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Knowledge Management
in Theory and Practice
Second Edition
Kimiz Dalkir foreword by Jay Liebowitz
Knowledge Management in Theory and Practice
Knowledge Management in Theory and Practice
Second Edition
Kimiz Dalkir
foreword by Jay Liebowitz
The MIT Press
Cambridge, Massachusetts
London, England
© 2011 Massachusetts Institute of Technology
All rights reserved. No part of this book may be reproduced in any form by any electronic or
mechanical means (including photocopying, recording, or information storage and retrieval)
without permission in writing from the publisher.
For information about special quantity discounts, please e-mail [email protected]
This book was set in Stone Sans and Stone by Toppan Best-set Premedia Limited. Printed and
bound in the United States of America.
Library of Congress Cataloging-in-Publication Data
Dalkir, Kimiz.
Knowledge management in theory and practice / Kimiz Dalkir ; foreword by Jay Liebowitz.
— 2nd ed.
p. cm.
Includes bibliographical references and index.
ISBN 978-0-262-01508-0 (hardcover : alk. paper)
1. Knowledge management. I. Title.
HD30.2.D354 2011
658.4’038 — dc22
2010026273
10 9 8 7 6 5 4 3 2 1
Contents
Foreword: Can Knowledge Management Survive? xiii Jay Liebowitz
1 Introduction to Knowledge Management 1 Learning Objectives 1 Introduction 2 What Is Knowledge Management? 5 Multidisciplinary Nature of KM 8 The Two Major Types of Knowledge: Tacit and Explicit 9 Concept Analysis Technique 11 History of Knowledge Management 15 From Physical Assets to Knowledge Assets 19 Organizational Perspectives on Knowledge Management 21 Library and Information Science (LIS) Perspectives on KM 22 Why Is KM Important Today? 22 KM for Individuals, Communities, and Organizations 25 Key Points 26 Discussion Points 27 References 27 2 The Knowledge Management Cycle 31 Learning Objectives 31 Introduction 32 Major Approaches to the KM Cycle 33 The Meyer and Zack KM Cycle 33 The Bukowitz and Williams KM Cycle 38 The McElroy KM Cycle 42 The Wiig KM Cycle 45 An Integrated KM Cycle 51 Strategic Implications of the KM Cycle 54
vi Contents
Practical Considerations for Managing Knowledge 57 Key Points 57 Discussion Points 57 References 58 3 Knowledge Management Models 59 Learning Objectives 59 Introduction 59 Major Theoretical KM Models 62 The Von Krogh and Roos Model of Organizational Epistemology 62 The Nonaka and Takeuchi Knowledge Spiral Model 64 The Choo Sense-Making KM Model 73 The Wiig Model for Building and Using Knowledge 76 The Boisot I-Space KM Model 82 Complex Adaptive System Models of KM 85 The European Foundation for Quality Management (EFQM) KM Model 89 The inukshuk KM Model 90 Strategic Implications of KM Models 92 Practical Implications of KM Models 92 Key Points 93 Discussion Points 93 References 95 4 Knowledge Capture and Codifi cation 97 Learning Objectives 97 Introduction 98 Tacit Knowledge Capture 101 Tacit Knowledge Capture at the Individual and Group Levels 102 Tacit Knowledge Capture at the Organizational Level 118 Explicit Knowledge Codifi cation 121 Cognitive Maps 121 Decision Trees 123 Knowledge Taxonomies 124 The Relationships among Knowledge Management, Competitive Intelligence, Business Intelligence,
and Strategic Intelligence 131 Strategic Implications of Knowledge Capture and Codifi cation 133 Practical Implications of Knowledge Capture and Codifi cation 134 Key Points 135 Discussion Points 135 References 136
Contents vii
5 Knowledge Sharing and Communities of Practice 141 Learning Objectives 141 Introduction 142 The Social Nature of Knowledge 147 Sociograms and Social Network Analysis 149 Community Yellow Pages 152 Knowledge-Sharing Communities 154 Types of Communities 158 Roles and Responsibilities in CoPs 160 Knowledge Sharing in Virtual CoPs 163 Obstacles to Knowledge Sharing 168 The Undernet 169 Organizational Learning and Social Capital 170 Measuring the Value of Social Capital 171 Strategic Implications of Knowledge Sharing 173 Practical Implications of Knowledge Sharing 175 Key Points 175 Discussion Points 176 References 177 6 Knowledge Application 183 Learning Objectives 183 Introduction 184 Knowledge Application at the Individual Level 187 Characteristics of Individual Knowledge Workers 187 Bloom ’ s Taxonomy of Learning Objectives 191 Task Analysis and Modeling 200 Knowledge Application at the Group and Organizational Levels 207 Knowledge Reuse 211 Knowledge Repositories 213 E-Learning and Knowledge Management Application 214 Strategic Implications of Knowledge Application 216 Practical Implications of Knowledge Application 217 Key Points 218 Discussion Points 218 Note 219 References 219
viii Contents
7 The Role of Organizational Culture 223 Learning Objectives 223 Introduction 224 Different Types of Cultures 227 Organizational Culture Analysis 229 Culture at the Foundation of KM 232 The Effects of Culture on Individuals 235 Organizational Maturity Models 238 KM Maturity Models 239 CoP Maturity Models 244 Transformation to a Knowledge-Sharing Culture 246 Impact of a Merger on Culture 256 Impact of Virtualization on Culture 258 Strategic Implications of Organizational Culture 258 Practical Implications of Organizational Culture 259 Key Points 262 Discussion Points 262 References 263 8 Knowledge Management Tools 267 Learning Objectives 267 Introduction 268 Knowledge Capture and Creation Tools 270 Content Creation Tools 270 Data Mining and Knowledge Discovery 271 Blogs 274 Mashups 275 Content Management Tools 276 Folksonomies and Social Tagging/Bookmarking 277 Personal Knowledge Management (PKM) 279 Knowledge Sharing and Dissemination Tools 280 Groupware and Collaboration Tools 281 Wikis 285 Social Networking, Web 2.0, and KM 2.0 288 Networking Technologies 292 Knowledge Acquisition and Application Tools 297 Intelligent Filtering Tools 298 Adaptive Technologies 302 Strategic Implications of KM Tools and Techniques 303 Practical Implications of KM Tools and Techniques 304
Contents ix
Key Points 304 Discussion Points 305 References 306 9 Knowledge Management Strategy 311 Learning Objectives 311 Introduction 311 Developing a Knowledge Management Strategy 316 Knowledge Audit 318 Gap Analysis 322 The KM Strategy Road Map 325 Balancing Innovation and Organizational Structure 328 Types of Knowledge Assets Produced 333 Key Points 336 Discussion Points 337 References 338 10 The Value of Knowledge Management 339 Learning Objectives 339 Introduction 339 KM Return on Investment (ROI) and Metrics 343 The Benchmarking Method 345 The Balanced Scorecard Method 351 The House of Quality Method 354 The Results-Based Assessment Framework 356 Measuring the Success of Communities of Practice 359 Key Points 360 Discussion Points 362 References 362 Additional Resources 364 11 Organizational Learning and Organizational Memory 365 Learning Objectives 365 Introduction 365 How Do Organizations Learn and Remember? 368 Frameworks to Assess Organizational Learning and Organizational Memory 369 The Management of Organizational Memory 370 Organizational Learning 377 The Lessons Learned Process 378 Organizational Learning and Organizational Memory Models 379
x Contents
A Three-Tiered Approach to Knowledge Continuity 385 Key Points 390 Discussion Points 391 References 392 12 The KM Team 397 Learning Objectives 397 Introduction 398 Major Categories of KM Roles 402 Senior Management Roles 403 KM Roles and Responsibilities within Organizations 410 The KM Profession 412 The Ethics of KM 413 Key Points 419 Discussion Points 420 Note 421 References 421 13 Future Challenges for KM 423 Learning Objectives 423 Introduction 424 Political Issues Regarding Internet Search Engines 425 The Politics of Organizational Context and Culture 427 Shift to Knowledge-Based Assets 429 Intellectual Property Issues 433 How to Provide Incentives for Knowledge Sharing 435 Future Challenges for KM 440 KM Research 442 A Postmodern KM 446 Concluding Thought 447 Key Points 448 Discussion Points 449 References 450 14 KM Resources 453 The Classics 453 KM for Specifi c Disciplines 454 International KM 455 KM Journals 455 Key Conferences 456
Contents xi
Key Web Sites 457 KM Glossaries 457 KM Case Studies and Examples 458 KM Case Studies 458 KM Examples 459 KM Wikis 459 KM Blogs 459 Visual Resources 460 YouTube 460 Other Visual Resources 460 Some Useful Tools 460 Other Visual Mapping Tools 460 Note 460
Glossary 461 Index 477
Foreword: Can Knowledge Management Survive?
The title of this foreword, “ Can Knowledge Management Survive? ” is perhaps rather
strange for this second edition of this leading textbook on knowledge management
(KM). However, as the KM fi eld has taught us to be “ refl ective practitioners, ” this
question is worth pondering.
Knowledge management has been around for twenty years or more, in terms of its
growth as a discipline. Even though the roots of knowledge management go back far
beyond that, is knowledge management generally accepted within organizations, and
is KM a lasting fi eld or discipline?
To answer the fi rst question, we can review some anecdotal evidence that suggests
KM is more widely accepted within certain industries than others. Over the years,
the pharmaceutical, energy, aerospace, manufacturing, and legal industries have
perhaps been some of the leaders in KM organizational adoption. In looking toward
the future, the public health and health care fi elds are certainly well positioned to
leverage knowledge throughout the world. And as the graying workforce ensues and
the baby boomers retire, knowledge retention will continue to play a key role in
many sectors, such as in government, nuclear energy, education, and others. So, KM
has permeated many organizations and has the propensity to propagate to others.
However, there are still many organizations that equate KM to be IT (information
technology), and do not fully grasp the concept of building and nurturing a knowl-
edge sharing culture for promoting innovation. Many organizations do not have KM
seamlessly woven within their fabric, and many organizations do not recognize or
reward their employees for knowledge sharing activities. It is getting harder to fi nd
the title of a “ chief knowledge offi cer ” or a “ knowledge management director ” in
organizations, suggesting two possibilities. The fi rst is that KM is indeed embedded
within the organization ’ s culture so there is no need to single it out. The second
proposition is that KM has lost its appeal and importance, so there is no need to
have a CKO or equivalent position, especially in these diffi cult economic times.
xiv Foreword
Probably, both propositions are true, depending perhaps on the type and nature of
the organization.
So, returning to the fi rst question about KM being widely accepted within today ’ s
organizations, the jury is still out. It may be simply an awareness issue in order to
show the value-added benefi ts of KM initiatives. Or it may be that KM was the “ man-
agement fad of the day ” and we are ready to move on. I believe that KM can have
tremendous value to organizations by stimulating creativity and innovation, building
the institutional memory of the fi rm, enabling agility and adaptability, promoting a
sense of community and belonging, improving organizational internal and external
effectiveness, and contributing toward succession planning and workforce develop-
ment. KM should be one of the key pillars underpinning a human capital strategy for
the organization. As with anything else, some organizations are leaders and some are
laggards. Those who recognize the importance of KM to the organization ’ s overarching
vision, mission, and strategy should hopefully be in the winning side of the equation
in the years ahead.
Let us now address the second question posed, “ is KM a lasting fi eld? ” In other
words, does KM have endurance to stand on its own in the forthcoming years? This
relates back to whether KM is more an art than a science. KM is certainly both, and
as the KM fi eld has developed over the years, an active KM community of both prac-
titioners and researchers has emerged. There are already well over ten international
journals specifi cally devoted to knowledge management. Worldwide KM conferences
abound, and individuals can take university coursework in knowledge management,
as well as being certifi ed in knowledge management by KM-related professional societ-
ies and other organizations. There are funded research projects in knowledge manage-
ment worldwide, both from basic and applied perspectives. In addition, there are
many KM-related communities of practice established worldwide. So certainly there
is an active group of practitioners and researchers who are trying to put more rigor
behind KM to accentuate the “ science ” over the “ art ” in order to give the KM fi eld
lasting legs.
On the other hand, there is the “ art ” side of KM. Like many fi elds that draw from
a multidisciplinary approach, especially from the social sciences, there is art along
with the science. Whether KM contributes to “ return on vision ” versus “ return on
investment ” indicates some of the diffi culty in quantifying KM returns. There certainly
is a “ touchy-feely ” side to KM, but there is a sound methodological perspective to KM,
too.
Here again, the jury is still out on whether the KM fi eld will last. So what needs to
be done? This is where textbooks such as Knowledge Management in Theory and Practice
Can Knowledge Management Survive? xv
play an important role. This textbook, in its second edition, marries the theory and
practice of knowledge management; namely, it provides the underlying methodolo-
gies for knowledge management design, development, and implementation, as well
as applying these methodologies and techniques in various cases and vignettes sprin-
kled throughout the book. It addresses my fi rst question of having knowledge manage-
ment being more widely accepted in organizations by discussing how KM has been
utilized in various industry sectors and organizational settings. The book also empha-
sizes the “ science ” behind the “ art ” in order to address my second question regarding
providing more rigor behind KM so that the fi eld will endure in the years ahead.
Professor Dalkir, a leading KM researcher, educator, and practitioner, uses her
insights and experience to highlight the important areas of knowledge management
in her book. People, culture, process, and technology are key components of knowl-
edge management, and the book provides valuable lessons learned in each area. This
book is well-suited as a reference text for KM practitioners, as well as a textbook for
KM-related courses.
This book, and others, is needed to continue to take the mystique out of KM and
provide the tangible value-added benefi ts that CEOs and organizations demand. Pro-
fessor Dalkir should be commended on this new edition, which will hopefully propel
others to be believers in the power of knowledge management. As this happens, the
answers to my two KM questions will be quite obvious! Enjoy!
Jay Liebowitz, D.Sc.
Professor, Carey Business School
Johns Hopkins University
1 Introduction to Knowledge Management
A light bulb in the socket is worth two in the pocket.
— Bill Wolf (1950 – 2001)
This chapter provides an introduction to the study of knowledge management (KM).
A brief history of knowledge management concepts is outlined, noting that much of
KM existed before the actual term came into popular use. The lack of consensus over
what constitutes a good defi nition of KM is addressed and the concept analysis tech-
nique is described as a means of clarifying the conceptual confusion that still persists
over what KM is or is not. The multidisciplinary roots of KM are enumerated together
with their contributions to the discipline. The two major forms of knowledge, tacit
and explicit, are compared and contrasted. The importance of KM today for individu-
als, for communities of practice, and for organizations are described together
with the emerging KM roles and responsibilities needed to ensure successful KM
implementations.
Learning Objectives
1. Use a framework and a clear language for knowledge management concepts.
2. Defi ne key knowledge management concepts such as intellectual capital, organiza-
tional learning and memory, knowledge taxonomy, and communities of practice
using concept analysis.
3. Provide an overview of the history of knowledge management and identify key
milestones.
4. Describe the key roles and responsibilities required for knowledge management
applications.
2 Chapter 1
Introduction
The ability to manage knowledge is crucial in today ’ s knowledge economy. The cre-
ation and diffusion of knowledge have become increasingly important factors in
competitiveness. More and more, knowledge is being thought of as a valuable com-
modity that is embedded in products (especially high-technology products) and
embedded in the tacit knowledge of highly mobile employees. While knowledge is
increasingly being viewed as a commodity or intellectual asset, there are some para-
doxical characteristics of knowledge that are radically different from other valuable
commodities. These knowledge characteristics include the following:
• Using knowledge does not consume it.
• Transferring knowledge does not result in losing it.
• Knowledge is abundant, but the ability to use it is scarce.
• Much of an organization ’ s valuable knowledge walks out the door at the end of the
day.
The advent of the Internet, the World Wide Web, has made unlimited sources of
knowledge available to us all. Pundits are heralding the dawn of the Knowledge Age
supplanting the Industrial Era. Forty-fi ve years ago, nearly half of all workers in
industrialized countries were making or helping to make things . By the year 2000,
only 20 percent of workers were devoted to industrial work — the rest was knowledge
work ( Drucker 1994 ; Barth 2000 ). Davenport (2005, p. 5) says about knowledge
workers that “ at a minimum, they comprise a quarter of the U.S. workforce, and at
a maximum about half. ” Labor-intensive manufacturing with a large pool of relatively
cheap, relatively homogenous labor and hierarchical management has given way to
knowledge-based organizations. There are fewer people who need to do more work.
Organizational hierarchies are being put aside as knowledge work calls for more col-
laboration. A fi rm only gains sustainable advances from what it collectively knows,
how effi ciently it uses what it knows, and how quickly it acquires and uses new
knowledge ( Davenport and Prusak 1998 ). An organization in the Knowledge Age is
one that learns, remembers, and acts based on the best available information, knowl-
edge, and know-how.
All of these developments have created a strong need for a deliberate and systematic
approach to cultivating and sharing a company ’ s knowledge base — one populated
with valid and valuable lessons learned and best practices. In other words, in order to
be successful in today ’ s challenging organizational environment, companies need to
learn from their past errors and not reinvent the wheel. Organizational knowledge is
Introduction to Knowledge Management 3
not intended to replace individual knowledge but to complement it by making it
stronger, more coherent, and more broadly applied. Knowledge management repre-
sents a deliberate and systematic approach to ensure the full utilization of the
organization ’ s knowledge base, coupled with the potential of individual skills, com-
petencies, thoughts, innovations, and ideas to create a more effi cient and effective
organization.
Increasingly, companies will differentiate themselves on the basis of what they know. A relevant
variation on Sidney Winter’s defi nition of a business fi rm as an organization that knows how to do
things would defi ne a business fi rm that thrives over the next decade as an organization that knows
how to do new things well and quickly . ( Davenport and Prusak 1998 , 13)
Knowledge management was initially defi ned as the process of applying a system-
atic approach to the capture, structuring, management, and dissemination of knowl-
edge throughout an organization to work faster, reuse best practices, and reduce costly
rework from project to project (Nonaka and Takeuchi, 1995; Pasternack and Viscio
1998; Pfeffer and Sutton, 1999; Ruggles and Holtshouse, 1999). KM is often character-
ized by a pack rat approach to content: “ save it, it may prove useful some time in the
future. ” Many documents tend to be warehoused, sophisticated search engines are
then used to try to retrieve some of this content, and fairly large-scale and costly KM
systems are built. Knowledge management solutions have proven to be most successful
in the capture, storage, and subsequent dissemination of knowledge that has been
rendered explicit — particularly lessons learned and best practices.
The focus of intellectual capital management (ICM), on the other hand, is on those
pieces of knowledge that are of business value to the organization — referred to as intel-
lectual capital or assets. Stewart (1997) defi nes intellectual capital as “ organized knowl-
edge that can be used to produce wealth. ” While some of these assets are more visible
(e.g., patents, intellectual property), the majority consists of know-how, know-why,
experience, and expertise that tends to reside within the head of one or a few employ-
ees ( Klein 1998 ; Stewart 1997 ). ICM is characterized less by content — because content
is fi ltered and judged, and only the best ideas re inventoried (the top ten for example).
ICM content tends to be more representative of the real thinking of individuals (con-
textual information, opinions, stories) because of its focus on actionable knowledge
and know-how. The outcome is less costly endeavors and a focus on learning (at the
individual, community, and organizational levels) rather than on the building of
systems.
A good defi nition of knowledge management would incorporate both the capturing
and storing of knowledge perspective, together with the valuing of intellectual assets.
For example:
4 Chapter 1
Knowledge management is the deliberate and systematic coordination of an organization ’ s
people, technology, processes, and organizational structure in order to add value through reuse
and innovation. This is achieved through the promotion of creating, sharing, and applying
knowledge as well as through the feeding of valuable lessons learned and best practices into
corporate memory in order to foster continued organizational learning.
When asked, most executives will state that their greatest asset is the knowledge
held by their employees. “ When employees walk out the door, they take valuable
organizational knowledge with them ” ( Lesser and Prusak 2001 , 1). Managers also
invariably add that they have no idea how to manage this knowledge! Using the intel-
lectual capital or asset approach, it is essential to identify knowledge that is of value
and is also at risk of being lost to the organization through retirement, turnover, and
competition.. As Lesser and Prusak (2001, 1) note: “ The most knowledgeable employ-
ees often leave fi rst. ” In addition, the selective or value-based knowledge management
approach should be a three-tiered one, that is, it should also be applied to three orga-
nizational levels: the individual, the group or community, and the organization itself.
The best way to retain valuable knowledge is to identify intellectual assets and then
ensure legacy materials are produced and subsequently stored in such a way as to make
their future retrieval and reuse as easy as possible ( Stewart 2000 ). These tangible by-
products need to fl ow from individual to individual, between members of a commu-
nity of practice and, of course, back to the organization itself, in the form of lessons
learned, best practices, and corporate memory.
Many knowledge management efforts have been largely concerned with capturing,
codifying, and sharing the knowledge held by people in organizations. Although there
is still a lack of consensus over what constitutes a good defi nition of KM (see next
section), there is widespread agreement as to the goals of an organization that under-
takes KM. Nickols (2000) summarizes this as follows: “ the basic aim of knowledge
management is to leverage knowledge to the organization ’ s advantage. ” Some of
management ’ s motives are obvious: the loss of skilled people through turnover, pres-
sure to avoid reinventing the wheel, pressure for organization-wide innovations in
processes as well as products, managing risk, and the accelerating rate with which new
knowledge is being created. Some typical knowledge management objectives would
be to:
• Facilitate a smooth transition from those retiring to their successors who are recruited
to fi ll their positions
• Minimize loss of corporate memory due to attrition and retirement
• Identify critical resources and critical areas of knowledge so that the corporation
knows what it knows and does well — and why
Introduction to Knowledge Management 5
• Build up a toolkit of methods that can be used with individuals, with groups, and
with the organization to stem the potential loss of intellectual capital
What Is Knowledge Management?
An informal survey conducted by the author identifi ed over a hundred published
defi nitions of knowledge management and of these, at least seventy-two could be
considered to be very good! Carla O ’ Dell has gathered over sixty defi nitions and has
developed a preliminary classifi cation scheme for the defi nitions on her KM blog (see
http://blog.simslearningconnections.com/?p=279) and what this indicates is that KM
is a multidisciplinary fi eld of study that covers a lot of ground. This should not be
surprising as applying knowledge to work is integral to most business activities.
However, the fi eld of KM does suffer from the “ Three Blind Men and an Elephant ”
syndrome. In fact, there are likely more than three distinct perspectives on KM, and
each leads to a different extrapolation and a different defi nition.
Here are a few sample defi nitions of knowledge management from the business
perspective:
Strategies and processes designed to identify, capture, structure, value, leverage, and share an
organization’s intellectual assets to enhance its performance and competitiveness. It is based on
two critical activities: (1) capture and documentation of individual explicit and tacit knowledge,
and (2) its dissemination within the organization. ( The Business Dictionary , http://www.business-
dictionary.com/defi nition/knowledge-management.html)
Knowledge management is a collaborative and integrated approach to the creation, capture,
organization, access, and use of an enterprise ’ s intellectual assets. ( Grey 1996)
Knowledge management is the process by which we manage human centered assets . . . the
function of knowledge management is to guard and grow knowledge owned by individuals, and
where possible, transfer the asset into a form where it can be more readily shared by other
employees in the company. ( Brooking 1999 , 154)
Further defi nitions come from the intellectual or knowledge asset perspective:
Knowledge management consists of “ leveraging intellectual assets to enhance organizational
performance. ” ( Stankosky 2008 )
Knowledge management develops systems and processes to acquire and share intellectual assets.
It increases the generation of useful, actionable, and meaningful information, and seeks to
increase both individual and team learning. In addition, it can maximize the value of an orga-
nization ’ s intellectual base across diverse functions and disparate locations. Knowledge manage-
ment maintains that successful businesses are a collection not of products but of distinctive
knowledge bases. This intellectual capital is the key that will give the company a competitive
6 Chapter 1
advantage with its targeted customers. Knowledge management seeks to accumulate intellectual
capital that will create unique core competencies and lead to superior results. ( Rigby 2009 )
A defi nition from the cognitive science or knowledge science perspective:
Knowledge — the insights, understandings, and practical know-how that we all possess — is the
fundamental resource that allows us to function intelligently. Over time, considerable knowledge
is also transformed to other manifestations — such as books, technology, practices, and tradi-
tions — within organizations of all kinds and in society in general. These transformations result
in cumulated [sic] expertise and, when used appropriately, increased effectiveness. Knowledge is
one, if not THE, principal factor that makes personal, organizational, and societal intelligent
behavior possible. ( Wiig 1993 )
Two diametrically opposed schools of thought arise from the library and informa-
tion science perspective: the fi rst sees very little distinction between information
management and knowledge management, as shown by these two defi nitions:
KM is predominantly seen as information management by another name (semantic drift).
( Davenport and Cronin 2000 , 1)
Knowledge management is one of those concepts that librarians take time to assimilate, only to
refl ect ultimately “ on why other communities try to colonize our domains. ” ( Hobohm 2004 , 7)
The second school of thought, however, does make a distinction between the manage-
ment of information resources and the management of knowledge resources.
Knowledge management “ is understanding the organization ’ s information fl ows and implement-
ing organizational learning practices which make explicit key aspects of its knowledge base. . . .
It is about enhancing the use of organizational knowledge through sound practices of informa-
tion management and organizational learning. ” ( Broadbent 1997 , 8 – 9)
The process-technology perspective provides some sample defi nitions, as well:
Knowledge management is the concept under which information is turned into actionable
knowledge and made available effortlessly in a usable form to the people who can apply it. (Patel
and Harty, 1998)
Leveraging collective wisdom to increase responsiveness and innovation. (Carl Frappaolo, Delphi
Group, Boston, http://www.destinationkm.com/articles/default.asp?ArticleID=949)
A systematic approach to manage the use of information in order to provide a continuous fl ow
of knowledge to the right people at the right time enabling effi cient and effective decision making
in their everyday business. (Steve Ward, Northrop Grumman, http://www.destinationkm.com/
articles/default.asp?ArticleID=949)
A knowledge management system is a virtual repository for relevant information that is
critical to tasks performed daily by organizational knowledge workers. (What is KM? http://www
.knowledgeshop.com)
Introduction to Knowledge Management 7
The tools, techniques, and strategies to retain, analyze, organize, improve, and share business
expertise. ( Groff and Jones 2003 , 2)
A capability to create, enhance, and share intellectual capital across the organization . . . a short-
hand covering all the things that must be put into place, for example, processes, systems, culture,
and roles to build and enhance this capability. ( Lank 1997 )
The creation and subsequent management of an environment that encourages knowledge to be
created, shared, learnt [ sic ], enhanced, organized and utilized for the benefi t of the organization
and its customers. ( Abell and Oxbrow 2001 )
Wiig (1993, 2002) also emphasizes that, given the importance of knowledge in
virtually all areas of daily and commercial life, two knowledge-related aspects are vital
for viability and success at any level. These are knowledge assets that must be applied,
nurtured, preserved, and used to the largest extent possible by both individuals and
organizations; and knowledge-related processes to create, build, compile, organize,
transform, transfer, pool, apply, and safeguard knowledge. These knowledge-related
aspects must be carefully and explicitly managed in all affected areas.
Historically, knowledge has always been managed, at least implicitly. However, effective and
active knowledge management requires new perspectives and techniques and touches on almost
all facets of an organization. We need to develop a new discipline and prepare a cadre of knowl-
edge professionals with a blend of expertise that we have not previously seen. This is our chal-
lenge! (Wiig, in Grey 1996 )
Knowledge management is a surprising mix of strategies, tools, and techniques —
some of which are nothing new under the sun: storytelling, peer-to-peer mentoring,
and learning from mistakes, for example, all have precedents in education, training,
and artifi cial intelligence practices. Knowledge management makes use of a mixture
of techniques from knowledge-based system design, such as structured knowledge
acquisition strategies from subject matter experts ( McGraw and Harrison-Briggs 1989 )
and educational technology (e.g., task and job analysis to design and develop task
support systems; Gery 1991 ).
This makes it both easy and diffi cult to defi ne what KM is. At one extreme, KM
encompasses everything to do with knowledge. At the other extreme, KM is narrowly
defi ned as an information technology system that dispenses organizational know-
how. KM is in fact both of these and much more. One of the few areas of consensus
in the fi eld is that KM is a highly multidisciplinary fi eld.
8 Chapter 1
Multidisciplinary Nature of KM
Knowledge management draws upon a vast number of diverse fi elds such as:
• Organizational science
• Cognitive science
• Linguistics and computational linguistics
• Information technologies such as knowledge-based systems, document and informa-
tion management, electronic performance support systems, and database technologies
• Information and library science
• Technical writing and journalism
• Anthropology and sociology
• Education and training
• Storytelling and communication studies
• Collaborative technologies such as Computer-Supported Collaborative Work (CSCW)
and groupware as well as intranets, extranets, portals, and other web technologies
The above is by no means an exhaustive list but serves to show the extremely varied
roots that KM grew out of and continues to be based upon today. Figure 1.1 illustrates
some of the diverse disciplines that have contributed to KM.
The multidisciplinary nature of KM represents a double-edged sword: on the one
hand, it is an advantage as almost anyone can fi nd a familiar foundation upon which
to base an understanding and even practice of KM. Someone with a background in
Library and Information Sciences
Web Technologies
Decision Support Systems
Document and
Information Management
Electronic Performance
Support Systems
Organizational Science
Collaborative Technologies
Database Technologies
Help Desk Systems
Cognitive Science
Technical Writing
Artificial Intelligence
KM Disciplines
Figure 1.1 Interdisciplinary nature of knowledge management
Introduction to Knowledge Management 9
journalism, for example, can quickly adapt this skill set to capture knowledge from
experts and reformulate this knowledge as organizational stories to be stored in cor-
porate memory. Someone coming from a more technical database background can
easily extrapolate his or her skill set to design and implement knowledge repositories
that will serve as the corporate memory for that organization. However, the diversity
of KM also results in some challenges with respect to boundaries. Skeptics argue that
KM is not and cannot be said to be a separate discipline with a unique body of knowl-
edge to draw upon. This attitude is typically represented by statements such as “ KM
is just IM ” or “ KM is nonsensical — it is just good business practices. ” It becomes very
important to be able to list and describe what attributes are necessary and in them-
selves suffi cient to constitute knowledge management both as a discipline and as a
fi eld of practice that can be distinguished from others.
One of the major attributes lies in the fact that KM deals with knowledge as well
as information. Knowledge is a more subjective way of knowing, typically based on
experiential or individual values, perceptions, and experience. Consider the example
of planning for an evening movie to distinguish between data, information, and
knowledge.
Data Content that is directly observable or verifi able: a fact; for example, movie list-
ings giving the times and locations of all movies being shown today — I download the
listings.
Information Content that represents analyzed data; for example, I can ’ t leave before
5, so I will go to the 7 pm show at the cinema near my offi ce.
Knowledge At that time of day, it will be impossible to fi nd parking. I remember the
last time I took the car, I was so frustrated and stressed because I thought I would miss
the opening credits. I ’ ll therefore take the commuter train. But fi rst, I ’ ll check with
Al. I usually love all the movies he hates, so I want to make sure it ’ s worth seeing!
Another distinguishing characteristic of KM, as opposed to other information
management fi elds, is the fact that knowledge in all of its forms is addressed: tacit
knowledge and explicit knowledge.
The Two Major Types of Knowledge: Tacit and Explicit We know more than we can tell.
— Polanyi 1966
Tacit knowledge is diffi cult to articulate and diffi cult to put into words, text, or
drawings. Explicit knowledge represents content that has been captured in some
10 Chapter 1
tangible form such as words, audio recordings, or images. Tacit knowledge tends to
reside within the heads of knowers , whereas explicit knowledge is usually contained
within tangible or concrete media. However, it should be noted that this is a rather
simplistic dichotomy. In fact, the property of tacitness is a property of the knower:
that which is easily articulated by one person may be very diffi cult to externalize by
another. The same content may be explicit for one person and tacit for another.
There is also somewhat of a paradox at play here: highly skilled, experienced, and
expert individuals may fi nd it harder to articulate their know-how. Novices, on the
other hand, are more apt to easily verbalize what they are attempting to do because
they are typically following a manual or how-to process. Table 1.1 summarizes some
of the major properties of tacit and explicit knowledge.
Typically, the more tacit knowledge is, the more valuable it tends to be. The
paradox lies in the fact that the more diffi cult it is to articulate a concept such as story ,
the more valuable that knowledge may be. This is often witnessed when people make
reference to knowledge versus know-how, or knowing something versus knowing how
to do something. Valuable tacit knowledge often results in some observable action
when individuals understand and subsequently make use of knowledge. Another
perspective is that explicit knowledge tends to represent the fi nal end product whereas
tacit knowledge is the know-how or all of the processes that were required in order
to produce that fi nal product.
We have a habit of writing articles published in scientifi c journals to make the work as fi nished
as possible, to cover up all the tracks, to not worry about the blind alleys or how you had the
wrong idea at fi rst, and so on. So there isn ’ t any place to publish, in a dignifi ed manner, what
you actually did in order to do the work. (Feynman 1966).
Table 1.1 Comparison of properties of tacit versus explicit knowledge
Properties of tacit knowledge Properties of explicit knowledge
Ability to adapt, to deal with new and exceptional situations
Ability to disseminate, to reproduce, to access and re-apply throughout the organization
Expertise, know-how, know-why, and care-why
Ability to teach, to train
Ability to collaborate, to share a vision, to transmit a culture
Ability to organize, to systematize, to translate a vision into a mission statement, into operational guidelines
Coaching and mentoring to transfer experiential knowledge on a one-to-one, face-to-face basis
Transfer knowledge via products, services, and documented processes
Introduction to Knowledge Management 11
A popular misconception is that KM focuses on rendering that which is tacit into
more explicit or tangible forms, then storing or archiving these forms somewhere,
usually some form of intranet or knowledge portal. The “ build it and they will come ”
expectation typifi es this approach: Organizations take an exhaustive inventory of
tangible knowledge (i.e., documents, digital records) and make them accessible to all
employees. Senior management is then mystifi ed as to why employees are not using
this wonderful new resource. In fact, knowledge management is broader and includes
leveraging the value of the organizational knowledge and know-how that accumulates
over time. This approach is a much more holistic and user-centered approach that
begins not with an audit of existing documents but with a needs analysis to better
understand how improved knowledge sharing may benefi t specifi c individuals, groups,
and the organization as a whole. Successful knowledge-sharing examples are gathered
and documented in the form of lessons learned and best practices and these then form
the kernel of organizational stories.
There are a number of other attributes that together make up a set of what KM
should be all about. One good technique for identifying these attributes is the concept
analysis technique.
The Concept Analysis Technique
Concept analysis is an established technique used in the social sciences (i.e., philoso-
phy and education) in order to derive a formula that in turn can be used to generate
defi nitions and descriptive phrases for highly complex terms. We still lack a consensus
on knowledge management – related terms, and these concepts do appear to be complex
enough to merit the concept analysis approach. A great deal of conceptual complexity
derives from the fact that a word such as knowledge is necessarily subjective in nature,
not to mention value laden in interpretation.
The concept analysis approach rests on the obtaining consensus around three major
dimensions of a given concept (shown in fi gure 1.2 ).
1. A list of key attributes that must be present in the defi nition, vision, or mission
statement
2. A list of illustrative examples
3. A list of illustrative nonexamples
This approach is particularly useful in tackling multidisciplinary domains such
as intellectual capital, because clear criteria can be developed to enable sorting
into categories such as knowledge versus information, document management versus
knowledge management, and tangible versus intangible assets. In addition, valuable
12 Chapter 1
contributions to the organization ’ s intellectual capital are derived through the produc-
tion of ontologies (semantic maps of key concepts), identifi cation of core competen-
cies, and identifi cation of knowledge, know-how, and know-why at risk of being lost
through human capital attrition.
Concept analysis is a technique used to visually map out conceptual information
in the process of defi ning a word ( Novak 1990, 1991 ). This is a technique derived from
the fi elds of philosophy and science education ( Bareholz and Tamir 1992 ; Lawson
1994 ) and is typically used in clearly defi ning complex, value-laden terms such as
democracy or religion . It is a graphical approach to help develop a rich, in-depth under-
standing of a concept. Figure 1.2 outlines the major components of this approach.
Davenport and Prusak (1998) decry the ability to provide a defi nitive account of
knowledge management since “ epistemologists have spent their lives trying to under-
stand what it means to know something. ” In his 2008 keynote address, Michael
Stankosky reiterated this disappointment that we still “ don’t know what to call it! ” If
Concept Name
Key Attributes Examples Nonexamples
1.
2.
3.
4.
5.
6.
7.
1.
2.
3.
4.
5.
6.
7.
1.
2.
3.
4.
5.
6.
7.
Figure 1.2 Illustration of the Concept Analysis Technique
Introduction to Knowledge Management 13
you can’t manage what you cannot measure, then you can’t measure what you cannot
name. Knowledge management, due to this still ongoing lack of clarity and lack of
consensus on a defi nition, presents itself as a good candidate for this approach. In
visioning workshops, this is the fi rst activity that participants are asked to undertake.
The objective is to agree upon a list of key attributes that are both necessary and suf-
fi cient in order for a defi nition of knowledge management to be acceptable. This is
completed by a list of examples and nonexamples, with justifi cations as to why a
particular item was included on the example or nonexample list. Semantic mapping
( Jonassen, Beissner, and Yacci 1993 ; Fisher 1990 ) is the visual technique used to extend
the defi nition by displaying words related to it. Popular terms to distinguish clearly
from knowledge management include document management, content management,
portal, knowledge repository, and others. Together, the concept and semantic maps
visually depict a model-based defi nition of knowledge management and its closely
related terms.
In some cases, participants are provided with lists of defi nitions of knowledge
management from a variety of sources can so they can try out their concept map of
knowledge management by analyzing these existing defi nitions. Defi nitions are typi-
cally drawn from the knowledge management literature as well as internally, from
their own organization. The use of concept defi nition through concept and semantic
mapping techniques can help participants rapidly reach a consensus on a formulaic
defi nition of knowledge management, that is, one that focuses less on the actual text
or words used but more on which key concepts need to be present, what comprises
a necessary and suffi cient (complete) set of concepts, and rules of thumb to use in
discerning what is and what is not an illustrative example of knowledge
management.
Ruggles and Holtshouse (1999) identifi ed the following key attributes of knowledge
management:
• Generating new knowledge
• Accessing valuable knowledge from outside sources
• Using accessible knowledge in decision making
• Embedding knowledge in processes, products and/or services
• Representing knowledge in documents, databases, and software
• Facilitating knowledge growth through culture and incentives
• Transferring existing knowledge into other parts of the organization
• Measuring the value of knowledge assets and/or impact of knowledge management
14 Chapter 1
Some key knowledge management attributes that continue to recur include:
• Both tacit and explicit knowledge forms are addressed; tacit knowledge ( Polanyi
1966 ) is knowledge that often resides only within individuals, knowledge that is dif-
fi cult to articulate such as expertise, know-how, tricks of the trade, and so on.
• There is a notion of added-value (the so what? of KM).
• The notion of application or use of the knowledge captured, codifi ed, and dissemi-
nated (the impact of KM).
It should be noted that a good enough or suffi cient defi nition of knowledge has been
shown to be effective (i.e., settling for good enough as opposed to optimizing; when 80
percent is done because the incremental cost of completing the remaining 20 percent
is disproportionately expensive and/or time-consuming in relation to the expected
additional benefi ts). Norman (1988 , 50 – 74) noted that knowledge might reside in two
places — in the minds of people and/or in the world. It is easy to show the faulty nature
of human knowledge and memory. For example, when typists were given caps for
typewriter keys, they could not arrange them in the proper confi guration — yet all
those typists could type rapidly and accurately. Why the apparent discrepancy between
the precision of behavior and the imprecision of knowledge? Because not all of the
knowledge required for precise behavior has to be in the mind. It can be distributed —
partly in the mind, partly in the world, and partly in the constraints of the world.
Precise behavior can thus emerge from imprecise knowledge ( Ambur 1996 ). It is for
this reason that once a satisfactory working or operational defi nition of knowledge
management has been arrived at, then a knowledge management strategy can be
confi dently tackled.
It is highly recommended that each organization undertake a concept analysis
exercise to clarify their understanding of what KM means in their own context. The
best way to do this would be to work as a group in order to achieve a shared under-
standing at the same time that a clearer conceptualization of the KM concept is
developed. Each participant can take a turn to contribute one good example of what
KM is and another example of what KM is not. The entire group can then discuss this
example/nonexample pair in order to identify one (or several) key KM attributes.
Miller ’ s (1956) magic number can be used to defi ne the optimal number of attributes
a given concept should have — namely, seven plus or minus two attributes. Once the
group feels they have covered as much ground as they are likely to, the key attributes
can be summarized in the form of a KM concept formula such as:
In our organization, knowledge management must include the following: both tacit
and explicit knowledge; a framework to measure the value of knowledge assets; a
process for managing knowledge assets . . .
Introduction to Knowledge Management 15
The lack of agreement on one universal formulation of a defi nition for knowledge
management makes it essential to develop one for each organization (at a very
minimum). This working or operational defi nition, derived through the concept analysis
technique, will render explicit the various perceptions people in that company may
have of KM and bring them together into a coherent framework. It may seem strange
that KM is almost always defi ned at the beginning of any talk or presentation on the
topic (imagine if other professionals such as doctors, lawyers, or engineers began every
talk with “ here is a defi nition of what I do and why ” ) but this is the reality we must
deal with. Whether the lack of a defi nition is due to the interdisciplinary nature of
the fi eld and/or because it is still an emerging discipline, it certainly appears to be
highly contextual. The concept analysis technique allows us to continue in both
research and practice while armed with a common, validated, and clear description
of KM that is useful and adapted to a particular organizational context.
History of Knowledge Management
Although the term knowledge management formally entered popular usage in the late
1980s (e.g., conferences in KM began appearing, books on KM were published, and
the term began to be seen in business journals), philosophers, teachers, and writers
have been making use of many of the same techniques for decades. Denning (2002)
related how from “ time immemorial, the elder, the traditional healer, and the midwife
in the village have been the living repositories of distilled experience in the life of the
community ” (http://www.stevedenning.com/ knowledge_management.html).
Some form of narrative repository has been around for a long time, and people
have found a variety of ways to share knowledge in order to build on earlier experi-
ence, eliminate costly redundancies, and avoid making at least the same mistakes
again. For example, knowledge sharing often took the form of town meetings, work-
shops, seminars, and mentoring sessions. The primary vehicle for knowledge transfer
was people themselves — in fact, much of our cultural legacy stems from the migration
of different peoples across continents.
Wells (1938) , while never using the actual term knowledge management , described
his vision of the World Brain that would allow the intellectual organization of the sum
total of our collective knowledge. The World Brain would represent “ a universal orga-
nization and clarifi cation of knowledge and ideas ” (Wells 1938, xvi). Wells in fact
anticipated the World Wide Web, albeit in an idealized manner, when he spoke of
“ this wide gap between . . . at present unassembled and unexploited best thought and
knowledge in the world . . . we live in a world of unused and misapplied knowledge
and skill ” (p. 10). The World Brain encapsulates many of the desirable features of the
16 Chapter 1
intellectual capital approach to KM: selected, well-organized, and widely vetted
content that is maintained, kept up to date, and, above all, put to use to generate
value to users, the users ’ community, and their organization.
What Wells envisioned for the entire world can easily be applied within an orga-
nization in the form of an intranet. What is new and termed knowledge management
is that we are now able to simulate rich, interactive, face-to-face knowledge encoun-
ters virtually through the use of new communication technologies. Information tech-
nologies such as an intranet and the Internet enable us to knit together the intellectual
assets of an organization and organize and manage this content through the lenses
of common interest, common language, and conscious cooperation. We are able to
extend the depth and breadth or reach of knowledge capture, sharing and dissemina-
tion activities, as we had not been able to do before and fi nd ourselves one step
closer to Wells ’ (1938) “ perpetual digest . . . and a system of publication and distri-
bution ” (pp. 70 – 71) “ to an intellectual unifi cation . . . of human memory ” (pp.
86 – 87).
Drucker was the fi rst to coin the term knowledge worker in the early 1960s ( Drucker
1964 ). Senge (1990) focused on the learning organization as one that can learn from
past experiences stored in corporate memory systems. Dorothy Barton-Leonard (1995)
documented the case of Chapparal Steel as a knowledge management success story.
Nonaka and Takeuchi (1995) studied how knowledge is produced, used, and diffused
within organizations and how this contributes to the diffusion of innovation.
The growing importance of organizational knowledge as a competitive asset was
recognized by a number of people who saw the value in being able to measure intel-
lectual assets (see Kaplan and Norton; APQC 1996 ; Edvinsson and Malone 1997,
among others). A cross-industry benchmarking study was led by APQC ’ s president
Carla O ’ Dell and completed in 1996. It focused on the following KM needs:
• Knowledge management as a business strategy
• Transfer of knowledge and best practices
• Customer-focused knowledge
• Personal responsibility for knowledge
• Intellectual asset management
• Innovation and knowledge creation ( APQC 1996 )
The Entovation timeline (available at http://www.entovation.com/timeline/
timeline.htm) identifi es a variety of disciplines and domains that have blended
together to emerge as knowledge management. A number of management theorists
have contributed signifi cantly to the evolution of KM such as Peter Drucker, Peter
Introduction to Knowledge Management 17
Senge, Ikujiro Nonaka, Hirotaka Takeuchi, and Thomas Stewart. An extract of this
timeline is shown in fi gure 1.3 .
The various eras we have lived through offer another perspective on the history of
KM. Starting with the industrial era in the 1800s, we focused on transportation tech-
nologies in 1850, communications in 1900, computerization beginning in the 1950s,
and virtualization in the early 1980s, and early efforts at personalization and profi ling
technologies beginning in the year 2000 ( Deloitte, Touche, Tohmatsu 1999 ). Figure
1.4 summarizes these developmental phases.
With the advent of the information or computer age, KM has come to mean the
systematic, deliberate leveraging of knowledge assets. Technologies enable valuable
knowledge to be remembered , via organizational learning and corporate memory; as
well as enabling valuable knowledge to be published , that is, widely disseminated to
all stakeholders. The evolution of knowledge management has occurred in parallel
with a shift from a retail model based on a catalog (e.g., Ford ’ s famous quote that you
can have a car in any color you like — as long as it is black) to an auction model (as
exemplifi ed by eBay) to a personalization model where real-time matching of user
needs and services occur in a win-win exchange model.
In 1969, the launch of the ARPANET allowed scientists and researchers to com-
municate more easily with one another in addition to being able to exchange large
data sets they were working on. They came up with a network protocol or language
that would allow disparate computers and operating systems to network together
Certification
of knowledge
innovation
standards
1969 1985 1988 1991 1994 1997 2000 +
Knowledge
Creating
Company
HBR Nonaka
Emergence
of virtual
organizations
Your Company’s
Most Valuable
Asset:
Intellectual
Capital
Stewart
ARPANET
Organizational
Learning
Sloan Mgmt.
Measurement
of intellectual
assets
Community
of Practice
Brown
Proliferation
of information
technology
Fifth
Discipline
Senge
First CKO
Edvinsson
Corporation
Knowledge
Management
Foundations
Wiig
The Balanced
Scorecard
Kaplan and Norton
APQC
benchmarking
First KM
programs in
universities
Figure 1.3 A summary timeline of knowledge management
18 Chapter 1
across communication lines. Next, a messaging system was added to this data fi le
transfer network. In 1991, the nodes were transferred to the Internet and World Wide
Web. At the end of 1969, only four computers and about a dozen workers were
connected.
In parallel, there were many key developments in information technologies devoted
to knowledge-based systems: expert systems that aimed at capturing experts on a dis-
kette , intelligent tutoring systems aimed at capturing teachers on a diskette and artifi cial
intelligence approaches that gave rise to knowledge engineering, someone tasked with
acquiring knowledge from subject matter experts, conceptually modeling this content,
and then translating it into machine-executable code ( McGraw and Harrison-Briggs
1989 ). They describe knowledge engineering as “ involving information gathering,
domain familiarization, analysisand design efforts. In addition, accumulated knowl-
edge must be translated into code, tested and refi ned ” (McGraw and Harrison Briggs,
5). A knowledge engineer is “ the individual responsible for structuring and/or con-
structing an expert system ” (5). The design and development of such knowledge-based
systems have much to offer knowledge management that also aims at the capture,
validation, and subsequent technology-mediated dissemination of valuable knowl-
edge from experts.
Industrialization 1800
Transportation 1850
Communications 1900
Computerization 1950*
Virtualization 1980
Personalization 2000 ++
* Birth of the Internet, 1969
Figure 1.4 Developmental phases in KM history
Introduction to Knowledge Management 19
By the early 1990s, books on knowledge management began to appear and the fi eld
picked up momentum in the mid 1990s with a number of large international KM
conferences and consortia being developed. In 1999, Boisot summarized some of these
milestones. Table 1.2 shows an updated summary.
At the 24th World Congress on Intellectual Capital Management in January 2003,
a number of KM gurus united in sending out a request to academia to pick up the KM
torch. Among those attending the conference were Karl Sveiby, Leif Edvinsson, Debra
Amidon, Hubert Saint-Onge, and Verna Allee. They made a strong case that KM had
up until now been led by practitioners who were problem-solving by the seat of their
pants and that it was now time to focus on transforming KM into an academic disci-
pline, promoting doctoral research in the discipline, and providing a more formalized
training for future practitioners. Today, over a hundred universities around the world
offer courses in KM, and quite a few business and library schools offer degree programs
in KM ( Petrides and Nodine 2003) .
From Physical Assets to Knowledge Assets
Knowledge has increasingly become more valuable than the more traditional physical
or tangible assets. For example, traditionally, an airline organization ’ s assets included
the physical inventory of airplanes. Today, however, the greatest asset possessed by
Table 1.2 Knowledge management milestones
Year Entity Event
1980 DEC, CMU XCON Expert System
1986 Dr. K. Wiig Coined KM concept at UN
1989 Consulting Firms Start internal KM projects
1991 HBR article Nonaka and Takeuchi
1993 Dr. K. Wiig First KM book published
1994 KM Network First KM conference
Mid 1990s Consulting Firms Start offering KM services
Late 1990s Key vertical industries Implement KM and start seeing benefi ts
2000 – 2003 Academia KM courses/programs in universities with KM texts
2003 to present Professional and Academic Certifi cation
KM degrees offered by universities, by professional institutions such as KMCI (Knowledge Management Consortium International; information available at: http://www.kmci.org/) and PhD students completing KM dissertations
20 Chapter 1
an airline is the SABRE reservation system, software that enables the airline to not
only manage the logistics of its passenger reservations but also to implement a seat-
yield management system. The latter refers to an optimization program that is used
to ensure maximum revenue is generated from each seat sold — even if each and every
seat carried a distinct price. Similarly, in the manufacturing sector, the value of non-
physical assets such as just-in-time (JIT) inventory systems is rapidly proving to
provide more value. These are examples of intellectual assets , which generally refer to
an organization ’ s recorded information, and human talent where such information is
typically either ineffi ciently warehoused or simply lost, especially in large, physically
dispersed organizations ( Stewart 1991 ).
This has led to a change in focus to the useful lifespan of a valuable piece of
knowledge — when is some knowledge of no use? What about knowledge that never
loses its value? The notion of knowledge obsolescence and archiving needs to be
approached with a fresh lens. It is no longer advisable to simply discard items that
are past their due date . Instead, content analysis and a cost-benefi t analysis are needed
in order to manage each piece of valuable knowledge in the best possible way.
Intellectual capital is often made visible by the difference between the book value
and the market value of an organization (often referred to as goodwill ). Intellectual
assets are represented by the sum total of what employees of the organization know
and know how to do. The value of these knowledge assets is at least equal to the cost
of recreating this knowledge. The accounting profession still has considerable diffi -
culty in accommodating these new forms of assets. Some progress has been made (e.g.,
Skandia was the fi rst organization to report intellectual capital as part of its yearly
fi nancial report), but there is much more work to be done in this area. As shown in
fi gure 1.5 , intellectual assets may be found at the strategic, tactical, and operational
levels of an organization.
Some examples of intellectual capital include:
Competence The skills necessary to achieve a certain (high) level of performance
Capability Strategic skills necessary to integrate and apply competencies
Technologies Tools and methods required to produce certain physical results
Core competencies are the things that an organization knows how to do well, that
provide a competitive advantage. These are situated at a tactical level. Some examples
would be a process, a specialized type of knowledge, or a particular kind of expertise
that is rare or unique to the organization. Capabilities are found at a more strategic
level. Capabilities are those things that an individual knows how to do well, which,
under appropriate conditions, may be aggregated to organizational competencies.
Introduction to Knowledge Management 21
Capabilities are potential core competencies and sound KM practices are required
in order for that potential to be realized. A number of business management texts
discuss these concepts in greater detail (e.g., Hamel and Prahalad 1990 ). It should be
noted that the more valuable a capability is, and the less it is shared among many
employees, then the more vulnerable the organization becomes should that employee
leave.
Organizational Perspectives on Knowledge Management
Wiig (1993) considers knowledge management in organizations from three perspec-
tives, each with different horizons and purposes:
Business perspective Focusing on why, where, and to what extent the organization
must invest in or exploit knowledge. Strategies, products and services, alliances, acqui-
sitions, or divestments should be considered from knowledge-related points of view.
Management perspective Focusing on determining, organizing, directing, facilitating,
and monitoring knowledge-related practices and activities required to achieve the
desired business strategies and objectives
Hands-on perspective Focusing on applying the expertise to conduct explicit knowl-
edge-related work and tasks
Intellectual capital
Operational
Tactical
Strategic
Increasing complexity
Technical integration
Mainly objective
Political negotiation
Mainly subjective
Figure 1.5 Three levels of intellectual capital
22 Chapter 1
The business perspective easily maps onto the strategic nature of knowledge man-
agement, the management perspective to the tactical layer, and the hands-on perspec-
tive may be equated with the operational level.
Library and Information Science (LIS) Perspectives on KM
Although not everyone in the LIS community is positively inclined toward KM
(tending to fall back on arguments that IM is enough and that KM is encroaching
upon this territory, as shown in some of the earlier defi nitions), others see KM as a
means of enlarging the scope of activities that information professionals can partici-
pate in. Gandhi (2004) notes that knowledge organization has always been part of the
core curriculum and the professional toolkit of LIS; and Martin et al. (2006, 15) point
out that LIS professionals are also expert in content management. The authors go on
to state that
Libraries and information centers will continue to perform access and intermediary roles which
embrace not just information but also knowledge management (Henczel 2004). The difference
today is that these traditional roles could be expanded if not transformed . . . through activities
aimed at helping to capture tacit knowledge and by turning personal knowledge into corporate
knowledge that can be widely shared through the library and applied appropriately.
Blair (2002) notes that the primary differences between traditional information
management practiced by LIS professional and knowledge management consist of
collaborative learning, the transformation of tacit knowledge into explicit forms, and
the documentation of best practices (and presumably their counterpart, lessons
learned). The author often uses the phrase “ connecting people to content and con-
necting people to people ” to highlight the addition of non-document-based resources
that play a critical role in KM.
As with KM itself, there is no best or better perspective; instead, the potential added
value is to combine the two perspectives in order to get the most out of KM. One of
the easiest ways of doing so would be to ensure that both perspectives — and both
types of skill sets — are represented on your KM team.
Why Is KM Important Today?
The major business drivers behind today ’ s increased interest and application of KM
lie in four key areas:
1. Globalization of business Organizations today are more global — multisite, multi-
lingual, and multicultural in nature.
Introduction to Knowledge Management 23
2. Leaner organizations We are doing more and we are doing it faster, but we also
need to work smarter as knowledge workers — increased pace and workload.
3. Corporate amnesia We are more mobile as a workforce, which creates problems of
knowledge continuity for the organization, and places continuous learning demands
on the knowledge worker — we no longer expect to work for the same organization for
our entire career.
4. Technological advances We are more connected — information technology advances
have made connectivity not only ubiquitous but has radically changed expectations:
we are expected to be on at all times and the turnaround time in responding is now
measured in minutes, not weeks.
Today ’ s work environment is more complex due to the increase in the number of
subjective knowledge items we need to attend to every day. Filtering over two hundred
e-mails, faxes, and voice mail messages on a daily basis should be done according to
good time management practices and fi ltering rules, but more often than not, workers
tend to exhibit a Pavlovian refl ex to beeps announcing the arrival of new mail or the
ringing of the phone that demands immediate attention. Knowledge workers are
increasingly being asked to think on their feet with little time to digest and analyze
incoming data and information, let alone time to retrieve, access, and apply relevant
experiential knowledge. This is due both to the sheer volume of tasks to attend to, as
well as the greatly diminished turnaround time. Today ’ s expectation is that everyone
is on all the time — as evidenced by the various messages embodying annoyance at not
having connected, such as voice mails asking why you have not responded to an
e-mail, and e-mails asking why you have not returned a call!
Knowledge management represents one response to the challenge of trying to
manage this complex, information overloaded work environment. As such, KM is
perhaps best categorized as a science of complexity. One of the largest contributors to
the complexity is that information overload represents only the tip of the iceberg —
only that information that has been rendered explicit. KM must also deal with the
yet to be articulated or tacit knowledge. To further complicate matters, we may not
even be aware of all the tacit knowledge that exists — we may not know that we don ’ t
know . Maynard Keynes (in Wells 1938 , 6) hit upon a truism when he stated “ these
. . . directive people who are in authority over us, know scarcely anything about the
business they have in hand. Nobody knows very much, but the important thing to
realize is that they do not even know what is to be known. ” Though he was address-
ing politics and the economic consequences of peace, today ’ s organizational leaders
have echoed his words countless times.
24 Chapter 1
In fact, we are now entering the third generation of knowledge management, one
devoted to content management. In the fi rst generation, the emphasis was placed on
containers of knowledge or information technologies in order to help us with the
dilemma exemplifi ed by the much quoted phrase “ if only we knew what we know ”
( O ’ Dell and Grayson 1998 ). The early adopters of KM, large consulting companies that
realized that their primary product was knowledge and that they needed to inventory
their knowledge stock more effectively, exemplifi ed this phase. A great many intranets
and internal knowledge management systems were implemented during the fi rst KM
generation. This was the generation devoted to fi nding all the information that had
up until then been buried in the organization with commonly produced by-products
encapsulated as reusable best practices and lessons learned.
Reeling from information overload, the second generation swung to the opposite
end of the spectrum, to focus on people; this could be phrased as “ if only we knew
who knows about. ” There was growing awareness of the importance of human and
cultural dimensions of knowledge management as organizations pondered why the
new digital libraries were entirely devoid of content (i.e., information junkyards) and
why the usage rate was so low. In fact, the information technology approach of the
fi rst KM generation leaned heavily toward a top-down, organization-wide monolithic
KM system. In the second generation, it became quite apparent that a bottom-up or
grassroots adoption of KM led to much greater success and that there were many
grassroots movements — which were later dubbed communities of practice . Communities
of practice are good vehicles to study knowledge sharing or the movement of knowl-
edge throughout the organization to spark not only reuse for greater effi ciency but
knowledge creation for greater innovation.
The third stage of KM brought about an awareness of the importance of content —
how to describe and organize content so that intended end users are aware it exists,
and can easily access and apply this content. This phase is characterized by the advent
of metadata to describe the content in addition to the format of content, content
management, and knowledge taxonomies. After all, if knowledge is not put to use to
benefi t the individual, the community of practice, and/or the organization, then
knowledge management has failed. Bright ideas in the form of light bulbs in the pocket
are not enough — they must be plugged in and this can only be possible if people know
what there is to be known, can fi nd it when they need, can understand it, and, perhaps
most important, are convinced that this knowledge should be put to work. A
slogan for this phase might be something like: “ taxonomy before technology ” ( Koenig
2002 , 3).
Introduction to Knowledge Management 25
KM for Individuals, Communities, and Organizations
Knowledge management provides benefi ts to individual employees, to communities
of practice, and to the organization itself. This three-tiered view of KM helps empha-
size why KM is important today (see fi gure 1.6 ).
For the individual, KM:
• Helps people do their jobs and save time through better decision making and
problem solving
• Builds a sense of community bonds within the organization
• Helps people to keep up to date
• Provides challenges and opportunities to contribute
For the community of practice, KM:
• Develops professional skills
• Promotes peer-to-peer mentoring
• Facilitates more effective networking and collaboration
• Develops a professional code of ethics that members can adhere to
• Develops a common language
For the organization, KM:
• Helps drive strategy
• Solves problems quickly
• Diffuses best practices
• Improves knowledge embedded in products and services
• Cross-fertilizes ideas and increases opportunities for innovation
• Enables organizations to better stay ahead of the competition
• Builds organizational memory
Containers Communities
Content
Figure 1.6 Summary of the three major components of KM
26 Chapter 1
Some critical KM challenges are to manage content effectively, facilitate collabora-
tion, help knowledge workers connect, fi nd experts, and help the organization to learn
to make decisions based on complete, valid, and well-interpreted data, information,
and knowledge.
In order for knowledge management to succeed, it has to tap into what is important
to knowledge workers, what is of value to them and to their professional practice as
well as what the organization stands to gain. It is important to get the balance right.
If the KM initiative is too big, it risks being too general, too abstract, too top-down,
and far too remote to catalyze the requisite level of buy-in from individuals. If the KM
initiative is too small, however, then it may not be enough to provide suffi cient inter-
action between knowledge workers to generate synergy. The KM technology must be
supportive and management must commit itself to putting into place the appropriate
rewards and incentives for knowledge management activities. Last but not least, par-
ticipants need to develop KM skills in order to participate effectively. These KM skills
and competencies are quite diverse and varied, given the multidisciplinary nature of
the fi eld, but one particular link is often neglected, and that is the link between KM
skills and information professionals ’ skills. KM has resulted in the emergence of new
roles and responsibilities. Many of these new roles can benefi t from a healthy founda-
tion from not only information technology (IT) but also information science. In fact,
KM professionals have a crucial role to play in all processes of the KM cycle, which is
described in more detail in chapter 2.
Key Points
• KM is not necessarily something completely new but has been practiced in a wide
variety of settings for some time now, albeit under different monikers.
• Knowledge is more complex than data or information; it is subjective, often based
on experience, and highly contextual.
• There is no generally accepted defi nition of KM, but most practitioners and profes-
sionals concur that KM treats both tacit and explicit knowledge with the objective of
adding value to the organization.
• Each organization should defi ne KM in terms of the business objective; concept
analysis is one way of accomplishing this.
• KM is all about applying knowledge in new, previously unencumbered or novel
situations.
• KM has its roots in a variety of different disciplines.
Introduction to Knowledge Management 27
• The KM generations to date have focused fi rst on containers, next on communities,
and fi nally on the content itself.
Discussion Points
1. Use concept analysis to clarify the following terms:
a. Intellectual capital versus physical assets
b. Tacit knowledge versus explicit knowledge
c. Community of practice versus community of interest
2. “ Knowledge management is not anything new. ” Would you argue that this
statement is largely true? Why or why not? Use historical antecedents to justify your
arguments.
3. What are the three generations of knowledge management to date? What was the
primary focus of each?
4. What are the different types of roles required for each of the above three
generations?
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2 The Knowledge Management Cycle
A little knowledge that acts is worth infi nitely more than much knowledge that is idle.
— Kahlil Gibran (1883 – 1931)
This chapter provides a description of the major phases involved in the knowledge
management cycle, encompassing the capture, creation, codifi cation, sharing, access-
ing, applying, and reuse of knowledge within and between organizations. Four
major approaches to KM cycles are presented from Meyer and Zack (1996) , Bukowitz
and Williams (2000) , McElroy (1993, 2003), and Wiig (1993) . A synthesis of these
approaches is then developed as a framework for following the path that information
takes to become a valuable knowledge asset for a given organization. This chapter
concludes with a discussion of the strategic and practical implications of managing
knowledge throughout the KM cycle.
Learning Objectives
1. Describe how valuable individual, group, and organizational knowledge is captured,
created, codifi ed, shared, accessed, applied, and reused throughout the knowledge
management cycle.
2. Compare and contrast major KM life cycle models including the Meyer and Zack,
Bukowitz and Williams, McElroy, and Wiig life cycle models.
3. Defi ne the key steps in each process of the KM cycle and provide concrete examples
of each.
4. Identify the major challenges and benefi ts of each phase of the KM cycle.
5. Describe how the integrated KM cycle combines the advantages of other KM life
cycle models.
32 Chapter 2
Introduction
Effective knowledge management requires an organization to identify, generate,
acquire, diffuse, and capture the benefi ts of knowledge that provide a strategic advan-
tage to that organization. A clear distinction must be made between information —
which can be digitized — and true knowledge assets — which can only exist within the
context of an intelligent system. As we are still far from the creation of artifi cial intel-
ligence systems, this means that knowledge assets reside within a human knower — not
the organization per se. A knowledge information cycle can be envisioned as the route
that information follows in order to become transformed into a valuable strategic asset
for the organization via a knowledge management cycle.
One of the major KM processes identifi es and locates knowledge and knowledge
sources within the organization. Valuable knowledge is then translated into explicit
form, often referred to as codifi cation of knowledge, in order to facilitate more wide-
spread dissemination. Networks, practices, and incentives are instituted to facilitate
person-to-person knowledge transfer as well as person – knowledge content connec-
tions in order to solve problems, make decisions, or otherwise act based on the best
possible knowledge base. Once this valuable, fi eld-tested knowledge and know-how is
transferred to an organizational knowledge repository, it is said to become part of
corporate memory . This is sometimes also referred to as ground truth .
As was the case with a generally accepted defi nition of KM, a similar lack of con-
sensus exists with respect to the terms used to describe the major steps in the KM
cycle. Table 2.1 summarizes the major terms found in the KM literature.
However, upon closer inspection, the differences in term defi nitions are not really
that great. The terms used differ, but there does appear to be some overlap with regard
to the different types of steps involved in a KM cycle. To this end, four models were
selected as they met the following criteria:
• Implemented and validated in real-world settings
• Comprehensive with respect to the different types of steps found in the KM
literature
• Included detailed descriptions of the KM processes involved in each of the steps
These four KM cycle approaches are from Meyer and Zack (1996) , Bukowitz and
Williams (2000) , McElroy (1999, 2003), and Wiig (1993) .
The Knowledge Management Cycle 33
Major Approaches to the KM Cycle
The Meyer and Zack KM Cycle
The Meyer and Zack KM cycle is derived from work on the design and development
of information products ( Meyer and Zack 1996 ). Lessons learned from the physical
products cycle can be applied to the management of knowledge assets. Information
products are broadly defi ned as any information sold to internal or external custom-
ers such as databases, news synopses, customer profi les, and so forth. Meyer and
Zack ( 1996 ) propose that research and knowledge about the design of physical
products can be extended into the intellectual realm to serve as the basis for a KM
cycle.
This approach provides a number of useful analogies such as the notion of a product
platform (the knowledge repository) and the information process platform (the knowl-
edge refi nery) to emphasize the notion of value-added processes required in order to
leverage the knowledge of an organization. The KM cycle consists primarily of creating
a higher value-added knowledge product at each stage of knowledge processing. For
example, a basic database may represent an example of knowledge that has been
created. Value can then be added by extracting trends from these data. The original
information has been repackaged to now provides trend analyses that can serve as the
basis for decision making within the organization. Similarly, competitive intelligence
can be gathered and synthesized in order to repackage raw data into meaningful,
interpreted, and validated knowledge that is of immediate value to users, that is, it
can be put into action directly. Yet another example is a news gathering service that
Table 2.1 A comparison of key KM cycle processes
Wiig (1993) McElroy (1999) Rollet (2003)
Bukowitz and
Williams (2000)
Meyer and
Zack (1996)
Creation Individual and group learning
Planning Get Acquisition
Sourcing Knowledge claim validation
Creating Use Refi nement
Compilation Information acquisition Integrating Learn Store/retrieve
Transformation Knowledge validation Organizing Contribute Distribution
Dissemination Knowledge integration Transferring Assess Presentation
Application Maintaining Build/sustain
Value realization Assessing Divest
34 Chapter 2
summarizes or repackages information to meet the needs of distinct individuals
through profi ling and personalization value-added activities.
Meyer and Zack echoed other authors in stressing “ the importance of managing
the evolution and renewal of product architecture for sustained competitive success
. . . different architectures result in different product functionality, cost, quality and
performance. Architectures are . . . a basis for product innovation ” (Meyer and Zack
1996, 44). Research and knowledge about the design of physical information products
can inform the design of a KM cycle. In Meyer and Zack ’ s approach, the interfaces
between each of the stages are designed to be seamless and standardized. Experience
suggests the critical importance of specifying internal and external user interfaces in
order to do so.
The Meyer and Zack KM cycle processes are composed of the technologies, facilities,
and processes for manufacturing products and services. He suggests that information
products are best viewed as a repository comprising information content and structure.
Information content is the data held in the repository that provides the building
blocks for the resulting information products. The content is unique for each type of
business or organization. For example, banks have content relating to personal and
commercial accounts, insurance companies hold information on policies and claims,
and pharmaceutical companies have a large body of scientifi c and marketing knowl-
edge around each product under design or currently sold.
In addition to the actual content, the other important elements to consider are the
overall structure and approach as to how the content is stored, manipulated, and
retrieved. The information unit is singled out as the formally defi ned atom of informa-
tion to be stored, retrieved, and manipulated. This notion of a unit of information is
a critical concept that should be applied to knowledge items as well. A focus at the
level of a knowledge object distinguishes KM from document management. While a
document management system (DMS) stores, manipulates, and retrieves documents
as integral wholes, KM can easily identify, extract, and manage a number of different
knowledge items (sometimes referred to as “ knowledge objects ” ) within the same
document. The unit under study is thus quite different — both in nature and scale. This
again links us back to the notion that KM is not about the exhaustive collection of
voluminous content but rather more selective sifting and modifi cation of existing
captured content. The term often used today is “ content management systems. ”
Different businesses once again make use of unique meaningful information units.
For example, a repository of fi nancial statements is held in Mead ’ s Data System Lexis/
Nexis and the footnotes can be defi ned as information units. A user is able to select
a particular fi nancial statement for analysis based on key attributes of the footnotes.
The Knowledge Management Cycle 35
An expertise location system may have, as knowledge objects, the different categories
of expertise that exist within that organization (e.g., fi nancial analysis) and these
attributes are used to search for, select, and retrieve specifi c knowledgeable individuals
within the company.
A well-designed repository will include schemes for labeling, indexing, linking, and
cross-referencing the information units that together comprise its content. Although
Meyer and Zack (1996) specifi cally address information products, their work is more
broadly applicable to knowledge products as well . Whereas knowledge does indeed
possess unique attributes not found in information (as discussed in chapter 1), this
does not necessitate adopting a tabula rasa approach and reinventing decades of tried,
tested, and true methods. This is especially true when managing explicit knowledge
(formal, codifi ed), which has the greatest similarity to information management. In
the case of tacit knowledge, new management approaches need to be used, but these
should, once. again, build on solid content management processes.
The repository becomes the foundation upon which a fi rm creates its family of
information and knowledge products. This means that the greater the scope, depth,
and complexity, the greater the fl exibility for deriving products and thus the greater
the potential variety within the product family. Such repositories often form the fi rst
kernel of an organizational memory or corporate memory for the company. A sample
repository for a railway administration organization is shown in fi gure 2.1 .
Meyer and Zack analyzed the major developmental stages of a knowledge repository
and these stages were mapped on to a KM cycle consisting of acquisition, refi nement,
storage/retrieval, distribution, and presentation/use. Meyer and Zack refer to this as
the “ refi nery. ” Figures 2.2 and 2.3 summarize the major stages in the Meyer and Zack
cycle.
Acquisition of data or information addresses the issues regarding sources of raw
materials such as scope, breadth, depth, credibility, accuracy, timeliness, relevance,
cost, control, exclusivity, and so on. The guiding principle is the well-known adage
of “ garbage in garbage out, ” that is, source data must be of the highest quality, oth-
erwise the intellectual products produced downstream will be inferior.
Refi nement is the primary source of added value. This refi nement may be physical
(e.g., migrating form one medium to another) or logical (restructuring, relabeling,
indexing, and integrating). Refi ning also refers to cleaning up (e.g., sanitizing content
so as to ensure complete anonymity of sources and key players involved) or standard-
izing (e.g., conforming to templates of best practice or lessons learned as used within
that particular organization). Statistical analyses can be performed on content at this
stage to conduct a meta-analysis (e.g., a high-level summary of key themes, or patterns
36 Chapter 2
Upcoming events
Safety related news
One critical, 96 hurt as Amtrak train derails in...
Latest accident reports
New publications
New members
What’s new Head office ReportsLinksRegions
Repository
administration
Help
Glossary
Actions
Simple search
Advanced search
Figure 2.1 Example screen for a repository
Repository
Content
StructureS o
u rc
e s
U s e
rs
Product platform Product family
Content
Packaging format
Access distribution
Interactivity
Acquisition Refinement Storage retrieval
Distribution Presentation
Figure 2.2 High-level view of the Zack Information Cycle
The Knowledge Management Cycle 37
found in a collection of knowledge objects). This stage of the Meyer and Zack cycle
adds value by creating more readily usable knowledge objects and by storing the
content more fl exibly for future use.
Storage/retrieval forms a bridge between the upstream acquisition and refi nement
stages that feed the repository and downstream stages of product generation. Storage
may be physical (fi le folders, printed information) or digital (database, knowledge
management software).
Distribution describes how the product is delivered to the end user (e.g., fax, print,
e-mail) and encompasses not only the medium of delivery but also its timing, fre-
quency, form, language, and so on.
The fi nal step is presentation or use. It is here that context plays a very important
role. The effectiveness of each of the preceding value-added steps is evaluated here:
does the user have suffi cient context to be able to make use of this content? If not,
the KM cycle has failed to deliver value — to the individual and ultimately to the
organization.
Decompose into
k units, index,
and link
S o
u rc
e s U
s e
rs
Repository
of research
results
Reports
newsletters
bulletins
Acquire Refine Store Distribute Present
Calls and
surveys
Analyze,
interpret, report
Edit and format
Indexed and
linked
knowledge units
Online via Web
and groupware
Interactive
selection of
knowledge units
Figure 2.3 Detailed view of the Zack Information Cycle
38 Chapter 2
In order for the cycle to work as intended, front-end knowledge needs to be pro-
vided. This is typically in the form of rules on how to identify source information,
acquire it, refi ne it, and subsequently add it to the fi rm ’ s information repository. There
may also be a similar need at the fi nal stage, for rules on how content may be distrib-
uted and used, such as copyright, attribution, confi dentiality, and other restrictions
that may apply.
The repository and the refi nery together enable the management of valuable knowl-
edge of a fi rm. They need to in turn be supported by the fi rm ’ s core capabilities in
information technology, internal knowledge about their business, external knowledge
about current and emerging environments as well as how it organizes and manages
itself. The fl exibility with which the fi rm can create content-based products forms the
basis of the fi rm ’ s ability to realize market leverage from its information assets.
Although it is not explicitly described in the Meyer and Zack cycle, there is also a
notion of having to continually renew the repository and the refi nery in order to avoid
obsolescence. Renewal should be added to the cycle diagram in the form of a feedback
loop that involves rethinking the basic content and structure of the repository to
decide whether different, newer products or repackaging is required. This may mean
increasing the depth of an analysis, updating a report, greater integration, more
sophisticated cross-linking, or greater standardization of content.
The Meyer and Zack model is one of the most complete descriptions of the key
elements involved in the knowledge management model. Its strength derives primar-
ily from its comprehensive information-processing paradigm that is almost completely
adaptable to knowledge-based content. In particular, the notion of refi nement is a
crucial stage in the KM cycle and one that is often neglected.
The Bukowitz and Williams KM Cycle
Bukowitz and Williams (2000, 8) describe a knowledge management process
framework that outlines “ how organizations generate, maintain and deploy a strate-
gically correct stock of knowledge to create value. ” This framework is shown in
fi gure 2.4 .
In this framework, knowledge consists of knowledge repositories, relationships,
information technologies, communications infrastructures, functional skill sets,
process know-how, environmental responsiveness, organizational intelligence, and
external sources, among others. The “ get, ” “ learn, ” and “ contribute ” phases are tactical
in nature. They are triggered by market-driven opportunities or demands and typically
result in day-to-day use of knowledge to respond to these demands. The “ assess, ”
“ build/sustain, ” and “ divest ” stages are more strategic in nature, triggered by shifts in
The Knowledge Management Cycle 39
the macro environment. These focus on more long-range processes of matching intel-
lectual capital to strategic requirements.
The fi rst stage, get, consists of seeking out information needed in order to make
decisions, solve problems, or innovate. The challenge today is not so much in fi nding
information, but in dealing effectively with the enormous volume of information that
can be obtained. Technology has created great strides in providing access to an ever-
increasing pool of information. The resultant information overload has created a criti-
cal need to be able to sift through the vast volume of content, identify the knowledge
of value, and to then manage this knowledge effectively and effi ciently. Information
professionals have traditionally fulfi lled this role and they are certainly needed. User
needs must be well understood in order to match information seekers with the best
possible content. This involves knowing where knowledge resources exist and can be
accessed.
Where KM diverges from IM is that the getting of content encompasses not only
traditional explicit content (e.g., a physical or electronic document) but also tacit
knowledge. This means that the information that users need must not only be con-
nected to content, but also to content experts — people — where most of the valuable
tacit knowledge resides. The term “ cybrarian ” is sometimes used to describe the new
knowledge professional role. The key tasks are to organize knowledge content; main-
tain timeliness, completeness, and accuracy; profi le users ’ information needs; access/
navigate/fi lter voluminous content in order to respond to users ’ needs; and help train
users with new knowledge repository technologies (information literacy).
The use stage deals with how to combine information in new and interesting ways
in order to foster organizational innovation. The focus is primarily on individuals,
or: Divest
Build/Sustain
AssessGet
Use
Learn Contribute
Knowledge
Figure 2.4 The Bukowitz and Williams KM Cycle
40 Chapter 2
and then on groups. The narrow focus on innovation as the reason for making use of
intellectual assets is somewhat limiting in this KM cycle. The authors discuss a number
of techniques to promote serendipity, outside-of-the-box thinking, and creativity-
enhancing techniques. Although the notion of promoting the most fl uid fl ow of
knowledge is a worthwhile pursuit, the uses of knowledge are much wider in scope
than mere innovation.
The learn stage refers to the formal process of learning from experiences as a
means of creating competitive advantage. An organizational memory is created so
that organizational learning becomes possible — from both successes (best practices)
and failures (lessons learned). The links between learning and creating value are
harder to establish than those of getting and using information. Learning in organiza-
tions is important because it represents the transition step between the application
of ideas and the generation of new ones. Time must be taken to refl ect on experience
and consider its possible value elsewhere. There should be a strong link between
organizational strategy and organizational learning activities. Learning is absolutely
essential after the getting and using of content — otherwise, the content is simply
warehoused somewhere and not making a difference in how things are done within
the organization.
The contribute stage of the KM cycle deals with getting employees to post what
they have learned to the communal knowledge base (e.g., a repository). This is the
only way to make individual knowledge visible and available across the entire orga-
nization — where appropriate. The last caveat is added because there is a tendency to
warehouse all knowledge, which should not be the focus of KM. Many authors use
this sequence of steps and they have the unfortunate effect of creating the misconcep-
tion that KM is all about making public all that resides within the heads of individuals.
Needless to say, the impact on motivation of employees plummets considerably! The
point of the exercise is not to post everything on the company intranet, but to cull
those experiences from which others in the organization may also benefi t. This implies
that the experience has potential to be generalized. In fact, a great deal of content to
be shared organization-wide must fi rst be repackaged in a generic format in order to
be of use to a wider audience.
Examples of content that employees should be encouraged to contribute include
the transfer of best practices across the organization to apply the experience gained
from experience or unit to others and lessons learned which refer to less successful
outcomes that should be noted so that the same mistakes are not repeated by others.
The authors describe a number of carrots and sticks that can be used to promote
knowledge sharing. Practice has shown some methods that do not work: sharing does
The Knowledge Management Cycle 41
not happen with a direct pay-per-contribution scheme, and also does not happen if
there is a punish-the-withholders mentality. In order for successful knowledge sharing
to occur, it must make sense, that is, the benefi ts to both the organization and the
individuals must exist and be clearly perceived as such. The other critical success factor
appears to lie with the successful deployment of knowledge brokers — professionals
who assume the responsibility of gathering, repackaging, and promoting knowledge
nuggets throughout the organization. Third, a good system should be in place to
maintain the results of organizational learning — a good organizational memory man-
agement system, often in the form of an intranet of some sort. Part of good organi-
zational memory management practice should be to always maintain attribution,
require authorization for dissemination, provide feedback mechanisms, and keep track
of knowledge reuse. One of the best rewards of contributing is for the user to be noti-
fi ed of how popular his or her contributions were (which is analogous to a citation
index for scholarly publications).
The assess stage deals more with the group and organizational level. Assessment
refers to the evaluation of intellectual capital. This requires the organization to defi ne
mission-critical knowledge and map current intellectual capital against future knowl-
edge needs. The organization must also develop metrics to demonstrate that it is
growing its knowledge base and profi ting from its investments in intellectual capital.
The theory of the organization needs to be expanded to include capturing the impact
of knowledge on organizational performance. This includes identifying new forms of
capital such as human capital (competencies), customer capital (the customer relation-
ship), organizational capital (knowledge bases, business processes, technology infra-
structure, values, norms, and culture), and intellectual capital (the relationships among
human, customer, and organizational capital). The assessment must take into account
these new types of assets and focus on how easily and fl exibly the organization can
convert its knowledge into products and services of value to the customer. A new set
of frameworks, processes, and metrics that evaluate the knowledge base must be incor-
porated into the overall management process.
The build and sustain step in the KM cycle ensures that future intellectual capital
of the organization will keep the organization viable and competitive. Resources must
be allocated to the growth and maintenance of knowledge and they should be chan-
neled in such a way as to create new knowledge and reinforce existing knowledge. At
the tactical level, the inability to locate and apply knowledge to meet an existing need
results in a lost opportunity. At the strategic level, coming up short on the right
knowledge delivers a much more serious blow — loss of competitiveness and ultimately
of organizational viability.
42 Chapter 2
The fi nal step in the Bukowitz and Williams KM cycle is the divest step. The orga-
nization should not hold on to assets — physical or intellectual — if they are no longer
creating value. In fact, some knowledge may be more valuable if transferred outside
the organization. In this step of the KM cycle, organizations need to examine their
intellectual capital in terms of the resources required to maintain it and whether these
resources would be better spent elsewhere. This involves understanding the why,
when, where, and how of formally divesting parts of the knowledge base. An oppor-
tunity cost analysis of retaining knowledge should be incorporated into standard
management practice. This cost analysis is necessary in order to understand which
parts of the knowledge base will be unnecessary for sustaining competitive advantage
and industry viability.
Traditional divestiture decisions regarding knowledge include obtaining patents,
spinning off companies, outsourcing work, terminating a training program and/or
employees, replacing/upgrading technologies, and ending partnerships, alliances, or
contracts. However, KM requires a planned and purposeful form of divesting. This
means that the decision to be made is a strategic one, not an operational task. Ideally,
unnecessary knowledge should not have been acquired in the fi rst place — the organi-
zation should put into place processes to clearly discriminate between forms of knowl-
edge that can be leveraged and those that are of limited use. Knowledge that is a drain
on resources should be converted into value. This often involves converting rather
than getting rid of knowledge, for example, by redeploying the knowledge elsewhere,
either within or outside of the organization.
The Bukowitz and Williams KM cycle introduces two new critical phases: the learn-
ing of knowledge content and the decision as to whether to maintain this knowledge
or divest the organization of this knowledge content. This KM cycle is more compre-
hensive than the Meyer and Zack cycle as the notion of tacit as well as explicit knowl-
edge management has been incorporated.
The McElroy KM Cycle
McElroy (1999) describes a knowledge life cycle that consists of the knowledge pro-
cesses of knowledge production and knowledge integration, with a series of feedback
loops to organizational memory, beliefs, claims, and the business-processing environ-
ment. The high-level processes are shown in fi gure 2.5 .
McElroy emphasizes that organizational knowledge is held both subjectively in the
minds of individuals and groups and objectively in explicit forms. Together, they
comprise the distributed organizational knowledge base of the company. Knowledge
use in the business-processing environment results in outcomes that either match
The Knowledge Management Cycle 43
expectations or fail to do so. Matches reinforce existing knowledge, leading to its reuse,
whereas mismatches lead to adjustments in business processing behavior via single
loop learning ( Argyris and Schon 1978 ). Successive failures from mismatches will lead
to doubt and ultimately rejection of existing knowledge, which will in turn trigger
knowledge processing to produce and integrate new knowledge, this time via double-
loop learning ( Argyris and Schon 1978 ).
The term problem claim formulation represents an attempt to learn and state the
specifi c nature of the detected knowledge gap. The term knowledge claim formulation fol-
lows as a response to validated problem claims via information acquisition and indi-
vidual and group learning. New knowledge claims are tested and evaluated via
knowledge claim evaluation processes. Evaluation of knowledge claims lead to surviv-
ing knowledge claims which will be integrated as new organizational knowledge or
falsifi ed/undecided knowledge claims. The record of all such outcomes becomes part
of the distributed organizational knowledge base via knowledge integration. Once
integrated, they are used in business processing. Experience gained from the use of
knowledge in the organizational knowledge base gives rise to new claims and resulting
beliefs, triggering the cycle to begin all over again.
Knowledge processing environment
Knowledge production Organizational
knowledge Knowledge integration
Beliefs and claims
Double loop learning
Business processing environment Single loop learning
Beliefs and claims
Distributed
organizational
knowledge
base
Figure 2.5 High-level processes in the McElroy KM Cycle
44 Chapter 2
In knowledge production, the key processes are: individual and group learning,
knowledge claim formulation, information acquisition, codifi ed knowledge claim,
and knowledge claim evaluation. Figure 2.6 illustrates these knowledge production
processes.
Individual and group learning represents the fi rst step in organizational learning.
Knowledge is information until it is validated. Knowledge claim validation involves
codifi cation at an organizational level. A formalized procedure is required for the
receipt and codifi cation of individual and group innovations. Information acquisition
is the process by which an organization deliberately or serendipitously acquires knowl-
edge claims or information produced by others, usually external to the organization.
This stage plays a fundamental role in the formulation of new knowledge claims
at the organizational level. Examples include competitive intelligence, subscription
services, library services, research initiatives, think tanks, consortia, and personalized
information services. Knowledge claim evaluation is the process by which knowledge
claims are evaluated to determine their veracity and value. This implies that they
are of greater value than existing knowledge in the organizational knowledge base.
Figure 2.7 shows some of the components of this stage of the knowledge cycle.
Knowledge integration is the process by which an organization introduces new
knowledge claims to its operating environment and retires old ones. This includes all
knowledge transmission such as teaching, knowledge sharing, and other social activi-
ties that communicate either an understanding of previously produced organizational
Formulate
problem
claim
Information
acquisition
Individual
and group
learning
Knowledge
claim
formulation
Codified
knowledge
claim
Knowledge
claim
evaluation
Figure 2.6 Knowledge production processes in the McElroy KM Cycle
The Knowledge Management Cycle 45
knowledge to knowledge workers, or integrate newly minted knowledge. Figure 2.8
describes this stage of the KM cycle.
One of the great strengths of the McElroy cycle is the clear description of how
knowledge is evaluated and how a conscious decision is made as to whether or not it
will be integrated into the organizational memory. The validation of knowledge is a
step that clearly distinguishes knowledge management from document management.
The KM cycle does more than address the storage and subsequent management of
documents or knowledge that has been warehoused as is. The KM cycle focuses on
processes to identify knowledge content that is of value to the organization and its
employees.
The Wiig KM Cycle
Wiig (1993) focuses on the three conditions that need to be present for an organi zation
to conduct its business successfully: it must have a business (products and services)
Knowledge
production
Information about:
Surviving knowledge claim
Falsified knowledge claim
Undecided knowledge claim
Surviving
knowledge
claim
Falsified
knowledge
claim
Undecided
knowledge
claim
Organizational
knowledge
Figure 2.7 Knowledge claim evaluation processes in the McElroy KM Cycle
46 Chapter 2
and customers for them, it must have resources (people, capital, facilities), and it must
have the ability to act. The third point is emphasized in the Wiig KM cycle.
Knowledge is the principal force that determines and drives the ability to act intel-
ligently. With improved knowledge, we know better what to do and how to do it.
Wiig identifi es the major purpose of KM as an effort: “ to make the enterprise intelli-
gent-acting by facilitating the creation, cumulation [ sic ], deployment and use of
quality knowledge ” (Wiig 1993, 39). Working smarter means that we must approach
our tasks with greater expertise — that we must acquire as much relevant and high-
quality knowledge as possible and apply it better in a number of different ways.
Working smarter “ involves making use of all the best knowledge we have available ”
(Wiig 1993,51).
Wiig ’ s KM cycle addresses how knowledge is built and used as individuals or as
organizations. There are four major steps in this cycle, as shown in fi gure 2.9 :
1. Building knowledge
2. Holding knowledge
3. Pooling knowledge
4. Applying knowledge
Knowledge
production
Organizational
knowledge
Knowledge
integration
Broadcasting
Searching
Teaching
Sharing
Figure 2.8 Knowledge integration processes in the McElroy KM Cycle
The Knowledge Management Cycle 47
Although the steps are shown as independent and sequential, this is a simplifi cation
since some of the functions and activities may be performed in parallel. It is also pos-
sible to cycle back to repeat functions and activities performed earlier, using with a
different emphasis and/or level of detail. The cycle addresses a broad range of learning
from all types of sources: personal experience, formal education or training, peers, and
intelligence from all sources. We can then hold knowledge either within our heads or
in tangible form such as books or databases. Knowledge can then be pooled and used
in a variety of different ways depending on the context and the purpose.
The cycle focuses on identifying and relating the functions and activities that we
engage in to make products and services as knowledge workers.
Building knowledge refers to a wide range of activities ranging from market research,
focus groups, surveys, competitive intelligence, and data mining applications. Building
knowledge consists of fi ve major activities:
1. Obtain knowledge
2. Analyze knowledge
3. Reconstruct/synthesize knowledge
4. Codify and model knowledge
5. Organize knowledge
Build knowledge
Hold knowledge
Pool knowledge
Use knowledge
In people
In tangible forms (e.g., books)
KM systems (intranet, dbase)
Groups of people brainstorm
In work context
Embedded in work processes
Learn from personal experience
Formal education and training
Intelligence sources
Media, books, peers
Figure 2.9 Wiig KM Cycle
48 Chapter 2
Knowledge creation may occur through R & D projects, innovations by individuals
to improve the way in which they perform their tasks, experimentation, reasoning
with existing knowledge, and by hiring new people. Knowledge creation may also
be accomplished through knowledge importing (e.g., eliciting knowledge from
experts, from procedure manuals, by a joint venture to obtain technology, or by
transferring people between departments). Finally, knowledge may be created through
observing the real world (e.g., site visits, observing processes after the introduction of
a change).
Knowledge analysis consists of:
• Extracting what appears to be knowledge from obtained material (e.g., analyze tran-
scripts and identify themes, listen to an explanation, and select concepts for further
consideration)
• Abstracting extracted materials (e.g., from a model or a theory)
• Identifying patterns extracted (e.g., trend analysis)
• Explaining relations between knowledge fragments (e.g., compare and contrast,
causal relations)
• Verifying that extracted materials correspond to meaning of original sources (e.g.,
meaning has not been corrupted through summarizing, collating, etc.)
Knowledge synthesis or reconstruction consists of generalizing analyzed material
to obtain broader principles, generating hypotheses to explain observations, establish-
ing conformance between new and existing knowledge (e.g., corroborating validity in
light of what is already known), and updating the total knowledge pool by incorporat-
ing the new knowledge.
Codifying and modeling knowledge addresses how we represent knowledge in our
minds (e.g., mental models), how we then assemble the knowledge into a coherent
model, how we document the knowledge in books and manuals, and how we encode
it in order to post it to a knowledge repository.
Finally, knowledge is organized for specifi c uses and according to an established
organizational framework (e.g., standards, categories). Some examples would include
a help desk service or a list of frequently asked questions (FAQs) on the company
intranet. This organization is usually done using some form of knowledge ontology
(conceptual model) and taxonomy (classifi cation rules). Examples would include an
offi cial list of keywords or categories, knowledge object attribute specifi cations, and
guidelines for translation.
Holding knowledge consists of remembering, cumulating knowledge in reposito-
ries, embedding knowledge in repositories, and archiving knowledge. Remembering
The Knowledge Management Cycle 49
knowledge means that the individual has retained or remembered that item of knowl-
edge (e.g., knowledge has been internalized and understood by a given individual).
Accumulating knowledge in a repository means creating a computer-resident knowl-
edge base and encoding knowledge so that it can be stored in organizational memory.
Knowledge is then embedded in the repository by ensuring they are part of business
procedures (e.g., added to a procedures manual, training course). Finally, knowledge
must be archived by creating a scientifi c library and by systematically retiring out-of-
date, false, or no longer relevant knowledge from the active repository. The latter
typically involves storing the content in another, less costly, or less bulky medium for
less frequent future retrieval.
Examples of knowledge held by companies includes intellectual property, patents,
knowledge documented in the form of research reports, and technical papers, or tacit
knowledge, which remains in the minds of individuals but which may be elicited and
embedded in the knowledge base or repository (e.g., tips, tricks of the trade, case
studies, videotapes of demonstrations by experts, and task support systems). In this
way, the valuable knowledge held by the organization is documented in repositories
or in people and therefore available for future reference and use.
Knowledge pooling consists of coordinating knowledge, assembling knowledge,
and accessing and retrieving knowledge. Coordination of knowledge typically requires
the formation of collaborative teams to work with particular content in order to create
a “ who knows what ” network. Once knowledge sources are identifi ed, they are then
assembled into background references for a library or repository in order to make
subsequent access and retrieval easier. Focus groups are often used in order to arrive
at a consensus as to how this can best be achieved. Access and retrieval then addresses
being able to consult with knowledgeable people about diffi cult problems, obtaining
a second opinion from an expert, or discussing a diffi cult case with a peer.
Knowledge can be accessed and retrieved directly from the repository as well (e.g.,
using a knowledge based system to obtain advice on how to do something, or reading
a knowledge document in order to be able to arrive at a decision).
Organizations may pool knowledge in a variety of ways. An employee may realize
that he or she does not have the necessary knowledge and know-how to solve a par-
ticular problem. The individual can contact others in the organization who have faced
and solved similar problems by either obtaining the information from the organiza-
tional knowledge repository or by fi nding an expert through the expertise locator
network and contacting that person directly to obtain help. The individual can then
organize all this information and request that more experienced knowledge workers
validate the content.
50 Chapter 2
Finally, there are too many potential ways to apply the knowledge to list exhaus-
tively. Some examples include:
• Use established knowledge to perform a routine task, for example, make standard
products, provide a standard service, or use the expert network to fi nd out who is
knowledgeable about a particular area.
• Use general knowledge to survey exception situations at hand, for example, deter-
mine what the problem is and estimate potential consequences.
• Use knowledge to describe situation and scope, for example, identify the problem
and in general how it should be handled.
• Select relevant special knowledge to handle the situation, for example, identify who
you need to consult with or have address the problem.
• Observe and characterize a situation with special knowledge, for example, compare
with known patterns and history, followed collecting and organizing the required
information to act.
• Analyze situation with knowledge, for example, judge whether it can be handled
internally or if outside help will be required.
• Synthesize alternative solutions with knowledge, for example, identify options,
outline different approaches that may be taken.
• Evaluate potential alternatives using special knowledge, for example, determine risks
and benefi ts of each possible approach.
• Use knowledge to decide what to do, for example, rank alternatives, select one and
do a reality check.
• Implement selected alternatives, for example, execute the task, and authorize the
team to proceed.
When knowledge is applied to work objects, routine and standard tasks are
approached in a different way from diffi cult or unusual tasks. Routine or standard
tasks are typically carried out using compiled knowledge that we can readily access
and use almost unconsciously or automatically. Diffi cult tasks are usually performed
in a more deliberate and conscious manner, since knowledge workers cannot use
automated knowledge in unanticipated situations.
Figure 2.10 summarizes the key activities in the Wiig KM cycle.
One of the major advantages of the Wiig approach to the KM cycle is the clear and
detailed description of how organizational memory is put into use in order to generate
value for individuals, groups, and the organizational itself. The myriad of ways in
which knowledge can be applied and used are linked to decision making sequences
The Knowledge Management Cycle 51
and individual characteristics. Wiig also emphasizes the role of knowledge and skill,
the business use of that knowledge, constraints that may prevent that knowledge from
being fully used, opportunities, and alternatives to managing that knowledge and the
expected added value to the organization.
An Integrated KM Cycle
A synthesis of the preceding steps from the four approaches to a KM cycle is shown
in table 2.2 .
While the authors use different labels to describe each of the KM cycle stages, they
often refer to the same general type of knowledge processing. Table 2.3 represents an
amalgamation of the major KM cycle steps that each of the four approaches had in
common. The combined steps have been placed in a logical chronological order. The
additional steps contributed by each of the four approaches were then added to this
table, providing a comprehensive overview of knowledge processing throughout the
organizational lifecycle of knowledge.
Some of these processing steps are alternatives — for example, new knowledge must
be created and/or existing knowledge captured and knowledge is either reused or
divested. Regrouping by alternative processing choices thus yields ten major knowl-
edge processing steps:
1. Knowledge capture/creation/contribution
2. Knowledge fi ltering/selection
Build Hold Pool Apply
Obtain
Analyze
Reconstruct
Synthesize
Codify
Model
Organize
Remember
Cumulate in repositories
Embed in repositories
Archive
Coordinate
Assemble
Reconstruct
Synthesize
Access
Retrieve
Perform tasks
Survey, describe
Select
Observe, analyze
Synthesize
Evaluate
Decide
Implement
Figure 2.10 Summary of the Key Wiig KM Cycle activities
52 Chapter 2
Table 2.2 A synthesis of the key KM cycle steps from each of the four approaches
Meyer and Zack
(1999)
Bukowitz and
Williams (2000) McElroy (1999) Wiig (1993)
Acquisition Get Individual and group learning Creation
Refi nement Use Knowledge claim validation Sourcing
Store/retrieve Learn Information acquisition Compilation
Distribution Contribute Knowledge validation Transformation
Presentation Assess Knowledge integration Dissemination
Build/sustain Application
Divest Value realization
Sources: Meyer and Zack, (1999) ; Bukowitz and Williams (2000) ; McElroy, (1999) ; and Wiig
(1993) .
Table 2.3 Synthesis of knowledge processing steps contributed by each of the approaches
Steps in common Step added by
1. Knowledge capture
2. Knowledge creation
2a. Knowledge contribution Bukowitz and Williams (2000)
2b. Knowledge fi ltering and selection Bukowitz and Williams (2000)
3. Knowledge codifi cation
3a. Knowledge refi nement Meyer and Zack (1999); Bukowitz and Williams (2000)
4. Knowledge sharing
5. Knowledge access
5a. Knowledge learning Bukowitz and Williams (2000)
6. Knowledge application
6a. Knowledge evaluation McElroy (1999); Bukowitz and Williams (2000)
7. Knowledge reuse
7a. Knowledge reuse or divestment Bukowitz and Williams (2000)
The Knowledge Management Cycle 53
3. Knowledge codifi cation
4. Knowledge refi nement
5. Knowledge sharing
6. Knowledge access
7. Knowledge learning
8. Knowledge application
9. Knowledge evaluation
10. Knowledge reuse/divestment
Next, an integrated KM cycle can be distilled from our preceding study of some
of the major approaches that have been undertaken to describe the key processes that
should make up the KM cycle. The integrated cycle subsumes most of the steps
involved in the KM cycles discussed in this chapter and classifi es them into three
major stages:
1. Knowledge capture and/or creation
2. Knowledge sharing and dissemination
3. Knowledge acquisition and application
In the transition from knowledge capture/creation to knowledge sharing and dis-
semination, knowledge content is assessed. Knowledge is then made contextual in
order to be understood (acquired) and used (application). This stage then feeds back
into the fi rst one in order to update the knowledge content. The integrated KM cycle
is outlined in fi gure 2.11 .
Knowledge capture refers to the identifi cation and subsequent codifi cation of exist-
ing (usually previously unnoticed) internal knowledge and know-how within the
organization and/or external knowledge from the environment. Knowledge creation
is the development of new knowledge and know-how — innovations that did not have
a previous existence within the company. When knowledge is inventoried in this
manner, the next critical step must be some form of assessment against selection
criteria that will closely follow the organizational goals. Is this content valid? Is it new
and better, in other words, is it of suffi cient value to the organization that it should
be added to the store of intellectual capital?
Once it has been decided that the new or newly identifi ed content is of suffi cient
value, the next step lies in contextualizing this content. This involves maintaining a
link between the knowledge and those knowledgeable about that content: the author
or originator of the idea, subject matter experts, and also those who have garnered
54 Chapter 2
signifi cant experience in making use of this content. Contextualization also implies
identifying the key attributes of the content in order to better match to a variety of
users; for example, personalization to translate the content into one preferred by the
end user or the creation of a short executive summary to better accommodate the time
constraints of a senior manager. Finally, contextualization will often succeed when
the new content is fi rmly yet seamlessly embedded in the business processes of the
organization.
The knowledge management cycle is then reiterated as users understand and decide
to make use of content. The users will validate usefulness, that is, they will signal
when it becomes out of date or when situations are encountered where this knowledge
is not applicable. Users will help validate the scope of the content or to what extent
the best practices and lessons learned can be generalized. They will also, quite often,
come up with new content, which they can then contribute to the next cycle
iteration.
Strategic Implications of the KM Cycle
Knowledge represents the decisive basis for intelligent, competent behavior — at all
three levels: individual, group, and the organization itself. Only a conscious and
organized refl ection of lessons learned and best practices discovered will allow com-
panies to leverage their hard-won knowledge assets. A knowledge architecture needs
to be designed and implemented in order to enable the staged processing and trans-
Assess
Update
Contextualize
Knowledge capture
and/or creation
Knowledge sharing
and dissemination
Knowledge acquisition
and application
Figure 2.11 An Integrated KM Cycle
The Knowledge Management Cycle 55
Context: A major international consulting organization wanted to document lessons
learned from its major projects. This represented a fi rst step toward becoming a learning
organization. From a scan of what other similar companies were doing, their competitive
intelligence led them to select the implementation of an after action review (AAR) in the
form of a project postmortem. The AAR was a new procedure and it was initially piloted
with a group of experienced consultants. Project managers who became experienced with
the postmortem were subsequently asked to become resource people for those willing to
learn and try it out. A new role of knowledge journalist was created in order to have a
neutral, objective person who had not been a member of the original project team who
could facilitate the postmortem process and capture the key learning outcomes from the
project. Finally, the postmortem was added as an additional step to be completed by all
project managers before they could offi cially check off that a project was deemed formally
completed.
Knowledge Processing Steps
1. Knowledge capture/creation/contribution An after-action review process is created within
the organization such that at the end of each project, a meeting is held to have project
team members contribute ideas as to what could have been improved.
2. Knowledge fi ltering/selection During the meeting, the facilitator helps establish criteria
for lessons learned such as was it a factor beyond the control of team members (in which
case nothing much can be done in the future to mitigate against this event). Project team
members must reach a consensus on the criteria that will be used to decide which lessons
learned will be documented and why.
3. Knowledge codifi cation The meeting notes are transcribed and the KM team (including
the knowledge journalist) along with the project team agree on how the lessons learned
will be written up (e.g., format, length, classifi cation tags for future retrieval).
4. Knowledge refi nement The KM team then improves upon the original text of the lessons
learned (e.g., sanitizing or removing information that can identify the project and/or the
people involved, abstracting so that the lessons to be learned are more generalized and
therefore applicable to more than one specifi c context).
5. Knowledge sharing The existence of the lessons learned are publicized and made avail-
able to others (may be organization-wide, may be to specifi c targeted groups).
6. Knowledge access The lessons learned are stored in a database with adequate metadata
or tags that will enable easy access and retrieval (e.g., tagging by the type of lesson such
as “ poor team communication, ” by date, by type of project, and other meaningful tags).
7. Knowledge learning Some of the lessons learned are incorporated into an employee
orientation session and others into a project management – training course. In this way,
the material is used to enable role-playing and to provide themes for group discussion. An
Box 2.1 A vignette: A typical day in the life of knowledge in an organization
56 Chapter 2
example would be a lessons learned that addressed attitudes that were not compatible for
good teamwork. Another project team may decide to use some of the documented lessons
learned for storytelling sessions where participants are asked to take on the perspective of
another team member. In this way, the team members acquire some experience in walking
in someone else ’ s shoes that should afford them a different view on the events that
occurred.
8. Knowledge application A project manager embarking on a new project calls up the
lessons learned from similar projects from the organization ’ s lessons learned database. A
quick scan of the sorts of things that went wrong in the past help the manager to prepare
a risk management and contingency plan for these known challenges. At best, the same
mistakes will not be repeated (which is not to say that human creativity being what it is,
new ones will not arise!)
9. Knowledge evaluation A few people in the organization access the same learned lesson
but fi nd that the lesson is neither quite relevant nor valid in their particular contexts.
They contact the KM team to have additional tags added to this documented lesson — tags
that indicate the specifi c situations in which this is a valid lesson as well as the specifi c
conditions under which the lesson is not to be applied (an example may be one subsidiary
where the workforce is represented by a union and another subsidiary that is not
unionized).
10. Knowledge reuse/divestment The KM team performs its annual cleanup of the lessons
learned database and fi nds that some can be replaced by newer and more comprehensive
lessons. A few lessons are no longer relevant due to changes in the organization, changes
in the business environment, or both (e.g., technology issues with an older version of
software that are now moot with the newer version being used).
Box 2.1 (continued)
formation of knowledge, much like information products are processed, in order to
ensure that the knowledge objects reach the intended end users and are put to good
use. The objective is to retain and share knowledge with a wider audience. Informa-
tion and communication technologies such as groupware, intranets, and knowledge
bases or repositories provide the necessary infrastructure to do so. Business processes
and cultural enablers provide the necessary incentives and opportunities for all
knowledge workers to become active participants throughout the knowledge manage-
ment cycle.
The Knowledge Management Cycle 57
Practical Considerations for Managing Knowledge
It is important to understand the different stages of managing knowledge throughout
the KM cycle; however, it is not enough. From a practical perspective, in order to
manage knowledge, it is also necessary to have an organizing principle — a frame-
work — to classify the different types of activities and functions needed to deal with
all knowledge-related work within and between organizations. This framework is often
encapsulated in the form of a theory or model of KM. Several major KM models are
presented in the next chapter.
Key Points
• There are a number of different approaches to the knowledge management cycle
such as those by McElroy, Wiig, Bukowitz and Willams, and Meyer and Zack.
• By comparing and contrasting these and by validating them through experience
gained to date with KM practice, the major stages are identifi ed as knowledge capture
and creation, knowledge sharing and dissemination, and knowledge acquisition and
application.
• The critical processes throughout the KM cycle assess the worth of content based on
organizational goals contextualize content in order to better match with a variety of
users and continuously update with a focus on updating, archiving as required, and
modifying the scope of each knowledge object.
Discussion Points
1. Discuss the different KM cycles approaches and how they may be integrated into
a comprehensive, integrated approach to the effective management of knowledge
within an organization.
2. Provide an example of how each of the major KM cycle stages listed below can add
value to knowledge and increase the strategic worth of the knowledge asset:
a. Capture
b. Codify
c. Create
d. Share
e. Acquire
f. Apply
58 Chapter 2
3. Where are the go/no decisions in the KM cycle? What types of information would
you require in order to decide whether or not the knowledge content would continue
on to the next step of the cycle?
References
Argyris , C. , and D. Schon . 1978 . Organizational learning: A theory of action perspective . New York :
McGraw-Hill .
Bukowitz , W. , and R. Williams . 2000 . The knowledge management fi eldbook . London, UK :
Prentice-Hall .
McElroy , M. 1999 . The knowledge life cycle. ICM Conference on KM , Miami, FL.
McElroy , M. W. 2003 . The New Knowledge Management: Complexity, Learning, and Sustainable
Innovation , Burlington, MA : KMCI Press/Butterworth-Heinemann.
Meyer , M. , and M. Zack . 1996 . The design and implementation of information products. Sloan
Management Review 37 ( 3 ): 43 – 59 .
Rollett , H. 2003 . Knowledge management: Processes and technologies . Boston : Kluwer Academic
Publishers .
Wiig , K. 1993 . Knowledge management foundations . Arlington, TX : Schema Press .
3 Knowledge Management Models
Furious activity is no substitute for understanding.
— H. H. Williams (1858 – 1940)
A robust theoretical foundation is required as the basis of any knowledge management
initiative that is to succeed. The major KM activities described in the KM cycle in the
previous chapter must have a conceptual framework to operate within, otherwise the
activities will not be coordinated and will not produce the expected KM benefi ts. Eight
different knowledge management models are described in this chapter. The models
all present distinct perspectives on the key conceptual elements that form the infra-
structure of knowledge management. This chapter describes, compares, and contrasts
each in order to provide a sound understanding of the discipline of KM.
Learning Objectives
1. Understand the key tenets of the major knowledge management theoretical models
in use today.
2. Link the KM frameworks to key KM concepts and the major phases of the KM cycle.
3. Explain the complex adaptive system model of KM and how it addresses the subjec-
tive and dynamic nature of content to be managed.
Introduction
In an economy where the only certainty is uncertainty, the one sure source of lasting competi-
tive advantage is knowledge.
— I. Nonaka and Takeuchi (1995)
60 Chapter 3
Although few would argue that knowledge is unimportant, the overriding problem is
that few managers and information professionals understand how to manage knowl-
edge in knowledge-creating organizations. There is a tendency to focus on “ hard ” or
quantifi able knowledge; and KM is often seen as some sort of information processing
machine. The advent of knowledge management was initially met with a fair degree
of criticism — many people felt this was yet another buzzword and bandwagon that
they were expected to jump on. One of the reasons that KM has now established itself
more credibly as both an academic discipline of study and a professional fi eld of
practice is the work that has been done on theoretical or conceptual models of knowl-
edge management. Early on, more pragmatic considerations about the processes of
KM were complemented by the need to understand what was happening in organiza-
tional knowing, reasoning, and learning.
A more holistic approach to KM has become necessary as the complex, subjective,
and dynamic nature of knowledge has developed. Cultural and contextual infl uences
further increased the complexity involved in KM, and these factors also had to be
taken into account in a model or framework that could situate and explain the key
KM concepts and processes. Last but not least, measurements were needed in order to
be able to monitor progress toward and attainment of expected KM benefi ts.
This holistic approach is one that encompasses all the different types of content to
be managed, from data, to information, to knowledge, but also conversions from tacit
to explicit and back to tacit knowledge types. The KM models presented in this chapter
all attempt to address knowledge management in a holistic and comprehensive
manner.
Davenport and Prusak (1998 , 2) provide the following distinctions among data,
information, and knowledge, which recap the examples in chapter 1:
Data A set of discrete, objective facts about events.
Information A message, usually in the form of a document or an audible or visible
communication.
Knowledge A fl uid mixing of framed experiences, values, contextual information, and
expert insight that provide a framework for evaluating and incorporating new experi-
ences and information. It originates and is applied in the minds of those who know.
In organizations, it often becomes embedded not only in documents or repositories,
but also in organizational routines, processes, practices, and norms.
Davenport and Prusak (1998) refer to the distinctions among data, information,
and knowledge as operational, and argue that we can transform information into
knowledge by means of comparison, consequences, connections, and conversation.
Knowledge Management Models 61
They stress that knowledge-creating activities take place between people and within
each human being, and that we have to consider knowledge to be among the most
important corporate assets.
Since there are many overlapping categories of types of knowledge, it is tempting
to look for the defi nitive method of knowledge management. While we study many
methods, there is no need to choose one method over another for all of the many
different types of content. Respecting the diversity of types of knowledge, content
management may be a better, more general term than knowledge management.
Nonaka and Takeuchi (1995) provide a more philosophical distinction: starting
from the traditional defi nition of knowledge as “ justifi ed true belief. ” They defi ne
knowledge as “ a dynamic human process of justifying personal belief toward the truth ”
(Nonaka and Takeuchi, 58, emphasis added). They contend that it is necessary to
create knowledge in order to produce innovation. For them, organizational knowledge
creation is: “ The capability of a company as a whole to create new knowledge, dis-
seminate it throughout the organization and embody it in products, services, and
systems (p. 58). ”
The concept of tacit knowledge, as we saw in chapter 1, has been clarifi ed by Polanyi
(1966) who stresses the importance of the “ personal ” mode of knowledge construc-
tion, affected by emotions and acquired at the end of a process of every individual ’ s
active creation and organization of the experiences. When a person tacitly knows, he
or she does and acts without distance, uses the body, and has great diffi culty explain-
ing in words the rules and algorithms the process he or she is involved in. The act of
tacitly knowing is without distance from things and performances and the knowing
interaction between persons is one of an unaware observation and a social, commu-
nitarian closeness.
A thesis of Polanyi is that all knowledge is either tacit or rooted in tacit knowledge.
Tacit knowledge is hard to express in formalized ways, is context-specifi c, personal,
and diffi cult to communicate. On the other hand, explicit knowledge is codifi ed,
expressed in formal and linguistic ways, easily transmittable and storable, and express-
ible in words and algorithms; however, explicit knowledge represents only the tip of
the iceberg of the entire body of knowledge. This defi nition of the tacit/explicit
concepts makes clear the importance of adequately considering the tacit dimension.
The 80/20 rule appears to apply here — roughly 80 percent of our knowledge is in
tacit form as individuals, as groups, and as an organization. Only 15 – 20 percent of
valuable knowledge has typically been captured, codifi ed, or rendered tangible and
concrete in some fashion. This is usually in the form of books, databases, audio or
video recordings, graphs or other images, and so forth. The tacit/explicit mobilization
62 Chapter 3
(in the epistemological dimension) and the individual/group/organizational sharing
and diffusion (in the ontological dimension) have to take place in order to create
knowledge and produce innovation. Each of the KM models presented in the next
section addresses this point in different but complementary ways.
Major Theoretical KM Models
Major theoretical KM models were chosen for this section based on the following
criteria:
• They represent a holistic approach to knowledge management (i.e., they are com-
prehensive and take into consideration people, process, organization and technology
dimensions).
• They have been reviewed, critiqued, and discussed extensively in the KM literature —
by practitioners, academics, and researchers.
• The models have been implemented and fi eld tested with respect to reliability and
validity.
This is not meant to be an exhaustive list or a defi nitive short list; but the models
have been selected with a view to providing the widest possible perspective on KM as
a whole combined with a deeper, more robust theoretical foundation to explain,
describe, and better predict how best to manage knowledge.
The von Krogh and Roos Model of Organizational Epistemology
The von Krogh and Roos KM model ( 1995 ) distinguishes between individual knowl-
edge and social knowledge. Von Krogh and Roos take an epistemological approach to
managing organizational knowledge: the organizational epistemology KM model.
While pinning down a defi nition of organizational has been problematic, and the term
is often used interchangeably with information , there are a number of issues that must
be addressed:
• How and why individuals within an organization come to know
• How and why organizations, as social entities, come to know
• What counts for knowledge of the individual and the organization
• What are the impediments in organizational KM?
The cognitive perspective (e.g., Varela 1992 ) proposes that a cognitive system,
whether it is a human brain or a computer, creates representations (i.e., models) of
reality and that learning occurs when these representations are manipulated. A cogni-
Knowledge Management Models 63
tive organizational epistemology views organizational knowledge as a self-organizing
system in which humans are transparent to the information from the outside (i.e., we
take in information through our senses and use this information to build our mental
models). The brain is a machine based on logic and deduction that does not allow
any contradictory propositions. The organization thus picks up information from its
environment and processes it in a logical way. Alternative courses of action are gener-
ated through information search and the cognitive competence of an organization
depends on the mobilization of individual cognitive resources, that is, a linear sum-
mation of individuals to form the organizational whole.
The connectionist approach, on the other hand, is more holistic than reductionist
in nature. The brain is not assumed to sequentially process symbols but to perceive
wholeness, global properties, patterns, synergies, and gestalts. Learning rules govern
how the various components of these whole networks are connected. Information is
not only taken in from the environment but also generated internally. Familiarity and
practice lead to learning. Individuals form nodes in a loosely connected organizational
system and knowledge is an emergent phenomenon that stems from the social inter-
actions of these individuals. From this perspective, knowledge resides not only in the
minds of individuals, but also in the connections among these individuals. A collective
mind is formed as the representation of this network; and it is this mind that lies at
the core of organizational knowledge management.
Von Kroch and Roos adopt the connectionist approach. In their organizational
epistemology KM model, knowledge resides in both the individuals of an organization;
and at the social level, in the relations between the individuals. Knowledge is charac-
terized as “ embodied ” that is, “ everything known is known by somebody ” ( von Krogh
and Roos 1995 , 50). Unlike the cognitive perspective, where knowledge is viewed as
an abstract entity, connectionism maintains that there cannot be knowledge without
a knower. This fi ts nicely with the concept that tacit knowledge is very diffi cult to
abstract out of someone and make more concrete. It also reinforces the strong need
to maintain links between knowledge objects and those who are knowledgeable about
them — authors, subject matter experts, and experienced users who have applied the
knowledge, successfully and unsuccessfully.
In 1998, von Krogh, Roos, and Kleine examined the fragile nature of KM in orga-
nizations. They describe this fragility in terms of the mindset of the individuals, com-
munication in the organization, the organizational structure, the relationship between
the members, and the management of human resources. These fi ve factors could
impede the successful management of organizational knowledge for innovation, com-
petitive advantage, and other organizational goals. For example, if individuals do not
64 Chapter 3
perceive knowledge to be a crucial competence of the fi rm, then the organization will
have trouble developing knowledge-based competencies. If there is no legitimate lan-
guage to express new knowledge in the individual, then contributions will fail. If the
organizational structure does not facilitate innovation, then KM will fail. If individual
members are not eager to share their experiences with their colleagues on the basis of
mutual trust and respect, then there will be no generation of social, collective knowl-
edge within that organization. Finally, if those contributing knowledge are not evalu-
ated highly and acknowledged by top management, they will lose their motivation
to innovate and develop new knowledge for the fi rm.
Organizations need to put knowledge enablers in place who serve to stimulate
individual knowledge development, group sharing of knowledge, and organizational
retention of valuable knowledge-based content. This approach was further refi ned
( von Krogh, Ichijo, and Nonaka 2000 ) to propose a model of knowledge enabling,
rather than knowledge management. Knowledge enabling refers to the “ overall set of
organizational activities that positively affect knowledge creation ” (p. 4). This typically
involves facilitating relationships and conversations as well as sharing local knowledge
across an organization and across geographical and cultural borders.
The connectionist approach appears to be the more appropriate one to underpin a
theoretical model of knowledge management, especially due to the fact that the
linkage between knowledge and those who absorb and make use of the knowledge is
viewed as an unbreakable bond. The connectionist approach provides a solid theoreti-
cal cornerstone for a knowledge model and is a component of the models discussed
in this chapter.
The Nonaka and Takeuchi Knowledge Spiral Model
Nonaka and Takeuchi (1995) studied how Japanese companies were successful in
achieving creativity and innovation. They quickly found that it was far from a mecha-
nistic processing of objective knowledge. Instead, they found that organizational
innovation often stemmed from highly subjective insights that can best be described
in the form of metaphors, slogans, or symbols. The Nonaka and Takeuchi model of
KM has its roots in a holistic model of knowledge creation and the management of
“ serendipity. ” The tacit/explicit spectrum of knowledge forms (the epistemological
dimension) and the individual/group/organizational or three-tier model of knowledge
sharing and diffusion (the ontological dimension) are both needed in order to create
knowledge and produce innovation.
Nonaka and Takeuchi argue that a key factor behind the successful track record in
innovation of Japanese enterprises stems from the more tacit-driven approach to
Knowledge Management Models 65
knowledge management. They argue that Western culture considers knower and
known as separate entities (harking back to the cognitive approach, which stresses the
importance of communicating and storing explicit knowledge). In contrast, the struc-
tural characteristics of the Japanese language and infl uences such as Zen Buddhism
led the Japanese to consider that there is a oneness of humanity and nature, body and
mind, and self and the other ( Nonaka and Takeuchi 1995 ). It follows that it may be
easier for Japanese managers to engage in the process of indwelling , a term used by
Polanyi (1966) to defi ne the involvement of the individuals with objects through self-
involvement and commitment, in order to create knowledge. In such a cultural envi-
ronment, knowledge is principally “ group knowledge, ” easily converted and mobilized
(from tacit to explicit, along the epistemological dimension) and easily transferred
and shared (from the individual to the group to the organization, in the ontological
dimension).
Nonaka and Takeuchi emphasize the necessity of integrating the two approaches,
from the cultural, epistemological, and organizational points of view, in order to
acquire new cultural and operational tools to better build knowledge-creating organi-
zations. Their construct of the “ hypertext organization ” is the formalization of the
need for an integration of the traditionally opposed Western and Japanese schools of
thought.
The Knowledge Creation Process Knowledge creation always begins with the indi-
vidual. A brilliant researcher has an insight that ultimately leads to a patent. A middle
manager has an intuition about market trends that becomes the catalyst for an impor-
tant new product concept. A shop fl oor worker draws upon years of experience to
come up with a process innovation that saves the company millions of dollars. In
each of these scenarios, an individual ’ s personal, private knowledge (predominantly
tacit in nature) is translated into valuable, public organizational knowledge. Making
personal knowledge available to others in the company is at the core of this KM model.
This type of knowledge creation process takes place continuously and it occurs at all
levels of the organization. In many cases, the creation of knowledge occurs in an
unexpected or unplanned way.
According to Takeuchi and Nonaka, there are four modes of knowledge conversion
that:
Constitute the engine of the entire knowledge-creation process. These modes are what the
individual experiences. They are also the mechanisms by which individual knowledge gets
articulated and amplifi ed into and throughout the organization. (p. 57, emphasis added)
66 Chapter 3
Organizational knowledge creation, therefore, should be understood as a process that organiza-
tionally amplifi es the knowledge created by individuals and crystallizes it as a part of the knowl-
edge network of the organization. (p. 59)
Knowledge creation consists of a social process between individuals in which knowledge trans-
formation is not simply a unidirectional process but it is interactive and spiral. (pp. 62 – 63)
Knowledge Conversion There are four modes of knowledge conversion, as shown in
fi gure 3.1 :
1. From tacit knowledge to tacit knowledge: process of socialization
2. From tacit knowledge to explicit knowledge: process of externalization
3. From explicit knowledge to explicit knowledge: process of combination
4. From explicit knowledge to tacit knowledge: process of internalization
Socialization (tacit-to-tacit) consists of the sharing of knowledge in face-to-face,
natural, and typically social interactions. This involves arriving at a shared understand-
ing through the sharing of mental models, brainstorming to come up with new ideas,
apprenticeship or mentoring interactions, and so on. Socialization is among the easiest
forms of exchanging knowledge, because it is what we do instinctively when we gather
at the coffee machine or engage in impromptu corridor meetings. The greatest advan-
tage of socialization is also its greatest drawback: because knowledge remains tacit, it
is rarely captured, noted, or written down anywhere. It remains in the minds of the
Tacit knowledge
from
Explicit knowledge
Tacit knowledge to Explicit knowledge
Socialization Externalization
Internalization Combination
Figure 3.1 The Nonaka and Takeuchi model of knowledge conversion
Knowledge Management Models 67
original participants. Although socialization is a very effective means of knowledge
creation and sharing, it is one of the more limited means. Furthermore, it is diffi cult
and time-consuming to disseminate all knowledge using only the socialization mode.
Davenport and Prusak (1998, 70) point out that “ tacit, complex knowledge, devel-
oped and internalized by the knower over a long period of time, is almost impossible
to reproduce in a document or a database. Such knowledge incorporates so much
accrued and embedded learning that its rules may be impossible to separate from how
an individual acts. ”
This means that the process of acquiring tacit knowledge is not strictly tied to the
use of language but to experience and to the ability to transmit and to share it. This
must not be confused with the idea of a simple transfer of information because there
is no knowledge creation if we abstract the transfer of information and experiences
away from the associated emotions and specifi c contexts in which they are embedded.
Socialization consists of sharing experiences through observation, imitation, and
practice.
For example, Honda organizes “ brainstorming camps ” during which there are
detailed discussions to solve diffi cult problems in development projects. These infor-
mal meetings are usually held outside the workplace, off-site, where everyone is
encouraged to contribute to the discussion and no one is allowed to refer to the status
and qualifi cation of employees involved. The only behavior not allowed during these
discussions is simple criticism not followed by constructive suggestions. Brainstorming
meetings are used by Honda not only to develop new products, but also to improve
its managerial systems and its commercial strategies. Brainstorming can represent
occasions for creative dialogue. And brainstorming provides a moment of shared
experience, followed by sharing tacit knowledge. During brainstorming, people create
harmony among themselves, they feel engaged as part of a whole, and they feel
themselves allied by the same goal. Many other organizations organize similar “ Knowl-
edge Days ” or “ Knowledge Caf é s ” to encourage this type of tacit-to-tacit knowledge
sharing.
Externalization (tacit-to-explicit) is a process that gives a visible form to tacit knowl-
edge and converts it to explicit knowledge. It can be defi ned as “ a quintessential
knowledge creation process in that tacit knowledge becomes explicit, taking the shapes
of metaphors, analogies, concepts, hypotheses, or models ” ( Nonaka and Takeuchi
1995 , 4). In this mode, individuals are able to articulate the knowledge and know-how
and, in some cases, the know-why and the care-why. Knowledge that was previously
tacit can somehow be written down, recorded, drawn, or made tangible or concrete
in some manner. An intermediary is often needed at this stage, because it is always
68 Chapter 3
diffi cult to transform one type of knowledge into another. A knowledge journalist is
someone who can interview knowledgeable individuals in order to extract, model, and
synthesize in a different way (format, length, level of detail, etc.) in order to increase
its scope (i.e., so that a wider audience can understand and apply this content).
Once externalized, knowledge is now tangible and permanent. It can be shared
more easily with others and leveraged throughout the organization. Good principles
of content management will need to be brought into play in order to make future
decisions about archiving, updating, and retiring externalized knowledge content. It
is particularly important not to lose attribution and authorship information when
tacit knowledge is made explicit. This involves codifying metadata or information
about the content along with the actual content.
For example, Canon decided to design and produce a mini-copier that can be used
occasionally for personal use. This new product was very different from expensive
industrial copiers, which also engendered high maintenance costs. Canon had to
design something that was relatively inexpensive with reasonable maintenance costs.
The Canon mini-copier project members understood that the most frequent problem
was with the drums, so they designed a type of drum that would last through a fair
amount of usage. They then had to be creative and design a drum that did not cost
more than the mini-copier! How did they come up with this innovation? After long
discussions, one day the leader of the unit that had to solve this problem brought
along some cans of beer and as the team was brainstorming, someone noted that beer
cans had low costs and used the same type of aluminum as copier drums did . . . the
rest, as they say, is history.
The next stage of knowledge conversion in the Nonaka and Takeuchi model is that
of combination (explicit-to-explicit), the process of recombining discrete pieces of
explicit knowledge into a new form. Some examples would be a synthesis in the form
of a review report, a trend analysis, a brief executive summary, or a new database to
organize content. No new knowledge is created per se — it is a new combination or
representation of existing or already explicit knowledge. In other words, combination
happens when concepts are sorted and systematized in a knowledge system. Some
examples would be populating a database, when we teach, when we categorize and
combine concepts, or when we convert explicit knowledge into a new medium such
as a computer-based tutorial. For example, in developing a training course or curricu-
lum for a university course, existing, explicit knowledge would be recombined into a
form that better lends itself to teaching and to transferring this content.
Another example is that of Kraft General Foods when they planned and developed
a new point-of-sale (POS) system, one that would track not only items sold but also
Knowledge Management Models 69
information about the buyers. Their intent was to use this information to plan
new models to sell, new combinations of products, of products and service, of
service, and so on. The POS system collects and analyzes information and then
helps marketing people plan information-intensive marketing programs called
“ micro-merchandising. ”
Finally, the last conversion process, internalization (explicit-to-tacit) occurs through
the diffusion and embedding of newly acquired behavior and newly understood or
revised mental models. Internalization is very strongly linked to “ learning by doing. ”
Internalization converts or integrates shared and/or individual experiences and knowl-
edge into individual mental models. Once new knowledge has been internalized, it is
then used by employees who broaden it, extend it, and reframe it within their own
existing tacit knowledge bases. They understand, learn, and buy into the new knowl-
edge and this is manifest as an observable change, that is, they now do their jobs and
tasks differently.
For example, General Electric has developed a system of documenting all customer
complaints and inquiries in a database that can be accessed by all its employees. This
system allows the employees to fi nd answers to new customers ’ questions much more
quickly because it facilitates the sharing of employees ’ experiences in problem solving.
This system helps the workers to internalize others ’ experiences in answering ques-
tions and solving problems.
Knowledge, experiences, best practices, lessons learned, and so on go through the
conversion processes of socialization, externalization, and combination. It is crucial
that knowledge is not halted at any one of these stages. The reason is that it is only
when knowledge is internalized into individuals ’ tacit knowledge bases in the form
of shared mental models or technical know-how that this knowledge becomes a valu-
able asset — to the individual, to their community of practice, and to the organization.
In order for organizational knowledge creation to take place, however, the entire
conversion process has to begin all over again: the tacit knowledge accumulated at
the individual level needs to be brought into contact with other organizational
members, thereby starting a new spiral of knowledge creation ( Nonaka and Takeuchi
1995 , 69). When experiences and information are transferred through observation,
imitation, and practice, then we are back in the socialization quadrant. This knowledge
is then formalized and converted into explicit knowledge, through the use of analogy,
metaphor, and model, in the externalization quadrant. This explicit knowledge is then
systemized and recombined in the combination quadrant — whereupon it once again
becomes part of individuals ’ experience. In the internalization quadrant, knowledge
has once again thus become tacit knowledge.
70 Chapter 3
Knowledge Spiral Knowledge creation is not a sequential process, but depends on a
continuous and dynamic interaction between tacit and explicit knowledge throughout
the four quadrants. Organizations articulate, organize, and systematize individual tacit
knowledge, produce and develop tools, structures, and models to accumulate it and
share it to create new knowledge through the knowledge spiral as illustrated in fi gure
3.2 . The knowledge spiral is a continuous activity of knowledge fl ow, sharing, and
conversion by individuals, communities, and the organization itself.
The two steps that are the most diffi cult are those involving a change in the type
of knowledge, namely, externalization, which converts tacit into explicit knowledge,
and internalization, which converts explicit knowledge into tacit. These two steps
require a high degree of personal commitment and they will typically involve mental
models, personal beliefs, and values, and a process of reinventing oneself, one ’ s group,
and the organization as a whole. A metaphor is a good way of expressing this “ inex-
pressible ” content. For example, a slogan, a story, an analogy, or a symbol of some
type can encapsulate complex contextual meanings. A metaphor is often used to
convey two ideas in a single phrase and may be defi ned as a phrase that “ accomplishes
in a word or phrase what could otherwise be expressed only in many words, if at all ”
( Sommer and Weiss 1995 , vii). All of these vehicles are good models to represent a
consistent, systematic, and logical understanding of content without any contradic-
tions. The better and the more coherent the model, and the better the model fi ts with
existing mental models, the higher the likelihood of successful implementation of a
knowledge spiral.
Dialogue
Socialization Externalization
Linking
explicit
knowledge
Field building
Internalization
Learning by doing
Combination
Figure 3.2 The Nonaka and Takeuchi knowledge spiral
Knowledge Management Models 71
It is possible to structure metaphors, models, and analogies in an organizational
KM design. The fi rst principle is to have built-in redundancy to make sure that there
is overlapping information. Redundancy will make it easier to articulate content, to
share content, and to make use of it. An example is to set up several competing groups,
to build in a rotational strategy so workers do a variety of jobs, and to provide easy
access to company information via a single integrated knowledge base.
Knowledge sharing and use happens through the knowledge spiral that, “ starting
at the individual level and moving up through expanding communities of interaction
[. . .] crosses sectional, departmental, divisional and organizational boundaries ”
(Nonaka and Takeuchi 1995, 72). Nonaka and Takeuchi argue that an organization
has to promote a facilitating context in which both the organizational and the indi-
vidual knowledge-creation processes can easily take place, acting as a spiral. They
describe the following “ enabling conditions for organizational knowledge creation ” :
Intention An organization ’ s aspiration to its goals (strategy formulation in a business
setting)
Autonomy To allow individuals to act autonomously, according to the “ minimum
critical specifi cation ” principle, and involved in cross-functional self-organized teams
Fluctuation and creative chaos To stimulate the interaction between the organization
and the external environment and/or create fl uctuations and breakdowns by means
of creative chaos or strategic “ equivocality ”
Redundancy Existence of information that goes beyond the immediate operational
requirements of organizational members; competing multiple teams on the same issue;
strategic rotation of personnel
Requisite variety Internal diversity to match the variety and complexity of the environ-
ment; to provide to everyone in the organization the fastest access to the broadest
variety of necessary information; fl at and fl exible organizational structure interlinked
with effective information networks
The Nonaka and Takeuchi model has proven to be one of the more robust in the
fi eld of KM and it continues to be applied in a variety of settings. One of its greatest
strengths is the simplicity of the model — both in terms of understanding the basic
tenets of the model and in terms of being able to quickly internalize and apply the
KM model. One of the major shortcomings is that while valid, it does not appear to
be suffi cient to explain all of the stages involved in managing knowledge. The Nonaka
and Takeuchi model focuses on the knowledge transformations between tacit and
explicit knowledge, but the model does not address larger issues of how decision
making takes place by leveraging both these forms of knowledge.
72 Chapter 3
Box 3.1 A vignette: Skidmore, Owings, & Merrill LLP (SOM)
SOM (http://www.som.com) is a leading architecture, urban design and planning, engi-
neering, and interior architecture fi rm in the US ( Pulsifer 2008 ). Founded in 1936, SOM
has completed more than ten thousand projects in over fi fty countries. Most architectural
and engineering fi rms operate in an environment fi lled with guidelines and regulations
derived from best practices and standards that are often disseminated through the com-
pany ’ s intranet. SOM also has CAD (computer-aided design) libraries, drafting standards,
employee directories, and social networks — in other words, bits and pieces of KM. So why
did they need a KM model in addition to these piecemeal implementations? The model
is necessary in order to have a deeper understanding of how KM contributes to the goals
of the company. In this type of industry, as with many others, tacit knowledge consists
of creative and innovative knowledge — pretty much the polar opposite of such well-
documented explicit knowledge as guidelines and standards. A KM model helps SOM to
harness both types of knowledge in order to perform effi ciently, effectively, and competi-
tively. A comprehensive, easy-to-apply KM model can help decision makers and all
employees. With it, they can make the best use of tacit and explicit knowledge and apply
processes to transform knowledge from one form to the other. A KM model, together with
the KM process cycle discussed in the previous chapter, can be used by SOM as a checklist —
to ensure that all key KM components have been addressed — not just addressed well but
also addressed coherently, since KM components are highly interdependent and integrated
with one another. In the absence of a model, the fi rm can continue implementing KM
pieces in an ad hoc fashion, but will rarely succeed in bringing the pieces together in order
to better attain company goals and objectives.
A good KM model is a framework that positions goals, procedures, and enablers to help
the fi rm capitalize on their valuable knowledge assets. With a KM model, everyone can
understand what KM is expected to do for SOM, why they should share their knowledge,
how they should share, and how they can assess the costs and benefi ts that result. The
KM model will help ensure everyone shares the same understanding of the role of KM
throughout their career — from their employee orientation as new hires to their exit inter-
view and knowledge handover at the end of their career. The SOM KM framework helps
ensure that valuable knowledge is not lost when senior employees leave, that information
and knowledge fl ows among departments, that work is not duplicated, and that errors are
minimized. The company is better able to centrally gather, measure, and analyze how well
they have met their goals. Finally, the KM model helps SOM leadership to better shape
and support the fi rm ’ s business strategy. Each group within SOM needs to operate on this
common KM framework in order to promote individual, departmental, and organizational
success.
Knowledge Management Models 73
The Choo Sense-Making KM Model
Choo (1998) has described a model of knowledge management that stresses sense
making (largely based on Weick 2001 ), knowledge creation (based on Nonaka and
Takeuchi 1995 ), and decision making (based on, among others, bounded rationality,
Simon 1957 , among others). The Choo KM model focuses on how information ele-
ments are selected and subsequently fed into organizational actions. Organizational
action results from the concentration and absorption of information from the external
environment into each successive cycle, as illustrated in fi gure 3.3 . Each of the phases,
sense making, knowledge creation, and decision making, has an outside stimulus or
trigger.
The sense-making stage is the one that attempts to make sense of the information
streaming in from the external environment. Priorities are identifi ed and used to fi lter
Shared meanings
Shared meanings
Knowledge creating
New knowledge, new capabilities
External information
and knowledge
Decision making
Next knowing
cycle
Goal-directed adaptive behavior
Streams of
experience
Sense making
Figure 3.3 Overview of Choo ’ s (1998) knowledge management model
74 Chapter 3
the information. Common interpretations are constructed by individuals from the
exchange and negotiation of information fragments combined with their previous
experiences. Weick (2001) proposed a theory of sense making to describe how chaos
is transformed into sensible and orderly processes in an organization through the
shared interpretation of individuals. A loosely coupled system is a term used to describe
systems that can be taken apart or revised without damaging the entire system. For
example, a human being is tightly coupled, but the human genome is loosely coupled.
Loose coupling permits adaptation, evolution, and extension. Sense making can be
thought of as a loosely coupled system where individuals construct their own repre-
sentation of reality by comparing current with past events.
Weick (2001) claims that sense making in organizations consists of four integrated
processes:
• Ecological change
• Enactment
• Selection
• Retention
Ecological change is a change in the environment that is external to the organiza-
tion — one that disturbs the fl ow of information to participants. This triggers an eco-
logical change in the organization. Organizational actors enact their environment by
attempting to closely examine elements of the environment.
In the enactment phase, people try to construct, to rearrange, to single out, or to
demolish specifi c elements of content. Many of the objective features of their environ-
ment are made less random and more orderly through the creation of their own
constraints or rules. Enactment clarifi es the content and issues to be used for the
subsequent selection process.
Selection and retention are the phases where individuals attempt to interpret the
rationale for the observed and enacted changes by making selections. The retention
process in turn furnishes the organization with an organizational memory of success-
ful sense-making experiences. This memory can be reused in the future to interpret
new changes and to stabilize individual interpretations into a coherent organizational
view of events and actions. These phases also serve to reduce any uncertainty and
ambiguity associated with unclear, poorly defi ned information.
Knowledge creating is seen as the transformation of personal knowledge between
individuals through dialog, discourse, sharing, and storytelling. This phase is directed
by a knowledge vision of “ as is ” (current situation) and “ to be ” (future, desired state).
Knowledge creation widens the spectrum of potential choices in decision making
Knowledge Management Models 75
through the provision of new knowledge and new competencies. The result feeds the
decision-making process with innovative strategies that extend the organization ’ s
capability to make informed, rational decisions. Choo (1998) draws upon the Nonaka
and Takeuchi (1995) model for a theoretical basis of knowledge creation.
Decision making is situated in rational decision-making models that are used
to identify and evaluate alternatives by processing the information and knowledge
collected to date. There are a wide range of decision-making theories such as the
theory of games and economic behavior (e.g., Dixit and Nalebuff 1991 ; Bierman and
Fernandez 1993 ), chaos theory, emergent theory, and complexity theory (e.g., Gleick
1987 ; Fisher 1984 ; Simon 1969 ; Stewart 1989 ; Stacey 1992 ), and even a garbage can
theory of decision making (e.g., Daft 1982 ; Daft and Weick 1984 ; Padgett 1980 ).
The garbage can model (GCM) of organizational decision making was developed
in reference to “ ambiguous behaviors, ” that is, explanations or interpretations of
behaviors that at least appear to contradict classical theory. The GCM was greatly
infl uenced by the realization that extreme cases of aggregate uncertainty in decision
environments would trigger behavioral responses, which, at least from a distance,
appear irrational or at least not in compliance with the total/global rationality of
economic man (e.g., “ act fi rst, think later ” ). The GCM was originally formulated in
the context of the operation of universities and their many interdepartmental com-
munications problems.
The garbage can model tried to expand organizational decision theory into the then
uncharted fi eld of organizational anarchy, which is characterized by problematic
preferences, unclear technology, and fl uid participation. “ The theoretical breakthrough
of the garbage can model is that it disconnects problems, solutions and decision
makers from each other, unlike traditional decision theory. Specifi c decisions do not
follow an orderly process from problem to solution, but are outcomes of several rela-
tively independent streams of events within the organization ” ( Daft 1982 , 139).
Simon (1957, 198) identifi ed the principle of bounded rationality as a constraint
for organizational decision making, stating that “ the capacity of the human mind for
formulating and for solving complex problems is very small compared with the size
of the problems whose solution is required for objectively rational behavior in the real
world — or even for a reasonable approximation to such objective rationality. ”
Simon suggested that persons faced with ambiguous goals and unclear means of
linking actions to those goals seek to fulfi ll short-term subgoals. Subgoals are objectives
that the individual believes can be achieved by allocating resources under his or her
control. These subgoals are generally not derived from broad policy goals, but rather
from experiences, education, the community, and personal needs. Bounded rationality
76 Chapter 3
theory was fi rst proposed by Simon (1976) as a limited or constrained rationality to
explain human decision-making behavior. When confronted with a highly complex
world, the mind constructs a simple mental model of reality and tries to work within
that model. The model may have weaknesses, but the individual will try to behave
rationally within the constraints or boundaries of that model.
Individuals can be bound in a decisional process by a number of factors,
such as:
• Limits in knowledge, skills, habits, and responsiveness
• Availability of personal information and knowledge
• Values and norms held by the individual that may differ from those of the
organization
This theory has long been accepted in organizational and management sciences.
Bounded rationality is characterized by individuals ’ use of limited information analy-
sis, evaluation, and processing, shortcuts and rules of thumb (sometimes called heu-
ristics), and “ satisfi cing ” (i.e., a combination of satisfying and suffi cing) behavior,
which means it may not be fully optimized, but it is good enough. The 80/20 rule
(e.g., Clemson 1984 ) is a good example of the application of satisfi cing behavior — for
example, in a brainstorming session, when the group may not have fully exhausted
all the possibilities but did manage to capture roughly 80 percent of them. Continuing
on would result in the law of diminishing returns — so much more effort would be
required to incorporate the remaining 20 percent — that generally participants would
agree that what they have so far is good enough to proceed with.
One of the strengths of the Choo KM model is the holistic treatment of key KM
cycle processes extending to organizational decision making, which is often lacking
in other theoretical KM approaches. This makes the Choo model one of the more
realistic or feasible models of KM as the model represents organizational actions
with high fi delity . The Choo KM model is particularly well suited to simulations and
hypothesis or scenario-testing applications.
The Wiig Model for Building and Using Knowledge
Wiig (1993) approached his KM model with the following principle: in order for
knowledge to be useful and valuable, it must be organized. Knowledge should be
organized differently depending on what the knowledge will be used for. For example,
in our own mental models, we tend to store our knowledge and know-how in the
form of semantic networks. We can then choose the appropriate perspective based on
the cognitive task at hand.
Knowledge Management Models 77
Knowledge organized in a semantic network way can be accessed and retrieved
using multiple entry paths that map onto different knowledge tasks to be completed.
Some useful dimensions to consider in Wiig ’ s KM model include:
• Completeness
• Connectedness
• Congruency
• Perspective and purpose
Completeness addresses the question of how much relevant knowledge is available
from a given source. Sources may be human minds or knowledge bases (i.e., tacit or
explicit knowledge). We fi rst need to know that the knowledge is out there. The
knowledge may be complete in the sense that all that is available about the subject is
there but if no one knows of its existence and/or availability, they cannot make use
of this knowledge.
Connectedness refers to the well-understood and well-defi ned relations between
the different knowledge objects. There are very few knowledge objects that are totally
disconnected from the others. The more connected a knowledge base is (i.e., the
greater the number of interconnections in the semantic network), then the more
coherent the content and the greater its value.
A knowledge base is said to be congruent when all the facts, concepts, perspectives,
values, judgments, and associative and relational links between the knowledge objects
are consistent. There should be no logical inconsistencies, no internal confl icts, and
no misunderstandings. Most knowledge content will not meet such ideals where
congruency is concerned. However, concept defi nitions should be consistent and
the knowledge base as a whole needs to be constantly fi ne-tuned to maintain
congruency.
Perspective and purpose refer to the phenomenon where we know something,
but often from a particular point of view or for a specifi c purpose that we have in
mind. We organize much of our knowledge using the dual dimensions of perspective
and purpose (e.g., just-in-time knowledge retrieval or just enough or “ on-demand ”
knowledge).
Semantic networks are useful ways of representing different perspectives on the
same knowledge content. Figures 3.4 through 3.8 show examples of different perspec-
tives on the same knowledge object (i.e., a car) using semantic networks.
Wiig ’ s KM model goes on to defi ne different levels of internalization of knowledge.
Wiig ’ s approach can be seen as a further refi nement of the fourth Nonaka and
Takeuchi quadrant of internalization. Table 3.1 briefl y defi nes each of these levels. In
78 Chapter 3
Car
Maintain
Commute
Vacation
Driving
Car
Maintain
Commute
Vacation
Carpool
Traffic jams
Gas prices
Driving
Car
Maintain
Commute
Vacation
Scheduled maintenance
Funny noise
Car wash
Driving
Figure 3.4 Example of a semantic network
Figure 3.5 Example of a semantic network — “ commute ” view
Figure 3.6 Example of a semantic network — “ maintain ” view
Knowledge Management Models 79
general, there is a continuum of internalization, starting with the lowest level, the
novice, who “ does not know he does not know, ” that is, who does not even have
an awareness that the knowledge exists, to the mastery level, where there is a deep
understanding not just of the know-what, but the know-how, the know-why, and the
care-why (i.e., values, judgments, and motivations for using the knowledge).
Wiig (1993) also defi nes three forms of knowledge: public knowledge, shared
expertise, and personal knowledge. Public knowledge is explicit, taught, and routinely
shared knowledge that is generally available in the public domain. An example would
be a published book or information on a public web site. Shared expertise is proprietary
knowledge assets that are exclusively held by knowledge workers and shared in their
work or embedded in technology. This form of knowledge is usually communicated
via specialized languages and representations. Although he does not use the term,
Car
Maintain
Commute
Vacation Book time off
Map out trip
Sunglasses
Driving
Car
Maintain
Commute
Vacation
Driver’s license
Optometrist visit
Cell phone
Weather report
Driving
Figure 3.7 Example of a semantic network — “ vacation ” view
Figure 3.8 Example of a semantic network — “ driving ” view
80 Chapter 3
this knowledge form would be common in communities of practice, informal net-
works of likeminded professionals who typically interact and share knowledge in
order to improve the practice of their profession. Finally, personal knowledge is the
least accessible but most complete form of knowledge. Personal knowledge is typically
more tacit than explicit knowledge, and is used unconsciously in work, play, and
daily life.
In addition to the three major forms of knowledge (personal, public, and shared)
Wiig (1993) defi nes four types of knowledge (factual, conceptual, expectational, and
methodological). Factual knowledge deals with data and causal chains, measurements,
readings — typically directly observable and verifi able content. Conceptual knowledge
deals with systems, concepts, and perspectives (e.g., concept of a track record, a bull
market). Expectational knowledge concerns judgments, hypotheses, and expectations
held by knowers. Examples are intuition, hunches, preferences, and heuristics that we
make use of in our decision making. Finally, methodological knowledge deals with rea-
soning, strategies, decision-making methods, and other techniques. Examples would
be learning from past mistakes or forecasting based on analyses of trends.
Together, the three forms of knowledge and the four types of knowledge combine
to yield a KM matrix that forms the basis of the Wiig KM model. Table 3.2 summarizes
the Wiig KM model.
To summarize, Wiig (1993) proposes a hierarchy of knowledge that consists of
public, shared, and personal knowledge forms. Wiig ’ s hierarchy of knowledge forms
is shown in fi gure 3.9 .
Table 3.1 Wiig KM model — degrees of internalization
Level Type Description
1 Novice Barely aware or not aware of the knowledge and how it can be used
2 Beginner Knows that the knowledge exists and where to get it but cannot reason with it
3 Competent Knows about the knowledge, can use and reason with the knowledge given external knowledge bases such as documents and people to help
4 Expert Knows the knowledge, holds the knowledge in memory, understands where it applies, reasons with it without any outside help
5 Master Internalizes the knowledge fully, has a deep understanding with full integration into values, judgments, and consequences of using that knowledge
Knowledge Management Models 81
Knowledge
Public Shared Personal
Coded, accessible Coded, inaccessible Uncoded, inaccessible
Passive Active Active ActivePassive Passive
Library
books,
manuals
Experts,
knowledge
bases
Products,
technologies
Information
sytems,
services
Isolated
facts,
recent
memory
Habits,
skills,
procedural
knowledge
Figure 3.9 Wiig hierarchy of knowledge forms
Table 3.2 Wiig KM matrix
Type of knowledge
Form of
knowledge
Factual Conceptual Expectational Methodological
Public Measurement, reading
Stability, balance
When supply exceeds demand, price drops
Look for temperatures outside the norm
Shared Forecast analysis Market is hot A little water in the mix is OK
Check for past failures
Personal The “ right ” color, texture
Company has a good track record
Hunch that the analyst has it wrong
What is the recent trend?
82 Chapter 3
The major strength of the Wiig model is that despite having been formulated in
1993, the organized approach to categorizing the type of knowledge to be managed
remains a very powerful theoretical model of KM. The Wiig KM model is perhaps the
most pragmatic of the models in existence today and can easily be integrated into any
of the other approaches. This model enables practitioners to adopt a more detailed or
refi ned approach to managing knowledge based on the type of knowledge, but going
beyond the simple tacit/explicit dichotomy. The major shortcoming is that very little
has been published in terms of research and/or practical experience in implementing
this model.
The Boisot I-Space KM Model
The Boisot KM model is based upon the key concept of an “ information good ” that
differs from a physical asset. Boisot distinguishes information from data by emphasiz-
ing that information is what an observer will extract from data as a function of his or
her expectations or prior knowledge . The effective movement of information goods
is very much dependent on senders and receivers sharing the same coding scheme or
language. A “ knowledge good ” is a concept that in addition possesses a context within
which it can be interpreted. Effective knowledge sharing requires that senders and
receivers share the context as well as the coding scheme.
Boisot (1998) proposes the following two key points:
The more easily data can be structured and converted into information, the more diffusible it
becomes.
The less data that has been so structured requires a shared context for its diffusion, the more
diffusible it becomes.
Together, they underpin a simple conceptual framework, the information space or
I-Space KM model. The data are structured and understood through the processes of
codifi cation and abstraction. Codifi cation refers to the creation of content categories —
the fewer the number of categories, the more abstract the codifi cation scheme. The
assumption is that well-codifi ed abstract content is much easier to understand and
apply than highly contextual content. Boisot ’ s KM model does address the tacit form
of knowledge by noting that in many situations, the loss of context due to codifi ca-
tion may result in the loss of valuable content. This content needs a shared context
for its interpretation and that implies face-to-face interaction and spatial proximity —
which is analogous to the socialization quadrant in the Nonaka and Takeuchi model
(1995).
The I-Space model can be visualized as a three-dimensional cube with the following
dimensions (refer to fi gure 3.10 ):
Knowledge Management Models 83
• Codifi ed — uncodifi ed
• Abstract — concrete
• Diffused — undiffused
The activities of coding, abstracting, diffusing, absorbing, impacting, and scanning
all contribute to learning. Where they take place in sequence — and to some extent
they must — together they make up the six phases of a social learning cycle (SLC).
These are described in table 3.3 .
The strength of the Boisot model is that it incorporates a theoretical foundation of
social learning. The Boisot model serves to link together content management, infor-
mation management, and knowledge management in a very effective way. In a very
approximate sense, the codifi cation dimension is linked to categorization and classi-
fi cation; the abstraction dimension is linked to knowledge creation through analysis
and understanding; and the third diffusion dimension is linked to information access
and transfer. There is a strong potential to make use of the Boisot I-Space KM model
to map and manage an organization ’ s knowledge assets as an SLC — something that is
not directly addressed by the other KM models. However, the Boisot model appears
to be somewhat less well known, less accessible, and as a result has not had widespread
implementation. More extensive fi eld-testing of this KM model would provide feed-
back regarding its applicability as well as provide more guidelines on how best to
implement the I-Space approach.
Codified
Uncodified
Abstract Concrete
Undiffused
Diffused
Figure 3.10 The Boisot I-Space KM model
84 Chapter 3
Table 3.3 The social learning cycle in Boisot ’ s I-Space KM model
Phase Name Description
1 Scanning • Identifying threats and opportunities in generally available but often fuzzy content • Scanning patterns such as unique or idiosyncratic insights that then become the possession of individuals or small groups • Scanning may be very rapid when the data is well codifi ed and abstract and very slow and random when the data is uncodifi ed and context-specifi c
2 Problem solving • The process of giving structure and coherence to such insights, that is, codifying them • In this phase they are given a defi nite shape and much of the uncertainty initially associated with them is eliminated • Problem solving initiated in the uncodifi ed region of the I-Space is often both risky and confl ict-laden
3 Abstracting • Generalizing the application of newly codifi ed insights to a wider range of situations • Involves reducing them to their most essential features, that is, conceptualizing them • Problem solving and abstraction often work in tandem
4 Diffusing • Sharing the newly created insights with a target population • The diffusion of well codifi ed and abstract content to a large population will be technically less problematic than that of content which is uncodifi ed and context-specifi c • Only a sharing of context by sender and receiver can speed up the diffusion of uncodifi ed data • The probability of a shared context is inversely proportional to population size
5 Absorbing • Applying the new codifi ed insights to different situations in a “ learning by doing ” or a “ learning by using ” fashion • Over time, such codifi ed insights come to acquire a penumbra of uncodifi ed knowledge which helps to guide their application in particular circumstances
6 Impacting • The embedding of abstract knowledge in concrete practices • The embedding can take place in artifacts, technical or organizational rules, or in behavioral practices • Absorption and impact often work in tandem
Source: Adapted from Boisot (1998).
Knowledge Management Models 85
Complex Adaptive System Models of KM
The intelligent complex adaptive systems (ICAS) KM theory of the organization views
the organization as an ICAS (e.g., , 1989 1981 ; Bennet and Bennet 2004 ). Beer (1981)
was a pioneer in the treatment of the organization as a living entity. In his viable
system model (VSM), a set of functions is distinguished that ensure the viability of
any living system and organizations in particular. The VSM is based on the principles
of cybernetics or systems science that make use of communication and control mecha-
nisms to understand, describe, and predict what an autonomous or viable organization
will do.
Complex adaptive systems consist of many independent agents that interact with
one another locally. Together, their combined behavior gives rise to complex adaptive
phenomena. Complex adaptive systems are said to “ self-organize ” through this form
of emergent phenomena. There is no overall authority that is directing how each one
of these independent agents should be acting. An overall pattern of complex behavior
arises or emerges as a result of all of their interactions.
The VSM has been applied to a wide range of complex situations, including the
modeling of an entire nation (implemented by President Salvador Allende in Chile in
1972). The model enables managers and their consultants to elaborate policies and to
develop organizational structures with a clear understanding of the recursions in
which they are supposed to operate, and to design regulatory systems within those
recursions that obey certain fundamental laws of cybernetics (e.g., Ashby ’ s Law of
Requisite Variety). As such, the usefulness of the VSM as a theoretical grounding for
KM becomes quite clear.
A number of researchers have made use of complex adaptive system theories in
deriving a theoretical basis for KM. Snowden (2000, 1) the director of Cynefi n, a
research group at IBM, describes his approach as follows: “ Complex adaptive systems
theory is used to create a sense-making model that utilizes self-organizing capabilities
of the informal communities and identifi es a natural fl ow model of knowledge cre-
ation, disruption and utilization. ”
Cynefi n is a Welsh word with no direct equivalent in English that can be translated
as “ habitat, ” or as an adjective, “ acquainted ” or “ familiar. ” The Cynefi n research
center focuses on action research in organizational complexity and is open to indi-
viduals and to organizations. One of the major points emphasized by Snowden (2000)
is that the focus on tacit-explicit knowledge conversion (e.g., the Nonaka and Takeuchi
model, 1995) that has dominated knowledge management practice since 1995 pro-
vides a limited, but useful, set of models and tools. The Cynefi n model instead pro-
poses the following key types of knowledge: known, knowable, complex, and chaotic.
86 Chapter 3
Snowden ’ s Cynefi n model is less concerned with tacit-explicit conversions because of
its focus on descriptive self-awareness rather than prescriptive organization models.
Bennet and Bennet (2004) also describe a complex adaptive system approach to
KM but the conceptual roots are somewhat different from the Beer VSM. Bennet and
Bennet believe strongly that the traditional bureaucracies or popular matrix and fl at
organizations are not suffi cient to provide the cohesiveness, complexity, and selective
pressures that ensure the survival of an organization. A different model is proposed,
one in which the organization is viewed as a system that is in a symbiotic relationship
with its environment, that is, “ turning the living system metaphor into reality ”
(Bennet and Bennet 2004, 25). The ICAS model is composed of living subsystems that
combine, interact, and coevolve to provide the capabilities of an advanced, intelligent,
technological, and sociological adaptive enterprise. Complex adaptive systems are
organizations that are composed of a large number of self-organizing components,
each of which seeks to maximize its own specifi c goals but which also operate accord-
ing to the rules and context of relationships with the other components and the
external world.
In an ICAS, the intelligent components consist of people who are empowered to
self-organize, but who remain part of the overall corporate hierarchy. The challenge
is to take advantage of the strengths of people while getting them to cooperate
and collaborate to leverage knowledge and to maintain a sense of unity of purpose.
Organizations take from the environment, transform those inputs into higher-value
outputs, and provide them to customers and stakeholders. Organizational intelligence
becomes a form of competitive intelligence that helps facilitate innovation, learning,
adaptation, and quick responses to unanticipated situations. Organizations solve pro-
blems by creating options, and they use internal and external resources to add value
above and beyond the value of the initial inputs. They must also do this in an effec-
tive and effi cient manner. Knowledge becomes a valuable resource because it is critical
in taking effective action in a variety of uncertain situations. The actions taken can
be used to distinguish between information management (predictable reactions to
known and anticipated situations) and knowledge management (use existing or
create new reactions to unanticipated situations). Knowledge will typically consist of
experience, judgment, insight, context, and the right information. Understanding and
meaning become prerequisites to taking effective action and they create value by
ensuring the survival and the growth of the organization.
The fi ve key processes in the ICAS KM model can be summarized as:
1. Understanding
2. Creating new ideas
Knowledge Management Models 87
3. Solving problems
4. Making decisions
5. Taking actions to achieve desired results
Since only people or individuals can make decisions and take actions, the emphasis
of this model is on the individual knowledge worker and his or her competency,
capacity, learning, and so on. These are leveraged through multiple networks (e.g.,
communities of practice) to make available the knowledge, experience, and insights
of others. This type of tacit knowledge leveraged through dynamic networks makes a
broader “ highway ” available to connect data, information, and people through virtual
communities and knowledge repositories.
To survive and successfully compete, an organization will also require eight emer-
gent characteristics, according to this model:
1. Organizational intelligence
2. Shared purpose
3. Selectivity
4. Optimum complexity
5. Permeable boundaries
6. Knowledge centricity
7. Flow
8. Multidimensionality
An emergent characteristic is the result of nonlinear interactions, synergistic inter-
actions, and self-organizing systems. The ICAS KM model follows along the lines of
the other approaches in that it is connectionist and holistic in nature. The emergent
ICAS characteristics are outlined in fi gure 3.11 . These emergent properties serve to
endow the organization with the internal capability to deal with the future unantici-
pated environments yet to be encountered.
Organizational intelligence refers to the capacity of the fi rm to innovate, acquire
knowledge, and apply that knowledge to relevant situations. In the ICAS model, this
property refers to the ability of the organization to perceive, interpret, and respond
to its environment in such a way as to meet its goals and satisfy its stakeholders.
This is very similar to the Choo sense-making model approach. Unity and a shared
purpose represent the ability of the organization to integrate and mobilize resources
through a continuous, two-way communication with its large number of relatively
independent subsystems, much like the VSM. Optimum complexity represents
the right balance between internal complexity (i.e., the number of different relevant
88 Chapter 3
Organizational intelligence
Shared
purpose
Multi-
dimensionality
Knowledge
centricity
Optimum
complexity
Flow Selectivity
Permeable boundaries
Creativity Complexity Change
Figure 3.11 Overview of ICAS knowledge management model
organizational states) to deal with the external environment without losing sight of
the overall goal and the notion of a “ one-fi rm fi rm ” or common identity. The major
difference here with VSM is the notion of relevant states — not all possible states. This
selectivity is in keeping with the notion of evaluating value of content in KM as
opposed to a more exhaustive warehousing approach.
The process of selectivity consists of the fi ltering of incoming information from the
outside world. Good fi ltering requires broad knowledge of the organization, specifi c
knowledge of the customer, and a strong understanding of the fi rm ’ s strategic goals.
Knowledge centricity refers to the aggregation of relevant information from self-
organization, collaboration, and strategic alignment. Flow enables knowledge centric-
ity and facilities the connections and the continuity needed to maintain unity and
Knowledge Management Models 89
give coherence to organizational intelligence. Permeable boundaries are essential if
ideas are to be exchanged and built upon. Finally, multidimensionality represents
organizational fl exibility that ensures that the knowledge workers have the competen-
cies, perspectives, and cognitive ability to address issues and solve problems. This is
sometimes seen as being analogous to developing human instinct.
Each of these characteristics must emerge from the nature of the organization. They
cannot be designed by managerial decree — only nurtured, guided, and helped along.
In summary, there are four major ways in which the ICAS model describes organiza-
tional knowledge management:
1. Creativity
2. Problem solving
3. Decision making
4. Implementation
Creativity is the generation of new ideas, perspectives, understanding, concepts,
and methods to help solve problems, build products, offer services, and so on. Indi-
viduals, teams, networks, or virtual communities can solve problems and they take
the outputs of the creative processes as their inputs. Decision making is the selection
of one or more alternatives that were generated during the problem solving process
and implementation is the carrying out of the selected alternative(s) in order to obtain
the desired results.
Complex-adaptive-system-theory-based KM models are defi nitely showing both an
evolution and a return to systems-thinking roots in the KM world. All of the models
presented in this chapter are relevant and each offers valuable theoretical foundations
in understanding knowledge management in today ’ s organizations. What they all
share is a connectionist and holistic approach to better understand the nature of
knowledge as a complex adaptive system that includes knowers, the organizational
environment, and the “ bloodstream ” of organizations — the knowledge-sharing
networks.
The European Foundation for Quality Management (EFQM) KM Model
The EFQM model ( Bhatt 2000 , 2001 , 2002 ) looks at the way in which knowledge
management is used to attain the goals of an organization. This model is based on
traditional models of quality and excellence, so there are very strong links between
KM processes and expected organizational results. Figure 3.12 shows the major com-
ponents of the EQFM KM model.
90 Chapter 3
The major components are: leadership, people, policy and strategy, partnerships
and resources, processes, and the ultimate key, performance results. The role of KM
as a whole is thus clearly positioned as an enabler that helps a company achieve
its goals — that is to say, the company ’ s goals, and not KM-oriented goals. This is an
excellent depiction of the role of KM. One of the major reasons why KM fails occurs
when KM is pursued for the sake of KM itself. This is analogous to producing incom-
plete sentences when attempting to articulate the justifi cation for KM. For example,
“ the objective of the KM program is to promote greater sharing of knowledge ” as
opposed to “ the objective of the KM program is to promote the greater sharing of
knowledge so that our sales force can collectively benefi t from all the best practices
and lessons learned accumulated to date in order to provide faster and better front-line
service. ”
The inukshuk KM Model
The inukshuk KM model ( Girard 2005 ) was developed to help Canadian government
departments to better manage their knowledge. This model was developed by both
reviewing existing major models to extract fi ve key enablers (technology, leadership,
culture, measurement, and process) and by conducting quantitative research to
Leadership
People
Processes
Enablers Results
Key
performance
results
(people,
customer,
society)
Policy
and
stategy
Partnerships
and
resources
Figure 3.12 The key components of the EFQM model
Knowledge Management Models 91
validate these enablers. The name inukshuk is derived from the human-shaped fi gures
built by piling stones on one another by the Inuit in the northern part of Canada to
serve as navigational aids. There were three main reasons for choosing this symbol to
represent KM: it is well-recognized in Canada, it emphasizes the key role played by
people in KM, and while all inukshuks are similar they are not identical, refl ecting the
variations in KM implemented in different organizations. Figure 3.13 depicts the
major components of the inukshuk KM model.
The process element is directly derived from the SECI model ( Nonaka and Takeuchi
1995 ). Technology and culture represent critical structural elements that help main-
tain the integrity of the fi gure. Measurement and leadership are placed at the very top
to represent the importance of the overarching functions of measuring the impact of
KM and providing leadership and support for its implementation. This last model is
a good note to end on, as it represents a good aggregation of the key elements from
most KM models. While there remains diversity in terms of KM models, the major
components are beginning to gain more consensus and acceptance. Few KM research-
ers and practitioners would argue against including KM measurement, leadership,
technology, culture, and process in a solid KM model.
Tacit knowledge
LEADERSHIP
TECHNOLOGY
Socialization
Internalization
Externalization
Combination
CULTURE
Explicit knowledge
MEASUREMENT
Figure 3.13 Overview of the inukshuk KM model
92 Chapter 3
Strategic Implications of KM Models
Models help us to put the disparate pieces of a puzzle together in a way that leads to
a deeper understanding of both the pieces and the ensemble that they make up.
Models supplement the concept analysis approach outlined in the fi rst chapter in
order to take our understanding to a deeper level. KM models are still fairly new to
the practice or business of knowledge management, and yet they represent the way
forward. A coherent model of knowledge-driven processes is crucial in order for stra-
tegic business goals to be successfully albeit partially addressed by KM initiatives. KM
is not a silver bullet and it will not solve all organizational problems. Those areas of
knowledge-intensive work and intellectual capital development that are amenable to
KM processes, on the other hand, require a solid foundation of understanding what
KM is, what the key KM cycle processes are, and how these fi t in to a model that
enables us to interpret, to establish cause and effect, and to successfully implement
knowledge management solutions.
Practical Implications of KM Models
For many years now, KM practitioners have been practicing “ KM on the fl y. ” Many
valuable empirical lessons and best practices have been garnered through experience
with many diverse organizations. However, KM needs to be grounded in more robust,
sound theoretical foundations — something more than “ it worked well last time, so
. . . ” The key role played by KM models is to ensure a certain level of completeness
or depth in the practice of KM: a means of ensuring that all critical factors have been
addressed. The second practical benefi t of a model-driven KM approach is that models
enable not only a better description of what is happening but they help provide a
better prescription for meeting organizational goals. KM models help to explain what
is happening now, and they provide us with a valid blueprint or road map to get
organizations to where they want to be with their knowledge management efforts. Lai
and Chu (2000) reviewed the infl uence that major KM models have had on KM prac-
tice and found that measurement was the most infl uential component. The next in
terms of level of infl uence were culture (including reward and motivation compo-
nents) followed by technology as a strong enabler of KM.
Knowledge Management Models 93
Key Points
• Knowledge management encompasses data, information, and knowledge (some-
times referred to collectively as “ content ” ), and it addresses both tacit and explicit
forms of knowledge.
• The von Krogh and Roos KM model take an organizational epistemology approach
and emphasize that knowledge resides both in the minds of individuals and in the
relations they form with other individuals.
• The Nonaka and Takeuchi KM model focuses on knowledge spirals that explain the
transformation of tacit knowledge into explicit knowledge and then back again as the
basis for individual, group, and organizational innovation and learning.
• Choo and Weick adopt a sense-making approach to model knowledge management
that focuses on how information elements are fed into organizational actions through
sense making, knowledge creation, and decision making.
• The Wiig KM model is based on the principle that in order for knowledge to be
useful and valuable, it must be organized through a form of semantic network that is
connected, congruent, and complete and has perspective and purpose.
• The Boisot model introduces three key dimensions of knowledge beyond tacit and
explicit; codifi ed, abstract, and diffused knowledge.
• Complex adaptive systems are particularly well suited to model KM as they view the
organization much like a living entity concerned with independent existence and
survival. Beer and Bennet (1989) and Bennet (1981) have applied this approach to
describe the cohesiveness, complexity, and selective pressures that operate on ICAS.
• The EFQM model introduces the major components of leadership, people, policy
and strategy, and partnerships and resources, in addition to processes, as being key
enablers of organizational success.
• The inukshuk model reprises the key enablers that form part of most KM models
and assembles these components in a highly visual and symbolic fashion to depict
the key importance that people play in KM. Canadian government leaders have
applied this model.
Discussion Points
1. Compare and contrast the cognitive and connectionist approaches to knowledge
management. Why is the connectionist approach more suited to the von Krogh KM
94 Chapter 3
model? What are the strengths of this approach? What are its weaknesses? Use exam-
ples to make your points.
2. Describe how the major types of knowledge (i.e., tacit and explicit) are transformed
in the Nonaka and Takeuchi knowledge spiral model of KM. Use a concrete
example to make your point (e.g., a bright idea that occurs to an individual in the
organization).
a. Which transformations would prove to be the most diffi cult? Why?
b. Which transformation would prove to be fairly easy? Why?
c. What other key factors would infl uence how well the knowledge spiral model
worked within a given organization?
3. In what ways is the Choo and Weick KM model similar to the Nonaka and Takeuchi
KM model? In what ways do they differ?
a. How does the integration of a bounded rationality approach to decision making
strengthen this model? Give some examples.
b. List some of key triggers that are required in order for the sense-making KM model
approach to be successful.
4. How is the Wiig KM model related to the Nonaka and Takeuchi model? In what
important ways do they differ?
a. List some examples of internalization to illustrate how each of the fi ve levels
differs.
b. How do public, private, and shared knowledge differ? What are the implications
of managing these different types of knowledge according to the Wiig KM model?
5. Outline the general strategy you would use in order to implement the Boisot I-Space
KM model. Where would you expect to encounter diffi culties? What would be some
of the expected benefi ts to the organization of applying this approach?
6. What is the major advantage of a complex adaptive system approach to a KM
model? What are some of the drawbacks?
a. Provide an everyday example of requisite variety. Next, apply this to the manage-
ment of knowledge in an organization. What are the elements needed in order to
successfully regulate a complex adaptive system? Why?
7. What additional factors do the EFQM and inukshuk KM models introduce?
8. How would you go about selecting a KM model for a given organization? What are
some of the questions you would ask of the employees? Of the senior managers?
Others?
Knowledge Management Models 95
9. How would you justify the need for a KM model?
10. What is the relationship between the KM processes described in chapter 2 and the
KM models outlined in this chapter?
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4 Knowledge Capture and Codifi cation
If written directions alone would suffi ce, libraries wouldn ’ t need to have the rest of the universi-
ties attached.
— Judith Martin (1938 – )
This chapter addresses the fi rst phase of the knowledge management cycle, knowledge
capture and/or creation. The major approaches, techniques, and tools used to elicit
tacit knowledge, to trigger the creation of new knowledge, and to subsequently orga-
nize this content in a systematic manner (codifi cation) are presented. These approaches
represent a multidisciplinary methodology that integrates what we have found to be
successful in a variety of other fi elds such as knowledge acquisition for the develop-
ment of expert systems, instructional design techniques for course content creation
and organization, task analysis techniques used in the development of performance
support systems, and taxonomic approaches that originate from library and informa-
tion studies. Knowledge capture and codifi cation are the primary activities involved
in knowledge retention strategies and the management of strategic human capital.
Learning Objectives
1. Become familiar with the basic terminology and concepts related to knowledge
capture and codifi cation.
2. Describe the major techniques used to elicit tacit knowledge from subject matter
experts.
3. Defi ne the major roles and responsibilities that come into play during the knowl-
edge capture and codifi cation phase.
4. Outline the general taxonomic approaches used in classifying knowledge that has
been captured.
98 Chapter 4
5. Analyze the type of knowledge to be captured and codifi ed, select the best approach
to use, and discuss its advantages and shortcomings for a given knowledge elicitation
application.
Introduction
The fi rst high-level phase of the knowledge management cycle, as seen in fi gure 4.1 ,
begins with knowledge capture and codifi cation. More specifi cally, tacit knowledge is
captured or elicited and explicit knowledge is organized or coded.
In knowledge capture, a distinction needs to be made between the capture and
identifi cation of existing knowledge and the creation of new knowledge. In most
organizations, explicit or already identifi ed and coded knowledge typically represents
only the tip of the iceberg. Traditional information systems departments primarily
deal with highly structured (records or forms oriented) data that makes up much less
than 5 percent of a company ’ s information. In knowledge management, we need to
also consider knowledge that we know is present in the organization, which we can
then set out to capture. There remains, however, that interesting area of knowledge
that we do not know about. This as-yet-unidentifi ed knowledge will require additional
steps in its capture and codifi cation. Finally, there is knowledge that we know we do
not have. We will need to facilitate the creation of this new, innovative content (refer
to fi gure 4.2 ).
Assess
Update
Contextualize
Knowledge capture
and/or creation
Knowledge sharing
and dissemination
Knowledge acquisition
and application
Figure 4.1 An integrated KM cycle
Knowledge Capture and Codifi cation 99
Capturing the knowledge in an organization is not purely about technology.
Indeed, many fi rms fi nd that information technology (IT) plays only a small part in
ensuring that information is available to those who need it. The approach needed
depends on the kind of business, its culture, and the ways in which people solve
problems. Some organizations generally deliver standard products and services, while
others are constantly looking for new ways of doing things. Knowledge capture can
therefore span a whole host of activities, from organizing customer information
details into a single database to setting up a mentoring program. We need to capture
both types of knowledge — explicit and tacit. Knowledge about standardized work, for
example, can be described explicitly and is easily captured in writing. On the other
hand, where there is innovation and creativity, people will also need some direct
contact ( Moorman and Miner 1997 ). Knowledge capture cannot, therefore, be a
purely mechanistic “ add-on, ” because it has to do with the discovery, organization,
and integration of knowledge into the fabric of the organization. Knowledge has to
be captured and codifi ed in such a way that it can become a part of the existing
knowledge base of the organization. Every organization has a history, which provides
a backdrop to the growth and evolution of the organization. Every organization has
a memory. The embodiment of the organizational memory is the experience of its
employees combined with the tangible data and knowledge stores in the organization
( Walsh and Ungson 1991 ). Bush (1945) envisioned “ instruments . . . which, if prop-
erly developed, will give man access to and command over the inherited knowledge
Information sources
Known
Known
Unknown
Unknown
User
awareness
Know that
we know
Know that
we don’t know
Don’t know
that we know
Don’t know that
we don’t know
Figure 4.2 The known-unknown matrix ( Frappaolo 2006 )
100 Chapter 4
of the ages. ” Knowledge that is not captured in this way becomes devalued and
eventually ignored. Knowledge is more than statements, declarations, and observa-
tions: it represents an intellectual currency that produces the most value when
circulated. It may have unrealized potential and value, but unless it is spent, its value
is not tested.
In today ’ s fast-paced economy, an organization ’ s knowledge base is quickly becom-
ing its only sustainable competitive advantage. As such, this resource must be pro-
tected, cultivated, and shared among organizational members. Until recently,
companies could succeed based upon the individual knowledge of a handful of stra-
tegically positioned individuals. Increasingly, however, competitive advantage is to be
gained by making individual knowledge available within the organization, which then
becomes organizational knowledge. Organizational knowledge complements individ-
ual knowledge and makes it stronger and broader. The full utilization of an organiza-
tion ’ s knowledge base, coupled with the potential of individual skills, competencies,
thoughts, innovations, and ideas, will enable a company to compete more effectively
in the future. Competitiveness is becoming increasingly dependent on an organiza-
tion ’ s agility or ability to respond to changes in a very timely manner. The major
component of agility lies in the skills and learning abilities of the knowledge workers
within that organization.
There is no doubt that knowledge capture may be diffi cult, particularly in the case
of tacit knowledge. Tacit knowledge management is the process of capturing the
experience and expertise of the individual in an organization and making it available
to anyone who needs it. The capture of explicit knowledge is the systematic approach
of capturing, organizing, and refi ning information in a way that makes information
easy to fi nd, and facilitates learning and problem solving. Knowledge often remains
tacit until someone asks a direct question. At that point, tacit can become explicit,
but unless that information is captured for someone else to use again at a later date,
learning, productivity, and innovation are stifl ed.
Once knowledge is explicit, it should be organized in a structured document that
will enable multipurpose use. The best KM tools enable knowledge creation once and
then leverage it across multiple channels, including phone, e-mail, discussion forums,
Internet telephony, and any new channels that come online. There are a wide variety
of techniques used to capture and codify knowledge and many of these have their
origins in fi elds other than knowledge management (e.g., artifi cial intelligence, sociol-
ogy, instructional design), which are described here.
Knowledge Capture and Codifi cation 101
Tacit Knowledge Capture
Traditionally, knowledge capture has emphasized the individual ’ s role in gathering
information and creating new knowledge. The literature shows a lack of consensus on
the role of the individual in knowledge acquisition. Some authors (e.g., Nelson and
Winter 1982 ) purport that the fi rm is a learning entity unto itself — that is, it has some
cognitive capabilities that are quite apart from the individuals who comprise it. In
contrast, other authors (e.g., Dodgson 1993 ) do not believe that organizations per se
can acquire knowledge and learn, only individuals can learn. A middle ground is
needed where individuals in the fi rm play a critical role in organizational knowledge
acquisition.
Learning at the individual level, however, is widely accepted to be a fundamentally
social process — something that cannot occur without group interaction in some form.
Individuals thus learn from the collective and at the same time the collective learns
from the individuals (e.g., Crossan, Lane, and White 1999 ). According to Crossan ’ s 4I
model (see fi gure 4.3 ), organizational learning involves a tension between assimilating
new learning (exploration) and using what has been learned (exploitation). Individual,
group, and organizational levels of learning are linked by the social and psychological
Organization
Group
Individual
FEEDBACK
FEED FORWARD
Individual Group Organization
Interpret
Integrate Experimenting
Intuiting
attending
Institutionalize
knowledge
Figure 4.3 The 4I model of organizational learning ( Crossan, Lane, and White 1999 )
102 Chapter 4
processes of intuiting, interpreting, integrating, and institutionalizing (the four I ’ s).
Zietsma et al. (2002) modifi ed this slightly by including the process of attending at
the stage of intuiting and the process of experimenting at the stage of interpreting.
In KM, this knowledge creation or capture may be done by individuals who perform
this role for the organization or a group within that organization, by all members of
a community of practice (CoP) or a dedicated CoP individual — but it is really being
done on a personal level as well. Almost everyone performs some knowledge creation,
capture, and codifi cation activities in carrying out their job. Cope (2000) refers to this
as PKM (personalized KM). Within the fi rm, individuals share perceptions and jointly
interpret information, events, and experiences ( Cohen and Levinthal 1990 ) and at
some point, knowledge acquisition extends beyond the individuals and is coded into
corporate memory ( Inkpen 1995 ; Spender 1996 ; Nonaka and Takeuchi 1995 ). Unless
knowledge is embedded into corporate memory, the fi rm cannot leverage the knowl-
edge held by individual members of the organization. Organizational knowledge
acquisition is the “ amplifi cation and articulation of individual knowledge at the fi rm
level so that it is internalized into the fi rm ’ s knowledge base. ” ( Malhotra 2000 , 334)
The value of tacit knowledge sharing was discovered in a surprising way at Xerox
( Roberts-Witt 2002 ), which will be discussed later in this chapter.
Many of the tacit knowledge capture techniques described in this chapter stem
from techniques that were originally used in artifi cial intelligence, more specifi cally,
in the development of expert systems. An expert system incorporates know-how gath-
ered from experts and is designed to perform as experts do. The term “ knowledge
acquisition ” was coined by the developers of such systems and referred to various
techniques such as structured interviewing, protocol or talk aloud analysis, question-
naires, surveys, observation, and simulation. Some authors (e.g., Keritsis 2001 ) even
use the term digital cloning . Knowledge management in business settings is similarly
concerned with knowledge capture, fi nding ways to make tacit knowledge explicit
(e.g., documenting best practices) or creating expert directories to foster knowledge
sharing through human – human collaboration ( Smith 2000 ). In 1989 , for example,
Feigenbaum contrasted traditional libraries as “ warehouses of passive objects where
books and journals wait for us to use our intelligence to fi nd them, to interpret them
and cause them fi nally to divulge their stored knowledge ” (p. 122) with a library of
the future where books would interact and collaborate with users.
Tacit Knowledge Capture at the Individual and Group Levels
Knowledge acquisition from individuals or groups can be characterized as the transfer
and transformation of valuable expertise from a knowledge source (e.g., human expert,
documents) to a knowledge repository (e.g., corporate memory, intranet). This process
Knowledge Capture and Codifi cation 103
involves reducing a vast volume of content from diverse domains into a precise, easily
usable set of facts and rules.
The idea of acquiring knowledge from an expert in a given fi eld for the purpose of designing a
specifi c presentation of the acquired information is not new. Reporters, journalists, writers,
announcers and instructional designers have been practicing knowledge acquisition for years
. . . system analysts have functioned in a very similar role in the design and development of
conventional software systems. ( McGraw and Harrison-Briggs 1989 , 8 – 9)
The approach used to capture, describe, and subsequently code knowledge
depends on the type of knowledge: explicit knowledge is already well described, but
we may need to abstract or summarize this content. Tacit knowledge, on the other
hand, may require much more signifi cant up-front analysis and organization before
it can be suitably described and represented. The ways in which we can tackle tacit
knowledge range from simple graphical representations to sophisticated mathematical
formulations.
In the design and development of knowledge-based systems, or expert systems,
knowledge engineers interviewed subject matter experts, produced a conceptual model
of their critical knowledge and then “ translated ” this model into a computer execut-
able model such that an “ expert on a diskette ” resulted (e.g., Hayes-Roth, Waterman,
and Lenat 1983 ). The global aim of such systems was to extract and render explicit
the primarily procedural knowledge that comprised specialized know-how — typically
in a very narrow fi eld. Procedural knowledge is knowledge of how to do things, how
to make decisions, how to diagnose and prescribe. The other type of knowledge,
declarative knowledge, was used to denote descriptive knowledge or knowing what as
opposed to knowing how . It soon became apparent that certain types of content were
easily extracted and modeled in this manner — anything that was similar to an interac-
tive online manual or help function in such fi elds as engineering, manufacturing,
decision support, and medicine.
A wonderful by-product of the work in artifi cial intelligence was the array of inno-
vative knowledge acquisition techniques that were created. The interactions with
subject matter experts that were needed to render tacit knowledge explicit made up
the knowledge engineer ’ s toolkit. Quite a few of these techniques are imminently
relevant and applicable to the process of tacit knowledge capture in knowledge
management applications. The major tasks carried out by knowledge engineers
included:
• Analyzing information and knowledge fl ow
• Working with experts to obtain information
• Designing and implementing an expert system
104 Chapter 4
Only the last point would differ and it could be replaced by “ designing and imple-
menting a knowledge management system or knowledge repository. ” On the other
side were the subject matter experts, and they had to be able to:
• Explain important knowledge and know-how
• Be introspective and patient
• Have effective communication skills
Subject or domain experts were usually “ sole sources of information whose exper-
tise companies wish to preserve ” ( McGraw and Harrison-Briggs 1989 , 7). Today, many
organizations face knowledge continuity concerns due to a wave of retiring baby
boomers who represent knowledge walking out the door. The concerns are quite
similar and the techniques used show a great deal of overlap. For example, multiple
experts were often participants in knowledge engineering sessions in order to cover
the range of expertise they represented, to validate the content, to provide different
perspectives, and so on. A number of group knowledge acquisition techniques were
developed and used successfully with such groups. These approaches would be a
perfect fi t for knowledge acquisition at the community of practice level.
Another artifi cial intelligence researcher ( Parsaye 1988 ) outlined the following three
major approaches to knowledge acquisition from individuals and groups:
1. Interviewing experts
2. Learning by being told
3. Learning by observation
All three approaches are applicable to tacit knowledge capture, but it is critical to
note that no one approach should be used to the total exclusion of the others. In
many cases, a combination of these approaches will be required to capture tacit knowl-
edge. The following section presents a toolkit and guidelines on the strengths and
drawbacks of each tool in order to help select the best combination of techniques to
use for a variety of different knowledge capture situations.
Interviewing Experts A number of techniques can be used to optimize the interview-
ing of experts. Two of the more popular means include structured interviewing and
stories.
Structured Interviewing Structured interviewing of subject matter experts is the most
often used technique to render key tacit knowledge of an individual into more explicit
forms. In many organizations, structured interviewing is done through exit interviews
Knowledge Capture and Codifi cation 105
that are held when knowledgeable staff near retirement age. Content management
systems are well suited to publishing their lessons learned and best practices accumu-
lated over their years of experience at the organization. Structured interviewing tech-
niques place great demands on being highly skilled at communicating and
conceptualizing, as well as having a good grasp of the subject at hand. These sessions
yield specifi c data that is often declarative in nature in response to focused questions.
Structured interviews may also be used to clarify or refi ne knowledge originally elicited
during unstructured interactions. The interviewer should outline specifi c goals and
questions for the knowledge acquisition session. The interviewee should be provided
with session goals and sample lines of questioning, but usually not the specifi c ques-
tions to be asked.
Two major types of questions are used in interviewing: open and closed questions.
Open questions tend to be broad and place few constraints on the expert. Open ques-
tions are not followed by choices, as they are designed to encourage free response
( Oppenheim 1966 ). These types of questions allow interviewers to observe the expert ’ s
use of key vocabulary, concepts, and frames of reference. The expert can also offer
information that was not specifi cally asked for. Some examples would be:
• How does that work?
• What do you need to know before you decide?
• Why did you choose this one rather than that one?
• What do you know about . . .
• How could . . . be improved?
• What is your general reaction to . . .
Closed questions set limits on the type, level, and amount of information an expert
will provide. A choice of alternatives is always given. A moderately closed question
would be something like: “ which symptom led you to conclude that. . . . ” A very
strong closed question is one that can only be answered by yes or no.
The structured interviewing process is primarily a people-focused one and as
such, techniques that serve to facilitate the interactions can greatly contribute to the
successful outcome of such sessions. The four major techniques used in refl ective
listening include: paraphrasing, clarifying, summarizing, and refl ecting feelings.
Refl ective listening helps in cases where words may have multiple meanings, or where
the interview participants may hold very different mental models and personal char-
acteristics such as background, attitude, training, and level of comfort with the current
position in the organization. These factors may infl uence how an expert communi-
cates his or her knowledge.
106 Chapter 4
Paraphrasing is the restating of the perceived meaning of the speaker ’ s message but
using your own words. The goal is to check the accuracy with which the message was
conveyed and understood. Examples would include:
• What I believe you said was . . .
• If I am wrong, please correct me, but I understood you to say . . .
• In other words, . . .
• As I think I understand it . . .
Clarifying lets the expert know that their message was not immediately understand-
able. These responses encourage the expert to elaborate or clarify the original message
so that the interviewer gets a better idea of the intended message. Always focus on
the message and not the expert ’ s ability to communicate, and encourage them to
elaborate or explain by using open questions wherever possible. Examples would
include:
• I don ’ t understand . . .
• Could you please explain . . .
• Please repeat that last part again.
• Could you give me an example of that?
Summarizing helps the interviewer compile discrete pieces of information from a
knowledge acquisition session into a meaningful whole. Summarizing helps confi rm
that the expert ’ s message was heard and understood correctly. The summary should
be expressed in the words of the interviewer. Examples would be:
• To sum up what you have been saying . . .
• What I have heard you say so far . . .
• I believe that we are in agreement that . . .
Finally, refl ecting feelings mirrors back to the speaker the feelings that seem to have
been communicated. The main focus is on emotions, attitudes, and reactions, and not
the content itself. The purpose is to clear the air of some emotional reaction or nega-
tive impact of the message. Some examples are:
• You seem frustrated about . . .
• You seem to feel that you were put on the spot . . .
• I sense that you are uncomfortable with . . .
Transcripts of interviews are then analyzed in order to identify key concepts,
common themes, major methods, and techniques that were mentioned. If multiple
Knowledge Capture and Codifi cation 107
experts were interviewed for the same procedure or subject, then confl ict resolution
may be needed. Usually, each individual will be interviewed more than once. This
allows interviewers to validate their understanding of the knowledge that has been
elicited, to fi ll in any missing gaps, and to better conceptualize the content in an
organized manner. Each interview will raise additional questions, whether these are
aimed at clarifying, correcting, or expanding upon critical elements. After a number
of interviews and follow-up sessions, the interviewer will be able to start identifying
key themes and have a preliminary framework for organizing these. Unlike the initial
interview sessions, where new content is generated and captured, subsequent inter-
views are more focused and target a more detailed level.
The best test of whether enough content has been captured is to switch roles: the
interviewer can take on the role of a novice practitioner and verbally or physically go
through the key tasks that have been discussed to date. The interviewee can then vali-
date until such time that both are satisfi ed that the knowledge has been understood
and captured in as complete and valid a manner as possible.
Stories Stories are another excellent vehicle both for capturing and then subsequently
coding tacit knowledge. An organizational story is a detailed narrative of management
actions, employee interactions, and other intraorganizational events that are com-
municated informally within the organization. A story can be defi ned as the telling
of a happening or a connected series of happenings, whether true or fi ctitious ( Denning
2001 ). An organizational story can be defi ned as a detailed narrative of past manage-
ment actions, employee interactions, or other key events that have occurred and that
have been communicated informally ( Swap et al. 2001 ). Conveying information in a
story provides a rich context, remaining in the conscious memory longer and creating
more memory traces than information not in context. Stories can greatly increase
organizational learning and communicate common values and rule sets. Further,
stories remain an excellent vehicle for capturing, coding, and transmitting valuable
tacit knowledge.
However, there are a number of conditions that must be in place in order to ensure
that storytelling in its various enacted forms creates value in a particular organization.
Sole and Wilson (1999) argue that while all stories are narratives, not all narratives are
good knowledge-sharing stories. They use the example of movies that tell stories that
are designed primarily to entertain and therefore need not necessarily be authentic — or
even believable. In contrast, in organizational storytelling, stories are often used to
promote knowledge sharing, inform, and/or prompt a change in behavior, as well as
to communicate the organizational culture, and create a sense of belonging. In order to
108 Chapter 4
Interviewee 37 (name coded in order to protect anonymity) works in a large government
department and has been responsible for the implementation of knowledge management
in the past fi ve years. His own area of expertise lies in project management — he has over
twenty years experience managing large-scale (over $10 million) infrastructure projects
that typically required on average ten years to complete. One of the major catalysts for
implementing KM was the lack of a good handover process — the passing of the baton
when one project manager (PM) left and another took his or her place. Some turnover
was reasonable in such long-term and complex projects. The trouble was that while each
PM had the necessary training and skills, there was often little time to overlap with the
incumbent PM in order to get rapidly up to speed on the specifi cs of that particular project.
The purpose of the structured knowledge elicitation interviews with senior PMs was to
identify the types of tools and techniques they used to ensure that there was solid conti-
nuity in the management of these large infrastructure projects. Some PMs were scrupulous
and disciplined and kept detailed records (primarily paper-based) while others found ways
of embedding the knowledge about the project within the project itself (primarily digital
annotations). The departmental KM team had recently introduced facilitators to carry out
project debriefs and KM journalists to convert paper narratives into digital annotations,
and were in the process of setting up videotaping sessions to accommodate those PMs
who were more comfortable with verbal rather than textual communications.
An excerpt of the interview with PM #37 follows:
Q: How many project handovers have you been involved with to date? ( an icebreaker question to help the interviewee feel comfortable and to begin talking )
A: Over twenty at least — it seems to be getting worse actually — when I fi rst joined the department as a PM we were careerists — we made sure to hang around until the job got done — not like these younger mavericks — jumping from one project to another — even jumping ship and going to work for another department! ( subject getting off topic — starting to get a few things off his chest — prepare to cut in with next question )
Q: What were some of the hardest challenges you faced in doing a handover?
A: The stuff you can ’ t write down! I mean everyone spouts the same stuff — budget overrun, risk assess- ment fi gures off, and on and on and on. . . . the real stuff — we all know it in our gut but ****ed if I ’ m signing my name to it! ( he has quickly started discussing tacit knowledge to be transferred during a handover and his lack of comfort in documenting this in any way — the best way to dig deeper without increasing his level of discomfort is to reassure re anonymity of interview at this point and ask for an example in order to elicit substantive knowledge )
Q: Absolutely — it is certainly not the place to start assigning blame or signing names to statements — and yet, as you say, this is the content that is important for the next PM to know. What would be an example?
A: Well. . . . in one infamous case. . . . the team just dissolved . . . everyone went their own merry way. . . . and the supervisor was so concerned about not losing face with the PM that he just waited too long before saying anything . . . the disasters just snowballed from there. ( at this point, true tacit knowledge is beginning to surface and this part is particularly important to document as the type of PM handover knowledge to capture — next, we need to know how it was handed over ).
Box 4.1 A vignette: Excerpts of an expert interview
Knowledge Capture and Codifi cation 109
Q: How did you manage to talk about this situation with the incoming PM?
A: I shared my hard-earned wisdom and gray hairs with him! (Laughing) — I told him to forget about “ no news is good news ” — no news is unacceptable — don ’ t wait for the formal briefi ngs — keep your nose in it at all times — talk to everyone — walk around — get a feel for the morale and ask questions — just keep asking everyone the same question and you call the shots — get them in for a meeting the minute you sense there that something is off. . . . ( interviewee is not in full-blown tacit mode — a number of terms will need to be pinned down in later follow-up interviews — need to capture good memorable sounds bites such as “ no news is disastrous news!! ” and defi ne feelings such as “ feel the morale ” and “ get a sense that something is off ” — next in the interview template is a set of questions to assess how open the person is to new methods of doing handovers, e.g., videotaping ).
Q: Sounds like the sorts of things that have to be learned the hard way — what is the best way of getting the new PMs up to speed? Do you prefer to leave them some documentation or to meet with them face- to-face? How about this new initiative of videotaping PMs and leaving the clips on the intranet? ( up to this point in the interview, the subject was very relaxed, intent, engaged and appeared to be very comfortable; upon hearing this question, his level of agitation increased — he leaned forward, appeared to scowl )
A: Those oddballs — listen some people have too much free time on their hands — this isn ’ t the place for paparazzi — we are serious folks and we don ’ t need a bunch of techies pestering us — they don ’ t know what we do — all I need is a good heart to heart to put the fear of. . . . to get my points across — that ’ s it, that ’ s all — we don ’ t need anything fancy here. . . . ( defi nitely not open to new ways of transferring this knowledge ).
Q: Of course the best way is to meet face to face — but do you have the time to go over everything? You must have to refer to some documentation as the projects span so many years.
A: Well yeah — I also give them my notes and all that — they can sift through and fi nd out about all the details — but the real stuff is what I need to say to them — and that won ’ t be shown on YouTube any time soon!
Box 4.1 (continued)
achieve these organizational objectives, knowledge-sharing stories need to be auth-
entic, believable, and compelling. Stories need to evoke some type of response and,
above all, be concise ( Denning 2001 ) so that the moral of the story or the organizational
lesson to be learned can be easily understood, remembered, and acted upon. In other
words, organizational stories should have an impact: they should prevent similar
mistakes from being repeated, or they should promote organizational learning and
adoption of best practices stemming from the collective organizational memory.
Denning (2001) describes the power of a springboard story, knowledge that has
been captured in the form of a brief story that has the ability to create a strong impact.
He outlines a number of key elements required to use stories to encapsulate valuable
knowledge, such as:
• The explicit story should be relatively brief and just detailed enough so the audience
can understand it.
110 Chapter 4
• The story must be intelligible to the specifi c audience so they are “ hooked. ”
• The story should be inherently interesting.
• The story should spring the listener to a new level of understanding.
• The story should have a happy ending.
• The story should embody the change message.
• The change message should be implicit.
• The listeners should be encouraged to identify with the protagonist.
• The story should deal with a specifi c individual or organization.
• The protagonist should be prototypical of the organization ’ s main business.
• Other things being equal, true is better than invented.
• Test, test, and test again.
The use of fables such as those found in Aesop ( 1968 ) is often quite helpful in tacit
knowledge capture. A simple approach is to invite participants to a workshop where
they are given several classic fables to read, asked to recollect some they had heard,
and to identify the lesson to be learned in each. Fables are particularly useful with
multicultural groups since fables occur in all cultures but they defi nitely differ from
one culture to another. Next, participants are given a fable minus the “ punch line ”
and are asked to fi ll in the moral of the story. Asking for a punch line is a highly
effective way of acquainting participants with the objectives behind stories — the
purpose of organizational storytelling — that is, to have the reader learn from it. Sec-
ondly, participants also became aware of the fact that stories, like fables, need to be
concise. A fable can consolidate multiple viewpoints and recollections of different
individuals since it is not dependent on a single story to deliver its message ( Snowden
2001 ). Finally, the best way to end a fable — the punch line — is to have an ironic end
in which the reader realizes how a happy ending could have come about without the
narrative actually stating this in any form.
Two illustrations of the value of storytelling in the capture of tacit knowledge are
described in box 4.2 and box 4.3 .
Learning by Being Told In learning by being told, the interviewee expresses and
refi nes his or her knowledge, and the knowledge manager clarifi es and validates the
knowledge artifact that renders this knowledge in explicit form. This form of knowl-
edge acquisition typically involves domain and task analysis, process tracing, and
protocol analysis and simulations. Task analysis is an approach that looks at each of
the key tasks that an expert performs and characterizes them in terms of prerequisite
Knowledge Capture and Codifi cation 111
Knowledge disclosure is a key way of identifying the organizational culture. Knowledge
disclosure techniques such as storytelling allow us to uncover knowledge in the context
of its use. IBM views stories as a powerful means of knowledge discovery and knowledge
transfer. They are very good for conveying complex messages simply. Storytelling is a
uniting and defi ning component of all communities. Stories exist in all organizations;
managed and purposeful storytelling provides a powerful mechanism for the disclosure of
intellectual or knowledge assets in companies. It can also provide a nonintrusive, organic
means of producing sustainable cultural change. Storytelling is an excellent means of
conveying values and other complex tacit company knowledge.
Stories are endemic within each and every organization. They should be fostered, lever-
aged, and managed. We all tell stories in our daily work to share our experience and knowl-
edge. Tacit knowledge is the most powerful means of sharing knowledge and this knowledge
is usually shared through informal networks. Organizations need to accept that stories exist
in their organization, identify the stories that persist, leverage these stories to effect cultural
change, and foster an environment conducive to sharing knowledge and learning through
stories. The best teachers, presenters, and knowledge sharers tell stories naturally to convey
learning points and share their experiences. Stories put the knowledge in context, they
make the learning memorable, and they make the learning experience more compelling.
Failure stories, or lessons learned, help a community to learn from its mistakes.
IBM has a four-stage storytelling approach: the fi rst stage is anecdote elicitation through
interviews, observation, and story circles; the second is anecdote deconstruction to analyze
cultural issues, ways of working, values, rules, and beliefs to yield the story ’ s key messages;
the third phase is intervention/communication design with a story constructed or
enhanced; the fi nal phase is story deployment. Storytelling workshops can be run to elicit
the knowledge and cultural values of an organization as well as both its best and worst
practices. The value of capturing anecdotal or tacit knowledge is that it builds an accurate
picture of the existing culture, discloses enablers and inhibitors to sharing, and identifi es
business issues. Values are identifi ed: moral principles or standards. Rules are identifi ed:
the code of discipline that drives or conforms behavior. Finally, beliefs are elicited: the
collection of ideas that a community regards as true or shares faith in.
Storytelling is a cathartic process where employees can share experiences and build
social capital and networks. Perhaps most importantly of all, it achieves buy-in of partici-
pants. Once anecdotes are captured, they can be stored in a repository and aligned with
communities, processes, and subject areas. They can then be used to trigger and support
discussion forums (e.g., lunch and learn), databases, intellectual capital management
systems (e.g., training), document management systems, bulletin boards, online chats,
portals (e.g., community kickoff days), and intranets (e.g., competency/skill profi ling).
In the end, it is the people who make communities and effective communities have valu-
able stories. In order to help support effective communities, you need to understand what
their issues are, what they need, and what facilities and solutions would best suit them.
Box 4.2 An example: IBM
112 Chapter 4
It is, of course, not enough to create rich environments where people can share. Xerox
has lots of these: online Knowledge Universe with a catalog of best practices, chat rooms
for CoPs, a company Yellow Pages and a section of the public Web site, Knowledge Street,
devoted to promoting knowledge sharing. What are also required are good ideas, leader-
ship, and motivated people. A few years ago, Jack Whalen, a sociologist, spent some time
in a Xerox customer service call center outside Dallas studying how people used Eureka.
The trouble was, employees were not using it. Management decided workers needed an
incentive to change. To this end, they held a contest: workers could win points (convert-
ible into cash) each time they solved a customer problem, by whatever means. The winner
was an eight-year veteran named Carlos, who had more than 900 points. Carlos really
knew his stuff and everyone else knew this too. Carlos never used the software.
The runner-up however was a shock to everyone. Trish had been with the company
only a few months, had no previous experience with copiers, and didn ’ t even have the
software on her machine. Yet her 600 points doubled the score of the third-place winner.
Her secret: she sat right across from Carlos. She overheard him as he talked and she
persuaded him to show her the inner workings of copiers during lunch breaks. She asked
other colleagues for tips too. This story illustrates how knowledge gets shared. The point
is not the software, but how many people can sit next to Carlos? There is no single best
practice for sharing knowledge — both technology and subject matter experts are needed.
And sometimes storytelling is the best way to transfer knowledge. Most managers see
this as a waste of time, and concentrate on breaking up the coffee machine cliques.
However, companies should make opportunities for storytelling at informal get-togethers
that are loosely organized as an off-site meeting, and through videotapes and bragging
sessions.
Box 4.3 An example: Xerox
knowledge/skills required, criticality, consequences of error, frequency, diffi culty,
interrelationships with other tasks and individuals, as well as how the task is perceived
by the person (routine, dreaded, or looked forward to).
Process tracing and protocol analysis are adapted from psychological techniques.
This method involves asking the subject matter expert to “ think aloud ” as he or
she solves a problem or undertakes a task. The information used, questions asked,
actions taken, alternatives considered, and decisions taken are the types of knowledge
that are acquired in such sessions (e.g., Svenson 1979 ; McGraw and Seale 1987 ;
Gammack and Young 1985 ). Simulations are especially effective for later stages
of knowledge acquisition, to validate, refi ne, and complete the knowledge capture
process. Tools may include software programs and “ props ” such as models, schematics,
and maps.
Knowledge Capture and Codifi cation 113
Learning by Observation There are at least two types of discernible expertise: skill or
motor based (e.g., operating a piece of machinery, riding a bike) and cognitive exper-
tise (e.g., making a medical diagnosis). Expertise is a demonstration of the application
of knowledge. The learning-by-observation approach involves presenting the expert
with a sample problem, scenario, or case study that the expert then solves. Although
we cannot observe someone ’ s knowledge, we can observe and identify expertise. The
key is to use audio or video to record what the expert knows. People think of video
mainly as a presentation device. However, experience has shown again and again that
video recordings of informal and unrehearsed expert demonstrations form a perma-
nent record of task knowledge — one that can be mined repeatedly. However, one
should always accommodate the particular expert or interviewee at all times — many
individuals end up feeling much less comfortable if they know they are being recorded.
The happy medium is to bring along recording equipment but allow the subject the
choice and hand over the controls to them — so they can mute whenever they wish
to “ speak off the record. ” For physical demonstrations, inexpensive digital camcorders
are recommended. For software demonstrations, screen capture movie software that
records the action directly from the desktop is recommended. Together, simple equip-
ment and simple techniques can capture an amazing range of information and
demonstrations.
Other Methods of Tacit Knowledge Capture A number of other techniques may be
used to capture tacit knowledge from individuals and from groups, including:
• Ad hoc sessions
• Road maps
• Learning histories
• Action learning
• E-learning
• Learning from others through business guest speakers and benchmarking against
best practices
Ad hoc sessions are a means of rapidly mobilizing a community of practice or
informal professional network to a member ’ s call for help. These are usually brain-
storming sessions of no more than thirty minutes and can take place as face-to-face
meetings or make use of technologies such as instant messaging, e-mail, teleconfer-
ence, and chat rooms.
Road maps are more formal in nature. They tend to be facilitated problem-solving
meetings that are scheduled, convened, and that follow an agenda. The objective is
114 Chapter 4
to solve day-to-day problems in a public forum which often leads to the development
of guidelines and even standards for continuous process improvement within the
company. These sessions may also be “ registered ” so that they can be used for internal
benchmarking initiatives. Internal benchmarking consists of monitoring progress
against goals over time (comparing snapshots to an initial baseline) and/or comparing
the performance of one unit against another within the same company.
Learning histories ( Roth and Kleiner 2000 ) are a very useful means of capturing
tacit knowledge within group settings. They represent a retrospective history of sig-
nifi cant events that occurred in the organization ’ s recent past, as described in the voice
of the people who took part in them. Organizational history is often researched
through a series of initial individual interviews where participants are asked to remem-
ber and refl ect upon the event followed by a facilitated workshop with all participants
in order to capture that group ’ s memory.
The learning history process consists of:
1. Planning
2. Refl ective interviews
3. Distillation
4. Writing
5. Validation
6. Dissemination
Planning establishes the scope of the learning history to be captured. The scope
will be a function of the business objective that the learning history targets. Each
learning history exercise should be well founded on a problem or challenge that was
overcome by the organization. The learning history serves to describe what happened,
why it happened, how the organization reacted, and what current organizational
members should learn from this experience. The second phase, refl ective interviews,
consists of asking participants to talk about what happened from their own point of
view. By asking them about their analysis, evaluation, and the judgment they used,
insights will emerge. The capture and codifi cation of these insights will contribute to
increasing the refl ective capacity of the organization.
The fi nal phase, distillation, consists of synthesizing the information that was
gathered from the interviews into a summary format that will make it very easy for
others to access, read, and understand. The interview transcripts, along with notes
from the facilitated learning history workshop, can then be analyzed to identify
key themes and subthemes as well as specifi c quotes to be used. The key themes are
Knowledge Capture and Codifi cation 115
documented at a more abstract level (e.g., need not have specifi c dates or other
details in order to convey the major points to be made) and the quotes are verifi ed
and authorization obtained in order to print them with an attribution. The content
is then coded, summarized, and published as part of the organizational memory.
The results are often transcribed in a Q/A format as shown in table 4.1 . A learning
history is thus a systematic review of successes and failures in order to capture best
practices and lessons learned as they pertain to a signifi cant organizational event or
project. Some typical questions posed in learning history knowledge capture would
include:
• What was your role in the project/initiative?
• How would you judge its success or failure?
• What would you do differently if you could?
• What recommendations do you have for other people who may face a similar
situation?
• What innovative things were done along the way?
Learning histories are typically presented in two side-by-side columns with a nar-
rative in one column and evaluative comments in the other. This allows readers to
arrive at their own conclusions. The original participants must always validate the
learning history before it is fi nally disseminated throughout the organization. Dis-
semination works best when it is an organized activity. Action learning is based on
the fact that people tend to learn by doing. Small groups can be formed with partici-
pants who share common issues, goals, or learning needs. They can meet regularly,
report on progress, brainstorm alternatives, try out new things, and evaluate the
results. This is a form of task-oriented group work and learning that is well suited for
narrow, specialized domains and specifi c issues. One good theme for such small groups
would be to analyze a learning history, and to discuss what they would have done
differently and why in order to promote a better understanding of the event in
question.
E-learning solutions typically involve the capture of valuable procedural knowledge
and documenting a history of all procedural changes together with an explanation or
justifi cation for the change that was made ( George and Kolbasuk 2003 ). In this way,
a historical thread is maintained and the context within which changes were deemed
to be necessary does not become lost. In addition to a repository for such knowledge,
a process needs to be put into place whereby employees who are planning to leave
have the time and the necessary support to organize and store their reference
116 Chapter 4
Table 4.1 Sample learning history template
Theme title
For example, “ Repurposing of objectives for the ACME
Division in 1995 in response to new environmental
regulations ”
Part 1: Overview of theme Brief overview of the event, emphasizing why it was a signifi cant event in the organization ’ s history, why it needs to be well understood in order to better meet today ’ s objectives, who was involved, what triggered the event, etc.
Part 2: Description Chronological commentary, conclusions, and the questions that were asked together with the responses; quotes representing key responses to questions should appear as separate right-hand side column and be aligned with the content the quote refers to.
Part 3: Summary Brief summary of quotes, additional questions to provide more clarity to the theme; a stand-alone section that can be made available and be understood by those who were not participants in the original event.
Part 4: Best practices Describe any best practices that group consensus identifi ed. Include the following information:
• Date prepared
• Point of contact (name, contact information)
• Members who contributed to the development of the best practice
• Problem statement (what does best practice address)
• Background (enough context to understand the problem and the proposed solution)
• Best practice description (model, business rules — use graphics where appropriate)
Part 5: Lessons learned Describe any lessons learned identifi ed by the group. Include the following information:
• Date prepared
• Point of contact (name, contact information)
• Members who contributed to the development of the best practice
• Problem statement (what does best practice address)
• Background (enough context to what happened, what went wrong and how to prevent a recurrence)
• Lesson learned description (model, business rules — use graphics where appropriate)
Knowledge Capture and Codifi cation 117
materials, procedural experience accumulated throughout the years, and valuable
knowledge that would be of great benefi t to others in the future. For example, how
they solve problems would be a very valuable thing to capture. Next, online courses
could be created based on the information from threaded discussion archives. In this
way, traditional and computer-based training systems can be combined to both
capture and subsequently make available previously uncodifi ed, typically tacit knowl-
edge and know-how. The knowledge capture approach is very similar to how a subject
matter expert would work with an instructional designer to design course content and
accompanying hands-on activities.
An example is NASA, where 60 percent of aerospace workers were slated to reach
retirement age all within a few years of each other. These impending retirements
meant that valuable knowledge of the Apollo-era missions would be lost unless it could
be transferred to remaining and future workers in an effective manner. NASA began
a mentoring program that makes use of e-learning and virtual collaboration to capture
valuable knowledge and know-how and to keep this content online. The solution
included a mix of e-mail, threaded discussions, and live collaborative sessions. A
similar situation is faced by almost all major organizations around the world. The
demographic pressure created by the baby boomers, who have always led by their
sheer numbers, has created a growing need for knowledge continuity applications to
make sure that valuable knowledge does not “ walk out the door. ”
Learning from others can consist of a number of activities such as external bench-
marking, which involves learning about what the leaders are doing in terms of their
best practices, either through publications or site visits, and then adapting and adopt-
ing their best practices. Benchmarking is a way of identifying better ways of doing
business. Other sources would be through attending conferences, expositions, and
commissioning specifi c studies. Inviting guest speakers to an organization is another
opportunity to bring a fresh perspective or point of view. Speakers may be selected on
the basis of targeted interests and they may be internal or external to the organization.
Typically, the speakers would give a seminar or workshop and leave behind a set of
reference materials.
Figure 4.4 summarizes the key steps involved in knowledge acquisition at the indi-
vidual and group level. Identifi cation refers to the process of characterizing key
problem aspects such as participants, resources, goals, and existing reference materials.
Conceptualization involves specifying the key concepts and key relationships among
them in the form of a concept or knowledge map. Codifi cation renders this validated
content into an explicit form that can then be more readily disseminated throughout
the organization.
118 Chapter 4
Identification
Conceptualization
Codification
Refine
requirements
Refine
concepts
Model the knowledge
xyz
Organize and
externalize knowledge
What valuable knowledge
would be worthwhile
to capture?
Figure 4.4 Key knowledge acquisition phases
The importance of record keeping during knowledge capture, especially tacit knowl-
edge capture, cannot be emphasized enough. Original transcripts, recordings, and
reference materials need to be carefully organized in a knowledge acquisition database.
The source of each piece of key knowledge must be carefully recorded for future refer-
ence. The key fi ndings should also be systematically captured. Templates are often
used to structure and standardize knowledge acquisition processes. A sample knowl-
edge acquisition session template is shown in fi gure 4.5 . It is important to always send
back transcripts and summary forms to the people interviewed. This serves to validate
and complete the content but also gives the interviewee the chance to edit comments
so they are not taken out of context.
Tacit Knowledge Capture at the Organizational Level
Organizational knowledge acquisition is a qualitatively different process from those
that occur at the individual and group levels. Whereas in the latter we are primarily
concerned with identifying and coding valuable knowledge, which is mostly tacit in
nature, organizational knowledge capture takes place on a macro level. Malhotra
Knowledge Capture and Codifi cation 119
Knowledge Acquisition Session Notes
Project Name
Date
Person interviewed
Interviewer
Technique
Objective
Duration
Reference materials collected
Recorded session? Y/N
Next scheduled interview
Next topics to be addressed
Summary of key findings
Points to be clarified/followed up
Others to interview to complete knowledge acquisition
Special considerations
What worked well with this expert
What should be different next time
Key areas of expertise of interviewee
Number of years with the organization
Figure 4.5 Sample knowledge acquisition session template
120 Chapter 4
(2000) proposes a good approach by outlining four major organizational knowledge
acquisition processes:
1. Grafting
2. Vicarious learning
3. Experiential learning
4. Inferential processes
Grafting involves the migration of knowledge between fi rms — a learning process
whereby the fi rm gains access to task- or process-specifi c knowledge that was
not previously available within the fi rm. This is typically achieved through mergers,
acquisitions, or alliances in that there is a direct passing of knowledge between fi rms
( Huber 1991 ). An example would be technology transfer or other forms of explicit
knowledge.
Vicarious learning processes occur through one fi rm observing other fi rms ’ demon-
strations of techniques or procedures. For example, benchmarking studies where
companies can adopt the best practices of other industry leaders. This knowledge is
more tacit than that obtained through grafting ( Inkpen and Beamish 1997 ) as it
involves learning how to do something or know-how.
Experiential knowledge acquisition involves knowledge acquisition within a given
fi rm — knowledge that is created by doing and practicing. Repetition-based experience
relies on the learning curve to establish routines and procedures. This type of knowl-
edge is initially tacit but can be easily codifi ed and transferred ( Pennings, Barkema,
and Douma 1994 ; Starbuck 1992 ). Argyris and Schon (1978) refer to the processes of
single and double-loop learning. Single loop learning involves the refi nement and
improvement of existing procedures and technologies as opposed to developing new
ones (adapting for effi ciency). In inferential knowledge acquisition processes (e.g.,
Mintzberg 1990 ), learning is within the fi rm and occurs by doing; however, knowl-
edge acquisition occurs primarily through interpretation of events, states, changes,
and outcomes relative to the activities undertaken and decisions that were made.
The type of learning is experimental and deductive, and this type of learning
seeks to make sense of occurrences and to establish causal links between actions
and outcomes. This type of learning is sometimes referred to as double-loop learning,
as it involves changing underlying assumptions and frameworks (adapting for
effectiveness).
The results of all four types of organizational knowledge capture will ultimately
reside in some type of knowledge repository. This is the recipient of organizational
memory and containers are usually some form of database on an intranet or extranet.
Knowledge Capture and Codifi cation 121
The capture of such knowledge has, in large part, already occurred, which means we
can proceed directly to the codifi cation of this content.
Explicit Knowledge Codifi cation
Knowledge can be shared through the process of personal communication and interac-
tion. We saw this in the fi rst quadrant, socialization, of the Nonaka and Takeuchi KM
model. This occurs naturally all the time. While this process is very effective, it is
rarely very cost-effective. Knowledge codifi cation is the next stage of leveraging knowl-
edge. By converting knowledge into a tangible, explicit form such as a document, that
knowledge can then be communicated much more widely and with less cost. Interac-
tion is limited in scope to those within hearing or able to have face-to-face contact.
Documents can be disseminated widely over a corporate intranet and they persist over
time, which makes them available for reference as and when they are needed, both
by existing and by future staff. They constitute the only “ real ” corporate memory of
the organization.
There are, of course, costs and diffi culties associated with knowledge codifi cation.
The fi rst issue is that of quality, which encompasses:
• Accuracy
• Readability/understandability
• Accessibility
• Currency
• Authority/credibility
The pivotal role of knowledge codifi cation is that it allows the sharing and use of
what is collectively known. Knowledge held by a particular person enables that person
to be more effective. If people interact to share their knowledge within a community
of practice or work team, then that practice becomes more effective. If knowledge is
codifi ed in a material way (i.e., rendered explicit), then it can be shared more widely
both in terms of audience and time duration. In order to understand, maintain, and
improve knowledge as part of corporate memory, knowledge must be codifi ed. The
codifi cation of explicit knowledge can be achieved through a variety of techniques
such as cognitive mapping, decision trees, knowledge taxonomies, and task analysis.
Cognitive Maps
Once expertise, experience, and know-how have been rendered explicit, typically
through some form of interviewing, the resulting content can be represented as a
122 Chapter 4
cognitive map. A cognitive or knowledge map is a representation of the “ mental
model ” of a person ’ s knowledge and provides a good form of codifi ed knowledge. A
mental model is a symbolic or qualitative representation of something in the real
world. It is how human minds make sense of their complex environments. A cognitive
map is a powerful way of coding this captured knowledge because it also captures the
context and the complex interrelationships between the different key concepts. When
making cognitive maps, it is also very important to include individual views, percep-
tions, judgments, hypotheses, and beliefs, as they form part of the subjective world-
view of the interviewee. The nodes in a map are the key concepts and the links
represent the interrelationships between the concepts. These may be drawn manually,
by taping small note pages on a wall, by using a whiteboard, or through visualization
software (ranging from simple brainstorming mapping tools to 3D depictions). Figure
4.6 shows an example of a cognitive map in response to the question, “ What are the
major differences between tacit and explicit knowledge objects? ”
Cognitive mapping is based on concept mapping ( Leake et al. 2003 ), which allows
experts to directly construct knowledge models. Concept maps represent concepts and
relations in a two-dimensional graphical form with nodes representing key concepts
connected by links representing propositions. These are quite similar to semantic
networks used by such diverse disciplines as linguistics, education, and knowledge-
Tacit
knowledge
object
Explicit
knowledge
object
Knowledge
worker
Subject
matter
expert
Originator/
creator
Location
Accesses Shares
Sources
References
Codified
Format
Language Print/electronic
Experiences
with Practitioner
Figure 4.6 Example of a concept map
Knowledge Capture and Codifi cation 123
based systems. The goal of such systems is to better organize explicit knowledge and
to store it in corporate memory for long-term retention.
Another widely used tool for explicit knowledge coding is the CommonKADS
methodology ( Schreiber et al. 2000 ; Shadbolt, O ’ Hara, and Crow 1999 ), which is a
knowledge engineering methodology centered on fi ve types of models of an
organization:
1. Task model of the business processes of the organization
2. Agent model of the use of knowledge by executors, both human and artifi cial, to
carry out the various tasks in the organization
3. Knowledge model that explains in detail the knowledge structures and types
required for performing tasks
4. Communication model that models the communicative transactions between
agents
5. Design model that specifi es the architectures and technical requirements needed
to implement a system that embodies the functions detailed by the knowledge and
communication models
In order to implement KADS, the organization is analyzed to identify knowledge-
oriented problems, describe the organizational aspects that may affect knowledge
solutions (e.g., culture, resources), describe the business processes in terms of agents
required, location, knowledge assets deployed, and measures of knowledge intensive-
ness and signifi cance (e.g., mission criticality). Next, the knowledge used in the orga-
nization is described in terms of possessors, processes used in, and whether or not it
is in the right form and location, of the right quality, and available at the right times.
The feasibility of suggested solutions is then checked against the knowledge problems
identifi ed in the fi rst step. This approach allows a systematic cost-benefi t analysis to
be carried out for the processes of knowledge capture.
Decision Trees
Decision trees are another widely used method to codify explicit knowledge. This
representation is both compact and effi cient. The decision tree is typically in the form
of a fl owchart, with alternate paths indicating the impact of different decisions being
made at that juncture point. A decision tree can represent many “ rules ” and when
you execute the logic by following a path down it, you are effectively bypassing rules
that are not relevant to the case at hand. You do not have to look at every rule to see
if it “ fi res, ” and you also take the shortest route to the correct outcome. Their graphi-
cal nature makes them very easy to understand, and they are obviously very well suited
124 Chapter 4
for the coding of process knowledge. An example would be a preventive maintenance
process for factory equipment. The captured knowledge from maintenance workers
could be coded in a decision tree to help future maintenance workers carry out parts
replacement and other work on a schedule-based decision rather than reacting to parts
becoming worn out. Another example, shown in fi gure 4.7 , helps guide the decision
of whether to consolidate or to develop a new product as a risk management decision
tree.
Knowledge Taxonomies
Concepts can be thought of as the building blocks of knowledge and expertise. We
each have our own internal defi nitions of the concepts we use to make sense of the
world around us. Once key concepts have been identifi ed and captured, they can be
arranged in a hierarchy that is often referred to as structural knowledge taxonomy.
Knowledge taxonomies allow knowledge to be graphically represented in such a way
that it refl ects the logical organization of concepts within a particular fi eld of expertise
or for the organization at large. A knowledge dictionary is a good way to keep track
New product
Consolidation
Thorough
development
Rapid
development
Repurpose
product
Strengthen
product
Market reaction
Moderate
Good Poor
Moderate
Good Poor
Moderate
Good Poor
Moderate
Good Poor
Figure 4.7 Example of a decision tree
Knowledge Capture and Codifi cation 125
of key concepts and terms that are used. This may be compiled as you acquire and
code knowledge. It should clearly defi ne and clarify the professional jargon of the
subject matter domain.
Taxonomies are basic classifi cation systems that enable us to describe concepts and
their dependencies — typically in a hierarchical fashion. The higher up the concept is
placed, the more general or generic the concept is. The lower the concept is placed,
the more specifi c an instance it is of higher-level categories. An example is shown in
fi gure 4.8 .
An important concept that underlies taxonomies is the notion of inheritance.
Each node is a subgroup of the node above it. That means that all of the properties
of the higher-level node are automatically transferred from “ parent ” to “ child. ” As
shown in fi gure 4.8 , if the higher-level node is a houseplant and the lower level
nodes are foliage and fl owering plants, both of these two subgroups possess all the
characteristics of houseplants. In fact, taxonomies originated as biological classifi ca-
tion schemes.
Plants
Houseplants Landscaping plants Native/wild plants
Foliage Flowering
Cacti
Trees Ground cover
Deciduous Evergreen
Figure 4.8 Example of a knowledge taxonomy
126 Chapter 4
The construction of taxonomy involves identifying, defi ning, comparing, and
grouping elements ( Lambe 2007 ). Organizational knowledge taxonomies, however,
are not driven by basic fi rst principles or “ real ” attributes, but by consensus. All the
organizational stakeholders need to agree on the classifi cation scheme to be used to
derive the taxonomy — it cannot be theoretical but must be empirical — this is how we
code this type of knowledge in our work. The reason for this is that unlike traditional
taxonomies, such as the fi rst comprehensive biological species taxonomy developed
by Linnaeus (1767), the purpose of an organizational taxonomy is not to come up
with a universally accepted way of describing reality. Rather, an organizational tax-
onomy is a mixture of a depiction of concrete components and abstract concepts that
together make up the context of that particular company. Consensus is vital because
the taxonomy serves to help achieve the goals of the organization and it does
this by helping knowledge workers communicate better, code knowledge better, and
organize this coded knowledge in such a way that it can be used by everyone today
and by workers of the future when they need to retrieve and make use of this
knowledge.
A taxonomy is a classifi cation scheme that groups related items together, often
names the types of relationships concepts have to one another, and provides some
notion of more general categories versus examples or specifi c instances of a category.
Classifi cation schemes can be very personalized, such as the names we give our per-
sonal e-mail folders or PC desktop fi les. There is no problem as there is typically only
one user — you (and hopefully you can remember how you named your folders!). But
what happens if we are working with someone else? We usually refl ect a bit more
before typing in the e-mail subject heading and before naming a fi le to be sent as an
attachment. Why? The names must make sense to you but also to the recipient. In
the same way, we have no choice but to standardize a bit more and to achieve some
sort of consensus if there are a number of people working with the same content. At
the very basic level, a consensus on naming different versions of a document that has
multiple authors will be needed. The organizational level will require the highest level
of standardization and consensus. Perfect consensus is rarely feasible (and is not very
cost-effective), so we are fortunate to have a way of “ cheating ” : together with the
knowledge dictionary, it is often a good idea to develop an organizational thesaurus.
The thesaurus will contain all the synonyms and cross-references prevalent in the
organization. For example, one group may have decided against using the term knowl-
edge management and prefer knowledge sharing , and yet another division may adopt
knowledge networks . All three would appear in the thesaurus, with KM highlighted as
the formally accepted term for the organization as a whole, while allowing for some
Knowledge Capture and Codifi cation 127
customization at the level of the different groups. Another benefi t of a good thesaurus
is that a keyword search engine can use each term to retrieve all relevant content (see
chapter 8).
A number of concept sorting techniques may be used in coding organizational
knowledge, ranging from manual to completely automated processes. An example
of a manual process would be to have participants sort cards into groupings. An
automated example would be something like the RepGrid technique developed by
Shaw (1981) based on Kelly ’ s (1955) personal construct theory. Most automated
systems use a form of cluster analysis to identify groupings in a set of data (e.g.,
hierarchical cluster analysis, Johnson 1967 ), multidimensional scaling (e.g., Kruskal
1977 ) or network scaling (e.g., Schvaneveldt, Durso, and Dearholt 1985 ). Cluster
analysis is a method of producing classifi cations from data that is initially unclassi-
fi ed. In hierarchical cluster analysis, the groupings are arranged in the form of a
hierarchical tree. Repertory grid analysis is a technique based on a theory that states
each person functions as a scientist who classifi es or organizes his or her world. Based
on these classifi cations, the individual is able to construct theories and act based on
these theories. A repertory grid depicts this theoretical framework for a given indi-
vidual. The different taxonomic approaches to the codifi cation of explicit knowledge
are summarized in table 4.2 .
In addition to the hierarchy, taxonomies can organize knowledge as lists, trees,
poly-hierarchies, matrices, facets, or system maps (Lambe 2007). Organizational
knowledge is often best represented using a multifaceted taxonomy or poly-hierarchy
that makes use of more than one classifi cation rule (or “ facet ” ). The general guideline
is that each facet must be clearly distinguishable from the others (e.g., shape, color,
and cost are three facets that do not overlap in any way). Another guideline is that
each facet should be clearly understood by all users (and if not, then a thesaurus
should keep track of equivalent terms). Good examples of a faceted taxonomy may
be found at http://wine.com, where wine is classifi ed according to region, taste, price,
and so on, and http://www.epicurious.com, where recipes can be classifi ed according
to type of event, type of cuisine, and time to prepare. A multifaceted taxonomy is
often used for business content, as it is the most fl exible and can deal with the often
messy, overlapping, ill-defi ned nature of knowledge used in a company. Facets are
relatively easy to add, remove, or modify in order to accommodate changes in the
organization, changes in user types, and changes in tasks. Finally, from a user perspec-
tive, each facet can serve as a search term to locate and retrieve content.
Most small and medium-sized organizations will primarily use manuals as a
means of developing taxonomy while larger organizations may be better positioned
128 Chapter 4
Table 4.2 Major taxonomic approaches to knowledge codifi cation
Taxonomic approach Key features
Cognitive or concept map • Each key content item is represented as a node in a graph and the relationships between these key concepts are explicitly defi ned.
• Can show multiple perspectives or views on the same content.
• Fairly easy to produce and intuitively simple to understand but diffi cult to use for knowledge related procedures.
Decision tree • Hierarchical or fl owchart type of representation of a decision process.
• Very well suited to procedural knowledge — less able to capture conceptual interrelationships.
• Easy to produce and easy to understand.
Manual knowledge taxonomy
• Object-oriented approach that allows lower or more specifi c knowledge to automatically incorporate all attributes of higher-level or parent content they are related to.
• Very fl exible — can be viewed as a concept map or as a hierarchy.
• More complex, therefore will require more time to develop, as they must refl ect user consensus.
Automated knowledge taxonomy
• A number of tools are now commercially available for taxonomy construction.
• Most are based on statistical techniques such as cluster analysis to determine which types of content are more similar to each other and can constitute subgroups or thematic sets.
• Good solution if there is a large amount of legacy content to sort through.
• More expensive and still not completely accurate — will need to be validated and refi ned for maximum usefulness.
Knowledge Capture and Codifi cation 129
to purchase the fairly expensive automated software tools available. In all cases,
however, a hybrid approach is best. While automated systems can help provide a
good head start, especially in cases where there is a signifi cant volume of existing
legacy content, human intervention is almost always needed to correct and refi ne
the classifi cation — and, of course, to ensure consensus. A number of manual tax-
onomy techniques can be used to help groups work together to create the categories,
decide on the facets, and develop a thesaurus. The most popular techniques
used are card sorting (Nielsen 1994 2009 ) and affi nity diagramming ( Farnum 2002 ;
Gaffney 2000 ).
Card sorting is a very low-tech method of understanding users ’ mental models of
how knowledge should be organized. The best tools to use are sticky-note cards
preprinted with key concepts already known (typically derived from a survey of
documents and of intranet content). There should be some blank cards so users can
add terms. There are two general types of card sorting: open and closed. In open card
sorting, there are no preestablished groupings, whereas in closed card sorting, there
is already a preliminary taxonomy in place. Open card sorting is useful to better
understand participants ’ perceptions, while closed card sorting is useful to validate an
existing taxonomy (e.g., document classifi cation scheme or web navigation design).
The general steps involved are to distribute the cards to each participant and ask
them to group together those cards in a way that makes sense to them and to name
each grouping. The piles can be of different sizes and users can elect not to use some
of the cards (as long as they jot down why they were rejected). The user groups should
be representative, and they can be homogenous (if we are looking at a consensus) and
heterogeneous (in order to have a taxonomy that is broader in scope and to create a
thesaurus). Both types of groups are recommended if time permits. The recommended
number of participants is a minimum of six and the recommended time is a minimum
of thirty minutes to sort fi fty cards.
Users can stop when they feel they have exhausted all the possibilities. The facilita-
tor may ask them to try to aggregate into bigger groups if there are too many groups
(a good rule of thumb is Miller ’ s magic number of seven plus or minus two, which
appears to be the number of items our cognitive abilities are best able to handle). Once
everyone has fi nished, the facilitator enters everyone ’ s results onto a spreadsheet.
There will be some agreement right at the outset about groupings, while others
will differ. A statistical analysis called cluster analysis can be used to obtain a visual
representation of the results. For those groupings that were different, it may be due
to using different labels to denote the same concept, or additional subcategories may
be required. When the resulting preliminary taxonomy has been completed, the same
130 Chapter 4
participants may be asked to validate this classifi cation scheme through a closed card
sorting exercise.
Jiro Kawakita, an anthropologist, created the affi nity diagramming method in the
1960s ( Kawakita 1991 ) as a means of grouping large numbers of brainstormed ideas
into groups. The resulting groupings were represented visually as boxes. The general
process is to conduct a brainstorming meeting and record all the generated ideas on
sticky notes or index cards. The group of users sort the notes/cards based on what
items they feel are related. Each group is then given a name. The group is then asked
to explain both their grouping and their naming. The same idea may belong to more
than one group. Again, the most effi cient grouping gives small numbers of groups
(seven plus or minus two groupings).
It is vitally important to identify content owners when creating the knowledge
taxonomy of the organization to help ensure that content will always be kept up to
date. The organization will also have a clear idea of which of the staff are holders of
specialized knowledge. This knowledge taxonomy (also referred to as a knowledge map
or corporate organizational memory) should also make use of metadata tagging on
“ information about information. ” For example, tagging content with content owners,
“ best before ” dates, classifi cation information such as key words, business specifi c
information such as intended audience, and vertical industry should all be addressed.
An illustration appears in box 4.4 .
The Siemens AG ShareNet system is essentially an intranet covering both codifi ed and
personalized knowledge. The ShareNet organization consists of a global editor, contri-
butors, a decision committee for the evolution of ShareNet, and about one hundred
ShareNet managers, one in each country, who support contributors in capturing project
experiences and marketing know-how. These managers drive the development of reusable
knowledge. They spend 50 percent of their time on this and are supported by an eighteen-
person-strong central team. Siemans rates the taxonomy as being very important. They
came up with a shared taxonomy for business processes. The incentive system is also quite
interesting: ShareNet shares are given for urgent responses, discussion group responses,
objects published, reuse feedback, and so on. An individual who garners three thousand
fi ve hundred shares is granted an invitation to a conference. Siemans continues to have
a KM department whose main responsibilities are to set up communities and provide a
central support service to these communities. For example, there are corporate-funded CoP
kickoff workshops. Their initial budget was US$600,000 and is now US$10m, mainly in
the form of ShareNet Managers ’ time.
Box 4.4 An example: Siemens
Knowledge Capture and Codifi cation 131
Information professionals are the ideal candidates to carry out knowledge creation,
capture, codifi cation, and organization. Information professionals have a solid founda-
tion in library and information science skills and are already very adept at such skills
as structured interviewing (as they conduct reference interviews) and the development
of classifi cation frameworks. The process of analyzing and reworking the tacit and
explicit information will help clarify what the organization knows and what it needs
to know. It is neither necessarily cheap nor easy, but it will capture key knowledge
and improve consistency and generalizability throughout the organization. Writing
good content is the best way of creating knowledge assets within an organization. An
example showing two facets of good knowledge creation is shown in fi gure 4.9 .
The Relationships among Knowledge Management, Competitive Intelligence,
Business Intelligence, and Strategic Intelligence
Knowledge management has historically focused on capturing knowledge from within
the organization and from past events in the history of the organization while com-
petitive intelligence has traditional focused on external resources ( Bouthillier and
Dalkir 2005 ). Competitive intelligence (CI) can be defi ned as “ A systematic and ethical
program for gathering, analyzing, and managing external information that can affect
your company ’ s plans, decisions, and operations. ” (SCIP, Society of Competitive Infor-
mation Professionals, http://www.scip.org/) However, both KM and CI are concerned
with “ strategic intelligence, ” that is, information resources that are needed for decision
making, which in turn benefi ts, the company ( Liebowitz 2006 ). Business intelligence
(BI) is often used as a synonym for CI, but really refers to the set of tools that allow
Facet 1: Audience Facet 2: Topic
Hate literature
Online hate literature
Online detection/monitoring
Cyberbullying
Adolescent issues
Peer Pressure
Bullying
Cyberbullying
Researcher
Technology transfer officer
Media liason officer
Donor relations officer
Social cognition, emotional IQ
Online hate content detection
Bullying, cyberbullying
Adolescent issues, peer pressure
Figure 4.9 Example of multifaceted taxonomy for cyberbullying
132 Chapter 4
A large North American university contacted its library school to help in developing a blue
book — a database of research expertise present at the university. The objective was to
provide the Donor Relations Group, the Media Group, and the Technology Transfer Group
with a good central reference tool that would enable them to contact the most appropriate
researcher quickly with respect to each of their needs: to present their research to a group
of potential philanthropists (for the Donor Relations Group), to fi nd someone who can
answer questions from the media regarding a current event (for the Media Group), and to
meet with prospect companies interested in commercializing some of the results of their
research (for the Technology Transfer Group). While a number of researcher profi les
existed, they tended to be scattered over personal Web sites, university departmental Web
pages, and other stand-alone applications. The challenge was how to present the same
research to three different target audiences, each with their own preferred terminology.
The library science students quickly set up meetings with representative users from
each of the three groups and conducted card sorting and affi nity diagramming workshops
with each. Existing research profi les and existing commercial taxonomies provided the
terms to be placed on the preprinted cards. The multifaceted taxonomy was the result
with an extensive thesaurus. The database captured the three different perspectives (four
really, counting the researcher ’ s preferred terminology and groupings). Each user group
became a facet and users could search the database using their own specifi c perspective
and their own specialized language.
For example, educational researchers work on social cognition and emotional intelli-
gence (terms used by the researchers themselves) issues to better understand the anteced-
ents of peer pressure and bullying. A cyber-bullying incident brings reporters to call the
Education Department to fi nd someone to speak on the topic ( Kowalski, Limber, and
Agatston 2008 ). Cyber-bullying is a term that has been popularized by the media. The
Donor Relations group showcases some of the research being done to target adolescents
to garner the interest of potential philanthropists who have expressed specifi c interest in
this age group. Finally, a computational linguistics company that has already done some
work in identifying online hate literature is interested in adapting their software to identify
instances of cyber-bullying. This small specialized fi eld of research has rapidly generated
at least eight different but related tags: social cognition, emotional intelligence, peer pres-
sure, bullying (a subgroup of peer pressure), cyber-bullying (a subgroup of bullying),
adolescent behaviors, online hate literature, and computational linguistics. The database
can easily substitute equivalent terms to better respond to the information seeker ’ s needs
and to better adapt to the terms they are more familiar with.
Box 4.5 A vignette: University blue book
Knowledge Capture and Codifi cation 133
information to be gathered and used in decision making. BI therefore represents the
tools used for not only CI but also for customer profi ling, market research, and other
analyses.
Strategic Implications of Knowledge Capture and Codifi cation
Knowledge capture and codifi cation are particularly critical when there is an issue
of knowledge continuity (e.g., Field 2003 ; Beazley, Boenisch, and Harden 2003 ).
Whereas knowledge management is concerned with capturing and sharing know-how
valuable to colleagues who are performing similar jobs throughout a company, knowl-
edge continuity management focuses on passing critical knowledge from exiting
employees to their replacements. Whereas most of the literature focuses on the knowl-
edge transfer from this departing individual to his or her successor, the problem is not
so localized. Knowledge continuity should not focus solely on the specifi c knowledge
to be transferred between individuals. Instead, it should also address strategic concerns
at the group and organizational levels. The organization needs to be aware of its criti-
cal knowledge assets — these are captured and codifi ed in the form of a knowledge map
or taxonomy. Organizations also need to take into account the impact of a departure,
whether due to a baby boomer retiring or other reasons, on the communities that
they are members of. Their leaving may literally leave a serious gap in the fabric of
the community network.
At its core, knowledge continuity management is about communication ( Field
2003 ). That is, employees need to understand just what it is that they know, that
others need to know, and why this content needs to be shared with their peers. The
more critical a job is to the company, the more important it is that it be part of a
continuity management system. The more sophisticated, complex and tacit the knowl-
edge a worker possesses, the more diffi cult it will be to pass on — and even more
important that it be passed on. These challenges raise important questions concerning
security and access in addition to a code of ethics that ensures that all concerned are
treated in a professional manner.
Some recommendations from Field (2003) include:
• Set up a knowledge profi le for all critical workers.
• Foster mentoring relationships.
• Encourage communities of practice.
• Ensure that knowledge sharing is rewarded.
134 Chapter 4
• Protect people ’ s privacy.
• Create a bridge to organizational memory for long-term retention of the valuable
content.
Practical Implications of Knowledge Capture and Codifi cation
While the benefi ts of capturing tacit knowledge and codifying explicit knowledge are
obvious to organizations, they can be fairly vague at the level of the individual knowl-
edge worker. The prevalence of the “ knowledge is power ” paradigm makes it diffi cult
to “ sell ” employees on the importance of having their knowledge retained by the
organization as a future hedge for when they are no longer working there. Knowledge
is a curious asset — one that cannot be owned but merely borrowed or rented. Some
knowledge remains within the organization when employees leave but this needs to
be the “ right ” kind of knowledge and workers will need to be able to access and make
use of it.
A number of recommendations include:
Acknowledge knowledge contributors Turning tacit knowledge into explicit knowledge
is diffi cult for many users and often faces resistance, despite the obvious benefi ts.
Acknowledge workers who not only create original content, but also help improve the
content over time by adding additional context from customer interactions. KM soft-
ware should offer reports to identify those who are contributing, or help to tap the
tacit knowledge by building profi les of experts based on their contributions.
Remember to forget The role of unlearning or reframing cannot be emphasized enough
(e.g., Fiol and Lyles 1985 ). The organizational knowledge base should not be viewed
as unlimited storage space to be fi lled. While there may not be any technological
constraints, there are certainly conceptual constraints to take into consideration.
Unlearning involves disposing of old frameworks and breaking away from the status
quo — a form of double loop learning. Van de Ven and Polley (1992) suggest that the
type of unlearning that involves responses to mistakes and failures can play an impor-
tant role in knowledge acquisition and deployment — if they are viewed as learning
opportunities. As Edison put it: “ I have not failed. I ’ ve just found 10,000 ways that
won ’ t work ” (Thomas A. Edison, as quoted in The World Book Encyclopedia (1993) Vol.
E, p. 78).
Do not spill any knowledge during transfer Conversion of tacit knowledge to explicit
knowledge must be accomplished without signifi cant loss of knowledge (e.g., Brown
and Duguid 2000 ). The advantages of communicability do not always outweigh the
Knowledge Capture and Codifi cation 135
disadvantages of “ knowledge leakage. ” It is crucial to maintain links to knowers, that
is, individuals within the organization who are adept at making use of complex knowl-
edge. The goal is to carry out the “ right ” amount of knowledge acquisition and
codifi cation.
Remember the paradox of knowledge value The more tacit knowledge is, the more value
it holds. Tacit knowledge is generally of greater value and of greater competitive
advantage to a fi rm than explicit knowledge. It may be in the fi rm ’ s interest to main-
tain that content at a certain minimal level of tacitness so that it is not easily acquired
or imitated by others.
Key Points
• Firms need to adapt and adjust to some degree if they are to survive.
• Firms need to learn — the question is whether they do so in an ad hoc informal
manner, or whether there is deliberate intention to learn.
• Emergent knowledge acquisition ( Malhotra 2000 ) is spontaneous and unplanned.
Because it is haphazard, there is no guarantee that anything will be retained in the
organization ’ s corporate memory.
• Methodical, systematic, intentional knowledge acquisition is of greater strategic
value to a fi rm.
• Knowledge bases must be populated and contents deployed in order to maximize
effi ciency and effectiveness throughout the organization.
Discussion Points
1. Why is it diffi cult to directly codify tacit knowledge?
2. What are some of the pitfalls that may be encountered in capturing tacit knowl-
edge? How would you address these?
3. What is the purpose of a learning history? What are its key components?
4. What are the major taxonomic approaches to codifying knowledge that has been
captured? What sorts of criteria would help you decide which one(s) to use in a given
organization? How would you maintain the taxonomy?
5. Defi ne knowledge continuity management and discuss its strategic implications for
knowledge capture and codifi cation.
136 Chapter 4
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5 Knowledge Sharing and Communities of Practice
Knowledge exists to be imparted.
— Ralph Waldo Emerson (1803 – 1882)
This chapter addresses the social nature of knowledge, knowledge sharing, and com-
munities of practice (CoP). A number of important conceptual frameworks are pre-
sented to study the social construction of meaning. Knowledge-sharing groups such
as communities of practice are situated in a historical context and their evolution in
organizations is described with particular emphasis on the development of social
capital. Techniques and technologies such as social networks are presented as means
of visualizing and analyzing knowledge fl ows during knowledge-sharing activities and
some common barriers to knowledge sharing are described. The dimensions of social
presence and media richness are introduced as a means of characterizing knowledge-
sharing channels.
Learning Objectives
1. Describe the key components of a community of practice.
2. Outline the major phases in the life cycle of a community and the corresponding
information and knowledge management (KM) needs for each.
3. Defi ne the major roles and responsibilities in a community of practice, with
particular emphasis on the integration of library and information professionals ’
skills.
4. Characterize knowledge-sharing channels with respect to the dimensions of social
presence and media richness.
5. Analyze the fl ow of knowledge in a community of practice using appropriate tools
and techniques to identify enablers and obstacles to knowledge sharing.
142 Chapter 5
6. Discuss how communities can be linked to organizational memory in order to foster
organizational learning and innovation.
Introduction
Once knowledge has been captured and codifi ed, knowledge needs to be shared and
disseminated throughout the organization (see fi gure 5.1 ).
With the advent of personal computers and the World Wide Web, it seems to be
implicitly assumed that web users are all good researchers or searchers. Unfortunately,
this has not been accompanied by any type of training or what is sometimes referred
to as information literacy , defi ned as “ a set of abilities requiring individuals to recognize
when information is needed and have the ability to locate, evaluate and use effectively
the needed information ” ( ALA 1989 ). “ Information seeking ” rarely appears as a
requirement in job descriptions, and yet the International Data Corporation ’ s Content
Technologies Group director, Susan Feldman ( 2004 ) estimates that knowledge
workers spend from 15 to 35 percent of their time searching for information. These
workers typically succeed in fi nding what they seek less than 50 percent of the time.
In parallel, economists raised the alarm about the productivity paradox , which refers
to a surprising decline in productivity (as measured by standard indices) despite
massive investment in computers ( Harris 1994 ).
Assess
Update
Contextualize
Knowledge capture
and/or creation
Knowledge sharing
and dissemination
Knowledge acquisition
and application
Figure 5.1 An integrated KM cycle
Knowledge Sharing and Communities of Practice 143
This means that although 80 to 85 percent of a company ’ s information is hard-
to-access tacit knowledge, it does not appear that explicit knowledge is any easier to
fi nd and use. One IDC estimate ( Feldman 2004 ) found that 90 percent of a company ’ s
accessible information is used only once. The amount of time spent reworking or
recreating information because it has not been found, or worse, going ahead and
making decisions based on incomplete information, is increasing at an alarming rate.
The IDC study estimates that an organization with one thousand knowledge workers
loses a minimum of $6 million per year in time spent just searching for information.
The cost of reworking information because it has not been found costs that organiza-
tion a further $12 million a year. We can only imagine but not yet calculate the
increase in creativity and original thinking that might be unleashed if knowledge
workers had more time to think instead of futilely trying to fi nd existing
information.
In 2000, the IBM Institute conducted a survey of forty managers at a large
accounting organization to identify the sources of information people used in orga-
nizations that had a well-developed knowledge management system or infrastructure
( Bartlett 2000 ). The results showed that people still fi rst turned to people in order
to fi nd information, solve problems, and make decisions. In fact, the company
The annual cost of a poorly designed knowledge base interface such as an intranet can
be easily calculated using the Excellent Intranet Cost Analyzer (extract from: : http://www
.dack.com/web/cost_analyzer.html).
There is a cost to not fi nding information. Although it is impossible to measure the exact cost of employees not fi nding information on a company ’ s intranet, the tool below gives a ballpark fi gure. Instructions:
1. Enter the number of a company ’ s employees.
2. Enter the average number of intranet pages each employee visits per day.
3. Enter the average number of seconds of confusion per page a company ’ s intranet users
will experience. That is, the number of seconds a user says “ This isn ’ t what I ’ m looking
for ” or “ Dammit! I ’ m lost. ” A typical range is between fi ve and twenty seconds.
4. Enter the average employee ’ s annual salary.
5. Push the Calculate button.
Source : http://www.dack.com/web/cost_analyzer.html
Box 5.1 An example: The cost of not fi nding information
144 Chapter 5
knowledge base was ranked only fourth among the fi ve choices for preferred sources
of information as shown in table 5.1 .
Cross and Parker (2004) found that people are the most critical conduits of infor-
mation and knowledge. Knowledge workers typically spend a third of their time
looking for information and helping their colleagues do the same. A knowledge worker
is fi ve times more likely to turn to another person rather than an impersonal source
such as a database or KM systems. Only one in fi ve knowledge workers consistently
fi nds the information needed to do his or her job, and Cross and Parker (2004) found
that knowledge workers spend more time recreating existing information they were
unaware of than creating original material.
A similar type of study was undertaken with a large aviation company in the United
States. This was a longitudinal study that took place over seven years and studied the
ways in which individuals in this large organization sought out and found informa-
tion. The research team actually sat down with and observed highly skilled profes-
sionals as they went about their daily work. Not only did these workers prefer to
contact other people in order to fi nd, retrieve, and make use of information, but this
also turned out to be a more successful strategy to use.
It turns out that, not only are other people the preferred source of information,
but that there are a number of reasons for this. One is of course that it is often faster,
but this is not the only reason. When we turn to another person, we not only end up
with the information we were looking for, but we also help learn where it was found.
In addition, the person turned to may help us to reformulate our question or query,
tell us whether we were on the right track or where we strayed, and, last but not least,
that the information is coming to us from a known and usually trusted, credible
source. In other words, people are the best means of getting not only a direct answer
but also “ metaknowledge ” about our search target and our search capabilities. Talking
to other people provides a highly valuable learning activity that is primarily a tacit-
Table 5.1 Results of the IBM Institute survey
Information source
Number of respondents
who chose this source
Percent of respondents
who chose this source
People 34 85 Prior material 16 40 Web 10 25 Knowledge base 4 12 Other 4 12
Knowledge Sharing and Communities of Practice 145
tacit knowledge transfer, as this type of knowledge is seldom rendered explicit or
captured in any form of document.
These studies all point to one key dimension, and that is that learning is a pre-
dominantly social event ( Cohen and Prusak 2001 ). Present day organizations have
diffi culty providing opportunities for such social one-to-one knowledge exchanges to
continue to exist in their traditional form, that is, as informal hallway, water cooler,
coffee machine, or even designated smoking area chats due to the large number of
employees and/or the fact that they may not all be in close proximity to one another.
Technology offers a new medium through which employees who share similar profes-
sional interests, problems, and responsibilities can share knowledge. This is typically
through e-mail groups, discussion groups, and other interactions in some sort of
virtual shared workspace that is typically hosted by the organization ’ s intranet and
they are often referred to as CoPs.
A community of practice refers to “ a group of people having common identity, pro-
fessional interests and that undertake to share, participate and establish a fellowship ”
( American Heritage Dictionary 1996 ). Communities of practice can also be defi ned as a
group of people, along with their shared resources and dynamic relationships, who
assemble to make use of shared knowledge, in order to enhance learning and create
a shared value for the group ( Seufert, Von Krogh, and Bach 1999 ; Adams and Freeman
2000 ). The term community suggests that these groups are not constrained by typical
geographic, business unit, or functional boundaries, but rather by common tasks,
contexts, and interests. The word practice implies knowledge in action — how individu-
als actually perform their jobs on a day-to-day basis as opposed to more formal policies
and procedures that refl ect how work should be performed. The concept of a com-
munity of practice as a knowledge-sharing community within organizational settings
originated with Lave and Wenger (1991) . Many organizations have implemented com-
munities of practice.
Demarest (1997) distinguished two basic orientations to KM: information-based
(codifying and storing content) and people or interaction-based KM (connecting
knowers). Information-based approaches focus primarily on knowledge capture and
codifi cation, as we saw in chapter 4. The information-based approach tends to empha-
size explicit knowledge over tacit and favors the externalization objective. The learner
is viewed as a tabula rasa or blank slate and into this container content is simply
poured in. Rodin ’ s “ The Thinker ” is an image that captures this notion well — an indi-
vidual, alone, deep in thought. This narrow focus, or “ tunnel vision, ” neglects context,
background, history, common knowledge, and social resources. As noted in Seely
Brown and Duguid (2000, xxv), “ information and individual are inevitably and always
146 Chapter 5
Jumping straight into deploying knowledge-management technology was a temptation
for telecommunications supplier Ericsson Canada Inc. “ We have a tendency to grab tech-
nology fi rst, ” says Anders Hemre, director of enterprise performance at the company ’ s
Montreal research unit. But Ericsson offi cials wisely took a step back to look at the com-
pany ’ s culture, values, and people before doing so.
Through surveys, Hemre found that the research group ’ s growth (doubling to 1,700
workers in four years by 1999) had undercut the sense of community. So Ericsson identi-
fi ed informal groups that had formed around work-related topics, such as Java program-
ming or the mobile Internet, and worked to help those cliques expand and form new
groups to further disseminate ideas and information. People gather informally to discuss
work outside their cubicles every day, but “ to capture that and put a little bit of structure
to it to help it along, without over-engineering or over-managing it, is the trick. ”
Once the groups were identifi ed by talking to employees in the various research
divisions, Ericsson appointed a community leader for each group and gave workers time
to meet on a regular basis; there was no agenda for these meetings, which still take place.
A community is formed for learning, but it is not necessarily organized or managed in a
heavy-handed way.
Box 5.2 An example: Ericsson (Gonsalves and Zaino 2001)
ICL Ltd. has restructured its entire organization into communities. These fall into two
types: professional and interest. All employees belong to a professional community depen-
dent on their function (Sales, Project Management, Consultancy, etc.) and any employee
can belong to one or more communities of interest (KM, Quality Improvement, etc.). For
example, a consultant will belong to the professional community of consultants and work
and develop within this framework. The consultant can also specialize in KM and therefore
belong to the KM community of interest where members share, discuss, and develop in
the KM fi eld. The KM community meets at regular intervals, guest speakers are invited to
meetings, and lots of tacit knowledge exchange takes place. A true community spirit
develops. The interest community will typically regulate itself and have an administrator
to facilitate the web space and other coordination activities.
Box 5.3 An example: ICL
Knowledge Sharing and Communities of Practice 147
part of rich social networks. ” Critics maintain that this oversimplifi es knowledge and
in particular, ignores the social context of knowledge (e.g., Seely Brown and Duguid
2000 ; Conrad and Poole 2002 ).
People or interaction-based approaches, on the other hand, place a great deal of
emphasis on knowledge-sharing interactions, which in today ’ s organizations tend to
be associated with CoPs ( Thomas, Kellogg, and Ericson 2001 ). This social constructivist
approach to learning and knowledge transfer seems to be much better suited to the
discipline of knowledge management.
The Social Nature of Knowledge
KM needs to view knowledge as something that is actively constructed in a social
setting ( McDermott 2000 ). Group members produce knowledge by their interactions
and a group memory is created. Social constructivism views knowledge not as an
objective entity but as a subjective, social artifact ( Berger and Luckmann 1966 ). Social
constructivists argue that knowledge is produced through the shared understandings
that emerge through social interactions. As individuals and groups of people com-
municate, they mutually infl uence each other ’ s views and create or change shared
constructions of reality ( Klimecki and Lassleben 1999 ). The social constructivist per-
spective views knowledge as context dependent and thus as something that cannot
be completely separated from “ knowers ” ( Lave and Wenger 1991 ). Context helps
distinguish between knowledge management and document management: whereas
the latter can be carried out in a more or less automated manner, the former cannot
be accomplished without involving people as well as tangible content.
Huysman and DeWit (2002 ) describe a collective acceptance of shared knowledge
as being the key method of generating value to the organization. Until knowledge is
collectively accepted and institutionalized across the organization, organizational level
learning cannot occur and organizational memory cannot be developed. Ortenblad
(2002) explained that unlike the functionalist paradigm in which learning starts in
the individual, the interpretive paradigm suggests that learning begins in the relation-
ships between individuals. As the community grows and its knowledge base is more
broadly shared across the organization, the community ’ s practices become regularly,
widely, and suffi ciently adopted so as to be described as institutionalized knowledge
( Huysman and DeWit 2002 ).
Since individual memory is limited, we need to embed this knowledge in useful,
more permanent forms such as documents, e-mails, and so on. This institutionalized
knowledge then becomes an organizational legacy that remains in the corporate
The Special Library at the Jet Propulsion Lab of the California Institute of Technology took
the lead in forming a CoP for information professionals. The purpose of this CoP was to
promote knowledge sharing and networking to help connect JPL employees. The CoP
adopted an inclusive approach — a “ more the merrier ” mentality — with respect to member-
ship. Everyone deemed to play a role in moving information along was invited to the fi rst
meeting. Invitees were encouraged to identify others like themselves who might want to
participate. No one was excluded and the fi rst meeting included people with a variety of
titles, affi liations, and responsibilities within JPL. Next, a referral directory was developed
to identify members of the network as well as organizations containing relevant informa-
tion who did not have a network representative. The referral directory is a form of corpo-
rate yellow pages, or expertise locator system (ELS) and included the following information
for each member or organization:
• Name
• Information collected/provided
• Contact person, phone, e-mail address, fax number
• Hours of operation
• URL, if applicable
Some of the member organizations included the JPL AV Library, document manage-
ment unit, KM program offi ce, project libraries (project document repositories), Engineer-
ing Standards Library, IT services, Engineering Document Services, Infrared Processing and
Analysis Centre (IPAC) Library, the Oceanic and Remote Sensing Library (ORSL), Photog-
raphy Lab, Planetary Data System (PDS) that distributes data from missions, the NASA
image collection unit, and internal communications. Members had access to an e-mail
distribution list, but the main CoP channel used was a face-to-face meeting that was held
quarterly. At these meetings, the referral database was updated, new projects were reviewed,
and news was exchanged with other attendees. At some meetings, speakers presented new
tools (e.g., the KM team presented a new knowledge capture template). While there were
only six people present at the inaugural meeting, the network gradually grew to about
thirty members who regularly attend all the meetings.
Over time, the library led initiative became a part of the organization. The JPL Informa-
tion Professionals CoP is a good example of an informal network that self-organizes or
evolves without directives from management sponsors. The library continues to play a lead
role that consists of coordinating and not actively managing the CoP. This type of CoP is
often referred to as an organic entity — one that is free from strict rules (e.g., membership
eligibility), is non-hierarchical, informal, participatory, and primarily face-to-face. The JPL
CoP has helped break down organizational silos through its interdisciplinary participation.
When you think about it, there are very few if any other such opportunities for people from
different departments to meet and discuss their mutual work (other than smoking areas and
the cafeteria!). During the CoP meetings, participants are comfortable as they are not report-
ing to anyone in a supervisory fashion — they are among their peers and they are therefore
quite open to sharing their knowledge in a mutually benefi cial manner.
Box 5.4 An example: JPL information providers network ( Bailey and Hendrickson 2004 )
Knowledge Sharing and Communities of Practice 149
memory for subsequent generations to learn from. What is critical to keep in mind is
that the context of each item of knowledge must also be captured: when it occurred,
who is knowledgeable about it, which one submitted it, and so on. Without this
context, the knowledge product is not complete and cannot be successfully used,
applied, or even understood.
Sociograms and Social Network Analysis
According to Krebs (2002), “ social network analysis is the mapping and measuring of
relationships and fl ows between people, groups, organizations, computers or other
information/knowledge processing entities. ” Social network analysis (SNA) can map
and measure relationships and fl ows between people, groups, organizations, comput-
ers, and other information/knowledge processing entities. The nodes in the network
Networks, by defi nition, connect everyone to everyone. Hierarchies, by defi nition, do not;
they create formal channels of communication and authority. When a network becomes
the main means by which information is conveyed and work gets done in an organization,
our hierarchical crutches are knocked down. Rank is unclear. Networks operate informally
with few rules. They depend on trust. The fi rst dimension of trust is competence: I can
trust you if you are good at what you do. Second, trust needs a community. Networks
naturally spawn internal groups of like-minded individuals. When these emerge around a
common discipline, they are CoPs. CoPs create and validate competence. The boss may
not know who is the best at the job, but the community will always know.
At Thomas & Betts Corp., a $2.2 billion electrical parts maker in Memphis, Tennessee,
motivation is decidedly nontechnical. Board games in which teams compete on solving
business problems teach managers the importance of sharing ideas and information. “ It
gives employees a good sense of the roles and functions other people play in the company, ”
says Gary Bodam, director of training and development. Once they realize that their will-
ingness to share knowledge affects the bottom line in games, they ’ re more open to making
changes in how they operate in the real world, he says. But Thomas & Betts also is using
technology to foster knowledge sharing. The company runs an E-learning-management
system from ThoughtWare Technologies Inc. that tracks employees ’ continuing education,
such as public speaking or engineering. The data are logged in an SAP human-resources
system and can be used by managers looking for the best candidates for jobs. Says Bodam,
“ It ’ s all become part of the overall knowledge base by which we ’ ll try to move the orga-
nization forward. ”
Box 5.5 An example: Thomas & Betts (Gonsalves and Zaino 2001)
150 Chapter 5
are the people and groups, while the links show relationships or fl ows between the
nodes (see fi gure 5.2 ). SNA provides both a visual and a mathematical analysis of
complex human systems to identify patterns of interaction such as the average number
of links between people in an organization or community, the number of subgroups,
the information bottlenecks, the knowledge brokers, and the knowledge hoarders.
In the context of KM, SNA enables relationships between people to be mapped in
order to identify knowledge fl ows: who do people seek information and knowledge
from? Who do they share their information and knowledge with? In contrast to an
organization chart that shows formal relationships — who works where and who
reports to whom, an SNA chart shows informal relationships — who knows whom and
who shares information and knowledge with whom (see fi gure 5.3 ). It therefore allows
managers to visualize and understand the many relationships that can either facilitate
or impede knowledge creation and sharing ( Anklam 2003 ). Because these relationships
are normally invisible, SNA is sometimes referred to as an organizational x-ray, showing
the real networks that operate underneath the surface organizational structure ( Donath
2002 ; Freeman 2004 ).
Once social relationships and knowledge fl ows can be seen, they can be evaluated
and measured. Network theory is sympathetic with systems theory and complexity
theory. Social networks are also characterized by a distinctive methodology encom-
passing techniques for collecting data, statistical analysis, visual representation, and
so on. The results of social network analyses can be used at the level of individuals,
departments, or organizations to clear up information bottlenecks and to accelerate
Portal
Jack Sue
Knowledge request
Knowledge response
Figure 5.2 Mapping the fl ow of knowledge
Knowledge Sharing and Communities of Practice 151
Group A
Group B
Babette Jack
Heinrich
Mucho
Oedipa Metzger
Emily and Hugh are
“hidden experts”
Group E
Group CEmily Hugh
Liz Leamus
George
Wanda
KurtApril
Group D
Vronksy
Anna Kitty
Figure 5.3 Knowledge fl ow analysis example (Adapted from Krebs 2000 )
the fl ow of knowledge and information across functional and organizational boundar-
ies. A social network should be thought of as a dynamic or moving target and will
need to be constructed more than once. For example, the data gathering and analysis
process can provide a baseline against which you can then plan and prioritize the
appropriate changes and interventions to improve the social connections and knowl-
edge fl ows within the group or network.
The process of social network analysis typically involves the use of questionnaires
and/or interviews to gather information about the relationships among a defi ned group
or network of people. The responses gathered are then mapped using a software tool
specifi cally designed for the purpose. Key stages of the process will typically include:
152 Chapter 5
• Identifying the network of people to be analyzed (e.g., team, workgroup, department)
• Clarifying objectives and formulating hypotheses and questions
• Developing the survey methodology and designing the questionnaire
• Surveying the individuals in the network to identify the relationships and knowledge
fl ows between them
• Use a software mapping tool to visually map out the network
• Analyzing the map and the problems and opportunities highlighted using interviews
and/or workshops
• Designing and implementing actions to bring about desired changes
• Mapping the network again after a suitable period of time
In order for SNA maps to be meaningful, it is important to know what information
you need to gather in order to build a relevant picture of your group or network. Good
survey design and questionnaire design are therefore key considerations. Questions
will be typically based on factors such as:
• Who knows who and how well?
• How well do people know each other ’ s knowledge and skills?
• Who or what gives people information about xyz ?
• What resources do people use to fi nd information/feedback/ideas/advice about xyz ?
• What resources do people use to share information about xyz ?
While there are quite a number of different SNA tools, there is a need for a user-
friendly end-to-end solution that can be applied in a variety of business settings ( Dalkir
and Jenkins 2004 ). Existing tools have little support, tend to be proprietary, have little
track record, and tend to be heavily weighted toward the statistical analysis of data
once it has been gathered with little support for the initial data collection activities.
Community Yellow Pages
Communities are all about connections between people and these connections are
often used to develop corporate yellow pages or an expertise location system. While
initially community-based, such expertise locators can eventually be integrated to
form a corporate-wide yellow pages. Lamont (2003) emphasizes their contribution to
organizational learning initiatives such as facilitating mentoring programs, identifying
knowledge gaps, and providing both performance support and follow-up to formal
training activities. Figures 5.4 and 5.5 illustrate a typical application for a large, dis-
tributed European publishing company.
Knowledge Sharing and Communities of Practice 153
Directories Libraries Discussion area Support
Products
Projects
External suppliers
Publishing companies
Network of experts
Best practices library
Lessons learned
Stories
Training modules
Discussion themes
Project management
Risk management
Glossary of terms
Frequently asked
questions
Figure 5.4 Example of a Yellow Pages
Function
Network of experts
Geographic area Business area Expertise
Expertise
Content
management
Electronic
production
Knowledge
management
Publishing
management
Vice president
Director
Line manager
Operator
Northeast
West coast
Midwest
South
Sales
Operations
Distribution
Finance
Content management
Jane Dennys
Will Jameson
Electronic production
Jan Zariski
Sarah Marxman
Head Office
Regional Office 6
Regional Office 6
Regional Office 6
555 434-4564
555 212-3212
555 212-3233
555 212-3232
Figure 5.5 Example of a Yellow Pages (Continued)
154 Chapter 5
Table 5.2 Software to develop yellow pages or expertise location systems
Name Description Web Site
Kamoon ’ s Connect Profi les set up by analyzing unstructured repositories to identify documented expertise
http://www.kamoon.com/
AskMe Web-based questionnaire used on a voluntary basis; can track Q & A to identify any knowledge gaps
http://www.askmecorp.com/
Sopheon ’ s Organik Q & A format, provides answers to questions and then stores the answers in a repository for future reference
http://www.sopheon.com/
Tacit ’ s KnowledgeMail
Learns about people automatically through analysis of e-mails as well as document repositories and Lotus Notes databases. Search results include experts and links to content.
http://www.tacit.com/
A wide range of software exists for the development of corporate yellow pages (see
table 5.2 for some examples). Most create an initial profi le of an individual ’ s expertise
based on an analysis of published documents, based on questionnaires or interviews,
while others focus on e-mails. These are very popular KM applications and they are
often the fi rst KM implementation a company will undertake primarily due to the fact
that they can be developed fairly quickly (on the order of one to two months) and
they can provide almost instantaneous benefi ts to individuals, communities, and the
organization itself.
Yellow pages, or expertise location systems, were among the earliest KM applica-
tions and they remain one of the best ways to initiate wider-scale knowledge
sharing in organizations. Two examples are explored here from Texaco and British
Petroleum.
Knowledge-Sharing Communities
The notion of a community is, of course, not necessarily a new concept. In fact, as
far back as 1887, writers such as the German sociologist Tonnies compared and con-
trasted the more direct, more total, and more signifi cant interactions to be found in
a community as opposed to the more formal, more abstract, and more instrument-
driven relationships to be found in a society (translated by Loomis, 1957). Tonnies
Knowledge Sharing and Communities of Practice 155
Texaco ’ s knowledge-management arsenal includes PeopleNet ( Gonsalves and Zaino 2001 ),
a custom-built application that lets employees build a personal profi le and post it as a Web
page on the company ’ s intranet. The content of the profi le does not have to be purely
work-related: Pictures and hobby lists coexist alongside users ’ summaries of their job
expertise. The PeopleNet content and the company ’ s e-mail systems are linked through
KnowledgeMail from Tacit Knowledge Systems Inc., which monitors an employee ’ s e-mail,
moving phrases that seem to refl ect a person ’ s expertise on a particular subject into a
private profi le accessible only to that employee. The person then chooses which phrases
to publish in a public directory to help others distinguish him or her as a potential expert
in an area. Someone searching for an expert in marketing crude oil, for example, would
get a list of people associated with that phrase; clicking on a name in that list would call
up a profi le of the person in KnowledgeMail, as well as a link to the person ’ s PeopleNet
profi le.
300 people at Texaco used KnowledgeMail through a pilot program in its fi rst year and
a half. It is considered to be a successful KM application. John Old, the company ’ s director
of information, recounts a meeting in which Texaco execs were sharing ideas on KM with
a business partner. In demonstrating KnowledgeMail, a colleague typed the word “ wire-
less ” and the top name on the retrieved list was a systems architect who was in the room,
but had never been identifi ed as someone knowledgeable in wireless technology. “ In any
large company, there are lots of conversations in e-mail that you ’ re not aware of, and there
are lots of hidden experts, ” Old says.
Box 5.6 An Example: Texaco
BP ’ s yellow pages (Cohen 1999) are entirely bottom up. About 20,000 employees (of
80,000) have personal pages. It takes about ten minutes to produce one using a form fi lling
approach, which contains a self-appraisal of skills and interests. No one vets the content,
but people rarely oversell themselves! People who leave BP may still have a page. Every
three seconds, someone makes a connection. The yellow pages are widely embedded in
the BP intranet; they are integrated into the search environment and are now a part of
how they do business.
Box 5.7 An Example: British Petroleum
156 Chapter 5
argued that there are two basic forms of human will: the essential will, which is the
underlying, organic, or instinctive driving force; and arbitrary will, which is delibera-
tive, purposive, and future (goal) oriented. Groups that form around essential will, in
which membership is self-fulfi lling, Tonnies called Gemeinschaft (often translated as
community). Groups that were sustained by some instrumental goal or defi nite end
he termed Gesellschaft (often translated as society). The family or neighborhood exem-
plifi ed Gemeinschaft; the city or the state exepmlifi ed Gesellschaft.
More recently, Anselm Strauss (1978) another sociologist, described Internet com-
munities as “ social worlds. ” Even before there was an Internet, there were “ invisible
colleges, ” which consisted of academics, who though spread out around the world,
nonetheless developed a sense of collective identity with their colleagues, their fi eld,
and their professional position within that fi eld via constant communications ( Price
1963 ). Their shared communications and mental models gave rise to a discipline, a
professional group. Sharing and circulating knowledge appears to be age-old effective
social glue. These early communities were made possible by the printing press and are
sometimes referred to as “ textual ” communities as they primarily circulated written
documents. An important characteristic that these early communities share with
today ’ s virtual communities is that they organized themselves. The biggest divergence
is that whereas documents tend to be fi xed, information or knowledge to be shared
is fl uid in nature.
The fi rst virtual communities emerged about a decade after the establishment of
the Internet. The Internet itself was an initiative called ARPANET, which was intended
as a means of making it easier to for researchers to share large data fi les. In the early
1980s, a network called USENET was set up to link university computing centers that
used the UNIX operating system. One function of USENET was to distribute “ news ”
on various topics throughout the network. Initially, all of the newsgroups focused on
technical or scholarly subjects, but so-called alt and rec groups that focused on non-
technical topics such as food, drugs, and music began to appear, which constituted
the fi rst evidence of people organizing themselves into virtual networks.
Before long, the number of newsgroups started to grow exponentially. USENET, for
example, had 158 newsgroups in 1984. The number grew to 1,732 groups in 1991 and
to 10,696 groups in 1994. Today there are more than 25,000 different newsgroups in
existence. The Well, based in the San Francisco Bay Area, fl ourished as a place where
online pioneers could gather to meet and talk with one another and is one of the
oldest virtual communities around. Rheingold (1993) was one of the fi rst to assert that
online networks were emerging as an important social force that could provide rich
Knowledge Sharing and Communities of Practice 157
and authentic community experiences. Hagel and Armstrong (1997) argued that
virtual communities have economic as well as social signifi cance. Like Rheingold, they
recognize that virtual communities are based on the affi nity among their participants
that encourages them to participate in ongoing dialog with each other. Knowledge
sharing between participants can generate “ webs of personal communication ” that
reinforce the sense of identifi cation with the community.
Although the literature discusses virtual communities in abundant detail, the
technology-mediated interactions were supplanted by a substantial amount of old-
fashioned telephone exchanges, face-to-face meetings, and general neighborliness
( Rheingold 1993 ). When videoconferencing fi rst began to be widely used as an alter-
native to face-to-face business meetings, it was quickly found that this medium worked
well but only after participants had met in person and established some sort of social
presence. If participants met one another for the fi rst time during a videoconference,
or a teleconference for that matter, the interactions were much more awkward and
slow, and the knowledge that was exchanged tended to be less signifi cant ( Hayden,
Hanor, and Harrison 2001 ). Psychologists have found that in face-to-face talks, only
7 percent of the meaning is conveyed by the words, while 38 percent is communicated
by intonation and 55 percent through visual cues, and that up to 87 percent of mes-
sages are interpreted on a nonverbal, visual level ( Telstra 2000 ).
Seely Brown and Duguid (2002 ) point out the neglect of the social aspects of knowl-
edge sharing, noting that documents do more than merely carry information. They
“ help structure society, enabling social groups to form, develop and maintain a sense
of shared identify ” (p. 189). The community-forming character of the Internet is by
now quite well known. In fact, a number of technologies that were originally intended
to transmit information such as the Minitel system in France used to book travel and
serve as an electronic phone book quickly became used as messaging systems between
users. Similarly, transactional Web sites such as eBay and Amazon.com hold value not
only in terms of their product offerings, but also in the ability of visitors to the site
to annotate content and thus communicate with other visitors.
While technology is a feature of some communities, technological means of inter-
acting are by no means a necessary component of communities. Technology comes
into play when members are more dispersed and when they have fewer occasions to
meet face-to-face. The critical components of a community lie in the sharing of
common work problems between members, a membership that sees clear benefi ts of
sharing knowledge among themselves and who have developed norms of trust, reci-
procity, and cooperation.
158 Chapter 5
Types of Communities
All communities share some basic characteristics, regardless of the type of community.
Wenger (1998) identifi es these as joint enterprise (a common goal), mutual engage-
ment (commitment by all members), and shared repertoire (typically a virtual work-
space for all members to be able to interact with one another) see ( fi gure 5.6 ).
Joint enterprise refers to the glue that binds members together — why they want to
interact with one another. Reasons for interacting with one another will typically be
a personal goal and contribution toward the community ’ s goal. Mutual engagement
refers to how members become part of the community. They do not automatically
belong because they say so, because they have a certain job title, or because they know
someone. There are membership rules and each member agrees to carry out certain
roles and responsibilities in order to help achieve the goals of the CoP. Finally, a shared
repertoire refers to the shared workspace where members can communicate, where
they can store and share knowledge products, their profi les, and so on. The shared
repertoire is typically space on a server — it may be an intranet within an organization
Typically the improvement
of members’ profession
Common goal
Virtual workspaceCommitment
Participation fueled by
trust, interest, credibility,
professionalism and
ethical behaviors
A place to store stories,
artifacts, tools,
discussions, glossaries,
historical events
Figure 5.6 Common characteristics of CoPs (adapted from Wenger 1998 )
Knowledge Sharing and Communities of Practice 159
or on the Internet. What is important is that there is a place for real-time exchange
and asynchronous discussion, and that this interaction leaves behind tangible
archives — the social capital and intellectual capital created by the community. All
communities thus need shared cultural objects, a means of sharing them and a means
of storing them.
In other words, networks form because people need one another to reach common
goals. Mutual help, assistance, and reciprocity are common to all functioning net-
works. Another important characteristics is that these networks are not only self-
organizing but self-regulating. For example, no one “ decrees ” that a community will
exist (although many organizations have made this mistake). It is not a top-down
formal organization as a task force or project team would be. There is no one person
“ in charge ” of the community, although there may be founding members. Similarly,
if someone is in it only for himself or herself, the other members will quickly realize
this. This is illustrated by Hardin ’ s (1968) tragedy of the commons scenario.
There are many types of CoPs and they are typically defi ned as a function of some
common focal points such as:
• A profession such as engineering, law, or medicine
• A work-related function or process such as production, distribution, marking, sales,
or customer service
Picture a pasture open to all. It is to be expected that each herdsman will try to keep as
many cattle as possible on the commons. Such an arrangement may work satisfactorily
for centuries because tribal wars, poaching, and disease keep the numbers of both man
and beast well below the carrying capacity of the land. Finally, however, comes the day
of reckoning, that is, the day when the long-desired goal of social stability becomes a
reality and logic of the commons remorselessly generates tragedy. As a rational being, each
herdsman seeks to maximize his gain. “ What is the utility to me of adding one more animal
to my herd? ” Since the herdsman receives all the proceeds from the sale of the additional
animal, the positive utility is nearly +1. The negative impact is the additional overgrazing
created by one animal. However all the herdsmen share the effect of overgrazing: the
negative utility for any particular herdsman being only a fraction of – 1. The only sensible
course for him to pursue is to add another animal to his herd — and another, and so forth.
But this is the conclusion reached by each and every rational herdsman sharing a commons.
Therein lies the tragedy.
Box 5.8 A vignette: Tragedy of the commons
160 Chapter 5
• A recurring, nagging problem situated in a process or function
• A topic such as technology, knowledge retention, or innovation
• An industry such as automotive, banking, healthcare, and so on
A CoP may also be described in terms of its goals such as the development of best
practices or benchmarking. A CoP may be self-organizing or sponsored by the
organization. A CoP may also be distinguished on the basis of the type of recognition
(or lack thereof) it has from the host organization ( Wenger 1998 ): unrecognized,
bootlegged, legitimized, supported, and institutionalized. These categories often refl ect
the maturity level of a community, but not all communities will necessarily aspire to
become institutionalized ( Iverson and McPhee 2002 ).
There are many forms that an online community can take, but most will contain:
• Member-generated content (e.g., profi les, home pages, ratings, reviews)
• Member-to-member interaction (e.g., discussion forums, member yellow pages)
• Events (e.g., guest events, expert seminars, virtual meetings, or demos)
• Outreach (e.g., newsletters, volunteer/leader/mentoring programs, or polls/surveys)
It is important to distinguish a community of practice from other groups such as
work teams or project groups. Many online communities may be termed communities
of interest as they have an open membership that is catalyzed by interest in a common
theme such as a hobby. A community of practice is more like a professional organiza-
tion. CoPs have a business case, a code of ethics, a mission statement, and so forth.
They are there for a reason, and they produce results that are of value to the profes-
sion. Typically, a CoP goal would have something to do with the improvement of the
common profession or professional theme that members are interested in. However,
the ways in which they are formed are quite unlike a professional organization as
communities self-organize and emerge in a bottom-up manner.
Roles and Responsibilities in CoPs
Communities consist of people, not technology ( Cook 1999 ). Community members
may take an active role by contributing to discussions or providing assistance to other
members — this is referred to as “ participation. ” Other members may simply read what
others have posted without taking an active role themselves. These types of members
used to be referred to as “ lurkers, ” but given the somewhat derogatory connotation
of the term, this has been replaced by “ legitimate peripheral participants ”
In almost every case, the more participation that occurs in the community, the
greater the value created for both community members and community creators.
Knowledge Sharing and Communities of Practice 161
However, it is important to keep in mind that in most communities, readers outnum-
ber posters by 10:1 or more. People who visit a community regularly but who do not
post anything typically represent 90 percent or more of the total community partici-
pation. Passive members are not really passive in most cases as they may be actively
using and applying the content they have accessed online.
Kim (2000) lists the key roles as:
• Visitors
• Novices
• Regulars
• Leaders
• Elders
Visitors may visit once or twice and may or may not join. At this point, they are
merely curious and seeking to fi nd out what the community is all about. Novices are
new members who typically stay on the periphery until they have learned enough
about the community and the other members. At this point, they become regulars,
members who provide regular contributions and who interact with other members on
a sustained basis. Leaders are members who have the time and energy to take on more
offi cial roles such as helping with the operation of the community. Elders are akin to
subject matter experts: they are familiar with the professional theme and the com-
munity and have become respected sources of both subject matter knowledge and
cultural knowledge. Elders maintain the community history and agree to be consulted
from time to time by other community members.
Communities of practice require a number of key roles to be fi lled. These need not
necessarily be a single individual working full-time — more often, they are revolving
roles much like everyone taking a turn at being a scribe at business meetings today.
However, there is real work to be done in order for the community to succeed, and
this translates into real time. Depending on the type of organization, the number of
members, and other scope variables, a good rule of thumb is to budget 10 – 20 percent
of a knowledge worker ’ s time as being devoted to CoP work.
Nickols (2000) defi nes more offi cial community roles. The major CoP roles include
a champion, a sponsor, a facilitator, a practice leader, a knowledge service center or
offi ce (KSO), and members. The champion ensures support at the highest possible
level, communicates the purpose, promotes the community, and ensures impact. The
sponsor serves as the bridge between the CoP and the rest of the formal organization,
communicates the company ’ s support for a CoP, and may remove barriers such as
time, funding, and other resources. The sponsor is instrumental in establishing the
162 Chapter 5
mission and expected outcomes for the community. Community members are
recruited for their expertise relevant to the practice or strategic services. They are there
to better share knowledge, know-how, and best practices to benefi t the business by
participating actively. They participate in discussions, raising issues and concerns
regarding common needs and requirements, alert other members to any changes in
conditions and requirements, are on the lookout for ways to enhance CoP effective-
ness (e.g., by recruiting high-value members), and, above all, they learn.
CoP facilitators have perhaps the most demanding role. They are responsible for
clarifying communications, making sure everyone participates, ensuring dissident
views are heard and understood. They are the chief organizers of events such as meet-
ings (face-to-face as well as virtual meetings). They administrate all communications
by drawing out reticent members, reconciling opposing points of view, posing ques-
tions to further discussion, and keeping discussions on topic. The practice leader is
the acknowledged leader of the CoP “ themes. ” The leader provides thought leadership
for the practice or strategic service, validates innovations and best practices, and
promotes adherence to them. He or she identifi es emerging patterns and trends in
CoP activities and knowledge base and in other areas that may impact the practice.
Leaders resolve confl icts, evaluate CoP performance with respect to expectations,
approve memberships, and lead the way in prioritizing issues and improvements to
be tackled. CoP practice leaders serve as model to coach other members or arrange to
provide coaching and they are always alert to the potential need for CoP changes (e.g.,
more members, different members, and different member composition).
CoP knowledge services are information/knowledge integrators who serve to
interface with all CoPs to ensure clarity and lack of duplication of the information
disseminated within and from the CoPs. They maintain information sharing relation-
ships with all CoPs, inform CoP members about relevant activities elsewhere, and
inform others about relevant CoP activities. The knowledge center coordinates infor-
mation from CoP members to avoid duplication, redundancies, and poor quality (e.g.,
in postings to CoP Web sites and forums), and they fi lter knowledge and requests for
help (e.g., yellow pages). Finally, all the members of the CoP share the responsibility
for marketing and promoting the CoP, generating interest in the CoP, generating
enthusiasm among current members, and demonstrating its value. Everyone must
ensure continued support and resources from sponsor(s), recruit high-potential pro-
spective members, and invite them to special CoP events. Members are expected to
better leverage the knowledge created and learning generated by the CoP, to write and
publish articles or results descriptions in company publications, and to publish articles
in external journals or magazines and then distribute them internally.
Knowledge Sharing and Communities of Practice 163
In addition, some new types of roles arise from CoPs, such as membership manag-
ers, discussion moderators, knowledge editors, knowledge librarians, archivists, usage
analysts, and knowledge brokers. A CoP membership manager has to deal with the
registration and ongoing membership directory work. A CoP moderator is much like
a radio or TV show host. They act as conversation managers who help keep discus-
sions focused, inject new topics, add provocative points of view when discussion
lags, and seed the discussion with appropriate content. They must often be critical
in order to ensure value generation. Knowledge editors collect, sanitize, and synthe-
size content created and they provide a value-added link for the content produced.
A knowledge librarian or community taxonomist is responsible for organizing
and managing the collection of knowledge objects generated by the community. A
knowledge archivist maintains and organizes content generated by participants over
time.
A CoP usage analyst studies data on participants ’ behaviors within the community
and makes recommendations to the host. Finally, a knowledge broker is someone who
can join up with a number of different communities in order to identify commonali-
ties and redundancies, create synergy, form alliances, and feed in to organizational
memory and learning (e.g., map of intellectual assets, yellow pages, or expertise direc-
tory, CoP best practices, and lessons learned).
Finally, there will be some new roles and structures at the organizational level. For
example, the World Bank inspired knowledge management at CIDA (Canadian Inter-
national Development Agency). CIDA has implemented over 400 best practices,
lessons learned, and 30 communities of practice. There is coordination of branch
sharing activities through the CIDA KM Secretariat. The CIDA KM Secretariat in the
Senior VP ’ s offi ce has a staff of four to fi ve, to enable better knowledge sharing within
and among branches. This offi ce works closely with two organizations: the Branch KM
Leaders group (which has a representative from each of the thirteen agency branches)
develops the KM agenda, expected results, communication strategy, and specifi c KM
issues. The Network (CoP) Leaders group (which consists of the leaders of each of the
pilot CoP networks) helps networks learn from each other, achieve their objectives,
share lessons learned, and solve problems.
Knowledge Sharing in Virtual CoPs
The establishment of a community identity depends heavily on knowledge sharing.
Even something as simple as an online or paper newsletter will provide the backbone
for a community to develop. A sense of community arises from reading the same
text, the same article, and the same announcement as discussions can grow around
164 Chapter 5
CIDA (http://www.acdi-cida.gc.ca/) focuses on the dissemination of information, results,
and lessons learned. A study showed that CIDA was spending about $100 million on
repeating and reinventing knowledge the organization already had. Knowledge is created
through bringing together partners and shareholders in the organization around issues
and practices to produce new ideas, perspectives, and insights. In the application of knowl-
edge, CIDA has requested that partners and shareholders collaborate online on specifi c
projects. As part of the Canadian government, CIDA needs to make all information and
services available to citizens electronically through a project called Government Online.
This means making information available outside of Canada as well, such as on immigra-
tion services, goods and trade, development assistance, and so on.
CIDA uses an extranet, which is a culmination of the various intranets and the Internet.
Access is controlled to promote free fl owing discussion and information sharing. CIDA
uses its extranets to promote knowledge sharing through its Partners Forum, Field
Representatives Forum, and Strategic Information Management Forum. Finally, regional
forums allow different CIDA branches to share among themselves. The fi rst step is to
disseminate information that can be used as formal or explicit knowledge. The second
step is to encourage members of each extranet to develop new knowledge through
online discussions. The third step entails the implementation of this new knowledge in
the design, development, and management of specifi c projects. The goal is to harvest
the results of this implementation effort and to disseminate those as formal/explicit
knowledge through the agency ’ s intranet. To date, CIDA has documented about 4000 best
practices and lessons learned.
Within CIDA there are about thirty CoPs involving about 1,200 people. A KM Forum
was organized involving about 150 people from various departments and partners. These
networks are the primary knowledge-sharing vehicles within CIDA. CIDA management
now provides support to the CoPs and has developed expert directories to promote interac-
tion from both within and outside the organization. CIDA is currently involved in profi l-
ing and metadata to map and identify appropriate forms of access to knowledge and
expertise within the agency. An example is the Online Project Management, which devel-
ops tools to support KM within the organization. CIDA is also extending knowledge skills
to its partners and encouraging interaction between them through its Strategic Informa-
tion Management Forum initiative.
Box 5.9 An example: Canadian International Development Agency (CIDA)
Knowledge Sharing and Communities of Practice 165
this kernel. Personalization efforts will, to some extent, work against this sense of
community as different members would receive different content.
Different knowledge-sharing technologies or channels should always be seen as
complementary and as mutually exclusive. All types of communications are some form
of conversation. Each communication medium has its strengths and weaknesses. It is
important to choose the appropriate mix of channels in order to optimize knowledge
sharing. Most communities organize their knowledge-sharing interactions as informal
exchanges between peers. Communication genres are chosen primarily on the basis
of the developing relationship between community members ( Zucchermaglio
and Talamo 2003 ). The choice of communication medium appears to be a function
of specifi c professional tasks and the stage of maturity of community development.
The authors conducted a longitudinal study over a three-year period of an inter-
organizational CoP. For example, it took about six months for communications
to become predominately informal and e-mail-based among community members.
Concurrent with this was an increasing formality in how community members
communicated with those external to the community, which indicates that a sense
of community boundary has been established.
One important type of knowledge sharing that occurs in a community involves the
evolution of a best practice (an improved way of doing things) or lessons learned
(learning from both successful and unsuccessful events). Figure 5.7 shows how a good
idea can evolve and be transferred within CoPs in order to be ultimately incorporated
Industry
best practice
Local best
practice
Good
practice Good idea
Has impact
within company
Technique,
method that
improves
performance
Used by other
groups on
different
assignments
•
•
•
Recognized by
company experts
Shown to be best
approach for some
or all parts of the
organization
Available for
reuse throughout
company
•
•
•
Recognized by
outside experts
Acknowledged
as state-of-the-art
by industry
•
•
BP candidate
Unproven
Intuitive
Need to analyze
Used successfully
on one or a few
problems/projects
•
•
•
•
•
Figure 5.7 Knowledge-sharing example best practice/lesson learned (adapted from APQC 1999 , American
Productivity and Quality Centre, http://www/apqc.org).
166 Chapter 5
into the organizational memory or knowledge repository. The knowledge-sharing
processes involved include searching, evaluating, validating, implementing (transfer-
ring and enabling), reviewing, and routinizing ( Jarrar and Zairi 2000 ).
Table 5.3 shows the results of an APQC study that looked at how best practice
knowledge was shared and transferred within organizations ( APQC 1999 ). Their fi nd-
ings show that 51 percent of knowledge sharing occurred as part of a formal process
within the organization, 39 percent was ad hoc, more tacit, likely within a CoP and,
perhaps most striking, 10 percent of the best practices were never shared. This type
of obstacle in knowledge sharing or knowledge fl ow is very diffi cult to overcome.
Social network analysis (SNA) is one technique that can help to identify such knowl-
edge hoarding or knowledge “ black holes ” where content is received but nothing is
ever sent out.
Virtual CoPs must rely on technology-mediated knowledge-sharing channels to a
great extent. Two major characteristics are often used to characterize the channels
used for knowledge sharing: social presence and media richness. Thurlow, Engel, and
Tomic (2004 ) defi ne social presence as the degree to which the knowledge sharer feels
like he or she is talking with another person. The highest degree of social presence
will of course exist in a face-to-face exchange where knowledge sharers can easily hear
the tone of voice, see the facial expressions, and therefore easily infer nontextual cues.
A teleconference will provide the audio cues and a videoconference will provide both
visual and audio contexts. An e-mail or discussion forum, however, must rely upon
text, which has a lower social presence. One of the ways in which we try to overcome
this limitation is through the use of “ emoticons ” (e.g., a smiley face to indicate a joke),
uppercase letters to simulate shouting, shortcut expressions, and so forth.
The second attribute of technological knowledge-sharing channels is media rich-
ness, which is defi ned by Chua (2001) as the capacity for immediate feedback, ability
to support natural language, and social presence. Once again, synchronous commu-
Table 5.3 APQC (1999) study on how knowledge is transferred within a company
Verbally at team meetings 23%
Departmental meeting 21%
Written instructions 17%
Ad hoc verbally 16%
Intranet 9%
Video 5%
Knowledge Sharing and Communities of Practice 167
nications such as face-to-face meetings or instant messaging conversations will have
the fastest feedback (people can react right away to what has been said or typed),
participants can use natural language, and the degree of social presence is at a very
high level. Social presence and media richness do tend to go hand-in-hand, but there
are some channels that possess low media richness with a high degree of social pres-
ence, such as newsgroups, bulletin boards, personal Web pages, and blogs ( Dalkir
2007 ). Finally, when the knowledge to be shared is more tacit than explicit in nature,
it becomes more imperative to make use of channels that are quite high in both social
presence and media richness ( Vickery et al. 2004 ).
We can also look more closely at the types of exchanges that occur in knowledge
sharing. The majority of the knowledge exchanges consist of requests, revisions, modi-
fi cations, or some form of repackaging, publications, references (e.g., tell people about,
who knows about), recommendations, reuse, and reorganization (e.g., adding on of
categories, metadata). Reuse is also an excellent measure of the success of knowledge
sharing and it can be thought of as being analogous to a citation index. Scholars and
researchers produce a number of scientifi c publications but a metric that is perhaps
even more meaningful than the number of papers published is the citation index,
which keeps track of how many others have made use of this work. When others do
refer to their work, this is evidenced by specifi c citations and references to the original
work or a reuse of the original content. It is possible to track such reuse in a knowledge
management system as well and in some organizations, this is used to evaluate how
good a knowledge sharer a given employee is.
Knowledge-sharing communities are not just about providing access to data and
documents: they are about interconnecting the social network of people who pro-
duced the knowledge. A good knowledge management system should include infor-
mation not just on the people who produced the knowledge but those who will make
use of it. There is as much value in talking to people experienced in using knowledge
as there is in talking to the original authors (subject matter experts). One way this can
be achieved is by making the knowledge visible. This typically involves making the
interactions online visible in some way so that “ I know that you know x , y , and z ”
and “ I know that you know that I know a , b , and c . ” This helps create a mutual aware-
ness, mutual accountability, and mutual engagement to knit group members more
closely together.
Figure 5.8 shows a high-level representation of how a CoP can be rendered more
visible using social computing systems such as the Babble system ( Erickson and Kellogg
2000 ). Babble was designed as an online multiuser environment to support the cre-
ation, explanation, and sharing of knowledge through text-based conversations.
168 Chapter 5
Social computing refers to digital systems that draw upon social information and
context to enhance the activity and performance of people, organizations, and systems.
Examples include “ recommender ” systems such as those that advise you on which
books you would enjoy, which music you would like to hear, and which movies you
would like to see. Social presence is an important concept in virtual networks as it
refers to how much of a sense members have that other people are present. Since
communities are all about social interactions for learning and knowledge exchange,
it is very important that a social connection be felt. The use of buddy lists is another
example of establishing social presence. This is a feature that lets you know who else
is currently online when you log on to a virtual space.
Obstacles to Knowledge Sharing
There are a number of obstacles that can hinder knowledge sharing within organiza-
tions. Chief among these is the notion that knowledge is property and ownership is
very important. One of the best ways to counteract this notion is to reassure individu-
als that authorship and attribution will be maintained. In other words, they will not
lose the credit for a knowledge product they created. In fact, maintaining the connec-
tion between knowledge and the people that are knowledgeable about it is paramount
in any knowledge management system. There is a prevalent notion of knowledge as
power. The more that information is shared between individuals, the more opportuni-
ties for knowledge creation occur. There is, however, a risk in sharing what you know,
Logged on
but viewing
other conversations
Conversing
Figure 5.8 Making CoP interactions visible (adapted from the Babble system, Erickson and Kellogg 2000)
Knowledge Sharing and Communities of Practice 169
because in most cases, individuals are most commonly rewarded for what they know,
not what they share. As a result, hoarding of knowledge often leads to negative con-
sequences such as empire building, reinvention of wheels, feelings of isolation, and
resistance to ideas from outside an organization. The best way to address concerns is
to adapt the reward and censure systems that exist in the organization. In other words,
stop rewarding knowledge hoarding and start providing valued incentives for knowl-
edge sharing.
Another common reason given for not sharing knowledge is that either the provider
is unsure that the receiver will understand and correctly use the knowledge and/or
the recipient is unsure about the truth or credibility of the knowledge in question.
Both issues disappear in the context of a community, as it is a self-regulating system
that continually vets and validates both content and membership.
Last but not least, the organizational culture and climate may either help or hinder
knowledge sharing. An organizational culture that encourages discovery and innova-
tion will help, whereas one that nurtures individual genius will hinder. An organiza-
tion that rewards collective work will help create a climate of trust, whereas a culture
that is based on social status will hinder knowledge sharing. Without a receptive
knowledge sharing culture in place, effective knowledge exchanges cannot occur.
Signifi cant organizational changes may need to happen before effective knowledge
sharing can begin to take place.
Another caveat: while the assessment may show that organizational knowledge
sharing is weak due to any or all of the above factors, knowledge sharing may be
fl ourishing quite well — only it has not been detected. This is often referred to as the
phenomenon of the “ undernet. ”
The Undernet
Often, organizations conclude that knowledge sharing does not occur because no one
is using the organizational knowledge repository. The truth may be that there is a lot
of knowledge sharing going on — it is just that many employees choose to circumvent
the offi cial knowledge base — most likely because it is too diffi cult to fi nd what they
are looking for there. Since people are the best source of knowledge, it is no surprise
that knowledge workers are expert knowledge sharers — it is just that they use their
own networks, not the offi cial ones. This is in keeping with the increasingly prevalent
view that KM succeeds when it is a grassroots or demand-driven initiative rather than
a top-down technology push.
Knowledge fl ows appears to fl ow well when members perceive that there is a climate
of trust, that the members with whom they exchange knowledge are credible and that
knowledge exchange is bidirectional. In small organizations, these undernets bring
170 Chapter 5
different specialties together, such as engineering, design, and marketing. But in larger
organizations, these specialties tend to separate into their own groups. When that
happens, the communities develop different ways of working, even different vocabu-
laries, and they no longer understand each other. Knowledge still fl ows easily within
specialties, but not across them (Excerpt from CSC 2002).
Social network analysis is a very useful tool as it provides the means of identifying
the undernets in an organization ( Weinberger 1999 ). The undernet is defi ned as the
intranets that escape the offi cial gaze of the organization — they represent how people
really share knowledge and they constitute the skeleton of the communities of
practice that have emerged. Weinberger quite aptly refers to these undernets as
the “ lifeblood ” of the organization. In fact, many corporate top-down knowledge
management initiatives are met with lack of interest and lack of activity, and inves-
tigation invariably turns up the existence of the “ other ” network — the one people
really use!
Organizational Learning and Social Capital
Human capital refers to individuals ’ education, skills, and background necessary to be
productive in an organization or profession. However, sociologists such as Coleman
(1994) and Granovetter and Swedberg (2001 ) argue that there is much more to explain-
ing the differences in individual success than individual characteristics alone. The
concrete personal relationships and networks of relations generate trust, establish
expectations, and create and enforce norms. These webs of social relationships infl u-
ence individual behavior and ultimately organizational success. The term “ social
capital ” has been coined to refer to the institutions, relationships, and norms that
shape the quality and quantity of an organization ’ s social interactions ( Lesser and
Prusak 2001 ). Social capital is not just the sum of the individuals that comprise an
organization — it is the glue that holds them together.
Nahapiet and Ghoshal (1998) defi ne social capital as “ the sum of the actual and
potential resources embedded within, available through, and derived from the network
of relationships possessed by an individual or social unit. It thus comprises both the
network and the assets that may be mobilized through that network ” (p. 243). While
the concept is still evolving, there are increasing calls for expanded “ investment ” on
the part of business, government, and other organizations that promote the develop-
ment and maintenance of social capital. Social capital facilitates the creation of new
intellectual capital. Organizations, as institutional settings, are conducive to the devel-
opment of high levels of social capital. It is because of their more dense social capital
Knowledge Sharing and Communities of Practice 171
that fi rms, within certain limits, have an advantage over markets in creating and
sharing intellectual capital.
Knowledge-sharing communities are the primary producers of social capital as they
provide the opportunity for individuals to develop a network with members who share
similar professional interests. The community provides a “ Who ’ s who ” in the form of
yellow pages to help make connections between members. The community provides
a reference mechanism to quickly enable members to evaluate content, solve prob-
lems, and make decisions based on vetted, validated, and current knowledge. Social
networks can increase productivity by reducing the costs of doing business. Social
capital facilitates coordination and cooperation. However, social capital also has an
important downside ( Portes and Landolt 1996 ): communities, groups, or networks
that are isolated, parochial, or working at cross-purposes to the organization ’ s collec-
tive interests.
A broader understanding of social capital accounts for both the positive and nega-
tive aspects by including vertical as well as horizontal associations between people,
and includes behavior within and among organizations, such as fi rms. This view rec-
ognizes that horizontal ties are needed to give communities a sense of identity and
common purpose, but also stresses that without bridging ties that transcend various
social divides (e.g., religion, ethnicity, socioeconomic status), horizontal ties can
become a basis for the pursuit of narrow interests, and can actively preclude access to
information and material resources that would otherwise be of great assistance to the
community (e.g., tips about job vacancies, access to credit).
Measuring the Value of Social Capital
Organizations have begun to implement a large number of communities of practice
in the hopes of achieving such benefi ts as:
• Building loyalty and commitment on the part of stakeholders
• Promoting innovation through better sharing of best practices
• Improving effi ciency of processes
• Generating greater revenue and revenue growth
• Decreasing employee turnover and attrition
It remains a challenge to be able to evaluate whether or not communities in fact
achieve these objectives — or even to measure whether or not progress has been made
toward such goals. Communities of practice come packaged with a business plan —
they are there for a business reason and as such they must be evaluated just like any
172 Chapter 5
other business initiative in order to be able to calculate the return on the company ’ s
investment.
One way of measuring value is to calculate the additional value that a community
member represents in comparison to the average site visitor. For example, in a trans-
actional Web site, if a community member purchases twice as much per month as the
average user, then the community is generating additional revenue. Similar compari-
sons may be made with respect to usage for noncommercial sites. It appears that
communities that are actively managed have higher participation rates and conse-
quently bring greater value to the organization. Most companies lack experience in
community management and will have to fi nd resources that can possess the neces-
sary expertise, processes, tools, and infrastructure to get the job done.
Community development costs may be based on hardware and software costs (one-
time and ongoing), community strategy development costs (one time), and the
ongoing community management costs. Benefi ts other than usage are much more
diffi cult to assess. For example, the benefi ts of the closer relationship that builds
between the community members often leads to higher employee retention rates.
Organizational learning is likely accelerated and process effi ciencies attained as a
result, but it is diffi cult to quantify these valuable outcomes. Another example would
be the power of viral marketing or word of mouth that uses a community as a conduit.
Such recommendations would be much more targeted, relevant, and add to that the
fact that they come from trusted peer sources. In this case, the outcomes would be
much more favorable in terms of the internalization and application of this shared
content.
Another approach is to attempt to measure the value of the social capital that has
been produced as a result of the knowledge sharing. Social capital has been measured
in a number of innovative ways, though for a number of reasons obtaining a single
“ true ” measure is probably not possible, or perhaps even desirable. Measuring social
capital may be diffi cult, but it is not impossible, using different types and combina-
tions of qualitative, comparative, and quantitative research methodologies ( Woolcock
and Narayan 2000 ; Sveiby and Simons 2002 ). It is especially challenging because social
capital is comprised of concepts such as trust, community, and networks, which are
diffi cult to quantify. The challenge is increased when one considers that the quest is
to measure not just the quantity but also the quality of social capital on a variety of
scales. A useful form is that of a story or vignette of success due to the existence of a
knowledge-sharing community, such as the one working toward a cure for SARS.
It may also be possible to adapt methods used in measuring social capital of coun-
tries or societies. For example, in his research comparing north and south Italy,
Knowledge Sharing and Communities of Practice 173
Putnam (1995) examines social capital in terms of the degree of civic involvement,
as measured by voter turnout, newspaper readership, membership in choral societies
and football clubs, and confi dence in public institutions. Northern Italy, where all
these indicators are higher, shows signifi cantly improved rates of governance, insti-
tutional performance, and development when other orthodox factors were controlled
for. His recent work on the United States ( Putnam 2000 ) uses a similar approach,
combining data from both academic and commercial sources to show a persistent
long-term decline in America ’ s stock of social capital. Putnam validates data from
various sources against the fi ndings of the General Social Survey, widely recognized
as one of the most reliable surveys of American social life. Other examples include
the World Values Survey, which has measured interpersonal trust in 22 countries by
asking questions such as: “ Generally speaking, would you say that most people can
be trusted or that you can ’ t be too careful in dealing with people? ” ( Knack and Keefer
1997 ). The Social Capital Initiative at the World Bank funds social capital projects
which will help defi ne and measure social capital, its evolution, and its impact (e.g.,
Narayan and Cassidy 2001 ). Refer to chapter 10 for additional ways of measuring KM
and CoPs.
Strategic Implications of Knowledge Sharing
Some of the strategically important benefi ts of knowledge sharing include:
• Connect professionals across platforms and across distances
• Standardize professional practices
Global teams of scientists working on a vaccine for the SARS virus (severe acute respiratory
syndrome) have been collaborating online to store common knowledge on a Web site, to
look up experts, and to create communities. They make use of a KM tool from Knexa
(http://www.knexa.com) to stay in touch and to receive pertinent up-to-date information
without having to actively search for it. This Web site has become a virtual home to the
collection of international scientists working on the SARS problem. Although there has
been much published on how incentives are needed to get people to embark upon KM
solutions, this is not the case here. The major incentive is that this knowledge network
makes it easier for them to successfully do their job. Several groups can work simultane-
ously instead of sequentially to move ahead more quickly.
Box 5.10 A vignette: Knowledge sharing and the search for a SARS cure
174 Chapter 5
• Avoid mistakes
• Leverage best practices
• Reduce time to access talent
• Build reputation
• Take on stewardship for strategic capabilities
Knowledge resides in communities in the form of social capital. The key is often
connecting people to solve problems, to develop new capabilities (learn), to improve
work practices, and to share what is new in the fi eld. The type of knowledge that is
transferred is shared expertise. Unlike formal education and training where public
knowledge is transferred, CoPs provide apprenticing situations over long periods of
time. These need a shared background (context) and shared language in order to share
expertise and will also need to be technology-mediated using e-mail, telephone, group-
ware, videoconferencing. and intranets or Web sites.
Employees today are more often loyal to their profession than they are to a particu-
lar company. In turn, companies are no longer able to afford employment for life —
even in Japan where “ salarymen ” are expected to work at a company for life, layoffs
have occurred. One of the biggest benefi ts of communities of practice is that they help
retain employees. If a knowledge worker is working at an organization where he or
she is able to be an active member of one or more communities of practice, this will
be a signifi cant incentive to stay with that organization. Lesser and Storck (2001)
looked at the relationships that form in these communities and suggested that the
obligations, norms, trust, and identifi cation that come with being a community
member enhances the members ’ ability to share knowledge with and learn from com-
munity participants. The community also serves as a powerful tool to welcome new
members into the organization. New employees can quickly “ plug in ” to the network,
connect, get help, pick up the organizational culture, and quickly develop a sense of
identity and belonging.
Another key benefi t of communities lies in the now popular notion of “ six degrees
of separation ” where every person can be linked to another by six links (Watts 1999).
This stems from the famous 1967 experiment by Milgram (1967) where he asked 160
people in Kansas and Nebraska to each direct a letter to a particular person in
Massachusetts by sending it to an acquaintance whom they thought might be able
to forward it to the target. To Milgram ’ s surprise, 42 letters eventually arrived after
an average of only 5.5 hops. Networks are powerful conduits for the sharing of
knowledge — powerful in terms of the reach of the network and the speed with which
knowledge can be exchanged but also powerful in that content is not merely conveyed
Knowledge Sharing and Communities of Practice 175
but explicitly or implicitly “ vouched for ” because it is being sent to you from a trusted,
credible source.
Practical Implications of Knowledge Sharing
Whereas CoPs do emerge and run on their own, a minimal level of investment and
support is crucial ( Wenger, McDermott, and Snyder 2002 ). First and foremost, senior
management should ensure that the organizational climate or culture is one that
encourages networking. In addition to fi nancial support, it is important that employ-
ees are given the time they need to fulfi ll their knowledge-sharing roles and respon-
sibilities. They will need a physical place to meet for the face-to-face meetings that
should occur at least once a year. They should receive a travel budget if one is required.
Their group membership should be recognized and evaluated as part of the perfor-
mance review. Additional resources such as community moderators, journalists, librar-
ians, taxonomists, and archivists should be facilitated as well. Experience has shown
that one of the most important factors contributing to the success of a community is
that of an active and effective facilitator.
A conversation is more than an intellectual endeavor: it is a fundamentally social
process, as is learning. People need to connect. They need to speak to an audience,
note how they are being received, and adjust accordingly. People portray themselves
through conversations — bringing forth personal agendas, personal style, taking credit,
and sharing blame. In a virtual world, it is important to realize that all such connec-
tions and conversations are public, and that once digitized, conversations can persist.
This means that anyone can access them at some time in the future. It is important
for knowledge-sharing interactions to be maintained at a professional level at all times
and that all members of a virtual network are aware of and agree to adhere to a pro-
fessional code of ethics, both online and offl ine.
Key Points
• The cost of not fi nding information is extremely high — both for individuals and for
the organization as a whole.
• It is not always about knowing what, but “ knowing who knows what, ” which can
take the form of a corporate yellow pages or expertise location system.
• Learning is a primarily social activity.
176 Chapter 5
• Knowledge sharing occurs quite effi ciently and effectively in communities of practice
where members share a professional interest and goal.
• In order for effective knowledge sharing to occur in CoPs, a number of key roles
need to be in place, such as knowledge sponsor, champion, facilitator, practice leader,
KSO, membership manager, discussion moderator, knowledge editor, librarian, archi-
vist, usage analyst, and knowledge broker.
• Virtual communities are the primary sources of social capital produced that is of
value to the organization.
• Social network analysis can be used to visualize the people and their connections in
virtual communities.
• Social presence and media richness are two dimensions that can be used to assess
how well technological channels such as e-mail, blogs, wikis, and so forth can accom-
modate the sharing of both tacit and explicit knowledge.
• Some of the key obstacles to knowledge sharing are notions such as knowledge is
property, knowledge is power, credibility of the content and the source, organizational
culture, and the presence of undernets.
Discussion Points
1. What are the major distinguishing characteristics of a community of practice that
a community of interest would not possess?
2. Compare and contrast some different types of communities of practice. Describe
how they would differ with respect to their goals.
3. What are the key differences between the functionalist and the social constructivist
perspectives on knowledge? Why is the latter better suited to knowledge management?
4. Describe the roles and responsibilities of a knowledge broker in a virtual com-
munity. Provide examples of how they could help promote knowledge sharing and
increase the value of the social capital of the fi rm.
5. What is the difference between human and social capital?
6. What are some of the key deterrents to knowledge sharing and knowledge fl ow
within an organization? How could you help overcome them?
7. List some of the ways in which social network analysis techniques can be used to
better understand how knowledge is circulated within an organization.
8. What lesson can be learned from the tragedy of the commons? Provide some
modern-day examples and discuss how you would ensure effective knowledge
Knowledge Sharing and Communities of Practice 177
sharing takes place. Identify the types of knowledge-sharing channels you would use
and justify them with respect to their social presence and media richness.
9. What are some popular technologies used to develop corporate yellow pages? How
do they compare?
10. What are some of the key steps you would need to carry out in order to conduct
a social network analysis of an organization? What would you need to know before
you could start? What sorts of questions could the SNA answer?
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6 Knowledge Application
All that is gold does not glitter; not all those that wander are lost.
— J. R. R. Tolkien (1892 – 1973)
This chapter brings us to the fi nal step in the knowledge management cycle when
the knowledge that has been captured, coded, shared, and otherwise made available
is put to actual use. Unless this step is accomplished successfully, all of the KM efforts
have been in vain, for KM can only succeed if the knowledge is used. However, it
now becomes imperative to understand which knowledge is of use to which set of
people and how best to make it available to them so that they not only understand
how to use it, but believe that using this knowledge will lead to an improvement in
their work. The use of learning taxonomies, task support systems, and personalization
or profi ling techniques can help ensure the best possible match between user and
content. Expertise location systems and other collaboration aids can help groups of
people fi nd and apply valuable knowledge and know-how. Content management
systems can be designed to optimize knowledge application on an organization-wide
basis.
Learning Objectives
1. Understand how user and task modeling approaches can help promote effective
knowledge use at the individual, group, and organizational level.
2. Describe how an organizational KM architecture is designed.
3. Defi ne organizational learning and describe the links between individual and
organizational learning.
4. Compare and contrast learning and understanding with internalization of
knowledge.
184 Chapter 6
5. List the different knowledge support technologies that can help users put knowl-
edge into action.
Introduction
KM typically addresses one of two general objectives: knowledge reuse to promote
effi ciency and innovation to introduce more effective ways of doing things. Knowl-
edge application refers to the actual use of knowledge that has been captured or created
and put into the KM cycle (refer to fi gure 6.1 ).
Knowledge eventually is made accessible to all the knowledge workers in the orga-
nization, with an implicit assumption that the knowledge will be used. This turns out
to be a rather large and often unfounded assumption. In fact, if we recall the Nonaka
and Takeuchi model from chapter 3, we can see that having captured, coded, reorga-
nized, and made available, we are still only in the third quadrant. The knowledge
spiral needs to be completed by successful internalization of knowledge. This process
of internalization, it should be recalled, consists not only of accessing and understand-
ing the content but of consciously deciding that this is indeed a good — ideally better —
way of doing things and hence the knowledge is applied to a real world decision or
problem.
This is knowledge reuse, the process whereby useful nuggets of knowledge or knowl-
edge objects are made available in a library of such objects. These knowledge objects
Assess
Update
Contextualize
Knowledge capture
and/or creation
Knowledge sharing
and dissemination
Knowledge acquisition
and application
Figure 6.1 An integrated KM cycle
Knowledge Application 185
can be annotated references, components (programs or text), templates, patterns, or
other types of containers. For example, consulting companies often reuse project
proposal templates as they convey the company brand and they contain useful reus-
able objects such as testimonials, company description, and so on. The goal is to
reduce the time it takes to complete tasks as well as to help maintain higher standards
regarding the quality of the work to be done. The benefi ts to new employees are
enormous as they are able to attain “ day one ” performance with the help of such
a reuse library, that is, they are able to perform at a fairly high level on their fi rst
day on the job. The other major benefi t is the work that is not done — because it was
possible to see that someone else had already done it. The savings involved in not
“ reinventing the wheel ” can be considerable.
KM aims to support learning organizations that provide all employees with access
to corporate memory so that both the individuals and the organization as a whole
improve. Corporate memory is often incomplete, as it has captured only explicit
knowledge. KM attempts to also make accessible the valuable tacit knowledge and add
this to the corporate memory. While it is possible to reuse tacit knowledge and this
is done all the time during knowledge-sharing interactions, reuse tends to refer to
packaged explicit knowledge. Reuse of explicit knowledge affords a longer-term advan-
tage. Whereas tacit knowledge reuse can benefi t the individual who sought the advice
of a more experienced colleague, knowledge objects that are accessible through the
knowledge repository are accessible to all workers, and they remain available for as
long as they are useful.
That being said, it is imperative to try to include or at least be able to point to
where the tacit knowledge associated with a given knowledge object resides. It is never
possible or even desirable to try to render all knowledge explicit. If knowledge workers
can easily locate and communicate with individuals in the company that are con-
nected to a given knowledge object (e.g., they are familiar with how it is used, they
have been trained, etc.), then the ability to apply or to make use of this knowledge is
greatly increased. In the example of the proposal writing knowledge object or tem-
plate, hyperlinks can easily be included to not only good examples of past proposals
that were successful (best practices) but to the individuals who were involved in their
preparation so that they can be contacted for advice, a read through, or other forms
of help.
The essence of problem solving, innovation, creativity, intuitive design, good analy-
sis, and effective project management involves more tacit, rather than explicit, knowl-
edge. By putting tacit knowledge in a principal role and cultivating tacit knowledge
environments, KM can play an important role in application development, and
186 Chapter 6
particularly in reuse. Another aspect of the explicit knowledge problem is the fallacy
that documentation (explicit knowledge) equals understanding. We seek understand-
ing in order to successfully reuse a component. However, the larger and more complex
the component, the harder it is to gain the required understanding from documenta-
tion alone. Understanding, in this context at least, is a combination of documentation
and conversation — conversation about the component and the context in which that
component operates. No writer of documentation can anticipate all the questions a
component user may have. Even if this were possible, the resulting documentation
would be so extensive and cumbersome that potential users would simply develop
their own component rather than wade through the documentation.
Knowledge management systems that focus on gathering, recording, and accessing
reams of knowledge, at the expense of person-to-person interactions, have proven to
be expensive and less than satisfactory. Organizations that fail to understand tacit
knowledge will repeat many of the mistakes made with methodologies such as com-
puter assisted software engineering (CASE). A common assumption in the past was
that all relevant knowledge could be bundled up in nice, neat, easily accessible pack-
ages of “ best practices ” that practitioners could then “ repeat. ”
When we attack reuse as a KM problem, we begin to ask new questions, or at least
look for different avenues for fi nding solutions to the problem. How do we go about
fi nding the component we need? How do we gain confi dence that the component
does what we want it to do, and does not do strange things that we do not want?
What is the distance (organizationally or geographically) between the component
developer and users? Are there other people who have used this component that we
could talk to and learn from? Do we have access to the author of this component?
Have others found this component to be effective? How should we go about testing
this component? How easily will this component integrate into our environment?
Dixon (2000) outlines factors that affect knowledge transfer: characteristics of the
receiver (skills, shared language, technical knowledge), the nature of the task (routine,
nonroutine), and the type of knowledge being transferred (a continuum from explicit
to tacit). The author then identifi es fi ve categories of knowledge transfer that she has
observed, from near transfer ( “ transferring knowledge from a source team to a receiv-
ing team that is doing a similar task in a similar context but in a different location ” )
to serial transfer ( “ the source team and the receiving team are one and the same ” ).
Dixon then describes techniques that work well for each of these fi ve types of
transfer.
It is not the objective of this chapter to describe the practices of knowledge transfer
in detail, but rather to point out that merely coding a component and scratching out
Knowledge Application 187
a few lines of documentation will rarely be enough to facilitate knowledge transfer.
Other researchers such as Hatami, Galliers, and Huang (2003) found that a key to
organizational success in the face of global competition is the ability to capture orga-
nizational learning, to effectively reuse the knowledge through effi cient means, and
to synthesize these into more intelligent problem recognition, strategic analysis, and
choices in strategic directions. By tapping into their organization ’ s memory, decision
makers can make more intelligent business decisions. This is achieved when indivi-
duals access data, information, and knowledge residing in repositories. However,
retrieval alone is not enough. Knowledge application must follow. The success of
knowledge application appears to be a function of the characteristics of the individual,
of the knowledge content, the purpose of reuse for the particular task at hand, and
the organizational context or culture.
Knowledge Application at the Individual Level
Characteristics of Individual Knowledge Workers
Individual differences play a major role in knowledge-sharing behaviors ( Hicks and
Tochtermann 2001 ). Knowledge workers vary with respect to their familiarity with
the subject matter and their personality and cognitive styles. Cohen and Levinthal
(1990) found that sharing is more likely to occur when a foundation of prior relevant
knowledge exists. A number of studies (e.g., Ford et al. 2002 ; Kuhlthau 1993 ; Spink
et al. 2002 ) found signifi cant correlations between online searching behaviors and the
Paskian cognitive styles of holistic and operational learners. On the other hand, the
business world heavily favors the use of instruments such as the Myer-Briggs Type
Indicator (MBTI) personality style assessment ( Myers et al. 1998 ) to assess differences
in personality styles. Some research has been done to correlate MBTI type with
knowledge-sharing behaviors (e.g., Webb, 1998 ), found in a study of the consulting
fi rm Price Waterhouse Coopers that a strong outgoing personality was important in
knowledge sharing irrespective of qualifi cations and prior experience.
Characteristics of the individual who is seeking to apply or reuse knowledge are
likely to play a role in how effective they are at fi nding, understanding, and making
use of organizational knowledge. Individual characteristics may include, for example,
personality style, their preferences regarding how they best learn, and how they prefer
to receive their information, as well as how they can best be helped to put the knowl-
edge to work. This may range from something as simple as asking for and subsequently
accommodating the language the user prefers to work in to more sophisticated model-
ing of the user in terms of their abilities and their goals. One good framework that is
188 Chapter 6
of use here is the Bloom taxonomy of learning objectives ( Bloom, Mesia, and Krath-
wohl 1964 ) that was designed to help teachers set learning goals for learning activities.
The taxonomy can be easily adapted to knowledge application goals for each knowl-
edge object in a repository.
One way of visualizing personalization is to think of the one-person company or
the one-person library. All of the knowledge resources in a given repository can be
made to appear as if they were there at the disposal of a given person, refl ecting their
preferences, their background, and so forth. Figure 6.2 illustrates this concept of
“ many-to-one ” interactions.
Personalization and profi ling is currently a popular means of characterizing visitors
to a given web site. This is particularly true of virtual stores where customer data can
then be analyzed in order to improve marketing efforts. However, in KM, we are less
concerned with database marketing applications of personalization than with ensuring
that information retrieval; and knowledge application processes are tailor-made for
each knowledge worker. The easier it is for a knowledge worker to fi nd, understand,
and internalize the knowledge, the greater their success in actually applying this
knowledge. An alternative approach to user modeling is proposed in fi gure 6.3 .
Instead of using profi ling technologies to better understand all customers, we can
make use of similar techniques to follow or trace a given individual ’ s interactions with
a number of corporate memory interfaces. This alternative approach will yield a user
model. This model will help us to better understand the types of human-knowledge
interactions that have occurred in order to optimize knowledge application within the
Personalization: Many-to-one interactions
…….?
The one-person:
Office
Store
School
Library
Figure 6.2 Illustration of the personalization concept
Knowledge Application 189
organization. For example, push technologies are based on user models that look at
historical information requests in order to push or automatically send out similar new
content that becomes available.
We will need to be able to fi nd and use content based on an individual ’ s personal
model, that is, how they perceive the knowledge world around them. This is often
infl uenced by their particular background (e.g., IT vs. sociology), how long they
have been in the company, how expert they are in the topic as well as a whole
spectrum of preferences ranging from the linguistic to the format they prefer to receive
knowledge (e.g., visual types of people who prefer diagrams, or those who prefer to
read text). These are often represented as semantic networks (see fi gures 6.4 and 6.5 )
There are also systems that monitor users ’ tasks online and interpret them in
context, based on traces they leave behind. These systems work well for tasks that are
well identifi ed and where knowledge can be described in a clear ontology (e.g., a postal
address template). In general, this approach is based on a user interacting with a
computer system to perform a task that leads to changes in the system. An observer
agent (a software routine) observes these changes according to an observation model
to generate a log or trace of what the user has done. The trace is then analyzed to
identify and extract signifi cant episodes, and interpret them according to explained
task signatures. Each episode represents a pattern and each pattern can be mapped
onto a task, a subtask, or a more specifi c step that forms part of the subtask. For
example, if the user is trying to locate, open, and print out a particular fi le, there are
three distinct episodes that can be identifi ed: behaviors related to locating, opening,
Web server
Visitor 1
Visitor 2
Visitor 3
Visitor 4 Trace 1
Trace 2
Trace 3
Visitor 6
Visitor 5
Instead of Web-centric: profiling User-centric profiling
Figure 6.3 Alternative approach to personalization
190 Chapter 6
Tree of Porphyry, as it was drawn by the logician Peter of Spain (1329).
It illustrates the categories under substance, which is called the supreme genus
or the most general category.
Supreme genus
Differentiae
Subordinate genera
Differentiae
Subordinate genera
Differentiae
Differentiae
Proximate genera
Species
Substance
Material
Animate
Animate
Rational
Immaterial
Body
Living
Human
Spirit
Inanimate
Mineral
Insensitive
Animal Plant
Irrational
Beast
Socrates Plato Aristotle etc.Individuals
Figure 6.4 Example of a semantic network
and printing the fi le. Assistant agents that help the user to do what he or she is trying
to do can then reuse these episodes. The assistance episodes themselves can also be
reused in the future (see fi gure 6.6 ). In this way, the system has modeled how users
behave when they are undertaking these particular types of tasks.
The important factor to note here is that user modeling is an ongoing process, not
a one-shot deal. Dynamic profi ling systems need to be developed based on a mix
of human and automated trace facilities, in order to be able to continually adapt
to changes in the environment, changes in the organization, and changes in the
individuals themselves (e.g., different job responsibilities, different preferences, new
competencies, and new interests).
Knowledge Application 191
Birds
fly using theirbuild their nests in
eat
made of
Trees Worms Wings
Feathers
Figure 6.5 Example of a semantic network (continued)
Bloom ’ s Taxonomy of Learning Objectives
Bloom, Mesia, and Krathwohl (1964) divided knowledge into a hierarchical scheme
that distinguishes between psychomotor skills, the affective domain (e.g., attitudes),
and the cognitive domain (e.g., knowledge). The latter is the one that is more com-
monly used although attitudinal changes are often required in KM as well. Bloom
emphasizes that learning is hierarchical with learning (objectives) at the highest level
as dependent on the achievement of lower level knowledge and skills fi rst.
The cognitive domain taxonomy is shown in table 6.1 . The levels shown are from
low (1, knowledge) to high (6, evaluation). The affective domain includes the manner
in which we deal with things emotionally, such as feelings, values, appreciation,
enthusiasms, motivations, and attitudes. The fi ve major categories of affective domain
are listed in table 6.2 .
The psychomotor domain includes physical movement, coordination, and use of
the motor skill areas. Development of these skills requires practice and is measured in
terms of speed, precision, distance, procedures, or techniques in execution. The seven
major categories listed in table 6.3 .
These taxonomic categories can be used “ inside out ” to help understand what
users are trying to do. The level of internalization can be identifi ed for effective
192 Chapter 6
Behavior
model Dynamic
user
profile
Demographics,
pyschographics
Data resellers,
e.g., Polk, SRI
Sales, operational
data
Data warehouse
FormsFill out
Analyze data
(sequence, time,
frequency, ....)
Capture
log file
data
Validate
From cookies, internet,
intranet, personal
devices, different
countries, times...
Figure 6.6 Dynamic profi ling system design
performance, for example, setting a minimum threshold that must be reached in order
for the worker to be able to understand and make appropriate use of the knowledge
object. This can in turn be incorporated into a user model. The Bloom taxonomy
serves as a means of determining not only what knowledge workers are expected to
do (usually referred to as skills or expertise) but also the level of performance that is
expected (also referred to as mastery level). For example, using the cognitive skill
portion of the Bloom taxonomy, it is possible to characterize a particular knowledge
object, say a best practice procedure on how best to present a project team member ’ s
resume when preparing a project proposal. The knowledge worker who prepares the
bid would be expected to have a level of understanding that allows for critical judg-
ment in order to be able to execute this task at the required profi ciency level. He or
Knowledge Application 193
Table 6.1. Bloom taxonomy of the cognitive domain
Level Description Action verbs that can be used
1 Knowledge Remembering of previously learned material.
Recall, repeat, defi ne, describe, list, identify, label, match, name, state
2 Comprehension Ability to grasp the meaning of material e.g. translating from one form to another, estimating future trends, explaining or giving examples of.
Classify, convert, discuss, explain, generalize, give an example of, paraphrase, restate in your own words, summarize, and review.
3 Application Ability to use learned material in new and concrete situations by applying rules, methods, concepts, principles, laws and theories.
Articulate, assess, chart, computer construct, determine, develop, discover, establish, extend, operationalize, participate, predict, provide, show, solve, use, apply, demonstrate, sketch, practice, illustrate.
4 Analysis Ability to break down material into its component parts so that its organizational structure may be understood. Identifi cation of parts, relationships between parts, recognition of organizational principles.
Break down, correlate, diagram, differentiate, discriminate, distinguish, focus, infer, outline, point out, recognize, separate, subdivide, compare, contrast, inspect, inventory, relate, examine.
5 Synthesis Ability to put parts together to form a new whole. Creative behaviors stressed in the formulation of something new.
Adapt, categorize, collaborate, combine, communicate, compile, compose, create, design, devise, facilitate, formulate, generate, incorporate, individualize, initiate, integrate, model, plan, propose, assemble, and organize.
6 Evaluation Ability to judge the value of material based on defi nite criteria.
Appraise, conclude, criticize, decide, defend, judge, justify, support, evaluate, rate, value, score, prioritize, select.
Source: Adapted from Bloom 1956.
194 Chapter 6
Table 6.2 Affective domain as characterized in the Bloom taxonomy
Receiving phenomena: Awareness, willingness to hear, selected attention
Examples: Listen to others with respect Listen for and remember the name of newly introduced people
Keywords Asks, chooses, describes, follows, gives, holds, identifi es, locates, names, points to, selects, sits, erects, replies, uses
Responding to phenomena: Active participation on the part of the learners; attends and reacts to a particular phenomenon; learning outcomes may emphasize compliance in responding, willingness to respond, or satisfaction in responding (motivation)
Examples: Participates in class discussions Gives a presentation Questions new ideals, concepts, models, and so on, in order to fully understand them Knows the safety rules and practices them.
Keyword s Answers, assists, aids, complies, conforms, discusses, greets, helps, labels, performs, practices, presents, reads, recites, reports, selects, tells, writes
Valuing: The worth or value a person attaches to a particular object, phenomenon, or behavior; this ranges from simple acceptance to the more complex state of commitment; valuing is based on the internalization of a set of specifi ed values, while clues to these values are expressed in the learner ’ s overt behavior and are often identifi able
Examples: Demonstrates belief in the democratic process Is sensitive toward individual and cultural differences (values diversity) Shows the ability to solve problems Proposes a plan for social improvement and follows through with commitment Informs management on matters that one feels strongly about
Keywords Completes, demonstrates, differentiates, explains, follows, forms, initiates, invites, joins, justifi es, proposes, reads, reports, selects, shares, studies, works
Organization: Organizes values into priorities by contrasting different values, resolving confl icts between them, and creating a unique value system; the emphasis is on comparing, relating, and synthesizing values
Examples: Recognizes the need for balance between freedom and responsible behavior Accepts responsibility for one ’ s behavior Explains the role of systematic planning in solving problems Accepts professional ethical standardsCreates a life plan in harmony with abilities, interests, and beliefs Prioritizes time effectively to meet the needs of the organization, family, and self
Keywords Adheres, alters, arranges, combines, compares, completes, defends, explains, formulates, generalizes, identifi es, integrates, modifi es, orders, organizes, prepares, relates, synthesizes
Knowledge Application 195
Internalizing values (characterization): Has a value system that controls their behavior; the behavior is pervasive, consistent, predictable, and most importantly, characteristic of the learner;instructional objectives are concerned with the student ’ s general patterns of adjustment (personal, social, emotional)
Examples: Shows self-reliance when working independently Cooperates during group activities (displays teamwork) Uses an objective approach in problem solving Displays a professional commitment to ethical practice on a daily basis Revises judgments and changes behavior in light of new evidence Values people for who they are, not how they look
Keywords Acts, discriminates, displays, infl uences, listens, modifi es, performs, practices, proposes, qualifi es, questions, revises, serves, solves, verifi es
Source: Adapted from Bloom 1956.
Table 6.2 (continued)
she must not only be skilled in the selection of team members to be included in the
proposal but also be able to repackage their resumes in the form that has been shown
to be the best based on past successes. Another example, using the affective domain
Bloom taxonomy, once again can make use of this best practice but this time address
the best way to judge whether candidates who meet the technical skill requirements
also possess the appropriate “ soft skills ” such as being a good team player, having a
collaborative approach to work, and not being prone to knowledge hoarding or claim-
ing individual credit for group work.
The Bloom taxonomy provides a good basis for the assessment of knowledge appli-
cation. All too often in KM, simply having accessed content is taken to mean that
knowledge workers are using (and reusing) this content. It is far more useful to assess
the impact that the knowledge residing in the knowledge base has had on learning,
understanding, and “ buying in ” to a new way of doing things. It is only through
changes in behavior that knowledge use can be inferred and the taxonomy provides
a more detailed framework to evaluate the extent to which knowledge has been inter-
nalized (using the Nonaka and Takeuchi, 1995, model). For example, at the lower
cognitive skill levels, simply being aware that knowledge exists within the organiza-
tion is easily observed when knowledge workers are able to locate the content within
a knowledge repository. Access is typically tracked using log fi le statistics, which are
similar to the number of hits or visitors that a web site has attracted. Knowledge
application, however, requires that knowledge workers have attained much higher
levels of comprehension such as analysis, synthesis, and evaluation. It is only at these
196 Chapter 6
Table 6.3 Bloom taxonomy of the psychomotor domain
Perception: The ability to use sensory cues to guide motor activity; this ranges from sensory stimulation, through cue selection, to translation
Examples: Detects nonverbal communication cues Estimates where a ball will land after it is thrown and then moves to the correct location to catch the ballAdjusts heat of stove to correct temperature by smell and taste of food Adjusts the height of the forks on a forklift by comparing where the forks are in relation to the pallet
Keywords Chooses, describes, detects, differentiates, distinguishes, identifi es, isolates, relates, selects
Set: Readiness to act; This includes mental, physical, and emotional sets; these three sets are dispositions that predetermine a person ’ s response to different situations (sometimes called mind-sets)
Examples: Knows and acts upon a sequence of steps in a manufacturing process Recognize one ’ s abilities and limitations Shows desire to learn a new process (motivation) Note that this subdivision of the psychomotor domain is closely related to the “ responding to phenomena ” subdivision of the affective domain
Keywords Begins, displays, explains, moves, proceeds, reacts, shows, states, volunteers
Guided response: The early stages in learning a complex skill that include imitation and trial and error; adequacy of performance is achieved by practicing
Examples: Performs a mathematical equation as demonstrated Follows instructions to build a model. Responds to hand signals of instructor while learning to operate a forklift
Keywords Copies, traces, follows, reacts, reproduces, responds
Mechanism: This is the intermediate stage in learning a complex skill; learned responses have become habitual and the movements can be performed with some confi dence and profi ciency
Examples: Uses a personal computer Repairs a leaking faucet Drives a car
Keywords Assembles, calibrates, constructs, dismantles, displays, fastens, fi xes, grinds, heats, manipulates, measures, mends, mixes, organizes, sketches
Knowledge Application 197
Complex overt response: The skillful performance of motor acts that involve complex movement patterns; profi ciency is indicated by a quick, accurate, and highly coordinated performance, requiring a minimum of energy; this category includes performing without hesitation, and automatic performance; for example, players are often utter sounds of satisfaction or expletives as soon as they hit a tennis ball or throw a football, because they can tell by the feel of the act what the result will produce
Examples: Maneuvers a car into a tight parallel parking spot Operates a computer quickly and accurately Displays competence while playing the piano
Keywords Assembles, builds, calibrates, constructs, dismantles, displays, fastens, fi xes, grinds, heats, manipulates, measures, mends, mixes, organizes, sketches. (Note that the keywords are the same as for mechanism, but will have adverbs or adjectives that indicate that the performance is quicker, better, more accurate, and so on)
Adaptation: Skills are well developed and the individual can modify movement patterns to fi t special requirements
Examples: Responds effectively to unexpected experiences Modifi es instruction to meet the needs of the learners Perform a task with a machine that it was not originally intended to do (machine is not damaged and there is no danger in performing the new task)
Keywords Adapts, alters, changes, rearranges, reorganizes, revises, varies
Origination: Creating new movement patterns to fi t a particular situation or specifi c problem; learning outcomes emphasize creativity based upon highly developed skills
Examples: Constructs a new theory Develops a new and comprehensive training programming Creates a new gymnastic routine
Keywords Arranges, builds, combines, composes, constructs, creates, designs, initiate, makes, originates
Source: Adapted from Bloom 1956.
Table 6.3 (continued)
198 Chapter 6
levels that knowledge can truly be applied. In contrast to someone who can point to
a template in the knowledge base, knowledge application will be manifested by a
change in how a knowledge worker goes about doing his or her job.
The affective component is equally important to take into consideration when
analyzing knowledge application. Often, the reason knowledge is not being used is
not that it has not been understood. Rather, the knowledge worker was not convinced
that this new best practice or lesson learned represents any signifi cant improvement
over the way he or she is already working. An attitudinal change is more often than
not a critical prerequisite to internalization. It is not enough that someone be made
aware of and understand a given practice — the person must also believe that it is
indeed a better way of doing things and that he or she stands to gain by adopting
this new way of working.
The psychomotor domain is less widely used in KM, because it is often physical
work and skills. An illustration of individualized learning to facilitate knowledge
application appears in box 6.1.
A user model is, however, not enough for the facilitation of knowledge application.
We also need to know what the users are doing, and what their goals or purposes are
in applying this knowledge object. To this end, we will also require a task model. As
with the user model, the task model will serve to better characterize the different
reasons why someone would apply a particular knowledge item.
A user and task-adapted approach is highly recommended in order to facilitate
internalization processes. This means that we need to know enough about the user
and what they are trying to do in order to support them in the best possible way.
This is of course quite similar to what a good reference librarian or coach would
do, that is, try to understand who you are and what you are trying to accomplish
before beginning to help out. Someone who is browsing to pick up general informa-
tion and background on a subject of interest may be mistakenly taken for someone
who is lost in a sea of information. On the other hand, someone who has a looming
deadline to meet and is looking for a specifi c template to help him or her complete
the task at hand as quickly as possible without too many errors would not appreci-
ate being fl ooded with too much information. They are looking only for the
specially selected, vetted, and guided nuggets of knowledge — sometimes referred
to as just-in-time (JIT) knowledge or just-enough knowledge. Task support systems
or electronic performance support systems (EPSSs) best exemplify just-enough
knowledge.
Knowledge Application 199
Hughes Space and Communications (formerly part of Hughes Electronics Corporation, a
subsidiary of General Motors, now part of the Boeing Company). HSC has six thousand
employees who develop, produce, and launch state-of-the-art space and communications
systems for military, commercial, and scientifi c uses. It is the world ’ s largest producer of
commercial communication satellites. At HSC, KM is not viewed in terms of traditional
departmental boundaries. It is not a process, a function, or an organization. It is a skill
that is part of managing a business and should be one of the tools that every manager
possesses in his or her repertoire. Traditional management tends to take a “ top down ”
approach to implementation. In KM, it is better to lead not by direction but by service,
providing people with the necessary assistance to enable them to better do what they are
already doing.
For example, a lessons learned system can be described as a closed loop learning system.
People experience something in their work, either through analysis, discovery, or dialogue.
There are both good and bad discoveries, but in either event, something is learned. The
key is in extracting what was learned, and providing a connection between what was
learned and what is practiced. Lessons need to be documented and disseminated to the
masses in a form that is easily accessible to all. Feedback is then collected and incorporated
back into the documentation process. The challenge is continuously inserting these into
what is happening on the job.
HSC also has a coordinated business intelligence-gathering effort that includes a system
that pulls information from over sixty online sources, a process for analyzing it, and
ongoing dialoguing and sharing among HSC and other Hughes marketing people. This
began as a joint project of a few marketing people and the corporate library. It received a
boost when it was featured at a knowledge fair that showcased existing knowledge man-
agement activities to people from throughout HSC.
HSC does have an intranet that they did not simply install on everyone ’ s desktop and
then expected them to start using it effectively to do their jobs. Instead, they implemented
the intranet gradually, selectively deploying in pilot areas that focused on supporting a
high value business need such as lessons learned, gated processes, yellow pages, or a
common user interface to existing systems. Using one-on-one tutorials, each person was
trained on how to use the intranet and Internet to do their specifi c job. When pilots proved
successful, they were then deployed into enterprise-wide business applications.
Box 6.1 An example: Hughes Space and Communications
200 Chapter 6
Task Analysis and Modeling
Task analysis studies what knowledge workers must do with respect to specifi c actions
to be taken and/or cognitive processes that must be called upon to achieve a particular
task (e.g., Preece et al. 1994 ). The most commonly used method is task decomposition,
which breaks down higher-level tasks into their subtasks and operations. The lower
levels may make use of task fl ow diagrams, decision fl owcharts, or even screen layouts
to better illustrate the step-by-step process that has to be undertaken in order to com-
plete a task successfully. A good task analysis should show the sequencing of activities
by ordering them from left to right. In order to break down a task, the question should
be asked, “ How is this task done? ” If a subtask is identifi ed at a lower level, it is pos-
sible to build up the structure by asking “ Why is this done? ”
The task decomposition can be carried out using the following stages:
1. Identify the task to be analyzed.
2. Break this down into between four and eight subtasks. These subtasks should be
specifi ed in terms of objectives and, between them, should cover the whole area of
interest.
3. Draw the subtasks as a layered diagram, ensuring that it is complete.
4. Decide upon the level of detail into which to decompose. Making a conscious deci-
sion at this stage will ensure that all the subtask decompositions are treated consis-
tently. It may be decided that the decomposition should continue until fl ows are more
easily represented as a task fl ow diagram.
5. Continue the decomposition process, ensuring that the decompositions and num-
bering are consistent. It is usually helpful to produce a written account as well as the
decomposition diagram.
6. Present the analysis to someone else who has not been involved in the decomposi-
tion but who knows the tasks well enough to check for consistency.
Task fl ow analysis can include details of interactions between the user and the
current system, or other individuals, and any problems related to them. Copies of
screens from the current system may also be taken to provide details of interactive
tasks. Task fl ows will not only show the specifi c details of current work processes but
may also highlight areas where task processes are poorly understood, are carried out
differently by different staff, or are inconsistent with the higher level task structure.
An example of a task analysis is shown in table 6.4 .
Such task analyses are an important fi rst step in the design of knowledge applica-
tion support systems. A popular form of these has been around long before the term
KM came into common usage. EPSSs were and continue to be widely used provide
Knowledge Application 201
on-the-job learning and advice. E-learning is also currently enjoying a high level of
usage and can be seen as a subset of EPSSs, as described in the next sections.
EPSS In the groundbreaking book, Electronic Performance Support Systems , Gery (1991)
defi ned EPSSs as an integrated electronic environment that is available to and easily
accessible by each employee and is structured to provide immediate, individualized,
online access to the full range of information, software, guidance, advice, assistance,
data, images, tools, and assessment and monitoring systems to permit job performance
with minimal support and intervention by others.
An electronic performance support system can also be described as any computer
software program or component that improves employee performance by reducing
the complexity or number of steps required to perform a task, providing the perfor-
mance information an employee needs to perform a task, or providing a decision
support system that enables an employee to identify the action that is appropriate for
a particular set of conditions (see fi gure 6.7 ).
The EPSS point of view has been revolutionary. Its signifi cance was how it reframed
our thinking from the training paradigm of “ fi ll them up ” with knowledge and skills
and then “ put them to work. ” EPSS practitioners and business sponsors came to
Table 6.4 Example of a task analysis: Tying shoelaces
For novices For more experienced individuals
1. Pinch the laces.
2. Pull the laces.
3. Hang the ends of the laces from the corresponding sides of the shoe.
4. Pick up the laces in the corresponding hands.
5. Lift the laces above the shoe.
6. Cross the right lace over the left one to form a teepee.
7. Bring the left lace toward the student.
8. Pull the left lace through the teepee.
9. Pull the laces away from one another.
10. Bend the left lace to form a loop.
11. Pinch the loop with the left hand.
12. Bring the right lace over the fi ngers and around the loop.
13. Push the right lace through the hole.
14. Pull the loops away from one another.
1. Grab one lace in each hand.
2. Pull the shoelaces tight with a vertical pull.
3. Cross the shoelaces.
4. Pull the front lace around the back of the other.
5. Put that lace through the hole.
6. Tighten the laces with a horizontal pull.
7. Make a bow.
8. Tighten the bow.
202 Chapter 6
understand that people could be put on task far sooner — almost from day one — if we
provided an appropriate suite of integrated supports in the context of performing
real-work tasks.
Performance support systems such as EPSS help distill content into useful chunks.
The famous experiment by Miller (1956) found that our span of immediate memory is
severely limited. In fact, we can only hold seven (plus or minus two) discrete items in
our minds at the same time. Psychologists then did quite a bit of research on how
chunking, or combining items into more general categories, can help to overcome
this human information-processing bottleneck. This is also the reason why mnemonics
work in helping us to remember. For example, in trying to recall a list of things to
do, one mnemonic trick is to visualize each item as being in different room of your
house.
EPSSs capitalize on such useful methods by reducing a document into discrete
knowledge chunks (see fi gure 6.8 ). Each chunk then becomes a knowledge object and
the EPSS can direct you to the specifi c piece of knowledge you need in order to carry
out the task at hand. This is another important distinction in how KM carries out
content management as opposed to systems such as document management systems.
Task support system
Components: Task-Adapted
Database
Glossary, references,
documents, examples
Job aids
Decision aids
Coaching (tacit)
Manuals online
System support
Online help
Tutorial
Navigational help
Learning aids
“CBT” or E-learning
Simulations
Demonstrations
Figure 6.7 Components of an EPSS
Knowledge Application 203
KM operates at a fi ner level of granularity — the work has been done a priori, so users
need not wade through thick technical documents or other “ containers ” of knowl-
edge. These have been broken down into the valuable knowledge nuggets that are of
greatest use.
Content management in KM thus involves breaking down documents into their
conceptual components and mapping these out using concept indexes, semantic
networks, or hierarchical knowledge taxonomies. Decomposition is also a prerequisite
for the development of EPSSs. Understanding the EPSS vision remains far from uni-
versal. Indeed, misunderstanding of the EPSS vision is far more common — a result, in
part, of misapplication of the term by people who sought “ currency ” in being on the
bandwagon, despite the fact that they were selling traditional CBT, online reference
materials, and so on. Still, after roughly eight years since the phrase was coined, there
are quite a few success stories for “ true ” performance support systems. What we call
EPSS may change — there is a movement to replace the term with “ performance cen-
tered systems, ” an attempt to recapture the original intent and to better appeal to the
IS community — but the concept is here to stay, justifi ed by the value these systems
have provided to the visionary organizations that sponsored them.
EPSSs can help an organization to reduce the cost of training staff while increasing
productivity and performance. They can empower an employee to perform tasks with
a minimum amount of external intervention or training. By using this type of system,
an employee, especially a new employee, will not only be able to complete work more
quickly and accurately, but as a secondary benefi t will also learn more about the job
and the employer ’ s business. For an update on this approach, see Dickleman (2003) .
An EPSS application at Sun Microsystems is explored here (box 6.2).
Document 1 Videoclip 1 E-mail thread 1
Figure 6.8 Chunking in content management
204 Chapter 6
In 1997, Sun Microsystems launched SunWEB, (Monasco 2005 an intranet linking its
employees worldwide. The intranet has not only saved $25 million a year but has also
helped achieve big savings by enhancing its relationships with customers and suppliers
by putting knowledge online. Sun also began thinking about how to use this powerful
network to enhance the knowledge, skills, and capabilities of its employees and partners.
SunTAN is their intranet-based knowledge and training system, an interactive, network-
based curriculum management and sales support system. SUN has tremendous learning
and knowledge needs: 90 percent of its revenues are from products that are less than a
year old and it has consistently experienced widening product lines and shorter life cycles.
As a result, the company found it could not train its sales professionals fast or effectively
enough. It could no longer rely on traditional classroom-based training, which was too
long, overwhelmed people with information, and cost about $2,225 a week per individual
(not counting lost sales time).
SunTAN consolidates sales training information, sales support resources, product
updates and materials, competitive intelligence, and an array of other content on the Sun
intranet. This distributed learning architecture ensures the richest, most bandwidth-
intensive, and most actively used media (e.g., a video demonstrating the latest line of new
server products) is distributed to and stored on local servers at regional sales offi ces rather
than the company ’ s headquarters. Users can then download them at their convenience.
In the new world of distance training, you no longer need to retain knowledge. The only
knowledge you need to retain is knowledge of the location of where you can get the
information you need. It changes so often that it no longer makes sense to retain it. It is
a pull rather than a push model. It is critical that funding for this comes from business
units and that content also comes from resources other than a centralized training group.
In this way, SunTAN acts a just-in-time knowledge or performance support system enabling
sales personnel to rapidly access critical information while they have a customer on the
phone. Moreover, they can train in self-directed way at their desktops without abandoning
their customers for a weeklong training course.
SunTAN was originally developed for Sun ’ s direct sales reps and sales engineers, but it is
now available to the company ’ s twenty thousand resellers who account for more than 60
percent of worldwide sales. Additional features that will be integrated in this environment
include database technology to track and profi le individual usage of the system. This will be
used to create customized learning paths and alert employees when relevant resources
become available. A collaborative product called Kansas will be integrated into this environ-
ment to allow users to pull in as many as nine different video feeds onto a single screen for
a high-tech meeting or panel discussion. Another add-on technology will be a conceptual
indexer that will allow users to search and retrieve video content with keywords much in
the same way that they now search text. Some SUN customers are requesting that SunTAN ’ s
training content be made available within their own intranet fi rewalls. The SunTan system
remains an excellent example of a knowledge management application.
Box 6.2 An example: Sun Microsystems (now part of Oracle Systems)
Knowledge Application 205
Malcolm (1998) discussed the extension of the EPSS concept to apply to groups
(CoPs) and to house content that could be dynamically updated within an organiza-
tion ’ s knowledge repository. Performance support systems today have been designed
primarily for individual use: they support an individual as he or she works to accom-
plish some performance goal. On the commercial market, programs that help you
prepare your income tax returns, write a will, or create a newsletter template all
illustrate this level of support. In corporations, systems that support customer service
representatives — whether in a call center for fi nancial transactions or travel reserva-
tions, or face-to-face in the lobby of a hotel — also represent an individual ’ s use of
an EPSS. Imagine a group around a table with the means to project a computer
display. The group would work through the steps of the process together, brainstorm-
ing, and receiving group-processing advice from a built-in “ coach. ” The work product
belonged to the group and it was the group ’ s performance that had been enhanced
by the EPSS.
Another way to look at this challenge is to say that yet another conceptual merger
needs to take place — this time assimilating the discipline of KM, that is, capturing and
sharing vital business information from a variety of sources, not just top-down, in
order to enable better decision making in a dynamic business environment. We in the
fi eld of performance support have much to learn from it, just as those who study
knowledge capture and sharing have much to learn from us about how to integrate
various kinds of support into the context of performing work.
Examples are fairly common in the large consulting fi rms where dynamically
updated EPSSs are integrated within the organizational knowledge repository in order
to make the complex task of sharing critical business and personal development infor-
mation much easier.
An R & D organization relies upon a dedicated team of ten information professionals
who are continually updating their user and task models in order to optimize knowledge
services. For example, each researcher ’ s profi le is updated regularly to refl ect changing
interests, new skills, and/or new projects. In addition, each information request is also
analyzed periodically to assess the level of noise versus the level of hits — that is, how
often was the information judged to be useful? This analysis is used to further refi ne
or fi ne-tune the profi les so that the next information request will yield increasingly better
results.
Box 6.3 An example: A knowledge service center uses task modeling and user modeling
206 Chapter 6
Task Characteristics Features
Low
Moderate
High
Low
Moderate
High
Low
Moderate
High
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate Moderate
Moderate
Low
Moderate
High
High
High
N/A
N/A
High
High High
—
—
Knowledge
repositories
Manuals
LowLow
Daily
Daily
Monthly
Quarterly
Strategic
objectives
Business
units
Support
request
Problem
report
Tech.
Watch
Strategic
priorities
U(1)
Manager
U(2)
Technical
U(3)
Sales
Help
Desk
IT
Research
CKO
U(n) T(n)
Users Tasks Frequency Difficulty Complexity Desirability
Weekly
Monthly
Quarterly
Template
Example
Type of
support
Inter-
dependencies
Consequence
of errors
T(1)
T(2)
T(3)
T(2)
T(7)
T(8)
T(7), T(4)
T(1)
T(2)
T(3)
T(5)
Figure 6.9 Sample user and task model
Barron (2000) summarizes the current state of the art of EPSSs and related approaches
in the following manner: “ take an e-learning course; chunk it into discrete learning
bites; surround it with technology that assesses a learner ’ s needs and delivers the
appropriate learning nuggets; add collaborative tools that allow learners to share
information. What do you get? Something that looks a whole lot like knowledge
management. ”
The best approach, then, requires a user model or trace — a record of the interaction
between the user and the system. The user model would capture the objects of interest
or focus — that is, what content was accessed, when, how often, in which sequence,
and so on. A log of user interactions can be abstracted to produce a user and task
signature. Together, these will yield a model of the user and the task that the user is
attempting to perform and these two sources of information can help in providing
the best possible support for knowledge application in that particular case. Figure 6.9
illustrates a sample user and task model.
It is assumed that episodes related to particular tasks usually share some common
features or patterns. Once these common features have been identifi ed for a given
Knowledge Application 207
task, they can be considered a signature of the task, or evidence that the user is per-
forming this task.
Knowledge Application at the Group and Organizational Levels
Knowledge management systems (KMSs) are tools aimed at supporting KM. KMSs
evolved from information management tools that integrated many aspects of com-
puter-supported collaborative work environments (CSCW) with information and
document management systems ( Ganesan, Edmonds, and Spector 2001 ; Greif 1988 ;
Kling 1991 ). Key characteristics of KMSs are support for:
• Communication among various users
• Coordination of users ’ activities
• Collaboration among user groups on the creation, modifi cation, and dissemination
of artifacts and products
• Control processes to ensure integrity and to track the progress of projects
Systems that support KM provide specifi c functions related to communication
(e-mail and discussion forums); coordination (shareable calendars and task lists); col-
laboration (shareable artifacts and workspaces); and control (internal audit trails and
automatic version control). User-centered KMSs contribute to an organizational culture
of sharing by providing a sense of belonging to a community of users and by support-
ing reciprocity among users ( Marshall and Rossett 2000 ). KMSs extend the perspective
of employees as knowledge workers by providing them with the means to create
knowledge and to actively contribute to a shared and dynamic body of knowledge.
KMSs provide support for many information functions, including:
• Acquiring, indexing, capturing, and archiving
• Finding and accessing
• Creating and annotating
• Combining, collating, and modifying
• Tracking ( Edmonds and Pusch 2002 )
These KMS functions allow multiple individuals to organize meaningful activities
around shared and reusable artifacts to achieve specifi c goals. In short, KMSs address
the distributed nature of work and expertise ( Salomon 1993 ).
Within business and industry, KM technology is being used to support organiza-
tional learning ( Morecroft and Sterman 1994 ; Senge 1990 ). The dynamics of the global
208 Chapter 6
British Telecommunications and Futuremedia iLearning developed Solstra 2000, which is
a new model of the jointly developed net-based learning and knowledge management
system. It is the result of signifi cant product development based on increasingly sophisti-
cated and growing customer demand. Solstra 2000 is designed for hosting, delivering, and
managing online learning and job support information. Additional enhancements to the
new version include refi ned administration, management, and reporting capabilities, and
several new fl exible options that increase the availability of learning to groups and indi-
viduals at their PCs. Solstra 2000 also claims to provide the necessary technology to allow
any organization to set up a virtual “ Corporate University. ”
Highlights include the development of Solstra 2000 to map onto an organization ’ s
structure. This reportedly makes it intuitive and straightforward for HR, training, and line
managers to set up a familiar framework to administrate learning across all departments
and levels of the organization, providing the natural platform for a corporate university.
Also, the ability of all staff to “ raise their hand ” electronically, alerting colleagues to their
expertise, interests, and areas they are looking to improve, with their own Solstra 2000
personal homepage. Searchable throughout the organization, this information provides
the foundation for a knowledge management system. Solstra 2000 has increased scalabil-
ity, allowing it to be used by an unlimited number of participants. Terms and text can be
customized and translated into different languages, making it suitable for use by the largest
global organizations.
New participants joining a group or department using Solstra 2000 are automatically
able to access the learning content previously assigned to fellow group members, bringing
them instantly up to speed. Participants also have access to additional learning resources
as fi les can accompany learning content, to provide more information and recommend
related material. HR and training managers can create tailored FAQs within Solstra 2000,
as well as a “ news service ” alerting participants directly when new relevant learning
content becomes available.
Box 6.4 An example: British Telecommunications (Solstra 2000)
economy place a premium on organizational responsiveness and fl exibility. Partly as
a response to the demands of a highly competitive global economy, KMS technology
has emerged as a new generation of information management systems. In contrast
with previous information management systems, KMSs are designed for multiple users
with different and changing requirements.
Key enabling technologies include object orientation, broadband communications,
and adaptive systems. Object orientation provides for the creation of knowledge
objects that can be easily found, modifi ed, and reused. Broadband communication
allows users separated in time or space to work on large data objects effectively as a
Knowledge Application 209
Table 6.5 Examples: Knowledge application support technologies
Name Description Web site
Mindjet ’ s Mindman High-level visualization and mapping tool
http://www.mindjet.com
Groove Collaboration software http://www.groove.net
Visio High-end fl owcharting tool
http://www.microsoft.com/offi ce/visio/
Themescape Topographical knowledge maps
http://www.micropat.com/0/pdf/ themescape.pdf
OpenText ’ s eDocs and Livelink
Automatic taxonomy creation
http://www.opentext.com/2/global/ sol-products/sol-pro-knowledge- management.htm
ClearForest ’ s ClearTags
Automatic taxonomy creation
http://www.clearforest.com/
LotusNotes Websphere
Knowledge repository http://www.lotus.com/home.nsf/ welcome/kstation
Vignette Content management software
http://www.vignette.com/
EPSS Central Electronic performance support systems
http://www.pcd-innovations.com/
team. Adaptive systems recognize that different users may have different requirements
and preferred working styles.
KMSs can be viewed as activity systems that involve people making use of objects
(tools and technologies) to create artifacts and products that represent knowledge in
order to achieve a shared goal. Previous information management systems focused on
a small portion of such a system, such as a narrow set of objects in the form of a col-
lection of records or simple communication between team members. KMSs embrace
the entire activity system but maintain a focus on the human-use aspects (people with
shared goals) as opposed to the underlying or enabling technology aspects. KMSs have
already met with signifi cant success in the business sector and are spreading to other
sectors, including education ( Marshall and Rossett 2000 ) and instructional design
( Ganesan, Edmonds, and Spector 2001 ). Table 6.5 provides some examples of KM
systems.
The organizational knowledge management architecture will be comprised of at
least three levels: the data layer, which is the unifying abstraction across different
types of data with potentially different storage mechanisms (e.g., database, text docu-
ments, video, audio); the process layer, which describes the logic that links the data
210 Chapter 6
with its use and its users (other people or other systems who use that data); and the
user interface, which provides access to the information assets of the company via the
logic incorporated in the process layer. The KM organizational architecture is shown
in fi gure 6.10 .
KM cannot be supported by the simple amalgamation of masses of data. KM
requires the structuring and navigation of this content supported by metadata, the
formal description of the content and its interrelationships with other content or other
knowledge objects. Metadata encompasses information about physical structures, data
types, access methods, and the actual content. There are a variety of tools and tech-
niques available for the knowledge application phase of the KM cycle. Dissemination
and publication tools typically involve some type of knowledge repository design.
They will have features such as the routing and delivery of information to those who
have a need or who have subscribed (push vs. pull approach). E-mail and workfl ow
are examples of push technologies that notify users of any changes such as newly
posted content or expired content. Pattern matching can be done against user profi les
in order to better target where pushed content should go.
Unifying user interface Profiles for
personalization
User views
or representations
Applications Functions for KM
Help
system
Locate
experts
Find
associations
Alert to
new factors
Metadata
Data sources Data types Data formats
UI layer
Process
layer
Data
layer
Record
BPs
Figure 6.10 KM organizational architecture
Knowledge Application 211
Other tools help structure and navigate the content. They provide a classifi cation
scheme for the organization ’ s knowledge assets. We saw examples of these knowledge
taxonomies in the previous chapter. The user interface layer is where such navigation
guides are to be found. Once the content has been properly indexed and organized,
multiple views can be made available for the same underlying content in order to
accommodate user and task needs. Electronic linkages can be used to cross-reference
this content and a thesaurus can encapsulate these cross linkages. Similarly, expertise
location systems should be available from the user interface layer of the KM architec-
ture. In this way, links are made from the user interface topics to the relevant KM
content, people, and processes.
Knowledge Reuse
Reusing knowledge involves recall and recognition, as well as actually applying the
knowledge, if we use Bloom ’ s taxonomy. Reusing knowledge typically begins with the
formulation of a search question. It is here that expert-novice differences quickly
become apparent, as experts know the right questions to ask. Next, experts are searched
for and located, using expertise location systems or yellow pages as we saw in chapter
5. The appropriate expert and/or advice are then chosen and the knowledge nugget
is applied. Knowledge application may involve taking a general guide and making it
specifi c to the situation at hand which is sometimes referred to as “ recontextualiza-
tion ” of knowledge (where decontextualization to some degree occurred during knowl-
edge capture and codifi cation). An example of knowledge reuse is described here
concerning the J. P. Morgan Chase company (box 6.5).
There are three major roles required for knowledge reuse: the knowledge producer,
the person who produced or documented the knowledge object; the knowledge inter-
mediary, who prepares knowledge for reuse by indexing, sanitizing, packaging, and
even marketing the knowledge object; and the knowledge reuser, who retrieves, under-
stands, and applies it. Of course, these roles are neither permanent nor dedicated
roles — individuals will perform all three at some time during their knowledge work.
Knowledge repackaging is an important value-added step that may involve people,
information technology, or, as is often the case, a mixture of the two. For example,
there are automatic classifi cation systems that can index content, but a human is
almost always needed in the loop to validate and to add context, caveats, and other
useful indicators for the most effective use of that knowledge object.
Markus (2001) suggests there are four distinct types of knowledge reuse
situations according to the individual who is doing the reusing and the purpose
of knowledge reuse, which is quite compatible with the user- and task-adapted
212 Chapter 6
approach that has been outlined in this chapter. Markus ’ s types of knowledge reuse
situations are:
1. Shared work producers, who produce knowledge they later reuse
2. Shared work practitioners, who reuse each other ’ s knowledge contributions
3. Expertise-seeking novices
4. Secondary knowledge miners
Shared work producers usually consist of teams or workgroups who have collabo-
rated together. A common example is a physician who consults a patient ’ s chart to
see what medications had been prescribed recently by other members of the practice;
or special education teachers and therapists who share student fi les to see what
sorts of interventions worked and which ones did not have any effect. This is the
easiest form of knowledge reuse as everyone is quite familiar with the knowledge
content — they share the same context, which makes knowledge application rapid and
effective.
Shared work practitioners are members of the same community of practice. They
are peers who share a profession. This form of knowledge reuse will require a higher
degree of fi ltering and personalization, typically done by CoP knowledge librarians.
Reusers would need more reassurance about the source ’ s credibility — they would need
Reuse KM initiatives have taken hold at LabMorgan, the Internet strategy and incubation
unit of J. P. Morgan Chase & Co. The lab uses Intraspect Software technology to help
employees fi lter the hundreds of business-plan referrals received for investment or incuba-
tion possibilities each month. The platform lets users access all previous expertise and
feedback on similar propositions the company has received, so they can measure new
proposals against them and know what questions to ask to further probe a new plan ’ s
merits. Since the deployment, the lab says it has been able to avoid duplicate screenings
of similar proposals and has generated signifi cant gains.
But the lab thought fi rst about how it works as an organization before jumping into
the technology. “ The collaborative tool pushed thinking about our processes and how we
work together, ” Feldhusen says. “ The core has to be a mind-set of sharing and accomplish-
ing a common goal. We designed the software to support the processes we use. ” But she
acknowledges that deploying KM initiatives might be more challenging in dealing with
very established processes. “ How do you motivate people to move to new ways? [Our
advantage is that] we ’ re in an area that ’ s highly innovative. ”
Box 6.5 An example: J. P. Morgan Chase
Knowledge Application 213
to be able to trust that the content is valid and should be applied. They are less likely
to completely overlap in context, so it is likely that knowledge reuse would require
contact with others knowledgeable about the knowledge object.
Expertise-seeking novices are often in a learning scenario. Unlike the previous two
types of reusers, novices are the most distant or different from the knowledge object
authors and those experienced with its use. Knowledge intermediaries have a much
greater role to play here in making sure novices begin by accessing more general
information (e.g., FAQs, introductory texts, glossaries) before they attempt to apply
the knowledge object or directly contact those who are more expert in using the
knowledge object. EPSSs and other performance support aids such as e-learning 1
modules would also be of great use to such reusers.
Secondary knowledge miners are analysts who attempt to extract interesting and
hopefully meaningful patterns by studying knowledge repository use. They are analo-
gous to the usage analysts who perform similar roles for a CoP library as discussed in
chapter 5. They are also analogous to librarians who periodically assess the collective
holdings of a library, whether physical or digital, to see which items are no longer
being actively accessed and should perhaps be archived, which have been superseded
by newer and better best practices, and so forth.
Different types of reusers will interface differently with knowledge repositories and
they will differ in their support needs. Repositories therefore need to be able to per-
sonalize — either at the extreme of treating each individual differently or at the very
least, personalizing at the level of a community of practice. Since CoPs revolve around
organizational and professional themes, it makes sense to partition the global knowl-
edge repository along similar lines. Careful attention must also be paid to the roles of
intermediaries needed to develop and maintain the organization ’ s corporate memory.
Content authors are as vital to successful knowledge application and reuse as are
container maintainers.
Knowledge Repositories
Knowledge repositories are usually intranets or portals of some kind that serve to
preserve, manage, and leverage organizational memory (discussed further in chapters
8 and 11). There are many different types of knowledge repositories in use today
and they can be categorized in a number of different ways. In general, a knowledge
repository will contain more than documents (document management system), data
(database), or records (record management system). A knowledge repository will
contain valuable content that is a mix of tacit and explicit knowledge, based on the
unique experiences of the individuals who are or were a part of that company as well
214 Chapter 6
as the know-how that has been tried, tested, and found to be successful in work
situations.
Davenport et al. (1998) makes a distinction between repositories that store external
knowledge such as that gathered from competitive intelligence, demographic or sta-
tistical data from data resellers, and other public sources, and internal knowledge
repositories that store informal information such as transcripts of group discussions,
e-mails, or other forms of internal communications. Internal knowledge repositories
will have a less constraining or less formal structure in order to be able to better
accommodate the fl uid and subjective knowledge content required.
Zack (1999) classifi es repositories based on the type of content they contain such
as general knowledge (e.g., published scientifi c literature) and specifi c knowledge
(which includes knowledge of the local context of the organization). This distinction
is most useful, as knowledge reusers need to know whether the credibility of the
knowledge comes from general or common knowledge, or whether this is something
that was discovered by their colleagues.
E-Learning and Knowledge Management Application
Many organizations have integrated KM applications with e-learning or technology-
mediated learning (as opposed to traditional classroom-based teaching). There are a
number of ways in which KM can intersect with e-learning ( Khan 2005 ): one is as a
major part of the KM cycle where knowledge is reused and applied — and, in order to
do so, knowledge must be understood, learned, and/or internalized. E-learning can
therefore be seen as another type of knowledge-sharing channel, one that makes use
of technologies such as computers or the Web and one that also requires a very high
degree of social presence and media richness (as discussed in chapter 5). The major
advantage of traditional in-class learning is that the interaction is face-to-face. The
corresponding disadvantage is that time and space constraints do not allow for in-
depth one-on-one interactions. With online learning, students have the ability to
relearn through replaying a video, viewing the lecture slides, and asynchronously
interacting with both classmates and instructors. The major advantage of e-learning
is the time and travel cost saved by not having people go off-site for a period of time.
More students can be registered in the same course. The major drawback is the lack
of face-to-face interaction, which is often compensated for through the use of a
blended learning model (a combination of some e-learning with some face-to-face
instruction, tutoring, or discussion).
E-learning has developed an innovative approach to learning through the use of
technologies such as the computer and the Web: learning objects. A learning object
The National Science Digital Library (NSDL; Marshall et al. 2003 ) has provided students
and educators with science education resources since 2002. Seamans and McMillan (1998)
defi ne a digital library as more than the digitization of a collection, but also consisting of
information management tools and responsibilities to bring together collections, services,
and people to create, use, disseminate, and preserve content. NSDL collections cover a
wide range of topics including astronomy, biology, economics, mathematics, and technol-
ogy. The NSDL GetSmart system is a good example of how KM and e-learning can be
integrated. GetSmart was designed by blending together learning and information seeking
theories, and it has been implemented as an integrated suite of tools for curriculum
support for teachers, search support for those seeking information, and for concept
mapping support to support student learning.
Curriculum tools are typically Learning Management Systems (LMS) that provide a
standardized environment to support classroom learning (e.g., WebCT and Blackboard,
www.blackboard.com). Digital library tools provide information seeking and retrieval to
help users navigating through the digital collection to locate the resources they are looking
for. Knowledge representation tools provide a visualization of the content (e.g., concept
maps) to allow users to visually review, capture, or develop knowledge. Concept maps
represent concepts and relationships as node-link diagrams or semantic maps. Such maps
and the very act of mapping have proven to be very effective ways of presenting informa-
tion and also serve to promote effective learning ( Chmielewski and Dansereau 1998 ). For
example, a text syllabus may be found in the curriculum e-learning tool, a search aid to
fi nd all relevant resources in the digital collection related to that course may be found in
the digital library tool, and a course map of learning objectives and prerequisite knowledge
may be found in the knowledge representation tool.
From a KM perspective, GetSmart is a system for the generation, codifi cation, and
representation of knowledge. GetSmart is organized to help individuals, groups, and com-
munities develop knowledge. Curriculum tools provide a context for individual and group
learning. As users construct concept maps, they explore available information and then
synthesize selected ideas into personal knowledge representations, which allows them to
learn by exploration (discovery learning). When group maps are created, several users
collaborate, clarifying concepts and relationships and fi tting them together. The search
and curriculum functions access repositories of community knowledge that tend to be
more formal and to use established vocabulary. The search tools help knowledge travel as
information to the user/learners. As information is transferred to the individual, it becomes
enriched, expanded, and synthesized into new or unique contexts. These processes are
viewed as information fl owing from experts and repositories to individuals and groups.
When a body of maps has been created, the information fl ow can be reversed.
Technologically, the GetSmart system is an XML browser based so that learners can
access it from a typical university computer lab. Microsoft SQL Server is used for the data-
bases and the map-drawing tool is a Java applet developed using Java 1.4.
At the GetSmart launch in 2002, over one hundred student users at the University of
Arizona and Virginia Tech created a database of more than one thousand student-prepared
concept maps with more than forty thousand relationships expressed in semantic, graphi-
cal, node-link representations.
Box 6.6 An example: GetSmart — An E-learning solution for the National Science Digital Library (NSDL)
216 Chapter 6
is a stand-alone unit of learning — a reusable online learning resource ( Morales et al.
2005 ). A set of learning objects make up an e-learning library or repository so that
once posted, other users can reuse the same learning object. The learning objects
may be used as is, or they may be adapted, modifi ed or otherwise changed to better
meet specifi c needs. Users are able to manage and reuse content according to their
needs without interoperability problems. Learning objects are good examples of
reusable knowledge — once they have been created, they then continue along the
KM cycle as they are shared, disseminated, and applied by other users. Examples
of learning objects would include a learning module on a given topic, lecture slides,
a test, a demonstration, or combinations of different content formats, including
multimedia.
Strategic Implications of Knowledge Application
Knowledge application implies that employees in an organization can quickly fi nd
answers to the following types of questions:
• What have we already written or published on this topic?
• Who are the experts in this area and how can I contact them?
• Have any of our partners, contacts, and clients addressed these issues?
• What sources did we use to prepare the publications on this topic?
• What are the best web sites or internal databases to go to for more information?
• How can I add my own experience applying this particular piece of knowledge?
A knowledge repository should be a one-stop shop for knowledge application.
Employees should be able to fi nd out what they need in order to access, understand,
and apply the cumulative experience and expertise of the organization. In this way,
knowledge workers can concentrate on doing their actual work and not lose precious
time trying to fi nd all the bits and pieces of knowledge and know-how that have
already been captured, coded, vetted, and made available to them. Reuse of proven
knowledge can serve to not only increase effi ciency and effectiveness, but can free up
knowledge workers to devote their efforts to innovative and creative knowledge to be
added to corporate memory, as opposed to reinventing what has already been devel-
oped or solved.
In many cases, reusing knowledge is nontrivial. This counterintuitive result is gen-
erally due to two particular problems. In an organization of more than moderate
complexity, locating the knowledge to be reused is diffi cult. Workers may be unaware
Knowledge Application 217
that the knowledge they need is available. The knowledge may be held in the orga-
nization and correctly identifi ed, but may simply be in the wrong form for the task —
the essential information may be only implicit in the repository. The knowledge may
have to be reconfi gured in some way to meet the requirements of the task in hand.
It may be that the knowledge requires some partial modifi cation (e.g., updating). Here,
understanding the knowledge requirements of both the users and their tasks is the
key to understanding, identifying, and using the correct knowledge from the various
sources. This in turn would enable more leverage to be gained from the knowledge
already at hand, thereby increasing the return on investment in those knowledge
assets.
Practical Implications of Knowledge Application
At a minimum, do these things:
• Create an organizational knowledge base to house the intellectual assets.
• Create a corporate yellow pages so that knowledge workers can fi nd out who is
knowledgeable in which areas of expertise.
• Capture best practices and lessons learned and make them available to all others in
the organization via the knowledge base.
• Empower a Chief Knowledge Offi cer to develop and implement a KM strategy for
the organization.
• Ensure that the organizational culture will help facilitate the key phases required for
the KM cycle (to capture, create, share, disseminate, acquire, and apply valuable
knowledge).
Make sure that it is fairly easy to continually update and feed the corporate
memory. Users should be able to contribute best practices, lessons learned, comments
and questions about content, tips and tools they would recommend, working exam-
ples, and case studies. Openly encouraging and applying new ideas fosters the coop-
eration and innovation that is critical to a learning organization.
Knowledge application is far more likely to succeed if the type of content that is
being made available can “ hit the ground running ” — in other words, it is not just a
repository of “ stuff ” but chunks of executable knowledge. The knowledge nuggets
should always include tacit and contextual knowledge of when this should be used,
where it can and cannot be applied, why and why not, and the ground truth or knowl-
edge of how things really work and what is required for successful performance.
218 Chapter 6
Key Points
• There are a number of ways of ensuring that individuals apply knowledge such as
deriving user and task models in order to better match knowledge content to indi-
vidual knowledge workers ’ preferences and requirements.
• EPSSs, the Bloom taxonomies of cognitive, affective, and psychomotor skills, and
content chunking are all good means of providing learning and task support to knowl-
edge workers who apply knowledge and of optimizing the match between user needs
and the content that is to be applied.
• A KM organizational architecture needs to be designed, developed, and implemented
in order to facilitate knowledge application at the organizational level.
• Knowledge reuse is a good measure of how well valuable content has been preserved
and managed in organizational memory management systems.
• KSSs are tools that can assist in organizational knowledge use and reuse, typically
through some form of knowledge repository or intranet application.
• KM and e-learning share many of the same goals and processes and their integration
can help solidify the application of knowledge — the use, reuse, and continuous
improvement of both knowledge resources and learning objects in an organizational
repository.
Discussion Points
1. Discuss personalization and profi ling approaches to model knowledge workers.
How would you make use of more information about users in order to better target
valuable knowledge content to them? How would you increase the likelihood of their
applying the content?
2. When would you make use of which Bloom taxonomy? Provide examples of some
knowledge applications where each of the three taxonomies could provide useful
information.
3. What are some of the tools used in organizational memory management?
4. What are the key components that should be addressed by an organizational KM
architecture? Why are these critical for organizational knowledge application?
5. What is reuse and why is it an important measure of the success of KM within an
organization?
6. Why is knowledge application the most important step in the KM cycle?
Knowledge Application 219
7. How does knowledge application relate to the internalization phase of the Nonaka
and Takeuchi knowledge spiral model that was presented in Chapter 3?
8. Discuss why counting the number of “ hits ” to a knowledge-repository (much like
Web site statistics) would not be the best measure of knowledge application within
an organization.
9. What is chunking? Why is this a good content management strategy? How
would you take advantage of chunking for individual and organizational
knowledge application situations? How could an e-learning system make good use of
chunking?
10. Provide an example of a task analysis for a task you are familiar with. What are
the major challenges in designing an EPSS based on such a task analysis? How would
you address these challenges?
Note
1. See the journal on KM and E-Learning at: http://kmel-journal.org/ojs/index.php/online-
publication.
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7 The Role of Organizational Culture
As the soil, however rich it may be, cannot be productive without cultivation, so the mind
without culture can never produce good fruit.
— Seneca (Roman Senator, c. 60 BC – c. AD 37)
This chapter examines the role played by organizational culture in more detail. Dif-
ferent types of organizational cultures are described with a view to better understand-
ing the key dimensions of the different microcultures that thrive in organizations.
Cultural enablers and obstacles to knowledge sharing are presented together with a
discussion on how to institute desired organizational changes to better accommodate
knowledge management. Finally, the long-term nature of organizational culture
dimensions is addressed by presenting major organizational and KM maturity models.
Learning Objectives
1. Defi ne what organizational culture is.
2. Understand the relation between organizational culture and the business context.
How does culture contribute to organizational innovation and success?
3. Appreciate the contribution of organizational culture to the management of change;
understand the analytic elements of organization culture, such as different types of
cultures and organizational maturity models.
4. Describe how organizational culture intersects with KM.
5. Discuss the key organizational culture enablers and the key obstacles to effective
knowledge sharing and KM.
6. List the major phases involved in initiating organizational change and review how
the organizational culture would have to evolve so that KM goals can be attained.
7. Discuss to what extent organizational culture can be managed.
224 Chapter 7
Introduction
There are a number of common myths that persist in the fi eld of KM. Among these
are the “ build it and they will come ” myth. Unfortunately, people rarely take the time
to learn new tools, technology does not always give them what they want/need, and
they often are not in a position to even know what they need. A second myth is that
“ technology will replace face-to-face. ” However, valuable tacit knowledge sharing and
the important role of informal networks and peer-to-peer learning cannot and should
not be ignored. The third common KM myth is that “ the fi rst thing to do is change
the organizational culture to one of learning. ” While a number of successful KM ini-
tiatives grew in organizations that already had a solid learning culture, in other orga-
nizations it is very hard and it takes a very long time to change (and subsequently
maintain) cultural change. If you begin with this challenge, you will end up waiting
a long time for KM to succeed. Most organizations can be envisaged to sit on a KM
readiness gradient: some are already “ there ” while others have to move up to a cultural
state that will more readily accommodate or enable KM to succeed. Regardless of posi-
tion, one thing is certain: the cultural environment that the organization fi nds itself
in will play a crucial role on what happens to knowledge management within that
organization (see fi gure 7.1 ).
What is organizational culture? The literature on organizational culture borrows
heavily from anthropology and sociology. Originally an anthropological term, culture
refers to the underlying values, beliefs, and codes of practice that makes a community
what it is. The customs of society, the self-image of its members, the things that make
it different from other societies, are its culture. Culture is powerfully subjective and
refl ects the meanings and understandings that we typically attribute to situations, and
the solutions that we apply to common problems. The idea of a common culture sug-
gests possible problems about whether organizations have cultures. Organizations are
only one constituent element of society. People join organizations from the surround-
ing community and bring their culture with them. It is still possible for organizations
to have cultures of their own as they possess the paradoxical quality of being both
part of and apart from society. They are embedded in the wider societal context but
they are also communities of their own with distinct rules and values.
Culture has long been on the agenda of management theorists. Culture change
must mean changing the corporate ethos, the images, and values that inform action
and this new way of understanding organizational life must be brought into the man-
agement process. There are a number of central aspects of culture. There is an evalu-
ative element involving social expectations and standards, the values and beliefs that
The Role of Organizational Culture 225
Assess
Update
Contextualize
Knowledge capture
and/or creation
Knowledge sharing
and dissemination
Knowledge acquisition
and application
Organizational culture
Organizational environment
Figure 7.1 The cultural component in an integrated KM cycle
people hold central and that bind organizational groups. Culture is also a set of more
material elements or artifacts. These are the signs and symbols that the organization
is recognized by, and further, they are the events, behaviors, and people that embody
culture. The medium of culture is social interaction, the web of communications
that constitute a community. Here a shared language is particularly important in
expressing and signifying a distinctive organizational culture. This is particularly
apparent in communities of practice where members tend to have their own “ jargon ”
or “ brand. ”
There are, not surprisingly, many defi nitions of culture. One of the earliest defi ni-
tions was provided by Morgan (1977) who more recently ( 1997 ) describes culture as
“ an active living phenomenon through which people jointly create and recreate the
worlds in which they live ” (p. 141). For Morgan, the three basic questions for cultural
analysts are:
• What are the shared frames of reference that make organization possible?
• Where do they come from?
• How are they created, communicated, and sustained?
226 Chapter 7
Schein (1999), who is generally considered the father of organizational culture,
provides the following defi nition: “ organizational culture is a pattern of basic assump-
tions — invented, discovered, or developed by a given group as it learns to cope with
its problems of external adaptation and internal integration — that has worked well
enough to be considered valid and, therefore, to be taught to new members as the
correct way to perceive, think and feel in relation to those problems ” (p. 385). Orga-
nizational culture can also be defi ned both in terms of its causes and effects. Using an
outcomes perspective, culture can be defi ned as a manifest pattern of behavior,
between-individuals behavioral consistencies, or “ the way we do things around here. ”
Culture thus defi nes consistent ways in which people perform tasks, solve problems,
resolve confl icts, treat customers, treat employees, and so on. Using a process perspec-
tive, culture can also be defi ned as a set of mechanisms such as informal values, norms,
and beliefs that control how individuals and groups in an organization interact with
each other and with people outside the organization.
Morgan (1977) found that some key elements of organizational culture include:
• Stated and unstated values
• Overt and implicit expectations for member behavior
• Customs and rituals
• Stories and myths about the history of the group
• Shop talk — typical language used in and about the group
• Climate — the feelings evoked by the way members interact with one another, with
outsiders, with their environment, including the physical space they occupy
• Metaphors and symbols — may be unconscious or embodied in other cultural
elements
Other authors defi ne corporate culture is the set of understandings (often unstated)
that members of a community share in common. Shared understandings consist of
norms, values, attitudes, beliefs, and paradigms ( Sathe 1985 ). Webster ’ s New Collegiate
Dictionary defi nes culture as the “ integrated pattern of human behavior that includes
thought, speech, action, and artifacts and depends on man ’ s capacity for learning and
transmitting knowledge to succeeding generations. ” Organizational culture can be
taught to new members of the organization as the “ correct ” or accepted way to think,
perceive, and feel with respect to organizational work, problems, and so forth.
Although every organization has its own culture, strong or weak, most organiza-
tions do not create their culture consciously. Culture is created and ingrained into
people ’ s lives unconsciously. Unless special effort is taken, people will not recognize
The Role of Organizational Culture 227
that the attitudes, beliefs, and visions they have always taken for granted are actually
standardized assumptions that they may pass to future generations. The diffi culty of
making sense of culture lies in the fact that even though the artifacts of culture can
be easily sensed, the core of the culture, values, which are defi ned as “ broad, nonspe-
cifi c feelings of good and evil, beautiful and ugly, normal and abnormal, rational and
irrational are often unconscious and rarely discussable ” ( Hofstede et al. 1990 , 291).
Cultural artifacts are both conceptual (such as language) and material. They mediate
interaction with the world, coordinating people ’ s activities with the physical world
and with each other.
There is a reciprocal relationship between organizational culture and communi-
cation ( Pepper 1995 ). On the one hand, communication is the tool that helps to
transmit organizational culture to each other and to the newcomers of the organiza-
tion, and it also enables the culture to be maintained and developed in its particular
way. In a sense, culture comes into being through constant communication among
the members of the organization, and communication changes the cultural assump-
tions over time. On the other hand, culture deeply shapes and alters the communi-
cation within this specifi c culture. “ The culture encourages certain topics for
communication and discounts others. The culture often determines who talks with
whom, on what occasions, and covering what matters ” ( Neher 1997 ). Organizational
culture, therefore, may be thought of as the manner in which an organization solves
problems to achieve its specifi c goals and to maintain itself over time. Moreover, it
is holistic, historically determined, socially constructed, and diffi cult to change
( Hofstede et al. 1990 ).
Different Types of Cultures
Of course, people do not always behave as expected, and the above cultural profi les
are very generic. There is a good analogy between organizational culture and the
climate control of a large building: although the temperature may be set at room
temperature throughout the company, there are in fact a series of different microcli-
mates depending on which part of the building you are in, how the offi ce furniture
is arranged, the number of people, the number of plants, and so forth. A similar situ-
ation exists with organizational culture: although an organization as a whole may be
characterized as having a particular type of culture, there will be in fact many different
types of microcultures in evidence throughout the company. Some of these may be
picked up in examining the CoPs that exist, the different types of professionals or skill
sets that make up the company ’ s human capital, and so forth.
228 Chapter 7
One way of exploring cultures is to classify them into types. There are many ways
to differentiate organizational culture. Goffee and Jones (2000) identifi ed four types
of organizational culture. In their research, they used two dimensions to create the
four distinct types. The fi rst dimension, sociability, is a measure of friendliness. A high
sociable culture indicates that people within the culture tend to be friendly to each
other without expecting something in return. Sociability is consistent with a high
people orientation, high team orientation, and focus on process rather than outcomes.
Solidarity, the second dimension, measures the task orientation. High solidarity means
that people can work together toward common goals very well even they may have
personal disputes or confl icts.
This classifi cation scheme produces four types of organizational cultures as shown
in table 7.1 . These are described in greater detail below:
• A communal culture can give its members a sense of belonging, though it also is
task-driven. Leaders of this culture are usually very inspirational and charismatic. The
major negative is that they often exert too much infl uence and other members are
rarely heard from.
• In a networked culture, members are treated as friends and family. People have
close contact with each other and love each other. People are willing to help each
other and share information. The disadvantage of this culture is that people are
so kind to each other that they are reluctant to point out and criticize the poor
performance.
• A mercenary culture focuses on strict goals. Members are expected to meet the goals
and get the job done quickly. Since everyone focuses on goals and objectivity, there
is little room for political cliques. The negative is that those with poor performance
may be treated inhumanely.
• In an organization with a fragmented culture, the sense of belonging to and iden-
tifi cation with the organization is usually very weak. The individualists constitute the
organizations, and their commitment is given fi rst to individual members and task
work. The downside is that there is a lack of cooperation.
Table 7.1 Four types of organizational culture
High solidarity Low solidarity
High sociability 1. Communal culture 2. Networked culture
Low sociability 3. Mercenary culture 4. Fragmented culture
The Role of Organizational Culture 229
There are a number of other ways of characterizing culture, and organizational
cultural analysis must be one of the fi rst steps to be taken in any KM initiative. One
of the fundamental prerequisites to a culture that fosters rather than hinders KM is
the notion of trust. When organizational members feel they are respected, that they
can expect to be treated in a professional manner and that they can trust the other
members of their group, then knowledge sharing is greatly enhanced. Trust removes
any potential barriers due to lack of confi dence that the person on the receiving end
will not attribute authors of knowledge or that they will make inappropriate use of
the knowledge shared.
Organizational Culture Analysis
Culture surrounds us all, and we need to understand how it is created, embedded,
developed, manipulated, managed, and changed. To understand the culture is to
understand your organization. Schein (1992) approaches this issue through his three
levels, as shown in table 7.2 . The third level is ultimately the basis for all values and
actions.
Artifacts are easy to detect (e.g., a dress code) but they may be diffi cult to under-
stand. They represent “ the tip of the iceberg, ” and it remains a challenge to discern
or decipher what lies underneath them (i.e., what is the reason for this type of dress
code or other visible structures and processes?). General and abstract statements that
express certain ideas and truths about human beings usually represent basic assump-
tions in organizational culture. They are the expression of a philosophy, of a general
concept on individuals and society. Given the diversity of such concepts and the
contradictory characteristics they have, these assumptions often have an eclectic,
heterogeneous, fragmentary, and unilateral aspect.
The values shared by the members of an organization represent the second
layer in culture analysis. From an organizational perspective, values express essential
Table 7.2 Levels of culture
Cultural level Description
Artifacts The visible organizational structures and processes
Values The stated strategies, goals, philosophies and justifi cations
Assumptions The basic underlying assumptions, unconscious, taken for granted beliefs, perceptions, thoughts, and feelings
Source: Adapted from Schein 1992 .
230 Chapter 7
meanings of basic assumptions. Therefore, values defi ne a set of organization expecta-
tions from its members. Values are expressed and often imposed by the managerial
elite and become, in some ways, a reference system for activity assessment. They are
included in attitudes and behaviors, in the organizational habitat. The two levels,
assumptions and values, represent the content of what we call an organization expres-
sive area or expressive culture. Its origins can be found both in organization history,
and in the personal history of its members.
Norms form the instrumental and visible area of organizational culture. They rep-
resent the most evident layer for someone who comes in contact with the organization
for the fi rst time. They derive from culture values and basic assumptions. Norms are
expressed in a set of rules and expectations that orient people ’ s behavior within the
organization. This is why, even for the organization personnel, norms constitute their
contact with culture and are the conveyor of values and basic assumptions. There are
two basic categories of norms: formal, institutional norms, produced by managers or
experts, hired for this purpose alone, and made mandatory; and informal norms,
produced by the organization ’ s personnel or by certain groups and disseminated
through legends, stories, or myths, or refl ected in ceremonies or rituals. They are the
expression of informal culture, based on certain values spread in an informal space.
An expressive culture is one that refl ects the emotions, feelings, and aspirations of
the organizations ’ personnel. An illustration of different styles of practice appears in
box 7.1.
Norms are directly involved in the change process, since they allow for interven-
tions in a fi eld that is very accessible to individuals. Those who want to comprehend
organizational culture refer to its philosophical and value layers. Those who want to
change culture and use it as a maintenance or development tool, refer mainly to its
normative layer or as a normative culture. A normative culture is one based on a set
of formal rules, norms, prescriptions, positions, and hierarchies; and it is a culture that
emphasizes compliance with the rules.
On the other hand, norms represent one of the premises for cultural unity, the
reference system for managers in personnel assessment. Such assessments strengthen
norms and are often accompanied by bonuses. Norms are thus a reference system for
personnel as well, whose attitude toward them represents the framework that produces
an organizational ethos.
Schein (1999) argues that the pattern of basic underlying assumptions can function
as a cognitive defense mechanism for individuals and the group; as a result, culture
change is diffi cult, time consuming, and anxiety provoking. Cultures are deep-seated,
pervasive, and complex, and it can be extremely diffi cult to bring the assumptions to
The Role of Organizational Culture 231
the surface. He uses the classic three-step approach to discuss change — unfreezing,
cognitive restructuring, and refreezing. The key issue for leaders is that they must
become marginal in their own culture to a suffi cient degree to recognize what may be
its maladaptive assumptions and to learn some new ways of thinking themselves as a
prelude to unfreezing and changing their organization.
A number of instruments exist that can help diagnose organizational culture (e.g.,
Harrison and Stokes 1992 ). These are typically surveys or questionnaires that help to
identify the critical aspects of an existing culture and will provide a profi le of your
organization ’ s culture, typically in the form of an orientation.
The most important dimensions of an organizational culture are that culture pro-
motes an ideal that mobilizes learning institutions in achieving it and that culture
can bring uniformity and unity, as well as diversity. Culture is customs and rights and
the organization ’ s “ own way, ” its norms, values, behavior patterns, rituals, and tradi-
tions. Culture implies structural stability, patterning, and integration. It arises from
shared history, and adaptation and change are not possible without making changes
that affect the culture. It is not always rational. For large organizations, there are issues
around the development of subcultures and the integration of newcomers. Organiza-
tional learning, development, and planned change cannot be understood without
Four groups of about ten individuals are all in the same park at the same lunch hour.
Soon, ominous rain clouds loom, threatening a serious downpour. In the fi rst group, one
person gets up and says, “ It is going to rain, follow me, this is what we will do. . . . ” In a
second group, someone says, “ I have a plan: each one of us will stand up, we will walk in
pairs of two towards the covered tent, we will maintain a distance of two feet from the
person in front and the person behind us. . . . ” In a third group, a few people start con-
versing, each putting out a different idea, “ why don ’ t we go over to that big tree there?
But what if there is lightning, it wouldn ’ t be safe. How about the tent? That makes more
sense plus there are picnic tables where we could continue our picnic lunch. ” In the last
group, someone stands up and says: “ This reminds me of the adventure we had during
the last rainstorm. Let me tell you that story. . . . ”
The above illustrates four different types of microculture in evidence:
Group 1: Authoritarian doctrine
Group 2: Micromanagement
Group 3: Grassroots brainstorming, collaborative, consensus-driven
Group 4: Storytelling to share knowledge of lessons learned and best practices.
Box 7.1 A vignette: Imagine the following situation (adapted from Kotter 1996 ):
232 Chapter 7
considering culture as the primary source of resistance to change ( Schein 1999 ). It is
at this junction — the resistance to any change in the organizational culture, that we
fi rst encounter the intersection between organizational culture and KM.
Culture at the Foundation of KM
KM implementations almost always require a cultural change — if not a complete
transformation, at least a tweaking of the existing culture in order to promote a culture
of knowledge sharing and collaboration. In almost all cases, KM will trigger a change
that will in turn trigger a maturing or evolutionary process. However, the instigator
of change rarely meets with a receptive audience. People do not necessarily always
oppose change just to be contrary, but they will oppose change if they perceive the
proposed change as an imposition rather than an improvement in their personal work
lives. They are also often left out of the loop and feel neither ownership nor vested
interest in whether or not the change succeeds. A knowledge sharing culture is one
that is built upon the foundation of trust and as such it is imperative to inform,
involve, and inspire organizational participants during the organizational changes
that are needed.
Corporate culture is a key component of ensuring that critical knowledge and
information fl ow within an organization. The strength and commitment of a corpo-
rate culture will almost always be more important than the communication technolo-
gies that are implemented to promote knowledge sharing. Traditionally, knowledge
fl ows were vertical, from supervisor to supervisee, following the lines of the organiza-
tional chart. Organizations today need to change their culture to one that rewards the
fl ow of knowledge horizontally as well.
Communication systems can be thought of as the disseminators of culture ( Bloom
2000 ). In more ancient times, physical transportation routes fulfi lled this role. For
example, the Egyptians used the Nile to unite towns across four thousand miles. The
Phoenicians sailed to shuttle goods and ideas 2,400 miles away. St. Paul used the
Roman highway systems to send his Epistles on 170-mile journeys. The Chinese
used land and river routes to pull together a three-million-square-mile empire. In all
of these systems, ideas fl owed, were shared, exchanged, or integrated. The Romans
did not just build highways — they spread a common language. The Chinese dissemi-
nated a common writing system, and the Incas disseminated a uniform system of
accounting based on knots. Knowledge dissemination therefore needs some type
of lingua franca, something in common like a language, standards, norms, protocols,
and so on.
The Role of Organizational Culture 233
The types of ideas that need to be disseminated for KM to be successfully imple-
mented include a change from perceiving knowledge and knowledge creation as being
a proprietary and a solo undertaking to a perception of participation and collabora-
tion. This idea can be linked back to earlier discussions on the social construction of
knowledge, and an understanding of the individual differences and organizational
contexts that can infl uence such perceptions.
A knowledge-sharing culture is one where knowledge sharing is the norm, not the
exception, where people are encouraged to work together, to collaborate and share,
and where they are rewarded for doing so. A paradigm shift has to occur from “ knowl-
edge is power ” to “ sharing knowledge is more powerful ” and culture will determine
what you can and will do with the knowledge assets of the organization.
Sveiby and Simons (2002) suggest that a collaborative climate is one the major
factors infl uencing effectiveness of knowledge work. They surveyed 8,277 respondents
from a diverse group of public and private organizations. The degree to which an
organizational culture is collaborative can be assessed, and this in turn will provide a
good indicator of how successful KM will be. It is not a surprise that the study found
that distance was bad for collaboration, that is, the more dispersed a company, the
less the climate is collaborative.
Gruber and Duxbury (2000) conducted an in-depth study of the research and
development department of a high technology company. They looked at the linkages
between organizational culture and knowledge sharing and used the variables of trust,
openness, top management support, and the reward structure of the organization to
try to explain any correlations. They interviewed 30 employees and their initial ques-
tions addressed the sharing of explicit knowledge. It was found that this was mostly
through databases, intranets, and shared drives, but 28 percent was still through face-
to-face contact (see table 7.3 ). The face-to-face sharing typically involved questions
such as “ Where is it? How do I get it? Who should I go see? ”
Table 7.3 Explicit knowledge sharing
Knowledge-sharing medium Percentage of respondents who selected this
Database (LotusNotes) 55 %
Intranet 40 %
Face-to-face 28 %
Shared drive 25 %
Source: From Gruber and Duxbury 2000.
234 Chapter 7
The study also elicited some information on what made it hard to share explicit
knowledge and suggestions as to how it could be made easier. The major diffi culties
mentioned were that it was hard to fi nd, there were different systems and no stan-
dards, the information was not where it should be, the tools were diffi cult to use, and
the database was diffi cult to access. Some suggestions that were made were to conduct
training on knowledge retrieval, to defi ne a knowledge strategy that would categorize
in a standard way, to standardize the information technologies, and to create project
web sites.
Next, the authors looked at how tacit knowledge was shared. The most popular
means used was face-to-face (90 percent), followed by informal networks (25 percent).
Some of the factors that made it diffi cult to share tacit knowledge included attitudes
that knowledge was power, not knowing who the expert was, not knowing if the
knowledge exists, and loss of knowledge when people left the company. Some sug-
gestions that were made to improve tacit knowledge sharing included recognizing
the value of tacit knowledge, improving relationships within the organization, and
increasing opportunities for people within different parts of the organization to
interact.
The ideal knowledge-sharing culture would thus emphasize communication and
coordination between groups, experts would not jealously guard their knowledge, and
knowledge sharing would be actively and visibly encouraged at all levels of the hier-
archy through the recognition and rewarding of knowledge sharing and through
embedding such statements in corporate and individual performance objectives. A
culture that promotes knowledge sharing would be one were tools and taxonomies
are standardized to make access and exchange easy, where there are a signifi cant
number of semi-social events such as workshops for sharing with experts and other
groups, where organizational goals explicitly include knowledge sharing, where trust
is prevalent in all interactions, and where the communication channels fl ow across
geographical, temporal, and thematic boundaries.
Gruber and Duxbury (2000) concluded that an environment that truly supports
the sharing of knowledge has the following characteristics:
Reward structure Recognition for knowledge sharing with peers
Openness/transparency No hidden agendas
Sharing supported Communication and coordination between groups
Trust Shared objectives
Top management support Upward and downward communication
The Role of Organizational Culture 235
The Effects of Culture on Individuals
How does organizational culture control the behavior of organizational members? If
consistent behavioral patterns are the outcomes or products of a culture, what is it
that causes many people to act in a similar manner? There are four basic ways in which
a culture, or more accurately members of a reference group representing a culture,
creates high levels of cross-individual behavioral consistency: social norms, shared
values, shared mental models, and social identities.
Social norms are the most basic and most obvious of cultural control mechanisms.
In its basic form, a social norm is simply a behavioral expectation that people will act
in a certain way in certain situations. Social sanctions enforced by other members of
a reference group support norms (as opposed to rules). Kilmann, Saxton, and Serpa
(1986) characterize norms by level.
• Peripheral norms are general expectations that make interactions easier and more
pleasant. Because adherence to these norms is not essential to the functioning of the
group, violation of these norms in general results in mild social sanctions.
• Relevant norms encompass behaviors that are important to group functioning.
Violation of these norms often results in noninclusion in important group functions
and activities.
• Pivotal norms represent behaviors that are essential to effective group functioning.
Individuals violating these norms are often subject to expulsion from the group.
Why do individuals comply with social norms? What explains the variance among
individuals with a group in the degree of compliance with norms? Why do some
members comply with all norms, while others seem to ignore them? Individuals moti-
vated primarily by means of acceptance, worth, and status and other forms of external
validation would be most likely to comply with social norms. Since social sanctions
involve the withholding of acceptance, these individual are most likely to comply.
Likewise, those characterized by weak self-concepts would be more likely to comply
with social norms than with those with strong self-concepts. Those with strong self-
concepts are less likely to need the acceptance and other forms of affi rmation contin-
gent upon compliance with norms.
Individuals who identify with the group, that is, who defi ne their social identity
in terms of the group, are more likely to comply with the group ’ s norms. One of the
most powerful bases of compliance or conformity is internalization, that is, believing
that the behavior dictated by the norm is truly the right and proper way to behave.
Over time, many group members begin to internalize pivotal and relevant norms.
High status members of a group are often exempt from peripheral norms, as are those
236 Chapter 7
with high amounts of what is called idiosyncratic credit. Idiosyncratic credit is gener-
ally awarded to group members who have contributed a lot to the group and have
earned the freedom to violate the norms free from sanctions.
As a cultural control mechanism, the key word in shared values is shared . The issue
is not whether or not a particular individual ’ s behavior can best be explained and/or
predicted by his or her values, but rather how widely that value is shared among
organizational members, and more importantly, how responsible the organization/
culture was in developing that value within the individual. Value is any phenomenon
that has some degree of worth to the members of giving groups: the conception of
the desirable that establishes a general direction of action rather than a specifi c objec-
tive. Values are the conscious, affective desires or wants of people that guide their
behavior.
Values infl uence individual behavior in a number of ways. For example, individuals
who internalize the value of honesty feel guilty when cheating or stealing. This nega-
tive affect state stops them from acting in a way inconsistent with their internalized
value. Public values arise when we believe that everyone around us holds a certain
value (social value). In this case, we often act in ways consistent with that value even
though we do not personally hold that value. This is done to gain acceptance and
support from the group.
A mental model or theory defi nes a causal relationship between two variables. The
idea that people rely on mental models can be traced back to Kenneth Craik ’ s (1943 )
suggestion that the mind constructs “ small-scale models ” of reality that it uses to
anticipate events. Mental models can be constructed from perception, imagination,
or the comprehension of discourse. They underlie visual images, but they can also be
abstract, representing situations that cannot be visualized. Each mental model repre-
sents a possibility. This phenomenon has been studied by a number of cognitive
scientists for the past few decades (e.g., Gentner and Stevens 1983; Johnson-Laird
1983 ; Rogers, Rutherford, and Bibby 1992 ; Oakhill and Garnham 1996 ). The belief
structure of managers can be represented as a complex set of mental models that they
use for diagnosing problems and making decisions. In organizations with strong cul-
tures, members of the organization begin to share common mental models about
employees, competition, customers, unions, and other important aspects of manage-
rial decision making. Mental models are often called basic underlying assumptions.
Mental models impact the behavior of individuals to a very large extent. Decisions
are often based on one or more of our mental models. For example, if a manager
believes that increasing satisfaction will increase employee performance, he or she is
The Role of Organizational Culture 237
likely to do things that eliminate dissatisfaction among employees and work hard to
increase their levels of satisfaction. When all managers of the organization share the
same mental models or theories, they are likely to make very similar decisions when
solving problems. This leads to a consistent way of doing things and solving problems
in an organization.
Cognitive schema are mental representations of knowledge. Cognitive scripts are
types of schema involving action or the way to do something. Schema are generally
enacted subconsciously, that is, we enact a script without much thought or delibera-
tion. In other words, cognitive scripts are like programs (like macros) we store and call
upon when certain stimuli are present. We develop scripts over time by performing a
certain task many times (like driving home from work). The fi rst time we perform a
task, we tend to think about every step and deliberate about the many alternative
ways we can perform each step. Over time, as we learn the best way to perform the
task, we “ lock in ” the script, or program, and do not think about each step again
(unless we experience a signifi cant problem). This is called direct schema development.
In some cases, we do not go through this deliberate step-by-step learning process; we
simply copy (or are told) how to perform a certain task from members of the reference
group (culture). This is called indirect schema development. In either case, when
schema become widely shared they are called consensual schema, and they account
for a large amount of cross individual behavioral consistency.
In summary, organizational culture:
• Establishes a set of roles (social identities)
• Establishes a set of role expectations (traits, competencies, and values) associated
with each identity
• Establishes the status or value/worth to the reference group of each social identity
• Provides values, cognitive schema, and mental models to infl uence how individuals
behave with respect to the various groups or communities they fi nd themselves a
member of (micro culture) as well as with respect to the organizational culture as a
whole
Note that organizational culture is not so much a discrete “ thing ” that can be
pointed to. Rather, organizational culture should be seen more as the medium that
the organization resides in. This medium is not only complex but it is also a moving
target — organizational culture as a whole is dynamic and always in the process of
changing. One way of studying this process is to look at the evolution or maturing of
a culture.
238 Chapter 7
Organizational Maturity Models
It is very important to keep in mind that culture is not a static object stored somewhere
in the organization. Culture is a fl uid, dynamic medium that encompasses the orga-
nization. In fact, there is usually a series of “ microcultures ” that are typical of different
work groups within a given organization. Culture is a complex entity that represents
a moving target of sorts. One of the ways in which culture changes within an orga-
nization is through a maturing process. As organizations mature, so does the culture
of that organization. The notion of an optimal point or a threshold point that
should be reached before effective KM can be implemented is inherent in a number
of organizational, KM, and community maturity models.
Maturity models have their roots in software engineering. The Carnegie Mellon
Software Engineering Institute defi nes a maturity model as “ a model that describes
the characteristics of good processes, thus providing guidelines for companies
developing or honing their own sets of processes. ” ( Grenier 2007, 1 ). There are a
number of organizational and KM maturity models, most derived from the capability
maturity model, CMM ( Paulk et al. 1995 ). The CMM was developed to better describe
the phases of software development processes and the model was subsequently
updated to the capability maturity model integration in 2000 ( CMMI Project Team
2002 ).
The CMM is an organizational model that describes fi ve evolutionary stages (levels)
in which an organization manages its processes. An organization should be able to
absorb and carry its software applications. The model also provides specifi c steps and
activities to get from one level to the next.
The fi ve stages of the CMM are:
Initial Processes are ad hoc, chaotic, or not well defi ned.
Repeatable Basic processes are established and there is a level of discipline to stick to
these processes.
Defi ned All processes are defi ned, documented, standardized, and integrated into each
other.
Managed Processes are measured by collecting detailed data on the processes and
their quality.
Optimizing Continuous process improvement is adopted and in place by quantitative
feedback and from piloting new ideas and technologies.
CMM is useful not only for software development, but also for describing evolution-
ary levels of organizations in general. The CMM and the CMMI can be extended to
The Role of Organizational Culture 239
cover KM processes that can in turn serve to assess the current level of readiness of
an organization for KM. For example, the maturity model shown in fi gure 7.2 shows
the major phases that an organization has to complete in order to integrate a new
way of doing things, a new technology, or a new process. This is very relevant for KM
initiatives as new processes and technologies will be introduced into the organization.
These phases can help better track how well KM has been accepted as a way of doing
business within the organization.
Table 7.4 shows a maturity model based on CMM but adapted in particular to
organizational change and organizational cultural dimensions. This model serves as a
good organizational culture diagnostic in that it is a fairly straightforward task to
establish the status quo a given organization is in. For example, if the organization
exhibits multiple local cultures that do not, as yet, have much in common, then it
would be advisable to select one or more of these microcultures as pilot sites for KM
interventions. If, on the other hand, the organizational maturity stage were closer to
a managed phase where there is more pervasive and cohesive culture, then it would
be advisable to focus on tightly aligning the KM strategy to the overall business strat-
egy and objectives of the organization.
KM Maturity Models
There are currently a half a dozen or so KM maturity models. One of the ones that
have been implemented in a variety of organizations to date is the Infosys model
( Kochikar 2000 ) shown in table 7.5 . The Infosys is also consistent with the others in
Time
Contact
Awareness
Understanding
Trial
Adoption
Institutionalization C
o m
m it
m e
n t
Figure 7.2 Organizational maturity model
240 Chapter 7
Table 7.4 Stages of organizational maturity
Maturity phase Description
Chaotic • Noncohesive culture • Decision making in-fl ight
• Leadership structure vague
• Operation model undefi ned
• Employees leaving
Ad hoc • Multiple local cultures, leadership structures, and operation models • Local decision making
• Employee turnover high in some job categories
Organized • Similar local cultures • Local decision making based on corporate strategy
• Local leadership linked to corporate leadership team
• Corporate operation model pushed down to local level
• Stable employee base
Managed • Cohesive corporate culture and operation model • Corporate strategy drives operational tactics
• Corporate leadership team coaches and empowers local leaders
• Employees recruited and retained based on strategic direction
Agile • Culture adapts strategically • Operation model changes dynamically based on environmental changes
• Professionals compete to work for corporation
Source: Adapted from Fujitsu Consulting (Cheryl White, personal communication)
that it is based on the CMM approach. In fact, the Infosys model is denoted KMM in
honor of the CMM on which it is based. The fi ve levels are: default, reactive, aware,
convinced, and sharing. The model associates a number of key results for each of the
fi ve levels.
The Infosys model is much more closely linked to specifi c KM behaviors that can
be detected at the organizational, group, and individual levels. It is possible to make
much more fi ne grained or specifi c types of organizational diagnoses in order to estab-
lish the current status quo of an organization. For example, if it is possible to detect
that the majority of the KM effort appears to be devoted to the capturing of content,
then KM initiatives aimed at promoting knowledge sharing would be considered to
be premature at this stage. Instead, the KM objective targets reuse when the organiza-
tion is at the reactive level of organizational capability. In time, however, as KM
awareness is increased and knowledge fl ows appear between disparate groups, then
The Role of Organizational Culture 241
Table 7.5 The Infosys KM maturity model
Level Organizational capability Characteristics/key result areas
Default Complete dependence on individual skills and abilities
Unstructured on-the-job learning, accidental knowledge reuse, informal knowledge sharing, teamwork virtually nonexistent
Reactive Ability to perform tasks constituting the basic business of the organization repeatedly
People are aware of knowledge as an asset through formal training and mentoring, some pockets of knowledge sharing, sporadic knowledge reuse, and some teamwork
Process focus is on basic content capture
Technology is information management
Aware Restricted ability for data-driven decision making
Restricted ability to leverage internal expertise
Ability to manage virtual teams well
People are educated on KM, some environmental scanning and knowledge dissemination
Process of content structure management, taxonomy of knowledge
Knowledge technology infrastructure, for example, portal
Dedicated KM group
Convinced Quantitative decision making for strategic and operational applications is widespread
High ability to leverage internal and external sources of expertise
Organization realizes measurable productivity benefi ts through knowledge sharing
Ability to sense and respond proactively to changes in technology and business environment
Customized enabling
Value-added content
Quantitative KM processes, for example, KM metrics such as percentage of content used, quality ratings
Knowledge infrastructure management for sustainable KM
Sharing Ability to manage organizational competence quantitatively
Strong ROI-driven decision making
Streamlined process for leveraging new ideas for business advantage
Ability to shape change in technology and business environment
Expertise integration (content and expertise available organization-wide)
Knowledge leverage through frictionless knowledge fl ows
Innovation management and cohesive teamwork
242 Chapter 7
the organization can be diagnosed as being at the sharing level of organizational
capability. At the sharing level, KM initiatives such as corporate yellow pages or exper-
tise location systems would be more appropriate priorities.
Paulzen and Perc (2002) have proposed a knowledge process quality model (KPQM)
based on the major tenets of quality management and process engineering. The under-
lying premise is that knowledge processes can be improved by enhancing the corre-
sponding management structures. The maturity model makes it possible to implement
a systematic or incremental KM implementation. The authors make the assumption
that since software development is a knowledge-based activity, it is valid to adapt
these models for KM. The Paulzen and Perc (2002) model is essentially a modifi cation
of the capability maturity model ( CMMI Project Team 2002 ) that addresses the specifi c
characteristics of knowledge processes and KM systems. The maturity model consists
of fi ve phases: (1) initial, (2) aware, (3) established, (4) quantitatively managed, and
(5) optimizing, as shown in table 7.6 .
Note that there is a good fi t with the organizational maturity models presented
earlier. The major advantage of these models is that they enable organizations to
progress in an orderly manner, without skipping any important stages, in order to
achieve the desired end results of effective knowledge transfer, sharing, storing, and
distribution of experiences, learning from past experiences, and so forth.
Table 7.7 shows the Forrester Group KM maturity model, which describes the
different stages of maturity in terms of how people are supported throughout the
KM cycle. In the fi rst phase, assisted, other people are needed in order for knowledge
workers to fi nd valuable content and to connect with subject matter experts. In the
second phase, self-service, employees are able to make use of KM systems such as
Table 7.6 The KPQM maturity model
Maturity phase Description
Initial Knowledge process quality not planned, changes randomly (chaotic)
Aware Need for quality has been recognized and initial structures have been put into place
Established There is systematic structure and defi nition of knowledge processes and they are specifi cally tailored to needs identifi ed
Quantitatively managed Performance measures are used to plan and track knowledge processes
Optimizing Structures implemented to ensure continuous improvement and self-optimization of knowledge processes
The Role of Organizational Culture 243
knowledge repositories, in order to fi nd content and link to experts by themselves. In
the fi nal phase, organic, KM has ceased to be an “ extra ” burden but has instead become
part and parcel of how the knowledge work gets done every day.
The Forrester KM maturity model is quite useful in determining the level of knowl-
edge support that will be needed for effective KM to be established within a given
organization. For example, an organization that is at the assisted phase stands to
benefi t greatly from an expertise location system and a knowledge support offi ce
(KSO), which is essentially a 24/7/365 (24 hours a day, 7 days a week, 365 days a year)
help desk for knowledge content. Employees typically have a 1-800 telephone number
as well as an e-mail address through which they can contact the KSO in order to obtain
help in locating, accessing, and making use of valuable knowledge content.
The wide variety of KM maturity models makes choosing one a diffi cult decision.
An alternative approach, advocated by Liebowitz and Beckman (2008) would be to
develop a comprehensive KM maturity model, which they refer to as K3M. This inte-
grated approach is needed to provide a foundation for KM strategy development. The
authors describe K3M as the fi rst KM maturity model that is based on learning, com-
petencies, and business strategy.
Table 7.7 Forrester Group KM maturity model
KM maturity model phase Description Typical KM initiatives
1. Assisted • Culture adapts strategically • Operation model changes dynamically based on environmental changes
• Professionals compete to work for corporation
• Employees fi nd info with the help of librarians
• KSO
• Yellow Pages
• Communities of Practice
2. Self-service • Employees codify on their own without help
• Employees fi nd info using search engines
• Push technologies
• Customized KM
3. Organic • KM happens in the background — it is embedded in business
• Info provided when needed (JIT, JET)
• Personalized KM
Source: Shevlin et al. 1997
244 Chapter 7
CoP Maturity Models
Maturity models have also been applied to the CoP life cycle. A CoP maturity
model can serve as a good road map to show what steps need to be taken to move
communities to the next stage. The CoP life-cycle model provides a good diagnostic
to assess whether informal networks exist within an organization and if they do,
whether they are recognized and supported by the organization. The life-cycle model
(see fi gure 7.3 ) shows that a community needs to have attained the maturing and
stewardship of knowledge levels in order to begin creating value for its members and
for the organization as a whole. The life-cycle model is particularly useful for aligning
any new KM roles and responsibilities that will be needed in order to optimize KM
efforts throughout the life cycle, for example, a knowledge journalist to help build,
identify, and extract valuable content from community members; a knowledge tax-
onomist to help organize content once it is being produced at a steady rate; and a
knowledge archivist to help distinguish between content that should be stored or that
is no longer considered active.
Organizational and KM maturity models help to assess the current level of knowl-
edge sharing and knowledge activities within an organization. In situating a given
company on a given maturity model, organizational change is greatly facilitated as it
becomes easier to visualize what is needed in order to step up to the next level. It is
Community maturity
and productivity
Value of
content
created
Potential
Coalescing
Maturing Stewardship
Transformation
Knowledge taxonomist
Knowledge archivist
Knowledge
journalist
Phase 1: Identity
Building trust
Phase 2: Value creation
Phase 3: Transition
Figure 7.3 Community of practice maturity model
The Role of Organizational Culture 245
important to note that there is a minimum level of maturity or readiness before KM
stands a good chance of succeeding.
The major features of the six maturity models presented are summarized in table
7.8 . Each can serve as a good framework for understanding how change is introduced
and eventually adopted within knowledge-based organizations. The current state an
organization is in can be diagnosed in order to better anticipate how both the orga-
nization, as a whole, and individual knowledge workers within that organization will
react to KM initiatives. A better understanding of the level or phase of maturity of the
organization will greatly help in better identifying the potential enablers and obstacles
to the organizational cultural change(s) required for KM to succeed.
Table 7.8 The six maturity models
Maturity model Key features
Paulk organizational maturity
Represents the adoption of a new technology or process within an organization, which is a very good match for the introduction of new KM functions
Fujitsu organizational maturity
Provides a fast and easy way of assessing how cohesive or pervasive a culture is within a given organization which can provide valuable guidance in selecting either pilot KM sites, if the organization is in the earlier stages, or focusing on closely aligning KM with the overall business strategy
Infosys KM A model that is much more specifi c and allows diagnosis of particular KM behaviors such as content capture, knowledge sharing, and KM metrics
Greater specifi city allows for more refi ned targeting of priority KM initiatives
Paulzen and Perc KPQM The KPQM is quite similar to the Infosys KM model and also allows for incremental introduction of KM initiatives into an organization based on the phase of KM maturity
Forrester Group KM maturity mode
A model that focuses on how employees acquire relevant content that is particularly well suited for an incremental introduction of knowledge support services within an organization.
Wenger CoP life-cycle model
The CoP life-cycle model can also provide a good indicator of the cultural evolution of an organization, particularly as it pertains to the coalescing of informal networks of peers who regularly share valuable knowledge with one another
The CoP life-cycle model can also help identify key KM roles and responsibilities that should be introduced at each phase
246 Chapter 7
Transformation to a Knowledge-Sharing Culture
How is culture developed, reinforced, and changed? It is often said in organizations
that “ we need to change the culture around here. ” What is usually meant is that
someone desires a behavioral change, such as employees paying more attention to
customers, or that they want managers to come to meetings on time, or some other
set of behaviors. While these patterns of behavior can be changed by changing the
organization ’ s structure (rule, regulations, reward systems), changing these behaviors
through culture involves changing the underlying mechanisms that drive these behav-
ioral patterns: namely norms, social values, or mental models. Since these underlying
cultural control mechanisms are often taken for granted and subconscious in nature,
they are diffi cult to change.
Changing structure by changing a rule and its enforcement mechanism is rather
simple when compared to changing a social value. Culture is resistant to change
because many of the cultural control mechanisms become internalized in the minds
of organizational members. That is what makes culture such a strong control mecha-
nism. Changing culture often means that members have to change their entire social
identity. Sometimes changes in the status of various roles or identities cause even more
resistance on the part of high-status role holders.
While changing behavior by changing structure may have more appeal because it
appears easier, in many cases this type of change is not successful because managers
have not changed the underlying culture and they fi nd that the culture and structure
are in confl ict. While organizational change is diffi cult and often lengthy to undertake,
it is a critical requirement for most if not all KM implementations. The key often
lies in symbolic action, that is, dealing with important symbols of values, norms,
and assumptions. Kilmann, Saxton, and Serpa (1986 ) provide some good general
guidelines:
• The notion of role modeling is crucial. People look to leaders for clues about what
is important in an organization. The most important thing a leader can do is act in a
manner consistent with the desired social value. When it comes to instilling culture
values, “ do as a say, not as I do ” does not work very well. When organizational
members observe a leader making a personal sacrifi ce for a value, it sends a strong
message that this value is important. For example, if senior managers are seen to be
“ practicing what they preach ” by actively sharing knowledge and rewarding collabora-
tive efforts, then the organizational members can see that this type of behavior is in
fact highly valued and practiced at all levels of the organization.
The Role of Organizational Culture 247
• Culture is often transmitted through stories and myths that extol certain virtues
held to be important to the organization. These stories are often told in informal set-
tings as well as published in company newsletters. For example, when new employees
join an organization, they are not only handed manuals and directed to databases
containing forms to be fi lled out, but they are regaled with stories of key events in
the organization ’ s history, stories relating spectacular successes and disappointing
failures. These stories have a strong message that relays “ how things are done around
here ” to the new employees.
• In reacting to crises, leaders can send strong messages about values and assumptions.
When a leader supports new values in the face of crisis, when emotions often run
high, he or she communicates that this value is very important. For example, if the
organization has repeatedly supported a strong notion of professional ethics and ends
up losing a bid to a competitor who did not bother about such niceties, it is even
more powerful if the organization ’ s leaders reinforce this message in the face of and
in spite of the crisis situation they are in. In this way, everyone can see that values
are not being treated as “ fair-weather friends, ” that is, values are not adhered to when
it is convenient to do so and dropped when challenges arise.
• In addition to motivating behavior directly, a reward system can send powerful
messages regarding what is important. For example, if a university declines to promote
a professor who has won the university-wide Outstanding Teaching award, this sends
out the strong message that teaching was not valued and only research productivity
is really valued at this particular institution.
• Important and public decisions also communicate the importance of certain values.
If the fi rst thing to be cut in budget crunches is training, it sends the message that
training is not valued. The criteria for allocation of resources often become what are
valued in an organization. For example, if budgets were determined by steady past
performance, it sends a different message than if they were determined by past inno-
vation and risk taking.
• Leaders communicate the importance of values by what they praise and what they
criticize. It is important to pay attention to what is said. Social values are often
changed through the selection process. As new members are hired, effort is made to
hire new members that hold the new value. Different organizations will elect to imple-
ment this reward (praise) and censure (criticize) cycle differently. For example, at
Buckman Labs, employees who have been voted the “ top 100 knowledge sharers ” are
invited to take a trip to the head offi ce where the President of the company bestows
a gift of a fully loaded laptop to them in recognition of their excellent KM work. This
organization is further described in box 7.2.
248 Chapter 7
Buckman Labs is a specialty chemical company serving the pulp and paper, water treat-
ment, leather, coatings, agricultural, and wood treatment industries. Its core competency
is its ability to create and manufacture innovative solutions to control the growth of
microorganisms. Buckman ’ s expertise also spans specialty chemicals such as microbicides,
scale inhibitors, corrosion inhibitors, polymers, dispersants, and defoamers. Evaluated in
1990 by Goldman Sachs, Buckman had a market value $175 million higher than its asset
value. The difference owes a lot to the company ’ s focus on KM and knowledge transfer as
effective tools to improve and sustain its competitive advantage. They saw the need for a
system that would facilitate growth in the value of knowledge that existed within the
company. The best brains in the company on a particular topic were not necessarily in
the US, but spread out around the eighty offi ces worldwide. Hence, a system was needed
to facilitate communication between sister companies so that the collective knowledge
and understanding of the entire organization could be brought to bear on any problem.
The resulting acceleration of knowledge would lead to a strategic advantage based on the
leverage of internal as opposed to external knowledge. This thinking culminated in the
Knowledge Transfer Department. Its goals were to accelerate the accumulation and
dissemination of knowledge by all Buckman Labs ’ associates worldwide, to provide easy
and rapid access to Buckman Labs ’ global knowledge bases, and to eliminate time and
space constraints in communication. The department was given a budget of about $8
million.
The primary tool employed by Buckman to enable employees to share knowledge is
called KNetix, the Buckman Laboratory Knowledge Network. KNetix is an interconnected
system of knowledge bases used by Buckman associates worldwide to share knowledge
electronically and to collaborate closely with each other, unfettered by time and distance.
The principal component of KNetix is Tech Forum, a private bulletin board that only
Buckman associates are allowed to access. An employee in Malaysia needing information
about a water treatment process can post a query to the bulletin board in the evening,
and the next morning fi nd answers from a researcher in microbiology based in the US
offi ce or from a fi eld engineer in South Africa. This method of knowledge sharing recog-
nizes that no single person can possibly know everything about a topic, and that knowl-
edge is generally decentralized in the heads of many people, not just in single subject
matter expert ’ s head.
Employees are encouraged to both solve their own problems and to provide solutions
to others ’ questions on Tech Forum. The top 150 people from around the world who
were rated as top level performers in the Tech Forum with respect to answering questions
are brought to the company ’ s headquarters each year and presented with a state-of-the-art
fully loaded IBM laptop by the CEO. Such incentives help boost employees ’ desire to
participate in knowledge sharing. Besides the Tech Forum, other media such as virtual
conference rooms, libraries, and e-mail help employees to access knowledge rapidly.
Box 7.2 An example: Buckman Labs
The Role of Organizational Culture 249
In most cases, individuals making decisions and solving problems do not question
their basic assumptions (underlying mental models). They simply use them, without
thinking, and arrive at a decision or solution to their problem. If the solution does
not work, they most likely question the inputs to their decision and attempt to make
a better decision next time. Argyris and Schon (1978, 1996) refer to this type of learn-
ing as single-loop learning. In some cases, the individual or group actually begins to
question the basic assumptions and models underlying the decision, which is called
double-loop learning. It is through double-loop learning that changes in shared mental
models take place. When attempting to change the shared mental models of a group,
it is important to take time out from the day-to-day problem-solving processes to
outline, challenge, and agree on changes to the shared mental model.
Most programs for changing culture inside of companies do not work because they
address content (the knowledge, structure, and data in a company) or process (the
activities and behaviors), but they never address the context in which both of those
elements reside. The sources of people ’ s actions are not what they know, but how they
perceive the world around them. Context can be an individual ’ s mind-set or the
organizational culture. It includes all of the assumptions and norms that are brought
to the table. Context is perception, as opposed to facts or data. People do not go off
and design their context — they just inherit it. Culture is also socially constructed and
refl ects meanings that are constituted in interaction and that form commonly accepted
defi nitions of the situation.
Culture is symbolic, which is why it is best described by telling stories about how
we feel about the organization. A symbol stands for something more than itself and
Itinerant employees are provided with laptops so that they can stay connected at all
times.
Tools are only one side of the equation however — Buckman believes that tools can only
act as facilitators — the company culture has to provide a good environment in which to
use these tools. The most important cultural factor in KM is that of trust. Each employee
must trust the other before they provide information to them. A distinctive feature at
Buckman is that the focus is on direct communication between individual employees in
order to minimize distortion and misunderstanding of the knowledge content.
Finally, Buckman freely shares its experience and expertise in KM with other organiza-
tions. Companies like AT & T and 3M have visited them to benchmark their internal KM
processes.
Box 7.2 (continued)
250 Chapter 7
can be many things, but the point is that a symbol is invested with meaning by us
and expresses forms of understanding derived from our past collective experiences.
The sociological view is that organizations exist in the minds of the members. Stories
about culture show how it acts as a sense-making device. Also, culture is unifying and
refers to the processes that bind the organization together. Culture is thus consensual
and not confl icted. The idea of corporate culture reinforces the unifying strengths
of central goals and creates a sense of common responsibility. Culture is also holistic
and refers to the essence — the reality of the organization; what it is like to work
there, how people deal with each other, and what behaviors are expected. The example
of the Nokia way, illustrated in box 7.3, describes one such holistic approach to
culture.
Culture is rooted deep in unconscious sources, but is represented in superfi cial
practices and behavior codes and embodied in cultural artifacts. In order to best
accommodate this, some initial steps to creating a knowledge-sharing culture could
include:
• Having knowledge journalists begin interviewing key people to document projects,
best practices, lessons learned, and good stories
• Instituting KM get-togethers, which could be breakfasts, lunch and learn sessions —
any type of informal gathering to help people get to know one another, sometimes
with thematic talks and to show managerial support
• Newsletters to publicize KM initiatives and celebrate good role models
• KM pilot projects such as expertise location systems, intranets with space devoted
to different communities of practice
• Change performance evaluation criteria to refl ect and assess knowledge-sharing
competencies and accomplishments
• Censure knowledge hoarders and reward effective knowledge-sharers
• Redesign workplaces to allow for gathering places ( Cook 1997 ; Gladwell 2000 ).
The redesign of workplaces extends beyond simple physical offi ce layout designs
to a process of facilitating more effective knowledge sharing. Owen (1997) developed
the notion of open space technology (OST) as a large group facilitation process.
In practice, OST meetings take on many forms and variations, but they follow the
same general guidelines. OST meetings begin with all the participants sitting in a
circle, and no items on the agenda. The meeting opens with an agenda setting
exercise following which the group self-organizes into smaller discussion groups.
Discussion group conveners are responsible for providing a report of the discussions,
Nokia views KM as a combination of people, processes, technologies, and culture. It is
through learning that organizations are able to improve what they do. Appropriate knowl-
edge sharing facilitates effective learning. Various management approaches can be used in
combination to produce a learning organization, which can in turn provide improved
service — these include competence management and performance management. Organi-
zational values must be refl ected in the day-to-day running of an organization in order to
impact on its knowledge strategy. The Nokia Way promotes a culture of learning that is
premised on four pillars: customer satisfaction, respect for the individual, achievement,
and continuous learning. The Nokia Way is facilitated through a series of mechanisms,
mainly interactions between managers, colleagues, and employees placing power in the
hands of the individual to develop in the organization. A jazz band analogy best captures
Nokia ’ s approach to KM: the company shares a common vision and creates the space for
an ensemble to perform in unison without controlling the music or constraining the
performance.
Change and people management are commonly believed to make up 80 percent of KM
while IT comprises only 20 percent. At Nokia, no one person owns the KM process —
everyone owns it. HR has a crucial role to play in implementing KM, as do IT, quality,
and corporate planning departments. Organizational learning overlaps performance man-
agement (individual focus), competency management (organizational focus), and KM
(thematic or team focus). Nokia is integrating these three approaches in order to identify
best practices and lessons learned.
Nokia uses a book, the Nokia Saga, which is a novel about Nokia ’ s history. It contains
about one hundred stories which many employees read in order to better understand the
company ’ s values. The storytelling provides examples of what managers do and how they
apply Nokia values. Nokia ’ s annual report is called “ No Limits, ” and it gives progress
reports on how the company culture is moving toward a knowledge-sharing culture — with
no limits on learning, participating, and building better futures.
Nokia does not have a CKO. They have a steering group of about ten persons from
different functional areas coordinating KM activities. The head of the steering committee
is also the head of the quality department. In many organizations, there is still a concern
that sharing all its knowledge means giving all its power away. Nokia was able to change
its culture to one of knowledge sharing by designing a fl at, networked, global, and mul-
ticultural organization. Speed, fl exibility, opportunity, and openness are the key features.
Nokia ’ s management evaluates how well employees do with respect to supporting KM in
terms of creating, sharing, and reusing knowledge. They do not have incentive systems,
as they believe knowledge sharing should be part of the company culture and not some-
thing that is rewarded with money. The intention is to try to capture as much organiza-
tional knowledge as possible. As in a good jazz band, the players share a common vision,
and are interested in producing good products through innovation and improvisation. It
is not always clear what the end result will be, but because there is a common vision
guiding their performance, these professionals allow their services to be shaped by the
feelings and interactions of the various players who are part of the company.
Box 7.3 An example: Nokia
252 Chapter 7
which is immediately added to a book of proceedings. At the conclusion of the
meeting, or very shortly thereafter, participants receive a copy of the proceedings
including all of the discussion groups ’ reports and any action plans that were
developed.
OST meetings operate on four principles and one law. The principles are:
• Whoever comes is the right person.
• Whatever happens is the only thing that could have happened.
• When it starts is the right time.
• When it ’ s over, it ’ s over.
And the law is known as the Law of Two Feet (sometimes referred to as the Law of
Mobility). It states that “ If you fi nd yourself in a situation where you are not learning
or contributing, go somewhere where you can. ”
Gladwell (2000) discusses how the setup and character of offi ces can infl uence
innovation and knowledge sharing. He notes the importance of frequent interaction
among colleagues and how far basic offi ce layout goes in shaping the human relation-
ships of a workplace. Gladwell states that innovation is at the heart of the knowledge
economy and it is a fundamentally social phenomenon. Companies will therefore
need to design for public and semi-public spaces to promote employee interaction.
Many companies provide comfortable seating and access to the knowledge repository
via a few workstations to promote both tacit and explicit knowledge sharing.
The cultural approach to open space technology serves to create an environment
for innovation, teamwork, and rapid change. Open space offers a chance to gather
the members of the organization in an open setting and have the work done effi ciently
and creatively. Open space involves much brainstorming, but it is not just brainstorm-
ing. It is the process by which people have the urge to raise the topic they are pas-
sionate about, and they are willing to share their own knowledge, especially tacit
knowledge.
Whether the open space can be successful depends on the extent to which the
participants are willing to share the knowledge, which is infl uenced by the organiza-
tional culture of those participants. For example, in an organizational culture with
high sociability, people know each other and respect their companions. Therefore,
they will be more likely to take an active part in the open space, and more likely to
offer their knowledge to other members. However, in a low sociability culture, where
people focus more on individualism and their own work, it can be expected that
members may feel uneasy about talking with people they are not familiar with, not
to mention sharing something that they are deeply concerned about.
The Role of Organizational Culture 253
There are other characteristics of an organizational culture that can either encour-
age or discourage the recognition of belonging to the organization, and consequently,
they will infl uence the member ’ s performance in the open space. Some examples of
characteristics that are more connected with open space are individual initiative,
integration, reward system, and ethical climate. The facilitators should not ignore the
impact of organizational culture of the group of people who will attend the open
space. Further, the facilitators should prepare for the possible outcome that is expected
from them. Then the facilitators can work out some methods to encourage the par-
ticipants to understand and execute the essence of the open space.
Other good practices in encouraging a knowledge-friendly culture include: do not
impose top-down solutions, allow cultural change to evolve over a period of time,
provide positive role models wherever possible, create opportunities for people to get
to know one another, and focus on connecting people rather than capturing content.
Some illustrations are provided, covering GE, Viant, and ICL (boxes 7.3 – 7.5).
Some lessons learned from cultural change initiatives include:
• Provide information about the skills and experience of employees to overcome
problems arising from the absence or diffi culty of establishing personal relationships
(e.g., virtual organizations)
• Provide support mechanisms such as feedback for effective knowledge sharing to
take place
• Active knowledge transfer requires a bidirectional communication channel
• Develop common goals and mutual trust
• KM is an evolutionary process that must be embedded into organizational culture
• The introduction of new communication/information technologies that are capable
of enhancing knowledge sharing can be used to catalyze cultural changes by external-
izing tacit knowledge, by building up a permanent organizational memory, and by
including all members in a participatory development of content, rules, goals, and
systems
As Gruber and Duxbury (2000) discovered: “ We have to move to a transparent
organization. This means all kinds of information and knowledge is shared across the
whole organization. Everyone can fi nd out what everyone else is doing. Any kind of
information that infl uences me and my project have to be made available to everyone
else. ” Tapscott and Ticoll (2003) discuss the notion of organizational transparency and
the importance of having good values of honesty and openness and being successful
as an organization.
254 Chapter 7
Sharing best practices is a “ way of life ” at GE — employees live and breathe it every day
( Stewart 2000 ). A culture of what the company calls “ boundarylessness ” ensures that at
GE, whatever one person knows, everyone knows. GE demonstrates how this process
works. Beyond competence, community, and commitment, trust needs communication,
both positive and negative, and both best practices and lessons learned. GE is riddled
with CoPs — manufacturing councils, fi nance councils, technology councils — literally hun-
dreds of interdisciplinary and inter-business groups. Here GE ’ s younger employees bring
their ideas to share at meetings, where other members test them, improve upon them,
and take them home to be implemented in their own businesses. Individual performance
reviews stress the skills that contribute to the culture. Executive evaluations cover two
major areas: performance and personal values. Performance is a quantitative measure, but
when it comes to the qualitative measure of an executive ’ s personal values, the only
category that supersedes boundarylessness is integrity. At GE, employees are at least as
well regarded for borrowing a best practice across business lines as they are for inventing
a best practice.
Face time is only one way GE shares best practices and other intellectual assets. MS
exchange is standard on 50,000 desktops. In addition, GE has an intranet with the goal
of making the right information available at the right place and at the right time. The
intranet is an important vehicle for dynamic publishing and sharing of best practices. In
all divisions, executives put even their undeveloped ideas online. Others use, and then
modify those ideas using collaborative tools. For example, executives from all twelve GE
divisions discuss benchmarking for computer usage via GE ’ s intranet. Another discussion
site is devoted to enterprise resource planning. GE ’ s Technological Leadership Program is
an online multimedia just-in-time training program, which is also available live on the
intranet.
Jack Welch, who was the CEO from 1981 to 2001, committed GE to a Six Sigma
Program where the goal is to allow fewer than 3.4 customer-perceived defects per 1 million
opportunities to err. The linchpin to the knowledge sharing necessary to achieve that goal
is an intranet-accessible data warehouse dedicated to knowledge about quality that is
shared. How important is knowledge sharing at GE? If you are a CEO at GE and you
mention that you have developed a great new business procedure, the fi rst question the
chairman will ask is, “ Whom have you shared this with? ” People who hoard an idea for
personal glory simply do not do well at GE.
Box 7.4 An example: General Electric
The Role of Organizational Culture 255
Viant ( Stewart 2000 ) is a consulting company in Boston, public since June 1999, and is
often touted as a leader in knowledge sharing. New employees start off with an initiation
course of three weeks in Boston. At the end of their three weeks, they now know someone
in each of Viant ’ s offi ces, and have a laptop fully loaded with off-the-shelf and proprietary
software. They learn team skills and consulting strategies, including a mock consulting
engagement. They bond and hear company folklore. In terms of workplace layouts, Viant
has a “ leaky knowledge environment, ” balancing openness and privacy. People tend to
underestimate how much private offi ces are used for meetings. At any given time, Viant ’ s
leadership team consists of a score of offi cial members and about an equal number of
rotating “ fellows ” nominated by their peers in the fi eld. Conventional reporting relation-
ships do not work with consultants who rotate in and out of assignments, so consultants
have no fi xed boss; instead senior people act as “ advocates ” for a number of “ advocatees. ”
Performance reviews are 360 degrees, of course, emphasizing the growth in an employee ’ s
skill levels, while stock options are used to recognize excellent knowledge sharers. As
part of their everyday work, consultants complete a “ quick sheet ” that describes the
knowledge they need, what can be leveraged from previous projects, what they will need
to create, along with the lessons they hope to learn from each assignment. A longer report,
a sunset review, is produced at a team meeting to learn what did and did not work well.
Almost every document ends up hot-linked to Viant ’ s intranet site. Sunset reviews are
always done with a facilitator who is not part of the team, which keeps everyone honest.
Every six weeks, the KM group prepares, posts, and pushes a summary of what has been
learned.
Viant is also unusual in that it picks “ project catalysts ” from top consultants in the
company. They are pulled off client work for several months and assigned to other projects
where they do not supervise. They are not, however, passive — they are there to help: What
are you doing? How can I help? Looks like you need an example of a business plan to
adapt for your client, let me get one, and soon. This is in-your-face KM — and they are
referred to as agitators. Knowledge sharing is natural, instinctive, and painless in all aspects
of our lives — except our corporate ones. Companies who succeed in sharing knowledge
somehow “ force the issue ” — at Viant, that is the job of the agitators.
Box 7.5 An example: Viant
256 Chapter 7
ICL Ltd ( Bhatt 2000 ) developed a “ conversation for change ” program whereby all employ-
ees are asked to provide input in setting directions. The CEO invites all employees to
participate in the program. In addition, all executives use online chat sessions to discuss
staff issues in an open and nonjudgmental environment. This style of openness generates
a feeling of “ wanting, ” which can be very powerful in generating commitment and loyalty.
The staff feels their views and opinions are wanted and whatever they say will infl uence
the future vision. Every view is considered valid and important. The CEO also set up a
web space whereby any questions asked of him are posted with replies for all to see. ICL
is an example of many companies where leaders are changing the way they lead. These
leaders are not simply providing lip service, but genuinely believe that knowledge is a key
asset and that asset largely consists of the people in the organization.
Box 7.6 An example: ICL
Xerox Corporation global service technicians exchange most of what they know through
informal networks ( Roberts-Witt 2002 ). Technicians recount war stories face-to-face, but
this is not effective across all the service teams. The Eureka system was designed to capture
this tacit knowledge and make it more widely available. Technicians generally take a great
deal of pride in their ability to innovate. Recognition, rather than fi nancial reward, turned
out to be a major motivator in the sharing of their stories. The author ’ s name is displayed
prominently next to each tip in the system in order to reinforce this incentive. Each tip
is peer reviewed. In its fi rst month, over 5,000 tips were entered into Eureka.
Box 7.7 An example: Xerox
Impact of a Merger on Culture
Culture has been called the DNA of organizations. It is about patterns of human
interaction that are often deeply ingrained. While not directly observable, culture is
the defi ning, and in many cases, limiting, factor in creating a new entity that will be
healthy, integrated, balanced, coherent, and effective. What is the impact of a merger
on the organizational culture of both organizations? One of the hopes of a merger is
a new organization, with a new culture that is more than the sum of its parts. Given
this, the question above can be asked in another way that is really more appropriate
for the situation: What is the impact of organizational culture on the merger process
and on the newly created entity?
Dayaram (2005) has shown that some of the most critical issues that arise in post-
merger integration are in the area of culture. When you have two organizations
The Role of Organizational Culture 257
coming together, the challenge is to create, intentionally, a new culture that refl ects
the most strategic aspects of the parent organizations. Cultural integration in a merger
situation is about understanding and melding what can be two very different “ shared
lives, ” and growing a new one in the process.
Those who are tasked with furthering cultural integration have to assess the issues
above for the premerger partners, and then address the questions below:
• What are the most compatible elements of our former organizations ’ cultures?
• What are the elements that suggest the greatest potential confl ict?
• What would we like the new organization ’ s culture to look like?
• What do we want to be certain to bring forward into the new culture?
• What will be some indicators of successful cultural integration in our new
organization?
Through a deliberate and inclusive process of considering and discussing these
issues, the new organization can build trust, camaraderie, and the beginnings of a new
culture that will develop and evolve over the new organization ’ s future. This can be
the most challenging and, in many ways, the most rewarding work of postmerger
integration.
Sigma is a team-oriented completely virtual German organization ( Lemken, Kahler, and
Rittenbruch 2000 ). They went from twenty founding members to two-hundred employ-
ees with home offi ces throughout the country. They introduced a bulletin board service
and local groups met biweekly or bimonthly. All employees met face-to-face once a
year. Each area, each branch ended up having its own local culture. There was a great
deal of resistance to any top-down implementation of a KM system as well as to any
attempts to change their culture. In the early years, Sigma was a small group of indi-
viduals who had no trouble networking. Rapid growth and increasing virtualization
changed the early culture of Sigma. Technology could not replace their tradition of
personal-network-based collaboration and oral sharing of knowledge. However, what
did succeed was a highly fl exible approach. Transparency about activities resulted in
the creation of a culture of trust. KM is thus an evolutionary process that needs to
be embedded into the organizational culture. By allowing organizational members to
participate in the development of content, rules, and goals, greater cohesion will result
and this will help move the organization to a higher level of organizational and KM
maturity.
Box 7.8 An example: Sigma
258 Chapter 7
Impact of Virtualization on Culture
The basic challenges that culture faces in a virtual organization are:
• No formalization, each person follows his own norms, styles and ideas
• No shared values, beliefs, ideas, or norms
• No frameworks or policies that guide individuals working in the organization
The interaction and communication between the members of virtual organizations
is so limited and through channels so impersonal (the computer) that the scope for
development of a shared sense of belonging or a climate in the organization is almost
nonexistent.
Virtual organizations are here to stay and what they need to do today is to build a
culture that would give an existence to the organization in the minds of its members
and a sense of identifi cation and belonging that will bring them together in spite of
limited interactions. Within this culture it is necessary for each individual to take his
or her own developmental path, which is actually the core of the functioning of virtual
organizations.
Strategic Implications of Organizational Culture
Kanter (1989) refers to the paradox implicit in linking culture with change. On the
surface, culture has essentially traditional and stable qualities; so how can you have
a “ culture of change ” ? ( Fullam 2001 ). Yet this is exactly what innovative organizations
need. If real change is to occur in organizations rather than cosmetic or short-lived
change, it has to happen at the cultural level. Corporate culture has many powerful
attractions as a lever for change. The problem is how to get a hand on the lever. Firstly,
cultures can be explicitly created; you have to be aware of what it takes to change an
existing culture.
The ability of companies to be culturally innovative is related to leadership. Top
management must be responsible for building strong cultures. Leaders construct the
social reality of the organization, shape values, and help to create and attain the vision
of the organization.
The knowledge culture change adoption process will necessarily be a long one. You
should not expect results overnight. In fact, the more dispersed the organization, the
longer it has been in existence, and the less stable its environment and workforce,
among other factors, the longer the cultural change period that will be needed. For
some organizations, this may be as long as ten years. However, this does not mean
that small, meaningful steps cannot be taken to progress toward the overall cultural
The Role of Organizational Culture 259
change goal. The following are some recommendations for bringing about the cultural
change needed for KM to succeed:
• Clearly defi ne desired cultural outcomes
• Assess the current cultural state
• Diagnose the existing culture with respect to desired knowledge-sharing
behaviors
• Assess tolerance to change
• Identify change enablers and barriers
• Assess the maturity level of KM within the organization
• Identify KM enablers and barriers
• Conduct a gap analysis to yield a map on how to get from where the organization
is currently to where they would like to be culturally
Practical Implications of Organizational Culture
At a minimum, the following solutions to potential cultural barriers should be put
into place in order to catalyze and successfully implement desired organizational
cultural changes (see table 7.9 ).
Cultural change is often thwarted by lack of attention to some of the more basic
requirements such as providing employees with a place to meet and legitimate time
Table 7.9 Common barriers to cultural change and possible solutions
Cultural barrier Possible solutions
Lack of time and meeting places Seminars, e-meetings, redesign of physical workspaces
Status and rewards to knowledge owners
Establish incentives, include in performance evaluations, develop role models
Lack of absorptive capacity Hire for openness, educate current workforce
Not-invented-here syndrome Nonhierarchical approach based on quality of ideas and not status of source
Intolerance of mistakes and need for help, lack of trust
Accept and reward creativity and collaboration, and ensure there is no loss of status for not knowing everything
Lack of common language (not just English vs. Spanish but engineer-speak vs. manager-speak)
Establish a knowledge taxonomy and knowledge dictionary for knowledge content, standard formats, translators, metadata, and knowledge support staff
260 Chapter 7
spent in such meetings. For example, one organization set up a series of expensive
employee lounges fi lled with computers that were linked up to the organizational
knowledge base. However, on any given day, these lounges were empty. The reason
was that employees who spent time there were subject to comments such as “ wow —
you must not have much work to do if you have time to spare. ” When senior man-
agement took visitors around for a site visit of the offi ce, an e-mail memo was sent
out ahead of time to warn employees to be hard at work at their workstations and not
“ chatting in the lounges ” lest the visitors leave with the wrong perception of the
company. The message was very clear. Management may have built the physical
knowledge-sharing places, but they did not provide employees with the clear message
that time spent sharing knowledge was time that was productively spent. Similar
examples are often found in organizations where employees are told to do KM activi-
ties outside of their normal working hours. In other words, KM is done in your spare
time, which conveys a view of KM activities as peripheral, secondary, or even hobby-
type activities when compared to “ real work. ”
The rewarding of knowledge hoarding is another common barrier to the cultural
change needed for effective KM implementations. An example is any science-based
organization where recognition, performance appraisals, and promotion criteria are
all linked to what has been accomplished by being the fi rst and by being the only one
who thought of a great new idea, product, or process. As long as your career prospects
are enhanced if you do not share knowledge, cultural change will not occur. To bring
about cultural change, it is imperative to integrate knowledge-sharing behaviors in
performance evaluation criteria. Management can also help by publicly rewarding
examples of collaboration, good teamwork, and knowledge reuse wherever possible.
An example of a KM incentive strategy at Hill and Knowlton is explored in further
detail (box 7.9).
Absorptive capacity refers to the individual and/or organizational openness to
change and innovation, and the capability or preparedness for being able to integrate
it. The term originally referred to the prior related knowledge that a fi rm already pos-
sesses by Cohen and Levinthal (1990) . If existing absorptive capacity is low in an
organization, it will be very diffi cult to carry out any signifi cant cultural changes. The
organization could augment its existing employee base by recruiting and hiring indi-
viduals who have been selected for their openness to new ideas, eagerness to learn,
and innovativeness in approach. The existing employees can be provided with aware-
ness seminars, creativity building workshops (e.g., thinking out of the box approaches),
and other training opportunities to give them a chance to reframe their perception of
themselves and of the planned cultural changes.
The Role of Organizational Culture 261
Change is greatly hindered if mistakes and any requests for help or collaboration
are perceived as undesirable behaviors and/manifestations of weakness or incompe-
tence. For example, if in an organization you are expected to have all of the answers
and asking someone for assistance implies that you are not qualifi ed to be in your job,
this will greatly diminish the number of requests for help. If, on the other hand, the
organization ’ s role models and reward systems actively promote, support, and value
such interactions, then cultural change will be greatly facilitated. Steps must be taken
to ensure that employees do not lose face or status if they admit to not knowing
everything and, concurrently, employees who provide knowledge and assistance are
rewarded.
Finally, another important cultural barrier lies in the lack of a common language
among knowledge workers. Natural language barriers exist, particularly in multina-
tional companies, and translation costs can be prohibitive. However, there are other
Hill and Knowlton International Public Relations-Public Affairs established a knowledge
commerce methodology for its 1,700 employees worldwide. The goal was to conduct
consultations in such a way that the absorbed experience of that project is captured in a
knowledge base and is reusable for a new client.
A product launch with a client in the US, for instance, could be replicated worldwide
without the same level of man-hours. Replication does not imply exact duplication, but
rather abstraction of the key points of what makes it an effective launch. Captured knowl-
edge could include a checklist of product launch activities, a critical path outlining execu-
tion priorities, and competitive intelligence. Hill and Knowlton ’ s approach to KM
implementation was a three-pronged one: Decide on a technology platform; get people
motivated to use the KM resources; and integrate KM practices with people ’ s daily work.
IT integrated the platform with in-house e-mail and also organized editors into roles as
coaches and knowledge arrangers and categorizers. Senior management rejected the idea
that compensation for knowledge contributions was best conducted through infrequent
performance reviews.
One of the biggest benefi ts of a knowledge economy has been the cross-pollination of
ideas and abstract thinking across the company. H & K ’ s work is organized around practice
area (i.e., crisis management or investor relations) and industry vertical (i.e., healthcare or
technology). H & K is trying to break down service silos quite a bit. If someone develops
an account plan in crisis management that could be applied to other groups, they try to
open up people ’ s minds and identify information applicable to those other areas, like
investor or government relations.
Box 7.9 An example: Hill and Knowlton
262 Chapter 7
types of languages, such as jargon or shared technical or professional languages that
can cause a great deal of confusion. For example, the word “ network ” may be under-
stood to mean contacts for sales and marketing people, whereas the interpretation of
the same word by telecommunications engineers would refer to a system of towers. A
knowledge dictionary of commonly used terms within the organization, together with
a good, up-to-date thesaurus that cross-references all known synonyms, would greatly
assist in overcoming this type of cultural change barrier.
Key Points
• Culture penetrates to the essence of an organization — it almost analogous with the
concept of personality in relation to the individual and this acute sense of what an
organization is — its mission, core values — seems to have become a necessary asset of
the modern company.
• There is the challenging question of whether or not organizational culture can be
changed and/or managed.
• Organizational culture consists of the set of norms, routines, and unspoken rules of
how things are done in that organization.
• An organization ’ s culture may be in differing states of maturity, and these can be
assessed using a variety of organizational and KM maturity models.
• It is particularly important to address organizational culture issues in the case of a
merger and in the case of a virtual or highly distributed organization.
Discussion Points
1. What is the culture of an organization? Why is it important to understand?
2. What is the contribution of organizational culture to the intellectual capital of the
organization?
3. What do we mean when we talk about changing the culture of an organization?
What would be some examples?
4. How would we go about assessing the cultural readiness of an organization with
respect to planned KM interventions? How would we modify our KM implementation
strategy based on the results of such an assessment?
5. What are some of the maturity models that can be used to situate a company
with respect to its KM culture? Discuss the strengths and weaknesses of each of these
maturity models.
The Role of Organizational Culture 263
6. What are some of the key enablers and major obstacles to effective knowledge
sharing that can be attributed to the overall organizational culture? To the diverse
microcultures?
7. Describe how you would initiate an organizational change initiative. Provide an
estimate of how long you believe each stage would last.
8. What are some of the ways of assessing whether or not the culture is changing, or
maturing, toward an intended end state? Provide examples.
9. What are some of the ways you would go about learning what an organization ’ s
values are? How would you collect and analyze stories, myths, and the typical language
used by a particular CoP?
10. How would you forge a bridge between the largely tacit cultural knowledge of an
organization and the largely explicit organizational memory system that should serve
to preserve this knowledge?
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tionize business . Toronto, Ontario : Viking Canada Press .
8 Knowledge Management Tools
Any suffi ciently advanced technology is indistinguishable from magic
— Arthur C. Clarke (1917 – 2008)
This chapter provides an overview of knowledge management (KM) tools, which are
all too often treated as black boxes (data goes in and knowledge magically comes out
the other end) by the majority of users. The new generation of millennials however
appear to have developed different technology skills and have differing expectations
of these new tools. New technologies are continually emerging, and many will have
some intersection with KM. Knowledge management implementations require a wide
range of quite diverse tools that come into play throughout the KM cycle. Technology
is used to facilitate primarily communication, collaboration, and content management
for better knowledge capture, sharing, dissemination, and application. The major
categories of KM tools are presented and described together with a discussion on how
they can be used in KM contexts.
Learning Objectives
1. Describe the key communication technologies that can be used to support knowl-
edge sharing within an organization.
2. Illustrate the major advantages and major drawbacks of synchronous versus asyn-
chronous KM technologies.
3. Defi ne data mining and list some cases where it would be used.
4. Compare and contrast the different types of intelligent agents and how they can
be used to personalize KM technologies.
5. Defi ne the difference between push and pull KM technologies.
268 Chapter 8
6. Characterize the major groupware tools and explain how they would be imple-
mented within an organization.
7. Sketch out the major components of a knowledge repository and explain how
organizations and organizational users would make optimal use of one.
8. Describe how e-learning and knowledge management intersect and in which ways
they differ.
9. Identify emerging technologies and describe how they may be applied in a KM
context.
10. Compare and contrast the skill set and technology expectations of the baby
boomer and the millennial generations.
Introduction
Technology is a moving target as new tools are being continuously developed and
adopted to varying degrees by users. Knowledge management has an added complica-
tion in that there is no single tool that will cover all the bases. A suite or toolkit of
technologies, applications, and infrastructures are required in order to address all
phases involved in capturing, coding, sharing, disseminating, applying, and reusing
knowledge. Yet another variable to further complicate the situation is that the users
themselves are continuously changing. While baby boomers have certain preferences,
such as preferring the phone to e-mail or meeting face to face, as well as certain
expectations of technology (e.g., they are quite tolerant of errors, willing to wait, and
quite accepting of asynchronous communications), the same cannot be said of the
new millennial generation ( Eisner 2005 ; Raines 2003 ).
The millennial generation is also referred to as the net generation (Tapscott,) or the
Y generation as it comes after generation X. The baby boomers are generally defi ned
as those born after World War II in the years between 1945 and 1965. Generation X
refers to those born between 1966 and 1980, while the Y generation refers to those
born between 1980 and the year 2000. Perhaps the best way to characterize generation
Y or the millennials is that they were the fi rst to grow up with television and the
Internet. Throughout all three waves, there has been a wide range of innovations and
new tools, both for public consumption and for the workplace. The millennials tend
to have high expectations of the workplace precisely because they are such avid users
of real-time tools in their personal lives. The generational differences thus introduce
an added level of complexity to the KM world.
Knowledge Management Tools 269
One strategy for navigating through all of this complexity is to categorize the dif-
ferent types of KM tools. Ruggles (1997) provides a good classifi cation of KM technolo-
gies as tools that intervene in the knowledge processing phases:
• To enhance and enable knowledge generation, codifi cation, and transfer
• That generate knowledge (e.g., data mining that discover new patterns in data)
• That code knowledge to make knowledge available for others
• That transfers knowledge to decrease problems with time and space when commu-
nicating in an organization
Rollet (2003) classifi es KM technologies according to the following scheme:
• Communication
• Collaboration
• Content creation
• Content management
• Adaptation
• E-learning
• Personal tools
• Artifi cial intelligence
• Networking
Rollet ’ s (2003) categories can also be grouped according to what phase of the KM
cycle they occur in (refer to fi gure 8.1 ).
The initial knowledge capture and creation phase does not make extensive use of
technologies. Methods of converting tacit knowledge into explicit knowledge were
discussed in chapter 4. A wide range of diverse KM technologies may be used to
support knowledge sharing and dissemination as well as knowledge acquisition and
application. Table 8.1 lists the major KM tools, techniques and technologies currently
in use. The underlying theme is that of a toolkit. Many tools and techniques are bor-
rowed from other disciplines and others are specifi c to KM. All of them need to be
mixed and matched in the appropriate manner in order to address all of the needs of
the KM discipline. The choice of tools to include in the KM toolkit must be consistent
with the overall business strategy of the organization.
270 Chapter 8
Assess
KM Technologies
Update
Contextualize
Knowledge capture
and/or creation
Knowledge sharing
and dissemination
Knowledge acquisition
and application
Organizational culture
Figure 8.1 An integrated KM cycle
Knowledge Capture and Creation Tools
Content Creation Tools
Robertson (2003a) predicts that content management systems (CMS) will become a
commodity in the future. Many content management system projects fail due to lack
of good implementation standards and a lack of understanding of usability issues.
Technology-only approaches will continue to generate unsuccessful projects. CMS
should be handled in a strategic way. Lessons learned from these failures provide a
valuable source of learning. The move toward open standards would greatly assist the
evolution of CMS. This is likely to proceed with the use of XML-based protocols for
communicating with and between content management systems. Additional stan-
dards are needed for storing, structuring, and managing content. There will eventually
be a convergence between content, documents, records and knowledge management
that will be of greatest benefi t to organizations. As yet, there is no merged platform
to accommodate such a convergence.
Authoring tools are the most commonly used content creation tools. Authoring
tools range from the general (e.g., word processing) to the more specialized (e.g., web
Knowledge Management Tools 271
Table 8.1 Major KM techniques, tools, and technologies
Knowledge creation and
codifi cation phase
Knowledge sharing and
dissemination phase
Knowledge acquisition and
application phase
Content creation
• Authoring tools
• Templates
• Annotations
• Data mining
• Expertise profi ling
• Blogs
• Mashups
Communication and collaboration technologies
• Telephone/Internet telephone/Fax
• Videoconferencing
• Chat rooms/instant messaging/iwitter
• E-mail/discussion forums/ wikis
• Groupware
• Work fl ow management
• Folksonomies
• Social networking
• Web 2.0/KM 2.0
E-learning technologies
• CBT
• WBT
• EPSS
Emerging technologies
• Folksonomies
• Metadata
Content management
• Taxonomies
• Folksonomies
• Metadata tagging
• Classifi cation
• Archiving
• Personal KM
Networking technologies
• Intranets
• Extranets
• Web servers, browsers
• Knowledge repository
• Portal
Artifi cial intelligence technologies
• Expert systems
• DSS
• Customization/personalization
• Push/pull technologies
• Recommender systems
• Visualization
• Knowledge maps
• Intelligent agents
• Automated taxonomy systems
• Text analysis — summarization
page design software). Annotation technologies enable short comments to be attached
to specifi c sections of a text document, often by a number of different authors (e.g.,
track changes feature in Word). This allows a running commentary to be built up and
preserved. Annotations may be public (visible to all who access and read the docu-
ment) or private (visible to author only).
Data Mining and Knowledge Discovery
Data mining and knowledge discovery are processes that automatically extract predic-
tive information from large databases based on statistical analysis (typically cluster
analysis). Using a combination of machine learning, statistical analysis, modeling
272 Chapter 8
techniques, and database technology, data mining detects hidden patterns and subtle
relationships in data and infers rules that allow the prediction of future results. Raw
data are analyzed to put forth a model that attempts to explain the observed patterns.
This model can then be used to predict future occurrences, and to forecast expected
outcomes (see fi gure 8.2 ).
A large number of inputs are required, usually over a signifi cant period of time, and
the types of models produced range from easy to almost impossible to understand.
Easy to understand models are decision trees, for example. Regression analyses are
moderately easy to understand and neural networks remain black boxes. The major
drawback of the black box models is that it becomes very diffi cult to hypothesize about
causal relationships (see fi gure 8.3 ).
If
If
Then xxxx
Then yyyy
Historical
data
Data
mining
Age
Education
Eye color
Model
How well will the
student perform on
the entrance exam?
Figure 8.2 Predictive models
Figure 8.3 Black box models
Knowledge Management Tools 273
Variables may be correlated but this relationship may not have any meaning or
usefulness. For example, a major bank found that there was a relationship between
the state an applicant lived in and a higher percentage of defaults on loans given out.
This should not be the basis for a policy that would automatically reject any applicants
from that state! Reality checks are always needed with statistics before any conclusions
can be drawn, as noted by British statesman Benjamin Disraeli, “ There are three kinds
of lies: lies, damned lies and statistics. ”
Typical applications of data mining and knowledge discovery systems include
market segmentation, customer profi ling, fraud detection, retail promotion evalua-
tion, credit risk analysis, and market basket analyses (as described in the vignette).
However, there are a few gems usually to be mined with data mining applications.
These are often unexpected correlations that upon further study yield some useful
(and often actionable) insights into what is occurring. The famous example is that of
the relationship between purchases of beer and purchases of diapers.
Some data mining tools that are currently in use include:
• Statistical analysis tools (e.g., SAS)
• Data mining suites (e.g., EnterpriseMiner)
• Consulting/outsourcing tools such as EDS, IBM, Epsilon (note that these tools are
models, not just software)
• Data visualization software that coherently present a large amount of information
in a small space. They make use of the human computer — your eyes — to detect
patterns, for example, virtual reality and simulation software — to walk around the data
points.
A chain of convenience stores conducted a market basket analysis to help in product
placement. Market basket analysis is a statistical analysis of items that consumers tend to
buy together (i.e., that are found in the same basket at checkout). One of their hypotheses
was to place all infant care-related items together and run a simple correlation check to
validate that mothers of newborns did in fact tend to buy items such as baby powder or
cream when they came in to purchase diapers. To their surprise, the highest correlation
for an item that tended to be bought at the same time as diapers (in the newborn size and
format) was in fact a case of beer. This was later explained by the observation that it was
the fathers of newborns who were more likely to be sent to the store to buy more diapers
and while they were there, they tended to pick up other equally essential items.
Box 8.1 A vignette: Beer with your diapers
274 Chapter 8
It is also possible to apply this technique and use these tools to mine content other
than data, namely text mining, thematic analysis, and web mining to look at what
content, how often, for how long (e.g., number of hits) which is very helpful
in content management. Similarly, skill mining or expertise profi ling can be used
to detect patterns in online curriculum vitae of organizational members. Expertise
location systems can be automatically created based on the content that has been
mined. Commercial software systems can also be used to mine e-mail data in order
to determine who is answering what types of queries or themes. Organizational experts
and expertise can be detected by looking at the patterns of questions and answers
contained within the e-mails. The same caveat applies to all of these data mining
applications — a human being is always needed in the loop in order to carry out “ reality
checks ” (i.e., to verify and validate that the patterns do indeed exist and that they
have been interpreted in a useful and valuable manner).
Blogs
A blog is a term for a web log — a popular and fairly personal content form on
the Internet. A blog is almost like an open diary; it chronicles what a person wants
to share with the world on an almost daily basis ( Blood 2002 ; see also http://www
.rebeccablood.net/). While the “ blogosphere ” started off as a medium for mostly per-
sonal musings, it has evolved into a tool that offers some of the most insightful
information on the web. Further, blogs are becoming much more common, as busi-
nesses, politicians, policy makers, and even libraries and library associations have
begun to blog as a way of communicating with their patrons and constituents.
Several librarians publish blogs that offer a wealth of information about social
software and its uses. SNTReport.com focuses on the social software industry and how
social software tools are being used to help people collaborate. Blogs not only offer a
new way to communicate with customers, they have internal uses as well. For example,
large organizations can use a well-formed blog to exchange ideas and information
about web development projects, training initiatives, or research issues. These ques-
tions and answers can be cross-indexed and archived, which helps build a knowledge
network among the participating members. Most important, the price of setting up a
well-formed, secure blog and leveraging it into a knowledge and content management
tool is a pittance when compared to other proprietary solutions.
Right now, the majority of blogs are published exclusively in text. The next genera-
tion of blogs, however, will implement audio and video elements, bringing a sophis-
ticated multimedia blend to the medium ( Dames 2004) . The overwhelming popularity
of YouTube (www.youtube.com) attests to the powerful draw of the image, and in
particular, the moving image. On YouTube, short video clips can be posted on practi-
Knowledge Management Tools 275
cally any topic. These are often self-fi lmed and self-indexed. It is possible to search
the YouTube web site for a clip on a particular topic. While many videos are mostly
entertaining, quite a few serve as educational resources (see listings in chapter 14).
Pikas (2004) added the notion of searching to blogs. Blogs are reverse chronologi-
cally arranged collections of articles or stories that are generally updated more
frequently than regular web pages. Just like any other information on the net, there
is no guarantee of authority, accuracy, or lack of bias. In fact, personal blogs are
frequently biased and can be good sources of opinion and information from the man
on the street. Because blogs can be updated on the fl y, they frequently have unfi ltered
information faster from war zones and sites of natural disasters than the mainstream
media outlets. Blogs are also good sources of unfi ltered information on either faulty
or very useful products.
In the beginning, blogs appeared in search results alongside regular web pages.
Since blogs are not technologically any different from other web pages (i.e., they are
HTML, XML, JavaScript, etc., and it is their format, not their coding, that is different.),
spiders and bots collect posts the same way they collect other online information.
Search engines that place greater value on sites that are recently and frequently
updated and are highly linked tend to rank blog posts very highly. Since the barrier
to publication is so low in blogs, arguably much lower than for standard web pages,
these high rankings were introducing a lot of noise into online searches. Odds are that
you have run across several archived blog posts if you have searched on a controversial
topic in the past year. Recently, most major search engines have altered their
algorithms to push blogs down in the search results. Engines that only return two
results from any one site use this feature to limit the impact of blogs on the search
results.
Blog searching breaks down into at least two categories: information from within
blogs/across blogs or addresses of feeds from blogs so that you may subscribe in your
aggregator. Feeds and blogs are two different things, but are closely linked because
most blogs have feeds and many feeds are generated by blogs. Just as in other web
search tools, there are search engines and directories. At this time, blog search engines
are where general search engines were before the Google Age. There are many compet-
ing smaller products but no outstanding products dominating the scene.
Mashups
A mashup is an innovative way of combining content ( Merrill 2006 ). Mashups are
web applications that offer an easy and rapid way of combining two or more differ-
ence sources of content into a single seamlessly integrated application. The term
originates from the practice of mixing tracks from two different songs. One of the fi rst
276 Chapter 8
applications was to combine real estate listings with the location map drawn from
Google Maps. The integration is typically undertaken by retrieving content from pub-
licly available sources, combining continuous web feeds such as RSS or using some of
the newly created mashup editors and programming languages. Mashups make it very
easy to combine different media such as text and images, videos, maps, and news
feeds. There is, however, an issue with intellectual property and information privacy
that will need to be ironed out with this new emergent technology ( Zang, Rosson,
and Nasser 2008 ).
Within a business context, however, if the content to be combined is clearly avail-
able for use by the company and its employees, then mashups become an intriguing
means of creating new content from old. Some popular business uses of mashups to
date have been to create presentations that contain aggregated content and to support
collaborative work such as joint authoring of content. In a way, mashups may also be
considered as knowledge portals — both are aggregate content. However, mashups do
so in a much more dynamic way (portals are discussed later in this chapter).
Content Management Tools
Content management refers to the management of valuable content throughout the
useful life span of the content. Content life span will typically begin with content
creation, handle multiple changes and updates, merging, summarization, and other
repackaging and will typically end with archiving. Metadata (information about the
content) is used to better manage content throughout its useful life span. Metadata
includes such information as source/author, keywords to describe content, date
created, date changed, quality, best purposes, annotations by those who have made
use of it, and an expiry or best before date where applicable. Additional attributes such
the storage medium, location, and whether or not it exists in a number of alternative
forms (e.g., different languages) are also useful to include. XML is increasingly being
used to tag knowledge content. Taxonomies serve to better organize and classify
content for easier future retrieval and use.
XML (eXtensible markup language) provides the ability to structure and add rele-
vance to chunks of information (that ’ s why many CM solutions use XML), and in
theory, exchange data more easily between applications, for example, with your sup-
pliers, customers, and partners. However, you may all use the same words (tags), but
if each of you defi nes and applies them differently, then we remain in the land of
Babel. Common agreed schemas are essential. Keep tabs with developments on the
schemas and metadata standards in your fi eld. Useful sources are XML.org (http://
www.xml.org) the W3C XML schemas section — http://www.w3.org/XML/Schema.
Knowledge Management Tools 277
Taxonomies — hierarchical information trees for classifying information — act like
your library subject catalog. They can help overcome differences of language usage in
different parts of an organization and even the use of different languages. Traditionally
manually intensive, the growing problem of information overload means that they
are receiving signifi cant attention. But how do you cope with the evolution of terms,
whose meaning seems to change from one year to the next? Automatic (or
semi-automatic) classifi cation of information objects — natural language analyzers, text
summarizers, and other technology — helps to understand some of the meaning —
the concepts — behind blocks of text and to tag and index it appropriately for to aid
subsequent retrieval. Many take advantage of the organization’s underlying knowledge
taxonomy.
Folksonomies and Social Tagging/Bookmarking
Metadata is literally translated as data about data and refers to specifi c information
about content contained in books, reports, articles, images, and other containers so
that they can be organized and retrieved in an orderly fashion. Metadata is also
referred to as tags or keywords. Taylor (2004) notes that metadata comes in three
general fl avors: administrative, structural and descriptive. The Oxford Digital Library
(ODL) (http://www.odl.ox.ac.uk/metadata.htm) defi nes three types. Administrative
metadata is the information needed to manage the information resource over its life
cycle such as data about how it was acquired, where it came from, licensing, intel-
lectual property rights, and attribution (e.g., was it scanned, what format is it stored
in, etc.). This is sometimes referred to as preservation metadata. Structural metadata
relates to the actual computer elements involved such as tables, columns, and indi-
ces — all the logical units of the information resource. Descriptive metadata refers more
to the content or subject matter of the information resource to help users fi nd it (e.g.,
cataloguing records, fi ndings aids, keywords). Descriptive metadata is of greatest
concern in KM because we often need to expand this type of data about data greatly
in order to increase the usability (and reusability) of a given unit of knowledge.
Metadata is very formal and tends to be created and updated by dedicated person-
nel such as catalogers and other library and information science professionals. This is
the highest standard in metadata but is time consuming to produce (Mathes 2004).
An alternative is to have authors create and add their own metadata for their own
works. The Dublin Core best exemplifi es author-created metadata (Greenberg et al.
2001). Both of these approaches work well for the person who develops the metadata
but not necessarily as well for other users (often referred to as unknown or unantici-
pated users). A third option exists — that of user-created metadata. This bottom-up or
278 Chapter 8
grassroots approach is referred to as a folksonomy or as social bookmarking or tagging.
The advantage of this third option is that metadata is created by the collectivity of
users. All users should more readily understand the tags or data about data, not just
their creators.
Social bookmarking is a method whereby users participate directly in the storage,
organization, searching, and managing of web resources. One way is by saving
personal bookmarks on a publicly accessible web site and then tagging these sites
with your own metadata. Early sites include: del.icio.us (http://www.delicious.com),
Furl (http://www.furl.net/), web page bookmarking sites, and Citeulike (http://www
.citeulike.org/), a social citation site for scholarly publications. Other users can then
view the bookmarks by category, search by key word or use other attributes. Users
make use of informal tags instead of more formal cataloguing methods. Since all the
tags originate from the intended end users, they are easier to understand than more
standardized or top-down indexing terms. The major drawback is this very lack of
standardization. There is no controlled vocabulary, that is, a list of standard keywords.
So many errors can occur due to misspelling, synonym confusion, tags with more than
one meaning, or tags that are too personalized. This situation brings us right back to
the problem faced by more traditional cataloguing approaches: How to tag so that
others can understand your tags?
In a KM context, social bookmarking makes it possible to share knowledge with
others in a new way by sharing not only the original knowledge but also what you
think about it (the metadata). The technology is easy to use with hardly any learning
curve to speak of. The real potential lies in what the metadata can be used for. For
example, if the knowledge resource (data) is a best practice, then the metadata (data
about data) can include annotations about what others think of the best practice,
testimonials, cautionary notes (when not to apply and why), and other contextual
information that can greatly increase the successful use and reuse (application) of this
knowledge. Social bookmarking is an excellent vehicle to peer-to-peer knowledge
sharing and may play a greater role in future communities of practice. In a given
community of practice (CoP), there is, in addition to a shared purpose and a shared
repository, a shared vocabulary. Since CoP members share the same jargon, tagging is
less likely to be a problem. Tagging for yourself should approximate tagging for your
peers, who are neither unknown nor unanticipated users.
As social bookmarking sites mature and ever-increasing numbers of users participate
in them, it becomes possible to see some patterns emerging with respect to the tags
that are most commonly used. This tag “ cloud ” can be found by looking at the right-
Knowledge Management Tools 279
hand side of individual tag pages, under related tags of most social bookmarking sites.
Tag clouds represent emergent or organically grown taxonomies — commonly referred
to as folksonomies, a term coined by Thomas van der Wal in 2004 ( Smith 2004 , in
Mathes 2004) as a combination of folk and taxonomy.
Folksonomies differ from traditional taxonomies in that there is no hierarchy, no
object-oriented style of inheritance from parent object to child object, just clusters of
tags that appear to be loosely related. They also do not follow taxonomy rules in that
folksonomies can have more than one type of relationship between the same terms.
In a typical folksonomy, terms will differ in their level of specifi city, they may be
qualitatively different, and they may not necessarily make sense! A folksonomy, in
other words, freely advocates mixing apples and oranges. The drawbacks are once
again lack of standardization, ambiguity, diminished rigor in classifying, and the use
of a fl at rather than hierarchical space. The advantages are being able to use the every-
day language that users have and unlimited expansion of keywords. Finding through
serendipity improves retrieval by being able to observe what others felt were related
knowledge.
As with social bookmarking, folksonomies appear particularly well suited to com-
munities of practice, where peer-to-peer sharing can be augmented through the folk-
sonomy approach. A folksonomy should help increase cooperation and knowledge
sharing among community members by making visible what often remains an invis-
ible model of who knows whom and who knows what or who is interested in what
topic. Folksonomies can therefore be considered as knowledge creation tools (creation
of tags) and knowledge sharing and dissemination tools (peer-to-peer sharing, public
posting of tags) as well as a knowledge application tool (metadata that contextualizes
when and where the knowledge should be used).
A fi nal note: folksonomies and more traditional knowledge organization schemes
(see chapter 4) need not be mutually exclusive. A folksonomy can be an excellent
starting point for a more formal taxonomy. The folksonomy can serve a needs-analysis
function and permit the users to make use of their own preferred vocabulary while
the designers link this to the more formal taxonomy through a thesaurus. This linkage
will also serve as a form of personalization of the search and retrieval interface for the
users.
Personal Knowledge Management (PKM)
Personal capital is a term coined by Cope (2000) as a divergence from the traditional
notion of capital, which is an asset owned by an organization. In fact, the future of
280 Chapter 8
KM will blur the boundaries between the individual, the group or community, and
the organization. KM will become a pervasive part of how we conduct our everyday
business lives. Personalized KM (PKM) will gain increasing importance given the ever-
increasing momentum of information overload that we must deal with. In other
words, some of the key principles, best practices, and business processes of KM that
have to date been focused at the organizational level will fi lter down to be used by
individuals managing their own personal capital.
PKM and traditional knowledge management differ depending on whether an
organizational or personal perspective is adopted. Tools for personal information
management are impressive and, if you think about e-mail and portals, are already
widely used. Newer tools such as blogs, news aggregators, instant messaging, and wikis
represent a new toolset for PKM.
The personal portal, what was once an enterprise portal, is now focused around the
needs of the individual. All of a person ’ s information and application needs harmoni-
ously are brought together and arranged on the desktop, mass customization in front
of your eyes! Again, the aims are laudable, but reality and theory are often miles apart.
PKM brings many of the key principles of KM to bear on the personal productivity
and specifi c work requirements of a given knowledge worker. Defi nitions of PKM
revolve around a set of core issues: managing and supporting personal knowledge and
information so that it is accessible, meaningful, and valuable to the individual; main-
taining networks, contacts, and communities; making life easier and more enjoyable;
and exploiting personal capital ( Higgison 2004 ). On an information-management
level, PKM involves fi ltering and making sense of information, organizing paper and
digital archives, e-mails, and bookmark collections.
Knowledge Sharing and Dissemination Tools
Rollet (2003) made a distinction between communication technologies, such as
telephone and e-mail, and collaboration technologies, such as work fl ow management.
Yet it is very diffi cult to draw a line between the two. Communication and collabora-
tion are invariably intertwined. It is quite diffi cult to establish where one ends and
the other begins. Both types of tools have been grouped under the category of
groupware or collaboration tools. Although all organizational members will make
use of communication and collaboration, including project teams and work units,
communities of practice will be particularly active in making use of many if
not all of the communication and collaboration technologies described in this
section.
Knowledge Management Tools 281
Groupware and Collaboration Tools
Groupware represents a class of software that helps groups of colleagues (work groups)
attached to a communication network (e.g., LAN) organize their activities. Typically,
groupware supports the following operations:
• Scheduling meetings and allocating resources
• Password protection for documents
• Telephone utilities
• Electronic newsletters
• File distribution
Communication technologies used typically include the telephone, fax, videocon-
ferencing, teleconferencing, chat rooms, instant messaging, phone text messaging
(SMS), Internet telephone (voice over IP or VOIP), e-mail, and discussion forums.
Communication is said to be dyadic when it occurs between two individuals, for
example, a telephone call. Teleconferencing, on the other hand, may have more
than two participants interacting with one another in real time. Videoconferencing
introduces a multimedia component to the communication channel as participants
can not only hear (audio) but also see the other participants (audiovisual). Desktop
videoconferencing is similar but does not require a dedicated videoconference facility.
Simple and inexpensive digital video cameras can be used to transmit images. The
visual component is especially useful when demonstrations are presented to all
participants.
Chat rooms are text based but synchronous. Participants communicate with one
another in real time via a web server that provides the interaction facility. Instant
messaging is also real-time communication, but in this case participants sign on to
the instant messaging system and they can immediately see who else is online or live
at that same time. Messages are exchanged through text boxes. The SMS (short mes-
saging system) allows text messages to be sent via a cell phone rather than through
the Internet.
E-mail continues to be one of the most frequently used communication channels
in organizations. Although e-mail messaging is dyadic, it can also be used in a more
broadcast mode (e.g., group mailings) as well as in an asynchronous group discussion
mode by forwarding previous discussion threads.
Communication technologies are almost always integrated with some form of
collaboration, whether it be planning for collaboration or organizing collaborative
282 Chapter 8
work. Collaboration technologies are often referred to as groupware or as work group
productivity software. It is technology designed to facilitate the work of groups. This
technology may be used to communicate, cooperate, coordinate, solve problems,
compete, or negotiate. While traditional technologies like the telephone qualify as
groupware, the term is ordinarily used to refer to a specifi c class of tech nologies relying
on modern computer networks, such as e-mail, newsgroups, videophones, or chat.
Groupware technologies are typically categorized along two primary dimensions
(see table 8.2 ):
• Whether users of the groupware are working together at the same time (real-time or
synchronous groupware) or different times (asynchronous groupware), and
• Whether users are working together in the same place (co-located or face-to-face) or
in different places (non-co-located or distance).
Coleman (1997) developed the taxonomy of groupware that lists twelve different
categories:
• Electronic mail and messaging
• Group calendaring and scheduling
• Electronic meeting systems
• Desktop video, real time synchronous conferencing
• Non-real time asynchronous conferencing
• Group document handling
• Work fl ow
• Work group utilities and development tools
• Groupware services
• Groupware and KM frameworks
• Groupware applications
• Collaborative Internet-based applications and products
E-mail is by far the most common groupware application (besides, of course, the
traditional telephone). While the basic technology is designed to pass simple messages
Table 8.2 Classifi cation of groupware technologies
Same time synchronous Different time asynchronous
Same place, colocated Voting presentation support Shared computers
Different place, distant Videophones Chat E-mail Work fl ow
Knowledge Management Tools 283
between two people, even relatively basic e-mail systems today typically include inter-
esting features for forwarding messages, fi ling messages, creating mailing groups, and
attaching fi les with a message. Other features that have been explored include auto-
matic sorting and processing of messages, automatic routing, and structured commu-
nication (messages requiring certain information).
Newsgroups and mailing lists are similar in spirit to e-mail systems except that they
are intended for messages among large groups of people instead of one-to-one com-
munications. In practice the main difference between newsgroups and mailing lists is
that newsgroups only show messages to a user when they are explicitly requested (an
on-demand service), while mailing lists deliver messages as they become available (an
interrupt-driven interface).
Work fl ow systems allow documents to be routed through organizations using a
relatively fi xed process. A simple example of a work fl ow application is an expense
report in an organization. An employee enters an expense report, submits it, a copy
is archived, and then routed to the employee’s manager for approval. The manager
receives the document, electronically approves it, and sends it on. The expense is
registered to the group ’ s account and forwarded to the accounting department for
payment. Work fl ow systems may provide features such as routing, development of
forms, and support for differing roles and privileges.
Hypertext is a system for linking text documents to each other with the web being
an obvious example. Whenever multiple people author and link documents, the
system becomes group work, constantly evolving and responding to others ’ work.
Some hypertext systems include capabilities for seeing who else has visited a certain
page or link or at least seeing how often a link has been followed, thus giving users a
basic awareness of what other people are doing in the system. Page counters on the
web are a crude approximation of this function. Another common multi-user feature
in hypertext that is not found on the web is allowing any user to create links from
any page, so that others can be informed when there are relevant links that the original
author was unaware of.
Group calendars allow scheduling, project management, and coordination among
many people and may provide support for scheduling equipment as well. Typical
features detect when schedules confl ict or fi nd meeting times that will work for every-
one. Group calendars also help to locate people. Typical concerns are privacy (users
may feel that certain activities are not public matters) and completeness and accuracy
(users may feel that the time it takes to enter schedule information is not justifi ed by
the benefi ts of the calendar).
Collaborative writing systems may provide both real-time support and non-
real-time support. Word processors may provide asynchronous support by showing
284 Chapter 8
authorship and by allowing users to track changes and make annotations to docu-
ments. Authors collaborating on a document may also be given tools to help plan
and coordinate the authoring process, such as methods for locking parts of the
document or linking separately authored documents. Synchronous support allows
authors to see each other ’ s changes as they make them and usually needs to provide
an additional communication channel to the authors as they work (via videophones
or chat).
Synchronous or real-time groupware is exemplifi ed by shared workspaces, telecon-
ferencing or videoconferencing, and chat systems. For example, shared whiteboards
allow two or more people to view and draw on a shared drawing surface even from
different locations. This can be used, for instance, during a phone call, where each
person can jot down notes (e.g., a name, phone number, or map) or to work col-
laboratively on a visual problem. Most shared whiteboards are designed for informal
conversation, but they may also serve structured communications or more sophisti-
cated drawing tasks, such as collaborative graphic design, publishing, or engineering
applications. Shared whiteboards can indicate where each person is drawing or point-
ing by showing tele-pointers, which are color coded or labeled to identify each
person.
Twitter is a newer technology that is about as real as real-time can get. The major
use of Twitter is to continuously answer the question, “ what are you doing now? ” It
is a miniblogging service that allows users to send tweets or minitexts up to 140 char-
acters in length to their user profi le web page. This information is then conveyed to
users who have signed up to receive the posts (typically a circle of friends or col-
leagues). Tweets can be received as web page updates RSS feeds, SMS text on phones,
through e-mail, on Facebook, and so on. Twitter started out in life as an R & D project
in podcasting ( Glaser 2007 ). While Twitter remains largely a novelty application used
by early adopters, there are potential applications within a KM context. Anthony
Bradley (2008) addressed this point and noted that Twitter is a people-based technol-
ogy and can serve as a good alerting service for people who are working together,
particularly if they are working together on time critical work. Twitter can also serve
as an ultra-rapid way of testing out ideas on a few trusted individuals — a quick forum
for feedback in real time (e.g., a presenter who checks to see how the talk is going, a
meeting coordinator who needs everyone in attendance ASAP, or a project manager
trying to physically locate his team). One potential application for real-time tweets
could be an expertise locator system — one that locates expertise in real-time as well
as a means of meeting some of the expectations of millennial knowledge workers
( Lee 2003 ).
Knowledge Management Tools 285
Video communications systems allow two-way or multi-way calling with live video,
essentially a telephone system with an additional visual component. Cost and compat-
ibility issues limited early use of video systems to scheduled videoconference meeting
rooms. Video is advantageous when visual information is being discussed, but may
not provide substantial benefi t in most cases where conventional audio telephones
are adequate. In addition to supporting conversations, video may also be used in less
direct collaborative situations, such as providing a view of activities at a remote
location.
Chat systems permit many people to write messages in real-time in a public space.
As each person submits a message, it appears at the bottom of a scrolling screen. Chat
groups are usually formed by listing chat rooms by name, location, number of people,
topic of discussion, and so on.
Many systems allow for rooms with controlled access or with moderators to lead
the discussions, but most of the topics of interest to researchers involve issues related
to unmediated real-time communication including anonymity, following the stream
of conversation, scalability with number of users, and abusive users.
While chatlike systems are possible using non-text media, the text version of chat
has the rather interesting aspect of having a direct transcript of the conversation,
which not only has long-term value, but allows for backward reference during con-
versation making it easier for people to drop into a conversation and still pick up on
the ongoing discussion.
Groupware applications from Teamware, the U.S. Army, Chevron, and BP are
further illustrated in boxes 8.2 and 8.3.
Wikis
Wikis are web-based software that supports concepts such as open editing, which
allows multiple users to create and edit content on a web site (for more information,
see: http://en.Wikipedia.org/Wiki/Wiki). A wiki site grows and changes at the will
of the participants. People can add and edit pages at will, using a Word-like screen
without knowing any programming or HTML commands. More specifi cally, a wiki
is composed of web pages where people input information and then create hyper-
links to another or new pages for more details about a particular topic. Anyone can
edit any page and add, delete, or correct information. A search fi eld at the bottom
of the page lets you enter a keyword for the information you want to fi nd. Today
two types of wikis exist: public wikis and corporate wikis. Public wikis were devel-
oped fi rst and are freewheeling forums with few controls. In the last year or two,
corporations have been harnessing the power of wikis to provide interactive forums
286 Chapter 8
Teamware Group, a Fujitsu subsidiary, implemented an interactive web community solu-
tion for the city of Kerava in Finland. The solution enhances communication between
and within the city managers, city board, city council, and other elected offi cials, and
offers them facilities to interact and distribute information regardless of time or location.
The objective of the system is to facilitate the daily work of the city administrators by
providing them with a new virtual means of interaction in addition to the traditional
meetings and sessions. “ It has become more and more diffi cult for the city administrators
to take care of their duties within the normal working hours and premises. Therefore,
it is essential to provide them with facilities to communicate and obtain information
without the boundaries of time or location, ” says IT manager Ari Sainio from the city of
Kerava.
The new system was built on the Teamware Pl@za platform and integrated with the
existing Teamware Offi ce groupware solution, which means that now e-mail, city archives,
electronic calendars, and bulletin boards will be available for the city administrators
through a standard web browser. In order to enhance interaction between the city offi cials,
the system is augmented with discussion facilities where individuals can exchange
opinions and discuss different issues. Various archives and fi les are created for content
management purposes. Different user groups are provided with their own virtual work-
spaces that can be accessed only by authorized members. Thanks to Teamware Pl@za ’ s
decentralized and easy-to-use updating functionality, the city offi cials can update the pages
themselves.
Box 8.2 An example: Teamware
for tracking projects and communicating with employees over their in-house
intranets.
An example is Wikipedia (http://en.Wikipedia.org/Wiki/Main_Page), a free ency-
clopedia written by literally thousands of people around the world. Wikis exist for
thousands of topics (http://www.worldwideWiki.et/Wiki/SwitchWiki). If one does not
exist for your favorite subject, you can start one on it and add it to the list.
Wikis support new types of communications by combining Internet applications
and web sites with human voices. That means people can collaborate online more
easily, whether they are working together on a brief or working with a realtor online
to tour offi ces space in another city. Outside the offi ce, it means customer service
representatives can interact with customers more readily, which should advance
e-commerce ( Leuf and Cunningham 2001 ). Cunningham, a programmer, decided to
build the most minimal working database possible and started the fi rst wiki in 1995.
The idea was to provide a simple web site where programmers could quickly and easily
Knowledge Management Tools 287
The Army ’ s after action review (AAR) is an excellent example of a process that ensures
lessons are learned after an event ( Bhatt 2000 ). British Petroleum (BP) and Chevron have
introduced similar systems whereby they learn before, during, and after the undertaking
of a large project. Major cost savings have been realized by introducing these learning
processes. For example, Chevron introduced a lessons learned tool for their drilling pro-
cesses. Every time they drill in a particular area, lessons are recorded. Next time drilling
takes place in a similar area, lessons learned during the last drilling operations are avail-
able. This results in fewer errors and less reinventing of the wheel. Chevron has also
recorded waste savings in their drilling operations.
The United States Air Force (USAF) is utilizing Open Text ’ s Livelink to manage its
Business Solutions Exchange (BSX), which involves integrating the people, process, and
policies of the USAF ’ s service contracting into a single system, paving the way for the
group to meet the Pentagon ’ s goal of a completely paper-free acquisition process. Prior to
installing Livelink, the USAF employed a variety of client-server based systems that had
diffi culty managing this process across different geographic locations. With the new col-
laborative KM approach, the USAF has reduced the time spent from identifying the point
of need to completing a performance requirement document (PRD) from seven months
to eight weeks, a 70% reduction in processing time.
The USAF ’ s KM initiative is part of the Pentagon ’ s requirement to simplify and modern-
ize the US Defense Department ’ s acquisition process in the area of contract writing,
administration, fi nance, and auditing. Since July 1998, the USAF has been using Livelink
on a variety of outsourcing projects. The fi rst and largest project can be found at the
Maxwell Air Force Base in Alabama. The goal of the business solutions exchange (BSX)
process is to continually improve USAF business practices. BSX goes to work as soon as a
requirement is identifi ed and a business strategy team is formed. The collaborative software
is used throughout the life cycle of the project, from requirements defi nition to contract
closeout, connecting a cross-functional team dispersed across a given base and the
command.. A team, often composed of people from six different locations within the US,
is formed to create a PRD and uses the collaborative software as its central knowledge
library to gather market research, establish an acquisition plan, record baseline costs,
eliminate regulatory constraints, draft requirements, and gather feedback from customers
and industry on the contract requirements. The BSX team works together throughout the
planning, execution, and supplier management phases. Teams use the public folders
(http://www.bsx.org) to gather feedback from industry on ways to improve existing
requirements documents. In addition, the public sites include process-oriented libraries of
best practices that are available to other agencies, whether or not they use the collabora-
tive capabilities of Livelink.
Box 8.3 An example: U.S. Army/Chevron/BP
288 Chapter 8
exchange information without waiting for a webmaster to update the site. He named
the site wiki, after the quick little Wiki-Wiki shuttle buses in Hawaii.
A public wiki survives thanks to the initiative, honesty, and integrity of its users.
Sites can be vandalized, derogatory remarks — called fl ames — can be posted, and mis-
information can be published. However, a vandalized site can be restored, a fl ame can
be erased, and information can be corrected by anyone who knows better. The com-
munity polices itself. Corporate wikis differ from public wikis in that they are more
secure and have many more navigation, usage, and help features. Corporate wikis are
used for project management and company communications and well as discussion
sites and knowledge databases. For example, a wiki can be established for a particular
project with the project team given access to update the status of tasks and add related
documents and spreadsheets. Its central location makes it easy to keep everyone
informed and up-to-date regardless of his or her home offi ce, location or time zone.
A wiki is more reliable than continually e-mailing updates back and forth to the team
members. It is faster than e-mail since updates are available instantly and more effi -
cient than e-mail since each team member does not have to maintain his or her own
copies. Managers like wikis because they can see what progress the team is making or
what issues it is facing without getting involved or raising concern (e.g., a new way
of doing of project management reporting).
For security reasons, corporations usually buy wiki software, rather than lease space
on the Internet, and set it up the wiki behind the company ’ s fi rewall as part of an
intranet or as an extranet if customers or vendors are allowed access. Also, corporations
look for wiki software that has authorization and password safeguards, roll-back ver-
sions for information to be restored to its former state, and easy upload capabilities
for documents and images. Some wikis notify users when new information is added,
an especially nice feature for corporate projects where fast responses are required.
Social Networking, Web 2.0, and KM 2.0
Social networking has rapidly become a part of everyday living and working, particu-
larly for the Y or millennial generation ( eMarketer 2008 ). As noted by Jones (2001 , 2),
“ knowledge management is inherently collaborative: thus a variety of collaboration
technologies can be used to support knowledge management practices. ” Social net-
works are dynamic people-to-people networks that represent relationships between
participants. A social network can serve to delimit or identify a community of practice
as it models the interaction between people. Wladawsky-Berger (2005 ) notes that
social networks are “ knowledge management done right ” (p. 1) as they address similar
goals to solve problems, increase effi ciency, and better achieve goals.
Knowledge Management Tools 289
Social network analysis (SNA; see http://www.insna.org) is a social science research
tool that dates back to the 1970s and has increasingly become used in KM applications
( Durkheim 1964 , Drucker 1989 , Granovetter 1973 , Lewin 1951 ). Valdis Krebs (2008)
defi nes SNA as the “ mapping and measuring of relationships and fl ows between
people, groups, organizations, computers, or other information/knowledge processing
entities. ” SNA can be used to identify communities and informal networks and to
analyze the knowledge fl ows (i.e., knowledge sharing, communication, and other
interaction) that occur within them ( Brown and Duguid 1991 ). SNA is one of the ways
of identifying experts and expertise to develop an expertise locator system. The basic
steps to develop a survey tool (e.g., a questionnaire) to collect the required data are
to identify network members and their exchange patterns. Next, the data are analyzed
using software such as Pajek (http://www.pajek.com) or UCINET (http://www
.analytictech.com) to identify patterns of interaction and emergent relationships. The
analyzed data can then be used to inform decision-making based on the objectives
( Scott 2000 ), for example, for change management, to establish a baseline in order to
later assess the effects of a technology introduction, or to improve upon the knowledge
fl ow and connections.
The combination of social networking, blogging, wikis, and other related technolo-
gies together defi ne Web 2.0 or the next generation of the web. Web 2.0 is a concept
that began with an interactive conference session between Tim O ’ Reilly and Dale
Dougherty that in turn led to the development of the annual Web 2.0 conference
( O ’ Reilly 2009 ). (http://en.oreilly.com/web2008/public/content/home). They defi ned
Web 2.0 as something without a hard boundary but rather a set of principles that
include:
• The web as a platform
• User control of your own data
• Services instead of packaged software
• An architecture of participation
• Cost-effective scalability
• Re-mixable data sources and data transformations
• Software that rises above the level of single device
• Harnessing of collective intelligence
A popular way of defi ning Web 2.0 is a form of concept analysis — the listing
of examples for both Web 1.0 and Web 2.0. For example, Netscape is an example of
Web 1.0 whereas Google exemplifi es Web 2.0. Microsoft Outlook e-mail is a Web 1.0
290 Chapter 8
application whereas Gmail (http://www.gmail.com) is a Web 2.0 application. Other
Web 2.0 examples include eBay, a digital marketplace (http:/www.ebay.com); BitTor-
rent, a free open source fi le-sharing application site for sharing large software and
media fi les (http://www.bittorrent.com); Wikipedia, a user-authored encyclopedia site,
(http://www.wikipedia.org); as well as folksonomies, viral marketing and open source
software sites. Many Web 2.0 sites contain RSS feeds — which allows someone to sub-
scribe to a webpage and be alerted to any changes. An RSS feed is much more reliable
than a link to what could be an ever-changing web site.
Finally the harnessing of the collective intelligence is a key attribute of Web 2.0
which means that the collective (i.e., the set of users) determine what is of value, what
is valid, and what is important ( Surowiecki 2004 ). The more people use a Web 2.0 site,
the more the site automatically improves. A key feature of Web 2.0 sites is that the
users of that site contribute the content.
IBM developed a social networking tool called Pass It Along (a free demonstration
is available at http://www.ibm.com/developworks/community/passitalong) to promote
knowledge sharing and skills development. Pass It Along integrates knowledge man-
agement, social networking, and Web 2.0 concepts to help users share and apply
information. Each user can decide how widely they want their content to be shared
and who they would like to collaborate with, for example, new hires, include external
partners or not or limit to a particular community of practice. Users can visually map
out their knowledge assets so others can see them.
KM 2.0 is analogous to Web 2.0 and refers to a more people-centric approach to
knowledge management. Companies are adopting KM 2.0 to varying degrees, mostly
based on their underlying culture and how well it promotes transparency and are less
concerned with control and availability of the underlying technologies. A surprising
example is the Central Intelligence Agency (see the vignette). Other examples include
IBM where a large collaborative online brainstorming session called InnovationJam
was held that included over 150,000 people ( Dearstyne 2007 ). Participants were not
only employees but also customers and business partners. The event ran for three days
with different topics being addressed in different moderated forums. The best ideas
generated were acknowledged and rewarded.
Lee and Lan (2007) suggest that traditional knowledge management (KM 1.0) is
based on knowledge repositories, the storing and preserving of knowledge but in
a largely static fashion. KM 2.0 represents a new paradigm and much like the core
attributes listed for Web 2.0, the authors propose corresponding attributes for KM
2.0 (p. 50). In building on a theme of collaborative intelligence, the following list of
Knowledge Management Tools 291
Web 2.0 technologies are enabling the CIA to share more information within their agency
in addition to their intelligence counterparts ( Wailgum 2008 ). The events of September
11, 2001, have catalyzed a series of reforms in the intelligence community, especially when
it became clear that key agencies were not able to connect the dots.
After 9/11, we asked ourselves: why was no one able to connect the dots? (David Ignatius, Associate Editor, The Washington Post). Could 9/11 have been prevented? In a number of crucial cases, mis- handled intelligence, bureaucratic tangles and legal hurdles blinded the CIA and the FBI to clues right in front of them. Individually, none of these was a smoking gun. But combined they were a four-alarm fi re. ( Frank 2004 )
The CIA is well aware of the post-9/11 analyses and reports that described how sixteen
government intelligence agencies were unable to puncture internal and external silos and
as a result critical information was not shared and was not aggregated to detect a pattern —
and a substantial threat. The CIA ’ s CIO Al Tarasiuk, introduced the notion of web 2.0 and
KM 2.0 into the sixty-one-year-old agency in the form of Intellipedia, modeled on Wiki-
pedia. Intellipedia is a bottom-up system that allows all US analysts to share their informa-
tion, their analyses, and even their insights with trusted peers over a secure network. The
new system is essentially a wiki for knowledge sharing that was implemented in 2006.
There is no anonymity as users log on and are authenticated each time they use Intelli-
pedia. There is a form of expertise locator system integrated within this system as users
can fi nd out who has expertise on a particular topic, a particular country, and so forth.
After two years in operation, Intellipedia has over forty thousand registered users who
have made almost two million edits on the web pages (which number around three
hundred thousand). It is interesting to note that the most prolifi c user of Intellipedia is
an employee who is preparing to retire, which indicates that such systems may also play
a role in organizational memory and knowledge continuity (see chapter 11).
In the old web 1.0 world, the content contained within Intellipedia would have been
shared with a limited amount of people and most likely through e-mail (which only served
to add to employee information overload). Intellipedia defi nes and enables the US intel-
ligence community and is a clear contrast to what prevailed before: a need to know basis
for knowledge sharing and one based on status, hierarchical relationships, and formal
authority. The major goal of Intellipedia is to enable collaboration across silos to help
participants solve complex problems and to connect all of the known dots. This requires
that participants speak the same language (i.e., share the same vocabulary and defi ne all
the dots in the same way). This new way of working also requires the motivation to share,
which in turn entails a change in organizational culture (see chapter 7). The major chal-
lenge is not with the technology but with a change in mind-set of the individuals and the
collective mind-set that prevails as the organizational culture.
Box 8.4 An example: Intellipedia at the CIA
292 Chapter 8
features may be considered as the objectives of knowledge contents development via
Web 2.0.
Contribution Every Internet user has the opportunity to freely provide their knowledge
content to the relevant subject domains.
Sharing Knowledge contents are freely available to others. Secured mechanisms may
be enforced to enable the knowledge sharing among legitimate members within spe-
cifi c communities.
Collaboration Knowledge providers collaboratively create and maintain knowledge
content. Internet users participating in the knowledge content can have conversations
as a kind of social interaction.
Dynamic Knowledge contents are updated constantly to refl ect the changing environ-
ment and situation.
Reliance Rnowledge contribution should be based on trust between knowledge pro-
viders and domain experts.
Once again, the best approach is one of inclusion rather than mutual exclusivity.
KM 1.0 is mainly focused on preserving valuable knowledge that has been created.
KM 2.0 is mainly concerned with user participation, knowledge fl ow and sharing, and
user-generated content with much more rapid feedback and revision of the knowledge.
The two can coexist in much the same way as taxonomies and folksonomies can
coexist. KM 2.0 is closer to the everyday operational concerns of knowledge workers
and serves as an excellent framework for collaboration and conversation with others.
KM 1.0 (as discussed in more detail in the next section) can then periodically access,
assess, incorporate the outputs of KM 2.0, and ensure that they are well preserved and
well organized for future retrieval and reuse.
Networking Technologies
Networking technologies consist of intranets (intra-organizational network), extranets
(inter-organizational network), knowledge repositories, knowledge portals, and web-
based shared workspaces. Liebowitz and Beckman (1998) defi ne knowledge reposito-
ries as an “ on-line computer-based storehouse of expertise, knowledge, experiences,
and documentation about a particular domain of expertise. In creating a knowledge
repository, knowledge is collected, summarized, and integrated across sources. ” Such
repositories are sometimes referred to as experience bases or corporate memories. The
repository can either be fi lled with knowledge by what Van Heijst, Van Der Spek, and
Kruizinga (1997 ) call passive collection, where workers themselves recognize what
knowledge has suffi cient value to be stored in the repository; or active collection,
Knowledge Management Tools 293
where some people in the organization are scanning communication processes to
detect knowledge.
Davenport and Prusak (1998) divide between three types of knowledge
repositories:
• External knowledge repositories (such as competitive intelligence)
• Structured internal knowledge repositories (such as research reports, product-
oriented market material)
• Informal internal knowledge repositories (such as lessons learned)
A knowledge repository differs from a data warehouse and an information reposi-
tory primarily in the nature of the content that is stored. Knowledge content will
typically consist of contextual, subjective, and fairly pragmatic content. Content in
knowledge repositories tends to be unstructured (e.g., works in progress, draft reports,
presentations). Knowledge repositories will also tend to be more dynamic than other
types of architectures because the knowledge content will be continually updated and
splintered into varying perspectives to serve a wide variety of different users and user
contexts. To this end, repositories typically end up being a series of linked mini-portals
distributed across an organization.
Most repositories will contain the following elements (adapted from Tiwana 2000):
• Declarative knowledge (e.g., concepts, categories, defi nitions, assumptions — knowl-
edge of what)
• Procedural knowledge (e.g., processes, events, activities, actions, manuals — knowl-
edge of how or know-how)
• Causal knowledge (e.g., rationale for decisions, for rejected decisions — knowledge
of why)
• Context (e.g., circumstances of decisions, informal knowledge, what is and what is
not done, accepted, etc. — knowledge of care-why)
The knowledge repository is the one-stop-shop for all organizational users to be
able to access all historical, current, and projected valuable knowledge content. All
users should be able to connect to and annotate content, connect to others who have
come into contact with the content, as well as contributing content of their own. The
interface to the repository or repositories should be user-friendly, seamless, and
transparent.
Personalization in the form of personalized news services through push technolo-
gies in the form of mini-portals for each community of practice and so forth will help
maintain the repository in a manageable state. To this end, the use of a term such as
294 Chapter 8
a knowledge warehouse should be strongly discouraged — the knowledge repository
should instead be visualized as a lens that is placed on top of the data and informa-
tion stores of the organization. The access and application of the content of a reposi-
tory should be as directly linked to professional practice and concrete actions as
possible.
The knowledge repository typically involves content management software tools
such as a LotusNotes platform and will be run as an intranet within the organization
with appropriate privacy and security measures in place. An example is described in
box 8.5.
Knowledge portals provide access to diverse enterprise content, communities,
expertise, and to internal and external services and information ( Collins 2003 ;
Price Waterhouse Coopers focused on sharing knowledge across what had been boundaries
following the merger of Price Waterhouse and Coopers & Lybrand. The chief knowledge
offi cer, Ellen Knapp, supported this effort by putting into place the KnowledgeCurve,
where employees can fi nd a repository of best practices, consulting methodologies, tax
and audit rules, news services, online training, directories of experts, and more, plus links
to specialized sites for various industries or skills. The site gets eighteen million hits a
month, mostly from workers downloading forms or checking news, but also from employ-
ees looking things up. Yet there is a feeling that it is underused. When looking for exper-
tise, most people still go down the hall.
In parallel, a British-based PWC consultant and his colleagues set up a network where
they could be more innovative. Over fi ve months they set up a Lotus Notes e-mail list
with no rules, no moderator, and no agenda other than what is set by the messages people
sent. Any employee was able to join. Kraken, as it came to be known, now has fi ve hundred
members and although it still has unoffi cial status, it has become the premier forum for
sharing. As an analogy, Kraken is to KnowledgeCurve what Carlos was to Eureka. On a
busy day, members may get fi fty Kraken messages but they are welcomed because they are
relevant and useful.
What are some of the reasons for this grassroots CoP success over corporate top-down
KM systems? It is demand-driven ( “ does anyone know … ” ); it gets at tacit knowledge; it
allows fuzzy questions rather than structured database queries; it is part of the everyday
routine; and it is full of opinions — points of view rather than dry facts. KnowledgeCurve
preserves explicit knowledge — Kraken enables the sharing of tacit knowledge. Kraken is
about learning; KnowledgeCurve is about teaching. You cannot have one without the
other.
Box 8.5 An example: Price Waterhouse Coopers (PWC)
Knowledge Management Tools 295
Firestone 2003 ). Portals are a means of storing and disseminating organizational
knowledge such as business processes, policies, procedures, documents, and other
codifi ed knowledge. They will typically feature searching capabilities through content
as well as through the taxonomy (categorized content). The option to receive personal-
ized content through push technologies as well as through pull technologies (intel-
ligent agents) may exist. Communities can be accessed via the portal for communication
and collaboration purposes. There may be a number of services that users can subscribe
to as well as web-based learning modules on selected topics and professional practices.
The critical content will consist of the best practices and lessons learned that have
been accumulated over the years and to which many organizational members have
added value.
The purpose of a portal is to aggregate content from a variety of sources into a
one-stop shop for relevant content. Portals enable the organization to access internal
and external knowledge that can be consolidated, analyzed, and used as inputs to
decision making. Ideally, portals will take into account the different needs of users
and the different sorts of knowledge work they carry out in order to provide the best
fi t with both the content and the format in which the content is presented (the portal
interface). Knowledge portals link people, processes, and valuable knowledge content
and provide the organizational glue or common thread that serves to support knowl-
edge workers. First generation portals were essentially a means of broadcasting infor-
mation to all organizational members. Today, they have evolved into sophisticated
shared workspaces where knowledge workers can not only contribute content and
share content but also acquire and apply valuable organizational knowledge. Knowl-
edge portals support knowledge creation, sharing, and use by allowing a high level of
bidirectional interaction with users.
Portals serve to promote knowledge creation by providing a common virtual space
where knowledge workers can contribute their knowledge to organizational memory.
Portals promote knowledge sharing by providing links to other organizational members
through expertise location systems. Communities of practice will typically have a
dedicated space for their members on the organizational portal and their own mem-
bership location system included in the virtual workspace. The portal organizes valu-
able knowledge content using taxonomies or classifi cation schemes to store both
structured (e.g., documents) and unstructured content (e.g., stories, lessons learned,
and best practices). Finally, portals support knowledge acquisition and application by
providing access to the accumulated knowledge, know-how, experience, and expertise
of all those who have worked within that organization. An application is described in
box 8.6.
296 Chapter 8
KPMG International implemented KWORLD, an advanced global knowledge management
system. KWORLD, an online messaging, collaboration, and knowledge-sharing platform,
is reportedly the fi rst system of its kind built entirely from standard Microsoft compo-
nents — Microsoft Windows NT Server, including Microsoft Exchange, Site Server, and
Microsoft Offi ce, Outlook, and Internet Explorer. KWORLD is KPMG ’ s digital nervous
system based on the Microsoft concept.
KPMG invested over one year and $100 million in developing this universally accessible
knowledge-sharing environment, which allows its nearly one hundred thousand profes-
sional workers to conduct active conferences and public exchanges, locate customized and
fi ltered external and internal news, and access global- and country-specifi c fi rm informa-
tion. As acknowledged by Microsoft, KPMG is one of only fi ve organizations to embark
on its fast-track program to exploit fully the power of the web browser, integrate Microsoft-
based messaging, collaboration and knowledge-sharing applications, and push current web
technology to the “ limit. ” Knowledge is content in context, and KPMG ’ s global communi-
ties of practice — who marry knowledge about complex services to specifi c industries —
determine KWORLD ’ s contextual frames. KWORLD brings qualifi ed internal content and
fi ltered external content to each community with a click. KPMG foresees developing
KWORLD extranets to make KPMG a virtual extension of its clients.
Box 8.6 An example: KPMG
Mashups were discussed in an earlier section as a form of portal (see the previous
section on Knowledge Creation and Codifi cation Tools). Both mashups and portals
aggregate content coming from different sources. However, there are some signifi cant
differences between the two tools. Portals are a somewhat older, more established tool
that serves to aggregate vetted and validated content to be stored for future use in an
organization. The purpose of a portal is to preserve organizational knowledge and to
make it available to all employees. Portals are well defi ned, often adhere to standards,
are updated according to an established schedule, only by those authorized to do so.
A portal is thus more formal in some ways. A mashup, on the other hand, is more of
a Web 2.0 application. Users tend to have complete control and autonomy in what
they choose to aggregate. This is often shared with others in a limited way (e.g., often
within their own community of practice). Mashups may have a limited life span as
they serve a specifi c purpose, such as putting together a presentation. Mashups are
not necessarily formalized nor do they need to be centralized in order to be useful
( Wong and Hong 2007 ).
Knowledge Management Tools 297
Knowledge Acquisition and Application Tools
A number of technologies play an important role in how successful knowledge workers
are in acquiring and applying knowledge content that is made available to them by
the organization. E-learning systems provide support for learning, comprehension,
and better understanding of the new knowledge to be acquired. Tools such as EPSS,
expert systems, and decision support systems (DSS) help knowledge workers to better
apply the knowledge on the job. Adaptive technologies can be used to personalize
knowledge content push or pull. Recommender systems can detect similarities or
affi nities between different types of users and make recommendations of additional
content that others like them have found to be useful to acquire and apply. Knowledge
maps and other visualization tools can help to acquire and apply valuable knowledge
better. A number of tools derived from artifi cial intelligence can at least partially
automate processes such as text summarization, content classifi cation, and content
selection.
E-learning applications started out as computer-based learning or tutoring systems
(CBT) and web-based training (WBT) applications. The common feature is the online
learning environment provided for learners. Courses can now be delivered via the web
or the company intranet. The particular knowledge and know-how to be acquired can
be scoped and delivered in a timely fashion in order to support knowledge acquisition.
E-learning technologies also greatly increase the range of knowledge dissemination as
knowledge that has been captured and coded or packaged as E-learning can be easily
made available to all organizational members, regardless of any time or distance
constraints.
Decision support systems are designed to facilitate groups in decision-making.
They provide tools for brainstorming, critiquing ideas, putting weights and probabi-
lities on events and alternatives, and voting. Such systems enable presumably more
rational and even-handed decisions. Primarily designed to facilitate meetings, they
encourage equal participation by, for instance, providing anonymity or enforcing turn
taking.
Visualization technologies and knowledge mapping are good ways of synthesizing
large amounts of complex content in order to make it easier for knowledge workers
to acquire and apply.
Artifi cial intelligence (AI) research addressed the challenges of capturing, represent-
ing, and applying knowledge long before the term knowledge management entered
popular usage. AI developed automated reasoning systems that could make use
of explicit knowledge representations in order to provide expert-level advice,
298 Chapter 8
troubleshooting, and other forms of support to knowledge workers. Expert systems
are decision support systems that do not execute an a priori program but instead
deduce or infer a conclusion based on the inputs provided. Natural language process-
ing also grew out of AI research. Linguistic technologies resulted in automating the
parsing (breaking into subsections) and analysis of text. Common applications today
are voice interfaces or natural language queries that can be typed in to search data-
bases. Similar AI technologies can also be applied to analyze and summarize text or
to automatically classify content (e.g., automated taxonomy tools). Many of the auto-
mated reasoning capabilities studied in AI research were encapsulated in autonomous
pieces of software code, called intelligent agents or software robots (softbots). These
agents act as proxies for knowledge workers and they can be tasked with information
searching, retrieving, and fi ltering tasks.
Intelligent Filtering Tools
Intelligent agents can generally be defi ned as software programs, which assist their
user and act on his or her behalf, such as a computer program that helps you in
newsgathering, acts autonomously and on its own initiative, has intelligence and can
learn, and improves its performance in executing its tasks ( Woolridge and Jennings
1995 ). They are autonomous computer programs, where their environment dynami-
cally affects their behavior and strategy for problem solving. They help users deal with
information. Most agents are Internet based, that is, software programs inhabiting the
Net and performing their functions there.
The following features are necessary to defi ne a true intelligent agent ( Khoo, Tor,
and Lee 1998 ):
Autonomy The ability to do most of their tasks without any direct assistance from an
outside source, which includes human and other agents, while controlling their own
actions and states.
Social ability The ability to interact with, when they deem appropriate, other software
agents and humans.
Responsiveness The ability to respond in a timely fashion to perceived changes in the
environment, including changes in the physical world, other agents, or the Internet.
Personalization The ability to adapt to its users needs by learning from how the user
reacts to the agent ’ s performance.
Initiative The ability of an agent to take initiatives by itself, autonomously (out of a
specifi c instruction by its user) and spontaneously, often on a periodical basis, which
makes the Agents a very helpful and time saving tool.
Knowledge Management Tools 299
Adaptivity The capacity to change and improve according to the experiences accumu-
lated. This has to do with memory and learning. An agent learns from its user and
progressively improves in performing its tasks. The most experimental bots even
develop their own personalities and make decisions based upon past experiences.
Cooperation The interactivity between agent and user is fundamentally different from
the one-way working of ordinary software.
There are many knowledge management applications that make use of intelligent
agents (e.g., see Elst et al. 2004). These include personalized information manage-
ment (such as fi ltering e-mail), electronic commerce (such as locating information
for purchasing and buying), and management of complex commercial and indus-
trial processes (such as scheduling appointments and air traffi c control). These
tasks/applications can generally be grouped into fi ve categories ( Khoo, Tor, and Lee
1998 ):
Watcher agents Look for specifi c information
Learning agents Tailor to an individual ’ s preferences by learning from the user ’ s past
behavior
Shopping agents Compare “ the best price for an item ”
Information retrieval agents Help the user to “ search for information in an intelligent
fashion ”
Helper agents Perform tasks autonomously without human interaction.
In the age of computers, information is readily available on the Internet, whether
it is useful or useless. There is so much data available that we often claim to be over-
loaded with information. Having too much data can cause as much trouble as having
no data, as we must shift through so much information to get what we need. We can
categorize this information overload problem into two divisions:
Information fi ltering We must go through an enormous amount of information to fi nd
the small portion that is relevant to us.
Information gathering There is not enough information available to us and we have
to search long and hard to fi nd what we need.
Information fi ltering is a particularly important function in KM, as users need a
way of fi ltering these data into a more manageable situation. Knowledge workers (such
as managers, technical professionals, and marketing personnel) need information in
a timely manner as it can greatly affect their success. Tasks that are redundant or
routine need to be minimized by some individuals that can otherwise spend their time
more productively ( Roesler and Hawkins 1994 ).
300 Chapter 8
Some companies receive so much e-mail that they have to employ clerical worker
to sift through the fl ood of e-mail, answering basic queries and forwarding others to
specialized workers. Others use intelligent fi ltering software such as GrapeVine for
Lotus, which reads a pre-established knowledge chart to determine who should receive
what mail. Intelligent agent services can supplement but not replace the value of
edited information. As information becomes more available, it becomes more and
more crucial to have strong editors fi lter that information ( Webb 1995 ). There is so
much content out there that the tools that fi lter content are going to be as important
as the content itself ( Wingfi eld 1995 ). As stated by the Rutherford Rogers, “ we are
drowning in information but starved for knowledge ” ( Rogers 1985 ).
An end user, required to constantly direct the management process, is the contrib-
uting factor to information overload. But having agents to do the tasks, such as search-
ing and fi ltering, can ultimately reduce the information overload to a degree. Maes
(1994) describes an electronic mail fi ltering agent called Maxims. Maxims is a type of
learning agent. The program learns to prioritize, delete, forward, sort, and archive mail
messages on behalf of a user. The program monitors the user and uses the actions the
user makes as a lesson on what to do. Depending upon threshold limits that are con-
stantly updated, Maxims will guess what the user will do. Upon surpassing a degree
of certainty, it will start to suggest to the user what to do.
Maes (1994) also describes an example of an Internet news-fi ltering program called
NewT. This program takes as input a stream of Usenet news articles and gives as output
a subset of these articles that is recommended for the user to read. The user gives NewT
examples of articles that would and would not be read, and NewT will then retrieve
articles. The user then gives feedback about the articles, and thus NewT will then be
trained further on which articles to retrieve and which articles not to retrieve. NewT
retrieves words of interest from an article by performing a full-text analysis using the
vector space model for documents. Some additional examples of information fi ltering
agents are shown in table 8.3 .
News agents are designed to create custom newspapers from a huge number of web
newspapers throughout the world. The trend in this fi eld is toward autonomous,
personalized, adaptive, and very smart agents that surf the net, newsgroups, databases,
and so on, and deliver selected information to their users. “ Push ” technology is strictly
connected to news bots development, consisting basically in the delivery of informa-
tion on the web that appears to be initiated by the information server rather than by
the client. Some examples are shown in table 8.4 .
Information overload is a problem of the world today, but intelligent agents help
reduce this problem. Using them to fi lter the oncoming traffi c of the information
Knowledge Management Tools 301
Table 8.3 Sample information fi ltering agents
Name Description Reference
Search pad An advanced bot that fi nds and categorizes relevant information based on the users preferences, also learning from them
http://www.searchpad.com
Copernic An agent that carries out net searches by simultaneously consulting the most important search engines on the web
http://copernic.com
Citizen 1 Finds thousands of the best databases on the Internet and indexes them into a hierarchy of fi les, making the Internet look like an extension of a PC fi le system
http://www.download.com/PC/ Result/TitleDetail/ 0,4,0-21278-g.html
NetAttachePro v1.0 A “ second generation web agent ” which features a powerful information-fi ltering intelligent agent that organizes off-line browsing
http://www.tympani.com/
Table 8.4 Examples of personalized news services
Name Description Reference
myCNN Personalized news service http://my.cnn.com
Excit News Tracker Pulls information from a collection of databases
http://nt.excite.com
Infoseek Personal News Personalized news service http://www.infoseek.com/ news?pg=personalize.html
Dogpile Fast, effi cient news service that draws upon a large database for its searches
http://www.dogpile.com
302 Chapter 8
highway can help reduce cost, effort, and time. Yet the development of intelligent
agents is still in its infancy. As they gain in popularity and use, we can expect to see
more sophisticated and better-developed intelligent agents.
Information studies research has studied information seeking behavior for over fi ve
decades now and this research can serve as an excellent theoretical basis for the study
of the Internet as an information source and intelligent agents as mediators in this
digital environment (e.g., Kulthau 1991 , 1993 ; Rasmussen, Pejtersen, and Goodstein
1994 ; Spink 1997 , Wilson 1981 , 1994 1999). Detlor (2003) used a case study to explore
how knowledge workers made use of Internet-based information systems and found
that information studies theory provides an appropriate framework for examining
Internet-based information seeking behaviors. Detlor, Sproule, and Gupta (2003) made
use of a similar conceptual framework to explore goal-directed behavior in online
shopping environments. Choo, Detlor, and Turnbull (2000a ) investigated how knowl-
edge workers use the web to fi nd information external to their organizations as part
of their daily work life. A typology of different complementary modes of using the
web as an information source was identifi ed and described (e.g., formal search, infor-
mal search).
Detlor (2004) adopted an information vantage point that views enterprise knowl-
edge portals as more than tools to merely deliver content. He instead see them as
shared workspaces that can facilitate communication and collaboration among knowl-
edge workers. Intelligent agents can play a signifi cant role to improve the interaction
between knowledge workers and knowledge portals for the successful completion of
everyday work tasks. Empirical research studies on information seeking helps defi ne
a web use model based on information seeking motives and modes. The advantage of
using a theoretical framework as a starting point is that online behavior and prefer-
ences can be better understood, explained, and predicted. These online behavioral
preferences can then be used to better design both online environments and mediators
such as intelligent agents.
Adaptive Technologies
Adaptive technologies are used to better target content to a specifi c knowledge worker
or to a specifi c group of knowledge workers who share common work needs. Custom-
ization refers to the knowledge worker manually changing their knowledge environ-
ment. For example, selecting user preferences to change the desktop interface,
specifying certain requirements in content to be provided to them (language, format),
or subscribing to certain news or listserv services.
Knowledge Management Tools 303
Personalization, on the other hand, refers to automatically changing content and
interfaces based on observed and analyzed behaviors of the intended end user. For
example, many MS Offi ce applications offer the option of dynamically reordering pop
down menu items based on frequency of usage (the ones used most often will be
displayed on the top). One way of automatically personalizing knowledge acquisition
makes use of recommender systems. Recommendations regarding content that is likely
to be considered useful and relevant by a given knowledge worker may be based on
a user profi le of that knowledge worker (e.g., with themes checked off) or the recom-
mendation may be based on affi nity groups. Affi nity groups make use of similarity
analysis of users in order to develop groups of individuals who appear to share the
same interests. Amazon uses affi nity groups for example, when after ordering a book
online, visitors to the site are provided with information on related books that others
who have bought the same book have also purchased.
Communities of practice are affi nity groups to some extent. Personalization tech-
nologies are often used to target or push certain types of content that is of interest to
a given community. Community profi les can be established just as individual profi les
and used in the same manner in order to better adapt content and interfaces to the
community members.
Strategic Implications of KM Tools and Techniques
Historically, the IT horse has always been placed before the KM carriage. It is crucial
to think of KM tools in strategic terms. It is often said that if we hold a hammer in
our hand, then all the problems we see look very much like nails. It is important to
avoid this bias in knowledge management. Tools and techniques are a means and not
an end. The business objectives must fi rst be clearly identifi ed and a consensus reached
on priority application areas to be addressed. For example, an initial KM application
will typically be some form of content management system on an internally managed
intranet site. This is a good building block for subsequent applications, such as yellow
pages or expertise fi nders and groupware tools to enable newly connected knowledge
workers to continue to work together. An illustration is provided in box 8.7.
A number of the techniques presented here address the phenomenon of emergence
that can help discover existing valuable knowledge, experts, communities of practice,
and other valuable intellectual assets that exist within an organization. Once this is
done, the intellectual assets can be better accessed, leveraged, and made use of. KM
tools and techniques have an important enabling role in ensuring the success of KM
applications.
304 Chapter 8
Practical Implications of KM Tools and Techniques
A number of techniques and tools, while never having been specifi cally developed for
or targeted to KM applications, have proven to be quite useful. A pragmatic toolkit
approach is needed for KM as there is no single end-to-end solution that can be simply
bought “ off the shelf ” in order to address all the critical dimensions of a knowledge
management initiative. It is therefore important to understand what is out there
already and what some of the new emerging tools are in order to adapt them and
make use of them for KM purposes.
Key Points
• Content creation and management tools are used to structure and organize knowl-
edge content for each retrieval and maintenance.
The Mercedes-Benz Customer Assistance Center in Maastricht, The Netherlands, serves as
a central customer contact point for the whole of Europe, handling all customer needs in
seventeen European countries, in twelve languages, twenty-four hours a day, 365 days a
year. In order to share knowledge of product information, technical information, and
business procedures as well as sample letters, FAQs, and best practices, a web-based knowl-
edge management solution was developed for Mercedes-Benz by CMG, a leading European
IT services business. Called BRAiN (backbone repository for archiving information), this
KM-based IT solution enables Mercedes-Benz Customer Assistance Center employees to
share and retrieve knowledge through the company ’ s corporate intranet. Full text search-
ing and dynamic knowledge maps allow users to navigate intuitively to the information
needed. Direct search facilities enable quick retrieval of all information related to a specifi c
vehicle, country, or market, and have been fi ne-tuned to support business needs. Web
technology facilitated a quick rollout within the organization and helps to minimize
maintenance. Attention was paid to all business aspects throughout the project phases. A
staged business approach, supported with incremental system development (RAD, rapid
application development), was applied. Both technical and organizational goals were
identifi ed at each stage. Procedures were defi ned for sharing knowledge, and these were
directly supported by the knowledge management system. BRAiN offers the possibility to
identify knowledge users, publishers, advanced publishers, and knowledge administrators,
each with their own rights and authorities.
Box 8.7 An example: Mercedes-Benz
Knowledge Management Tools 305
• Groupware and other collaboration tools are essential enablers of knowledge fl ow
and knowledge sharing activities among personnel.
• Data mining and knowledge discovery techniques can be used to discover or identify
emergent patterns that could not have otherwise been detected. Some of these may
provide valuable insights.
• Intelligent fi ltering agents are a KM technology that can help address the challenges
of information overload by selecting relevant content and delivering this in a just-in-
time and just-enough format.
• A knowledge repository will often be the most used and most visible aspect of a KM
technology. What is important is not so much the containers but the content and
how this content will be managed.
• Knowledge management technologies help support emergent phenomena involved
in the creation, sharing, and application of valuable knowledge assets.
Discussion Points
1. Discuss the pros and cons of the major technologies used in:
a. The knowledge creation and capture phase.
b. The knowledge sharing and dissemination phase.
c. The knowledge acquisition and application phase.
2. Data mining technologies can be used on a number of different types of knowledge
content. What are the major categories and what sorts of patterns would this technol-
ogy detect?
3. Describe an application of blog technology within an organization. What potential
benefi ts would accrue to the individual, the community of practice, and to the orga-
nization as a whole if blogs were implemented?
4. How would you categorize the different forms of groupware or collaboration tech-
nologies? What sort of criteria would you make use of in order to determine when
and where each type would be the best means of sharing and disseminating knowl-
edge? How would you adopt a cost-benefi t approach to such a technology selection
decision?
5. What role can a wiki play in promoting group collaboration? What advantages does
a wiki offer when compared to a discussion forum?
6. What role is played by e-learning tools in knowledge management?
306 Chapter 8
7. How can intelligent agents help knowledge workers fi nd relevant knowledge
content?
8. Describe how you would attempt to accommodate different user skill levels and
expectations in the same organization, in particular, what type of tools would be
recommended for the baby boomer versus the millennial generation of technology
users?
9. Select one new emerging technology and lists potential uses for knowledge manage-
ment. Make the connection between what the technology offers and each phase of
the KM cycle. For example, are some tools better suited to knowledge capture or
knowledge sharing?
10. Select any KM technology and describe how it may be applied at the individual,
group, and organizational level. Would they require different degrees of standardiza-
tion? Maintenance? Training?
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9 Knowledge Management Strategy
You have to be fast on your feet and adaptive or else a strategy is useless.
— Charles de Gaulle (1890 – 1970)
This chapter addresses the common building blocks that are developed in order to
apply and gain benefi t from knowledge management (KM) applications. The major
steps involved in developing a KM strategy are presented: the knowledge audit, the
gap analysis, the elicitation of KM objectives, the short-term road map, and the long-
term KM strategy. The general KM objectives of innovation and reuse will be discussed
in terms of how best to balance creativity with organizational structure.
Learning Objectives
1. Provide examples of major KM objectives and how specifi c KM initiatives can be
implemented to address them.
2. Illustrate the major elements of a KM strategy and discuss the processes involved
in each step.
3. Outline the key steps in the evolution of an innovative new idea and the institu-
tionalization of a best practice that forms the object of reuse.
4. Discuss and evaluate the different approaches that may be undertaken in order to
achieve an optimal balance between creativity and organizational structure.
5. List the different types of knowledge assets that result from KM initiatives.
Introduction
This chapter introduces the addition of a sound KM strategy that is linked to the overall
business objectives of the organization to the integrated KM cycle (see fi gure 9.1 ).
312 Chapter 9
The two most commonly encountered objectives of knowledge management are
innovation and reuse. Innovation is closely linked to the generation of new knowledge
or new linkages between existing knowledge. It is a popular misconception, however,
to think that innovation occurs in isolation — in fact, innovation rests fi rmly on a large
body of accumulated experiences, both positive and negative, based on what has
worked and what has not worked in the past. Creativity often involves lateral thinking
such as seeing an analogy in a completely different context. Similarly, reuse is often
mistakenly equated with dull, routine, and unproductive work. In actual fact, reuse
forms the basis for organizational learning and should be viewed more as a dissemina-
tion of innovation.
An evolutionary framework begins to emerge in which new knowledge in the form
of innovations eventually ends up becoming incorporated into organizational memory
to form the object of reuse so that the benefi ts of this new knowledge, know-how,
can be spread throughout the organization. The KM strategy provides the basic build-
ing blocks used to achieve this organizational learning and continuous improvement
so as to not waste time repeating mistakes and so that everyone is aware of new and
Assess
KM technologies
Update
Contextualize
Knowledge capture
and/or creation
Knowledge sharing
and dissemination
Knowledge acquisition
and application
Organizational culture
K M
t e
a m
KM strategy
Figure 9.1 An integrated KM cycle
Knowledge Management Strategy 313
better ways of thinking and doing. In addition, there will be a number of important
knowledge by-products that should be recognized and inventoried as knowledge assets
of the organization. These will typically include familiar, tangible items such as patents
as well as “ softer ” or more intangible assets such as core competencies.
Sveiby (2001) developed a framework for categorizing the different types of KM
initiatives. He uses three categories:
• External structure initiatives (e.g., gain knowledge from customers, offer customers
additional knowledge)
• Internal structure initiatives (e.g., build a knowledge-sharing culture, create new
revenues from existing knowledge, capture individual ’ s tacit knowledge, store it,
spread it and reuse it, and measure knowledge creating processes and intangible assets
produced)
• Competence initiatives (e.g., create careers based on KM, create microenvironments
for knowledge transfer and learn from simulations and pilot projects)
Lev (2001) uses different labels for the three main nexuses of sources of
intangibles:
• Discovery (innovation)
• Organizational practices
• Human resources
The sources of innovation and knowledge reuse consist of either internal or
external discoveries, or stem from business practices or from knowledge workers ’
competencies. More often, improvements will result from some combination of
these types of sources, as is illustrated in the discussion about the World Bank
(box 9.1).
A knowledge management strategy should target one or more of these objectives,
but the strategy must go further than high-level goals. Robertson (2004) points out
that a good KM strategy should identify the key needs and issues within the organiza-
tion, and provide a framework for addressing these. A number of different types of
business needs may trigger the need for KM. The most commonly encountered busi-
ness drivers include:
• Imminent retirement of key personnel
• Need for innovation to compete in dynamic, challenging business environment
• Need for internal effi ciencies in order to reduce cost and effort (e.g., time to market
a new product)
The World Bank has distinguished itself as a KM leader due to the swiftness with which
it was able to transform itself into the Knowledge Bank within only four years ( Pommier
2007 ). One of the major concerns that drove this transformation was being able to answer
queries faster and better — by drawing upon the collective knowledge of the Bank. In addi-
tion, the Bank faced the challenges of multiple databases and repositories, different IT
groups and tools, inconsistent information, and poor documentation and control. The
World Bank thus developed their KM mission statement: to develop a world-class reposi-
tory of their development experience and their cumulative knowledge.
One of the major success factors behind this rapid transformation was due to an inno-
vative technique, storytelling, which just happened to be developed by one of their own
employees, their KM champion, Stephen Denning. In fact, Denning came up with the
idea of a springboard story based on his years of frustration at trying to “ explain ” KM and
why they needed it to senior managers at the Bank. His idea was a story that would help
the audience — managers and decision makers — use the story as a springboard to leap to
an intuitive understanding of KM. Here is the story Denning used:
A health care worker in Zambia needed an anti-malarial preparation using only materials he had on hand. He sent a query via the World Bank ’ s Web site and he had a workable solution within 48 hours. He was able to harness the collective experience, expertise, and know-how of the World Bank to come up with the best possible answer in a timely way.
The World Bank KM program was off and running. The World Bank transformed itself
into a Knowledge Bank through its strategic goal of putting knowledge at the core of the
World Bank ’ s work. The elements of this strategy included:
People A focus on knowledge workers and connecting them via knowledge communities
(CoPs)
Culture Shifting the culture from an individualistic focus to a team and knowledge-
sharing culture
Accountability Clear roles and responsibilities established for knoweldge managers and
coordinators
Technology System to capture, organize, and disseminate knowledge to all stakeholders
of the Bank
Process Implement a series of small steps or quick hits and continually promoted aware-
ness and buy-in through “ relentless repetition ”
The World Bank has implemented corporate portals, knowledge repositories (including
image banks), a library of learning objects, video on demand and web casting content, a
live database, an expertise locator system, communities of practice (called “ thematic
groups ” ), after action reviews, peer learning, and fi eld visits and site tours to enhance learn-
ing. The major focus was on the thematic communities to restructure the Bank. Today,
there are about 123 thematic groups or communities of practice overseeing key areas such
as poverty, community development, and rural information technology infrastructures.
A small KM Board composed of fi ve people oversees all communities of practice. This
core KM team has overall coordination and facilitation responsibilities. They identify any
Box 9.1 A vignette: The World Bank
synergies or redundancies among communities, they identify opportunities for cross-
community knowledge sharing, they provide the link to organizational learning and
corporate memory systems, and they assess the value of the outputs of each of the com-
munities. A KM Council is the governance body that provides overall KM policy formula-
tion and has KM responsibility at the corporate level. In addition, knowledge sharing is
one of the four key behaviors that are evaluated in performance evaluations. Usage and
application of knowledge are behaviors that are rewarded — not numbers of hits or postings
on the intranet site. This is the major contribution required from the Human Resources
department. The World Bank spent roughly 3 percent of its total administrative budget
on KM. Of this, less than 10 percent was on technology (web, telephone, e-mail, and
videoconferencing) and 2 percent was for the operating costs of the central KM unit. The
rest went to fi nancing the thematic groups and the Knowledge Support Offi ce (KSO).
Operational managers in the communities and the regions are responsible for imple-
menting KM. Measurement, accountability, and budgets reside within the regions. Two
major forms of support are required from senior managers: that CoP leaders spend approxi-
mately 25 percent of their time on KM activities and that communities are supported by
KSOs that are best described as knowledge help desks.
The World Bank has established cost-effective, global connectivity with developing
countries to facilitate collaboration between offi ces, extend operational and administrative
information to staff at any location, and reduce the cost of doing business. For example,
the Bank provides an electronic venue for dialogue and knowledge sharing among members
of the development community. The Development Gateway is an Internet portal that
supports knowledge sharing and interactions to address the digital divide and poverty.
More than thirteen thousand staff in eighty countries are now linked together with high
speed and high quality so that everyone has access to the same work tools and informa-
tion. With the knowledge management system in place, the World Bank is able to provide
not only new services but higher quality services.
A primary indication that the World Bank made effective use of its knowledge is the
organizational innovation and entrepreneurial culture that was fostered partly as a result
of knowledge management and sharing initiatives. Some of the key concerns of the World
Bank such as timeliness or speed of creation of new knowledge, access to knowledge-
sharing methods, and innovation were also the focus of the measurements undertaken.
While it may be impossible to determine the contribution of KM with complete accuracy,
as is the case with most intangibles, it is possible to talk about the contributing role of
KM. In evaluating KM, a holistic approach was used in order to take into account human
and social as well as technological critical success factors.
In 2000, the American Productivity and Quality Centre (APQC) found the World Bank
to be one of the fi ve global best practice leaders. By 2001, The World Bank ranked fourth
place in the Most Admired Knowledge Enterprises Award and was been recognized again
in 2002, 2003, and 2004. The organizations in this study are recognized for their world-
class efforts to manage knowledge, leading to superior performance. Knowledge sharing
had become a way of doing business at the Bank.
Box 9.1 (continued)
316 Chapter 9
The resources and skills required to develop a KM strategy depend on the size and
complexity of the organizational unit and on the depth of information gathering and
analysis. The ideal mix of skills on the KM strategy team would be a KM expert, access
to people who are knowledgeable about the organization, and a KM advocate who
will “ sell ” the strategy to the senior member of management who mandated the
strategy development.
Developing a Knowledge Management Strategy
A KM strategy is a general, issue-based approach to defi ning operational strategy and
objectives with specialized KM principles and approaches ( Srikantajah and Koenig
2000 ). The result is a way of identifying how the organization can best leverage its
knowledge resources. Once this fundamental KM strategy is defi ned, baselining and
technology options may be explored. A KM strategy helps address the following
questions:
• Which KM approach, or set of KM approaches, will bring the most value to the
organization?
• How can the organization prioritize alternatives when any one or several of the
alternatives are appealing and resources are limited?
Once the KM strategy is defi ned, the organization will have a road map that can
be used to identify and prioritize KM initiatives, tools, and approaches in such a way
as to support long-term business objectives. The strategy is used to defi ne a plan of
action by undertaking a gap analysis. The gap analysis involves establishing the
current and desired states of knowledge resources and KM levers. Specifi c projects are
then defi ned in order to address specifi c gaps that were identifi ed and agreed upon as
being high priority areas.
A good KM strategy is composed of the following components:
1. An articulated business strategy and objectives
a. Products or services
b. Target customers
c. Preferred distribution or delivery channels
d. Characterization of regulatory environment
e. Mission or vision statement
2. A description of knowledge-based business issues
a. Need for collaboration
Knowledge Management Strategy 317
b. Need to level performance variance
c. Need for innovation
d. Need to address information overload
3. An inventory of available knowledge resources
a. Knowledge capital: tacit and explicit knowledge, know-how, expertise, experience
in the minds of individuals and in communities or embedded in work routines, pro-
cesses, procedures, roles, and artifacts such as documents or reports
b. Social capital: culture, trust, context, informal networks, and reciprocity (e.g., will-
ingness to experiment, take risks or able to fail without fear of repercussions)
c. Infrastructure capital: physical knowledge resources, for example, LAN/WAN, fi le
servers, intranets, PCs, applications, physical workspaces and offi ces, and the organi-
zational structure
4. An analysis of recommended knowledge leverage points that describes what can be
done with the above-identifi ed knowledge and knowledge artifacts and that lists KM
projects that can be undertaken with the intent to maximize ROI and business value,
for example
a. Collect artifacts and exploit them, for example, a best practices database, a lessons
learned database
b. Store for future use, for example, data warehouses, intelligence gathering for specifi c
issue/problem, data mining, text mining
c. Focus on connecting; connect “ knowers ” to each other and to a problem through
CoPs or expertise location systems; hypothesize to carry out scenario planning and
informal cross-pollination to produce new insights and breakthrough thinking
The major steps involved in developing a KM strategy are to fi rst understand the
organization in terms of its current state (as is) and its desired business objectives (to
be). The analysis of the difference between the two states is often referred to as a gap
analysis and the means of getting from the “ as is ” to the “ to be ” is often represented
in the form of KM strategic road map. The road map typically represents a three- to
fi ve-year strategy with clear milestones or targets to be achieved throughout that
time.
The current or baseline state of the organization is assessed using information
gathered from a variety of sources such as key documents (e.g., annual report) and by
interviewing key stakeholders (e.g., senior managers, human resources, information
technology and major business unit managers). It is at this point that existing KM
initiatives will also be identifi ed in the form of a knowledge audit or inventory.
318 Chapter 9
Knowledge Audit
A knowledge audit service identifi es the core information and knowledge needs and
uses in an organization. It identifi es gaps, duplications, fl ows, and how they contribute
to business goals. A knowledge inventory (sometimes called an information audit or
a knowledge map) is a practical way of coming to grips with “ knowing what you
know ” by applying the principles of information resources management (IRM). A
knowledge audit identifi es owners, users, uses, and key attributes of core knowledge
assets. Willard (1993) discusses fi ve key activities of IRM:
Identifi cation What information is there? How is it identifi ed and coded?
Ownership Who is responsible for different information entities and coordination?
Cost and value A basic model for making judgments on purchase and use
Development Increasing its value or stimulating demand
Exploitation Proactive maximization of value for money
A knowledge audit is often carried out in conjunction with a KM assessment, which
provides a baseline on which to develop a KM strategy ( Skyrme 2001 ). This typically
involves taking stock of current KM capabilities and is often carried out as part of a
KM strategy formulation exercise.
A knowledge audit would result in the following types of results:
• Identifi cation of core knowledge assets and fl ows — who creates, who uses
• Identifi cation of gaps in information and knowledge needed to manage the business
effectively
• Areas of information policy and ownership that need improving
• Opportunities to reduce information handling costs
• Opportunities to improve coordination and access to commonly needed
information
• A clearer understanding of the contribution of knowledge to business results
An example from Northrop Grumman is provided (box 9.2).
A KM program or system should never be implemented without a knowledge audit
having been conducted. Most importantly the precursor to spending a lot of money
on KM technology is a proper knowledge audit to determine exactly what tools and
solutions are most appropriate to enable better KM by the knowledge people in the
organization. It is people that will be required to use the newly procured technology
and adapt to the new KM system. It is therefore prudent that every attempt be made
to consult with all or most knowledge people in the organization before any KM
Knowledge Management Strategy 319
Northrop Grumman faced consolidation and downsizing during the late 1990s. The Air
Combat Systems (ACS) group in particular was in danger of losing the expertise it needed
to support and maintain a complex machine that would be fl ying — carrying precious lives
and cargo — for years to come. So ACS instituted KM procedures designed to capture tacit
knowledge about the B-2 that was locked in its employees ’ heads. But before designing a
program, ACS wanted to fi nd out what barriers, if any, prevented employees from sharing
knowledge with their peers. With a good picture of the knowledge culture attitudes, ACS
would then have a better road map for designing a unit-wide KM program. They conducted
a knowledge audit, surveying employees about their knowledge-sharing habits, polling
nearly fi ve thousand employees with a ninety-seven-question survey (KM2) to determine
their knowledge needs, sharing practices, and prejudices. The survey asked questions such
as, “ From your perspective, to what extent is the knowledge that you and your team
generate reused by other teams? ” This not only highlighted ACS ’ readiness for a formal
KM effort but also pointed out areas where sharing was not happening. The Delphi group
was hired to conduct the audit and derive a baseline pulse of the unit ’ s knowledge-sharing
culture. Participation was voluntary — employees were given a free lunch for giving 30
minutes of their time. The survey response rate was better than 70 percent (typically,
mail-in surveys return a 10 – 30 percent response). Delphi consultants analyzed the prelimi-
nary results and targeted 125 employees for face-to-face follow-up interviews.
ACS had established a ten-person KM team to identify subject matter experts and
capture the content of their expertise. After creating about one hundred knowledge cells
and identifying two hundred subject matter experts within those cells, the KM council
turned their attention to knowledge capture. The team created web sites for each of the
knowledge cells and logged information about the knowledge experts into an expert
locator system called Xref, short for cross-reference. Using Xref, employees can search for
information in any number of ways, including by employee name, program affi liation, or
skill area. If, for example, the B-2 landing gear is locking up, one can fi nd the landing gear
expert through Xref. The knowledge audit helped ensure that this centralized database
would not only be useful but would actually be used.
The results of the knowledge audit confi rmed that employees were eager to share their
knowledge in an automated, centralized system but that challenges, such as integrating
the systems across lines of business, remained. The willingness of employees to participate
in systems intended to minimize the impact of their own eventual layoff is, of course,
highly dubious. Other key fi ndings showed employees recognized the value of their fellow
employees ’ expertise. For example, they spent at least eight frustrating hours each week
looking for information they needed to do their job (costing $150 million annually), only
6 percent of their knowledge was reused by others, and 31 percent believed that ideas
generated by junior staffers were not valued and were likely to get smothered by the ACS
bureaucracy.
Box 9.2 An example: Northrop Grumman
320 Chapter 9
The ACS knowledge strategy based on these results made use of three dimensions. (1)
On the human side, the KM team set out to identify experts and communities of practice
to facilitate sharing among employees (e.g., the CoP of project managers on different ACS
programs). CoPs exist informally — it is important to identify the ones that are strategically
important, raise their visibility, and provide funding and support systems for them. (2)
On the process side, the KM team focused on fi nding out how people captured, organized,
and reused existing knowledge. A central repository was created to amalgamate knowledge
previously found in personal employee fi les in order to share lessons learned. The F/A-18
fi ghter jet program, for example, now has a web-based system that capitalizes on years
of technical expertise by tracking structural problems with the aircraft. When an issue
arises — a cracked part, for example — the fi rst thing an engineer does is search the tracking
system ’ s nine hundred previously encountered experiences. If it is a new problem, he or
she inputs the relevant information using a PowerPoint template that can include pic-
tures, drawings, and notes on the appropriate sections. Each week, engineers meet to
discuss unresolved issues. Once it is resolved, it is automatically entered as a lesson
learned. (3) The technology piece of the strategy serves as the glue lashing the KM initia-
tive together — the homegrown Xref system, collaboration applications, and document
management systems. The fi ve technology areas are portals, expert locator, knowledge
capture, media management, and collaboration, as these address the key barriers found
in the knowledge audit: paper-based fi ling systems, disparate locations, and inability to
locate internal expertise. Other initiatives, including portals that push personalized infor-
mation, are in the pilot phase. The KM team plans to conduct follow-up audits every
eighteen months or so to keep tabs on the evolution of KM initiatives and the knowledge-
sharing culture.
Box 9.2 (continued)
system is purchased and implemented. This is where the knowledge audit plays a
pivotal role in a new KM initiative. The company ’ s “ knowledge people ” are the core
of its knowledge audit and hence no knowledge person should be marginalized during
the knowledge audit initiative/process.
It is of vital importance that an organization ’ s KM initiators or practitioners always
seek to assess the company ’ s current KM health, before proceeding to implement KM.
The knowledge audit serves the purpose of providing evidence-based information and
knowledge of the audited units current knowledge status or “ knowledge health. ” This
evidence-based knowledge is the launching pad into a new KM program. The knowl-
edge audit is also extremely useful as a regular review and assessment of existing KM
practices in the company. Management and exploitation of corporate knowledge is
Knowledge Management Strategy 321
More often than not, KM practitioners fi nd themselves facing an organization that is
convinced they need KM but cannot say why. In one large business unit, the stakeholders
repeatedly insisted that knowledge sharing was blocked and no one knew whom to turn
to for expert advice. They were convinced that “ KM issues ” were preventing them from
carrying out one of the major mandates that was to assess the environmental health of a
particularly sensitive area. Upon conducting an audit, the results quickly aggregated into
one very strong theme: that of information management. Most respondents felt that they
were great at sharing knowledge but they just could not get their hands on the data and
information they needed. Some data sets were found to be over fi fty years old but still
critically needed to do trend analyses — and these old data sets were on a medium that no
one had a reader for. One was eventually tracked down in an archive and the data was
transferred to more modern media for preservation. A second data set was sitting in card-
board boxes because the scientist in charge of the project had retired. Actually, the boxes
were originally in the scientist ’ s basement and his family contacted the company when
he passed away, asking if they would like the boxes. The only drawback: the encryption
key needed to decode the data was nowhere to be found. A Library and Information Studies
intern had developed the key as a classifi cation and fi nding aid fi fteen years previously,
and no one had thought to make a backup of the key.
The knowledge audit results showed that problems existed at the information access,
preservation, and retrieval level. Much like the old adage that one should “ learn to walk
before running a marathon, ” this particular organization did not have a good sense of
where the immediate needs lay. KM was relegated to a more long-term strategy recom-
mendation and the action plan addressed more pressing information management
concerns, which will in turn be needed to provide a solid infrastructure for knowledge
management.
Box 9.3 A vignette: How do we know they need KM?
intrinsically intertwined in the corporate knowledge culture, which is in turn deter-
mined and maintained by the corporate knowledge people. This is why a knowledge
audit must be people-focused.
Stakeholder interviews can help identify key knowledge needs to yield a knowledge
map ( Robertson 2004 ). Sample questions will typically include:
• What is your job role and major responsibilities?
• How long have you been working for the organization?
• Who do you communicate with most frequently on work matters?
• Do you have policies or guidelines for your work? If so, how do you access these?
322 Chapter 9
• What information do you rely upon during a normal working day? Where do you
obtain this?
• If you have a question, where do you go to fi nd the answer?
• Who asks you what types of questions?
• What sort of orientation and refresher training have you received?
• How do you fi nd out what is happening in the organization?
• What sorts of news do you read regularly?
• What type of knowledge is needed to do your work?
• How do you add value to the organization? Where do your knowledge artifacts
reside?
• How could knowledge fl ow be improved, in your opinion?
• What would make your work easier?
A knowledge audit is typically carried out by interviewing individuals or groups or
by administering a survey questionnaire. It is highly recommended that audit ques-
tions be prepared ahead of time even if the interview method is chosen. A compre-
hensive questionnaire can serve as either a web-administered survey or as an
interviewing guide. In the questionnaire in table 9.1 (adapted from Liebowitz et al.,
2000 , 5 – 6), knowledge categories refer to the types that you need to know to do your
job; for example, a professor needs to know how to teach, conduct research, and
supervise graduate students; a lawyer needs to know about legislation; a doctor needs
to know about diagnostic techniques, and so on.
Knowledge mapping is an ongoing endeavor — not a one-time activity. The knowl-
edge map is a navigation aid to explicit/codifi ed information and tacit/uncodifi ed
knowledge (Grey 1999). The map should provide an inventory and evaluation of intel-
lectual or knowledge assets of an organization.
Once the “ as is ” portrait of the organization has been completed through informa-
tion gathering and the knowledge audit, a gap analysis can be performed.
Gap Analysis
The difference between the existing and desired KM state of the organization is
analyzed in terms of enablers and barriers to successful KM implementation. A good
gap analysis should address the following points ( Zack 1999 ; Skyrme 2001 ):
• What are the major differences between the current and desired KM states of the
organization?
Knowledge Management Strategy 323
Table 9.1 Sample knowledge audit questionnaire
Question Number Question text
1 List specifi cally the categories of knowledge you need to do your job
2 Which categories of knowledge listed in question 1 are currently available to you?
For each category of knowledge you specifi ed in question 1, answer the following:
3 How do you use this knowledge? Please list specifi c examples.
4 From how many sources can you obtain this knowledge? Which sources do you use? Why?
5 Besides yourself, who else might need this knowledge?
6 How often would you and the others cited in question 5 use this knowledge?
7 Who are potential users of this knowledge who may not be getting the knowledge now?
8 What are the key processes you use to obtain this knowledge?
9 How do you use this knowledge to produce a value-added benefi t to your organization?
10 What are the environmental/external infl uences impacting this knowledge?
11 What would help you identify, use or transform this knowledge more effectively?
12 Which parts of this knowledge do you consider to be (a) in excess/ abundance; (b) sparse; or (c) ancient/old/outlived its useful life?
Answer the remaining questions for knowledge you make use of in general:
13 How is knowledge currently being delivered? What would be a more effective method for delivering knowledge?
14 Who are the experts in your organization housing the type of knowledge you need?
15 In what form is the knowledge that you gained from the experts?
16 What are the key documents and external resources that you use or would need to make your job easier?
17 What are the types of knowledge that you will need as a daily part of your job (a. in the short term (one to two years)? (b. in the long term (three to fi ve years)?
Source: Adapted from Liebowitz et al. 2000, 6.
324 Chapter 9
• List barriers to KM implementation (e.g., culture where “ knowledge is power ” or
where individual possession of knowledge is consistently rewarded)
• List KM leverage points or enablers (e.g., existing initiatives that could be built upon)
• Identify opportunities to collaborate with other business initiatives (e.g., combine
knowledge continuity goals with succession planning initiatives in human resources)
• Conduct a risk analysis (e.g., knowledge that will soon “ walk out the door ” due to
imminent retirements or knowledge that is considered to be at risk because only a few
individuals are competent in this area and very little of their expertise exists in coded
or tangible knowledge assets)
• Identify redundancies within the organization (e.g., the case of the right hand not
knowing what the left hand is doing)
• Identify knowledge silos (e.g., groups, departments or individuals that hoard knowl-
edge or block fl uid knowledge fl ows to other groups, departments or colleagues)
• Determine how the organization ranks with respect to others within the industry
(e.g., are they early adopters of KM, KM leaders that are emulated by others, or are
they just becoming aware of KM needs within their organization)
One of the ways to perform gap analysis is to locate any gaps in knowledge. A good
way to do this is to once again survey and/or interview key stakeholders to fi nd out
what types of knowledge they would like to have in contrast to what they actually
have. A second set of questions (adapted from Liebowitz et al. 2000 , 7), as shown in
table 9.2 , can help complete this step of the analysis required for a KM strategy.
Next, the gap analysis will need a list of prioritized KM objectives to be addressed
by the organization. This list is typically gathered through interviews with senior
management and focus groups with the managers of all core business divisions. The
sessions are a form of brainstorming where participants are encouraged to think “ blue
sky ” thoughts, that is, to momentarily ignore constraints and reality checks and envi-
sion a more utopian version of their company. Typical questions would include: If all
were possible, what would your ideal day be like? What are some of the thorns in your
side that you would like taken care of immediately? What major changes would have
an enormous impact on your company ’ s effi ciency and effectiveness?
The differences between the “ as is ” situation, as assessed by the fi rst step in the
audit, serves to paint a portrait of the status quo, warts and all. The second stage asks
the stakeholders to put into words their visions for an improved version of their orga-
nization, one with an ideal culture, technological infrastructure, and skilled resources
and, above all, with no constraints. After this brief respite, the stakeholders are then
Knowledge Management Strategy 325
brought back to earth by asking them to now think about the feasibility, the cost –
benefi t, and the priority of each of these desired objectives.
The results of the gap analysis should be validated by returning to the stakeholders
who were initially involved in the information gathering and needs analysis phases.
The priorities should be determined by a consensus of the organization ’ s key stake-
holders. The result will be a KM strategy document that can be used as road map to
implement short-term KM initiatives within the organization (those with the highest
scores on feasibility, cost – benefi t, and priority) as well as a longer-term KM strategy
that will describe some of the longer, more complex initiatives.
The KM Strategy Road Map
The fi nal recommended strategy would typically cover a three- to fi ve-year period,
outlining the key priorities for each year. The road map addresses issues such as:
• How will the organization manage its knowledge better for the benefi t of the
business?
Table 9.2 Questionnaire to identify missing knowledge
Question number Question text
1 What kinds of knowledge do you reuse? Can you think of examples where reuse would be benefi cial but is not being done?
2 What types of questions do you have for which you cannot fi nd the answers? Are these questions related to your job performance or to administrative procedures?
3 What kinds of questions do you ask repeatedly?
4 Do you know whom you should direct your question to?
5 What kinds of questions are you asked? What do you do if you do not know the answer?
6 What mechanisms might be helpful for encouraging knowledge sharing and transfer in your organization?
7 What aspects of your organization seem to provide barriers to effective KM? What constraints impede knowledge sharing and transfer?
8 What are the main reasons why you could have made errors/mistakes on the job?
9 If your organization has considered outsourcing in the last 5 years: (a. In what areas was outsourcing considered? (b. If outsourcing was rejected, why? (c. If outsourcing occurred, why?
10 How much time do you spend looking for knowledge? (a. In a given day? (b. In a given week?
Source: Adapted from Liebowitz et al. 2000, 7
326 Chapter 9
The knowledge audit and gap analysis phases of the KM strategy will help determine what
the KM efforts should focus on within a given organization. While there are some high-
level goals such as effi ciency or innovation and some generic KM initiatives such as
implementing communities of practice or an expertise locator system, each strategy will
necessarily be unique. Every organizational context is different so a “ one size fi ts all ”
approach cannot work for a KM strategy. The audit or diagnostic phase ensures that the
core characteristics of the organization are well-understood and taken into account in
proposing KM recommendations.
For example, in a public utility company, an extensive audit revealed that while explicit
knowledge was formally shared quite extensively, there were few if any opportunities to
meet to share knowledge informally. As a result, the lessons learned were edited so as to
not cause any undue alarm, with the result that when they reached the eyes of the CEO,
the reports all read a bit like “ something terrible happened, we were not 100 percent
prepared, we dealt with it, all is now back to normal. ” In fact, the knowledge audit revealed
that this organization worked exceedingly effi ciently and effectively under normal
operational conditions. In the context of an emergency, however, work teams no longer
knew their roles, they could not collaborate in more dynamic, tacit ways preferring to
keep to “ the book ” or manuals and rules, and they often failed in carrying out their critical
duties.
For this particular organization, an emphasis on tacit knowledge and informal ways of
sharing this knowledge became a critical focus for the KM strategy. Employees were
encouraged to meet and discuss project postmortems with peers before reporting more
formally up the hierarchical levels of authority. Additional recommendations were made,
including short term training of teams so that they could better perform in crisis situations
through role playing and simulations in the short term; and beginning the journey to
cultural change by encouraging employees to send anonymous e-mails directly to the CEO
and rewarding them for risk-taking.
Another organization, an international aid outfi t, revealed quite a different focus
for KM during the course of their KM audit. This organization had branches around the
world and operated in a highly complex environment: multiple locations, multiple
languages, and multiple stakeholders, including funding agencies, partners in the various
countries, and a high turnover rate due to two-year mandates. The audit revealed that
tacit knowledge was being well shared throughout the organization, primarily through
informal contacts using Skype (voice over Internet) and occasional face-to-face meetings.
A number of bottom-up or grassroots communities of practice had emerged on their own,
further linking geographically dispersed workers around a common mandate theme. In
fact, this organization ’ s evolution in KM terms mimicked that of the World Bank, which
created over one hundred thematic communities to better harness the expertise that they
provided to third world countries.
Box 9.4 A vignette: What should KM focus on within our rrganization?
Knowledge Management Strategy 327
The gap analysis showed that the critical KM missing in this organizational context
was the formal capture and sharing of explicit knowledge. Meetings were often held
without an agenda, attendees changed at the last minute, and the proceedings seemed
quite chaotic to an outsider. For example, the topics to be addressed were arbitrarily
changed, priorities were suddenly announced, and discussions were very diffi cult to follow.
Attendees often interrupted one another, there was no set time for the meeting to end,
and there was no one to chair or to take the minutes. Employees explained that this was
the “ culture ” of the place — where everyone was involved in everything and every decision
was made by consensus. There was little systematic documentation of meeting results.
There was also very little refl ection on completed projects and what documentation did
exist was often very diffi cult to track down. Reports were written for each project, but the
reports varied in structure and content as each was dedicated to an external audience. KM
seemed to be invoked in order to fulfi ll very specifi c demands of external parties but rarely
was the KM lens turned inward.
As a result, the organization had to focus KM efforts on the knowledge capture and
codifi cation side of things. This would require the organization to identify the types of
knowledge they have and that they need to have, and to fi gure out how to render these
more visible and therefore easier to access by others.
Box 9.4 (continued)
• Content (management of explicit knowledge) and community (management of tacit
knowledge) priorities
• Identifi cation of processes, people, products, services, organizational memory,
relationships, knowledge assets as high priority knowledge levers to focus on
• What is the clear or direct link between KM levers and business objectives?
• What are some quick wins (i.e., early relatively inexpensive KM successes)?
• How will KM capability be sustained over the long term? (e.g., defi ned KM roles)?
A typical KM strategy document will contain the results of the audit, an inventory
of what exists, what KM initiatives were implemented or tried out, what types of
knowledge exists, who uses this knowledge, and how and whether or not knowledge
is being shared and disseminated throughout the organization. In parallel, it is also
important to assess the current status of the two key enablers of KM: the technological
infrastructure and the type of prevailing culture (or microcultures within different
units). All of the pieces of the audit can then be integrated to provide a snapshot of
the organization at this point in time and a high-level diagnostic: for example, the
level of organizational readiness for KM (based on KM maturity models, discussed in
328 Chapter 9
chapter 7), whether or not they have an intranet or other means to ensure that every-
one can connect with everyone else and access existing knowledge; as well as some
of the potential obstacles that may cause some issues with future KM implementations.
The prioritized “ wish list ” developed in the next phase serves to show where the
organization would like to be in the short-term (one to three years) and long-term
(three to fi ve years) time horizon. The gaps are thus the differences (measured by the
width of the gap) between what is and what should be and the strategy recommenda-
tions outline how the company should close these gaps.
The table of contents of a good KM strategy document is shown in table 9.3 . The
strategy should contain both diagnostic and prescriptive content. In addition, the
recommendations should not be so generic or abstract that it is not clear how they
could be implemented. In other words, the recommendations should be packaged
together with the resources needed for each recommendation such as cost and human
resources together with the required skill set and training (KM roles and responsibili-
ties are discussed in chapter 12) and a way of assessing whether or not implementation
was successful (KM metrics, discussed in chapter 10).
An illustration of the critical importance of closely aligning KM strategy to the
overall organizational business goals is described in the detailed look at Ford (box 9.5).
Balancing Innovation and Organizational Structure
Klein (1999) discusses the importance of maintaining a balance between fl uidity and
institutionalization as the dynamic equilibrium that should ideally exist between
innovation and organizational structure. The fl uid intellectual domain consists of
individuals with ideas originating and growing from a given person (intuition), per-
sonal networks that form outside formal organizational charts (CoPs), chance encoun-
ters that occur between people, and improvisation that ignores standard procedures
to discover better ways of doing things. In contrast, the organization strives to struc-
ture work, to control processes, and to measure outcomes. Explicit knowledge is clearly
defi ned in procedures, reports, memos, and databases. This knowledge is usually selec-
tively shared through offi cial chains of command or organizational hierarchies. How
then to strike the right balance?
If the organization is too fl uid, there will be no solid connection of knowledge work
to business goals, and it will be diffi cult to have clear accountability. If the balance
shifts too much in favor of institutionalization, however, the organization risks becom-
ing too formal, which can stifl e innovation and the open communication necessary
for creative work to take place (see fi gure 9.2 ).
Knowledge Management Strategy 329
Table 9.3 Recommended table of contents for a KM strategy
Section number Section title Comments
Metadata Document history/ information
Include information about authors, contact person, date last revised, authority owners, and distribution limits (usually not a public document)
1 Executive summary Maximum of two pages
2 Introduction The organizational context, the business drivers that led to a KM requirement
3 KM audit — key fi ndings Thematic summaries from stakeholder interviews; inventory of what exists (intranet, KM projects, knowledge categories); assessment of KM maturity; potential KM enablers and obstacles — where they are now
4 KM objectives Prioritized list, based on stakeholder consensus, on the company KM wish list — where they would like to be in the short and long term
5 Gap analysis — key fi ndings Assessment of how far apart the status quo is from the desired future state; analysis showing ranked gaps — from least to greatest
6 Recommendations The way forward — the major priorities that need to be addressed, when and how and by whom
6a Short term Action plan for the next one to three years with cost-benefi t analysis, resources, and metrics identifi ed
6b Long term Strategic objectives with results projected in the next three to fi ve years, clearly showing how this builds on the action plan
7 Conclusions Identify next steps; include governance (who approves strategy, when will it be updated, assessed, etc.)
8 Appendices Include (as documents or links to intranet) all data gathered (ensure participant confi dentiality — if conferred — is fully respected) so that the reader can dig deeper to fi nd sources and justifi cations if needed
330 Chapter 9
Ford and Firestone suffered the death of a thousand cuts, in part because of a catastrophic
failure to share knowledge. Information that might have alerted the companies to the
calamitous mismatch of Ford Explorers and Firestone tires was scattered in different places
in both companies, each item innocuous in isolation. Yet Ford ’ s knowledge-sharing
scheme is one of the best in the world. The company ’ s Best Practices Replication Process
has produced a billion-dollar benefi t for the automaker. Why did it not help in this case?
The Ford process was started in 1995 when a VP of manufacturing on a trip to Europe
saw that the plant there had ideas Americans could use and vice versa. Back home, he
assembled his operations people and asked them to fi gure out a way to share best practices.
At the same time, another Ford group was addressing reengineering issues through the
Rapid Actions for Process Improvement Deployment (RAPID) program. These were work-
shops aimed to eradicate small ineffi ciencies. They soon turned to the challenge of repli-
cating the solution so the RAPID need not be reinvented again. The two merged to become
Ford ’ s Best Practices Replication Process. In 4.5 years, more than 2,800 proven superior
practices have been shared across Ford ’ s manufacturing operations. The documented value
of this shared knowledge so far is $850 million. Another $400 million stands to be won
from work in progress, bringing the grand total to $1.25 billion. Royal Dutch/Shell and
Nabisco have licensed the process and portions have been patented.
Ford made three key decisions: fi rst, the process would be managed with distinct roles
and responsibilities. Second, no practice would get into the system unless proven. Third,
every improvement would be described in the language of the work group involved: time,
head count, gallons, and quality. These work groups are communities of practice. Each
CoP has a company-wide administrator, picked by the director of manufacturing. The role
takes a half a day a week. At the plant level, each CoP chooses someone as the focal point
and that role takes one to two hours a week. No one is paid extra. The best practices process
has forty-two steps. The focal point looks for a neat new process (or its inventors go to
him or her). He or she makes up a web page that prompts him or her to quantify benefi ts
such as time or material saved. The focal point then e-mails it to the community admin-
istrator, who compares it with other plants, and if it passes muster, designates it as a gem.
It is then immediately posted on the intranet and e-mailed to every focal point in the
community. One way or another, each focal point must report a decision: to adopt or
adapt it, and say when; to investigate it; or to reject it and explain why. The web displays
a scorecard to all users — by community and by plant. It may show, for example, that of
sixty-one gems in painting, the St. Louis plant has done or agreed to forty-two, was inves-
tigating two, had rejected seven as inapplicable and nine as economically not feasible, and
had originated and contributed two.
Box 9.5 An example: Ford
Knowledge Management Strategy 331
So if Ford is so good at knowledge sharing, why did no one know about the tire
problem? Two reasons: fi rst, knowledge is best shared within communities — people with
something in common talk more than strangers do. Neither Ford ’ s nor Firestone ’ s social
networks were rich enough to support the kind of extramural communication that might
have uncovered the problem. Second, the more widely dispersed the knowledge is, the
more powerful the force required to share it. Every year, Ford headquarters hands down
a “ task ” to managers — they are required to come up with a 5 – 7 percent gain in, say,
costs, throughput, or energy use. The best practices database is the fi rst place they turn
to — like a magnet, the task draws knowledge from its hiding places. This is an important
lesson for KM: if KM isn ’ t tightly linked to your business model, it will never amount
to much.
Box 9.5 (continued)
Where is the high-value IC??
Tacit Explicit
Knowledge
Institutional
• Structured
• Codified
• Controlled
• Measured
Fluid
• Spontaneous
• Creative
• Dynamic
• Experimental
Figure 9.2 Balance between fl uidity and institutionalization (adapted from Klein 1999 )
332 Chapter 9
Some companies such as Buckman Labs, 3M, Kao in Japan, AES, and others have
managed to strike the right balance ( Klein 1999 ). Some of their critical success factors
were:
• Consistency between core values, business strategy, and actual work environment
• Stress placed on personal freedom, cooperation, and community
• Top leaders serve as good role models — they “ walk the talk ”
AES set up a task force to conduct a historical study of the company ’ s ten biggest
mistakes. They also provided physical meeting space and time for people from differ-
ent parts of the company to meet and share what they were doing, and to get advice
on problems.
3M incorporated stories into their corporate training. 3M adopted the slogan “ con-
servatism with creativity ” and the company realized that 30 percent of revenues come
from products that are less than four years old. Technology was used to connect
knowledge workers to a database so they could share their expertise syste matically.
The 15 percent rule was used: 15 percent of each employee ’ s time should be set aside
to pursue personal research interests. 3M also instituted a storytelling culture with
such chestnuts as “ remember the time they tried to kill the Thinsulate idea. . . ” ).
KAO is a company that focused on organizational learning and based its approach
on values derived from Buddhist principles. Continuous cross-functional interactions
were encouraged and every meeting at KAO is open to all. The value-added network
(VAN) is KAO ’ s digital memory. ECHO is a system that adds customer call information
to VAN and they can receive about 250 calls per day. In this way, corporate experi-
ences are preserved and made available for future customer interactions.
Buckman Labs developed K ’ Netix as their knowledge network. This knowledge
repository is available in the ninety countries where Buckman has its offi ces. The users
are both the sales and technical workforce. K ’ Netix connects the Buckman CoPs. The
KM application consists of e-mail and forums residing in the knowledge repositories.
Each forum has a message bulletin board, library, and virtual conference room. In
confi guring for a balanced knowledge framework, successful companies such as these
need to identify strategic business drivers: What is the business all about? This is the
logical starting point to decide how to organize and manage intellectual assets. They
need to identify products, services, cost, value, quality, and differentiating factors, and
they need to characterize the environment in terms of competitive forces, regulations,
and socioeconomic trends. The organization can then establish the knowledge core
and interrelationships: What are the knowledge assets needed to maximize value
for customers, shareholders, employees, and other stakeholders? Both tangible and
intangible assets (e.g., values, culture, people, technology, business capabilities) need
Knowledge Management Strategy 333
to be clearly identifi ed together with where this critical knowledge exists and where
it goes (knowledge fl ow analysis). The knowledge fl ow can then be further analyzed
to assess how fl uid or how institutionalized the knowledge has become and whether
any gaps in key competencies exist.
In summary, there is a need to continually monitor and rebalance, to reconfi gure
or expand an organization ’ s knowledge assets as triggered by mistakes, changes in
environment, changes in competencies, and/or changes in performance. It is impor-
tant to remember that an organization is a complex adaptive system operating in a
complex dynamic environment, and the ultimate goal is that of a dynamic equilib-
rium between fl uidity and institutionalization pressures. Just-in-time discipline can be
applied, together with a focus on culture. The speed and accuracy with which knowl-
edge is transmitted must be optimal. The best example of nonoptimal conditions is a
reenactment of the telephone game — when the message that is transmitted to the fi rst
individual becomes progressively more garbled with each repetition. Other useful
questions to ask are:
• How changeable is the knowledge?
• What is the useful half-life of knowledge?
• What type of information technology is being used for knowledge sharing?
• What about innovation support systems?
Types of Knowledge Assets Produced
Intellectual assets (IA) are the intangible and often highly valuable assets that can
include brands, employee know-how, trade secrets, and technical information. IA also
covers intellectual property (IP), those assets such as patents and trademarks that are
formally protected by statute law. Generally, intellectual capital refers to the difference
between a company ’ s market value and its book value. It consists of organizational
knowledge and the ability of the organization ’ s members to act on it. Intellectual
capital is often used synonymously with the terms intangible assets, intellectual assets,
or knowledge assets.
Intellectual capital includes not only traditional intangible assets such as brand
names, trademarks, and goodwill, but new intangibles such as technology, skills, and
customer relationships. It is the resources that an organization could — and should —
make the most of to obtain competitive advantages.
Many present-day business managers are intrigued by the potential hidden value
that the intellectual capital perspective suggests lies untapped within their businesses.
However, managers do not know what kinds of value they could obtain from their
334 Chapter 9
company ’ s intangible assets or how they might go about it. They just know that there
is hidden value in their companies and that it is somehow wrapped up in the thoughts,
skills, innovations, and abilities of their employees. They want to learn more about
this value: how to harness it, direct it, and extract value from it (Sullivan 2000).
Intellectual assets are intellectual materials that have been formalized, captured,
and leveraged to produce higher value for the fi rm. As organizations more fully rec-
ognize the role these assets play in marketplace success, efforts to more accurately
identify and value them are becoming a top priority. While most managers readily
recognize that their most important organizational investments are in talents, capa-
bilities, skills, and ideas, often they must rely on surrogate, tangible-resource measures
such as people, capital, inventory, and money for performance decisions.
Historically, the intangibility of intellectual assets has made them diffi cult to
measure and manage. The accounting concept of “ goodwill, ” which is simply the
amount left after deducting measurable costs from the selling price, has and continues
to be used by many organizations as a type of miscellaneous category where intel-
lectual assets can be put in. A more organizationally appealing approach was intro-
duced by Stewart (1997) where intellectual assets are classifi ed as:
• A semipermanent body of tacit and explicit knowledge about a task, person, or
organization
• The capital resources (human, structural, and relational) that augment this body of
knowledge
This classifi cation scheme, if applied properly, produces intellectual asset measures
that can be targeted for KM value assessment.
Bolita (2001) states that with more than half the value of US corporations now
considered intellectual assets, organizations are increasingly looking for ways to iden-
tify, quantify, and capitalize on those intangibles. Over the last seven years, the value
of intellectual assets has increased by 700 percent. An organization ’ s intellectual assets
are computed in a number of ways (none of them precise). The difference between a
company ’ s book value and the value of all its fi xed assets is one measure. The Coca-
Cola Company (www.thecoca-colacompany.com) is often cited as a reference model
for evaluating intellectual assets. Discounting the extensive value of the sugar, water,
bottling facilities, and distribution system, the bulk of the company ’ s value lies in the
formula to make Coke, and the brand awareness the company has established.
For example, Microsoft (www.microsoft.com) paid $425 million for WebTV (www
.webtv.com), a company with few fi xed assets and only modest revenue. However,
WebTV held 35 patents for delivering the Internet over television. For that intellectual
Knowledge Management Strategy 335
property and the expectation of revenue it could generate, Microsoft was willing to
pay dearly. Documents, recordings, or images — all different structured data types, may
represent intellectual capital. Those data types embody the knowledge and a substan-
tial portion of the value of a company. Quantifying an organization ’ s intellectual
property should therefore begin by making it as tangible as possible. By converting
ideas, processes, concepts, and business intelligence into archived documents, CAD
drawings, database entries, procedure manuals, or even patents, organizations are
much better able to count intellectual assets in their bottom line.
Edvisson and Malone (1997) propose that knowledge assets can be placed in one
of these categories:
• Human capital , or all the brainpower that “ leaves at 5 PM. ” Human capital represents
the knowledge inherent in employees and contractors, and it is diffi cult to calculate.
The best way of assessing it is to calculate the potential inherent in human knowl-
edge — the value that has not yet manifested itself.
• Structural capital , or all the brainpower that “ stays after 5 PM. ” Structural capital
includes policies and procedures, customized software applications, training courses,
patents, and the like. The fi nancial community can more easily calculate the value of
structural capital because it has physical properties.
• Customer capital (also called relationship capital ), or all the corporate relationships
with customers and prospects. The value of customer relationships can be calculated
in terms of the business the customers have provided and the trend in those relation-
ships. (The value of future relationships or lapsed contracts is diffi cult to calculate.)
Organizations can take an inventory of these assets and, in some cases, can sell
them to others. (For example, organizations can sell training courses and license
patents.) Identifying and extracting intellectual assets is the process of determining
the obvious and nonobvious assets that a company owns. Often as a company goes
through a systematic process of inventorying its known assets, it fi nds many surprises.
For example, a company might start an inventory by listing its patents and patentable
discoveries. It then becomes clear that some of the company ’ s most valuable intel-
lectual assets are in the form of processes or know-how that are not patentable.
Examples that should be included in an inventory of intellectual assets are product
formulas, manufacturing processes, new product plans, packaging specifi cations,
product compositions, research directions, test methods, alliance relationships, busi-
ness plans, strategic directions, vendor terms, competitive analyses, customer lists,
marketing plans, sales projections, budgets, fi nancial projections, pricing analysis, and
employee lists.
336 Chapter 9
Intellectual assets also come from widening the aperture of the lens used to see
intellectual assets. For example, by looking to contractors and consultants who
develop intellectual assets for the company, the company is likely to discover assets
it owns that had not been considered. In the process that links identifying intellectual
assets to extracting them for profi t, a company will often see opportunities to create
new intellectual assets. A company can cultivate creativity to create assets, which can
be identifi ed and extracted for profi t to the organization.
Lev (2001) views intangible assets as nonscarce. Deployment of an intangible asset
is possible at the same time in multiple uses. Intangibles increase in value when used.
This is also referred to as scalability: the value of intangibles increases when the scale
at which they are used increases. Intangibles are not subject to diminishing returns
as are tangible assets, but have increasing returns. Intangibles also have strong network
effects. Although not exclusively applicable to intangibles, network effects are char-
acteristic for intangibles in the sense that intangibles often form the core of important
networks.
Intangibles create future value. All intangibles are future-oriented and because of
this they are ignored by traditional accounting systems based on conservatism and
materialism.
Intangibles are diffi cult to manage and to exclusively control. Taking full advantage
of the tacit knowledge residing in employees is more diffi cult than exploiting the value
of a building or a machine to its maximum. Copying or re-engineering of intellectual
assets is often relatively easy, and we have limited ability to protect using property
rights. Cost accounting systems are not well geared toward intangible assets, and are
even wholly inaccurate for managing intangible assets-intensive corporations. Intan-
gibles cannot be owned (except legal property rights). Intangibles investments are
therefore typically more risky due to the fact that intangibles play the most dominant
role in the early stages of the innovation process. Proper management can deal with
this, that is, R & D alliances and diversifi ed innovation project portfolios.
Intangible assets are nonphysical and therefore inherently diffi cult to trade. Legal
protection is weak. There are large sunk costs and low marginal costs. Open exchanges
for intangibles are in their infancy. Intangibles cannot directly be measured. Valuing
intangibles is diffi cult. Intangibles are not evidenced by fi nancial transactions (as
tangibles are).
Key Points
• KM auditing is often the fi rst step in any KM initiative as it serves to inventory what
knowledge-intensive resources exist within a company. This provides a snapshot of
Knowledge Management Strategy 337
the “ as is ” or current state of the organization with respect to KM, and helps in mea-
suring progress toward organizational culture change and other KM goals.
• The two most commonly encountered KM application goals are reuse and
innovation.
• A good KM strategy will diagnose the existing status of the organization, compare
this with what stakeholders want to achieve in the future, and come to an assessment
of how far apart the two are: a gap analysis.
• A short-term horizon of one to three years is best for detailed recommendations — an
action plan that includes cost, resources, and measuring components.
• The proposed KM strategy should not only clearly address business objectives (not
KM objectives) but should be compatible with the prevailing cultural and technologi-
cal enablers of the organization.
• It is crucial that a balance be maintained between fl uidity and institutionalization
in a given organization.
Discussion Points
1. Compare and contrast KM applications that are driven by an objective of reuse
versus those driven by an objective of innovation.
2. What are the major steps involved in developing a KM strategy? What sorts of
information is needed in order to recommend a KM strategy to an organization? List
the major categories of stakeholders who should be involved in the strategy formula-
tion process.
3. What are some of the pros and cons of a web-based questionnaire versus face-to-
face interviewing when conducting a knowledge audit (refer to chapter 4)?
4. Why is it important to conduct an audit before eliciting stakeholder
objectives?
5. What are some criteria that may be used to prioritize both KM objectives and KM
recommendations?
6. What are the major differences between the short-term and long-term strategy?
How do they fi t together?
7. Why is it important to maintain a balance between fl uidity and institutionaliza-
tion? What are some of the mechanisms that can be used to achieve this balance?
How can KM applications upset this balance?
8. List and provide examples for some different types of knowledge assets. What are
some typologies that can be used to categorize them?
338 Chapter 9
9. What are the relationships among human, structural, and relationship capital?
10. Why are intellectual assets diffi cult to manage?
References
Bolita , D. 2001 . Intellectual assets — Corporate value moves from top minds to bottom lines price
on (what ’ s in) your head . KM World 8 ( 2 ). http://www.kmworld.com/Articles/Editorial/Feature/
Intellectual-assets--Corporate-value-moves-from-top-minds-to-bottom-linesa-price-on-(what’s-
in)-your-head---9062.aspx / (accessed June 4, 2010).
Edvisson , L ., and M . Malone . 1997. Intellectual capital: Realizing your company ’ s true value by fi nding
its hidden brainpower . New York, N.Y .: Harper-Business.
Grey , D. 1999 . Knowledge mapping: A practical overview. http://kmguru.tblog.com/post/98920.
(accessed June 4, 2010).
Klein , D. 1999 . The strategic management of intellectual capital . Boston : Butterworth-Heinemann .
Lev , B . 2001 . Intangibles – management, measurement and reporting . Washington, D.C .: Brookings
Institute Press .
Liebowitz , J. , B. Rubenstein-Montano , D. McCaw , J. Buchwalter , C. Browning , B. Newman , and
K. Rebeck . 2000 . The knowledge audit. Knowledge and Process Management 7 ( 1 ): 3 – 10 .
Pommier , M. 2007 . How the World Bank launched a knowledge management program. http://
www.knowledgepoint.com.au/knowledge_management/Articles/KM_MP001a.html (accessed
June 4, 2010).
Robertson , J. 2004 . Developing a knowledge management strategy. KM Column . August 2 . http://
www.steptwo.com.au/papers/kmc_kmstrategy/ (accessed June 4, 2010).
Skyrme , D. 2001 . Capitalizing on knowledge: From e-business to k-business . Boston, MA :
Butterworth-Heinemann .
Srikantajah , T. , and M. Koenig . 2000 . Knowledge management for the information professional .
Medford, NJ : Information Today .
Stewart, T . 1997 . Intellectual Capital — The New Wealth of Organizations , 1st ed. New York :
Doubleday / Currency.
Sullivan , P . 2001 . Value driven intellectual capital: how to convert intangible corporate assets into
market value . New York : John Wiley and Sons .
Sveiby , K. 2001 , A Knowledge-based theory of the fi rm to guide astrategy formulation, Journal of
Intellectual Capital 2 ( 4 ): 344 – 358 .
Zack , M. 1999 . Developing a knowledge strategy. California Management Review 41 ( 3 ): 125 – 145 .
Willard , N . 1993 . Information Resources Management . Aslib Information 21 ( 5 ): 201 – 205 .
10 The Value of Knowledge Management
Price is what you pay. Value is what you get.
— Warren Buffet (1930 – )
This chapter addresses the major ways in which the value of knowledge management
(KM) is assessed. The major types of KM measurement frameworks are introduced:
benchmarking, the balanced scorecard method, the house of quality, and the results-
based assessment metric. In addition, the various ways in which value is produced by
communities of practice (CoPs) are discussed.
Learning Objectives
1. Understand the major advantages and shortcomings of the three KM metrics.
2. Apply the benchmarking, house of quality metric, balanced scorecard method,
and results-based metric to knowledge management performance measurement
systems.
Introduction
This chapter discusses different metrics framework to monitor progress toward those
organizational goals. An additional dimension is now part of the integrated KM cycle:
that of measurement or assessment of KM value (as shown in fi gure 10.1 ).
There are a variety of methods to assess how well KM is succeeding (milestones
and formative evaluation) and how well KM has helped attain organizational goals
(outcomes and summative evaluation). KM metrics include quantitative, qualitative,
and anecdotal methods. Each method presents different advantages and disadvan-
tages, and often a combination of different measures may be called for.
340 Chapter 10
The best place to start is with a KM measurement strategy that answers the fi ve
basic questions:
• Why are we measuring?
• What are we measuring?
• For whom are we measuring?
• When are we measuring?
• How are we measuring?
The justifi cation for an assessment of how well KM had done is often to be able to
show the value that has been added by the KM. Most KM initiatives must provide
some evidence of at least contributing toward organizational goals. If, for example, a
company wanted to improve knowledge sharing so that best practices were spread
more rapidly and more broadly, then this should be assessed in some way. Some pos-
sibilities may be that better and quicker knowledge sharing has reduced the number
of errors, has speeded up problem solving, or has complemented formal training to
Assess
KM technologies
Update
Contextualize
Knowledge capture
and/or creation
Knowledge sharing
and dissemination
Knowledge acquisition
and application
Organizational culture
K M
m e
tric sK
M t
e a
m KM strategy
Figure 10.1 An integrated KM cycle
The Value of Knowledge Management 341
improve upon employees ’ skills. Note that KM is never to be presented as a silver
bullet that will solve all organizational woes — hence the phrase “ contributes toward. ”
Causality is extremely diffi cult to prove in a complex organizational environment, but
while desired results may not be attributed completely to KM, there should be a way
of at least partially attributing the success to KM.
Another frequent reason why KM is measured is to convince management and
stakeholders that KM is adding value to the organizational equation. This form of
justifi cation will help with the resource allocation and budgeting — costs are unfortu-
nately all too visible, whereas KM benefi ts tend to be rather opaque and long-term.
Finally, there are two general types of evaluations: formative (or in progress feedback)
and summative (which is provided upon completion). Formative KM assessment can
help revise project plans and goals and identify areas that need improvement while
there is still time to effect changes. A summative evaluation is much like a report
card — the work has been “ handed in ” and the results have been assessed.
What do we want to measure? KM assessment should focus on meaningful mea-
sures that relate directly to specifi c targets and objectives. The level of granularity
should be detailed enough that the results provide a means of acting upon them. For
example, a large organization wanted to know if the four communities of practice
they had supported and invested in had resulted in some benefi ts. They decided to
measure member satisfaction. The old adage, “ be careful what you wish ” for led to an
assessment that read: “ 97% of employees are highly or very satisfi ed with their mem-
bership in their CoP. ” There are a number of problems with this approach. For
example, we know that people are happy being members, but did we measure the
right dimension? A better question would have been: “ Could you provide specifi c
examples to illustrate how your participation in the CoP has helped you to do your
job better? ” A different organization did in fact include this question and found results
such as: “ I had no notion that a group on the other side of the country was working
on the very same sorts of problems as I was — we are now collaborating together and
have established a new thematic CoP; I was able to access up-to-date information that
I did not even know existed because of the CoP news alert I received. ”
The question, “ Who are we measuring for? ” while at times obvious, does deserve
some attention. Typically, we need to be aware of who is concerned by the success or
failure of the KM initiatives and what their expectations are. Expectations can lend
themselves to a form of gap analysis: the higher the expectations, the more diffi cult
the measurement and the greater the gap between what stakeholders would like
KM to do and what KM actually did. There are typically three main categories of
stakeholders:
342 Chapter 10
Program funders Primarily in fi nancial measures, what the return was on the KM
investment, and how long it took for the KM investment to be “ paid back ” (referred
to as the breakeven or payback period)
Managers Mostly interested in how the KM tools and processes are working and how
much they are being used by their staff (referred to as adoption rate)
Employees/participants More concerned with practical and operational issues such as
how does this improve (or make worse) my everyday life at work?
It is therefore crucial to identify all stakeholders ’ objectives and ensure the KM
metrics will answer each of their concerns (another reason why often more than one
metric is required for a given KM project).
Next, the question of when to measure needs to be considered. The organizational
context is one of the fi rst things to consider: is the organization in a stable state? If
yes, then the assessment can be conducted. If however, there is instability, then you
should wait to do the assessment. For example, if there is an imminent merger with
another company, a major reorganization planned, or a downsizing where a great
number of employees are concerned about job security — any one of these would be
cause to wait for a KM assessment. Measuring KM when the organization is in crisis
mode will yield un-representative results. For example, during a downsizing, one
would not necessarily expect knowledge sharing to be at the top of an employee ’ s list
of priorities. The data collected will be skewed or biased because the organization is
not in its natural state.
For stable organizations, there are at least four possible points at which assessment
can occur (adapted from APQC 2001) . These four points refer to the different general
phases of a KM project (or really, any project), namely:
1. Preplanning
2. Start-up
3. Pilot project
4. Growth and expansion
A KM assessment can (and ideally should) be done at all four stages. The preplan-
ning stage assessment will provide a good baseline measure: a starting point against
which subsequent changes may be measured and compared. If we know from where
we are starting, then we have a better chance of measuring how far we got. In the
start-up phase, we can track basic progress toward KM goals. During a pilot project
phase, we can focus on measures that show how KM is impacting the business. During
the fi nal growth and expansion phase, we can apply more formal metrics to monitor
The Value of Knowledge Management 343
KM health and progress. The fi nal stage will usually consist of a combination of dif-
ferent metrics in order to show the value added across the organization and for its
different stakeholders.
As to how we should measure KM, there are a variety of anecdotal (e.g., one-off
stories or anecdotes garnered from employees) to quantitative (e.g., statistical and
mathematical analyses of large data sets such as a survey questionnaire administered
to two hundred people) to qualitative measures (more in-depth interpretative
approaches, such as interviewing ten people several times to gather narrative data that
is then thematically organized). Quantitative measures assign a numerical value to an
observable phenomenon and provide concrete evidence such as causality or fi nancial
metrics. Examples would include usage metrics from the company intranet, the time
spent accomplishing a task with and without KM (the latter being a baseline) and time
saved, for example, on product development or in answering client queries. Qualita-
tive measures provide more context and details about the value (e.g., perceptions),
which are often diffi cult to measure quantitatively. Qualitative measures can serve to
augment quantitative measures by providing more interpretation and more meaning
with respect to the data. Anecdotal data consists of “ serious stories, ” for example, an
interviewee describing a lesson they learned or an innovation they made use of. All
stakeholders love stories and they often help make a metrics report or presentation
“ more human. ”
KM Return on Investment (ROI) and Metrics
There are a variety of methods to assess how well KM is succeeding (milestones and
formative evaluation) and how well KM has helped attain organizational goals (out-
comes and summative evaluation). KM metrics include quantitative, qualitative, and
anecdotal methods. Each method presents different advantages and disadvantages and
often, a combination of different measure may be called for.
Many businesses are fi nding that in order to gain buy-in from senior management,
they need to prepare and present a solid KM business case. Unfortunately, traditional
accounting standards do not provide the guidance necessary in valuing all intangible
assets ( Lev 1997 ). The International Accounting Standard Number 38 named “ Intan-
gible Assets ” only discusses patents, copyrights, goodwill, and research and develop-
ment costs ( IASC 1998 ). Nothing is mentioned about employee knowledge, best
practices, or investments in training. Despite the diffi culty in valuing such intellectual
capital, it remains one of the more important KM techniques to learn and to apply in
practice ( Brown and Woodland 1999 ). Traditional fi nancial statements would not
344 Chapter 10
show the loss of intellectual capital, and the subsequent impact to the company, if
one thousand employees would suddenly leave the company ( Roos and Roos 1998 ).
However, KPMG ’ s research indicates that, after losing key employees, 43 percent of
organizations experienced damage to a main customer relationship, 50 percent had
lost knowledge of best practice information, and 10 percent had lost signifi cant
income ( Warren 1999 ).
Most current approaches place a value on intellectual capital in the following way:
for publicly traded companies, the value of intellectual capital (IC) is the difference
between the market capitalization and the book value (summation of assets less depre-
ciation) of the company ( Roos and Roos 1998 ). For example, Intel ’ s market capitaliza-
tion in 1997 was $110 billion, while its fi nancial book value was $17 billion. This
hidden value of $93 billion is stated as the value of Intel ’ s intellectual capital ( Sveiby
1997 ). Roos and Roos (1998) made a similar comparison with Microsoft. A recent study
by the Brookings Institute in Washington shows that this “ missing value ” grew from
38 percent of a company ’ s market capitalization in 1982 to 62 percent in 1995
( Dzinkowski 1999 ).
Skandia, a Swedish insurance company, has made strides to quantify its intellectual
capital through further exploration. Using work that won the 1992 Nobel Prize in
Economics, Skandia has divided IC into several subsets, customer capital, human
capital, and organizational capital ( Roos and Roos 1998 ). In Skandia ’ s annual Intel-
lectual Capital Prototype Report, these terms are defi ned with supporting details
regarding how calculations of value are made. Skandia ’ s advancements, as well as
efforts by KPMG ( Andriessen 2000 ), Buckman Laboratories, and McKinsey & Company
( Davenport 1996 ), are providing tools by which management can determine the com-
pany ’ s present IC value and foresee future IC growth (or shrinkage). These tools are
being used by Deutsche Bank to give loans with only IC as collateral ( Henry and King
1999 ).
The Skandia Intellectual Capital model is called the Skandia Navigator ( Wall,
Kirk, and Martin 2004 ). Four key dimensions of business form the core of this
model:
• Financial focus, represented in monetary terms
• Customer focus, a fi nancial and nonfi nancial measure of the value of customer
capital
• Process focus, addressing the effective use of technology within the organization
• Renewal and development focus, which attempts to capture the innovative capabi-
lities of the organization
The Value of Knowledge Management 345
All four dimensions are in turn related to a human focus, which is a measure of
the organization ’ s human capital. This model is quite similar to the balanced scorecard
method (BSC) discussed later. The navigator can be thought of as a combination of
Sveiby ’ s (1988) intangible assets monitor with the BSC.
The valuation of IC is receiving much attention in today ’ s literature. However, the
cost of implementing KM techniques is not as clear. McKinsey & Company has an
objective of spending 10 percent of revenues on developing and managing knowledge
( Davenport 1996 ). Keeping with the earlier Intel example, these estimates would place
the cost of managing knowledge within Intel between $595 million and $1.7 billion
in 1997. By not clearly understanding the “ intellectual liabilities, ” or cost of KM, it
remains diffi cult for companies to calculate any balance sheet effects. Buckman Labs
estimates that companies spend 3.5 percent of revenues on KM ( Davenport 1996 ). The
founder of Buckman Labs, Robert Buckman, estimates that the fi rst benefi ts from KM
were seen as an improved speed of new product development ( Angus 2003 ), which
increased to 30 – 35 percent from 13 – 18 percent a year. Some additional examples are
provided here in discussions of Accenture and Chevron (boxes 10.1 and 10.2).
The shift toward knowledge-driven business models has created a strong need for
knowledge management metrics. The literature has only recently begun to explore the
cost of KM, with little empirical data showing true organizational costs ( Harvey and
Lusch 1999 ). The KM measurement process will therefore consist of the following
major steps:
1. Defi ne the business objective(s) addressed by the KM initiative or project.
2. Defi ne are the stakeholders and determine what they need to know.
3. Determine which measurement framework(s) is best to align KM measures with the
business objectives.
4. Modify the framework(s) based on measurements are needed.
5. Decide on a data collection and analysis strategy.
6. Get management to sign off on the measurement strategy.
7. Implement measures and present the results in a form that is most appropriate for
each stakeholder.
Three popular approaches, benchmarking, the balanced scorecard method, and the
house of quality are presented here.
The Benchmarking Method
Benchmarking is the search for industry-wide best practices that lead to superior per-
formance ( Camp 1989 ). It usually consists of a study of similar companies to see how
346 Chapter 10
Accenture and the Intellectual Capital Management (ICM) Group (International Knowl-
edge Management News, October 1, 1999) formed an alliance to help organizations iden-
tify and measure the value of their intangible assets, and use those assets to generate new
revenue. Services provided to fi rms included evaluating a company ’ s intangible assets —
patents, licenses, trademarks, copyrights, and all the knowledge or know-how of its
employees — and then recommending and implementing systems and processes to manage
those assets. Clients can expect to pay in the region of $25,000 for an analysis of their
intellectual property portfolios.
In 1995, the ICM Group cofounded the ICM Gathering, which included more than
thirty global companies dedicated to improving the way they manage their intellectual
assets and maximizing their fi nancial return. ICM views intellectual assets as ideas that
can be converted into profi t. Organizations are sitting on untapped wealth in the form of
hundreds of ideas that were never developed. Arthur Andersen and the ICM Group enable
organizations to fi nd these hidden gems and translate them into increased revenue and
higher market value. The alliance also will emphasize the link between research and
development and business strategy, as organizations need to look at where new value is
being created and focus the dollars spent on R & D. Organizations need to understand how
intellectual assets are created and managed in order to get the most benefi t from those
assets. R & D can help organizations identify future market direction and the competitive
landscape.
Box 10.1 An example: Accenture
In Chevron ’ s case, the guiding concept of KM has not been a buzzword, but a culture,
dubbed “ The Chevron Way. ” This concept, which provides an integrated framework for
the company ’ s objectives and principles, actively encourages the internal transfer of infor-
mation to make every employee ’ s life easier. For Chevron — like other oil companies — the
sharing of knowledge is a necessity. By using best practice sharing, Chevron can cut costs,
reduce production cycle times, and still grow in targeted areas.
That extends to ensuring that the projects the company undertakes are the most
important ones, and offer the best rate of return. Knowledge is applied to the entire busi-
ness, and sharing knowledge is no longer merely a performance issue — it is a reputation
issue as well. Knowledge directly affects every major company ’ s ability to win new business
and keep top employees.
One of the drivers for Chevron ’ s focus on sharing best practices throughout the orga-
nization was a series of benchmarking studies that showed Chevron ’ s management that
Box 10.2 An example: Chevron
The Value of Knowledge Management 347
the company was spending more than its competitors on large projects. The oil industry
is very capital intensive — and any way of cutting investment costs will improve the com-
pany ’ s bottom line. Based on the survey results, a tool was created and deployed through-
out the company called the Chevron project development and execution process — better
known throughout Chevron as “ Chip-Dip ” — which is estimated to have resulted in a 15
percent improvement in capital effi ciency since 1991. Chip-Dip is, in effect, a best practice
sharing work process system involving networks of Chevron staff to help improve capital
project selection and execution. At the same time, achieving best practice sharing can also
have a marked effect on safety and environmental performance. In a world where disasters
are headline news — as Exxon found to its cost with the Alaskan oil disaster in 1989 —
Chevron believes its employee safety performance has improved by 50 percent through
facilitating the transfer of knowledge throughout the company. Overall, although there
are hundreds of individual areas within the company that contribute to best practice
sharing, key labels under which they could be categorized include: exploration, produc-
tion, refi ning operations, energy management, marketing, and transportation.
Chevron’s goal has been one of steady, “ continuous improvement, ” based more on
cultural, rather than technology, buy-in. The key factor for Chevron was not just that
everyone within the company had IT tools, but that the tools were “ standardized, compat-
ible, and connected. ” Web usage within the company is also growing rapidly, doubling
every hundred days. Training to encourage the growth of the knowledge-sharing culture
across the company, especially for new employees, is also important. Chevron ’ s best prac-
tice culture extends to the evaluation of employees for salary purposes. An individual ’ s
evaluation is based on individual growth and team performance. Those who practice the
sharing of knowledge are more likely to be the ones rising up the organizational ladder.
If staff are not ingrained with the culture, they will probably either not know who to share
information with, or they will not share their information because they do not feel it is
of value to anyone. It is establishing that culture — and most important, doing it for busi-
ness needs — that is the difference between those who practice knowledge management,
and those who just talk about it. Best practice sharing has helped Chevron to cut annual
operating costs by $1.8 billion, reduce cost structure by $400 million, reduce debt by $2.3
billion in two years, cut capital project costs by 15 percent since 1991, and improve
employee safety performance by 50 percent.
Box 10.2 (continued)
348 Chapter 10
things are done best in order to adapt these methods for their own use. This technique
is best summed up by the Hindu proverb: “ know the best to become the best. ” In fact,
benchmarking, which is the term preferred by KM, is really a form of competitive
intelligence, the term favored by information professionals.
Benchmarking as a tactical planning tool originated with Xerox Business Systems
in the late 1970s. Japanese affi liates were selling better quality copiers for less than
the manufacturing costs of similar products in the US. Xerox wanted to know why
this was so, and whether or not they could emulate the Japanese companies. Similarly,
one of the fi rst experiments in benchmarking was in the production logistics area
(warehousing, picking, packing, and shipping) when Xerox Business Services bench-
marked with L. L. Bean, a clothing manufacturer who had one of the best logistics
operations in the world.
Benchmarking is a fairly straightforward KM metric that often represents a good
starting point. There are two general types of benchmarking: internal benchmarking,
which involves comparisons against other units within the same organization or a
comparison of a single unit over different time periods, and external benchmarking,
which involves a comparison with other companies.
In one engineering organization, the senior management team wanted to implement an
after action review (AAR) for completed projects. They were unsure of where and how to
begin — with projects in progress? How far back to go when the employees concerned may
no longer be with the company? What should they document? They had a whole series
of questions and not a lot of models to work from. They decided to do some benchmark-
ing — both external, with organizations of similar size and mandates as theirs, and inter-
nally, as they had subsidiaries around the world. The internal benchmarking results proved
the most valuable — one of the subsidiaries, in the Netherlands, had been doing AARs for
three years. They had templates and a good process for conducting the AAR meetings with
a facilitator. They even had a rule of thumb: an AAR had to be conducted no later than
three months after project completion and once ten projects were completed, they were
compared to identify any commonalities. Once thirty projects were completed, the AARs
were sent to the KM team to be further analyzed in order to extract lessons learned that
could have organization-wide interest. The senior managers were quite impressed that
their learning curve had all but disappeared. They adapted the existing questionnaire and
meeting process and requested a teleconference with their colleagues overseas. In this way,
an internal benchmark revealed existing best practices within the same organization that
could be easily transferred and reused by others.
Box 10.3 A vignette: Benchmarking from within
The Value of Knowledge Management 349
Spendolini (1992) further describes three different types of benchmarking, industry
group measurements, best practice studies, and cooperative benchmarking.
Industry group measurements This involves the measurement of various facets of your
operation compared to similar measurements from other companies. Often, the mea-
sures have little to do with productivity, customer satisfaction, or “ best practices. ”
Many industry groups publish comparative data either privately (for members of the
group or service only) or publicly or both. The Institute of Internal Auditors ’ GAIN
(Global Audit Information Network) provides this kind of data privately to subscribers.
The Institute also publishes biannual salary surveys and occasionally special studies
of external audit fees and research on effective audit departments (best practices).
Best practice studies These are studies and lists of what works best. These are useful to
benchmarking research, but they are not useful as metrics. What works best for an
entity in its specifi c environment, may not work the same way in another environ-
ment. These studies can be useful stimulators, but they are not benchmarks per se.
There are books, consultants, and public accounting fi rms that report internal audit
best practices gathered from research and consulting practice. The IIA published a
book for audit committees that was a study of best practices.
Cooperative benchmarking This involves the measurement of key production functions
of inputs, outputs, and outcomes with the aim of improving them. In an internal
audit, we would study, for example, comparisons of costs per audit hour, time elapsed
to distribute fi nal report, and percentage of recommendations accepted. Cooperative
benchmarking is done with the assistance of the entity being studied (the benchmark
“ partner ” ). Often the entity chosen as a benchmark is one that has best practices in
the area of interest, or has won a major national or international quality award. Inter-
nal audit departments are increasingly interested in this method. A version of coopera-
tive benchmarking is collaborative benchmarking. In the collaborative method, both
entities study each other and work together to improve. Some audit departments are
now doing this.
Competitive benchmarking This is the study and measurement of a competitor without
their cooperation for the purposes of process or product quality improvement. The
latter is called reverse engineering. A version of competitive benchmarking is a select-
ing a third party to study a group of competitors and share the results with all. The
third-party consultant is the only one who knows what data belongs to which entity
(you obviously know your own, but not necessarily anyone else ’ s).
It should be noted that in the long term, this approach lacks suffi cient value and
fl exibility, which leads to other measurement tools and techniques eventually being
350 Chapter 10
brought in to measure the effectiveness of KM. Benchmarking is essentially a com-
parison that is undertaken with key leaders in the industry in order to identify any
best practices that the company can emulate in order to improve their own organiza-
tional effectiveness. Carla O ’ Dell at the American Productivity and Quality Center
(APQC, http://www.apqc.org) pioneered this technique. Benchmarking is a good way
of avoiding reinventing the wheel by looking at what has worked and what has not
worked for other companies operating in comparable environments or industrial
sectors.
The benefi ts of benchmarking are not limited to improvements in process or the
promotion of reuse. Tiwana (2000) lists the following potential benefi ts:
• Overall productivity of knowledge investments
• Service quality
• Customer satisfaction and the operational level of customer service
• Time to market in relation to other competitors
• Costs, profi ts, and margins
• Distribution
• Relationships and relationship management
Benchmarking can help an organization evolve to higher maturity levels to become
a learning organization by identifying where they stand with respect to KM in relation
to the competition.
Andersen Consulting (now Accenture) developed a knowledge management assess-
ment tool (KMAT) that is essentially a benchmarking questionnaire where responses
by a given company can easily be compared against industry standards in order to
come up with a relative standing or ranking for the company on specifi c indicators.
The KMAT was developed by the American Productivity & Quality Center and Arthur
Andersen in 1995 to help organizations self-assess where their strengths and oppor-
tunities lie in managing knowledge. The tool is divided into fi ve sections: the KM
process, leadership, culture, technology, and measurement. A subset of the items and
information in the KMAT, with a simplifi ed scoring system is available (see http://
www.kwork.org/White%20Papers/KMAT_BOK_DOC.pdf).
The fi rst step in benchmarking is to identify the companies that you will be com-
paring. Recent trends toward globalization indicate that international companies
should not be automatically excluded from your short list. In the end, it is a fairly
subjective decision as to which companies and which criteria you will be bench-
marking against. Some typical targets include: innovation metrics (How fast are new
The Value of Knowledge Management 351
products developed? How much is invested in R & D?), customer loyalty, KM integra-
tion, leveraging of IT, and quality management.
Tiwana (2000) adapted Spendolini ’ s (1992) key benchmarking steps in order to
arrive at a better fi t with KM. These key steps can be summarized as:
1. Determine what to benchmark: which knowledge processes, products, services?
Why? With what scope?
2. Form a benchmarking team.
3. Select benchmarking short list — which companies will you be benchmarking
against?
4. Collect and analyze data.
5. Determine what changes should be made as a result of the metrics obtained.
6. Repeat when an appropriate amount of time has lapsed to measure progress.
Benchmarking is of greatest value when a company has clearly identifi ed its stra-
tegic objectives and they have thought long and hard about which best practices might
or might not be transferable and effective within their own particular context, with
its own KM drivers and constraints.
The Balanced Scorecard Method
The balanced scorecard method (BSC) is a measurement and management system that
enables organizations to clarify their vision and strategy and translate them into action
(Kaplan and Norton 1992, 1993, 1996). It provides feedback around both the internal
business processes and external outcomes in order to continuously improve strategic
performance and results. The BSC is a conceptual framework for translating an orga-
nization ’ s vision into a set of performance indicators distributed among four dimen-
sions: fi nancial, customer, internal business processes, and learning and growth. The
“ balance ” in the balanced scorecard refers to the way a balance is maintained between:
• Long-term and short-term objectives
• Financial and nonfi nancial measures
• Internal and external perspectives
• Lagging and leading indicators
• Objective and subjective measures
• Performance results and drivers of future results
Indicators are maintained to measure an organization ’ s progress toward achieving
its vision; other indicators are maintained to measure the long-term drivers of success.
352 Chapter 10
Through the BSC, an organization monitors both its current performance (fi nances,
customer satisfaction, and business process results) and its efforts to improve processes,
motivate and educate employees, and enhance information systems — its ability to
learn and improve. A high-level balanced scorecard is shown in fi gure 10.2 .
Variations in the basic design are common. Typical changes include changes in the
categorization of perspectives (e.g., innovation and learning, or employees, in place
of learning and growth) and the number of perspectives (e.g., adding stakeholders as
a separate, fi fth perspective). Balance is achieved through the four perspectives,
through the decomposition of an organization ’ s vision into business strategy and then
into operations, and through the translation of strategy into the contribution each
member of the organization must make to successfully meet its goals.
The fi nancial dimension typically includes measures such as operating income,
return on capital employed, and economic value added. The customer dimensions
deals with such measures as customer satisfaction, retention, and market share in
targeted segments. The internal business process dimension includes measures such
as cost, throughput, and quality. The learning and growth dimension addresses mea-
sures such as employee satisfaction, retention, skill sets, and so on.
The major steps in applying the balanced scorecard metric are:
1. Translate the KM vision and strategy into measurable goals.
2. Validate these through the establishment of a consensus on the concrete, short-
term, specifi c goals.
Vision and
strategy
Financial dimension
Customer dimension
Learning and growth
dimension
Internal business
processes dimension
1
4
3
2
Figure 10.2 High-level balanced scorecard
The Value of Knowledge Management 353
3. Communicate and link: measure as you go through the objectives and look at how
well the reward system is linked to these objectives: are employees trained, motivated,
and rewarded to use KM as part of their everyday work?
4. Do a reality check — be sure that you are being detailed enough that you can
measure something to assess how well these objectives are being met.
5. Incorporate learning and feedback into your metrics — do a formative and a sum-
mative evaluation.
Each dimension of the BSC can be further expanded to include objectives, metrics,
targets, and initiatives, as shown in table 10.1 . Objectives are the major goals to be
achieved (e.g., profi table growth). Metrics are the parameters that will be monitored
in order to measure progress toward these stated goals (e.g., growth in net margin).
Targets are the specifi c thresholds to be met for each metric (e.g., 2 percent or greater
growth in net margin). Finally, initiatives describe the actions, projects, programs, and
so on, to be put into place in order to be able to meet the stated goals.
The balanced scorecard method was originally intended to be a performance
improvement metric, but it quickly became apparent that it also serves as an effective
strategic management system. It is applicable to both nonprofi t and for profi t organi-
zations as well as to both private and public sector companies. The BSC offers a
number of signifi cant advantages including the translation of abstract goals into
action items that can be continuously monitored. It provides objective measures of
the current situation and also helps in initiating the changes required to move from
the current to the desired future state of the company. The major shortcoming is that
unlike benchmarking, this is a much more diffi cult technique to use. Each BSC must
be developed “ from scratch ” as it is customized to individual organizations. Some
templates and automated tools are available to help in the implementation of a BSC
from, for example, Six Sigma (available at http://www.isixsigma.com/me/balanced
_scorecard/) and QPR (available at http://www.qpr.com/balancedscorecard/).
Table 10.1 Sample BSC implementation
Objectives Metrics Targets Initiatives
Financial
Customer
Internal processes
Learning and growth
354 Chapter 10
The House of Quality Method
The house of quality was originally developed to show the connections between true
quality, quality characteristics, and process characteristics. This was done using the
fi shbone diagram, with true quality in the heads and quality and process characteris-
tics in the bones. In 1988, Hauser and Clausing developed an evaluation matrix metric
that measures how customer needs are linked to business processes and internal deci-
sions of an organization. A simplifi ed matrix is shown in fi gure 10.3 .
This technique is also referred to as quality function deployment (QFD; Mazur
1993 ) as it links the needs of the customer with marketing, design, development,
engineering, manufacturing, and service functions (see also the Quality Function
Deployment Institute, http://www.qfdi.org). It can be used for service and software
products, as well.
As shown in fi gure 10.3 , the house of quality has, as its key elements, desired out-
comes, priorities attached to these outcomes, and appropriate metrics for each outcome.
The overwhelming focus of the house of quality is on maximizing customer satisfac-
tion as measured by metrics such as repeat business and market share. It focuses on
Relationships
Metrics for performance
Outcomes
Correlations
Benchmark values
Goals and values
Ranked issues
Desired results
Workgroup
performance
Importance of
issues
being measured
Workgroup’s
knowledge,
related goals
Figure 10.3 High-level house of quality matrix
The Value of Knowledge Management 355
delivering value by seeking out both spoken and unspoken needs, translating these
into design targets, and communicating this throughout the organization. Further,
the house of quality allows customers to prioritize their requirements, tells us how we
are doing compared to our competitors, and then directs us to optimize those features
that will bring the greatest competitive advantage.
As with the balanced scorecard, the desired outcomes need to be specifi c enough —
concrete, detailed, and therefore measurable. For example, a desired outcome of
“ better collaboration ” is diffi cult to assess. A better desired outcome would be to
“ improve knowledge sharing to a level where at least 20 percent of an employee ’ s work
is based on existing knowledge provided by peers and/or the knowledge repository
in the next three years. ” This second statement can be measured more directly and
compared to an existing baseline, by administering knowledge audit questionnaires for
knowledge (as described in chapter 9) and through usage statistics for the repository.
These goals and objectives are placed to the left of the house as shown in fi gure
10.3 . Ideally, these desired outcomes should be short to mid-term and observable.
Some examples would be:
• Increase the number of communities of practice by three
• Decrease the number of customer complaints by 50 percent
• Decrease the number of unsolved problems by 60 percent
• Decrease the time to market for newly developed products and services by 40 percent
Priorities are next assigned to each of these goals by placing weights to the right
of the house. Useful metrics can then be listed on top of the house (the ceiling). At
the center of the matrix, we will see the level of correlation between the metrics and
the performance outcomes. These can be numerical correlations or low, moderate, or
high type values. By analyzing these correlations, we can zoom in on those aspects of
KM that are more likely to have an impact on overall company performance and thus
will contribute more signifi cantly to progress made toward the stated goals.
Some popular house of quality metrics used for KM projects include:
• The expense of reinventing solutions per year (or rework)
• The information/knowledge seeking time spent on average per employee
• The number of ideas that were implemented from the suggestion box per year
• Time spent on systematic capture and codifi cation of know-how for future use when
a project is completed (e.g., postmortems and AARs)
• The percent of employees who are aware of what KM exists within their organization
(e.g., a lessons learned database)
356 Chapter 10
A blank house of quality template is also available(http://www.gsm.mq.edu.au/
cmit/hoq/Example%20HOQ%20Matrix.doc). Advice on interpreting, analyzing, and
reiterating the house of quality design is provided in the form of a checklist by Mazur
(1993 ; http://www.mazur.net/works/9checks.pdf).
Tiwana (2000) recommends using indicators and other useful parameters from the
Skandia Intellectual Capital annual report instrument as house of quality outcomes
in order to analyze KM effectiveness. These indicators include:
• Competence development expenses ($ per employee)
• Employee satisfaction
• Time spent on systematic packaging of know-how for future reuse when a project
has been completed
• Training expenses per employee
• Information gathering expenses per existing customer
• Total number of patents held
• Employee attrition rate
• Dollar fi gure value of loss per employee who leaves (and who leaves for a competing
fi rm)
• Expense of reinventing solutions per year
• Number of ideas implemented compared to those suggested (e.g., suggestion box)
The Results-Based Assessment Framework
The results-based management accountability framework (RMAF) has become a widely
used framework for general performance assessment, particularly within the Canadian
federal government. The Canadian Treasury Board (http://www.tbs-sct.gc.ca/eval/
pubs/RMAF-CGRR/guide/guide_e.asp) has published guidelines on its development
and application that has led to a fairly high degree of adoption and standardized use
of this instrument. A number of other organizations such as UN agencies, USAID,
and Fujitsu Consulting also implement this metrics framework. The terms “ results
map ” or “ results chain ” are often used as shorter synonyms or more generic terms.
It is fairly easy to adapt this metric to knowledge management. The advantage in
doing so lies with the emphasis RMAF places on realistic results, monitoring of
expected results, reporting, and describing measurable changes. In addition, explicit
linkages are used to show how each activity contributes to each expected outcome.
Figure 10.4 outlines the major components of the RMAF metric (adapted from Plan
net 2004).
The Value of Knowledge Management 357
The major attributes of a results chain are:
• Results chain Explores how resources and activities connect with changes (fl ow
type)
• Activities Actions to be undertaken within the scope of the project; outcomes (a.k.a.
outputs): short-term effects of the completed activity
• Intermediate outcomes Medium-term results, one step removed from activity
• Final outcomes (a.k.a. impact) Long-term big-picture results, contribution toward
ultimate goal (may not be visible during project)
• Indicators Evidence of progress, metrics
• Results Aggregate at each level
Identifying all of the desired impacts, outcomes, and outputs and then connecting
these with existing and planned KM initiatives forms the foundation of the results-
based metric. In this way, the contributions expected from KM toward attaining
organization goals can be easily visualized and progressively monitored via the
Indicators Indicators IndicatorsEvidence of progress metrics:
Activities Intermediate
outcomes
Description
of component:
Action to be
undertaken
within scope
of the project
Short-term
effects of the
completed
activity
Medium-term
results,
one step
removed
from activity
Long-term
big-picture results,
contribution
towards
ultimate goal
(may not be visible
during project)
Results
aggregate at
each level:
Immediate
outcomes
(outputs)
Final
outcomes
(impact)
Figure 10.4 High-level RMAF
358 Chapter 10
indicators that are chosen. The impacts are often very long-term so the focus in this
metric will be primarily at the output and outcome levels. Figure 10.4 shows a logic
model or visual representation of the goals and how to attain them. An alternative
data collection tool can be a document-based template, where stakeholders are asked
to input the activities, outputs, outcomes, and impacts (long-term outcomes) directly
on this template. Table 10.2 shows a sample results map template.
The results-based metric is easily adapted to include KM activities and outputs
that in turn can be connected to expected outcomes and impacts. This metric makes
it almost impossible not to link or align the KM efforts with the overall organiza-
tional goals. There is a very strong return on investment focus and while causality
still eludes us, there is a very visual way of at least capturing the expected contribu-
tions KM can make toward business goals. Metrics in general and KM metrics in
particular are still a long way from being an exact science. However, the result map
makes it much easier to defi ne indicators and outcomes at the most useful level of
detail. Result maps or chains provide a good means of working with clear and well-
defi ned results that is to the benefi t of the KM team and the organizational
stakeholders.
Table 10.2 Sample template for data collection using the results map metric
Organization: Purpose:
Business unit: Date:
Project name: Date last revised:
How? What? Why?
Inputs Activities Outputs Outcomes Impacts
Indicators
Assumptions and anticipated risks
The Value of Knowledge Management 359
Measuring the Success of Communities of Practice
Finally, there are a number of metrics that are particularly well suited to measuring
the value created by communities of practice. In general, there are three types of value
that can result ( Krebs 2008 ):
Structural value The creation of connections in a network; the amount of time spent
in interacting with others; the fl ow of knowledge between network members (typically
measured using social network analysis (SNA) techniques
Relational value The maintenance of connections; their longevity; the degree of reci-
procity in network interactions (typically assessed through surveys and anecdotes)
Cognitive value The commonality or cohesiveness of the network (which can be
assessed through SNA and interviewing techniques)
Stories are a good way to illustrate the links between community activities, perfor-
mance outcomes, and value. Some sample questions to elicit such stories would be:
• “ What would not have happened without this CoP in place? ”
• “ Did you save time because you had access to community resources, including other
people? Did you fi nd the answer to a question more quickly or did you solve a problem
more rapidly? ”
• “ Has your decision-making confi dence increased since you have been a member of
this CoP? ”
Social network analysis (SNA) is a good tool to map out the patterns of network
interactions (who interacts with whom, what knowledge products are exchanged,
what is the frequency or density of each interaction, are there interactions you would
have expected to be present, e.g., people working on projects together) that were not
in evidence? SNA can also be very useful in establishing a baseline measure for a given
CoP and can be used to track changes over time (such as greater coalescence, fl uctua-
tions in activity levels) as well as to identify “ hidden experts. ” Hidden experts are
readily visible in a social network map as they appear as a node at the center of dense
connections — a traffi c cop of sorts — who appears to be instrumental in maintaining
good knowledge circulation throughout the community. These valuable nodes tend
to be the “ go to ” people in an organization — people who can quickly connect you to
other people or to valuable content because they just know who knows what and
where the useful knowledge resides.
Finally, time-use studies can also be used to measure productivity and time saved
by CoP members. A time-use study is usually done with a self-report survey instrument
that asks people to report on the time they spend solving problems, making decisions,
360 Chapter 10
searching for information, processing information, and coordinating and interacting
with others. Participants are typically asked to keep this tabular checklist on their
desks and to jot down their answers every day for a period of time (a week minimum
to a month maximum). Time use should be measured either before and after a com-
munity of practice has been implemented or, alternatively, at regular intervals in order
to track changes over time.
A community of practice can also be evaluated on its health, on its outcomes, and
on the impact it has had on the organization ( Fontaine and Millen 2004 ; Lesser and
Storck 2001 ; McDermott 2002 ) Health refers to the number of participants, the fre-
quency and quality of knowledge sharing between them, and the level of community
activity in general. For example, the number of community meetings held would be
one indicator of the health or activity level of the community. Outcomes measure the
individual and group benefi ts derived from CoP membership such as personal knowl-
edge and learning, strength of relationships, and access to information of the other
members. Outcomes are usually detectable when a community has reached a certain
level of maturity or coalescence. The impact dimension measures the return on invest-
ment (ROI); the return on time (ROT) spent on community activities (or time saved
by being a community member), increased innovation, and increased organizational
capability. Impact is often not measured directly or mathematically, although some
formulas do exist to “ operationalize ” this metric.
Table 10.3 summarizes some of the major CoP metrics used at the individual, group,
and organizational benefi t levels (adapted from Fontaine and Millen 2004 ).
Key Points
• Traditional metrics tend to be fi nancial in nature and diffi cult to adapt to KM activi-
ties and outcomes.
• The costs of KM are too visible and too easy to measure while the benefi ts tend to
be soft, intangible and much more long-term in nature. This makes the return on
investment (ROI) and the payback period diffi cult to assess.
• A good measurement strategy should be formulated before measuring anything —
one that addresses who, what, when, why, and how of metrics.
• There are a number of fairly sophisticated KM measurement techniques now that
can help assess how well an organization is progressing. These include benchmarking,
the balanced scorecard method, the house of quality matrix, and the results-based
metric.
The Value of Knowledge Management 361
Table 10.3 Benefi ts of a CoP to an individual, to the community, and to the organization
Type of benefi t Measurable value
Individual (how does an individual participating in a CoP benefi t?)
Skills and know-how increased
Increased personal productivity
Increased job satisfaction
Enhanced personal reputation
Increased sense of belonging
Community (how does the collective benefi t
Increased availability and access to knowledge, expertise, and resources
Easier to reach a consensus
Faster problem solving
Enhanced community reputation and legitimacy
Increased trust between members
Organization (how does having this CoP benefi t the host organization?)
Improved operational effi ciency
Increased cost savings
Increased avoidance of problems
Improved quality of service
Increased speed of service
Increased employee retention/decreased turnover
362 Chapter 10
• Even though a community of practice is a grassroots-driven, organically evolving,
and somewhat elusive entity, there are a number of indicators that can be used to
assess the health and value created by the CoP.
• It is generally recommended that a combination of different metrics be used in order
to assess the entirety of a KM project or program.
Discussion Points
1. Why are traditional accounting-based measures not entirely suitable for KM?
2. What are some of the key challenges in developing a measurement strategy?
3. What are the major benefi ts of drawbacks of quantitative, qualitative, and anecdotal
measures?
4. KM metrics remains an issue, as it is often only too easy to measure the costs
of implementing KM whereas the benefi ts prove too elusive to measure. Discuss this
KM issue: what are some of the methods and measures that can be used to make KM
benefi ts less elusive?
5. Explain how you would approach intellectual assets in developing KM applications.
What are some of the key challenges? Why can ’ t we use a single measurement method
when dealing with intellectual assets?
6. Compare and contrast the three KM metrics of benchmarking, BSC, and house of
quality. What are their major advantages and major drawbacks in monitoring progress
toward strategic KM and business goals?
7. What does the results-based approach offer that other methods do not?
8. How would you go about assessing the value of a CoP:
a. To an individual
b. To the community
c. To the host organization
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11 Organizational Learning and Organizational Memory
Those who cannot learn from history are doomed to repeat it.
— George Santayana (1863 – 1952)
This chapter addresses the processes involved in organizational learning, or how an
organization can continually improve over time by learning from its successes (best
practices and innovations) and its failures (lessons learned). In order to be able to
learn, the organization must be able to document milestone events and “ remember ”
them through access to an organizational memory. The major processes involved in
organizational learning are outlined and a review of organizational memory models
is undertaken.
Learning Objectives
1. List the major benefi ts of documenting experiential organizational learning in the
form of an organizational memory.
2. Outline the major barriers to good organizational memory management.
3. Defi ne corporate amnesia and reasons why this may occur.
4. Outline the key steps in the evolution of an innovative new idea and the institu-
tionalization of a best practice that forms the object of reuse.
5. Compare and contrast the components of leading organizational memory
models.
Introduction
Organizational knowledge is being lost at an alarming rate as businesses continue to
downsize, to outsource, and to draw from a pool of increasingly mobile knowledge
366 Chapter 11
workers. The average length of time a highly skilled and experienced employee spends
at a particular company has shortened considerably. Increased turnover may be due
to downsizing, retirement, and high mobility in a given industry, or it may even be
intentional (e.g., rotations in the military or limited-term mandates). Tacit knowledge
has often been referred to as “ the knowledge that leaves at the end of the day ” and
companies are said to “ lease ” knowledge but not own it. Tacit knowledge in this sense
resides in the knowledge workers themselves and has not been documented to any
great extent. Uncaptured knowledge is therefore at risk of being lost to the organiza-
tion. In fact, organizational forgetting may be denoted as a form of “ corporate amnesia ”
( Kransdorff 1998 ). There is a high cost to the fi rm of losing know-how that resides
within the minds of individual employees who depart. In an era of knowledge workers,
individuals are increasingly responsible for value creation.
Although many organizations have succession plans in place, the process usually
involves transferring know-how from the departing employee to their successor, but
the whole process has to be repeated again for the next departure. Organizations need
to “ capture ” this know-how and transfer it to a stable, easily accessible, cumulative
knowledge base — an organizational memory — to retain and make accessible valuable
knowledge gained through the experiences of all knowledge in a continuous and
uninterrupted manner. The possibility of a critical mass of employees all retiring at
the same time has been anticipated as baby boomers reach retirement age. A proactive
approach is needed for organizations to effectively manage their organizational
memory in order to prevent the loss of essential knowledge, particularly knowledge
that resides predominantly in the heads of their knowledge workers and less in docu-
ments, procedures, and other tangible forms. More often than not, it is this diffi cult-
to-articulate know-how that is of greatest value in organizational competitiveness and
viability.
The National Aeronautic and Space Administration (NASA), for example, has pub-
licly admitted that the knowledge of how to put a man on the moon has been lost.
The lessons that were learned and the innovations that were sparked cannot be found
in the collective organizational memory of NASA. This means that NASA ’ s organiza-
tional memory cannot be used as a resource to plan a more effective mission to send
another manned fl ight to the moon or to Mars. A well-designed and well-managed
organizational memory not only combats corporate amnesia, but it ensures knowledge
continuity — the effective transfer of know-how among peers and to future generations
of knowledge workers. A better understanding of the nature of organizational memory,
what it should include (content), how it can best be retained (technological contain-
ers), and how the accumulated lessons learned and best practices can be used by
Organizational Learning and Organizational Memory 367
NASA faces a challenge in collecting and maintaining valuable knowledge in its organiza-
tional memory. There has been much publicity over the loss of knowledge with respect
to manned space fl ights. To make matters worse, there was also a recent admission by
NASA that it was no longer able to locate the original recordings of the landing on the
moon; they exist, but the people who know where they are located are long gone from
NASA.
Petch (1998) notes that NASA has forgotten how to put a man on the moon. The Apollo
mission documents — millions of pages of plans — have been reduced to microfi che. But
missing is the critical set of plans. Twenty-fi ve years ago someone threw away the blue-
prints for the Saturn booster, the only rocket with enough thrust to send a manned lunar
payload on its way. The Apollo missions were completed and project directors were
moving offi ces. No other set of Saturn blueprints have been found to date.
The Columbia disaster showed that the lessons learned from the Challenger accident
either went unlearned or were forgotten once learned. NASA has a culture that is resistant
to criticism and to change — no one else could possibly understand what the agency does
. . . only NASA possesses the unique knowledge about how to safely launch people into
space. These attitudes are coupled with ineffective communication, and a tendency to only
accept opinions that agree with their own. The bureaucratic structure kept important
information from reaching engineers and managers alike, stifl ing the spread of critical
information.
Even when documents endure, they can be devoid of meaning, and human context is
often needed. A computerized knowledge base was designed by Dr. Richard Ballard (see
the NASA web site http://km.nasa.gov/) which imposes a rational structure on existing
sources of knowledge, then automates the capture and communication of future text-based
knowledge. This knowledge base was unique in that it used semantic nets and represen-
tational modeling. This knowledge base combines data retention with contextual relation-
ships that provide meaning to information, and may stop the liquidation of knowledge
assets, prevent future knowledge loss, and provide above-the-line profi t opportunities, to
be thought of as group memory or organizational intelligence.
Box 11.1 An example: NASA organizational memory
368 Chapter 11
newcomers (connections), will help mitigate the cost of lost, forgotten, or untrans-
ferred knowledge and know-how.
How Do Organizations Learn and Remember?
Organizational learning (OL) can be defi ned as learning what worked and what
did not work from the past and effectively transferring this experientially learned
knowledge to present-day and future knowledge workers. Organizational learning is
therefore a process through which an organization is said to improve over time — by
making innovations available for reuse and by taking steps to ensure that mistakes do
not occur again or that someone else begins from scratch, not realizing they are
redoing work that has already been done. We can say that OL has occurred if we can
easily fi nd success stories and lessons learned from the past and from other offi ces
around the world. This implies a documentation “ process ” of what has worked and
what has not, a technological “ container ” (e.g., LotusNotes, a knowledge repository)
to allow us to plug in to this collective experience of the organization, and the ability
to obtain help in reusing or putting this collective knowledge to work — so each can
better perform their job.
The technological container (referred to above) represents organizational memory
(OM). The OM is a centralized technological system (often an intranet) where we can
fi nd all the by-products of OL: primarily the best practices and the lessons learned.
An OM is largely made up of the accumulated and aggregated experience of all the
knowledge workers of that organization. The role of an organizational memory is to
preserve valuable knowledge for future access and reuse, for example, from employees
who leave the organization to new hires who join the organization. OM is thus “ the
means by which organizational knowledge is transferred from the past to the present ”
( Stein and Zwass 1995 ).
The underlying assumption is that organizations capable of learning will be more
effi cient, more effective, more competitive, and more viable than those that cannot
( Senge 1990 ; Garvin 1993 ). A learning organization (LO) is a type of organization that
has successfully implemented the processes of organizational learning. Typically, an
assessment is done on an organization and if it meets the required features of an LO,
then it is said to be a learning organization. For example, Senge (1990) lists fi ve key
attributes that a learning organization should have. His book, The Fifth Discipline , was
one of the fi rst to identify the core competencies a learning organization should have:
• Mental models
• Shared vision
Organizational Learning and Organizational Memory 369
• Personal mastery
• Team learning
• Systems thinking
Mental models (refer to chapter 4) are the coherent set of understandings or
models that allow individuals to make sense of their world and to make decisions
accordingly. A mental model can consist of experiential learning, things “ learned the
hard way, ” perceptions, values, beliefs — all assembled in a personalized manner by
each individual. Shared vision refers to rendering parts of the individual mental
models visible so that they can be shared with others in the organization, understood
by others, and perhaps even appropriated by others. The process of sharing can and
often does lead to a modifi cation of existing models so that the individuals involved
can come closer together with respect to a shared mental model of their organization.
Personal mastery refers to a set of values and attitudes such that individuals are com-
mitted to lifelong learning — which in turn enables the organization to engage in
lifelong learning. The implicit assumption behind this core competency is that the
individuals ’ mental models are not so rigid as to prevent any new knowledge, that
is, learning, to be incorporated or added (which may trigger a change or updating of
the original mental model). Team learning is the organizational values and attitudes
that actively foster individual learning such as investment in training or encourage-
ment to participate in communities of practice (CoPs; often excellent vehicles of
learning as discussed in chapter 5). An organization that supports individual learning
is much more likely to be capable of organizational learning. Finally, “ systems think-
ing, ” the “ fi fth discipline, ” refers to the perception or defi nition of an organization
as a gestalt, an integral entity that cannot be reduced to a series of components. The
organization must be seen, studied, and treated as a whole where all the parts are
seamlessly connected to one another. Systems thinking is also an excellent way of
viewing KM: as an intact system made up of processes, people, culture, technology,
and so forth.
Frameworks to Assess Organizational Learning and Organizational Memory
There are a variety of frameworks that can be used to assess organizational learning,
in much the same way as maturity models can be used to assess the state of KM within
an organization (discussed in chapter 7). These organizational learning frameworks
serve to evaluate the organizational readiness or baseline state of a given organization
with respect to organizational learning processes, organizational memory containers,
and enablers of these, such as technology and culture.
370 Chapter 11
One framework, proposed by Probst and B ü chel (1997 ) looks at the following orga-
nizational factors:
1. Knowledge — the number of organizational learning instruments
a. Number of techniques for facilitating learning
b. Number of techniques for breaking down barriers
c. Process-oriented use of techniques
2. Ability — the learning level
a. Ability to cooperate and participate
b. Ability to communicate and achieve transparency
c. Ability to analyze problems and solve complex issues
d. Ability to store knowledge
3. Intention — the willingness to learn
a. Creates a structure which imparts meaning
b. Builds on an ethical basis
c. Wants to create a shared value system
Marquardt (2002) proposes three dimensions to consider in building the learning
capacity of an organization:
Speed of learning How quickly the organization is able to complete each learning cycle
(planning, implementing, and refl ecting)
Depth of learning Degree of learning the organization achieves at the end of each cycle,
which it achieves by questioning assumptions, and improving its capacity to learn in
the future
Breadth of learning How extensively the organization is able to transfer the new
insights and knowledge derived from the iteration of the learning cycle to other issues
and parts of the organization
Table 11.1 summarizes some of the characteristics of a learning organization and
associated best practices (adapted from the work of Senge et al. 1994 , and Argyris and
Schon 1996) .
The Management of Organizational Memory
Knowledge management is an essential capability in the emerging knowledge economy.
In particular, organizations have a valuable asset in the informal knowledge that is
Organizational Learning and Organizational Memory 371
Table 11.1 Key characteristics and associated best practices of successful learning organizations
Characteristic Defi nition Associated best practices Positive by-products
Self mastery — individual
The ability to honestly and openly see reality as it exists; to clarify one ’ s personal vision
1. Positive reinforcement from role models/ managers
2. Sharing experiences
3. More interaction time between supervisory levels
4. Emphasis on feedback
5. Balance work/nonwork life
Greater commitment to the organization and to work; less rationalization of negative events; ability to face limitations and areas for improvement; ability to deal with change
Mental models — individual
The ability to compare reality or personal vision with perceptions; reconciling both into a coherent understanding
1. Time for learning
2. Refl ective openness
3. Habit of inquiry
4. Forgiveness of oneself
5. Flexibility/adaptability
Less use of defensive routines in work; less refl exivity that leads to dysfunctional patterns of behavior; less avoidance of diffi cult situations
Shared vision — group
The ability of a group of individuals to hold a shared picture of a mutually desirable future
1. Participative openness
2. Trust
3. Empathy toward others
4. Habit of dissemination
5. Emphasis on cooperation
6. A common language
Commitment over compliance, faster change, greater within group trust; less time spent on aligning interests; more effective communication fl ows
Team learning — group
The ability of a group of individuals to suspend personal assumptions about each other and engage in “ dialogue ” rather than “ discussion ”
1. Participative openness
2. Consensus building
3. Top-down and bottom-up communication fl ows
4. Support over blame
5. Creative thinking
Group self-awareness; heightened collective learning; learning “ up and down ” the hierarchy; greater cohesiveness; enhanced creativity
Systems thinking — group
The ability to see interrelationships rather than linear cause and effect; the ability to think in context and appreciate the consequences of actions on other parts of the system
1. Practicing self mastery
2. Possessing consistent mental models
3. Possessing a shared vision
4. Emphasis on team learning
Long-term improvement or change; decreased organizational confl ict; continuous learning among group members; revolutionary over evolutionary change
372 Chapter 11
the daily currency of their knowledge workers, but this asset usually lives only in the
collective human memory, and thus is poorly preserved and managed. There are sig-
nifi cant technical and cultural barriers to capturing informal knowledge and making
it explicit. As outlined in chapter 8, groupware tools such as e-mail and Lotus Notes
tend to make informal knowledge explicit, but they generally fail to create an acces-
sible organizational memory. On the other hand, attempts to build organizational
memory systems have generally failed because they required additional documenta-
tion effort with no clear short-term benefi t, or, like groupware, they did not provide
an effective index or structure to the mass of information collected in the system.
Knowledge is the key asset of the knowledge organization ( Conklin 2001 ). Organi-
zational memory extends and amplifi es this asset by capturing, organizing, disseminat-
ing, and reusing the knowledge created by its employees. There are good reasons to
pursue creating organizational memory. Organizations routinely forget what they
have done in the past and why they have done it. These organizations have an
impaired capacity to learn, due to an inability to represent critical aspects of what
they know. Ott and Shafritz (1994) coined the term “ organizational incompetence ”
to refer to the lack of organizational capability to learn or as the antonym of organi-
zational intelligence.
There is a fourth barrier to organizational memory that should be mentioned.
Spurred by their legal departments, a few American corporations are adopting a policy
of systematic destruction of all unneeded personal notes and documents at regular
intervals. The thinking behind this policy is that, in the event of litigation or criminal
prosecution, it is dangerous for anything to exist in writing that could be used against
the corporation since the legal mechanism of discovery allows lawyers from the
outside access to any documents that are not explicitly protected under client attorney
privilege. The risk of expensive judgments against the corporation may have created
an economic incentive for amnesia. Such thinking, where it exists, creates a major
obstacle for the creation of organizational memory. It insists that only the most formal
and sanitized forms of knowledge may be allowed to persist. It puts everything that
is written down or stored in a computer under the lens of “ can this information pos-
sibly be used against us. ” Most adults know that you learn the most if, when you make
a mistake, you acknowledge it and refl ect on what you have learned from it. But in
an organizational amnesia environment, mistakes must be avoided at all costs, and
denied if they occur. How can organizational learning possibly take place in this
environment?
Organizational memory is not just a facility for accumulating and preserving, but
also for sharing knowledge. As knowledge is made explicit and managed, it augments
Organizational Learning and Organizational Memory 373
the organizational intellect, becoming a basis for communication and learning. Orga-
nizational memory contributes to the overall compliance with regulatory guidelines.
An organizational memory can also help increase the transparency of the organization
as well as how knowledge workers perceive this transparency. Once valuable knowl-
edge content has been entered into organizational memory, it can be shared among
individuals working alone, by teams needing a project memory, and by the organiza-
tion as a whole for between-team coordination and communication. Given the nature
of organizations and the competitive environment within which they exist, organiza-
tional learning and the accumulation of knowledge will be the source of immediate
health as well as long-term survival ( McMaster 1995 , 113).
An organizational memory that consists only of formal knowledge is bare and life-
less. Conklin (1993) likens this to describing a ball game by giving the statistics or the
mystery novel by simply relating the plot outline. Such formal, structured content
also lacks the history and context behind the formal documents, and as a result, the
organizational memory is essentially an immense heap of disconnected things, a giant
“ organizational attic. ” Documents that contain formal knowledge that the organiza-
tion has paid dearly to create, live somewhere on the corporate network with enlight-
ening names like h:\org\fi nan\arc\drg\9plan.doc.8. If, however, an organization
embraces its informal knowledge, then the rationale behind decisions and documents
becomes the glue that holds the formal knowledge documents together and preserves
their meaning ( Conklin 1993 ).
A specialized school for students with severe behavioral problems undertook to build a
repository of lessons learned and best practices. The primary motivation was driven by
the fact that there was a high turnover among teachers employed by the school. The
average stay was about two years and most teachers left due to burnout, as the responsi-
bilities are quite demanding. A number of best practices and lessons learned were gathered
and preserved. Templates were developed and used in order to facilitate this knowledge
capture process and access was provided through each student ’ s profi le. This is an example
of a nontraditional KM application, one that is not situated in a for-profi t commercial
organization. The same principles and methods apply and can be successfully used to
create a corporate memory. The greatest benefi t will be that the wheel will no longer have
to be reinvented each time a new teacher works with the same student. The new teacher
will have access to all of the accumulated successes and failures of the various techniques
that have been tried out by each previous teacher working with that student.
Box 11.2 Example: Lessons learned and best practices in teaching
374 Chapter 11
A frequently encountered barrier to effective organizational memory is that the
usual approach to organizational memory, preserving documents, fails to preserve the
context that gives the documents meaning, the very thing that allows them to be
useful in the future, when the context has changed. Because current notions of
organizational memory assume a repository of artifacts, they focus on preserving,
organizing, indexing, and retrieving only the formal knowledge, as it is stored in
documents and databases. For some tasks, formal knowledge alone is suffi cient; for
example, when it is time to write the new annual report, you might start with last
year ’ s annual report as a template. However, most knowledge work addresses problems
for which there is no clear and agreed upon defi nition of the problem, and, indeed,
in which the problem itself is apt to change over time. Decision making is character-
ized by making lots of assumptions, educated guesses, and decisions under conditions
of uncertainty. Decisions must frequently be revised or even retracted. Problem resolu-
tion requires both traditional linear techniques and a heavy dose of social interactions:
conversations, meetings, presentations, phone calls, e-mail, and so on. The primary
goal is not always to fi nd a right answer as to fi nd a solution and an understanding
of the problem that has broad ownership.
In this context, formal documents are simply not rich enough to support
knowledge work. For example, a team may come together for many meetings in the
course of resolving a problem, but the practice of creating and circulating meeting
minutes is a relatively laborious instrument for creating continuity and coherence
among these meetings. Meeting minutes are summaries that often represent only
one person ’ s point of view, and they usually capture only a small part of the con-
versations that took place. Projects can often stretch into months and years, so some
form of project memory will be needed. An explicit project memory provides more
continuity among these sessions, allowing the group to pick up where it left off,
with a minimum of repetition and loss of important issues. As team membership
changes over time, or the project is handed off to a completely new team, the project
memory can in principle reduce the likelihood of false starts and duplication of
previous work.
A shared memory for the project team or a community of practice can create coher-
ence within the mass of formal and informal project knowledge. The shared memory
often takes on the form of story about what occurred, a living document that tells the
story of the project. It preserves the context of the work as it evolves. This project
memory is most naturally represented in the form of a web of information that
includes facts, assumptions, constraints, decisions and their rationale, the meanings
of key terms, and, of course, the formal documents themselves.
Organizational Learning and Organizational Memory 375
The third challenge for an effective organizational memory system is that for a
system that includes informal knowledge, that knowledge tends to lose its relevance,
and thus its value, over time. Informal knowledge, being more contextual, is even
more dynamic in this way. An organizational memory system should therefore, like
human memory, have the capacity to recall whatever is relevant and salient to the
moment. Closely related to this is the problem of the sheer size of organizational
memory. There will be ever-increasing volumes of corporate knowledge accessible
online which will make it even more diffi cult to pinpoint those particular items that
are relevant to users.
To summarize, the obstacles to an effective organizational memory system fall
into two categories, cultural and technical. The cultural barriers include the
following:
• A cultural emphasis on artifacts and results to the exclusion of process
• Resistance to knowledge capture because of the effort required, the fear of litigation,
and the fear of loss of job security
• Resistance to knowledge reuse because of the effort required, and the low likelihood
of fi nding relevant knowledge
The technical barriers include:
• How to make the knowledge capture process easy or even transparent
• How to make retrieval and reuse easy or even transparent
• How to ensure relevance and intelligibility (i.e., through suffi cient context) of
retrieved knowledge
Workgroup computing, or groupware tools, take an important step in the direction
of facilitating knowledge work, and their databases inherently create some degree of
organizational memory. But the problem is that knowledge must be organized and
indexed as it is being captured, without creating a burden to the people who create
it. The concept of organizational memory, and the possibility of an effective organi-
zational memory system, has growing importance in the global knowledge economy,
but many organizations are letting their most valuable asset, informal knowledge,
disappear.
Current implementations of organizational memory fail for a variety of reasons,
including a broad cultural focus on work products over process and a lack of tools
which make capture and reuse of knowledge transparent. The challenge is to design
an organizational memory system that offers suffi cient short-term payoffs to knowl-
edge workers that they will use the system, both to capture knowledge as they are
376 Chapter 11
A large mining company was examining its predictive maintenance procedures. This form
of maintenance relies upon scheduled parts changes and “ tune-ups ” that take place accord-
ing to expected useful life spans of the various types of equipment used, as opposed to
waiting until something fails and brings the whole operation to a costly stop. In the case
of one particular type of valve used in the refi nery, technological advances had resulted
in the use of a new type of polymer that was just now available. The question was: could
this new polymer be used to cap the valves? Could it withstand the high temperatures
that the valve would be subjected to during operations? At fi rst, this seemed to be an easy,
almost trivial question. Engineers began looking for the equipment specifi cation docu-
ments. These proved, however, more elusive than expected. When, after about six weeks,
they were found, they were located not within the company, but within the archives of
a design fi rm that had been subcontracted to design that particular piece of equipment —
roughly twenty-fi ve years previously. Unfortunately, nothing in the specifi cations helped
answer the question. The use of a polymer would represent a signifi cant cost savings, but
the team was reluctant to move forward. The conventional wisdom said, “ a slow dime is
worth more than a fast penny, ” or in other words, we may save a few pennies now but if
the polymer melts under the high temperatures, the whole refi nery will have to be shut
down, costing many, many, more dollars to the company. Finally, after about six months
of searching, the HR department of the design company tracked down the original design
engineer who had worked on the equipment. He was happily retired and playing golf in
Florida but was still receiving a pension and that is how they found an address for him.
Luckily for the mining company, this engineer was a bit of a pack rat and/or nostalgic:
he had kept his original hand-drawn specifi cations with his own annotations. It was by
checking these annotations that he was able to confi dently answer “ No — the polymer
would not be a safe alternative — metal should continue to be used. ” The next question
posed by the mining team was: now, where can we write down this valuable information
down? Where is the company “ book ” where they can look this up when the next fi ve-year
cycle comes up?
Box 11.3 A vignette: Corporate amnesia
Organizational Learning and Organizational Memory 377
creating it and to look for and reuse existing knowledge. The next step in the evolu-
tion of organizational memory is the use of a display system to focus knowledge
workers on improving shared understanding and coherence in their project meetings,
and capture the group ’ s information and knowledge in context and link it with the
project ’ s formal products in an easy and natural way.
Once a team or organization has recognized the value in its informal knowledge,
and has begun to capture and manage it appropriately, the group has the key raw
ingredients of project memory, and ultimately of organizational memory. Of course,
as the size of the organization and its memory increases, new problems of scale emerge
that are both technical and cultural in nature. The good news is that the short-term
payoffs from using display systems generally pay for the cost of implementing them,
thus easing the evolution toward a complete organizational memory system.
Organizational Learning
The key processes required to both populate an organizational memory and to retrieve
valuable knowledge for reuse from the same memory consists of the same steps as in
the KM life cycle (refer to chapter 2). The knowledge content to be processed, however,
is defi ned much more narrowly as the key successes and key failures that have a suf-
fi cient degree of generalization. If a particular innovation or failure is too specifi c, then
this content will typically reside in the group memory — either a project database or a
community of practice archive. Aggregated results from a diverse set of projects, on
the other hand, can be analyzed thematically to identify recurring themes. An orga-
nizational lesson learned or best practice is one that has broader applicability — it is
not limited to a particular context or particular event and offers reuse potential to an
organization-wide audience.
Secchi (1999) defi nes a lesson learned in the following way.
A lesson learned is knowledge or understanding gained by experience. The experience may be
positive, as in a successful test or mission, or negative, as in a mishap or failure. . . . A lesson
must be signifi cant in that it has a real or assumed impact on operations; valid in that is factually
and technically correct; and applicable in that it identifi es a specifi c design, process, or decision
that reduces or eliminates the potential for failures and mishaps, or reinforces a positive result.
( Secchi 1999 )
In general, the concept of lessons learned includes the following aspects:
• Contains knowledge gained by experience
• Can be positive or negative, and address a success or a failure
378 Chapter 11
• Implies that the knowledge is captured and its reuse is promoted to increase orga-
nizational learning (i.e., to avoid recurrence or to promote repeat application)
Lessons learned are typically obtained after performing one or more project postmor-
tem sessions, after action reviews or any type of refl ective exercise that asks partici-
pants to identify what worked well and what could be improved. Other tools include
continuity books, knowledge books, dark-side reviews, and any other process that
documents what has been learned in order to preserve this knowledge in the organi-
zational memory and in order to be able to pass along or transfer this knowledge to
people who will have to perform the same tasks.
What then, is the difference between a lesson learned and a best practice? The term
best practice is often associated with a success, an innovative discovery, or a tried and
tested method for accomplishing a task (positive experiences); whereas a lesson learned
more often implies the documentation of a critical mistake or failure in order to avoid
repeating it (negative experiences). However, as the defi nitions given above illustrate,
lessons learned ideally address both positive and negative experiences.
In general, two types of learning occur in organizations; top down and
bottom up.
1. Top-down learning is a strategic learning method whereby management, at any
given level, decides that a certain piece of knowledge is vital to the organization and
must be learned by its employees.
2. Bottom-up learning happens in the actual “ doing ” of tasks, it is experiential
learning and results from both positive and negative events ( O ’ Dell and Grayson
2001 ).
Lessons learned are concerned with capturing the results of bottom-up learning, as
they are a distillation of valuable employee experiences.
The Lessons Learned Process
Effective knowledge management processes involves the identifi cation, creation,
acquisition, dissemination, and reuse of knowledge assets to provide a strategic advan-
tage. The lessons learned process has a similar cycle of activities, as described in
fi gure 11.1 (adapted from US GAO 2002 ).
The steps of the process include:
Collection Capture of lessons through structured or unstructured processes, such as
after-action or project reviews, meetings, training evaluations, and so on. Capture may
be done at all levels: individual, community, and organization.
Organizational Learning and Organizational Memory 379
Verifi cation Lessons are verifi ed before dissemination to ensure that they are valid and
applicable. This process may involve subject-matter experts or additional research, and
the lessons are typically verifi ed to ensure that they meet or exceed a set of defi ned
criteria outlined in established standards.
Store Once approved, lessons are stored in an accessible database in a format that
allows for easy search and retrieval of information. Some storage issues include catego-
rization, indexing, formatting, and structure.
Disseminate Active dissemination of lessons is essential for getting value out of a
lessons learned program; lessons are of little benefi t unless they are accessed and
reused. Dissemination can be active (lessons are pushed to potential users) or passive
(users access a repository to retrieve lessons).
An illustration from the NASA lessons learned database is presented in box 11.4 .
Organizational Learning and Organizational Memory Models
A knowledge resource can therefore be defi ned as valuable organizational knowledge
that has been packaged either as a discrete digital unit of content or that can be
Organizational
memory
Organizational learning
Project
memories Verify generalizability
and store in database
Capture lessons
learned and best
practices
Analyze usage
Get feedback
Revise/retire accordingly
Used as is
Modified
Not used
Publicize and share
with others
Figure 11.1 Lessons learned process
After a period of decreased budgets, a reduced work force, and some very public failures
such as the Mars Polar Lander, NASA (web site http://km.nasa.gov/) conducted a study in
2000 to identify actions to improve its approach to executing programs and projects). One
of the recommendations from this report dealt with the improvement of capturing and
applying lessons learned from projects and missions, to prevent NASA from having to
“ relearn ” lessons of the past. As a result of this study, NASA ’ s lessons learned program was
thoroughly evaluated by the Government Accountability Offi ce (GAO) in 2001 – 2002.
At the time of the study, NASA had an established, agency-wide lessons learned infor-
mation system (LLIS) that managers were required to review on an ongoing basis to gain
lessons from past programs and projects and to submit to in a timely manner about any
signifi cant lesson throughout the life of a project. NASA also used training, program
reviews, and periodic revisions to agency policies and guidelines to communicate lessons
learned. In addition, several NASA centers and programs maintained their own lessons
learned systems geared toward their own activities. However, this impeded agency-wide
sharing of lessons learned.
To improve the way it captured and shared information, NASA developed a strategic
plan, assembled a management team to coordinate knowledge management and activities
at NASA ’ s centers, and begun several information technology pilot projects. The LLIS was
revamped and its public interface can be found at the NASA Engineering Network (http://
www.nasa.gov/offi ces/oce/llis/home/). The new LLIS includes a multifaceted taxonomy to
improve searching and browsing, allowing navigation by year, mission directorate, NASA
center, collection, and topics. It also includes a new search engine.
NASA then conducted another survey to evaluate their lessons learned database. The
results showed that although failure reports were useful, users preferred a stronger focus
on positive lessons, as they were considered more helpful in many cases than negative
ones, providing more effi cient and effective solutions that could be emulated. Ideally, a
balance between positive and negative lessons should be maintained, as NASA explains:
“ if an organization focuses only on failures, its overall program ’ s effectiveness will be
reduced and it will miss opportunities to improve all its processes ” ( US GAO 2002 ).
There is another KM system for obtaining and sharing lessons learned from past mis-
sions — the NASA engineering network. Prior to the Columbia disaster, NASA had been
using a voluntary database to share lessons learned, but employees rarely checked the
database to get information. Now employees can search and browse forty-eight NASA
engineering repositories using semantic search technologies to search both structured and
unstructured data. Content is from only accredited data sources, not informal blogs or
notes. Next, NASA will deploy a CoP portal — part chat, part search — as an interactive
message board with online conversations recorded for future reference. They also plan on
implementing an expertise locator feature will allow users to fi nd experts by inputting a
keyword search. Finally, NASA has created an agency-wide lessons learned steering com-
mittee with members from each of the NASA centers. So far, people are getting a lot more
information easier and quicker than before.
Box 11.4 An example: NASA lessons learned information system (LLIS)
Organizational Learning and Organizational Memory 381
represented as one, by converting tacit into explicit knowledge (usually through inter-
viewing and modeling the appropriate people). Examples of a knowledge resource
would be the description of a best practice or innovation, a set of validated FAQs, a
how-to guide for a complex procedure, a set of lessons learned from a project, or an
anecdotal story that illustrates the cultural values of the company. It is essential to
process these valuable knowledge resources through a life cycle in order to create or
capture explicit knowledge, to share and disseminate this widely for use and reuse,
and to then store or remove this content so that the organization can benefi t from
best practices (e.g., become more effi cient, more innovative) and lessons learned (to
avoid repeating past mistakes).
Today ’ s information saturated society recognizes knowledge as the key to competi-
tive advantage and organizational success ( Marquardt 2002 ). Knowledge is defi ned as
information plus people (or human experience) as it incorporates many intangibles
such as experiential learning, judgment, and intuition, to create extra value for an
organization by informing decisions and improving actions. Choo, Detlor, and Turn-
bull (2000) note that information becomes knowledge at the point when people justify
or validate their true beliefs about the world. Some knowledge can be easily commu-
nicated, stored, and accessed for later use. Other knowledge, however, is largely in the
heads of individuals (tacit) and is never communicated until someone else needs to
reuse it (e.g., Nonaka and Takeuchi 1995 ). Explicit knowledge is tangible and visible
knowledge such as reports, user manuals, procedures, and e-mails and more often than
not tends to exist in digital form and be stored in databases, wikis, blogs, or intranets.
Knowledge management (KM) is the discipline that helps organizations systematically
build, renew, and apply both explicit and tacit knowledge within a given organization
( Wiig 1993 ). Effective KM initiatives help organizations to capture knowledge of sig-
nifi cant value and usefulness and to ensure its use and reuse to avoid reinventing the
wheel. The benefi ts of KM can be seen in improved performance on the individual,
group, and organizational levels, cost savings, advanced competitive standing, and
effective organizational learning ( Lesser and Prusak 2004 ).
Knowledge processing has been studied in such fi elds as information studies, infor-
mation architecture, and knowledge management, but in a fragmented way. This
has led to disparate theories and conceptual frameworks that only partially address
knowledge-processing practices. Most studies observed knowledge processing on a
short-term basis, yet successful and sustainable knowledge processing requires a sig-
nifi cant period of time.
“ Knowledge is more than just information. In addition, it contains experiences,
skills and insights. These forms of knowledge are produced during day-to-day
382 Chapter 11
interactions ” ( Huysman and de Wit 2002 , 21). Knowledge management consists of
the systematic processes for acquiring, organizing, sustaining, applying, sharing, and
renewing both tacit and explicit knowledge by employees to enhance the organiza-
tional per formance and create value ( Davenport and Prusak 1998 ; Allee 1997 ; Alavi
and Leidner 2001 ; Al-Hawamdeh 2003 ; Choo 2006 ). Knowledge processing (also
referred to as knowledge sharing in the KM literature) supports learning that occurs
in organizations.
Huysman and de Wit (2002) identify three fundamental stages in knowledge pro-
cessing: (1) internalization, where knowledge is learned and understood by the knowl-
edge sharers; (2) externalization, where knowledge is exchanged or reused and new
knowledge can be derived from the shared knowledge; and (3) objectifi cation, where
shared knowledge is accepted and institutionalized as organizational knowledge.
Keong and Al-Hawamdeh (2002) defi ne knowledge sharing as “ the deliberate act in
which knowledge is made reusable through its transfer from one party to another ”
(p. 49). Alavi and Leidner (2001) note, “ to be credible, KMS [The authors use the term
KMS, for knowledge management systems] research and development should preserve
and build upon the signifi cant literature that exists in different but related fi elds ”
(p. 107). Knowledge processing is highly dependent on having access to this content
in the fi rst place, which fi rmly roots it in the territory of information studies.
Employees cannot benefi t from the accumulated experience of an organization
unless that valuable experiential learning has been captured, coded, and made acces-
sible through the organizational memory. Organizational learning and organizational
memory systems are therefore integral components of KM that aim to facilitate the
access, use, and reuse of valuable knowledge resources ( Dieng-Kunz and Matta 2002 ).
Examples of valuable knowledge resources would be an innovation (improved practice,
policy), a postmortem to identify why a particular project failed (which is subsequently
documented as a lesson learned) and a library of reusable knowledge objects that
others may easily incorporate into their work (such as a company profi le, a tool to
show which topics are most active in a discussion forum, or a starter kit to get you
up and running on a new process or technique). The value of these knowledge
resources lies in the fact that they have been digitized (rendered explicit), and
that people other than the creators fi nd them useful and time-saving for their own
work.
Knowledge “ access ” refers to the ability to know about existing knowledge and to
easily fi nd it from collective organizational knowledge systems such as intranets (used
to preserve and make available organizational knowledge to individuals). Knowledge
“ use ” refers to the manner by which organizational members (e.g., policy makers,
Organizational Learning and Organizational Memory 383
practitioners, and researchers) use policy, evidence, and experience as knowledge. The
sub-concepts for knowledge use are: (1) distribution of knowledge through different
modalities such as newsletters, bulletins, policy briefs, and web-based resources to
targeted audiences; (2) sharing of knowledge through interpersonal communications
and dialogues via e-mails and discussion forums; (3) application of the knowledge as
new policies, guidelines, or practice routines. Majchrzak et al. (2004) specify two types
of knowledge resource “ reuse ” : the reuse of knowledge for routine tasks (e.g., use of
templates, boilerplates, existing solutions) versus reuse that stimulates knowledge
synthesis and innovation (e.g., searching a database to fi nd new ideas to add to exist-
ing knowledge). Knowledge reuse demonstrates that knowledge is being retrieved from
organizational memory ( Markus 2001 ) and provides an excellent indicator of the value
of that resource. Knowledge reuse promotes peer-to-peer learning and helps avoid situ-
ations where people reinvent the wheel by doing work that was already done by
others. Companies typically create both social and technical networks to promote such
reuse ( Huysman and de Wit 2002 ).
Huysman and de Wit (2002 ) studied ten different case studies of knowledge sharing
in companies. They selected companies that were mature and had worked on KM for
a number of years and conducted interviews and analyzed existing documentation at
each site. They found that obstacles to knowledge sharing were: lack of motivation,
lack of time, and lack of a means to share the knowledge. In particular, Huysman and
de Wit (2002 ) found that “ employees regularly avoided using the technology: “ In
practice, Information and Communication Technologies ’ (ICT) role seems limited
while social personal networks are more important to knowledge sharing. . . . The role
of ICT in this should be more about connecting people and not so much about acquir-
ing and disseminating knowledge ” (p. 159). Conklin (2001) also maintains that knowl-
edge management fails for a variety of reasons, including a broad cultural focus on
work products over process, and a lack of tools that make the sharing and reuse of
knowledge transparent.
“ Organizational learning is the process through which an organization constructs
knowledge or reconstructs existing knowledge ” ( Huysman and de Wit 2002 , 30). This
defi nition of organizational learning is anchored in social constructivism, where
individual knowledge is transformed into organizational knowledge, encapsulated
in rules, procedures, technologies, and other operational routines ( Berger and
Luckman 1966 ; Gergen 1994 ). Organizational knowledge is that knowledge that an
individual can access, use, and reuse because they are a part of that organization —
often referred to as corporate or organizational memory ( Kransdorff and Williams
2000 ).
384 Chapter 11
Research to date indicates that organizations need better guidelines to improve their
knowledge processing practices concerning valuable and reusable content ( Patriotta
2004 ). Alavi and Leidner (2001) undertook a comprehensive review of knowledge
management systems used to “ support and enhance the organizational processes of
knowledge creation, storage/retrieval, transfer and application ” (p. 114) and con-
cluded that “ while much theory exists . . . little empirical work has been undertaken ”
(p. 126) and that “ research is needed to address several important issues regarding
knowledge storage and retrieval ” (p. 128).
Given the lack of integrated theories for conceptualizing knowledge use and reuse,
the fi eld of information studies offers a number or relevant models and concepts that
can guide the inquiry. This research calls for a user orientation because knowledge use
will be examined at the micro and individual level. One relevant user-oriented model
is Wilson ’ s model of the information user who experiences an information need,
which may encompass cognitive and emotional needs, and will place demands on
information systems and information sources ( Wilson 1981 , 1999 , 2000 , 2010 ). If the
need is successfully fulfi lled, information will be used to some extent and ultimately
information transfer and/or information exchange will take place. It is easy to draw
a parallel model with knowledge users ’ needs and demands on knowledge infrastruc-
ture and knowledge resources for learning and innovating. On the other hand, infor-
mation users will use information systems and services only if they perceive some
value-added dimension such as system noise reduction, quality, adaptability, and time
and cost savings ( Taylor 1986 ). Similarly, a knowledge user will make an effort to use
a knowledge resource, if they perceive that the resource will add some value to exist-
ing knowledge.
Choo ’ s theory on organizational knowing is also highly relevant. Choo (2001 , 2003 ,
2006 ; Choo et al. 2006 ) views organizational knowing as mediated (with rules, roles,
and technology), situated (located in time and space), provisional (often tentative),
pragmatic (oriented toward goals), and contested (sometimes affected by confl icts).
More importantly, organizational knowing involves various processes of sense making,
knowledge creation, and decision making, which all work as a cycle and which, by
defi nition, affect knowledge use and reuse.
Crossan et al. (1999) presented a model of organizational learning called “ the 4I
framework ” that identifi ed four key processes (intuiting, interpreting, integrating, and
institutionalizing) as being critical to organizational learning (introduced in chapter
4; see also fi gure 4.3). This model was further refi ned with respect to the fi rst three
steps, but the fourth step, institutionalization, has not been explored extensively
( Crossan and Bedrow 2003 ). The fourth or institutionalization step is a prerequisite
for the complete processing of knowledge resources.
Organizational Learning and Organizational Memory 385
The major components of Wilson ’ s user-oriented model, Choo ’ s sense-making
model and the 4I model of organizational learning should ideally be integrated in
order to provide a sound theoretical framework for organizational learning and orga-
nizational memory — one that also integrates the diverse fi elds of management studies,
information studies, and knowledge management.
A Three-Tiered Approach to Knowledge Continuity
One of the major concerns facing companies today is not only to prevent knowledge
loss due to employee attrition but how to transfer valuable knowledge to others within
the organization. This challenge is often referred to as “ knowledge continuity ” (analo-
gous to business continuity or the ability to maintain operations if the company
suffers a disaster). Most successful organizations will state that their two greatest assets
are the people who work for them and the knowledge they possess. The imminent
turnover signals a potential for the loss of valuable accumulated knowledge and know-
how in the form of the competence and expertise possessed by the departing individu-
als. This valuable knowledge and know-how exists in both formal and tangible forms
(explicit knowledge), such as documents, but also in less visible forms — often referred
to as tacit or diffi cult to articulate knowledge. Particular emphasis must be placed on
the tacit form as this often resides within a given individual or group and is therefore
more easily and completely lost when the people leave the organization ( LaBarre
2001 ).
The traditional response has been to mentor, coach, or carry out job shadowing,
which is not only time-consuming and complex, but just not possible in many
cases, due to a lack of advance warning, lack of time, or a lack of mentoring skills.
This problem can be tackled from a different angle: by ensuring that tangible legacy
materials are produced, shared, and fed into the corporate storehouse of intellectual
capital in an ongoing and seamless manner. Intellectual capital management (ICM)
can help capture, transfer and retain valuable knowledge using a three-tiered approach
that addresses the individual, the community of practice, and the organization itself.
The approaches used for individual-to-individual knowledge transfer level include
structured subject matter, expert interviews, and knowledge mapping of their key
knowledge areas together with task support system prototyping.
Individual structured interviews focus on “ knowledge archeology, ” that is, past
success stories, disasters, problems that were not handled well, the history of how
processes came to be put in place, the evolution of competencies, and so on. The key
roles and responsibilities of the expert serve as a starting point and a number of key
case studies are reviewed in order to extract historical best practices and lessons
386 Chapter 11
learned. Anecdotes and stories capture the contextual and social dimensions of knowl-
edge, experience, and expertise. This is often the type of knowledge that is not docu-
mented in any formal way. Stephen Denning ( 2001 ) of the World Bank is a leading
advocate of storytelling to capture the tacit culture surrounding intellectual assets and
as a means of catalyzing the cultural changes that need to occur before an organiza-
tion becomes effective at knowledge sharing.
At the group level, knowledge is often circulated within project teams, organiza-
tional units, and more informal communities of practice. Wenger and Snyder ’ s (2000)
defi nition of a community of practice is a group formed so that members can share
what they know and so they can learn from one another regarding all aspects of their
practice. Such groups have been around for quite a long time, ever since people real-
ized they could benefi t from sharing their knowledge, insights, and experience with
others of similar interests and goals. A number of surveys such as the one by Johnson
(2004) have shown that even in a company with an effective KM infrastructure, far
and away, people rely on other people as sources of knowledge and help. In fact,
the company knowledge base was ranked fourth among fi ve choices. For the
most part, CoPs are voluntary, informal gatherings and sharing of expertise where
synergies occur, best practices are identifi ed and shared, lessons learned are analyzed
and discussed, problems are identifi ed, and often the seeds of innovation are sown.
The knowledge capture and transfer challenge lies in conveying what needs to be
understood or what employees need to know for business results. This can encompass
a company ’ s values, work climate, commitment, culture — in short, a communal
mental model of the company, how it works, and the environment in which it works.
To foster its learning capabilities and “ transfer knowledge at the organizational
level, ” an organization must fi rst be aware of its core competencies and its associated
knowledge. These knowledge assets must fi rst be made explicit to become a real or
practical asset. Organizational learning and corporate memory are two terms that are
often used to describe the transfer of knowledge from individuals and CoPs to the
organization as a whole. These are usually encapsulated in the form of lessons learned,
best practices, the organization ’ s “ way of doing things, ” anecdotes, myths, and case
studies.
Table 11.2 summarizes the three-tiered approach to knowledge capture and transfer,
together with the types of knowledge best addressed by each tier and the types of
tangible legacy products that can be produced for individual, group, and organiza-
tional knowledge transfer processes.
There is not one specifi c approach that should be used with each of the three tiers.
Rather, a wide range of knowledge retention and transfer approaches should be used
Organizational Learning and Organizational Memory 387
Table 11.2 The three-tiered approach to knowledge capture and transfer for knowledge continuity
Knowledge transfer (KT)
approaches Types of knowledge Tangible by-products
Individual structured interviews with expert
KT at individual level
Operational
Anecdotal
Lessons learned
Best practices
Where to fi nd knowledge and experts
Map of key knowledge
Map of key contacts, memberships
Glossary of discipline
Interview templates
Interview transcripts
Key tasks and task support systems
Facilitated workshops with community of practice members
KT at group level
Tactical
Knowledge fl ow facilitators
Knowledge fl ow blocks
Identifi cation of CoP
Workshop notes
Knowledge repository design and implementation
Map of social interactions within CoP and with external stakeholders
Storytelling workshops and individual interviews with key executives
KT at executive levels
Strategic consensus regarding key intellectual assets
Criteria for evaluation of intellectual assets ’ business value
Map of key intellectual assets of the organization
Organizational lexicon of key concepts
Springboard stories
Historical knowledge (organizational “ saga ” )
Source : Adapted from Dalkir 2002 .
at all three levels in order to identify what is fairly easy to transfer, hard to transfer,
and impossible to transfer from one individual to another, in a retirement or succes-
sion planning situation.
The three-tiered approach to knowledge capture and transfer described here helps
ensure that critical intellectual assets are identifi ed at the individual, community, and
organizational levels. By capturing all the individual, community, and organizational
intellectual assets explicitly in the form of a map, the organization is able to make use
of this to create and sustain competitive advantage, barriers to entry, and continued
innovation and learning ( Senge 1999 ). The map of the organization ’ s intellectual
assets will also make it much easier to identify knowledge areas at risk (imminent
retirement of an expert, disbanding of a community of practice, lack of tangible by-
products left behind as an organizational legacy).
388 Chapter 11
Transport Canada was a pioneer in the identifi cation of critical knowledge that was at risk
of being lost due to imminent retirements. They undertook a comprehensive pilot study
in order to develop a toolkit for knowledge transfer for succession planning. Initially, they
had specifi c questions to explore.
1. Identify critical human resources.
a. Whom do others turn to in a crisis?
b. Who are the subject matter experts (SMEs)?
c. Who has long-term corporate memory?
d. Who is doing a one-of-a-kind job?
e. Who has a unique set of skills/knowledge?
f. Who carries the ball on major projects?
2. Maximize retention.
3. Retain their critical knowledge.
4. Facilitate the transfer of this critical knowledge.
5. Expose the right people to that critical knowledge.
Some key lessons learned ( Transport Canada 2003 ) included:
• Obtain buy-in from senior management.
• Raise awareness, generate enthusiasm.
• Managers should take ownership of the process of KT.
• Human resources personnel should provide signifi cant and sustained support to manag-
ers and SMEs through the entire KT and succession planning process.
• Integrate KT and succession planning into the ongoing business planning process of the
department.
Good practices that emerged included:
• Analyze your organization demographics to identify your vulnerabilities (i.e., where
would the loss of personnel most seriously threaten the execution of your mandate?).
• Secure senior management support and funding (if possible, name a champion).
• Identify critical knowledge holders.
• Approach them to discuss what would motivate them to stay on.
• Prepare succession and knowledge transfer plans.
• To facilitate mentoring and one-on-one knowledge transfer, whenever possible, bring in
a replacement before the SME retires.
• Extract critical knowledge held by these experts, customizing your methods to fi t your
subjects.
Box 11.5 An example: Transport Canada knowledge continuity best practices
Organizational Learning and Organizational Memory 389
• Work with IM/IT personnel and librarians in your department to choose your codifi ca-
tion methods, information management software, and retrieval tools.
• Encourage/facilitate strong CoPs to help disseminate tacit knowledge into the
organization.
• Reward knowledge sharing.
• Involve retiring SMEs in the writing of their job descriptions and the selection of suc-
cessors wherever possible.
• Provide extensive hands-on support to individual managers and management team.
The Transport Canada knowledge transfer toolkit consists of the following key
components:
Stakeholder maps Identify internal and external interactions with stakeholders and part-
ners — personal and professional networks of SMEs
Knowledge maps Conceptual representation of job tasks, key resources, how to obtain and
reuse knowledge, and a summary of SME expertise
Task support systems Online tools to support specifi c processes and info needed to com-
plete specifi c tasks — glossaries, demos, templates, references, resource lists, case studies,
simulations, computer based training (CBT) modules
Dashboard Single stop shop, customized work tools to hold knowledge maps, stakeholder
maps, task support systems, and other information such as answers to frequently asked
questions (FAQs), relevant legislation and regulations, a calendar of events, scholarly
articles, recent news, and useful tools
Transport Canada found that it was necessary to address both explicit and tacit knowl-
edge. They found that IT worked best for explicit knowledge, while CoPs worked best for
tacit knowledge.
Other best practices included:
• Hire successors before incumbent leaves, if possible, to establish mentoring
relationship.
• Include knowledge transfer (KT) in results-based management and accountability frame-
work (RMAF).
• Document lessons learned, best practices, decisions made — include as much context as
possible (include the why ’ s, the justifi cation, why alternatives were discarded).
• Focus on intellectual capital.
• Be proactive — do not wait until key people retire.
• Promote intergenerational knowledge sharing (under 35, 35 – 45, and over 45) through
communities of practice.
Box 11.5 (continued)
390 Chapter 11
The overriding initial emphasis should be on knowledge capture — the creation of
concrete, tangible knowledge containers to transform tacit knowledge into explicit
knowledge. Ideally, this should be done before the departure of retirees and this should
be done on for knowledge and know-how that is of high business value to the orga-
nization. Always keep in mind that the point of the exercise is not to document
everything.
Next, given the highly collaborative nature of the knowledge work and knowledge
workers today, some form of shared virtual workspace should be put into place to
enable members to quickly access key information and easily contact key members of
their community. This would reduce some of the risks associated with the high
employee turnover expected over the next few years but only if supported by organi-
zational processes, procedures, rules, rewards, and censure that promote the existence
and use of the tools. The overriding emphasis should now be placed on an organiza-
tional culture and tools that facilitate knowledge sharing .
Finally, the task support systems should be embedded in the shared work environ-
ment in order to promote knowledge application, use, and reuse, as well as learning
or internalization of this knowledge, know-how, and know-why.
Organizations using this three-tiered approach to knowledge capture, retention,
and transfer will be in a better position to proactively stem the potential loss of
intellectual capital due to attrition of their most experienced and expert employees.
This approach was fi rst tried by Transport Canada and has subsequently become a
best practice for the Canadian government, as described further in the vignette
here.
Key Points
• Organizational learning is the process of applying knowledge from the past to
present-day work challenges. Learning organizations are those organizations that have
succeeded in implementing OL and OM.
• Organizational memory systems are containers that serve to identify, preserve, and
make available valuable lessons learned and best practices.
• Lessons learned and best practices are fl ip sides of the same coin — they represent the
accumulated results and learning from trial and error experiences that the organization
has accumulated.
• Corporate amnesia is a risk when no systematic approach has been applied in creat-
ing organizational memory systems.
Organizational Learning and Organizational Memory 391
• Managing organizational memory often means overcoming barriers to the process-
ing of experiential knowledge accrued by knowledge workers over time.
• Knowledge continuity is the process of ensuring that valuable knowledge is not
lost to the organization due to employee attrition. Ideally, knowledge transfer should
take place at the individual (knowledge worker), group (community of practice), and
organizational (organizational memory) levels.
Discussion Points
1. What are some of the key challenges in developing and managing an organizational
memory system? Outline some of the key obstacles that may be encountered and how
you would address each one.
2. What does the term corporate amnesia refer to? How would you characterize the
costs involved with corporate amnesia? Provide some examples to illustrate your
points.
3. What is the difference between OL, LO, OM, and organizational memory systems
(OMS) or organizational memory information systems (OMIS)?
4. How would you decide whether a particular knowledge element should go into
a record management system, database management systems, a fi le or document
management systems, the intranet or portal, the backup fi les, the archives, or the
organizational memory system? What major criteria are used to identify a lesson
learned or best practice that is “ worthy ” of being preserved in OM?
5. How would you decide whether something is a lesson learned or a best practice?
What additional work is involved in documenting a lesson learned so that it does not
lead to blame or to an inadvertent disclosure of private or confi dential content?
6. Name the major ways in which knowledge can be lost to an organization. Link
these causes to the knowledge processing cycle (see chapter 2). What are some good
methods to deal with such knowledge loss after the fact (in a reactive fashion)? How
would you institute a more proactive approach to preventing knowledge loss?
7. How would you assess the success of your OM systems, for example, a lessons
learned, best practice, or story database? What sorts of tools would you use? Who
would be involved? How would you act on the feedback you collected? What are some
ways in which you could boost the adoption rate of the content?
8. List the major steps you would have to undertake in order to develop a knowledge
continuity strategy. Include information on how you would identify potential areas
392 Chapter 11
for concern, how you would identify critical know-how that needs to be preserved at
all costs, and discuss some of the mechanisms you could use in order to effectively
carry out knowledge capture and transfer.
9. Compare and contrast some of the leading theories on organizational learning and
organizational memory. Why are there so many with so little intersection — what do
you think may have caused this fragmentation? How would you go about trying to
put the various pieces together in order to better understand the processes of OL?
10. How would you integrate OL and knowledge continuity objectives within a KM
metrics framework? What sorts of organizational goals would be addressed and what
would the KM contributions be? (Refer to chapter 10).
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12 The KM Team
He is wise who knows the sources of knowledge — where it is written and where it is to be found.
— A. A. Hodge (1823 – 1886)
This chapter provides an overview of the professionals who form part of the knowledge
management (KM) team. The key skill set required to carry out KM responsibilities is
described using a variety of frameworks. The roles of CKO (chief knowledge offi cer)
and CLO (chief learning offi cer) are introduced and their evolution from the more
traditional CIO (chief information offi cer) is discussed. The new role of chief human
capital offi cer is discussed. The different types of KM jobs that exist and potential KM
employers are outlined and the chapter concludes with a discussion of the emerging
KM profession and some of the ethical issues involved in its practice.
Learning Objectives
1. List the key KM skills required to carry out KM professional work and justify the
need for each one.
2. Describe the different roles that are required for a KM team and list the key respon-
sibilities of each.
3. Understand how a CIO role can evolve into a CKO role or even a CLO position.
4. Identify the different types of potential KM employers.
5. Relate the critical cognitive and attitudinal attributes that an ideal KM professional
should possess.
6. Critically evaluate ethical issues in KM situations in order to make recommenda-
tions on how to successfully prevent and correct any morally challenging hurdles to
KM implementations. Outline the key tenets that should be included in a KM code
of ethics and justify your recommendations.
398 Chapter 12
Introduction
This chapter introduces the fi nal component to complete the integrated KM cycle: the
KM team (see fi gure 12.1 ).
The brief historical overview of KM in chapter 1 discussed how the KM fi eld has
transformed from one led primarily by consultants and other KM practitioners to a
bona fi de discipline with a distinct body of knowledge. This has been paralleled by
the growing number of academic programs that offer KM as compared to the predomi-
nately private sector training that had been the only way to learn about KM up to
now (e.g., Al-Hawamdeh 2003 ).
One approach to forming an effective KM team is to defi ne the different types of
KM professionals and the types of skills, attributes, and background they should
ideally possess. The ultimate goal is to develop a list of cognitive, affective, and
psychomotor skills together with the required competency levels for each skill.
TFPL (www.tfpl.com) is a specialist recruitment, advisory, training, and research
services company with offi ces in London focusing on knowledge management, library
Assess
KM technologies
Update
Contextualize
Knowledge capture
and/or creation
Knowledge sharing
and dissemination
Knowledge acquisition
and application
Organizational culture
K M
m e
tric sK
M t
e a
m
KM strategy
Figure 12.1 The KM team in the integrated KM cycle
The KM Team 399
and information management, records management, and web and content manage-
ment. Since 1987, TFPL has worked with organizations in both the public and private
sectors to help them develop and implement knowledge and information strategies
and to recruit and train information and knowledge leaders and their teams. TFPL has
drafted a guide of KM skills and competencies to provide a clear and practical overview
of KM skills and competencies that draws on the practical experience of organizations
in a wide range of sectors and with varying approaches to KM. In general, these KM
skills include:
• Time management to use time and energy effectively for acquiring knowledge
(spending all day surfi ng the net is probably counterproductive)
• Use of different learning techniques to absorb key knowledge and learning quickly
• Effective skills of advocacy and inquiry to present knowledge to and gather knowl-
edge from others
• Informal networking skills to build infl uence to gain access to people with
knowledge
• Resource investigation skills
• Effective IT skills for recording and disseminating information
• Skills of cooperative problem solving
• Open dialogue skills.
• Flexibility and willingness to try new things and take educated risks
• Active review of learning from mistakes, risks, opportunities, and successes
The TFPL knowledge management skills map (http://www.tfpl.com/resources/skills_
map.cfm) is based on an extensive international research. The project team contacted
over fi ve hundred organizations involved in implementing KM and identifi ed the roles
that they had created, the skills that were needed in those roles, and the additional
skills that were required across the organization. These key skills included an under-
standing of the KM concept — the philosophy and theory; an awareness of the experi-
ence of other organizations in developing KM solutions and approaches; an
understanding of and the ability to identify the business value of KM activities to the
organization; and an appreciation of the range of activities, initiatives, and labels
employed to create an environment to create, share, and use knowledge to increase
competitive advantage and customer satisfaction (see table 12.1 ).
The KM team ’ s skill requirements can be built up from the set of critical skills or
core competencies, such as an ability to learn, autonomous, wait to be told, collabora-
tive team player, sees the big picture, makes connections, learns from mistakes, ability
400 Chapter 12
Table 12.1 Excerpt from the TPFL KM skills map
Business awareness/
experience Management skills Intellectual and learning skills
Business planning Change management Ability to deal with ambiguity
Entrepreneurial Coordination Analytical
Forward thinking Cost control Bigger picture view
Globalization issues Financial management Conceptual thinking
Industry/sector knowledge Leadership Emotional intelligence
Leadership Measurement performance impact, value
Self awareness, self motivation, persistence, read emotion in others
Organizational design People management Innovation
Organizational skills Project management Lateral thinking
Risk management Quality assurance Organizational skills
Strategic thinking Team building Original thinking
Strategic planning Time management Perspective
Understanding value chain Training and development Problem solving
Visioning Needs analysis Positive thinking
to think and do, with a focus on outcome and an appreciation of information man-
agement techniques.
A KM dream team would collectively possess the skills of communication, leader-
ship, expertise in KM methodology/processes/tools, negotiation, and strategic plan-
ning. It would also know the organization, remain connected to the top, adopt a
systems view, and be an intuitive risk taker.
TFPL has developed a competency framework that allows managers in consultation
with the staff who will hold the posts to defi ne knowledge and information manage-
ment roles and their competencies. The KM Skills Toolkit (http://www.tfpl.com/
skills_development/skills_toolkit.cfm) is a diagnostic tool that can help organizations
to assess recruitment needs and develop job descriptions and personnel specifi cations
for knowledge and information roles.
Moving up one level, Goad (2002) groups key KM skills along the following seven
categories:
1. Retrieving information
2. Evaluating/assessing information
3. Organizing information
4. Analyzing information
The KM Team 401
5. Presenting information
6. Securing information
7. Collaborating around information
The skill of retrieving information is everything from the low-tech skills of asking
questions and listening, and following up to the more complex skills of searching for
information using Internet search engines, electronic library databases, and relational
databases. Concepts of widening and narrowing one ’ s search, Boolean logic, and itera-
tive search practices are an important part of the effective exercise of this skill.
Evaluating information entails not only being able to the judge the quality of
information, but to determine its relevance to some question or problem at hand.
Though this has no necessary computer mechanism for implementation (though
Internet search engines have crude relevant raters), the greater availability of informa-
tion in the current information-rich environments makes this skill of far greater
importance.
Organizing information entails using various tools to draw connections between
items of information. In the manual environment, we use fi le folders, drawers, and
other mechanism for organizing information; in more high-tech environments, we
use electronic folders, relational databases, and web pages. Effective organizational
principles must underlie effective implementation of information organization regard-
less of the environment.
Analyzing information entails the challenge of tweaking meaning out of data.
Integral to analyzing information is the development and application of models, often
quantitative, to “ educe ” relationships out of the data. Tools such as electronic spread-
sheets and statistical software provide the means to analyze information. But the
human element is central in framing the models that are embodied in that
software.
The key aspect of presenting information is the centrality of audience. Presenting
information — whether through PowerPoint presentation, web site, or text — builds on
principles of chunking information to enable audiences to understand, remember, and
connect. Web styles and monographs on designing web site usability provide concrete
content for this KM skill.
While securing information differs from the other six KM skills, it is no less impor-
tant. Securing information entails developing and implementing practices that ensure
the confi dentiality, quality, and actual existence of information. Practices of password
management, backup, archiving, and use of encryption are important elements of this
effectively practiced KM skill.
402 Chapter 12
Increasingly, information technology tools called groupware are being provided to
support collaborative work. To use that technology effectively requires not just under-
standing how to use those tools but understanding underlying principles of effective
collaborative work. Principles of e-mail etiquette are an illustration of important
knowledge underlying the effective exercise of this KM skill.
Most organizations are still defi ning their KM roles. Some are repurposing or
extending existing roles in order to better accommodate knowledge work. While KM
in every organization is unique and necessarily tailor-made, there are a number of
“ generic ” KM roles that can be identifi ed. These are discussed in further detail below.
Major Categories of KM Roles
KM roles are quite diverse. They may include such categories as:
Strategic roles Chief human capital offi cer, human capital retention manager
Senior and middle management roles Chief knowledge offi cer, knowledge manager
Knowledge leaders Also referred to as KM champions, who are responsible for promot-
ing KM within the organization
Knowledge managers Responsible for the acquisition and management of internal and
external knowledge
Knowledge navigators Responsible for knowing where knowledge can be located, also
called knowledge brokers
Knowledge synthesizers Responsible for facilitating the recording of signifi cant knowl-
edge to organizational memory, also called knowledge stewards
Content editors Responsible for codifying and structuring content, also called content
managers; roles involving capturing and documenting knowledge — researchers,
writers, editors
Web developers Electronic publishers, intranet managers, content managers
Learning-oriented roles Such as trainers, facilitators, mentors, coaches — including those
with responsibility for developing information and knowledge skills
Human resources roles Specifi c responsibility for developing programs and processes
that encourage knowledge-oriented cultures and behaviors
Knowledge publishers Responsible for internal publishing functions, usually on an
intranet, also called webmasters, knowledge architects, knowledge editors
Coaches and mentors Responsible for assisting individuals throughout the business
unit or practice to develop and learn KM activities and disciplines
The KM Team 403
Help desk activities Delivery of KM and information related to training, also called KSO
(knowledge support offi ce)
In seeking to recruit relevant professionals for knowledge management roles, a
key challenge lies in defi ning the objectives and deliverables of those roles and in
specifying the skills and experience of the people needed to fi ll them. Some of these
roles may be newly created, while others may involve redefi ning or extending existing
roles.
Different organizations will necessarily have different approaches describing knowl-
edge management roles. A sample KM job description may look something like the
example given here (box 12.1).
KM professionals require a multidisciplinary skill set that consists of such compe-
tencies as fi nding, appraising, and using knowledge, reformulating questions, navigat-
ing content, evaluating the relevance of content, fi ltering out what is not needed, and
synthesizing from diverse sources to apply the knowledge (e.g., to make a decision).
Last but not least, they must contribute to recording such valuable experiences to
organizational memory systems.
Senior Management Roles
One may be familiar with the role of a chief executive offi cer (CEO), chief operating
offi cer (COO), and the chief fi nancial offi cer (CFO). There are also chief technology
offi cers (CTO) and chief information offi cers (CIO), positions typically reserved for
heads of information technology. An analogous role exists for a knowledge manage-
ment executive, sometimes referred to as the chief knowledge offi cer (CKO) or chief
learning offi cer (CLO). The CKO or CLO position heads the KM team and is primarily
responsible for:
• Knowledge management strategy
• Knowledge management operations
• Infl uencing change in the organization
• Managing knowledge management staff ( Rusonow 2003 )
In 2002, the Chief Human Capital Offi cers Act was enacted as part of the U.S. Home-
land Security Department (see Crumpacker and Crumpacker). This Act required that
chief human capital offi cers (CHCOs) be designated for all twenty-four agencies and
offi ces. The Act states that each CHCO serves as his or her agency ’ s chief policy advisor
on all human resources management issues and is charged with selecting, developing,
training, and managing a high-quality, productive workforce. The CHCO Act also
404 Chapter 12
Responsibilities will include:
• The systematic recording and storing of health-related information and expertise
• The “ packaging ” of organizational expertise, health information, knowledge, and learn-
ing for use by a variety of clients
• Maximizing the usability and usefulness of health resources/information products for
different user groups
• Promoting the meaning and purpose of information and knowledge resources/products
to clients within and outside of the organization
• Ensuring information/knowledge resources can be readily accessed and easily retrieved
Knowledge and Information Manager:
• Will provide leadership in the area of knowledge management as a technique for the
management of the intellectual assets of the organization
• Will assist with the development of knowledge and information as a core business func-
tion for all business units
• Will provide the “ hands-on ” expertise required to manage organizational expertise in
the form of both knowledge and information resources/products
Selection criteria:
• Tertiary qualifi cations preferably in relevant fi eld, for example, Information Science, KM
• In-depth appreciation of the capabilities and limitations of information technology
• The ability to manage knowledge and information via online databases, collaborative
technologies, and web-based services
• Understanding of knowledge processes such as organizational learning and
development
• Understanding of the principles of knowledge management as a management technique
to enable organizational development in the knowledge economy
• Excellent computer skills preferably with experience with database and web site
management
• Experience in systems development and implementation would be an advantage
• Experience managing small teams and budgets
• Leadership and planning skills
• Superior communication and relationship building skills
• Strong project management skills
Box 12.1 An example: Sample job description: Knowledge and Information Manager (posted on www
.brint.com)
The KM Team 405
Role responsibilities:
• Develop, implement, and achieve a knowledge management plan for the organization
• Establish a Health Information Centre for the knowledge and information resources/
products of the organization
• Develop and maintain a health Internet and intranet site
• Train and develop staff in information literacy and knowledge awareness, that is, in
systematically identifying, collecting, reviewing, sharing, and retaining high-value
knowledge
• Ensure compliance with relevant legislation, for example, copyright and intellectual
property
• Oversee development and achievement of business and project plans for the unit
• Monitor and report on relevant activity levels in operational and business plans
• Establish and maintain links with relevant internal and external stakeholders
Box 12.1 (continued)
established a chief human capital offi cers council to advise and coordinate the activi-
ties of members ’ agencies on such matters as the modernization of human resources
systems, improved quality of human resources information, legislation affecting
human resources operations, and organizations. 1
CHCOs are responsible for the strategic alignment of the workforce (present and
future) to the organization ’ s mission. CHCOs are therefore the KM executives who
most effectively manage all human capital policies and programs such as retention
strategies, knowledge transfer tools and methods, and workforce planning to avoid
knowledge gaps and operational breakages.
The KM executive must decide how information is evaluated, created, processed,
inventoried, retrieved, and archived, so that KM activities are aligned with the business
goals of the organization. There are huge ramifi cations when an organization creates
records, installs a new online catalog or a fi rewall, designs a web site, creates virtual
workplaces, copyrights information, and creates policies and procedures on how one
department communicates information to another (or too many times, there is no
communication between departments). The head of KM must be present in all these
events. This executive KM role often also incorporates change management.
Thurow (2003 , 2004 ) maintains that in our increasingly knowledge-based economy,
every company will eventually have a senior manager responsible for KM. Those that
get there fi rst will have a competitive edge. Just what this person will do is still being
406 Chapter 12
invented and will differ from industry to industry. The KM executive ’ s duties may be
as varied as recommending whether a company should buy, sell, or make its technolo-
gies, or determining where technology is going and where new competitors may arise.
KM executives identify critical knowledge needs within a company as well as any
knowledge gaps that need to be addressed. KM executives need to be good relationship
builders as the fundamental issues revolve around people, culture, roles, behaviors,
and the business processes in the organization.
Skyrme (1997) defi nes a CKO as a senior executive who is responsible for ensuring
that an organization maximizes the value it achieves through one of its most impor-
tant assets — knowledge. Although only a few companies have people with this
explicit title, those with similar responsibilities include Director of Intellectual Capital
and Director of Innovation. CKOs will typically contribute to the following KM goals:
• Maximize the returns on KM investment in knowledge — people, processes, and intel-
lectual capital.
• Exploit intangible assets, for example, know-how, patents, customer relationships.
• Repeat successes and share best practices.
• Improve innovation and the commercialization of ideas.
• Avoid knowledge loss and leakage after organizational restructuring.
The responsibilities associated with the job function of KM executive revolve around
converting the KM strategy into specifi c KM initiatives that help achieve organiza-
tional business goals. KM initiatives fall into general categories such as:
• Promoting the importance of knowledge sharing
• Creating a technical infrastructure to ease that sharing
• Promoting a cultural climate that rewards knowledge sharing behaviors
• Measuring the value to the organization of knowledge and KM practices
Potentially the most important part of the job function is promoting a corporate
culture that encourages knowledge sharing, a long-term proposition. The CKO works
as a change agent to build a cultural climate that rewards sharing behavior ( Earl and
Scott 1999 ). Because of the power associated with expertise, employees may be reluc-
tant to share their knowledge and skill. The old adage that knowledge equals power
may prevail as employees with specialized knowledge may elect to use this as a source
of personal power ( Gordon 2002 ).
The CKO argues against perceived reasons for hording knowledge, ( Stewart 1998 )
persuades workers that knowledge-sharing initiatives are to their benefi t ( Earl and
Scott 1999 ), and uses motivational techniques to reward a sharing climate. The CKO
The KM Team 407
also creates an environment that makes it easier to build communication networks
between employees who do not normally work together but would generate value
from exchanging information ( Earl and Scott 1999 ). The CKO works with formal and
informal communication networks and supports communities of practice or groups
of experts who could learn from knowledge exchange ( Stewart 1998 ).
Davenport and Prusak (1997) argue that these organizational changes will neces-
sarily require changes to the information technology structure, since IT is the key
enabler in leveraging intellectual capital. Having fostered a sharing culture, the CKO
uses IT to create a structured means of knowledge exchange, and as a way of generat-
ing opportunities to connect workers together across organizational units and geog-
raphies. The CKO designs ways for workers to present and receive knowledge and is
responsible for developing and maintaining an information infrastructure to harness
the collective knowledge of the organization.
While working to foster a cooperative culture and creating mechanisms to exchange
knowledge, the CKO keeps a sharp eye on the rewards of these endeavors. The results
of KM activities must translate into real business value. In business ventures, the
bottom line is the measure of success to an organization. The CKO evaluates the return
on investment before making cultural and design decisions and proceeding with KM
initiatives. A fi nal function for many CKOs is that of manager to a team of knowledge
professionals. Although not all CKOs have a team, Earl and Scott (1999) found that
most have a small staff of three to twelve specialists working under their supervision.
In addition to leading the management of intellectual capital in an organization, the
CKO must therefore also supervise the work and careers of their employees.
Some KM executives have the title of chief learning offi cer (CLO). There is a journal
dedicated to this new role, called chief learning offi cer (http://www.clomedia.com/).
Like CKOs, most chief learning offi cers are fi rst-generation incumbents. They typically
started their jobs less than three years ago and did so without clearly defi ned roles,
responsibilities, or daily activities. Chief knowledge offi cer positions are typically
created to leverage knowledge into tangible business benefi ts. Likewise, CLO positions
are designed to leverage learning through the culture of an organization, the type of
knowledge and learning it wants to emphasize, and how technologically focused it is.
Unlike CKOs, the roots for most chief learning offi cer positions are in human
resources, organization development, or sales and marketing (Bonner 2000). Most
incumbent CLOs have strong backgrounds in learning strategies and a strong orienta-
tion toward setting and reaching business goals. They have been selected from such
positions as director of training or vice president of sales and marketing. CLOs are
committed to the strategic integration of organizational and individual learning at all
408 Chapter 12
levels and across all functional silos. They often have as a primary objective to change
their organizations ’ mind-sets from training (usually defi ned as a classroom-based
delivery system) to continuous learning and human performance improvement and
to use a wider variety of delivery methods such as virtual learning options, corporate
universities, and self-directed learning.
Chief learning offi cers are not glorifi ed training directors. Baard (2002) points out
that the CLO role began as being primarily concerned with organizational learning
and initiatives such as e-learning, but the role has expanded to help transform the
organization into a learning organization. The primary success factor for being a CLO
is being a businessperson fi rst and understanding how to drive through a strategic
initiative. CLOs must be able to communicate in business-tangible results, think stra-
tegically, and talk the language of other executives. CLOs are strategic leaders who
help senior management translate learning into strategic business capabilities.
Gale (2003) describes Dell ’ s CLO role, fi lled by John Con é who retired as Dell ’ s chief learn-
ing offi cer in August 2001. The company never replaced him. The reason was not because
the CLO position is a passing concept. It was because Con é believed that his work as the
CLO was done. He had been with Dell since 1995 and was given the offi cial title of CLO
in 1999, although he says that he really always worked in that capacity. His job was to
defi ne the policies and infrastructure that would make Dell a distributed learning organiza-
tion where employees have access to training whenever and wherever they needed it.
Ultimately, that meant making learning such an inherent part of how they did their jobs
that it became an unremarkable event in employees ’ lives, he says. He achieved that goal
in part by making training a necessary piece of every new-product release. “ We wanted
training to be a natural part of the development process, ” he says. Today, new products
at Dell do not move forward unless the necessary training for the product release is in
place and deployed. Since Dell comes out with thousands of new products every year,
training quickly became a constant in employees ’ lives.
During his six years at the company, Con é also oversaw the organization ’ s vast e-learn-
ing program. His team transformed more than 90 percent of the company ’ s learning
content to technology-based formats, putting employees in control of their own learning,
twenty-four hours a day, seven days a week. Admittedly, Con é is not sure if he was suc-
cessful in making learning a permanent part of the culture at Dell. The traditional measures
for training success, including the number of hours people are in training, executive
involvement, and the percentage of payroll dedicated to learning, show that his efforts
are still going strong.
Box 12.2 An example: The CLO at Dell
The KM Team 409
Willis and May (2000) describe the CLO role as:
• A strategic, lead player in today ’ s business organization
• Responsible for making sure learning across an entire system is leveraged, not
sacrifi ced
• Accountable to the whole system and must have broad discretionary power
• Operates by using knowledge about how adults learn, how learning affects work,
how value systems operate, and how social and technical systems in an enterprise or
in their environment may either support or counteract each other
CLOs work with the know-how of knowledge — the tacit knowledge that is hard to
codify. They integrate thinking and acting and their work involves lots of errors and
mistakes. CLOs need to create an environment that fosters knowledge sharing infor-
mally so that they can interact with a team in a work context. The CLO ’ s work begins
and ends with the customer. Their work is applicable at each point in the continuous
cycle that becomes spirals of need and need satisfaction. Customers validate and
confi rm the mission of the organization, which in turn drives the business strategy.
Strategy involves inventing and choosing options, determines the culture needed to
accomplish the strategy, and leads to modifi cation of the systems in use to create
competitive advantage. If there is advantage to the customers, they are satisfi ed and
the mission of the company is once again ratifi ed. Some typical CLO initiatives would
include:
Cultural transformation Assisting with the development and communication of a new
vision and strategy for the organization and tending to the cultural transformation to
support the new corporate direction. Watkins and Marsick (1993) noted that training
programs can help deliver skills needed for organizations to change, but do not address
the deep-seated, mental models and attitudes or the organizational structures and
norms which perpetuate them.
Culture maintenance Designed to support the marketplace strategy and address defi -
ciencies in skills essential to maintain the new culture developed.
Contemporary initiatives Related to business development, like developing a new mar-
keting plan, account manager development, or promotional process redesign. These
require in-depth experience in the industry, comfort/ease in working across all func-
tions of the organization, and a whole systems viewpoint/thinking.
Due to the nature of the work, CLOs have a limited number of quantitative perfor-
mance indicators and most are budget related. The CLO ’ s job focuses mainly on
management of projects, preparing plan documents for projects including problem or
410 Chapter 12
opportunity synopsis, proposed solutions, action steps and timetable, deliverables, and
projected costs. A CLO ’ s performance is evaluated in terms of meeting objectives
on target, on time and on budget. The CLO is an unprecedented kind of catalyst
in organizations, serving to combine technical and social work factors through
communication and paving the way for employees to contribute their very best to the
collective enterprise.
KM executives, whether they have a CKO or CLO title, are primarily
responsible for ensuring that KM goals are in line with organizational strategies and
objectives.
KM Roles and Responsibilities within Organizations
The main types of KM roles observed in a wide range of private and public sector
organizations can be summarized as follows:
Designing information systems Designing, evaluating, or choosing information content,
database structures, indexing and knowledge representation, interfaces, networking,
and technology.
Managing information systems Maintaining the integrity, quality, currency of the data,
updating, modifying, improving the system and operating the system.
Information resources management Managing organizational information resources to
support organizational missions and for competitive advantage.
Training Coaching, mentoring, community of practice start-up and lifecycle
training support, and feeding back lessons learned and best practices into training
content.
Information agencies Acting as information consultants or guides for clients by advis-
ing, training, and guiding on information, information sources, information use;
acting as an agent on behalf of the client by gathering, evaluating, analyzing, synthe-
sizing, and summarizing information for clients.
Competitive intelligence Gathering and analysing intelligence to inform decision
making.
Customer relations for information systems/technology Acting as intermediaries between
clients and information system designers, translating client needs into functional
specifi cations, and sales.
Designing and producing information services and products publications Databases, infor-
mation systems, multimedia products, and stories from storytelling workshops.
Knowledge journalist Gathering organizational stories and coding tacit knowledge.
The KM Team 411
Organizational information and KM policy analysts Designing access to corporate organi-
zational information and KM policies, quality control, maintaining proprietary infor-
mation and KM, and mapping corporate intellectual assets.
Government KM policy analysts Formulating government policies at all levels regarding
such issues as the KM infrastructure, access to and use of government information,
intellectual property, privacy and public/ private roles in knowledge creation,
dissemination and use, government acquisition of information and information
technology.
The types of organizations where KM roles can be found are typically those orga-
nizations concerned primarily with information content, such as publishers, database
creators and providers, the press/mass media, new media companies (e.g., multimedia
developers), information collectors (e.g., Reuters), data service companies (e.g., Mead),
value-added providers (e.g., Standard and Poors), and societies covering a single dis-
cipline (e.g., American Chemical Society). Also, organizations concerned primarily
with information delivery offer a number of major KM roles. These would include
companies such as telecommunications and cable companies, database vendors, for
example, DIALOG and networks, service providers (e.g., BARNET, ANS).
Organizations concerned primarily with information technology have long had a
number of key KM positions. These include the software industry, computer hardware
companies and systems integrators, especially to develop criteria for hardware and
software and optimize systems for customers and instructional technology develop-
ment companies. Similarly, KM can be found in organizations concerned primarily
with information organization, access and preservation such as libraries (e.g., college
and university libraries, public libraries, corporate libraries, school libraries, research
libraries, other special purpose libraries such as hospital libraries), museums, archives,
data centers, and hospitals and other medical organizations.
KM can be found in almost every type of organization today: law fi rms, medical
practices, pharmaceutical companies, utilities, engineering fi rms, healthcare, govern-
ment departments, banks and insurance companies, and the military sector. KM roles
include the application of information technology — evaluation, selection, applica-
tions design and research and information-gathering, synthesis, and evaluation —
libraries, competitive intelligence units, and records management. The government
has been a KM leader in many areas. KM jobs are often found at governmental agen-
cies engaged in information production and distribution (e.g., Bureau of Labor Statis-
tics, Department of Commerce, National Center for Education Statistics, NTIS, ERIC,
US Geological Survey, NIH, Bureau of the Census, Patent and Trademark Offi ce, United
412 Chapter 12
Nations, World Bank, foreign governments); governmental agencies involved in infor-
mation regulation (e.g., PUCs regarding telecommunications regulation); governmen-
tal agencies involved in information technology assessment, development and policy;
information resources management to help agencies accomplish their missions (e.g.,
a recent GAO report criticized the Department of Energy for inadequate information
resources management which impeded its operations), the intelligence community
(e.g., CIA), and agencies involved in policy formulation/decision making as consumers
of information, for example, the Food and Drug Administration.
There are a number of important KM functions to be found in other academic and
research institutions such as large scientifi c enterprises (e.g., Human Genome Project,
Mission to Planet Earth) and in the design and management of discipline-specifi c
information systems. PhDs in KM also follow an academic career path at universities
or fi nd employment in information industry fi rms for R & D and government
agencies.
The KM Profession
Al-Hawamdeh (2003) refers to KM as an emerging profession. The fi eld of KM has
slowly evolved from a consulting service to an internal business function. It has
become an academic discipline being taught in universities worldwide. At the same
time, many organizations are still in the process of defi ning their KM roles. There are
a wide range of differing job titles and an even wider diversity in the backgrounds of
KM practitioners. These factors all contribute to the emergence of the KM profession.
The KM fi eld is fairly young when compared to older, more established professions
such as law, medicine, or engineering. As the KM skill set continues to grow and show
valuable contributions to the overall organizational goals, the profession will continue
to mature and coalesce as a distinct fi eld of professional activity. There are a number
of certifi cation initiatives underway that will help solidify KM ’ s position as a bona fi de
fi eld of professional practice (e.g., the KMCI Certifi cate in Knowledge and Information
Management, www.kmci.org). At the same time, university programs in KM are pro-
liferating, and new classes of KM graduates are entering the KM job market. In parallel
with the emergence and coalescence of KM as both an academic discipline and a
professional fi eld of practice is a growing awareness of the need to incorporate ethics
into the job description of each KM team member.
The Knowledge Management Resource Center (http://www.kmresource.com/
exp_university.htm) lists a large number of universities that offer knowledge manage-
ment courses and programs. In general, KM is found in the management, education,
The KM Team 413
and library and information studies departments of universities. Stand-alone special
interest courses have evolved into degree programs at the undergraduate and graduate
levels. Some sample KM courses and their syllabi can be found on Peter Gray ’ s site
(http://mint.mcmaster.ca/mint/OLKM_Syllabi.doc). Quite a few doctoral students are
doing their dissertations on KM topics and some of these are listed on the ICASIT web
site (http://www.icasit.org/km/academia/list_of_phd_dissertation.pdf).
Knowledge management has become more solidly established as a discipline
as well as a fi eld of professional practice. In parallel, KM qualifi cations now require
more than having had a course or two in the subject, as many employers now require
a degree or at least a specialization in KM. The fi eld of knowledge management
still maintains its wide diversity as the titles of these degrees range from computer
science, management or business, cognitive psychology, and library and information
science degrees. In parallel, a number of professional associations have created KM
chapters such as the Special Libraries Association (http://wiki.sla.org/display/SLAKM/)
that in addition to its excellent content is also a “ practice what you preach ” site
with wikis, communities of practice, and many web 2.0 features. Other associations
include:
• KMPro, Knowledge Management Professional Society (http://www.kmpro.org), with
wide-ranging chapters and a certifi cation process.
• AOK, Association of Knowledge Work (http://www.kwork.org).
• Knowledge Management Benchmarking Association (http://www.kwork.org/).
• Information and Knowledge Management Society (http://www.ikms.org/).
• Regional KM organizations listed at IT Toolbox ’ s web site (http://it.toolbox.com/
wiki/index.php/Knowledge_Management_Societies_ & _Associations).
Some sample KM job postings are shown here (adapted from TFPL: http://www.tfpl
.com/permanent_recruitment/clients/kmroles.cfm).
The Ethics of KM
Ethics establishes a framework for making decisions based on values and a determina-
tion of what is right and wrong. Laws create public policy built on government ’ s
presumption of what is best for its citizens. Legal aspects frequently attempt to codify
ethical responsibilities but often can differ from an individual or organizational moral
standard. An ethical code for a profession is a system of standards to which those in
the fi eld agree to conform ( Rogus 1997 ). Professionals in formal leadership roles have
a responsibility to model the highest possible standards for those whom they manage.
Perhaps our most important aspiration is that we understand how the larger culture
414 Chapter 12
Chief Knowledge Offi cer
• To take the lead in developing the infrastructure, resources, processes, and culture for
knowledge management to support creativity and competitiveness
• To supervise senior managers (IT, HR, business development)
• Prioritize KM initiatives
• Implement KM processes and procedures around a corporate memory
• Qualifi cations: Degree plus professional experience
• Skills: Demonstrated capacity for managing change, ability to negotiate and persuade;
presentation skills, team-building and motivational skills
• Personality traits: Effective, pragmatic, and action orientated, adaptable and fl exible in
approach, people orientated
Knowledge Manager
• To manage and promote the effective supply and use of knowledge
• Identifying local knowledge needs and prioritize in terms of value to the business
• Promoting the effective use of knowledge-sharing tools for all partners and staff
• Qualifi cations: Postgraduate qualifi cation in librarianship, information sciences, or a
related discipline. Business related qualifi cation desirable (or appropriate professional
experience)
• Experience: Over fi ve years senior experience in a business/fi nancial environment
• Skills: Management skills, good IT skills including maintaining quality databases, Lotus
Notes skills; in-depth understanding of the principles of knowledge management
• Personality traits: Good at building, motivating, and leading teams, good communicator;
pragmatist
Knowledge Coordinator/Information Specialist
• Purpose/objectives: To manage the effective supply and use of internal information and
its integration into the corporate knowledge base
• Responsibilities: Industry research using a variety of sources including the Internet and
Lotus Notes. Maintaining a collection of internal research. Assisting in the population of
the company ’ s existing information databases. End-user training in the use of desktop
information resources such as Lotus Notes. Knowledge management administration
including maintaining internal distribution lists and upkeep of hard-copy library.
• Education: Degree or postgraduate qualifi cation in librarianship, information sciences,
or a related discipline
• Experience: Over two years experience providing research services in a corporate or
industry specifi c environment
Box 12.3 An example: Sample KM job descriptions
The KM Team 415
• Skills: Added value research, project management, high competency in searching
CD-ROMs, DIALOG, Datastar, and the Internet. Instruction/training skills
• Personality: Ability to function in a high-pressure environment.; fast thinker with a
fl exible attitude
Knowledge Management Analyst
• Purpose/objectives: To provide information management support to knowledge teams.
To undertake analytical research to support business teams.
• Responsibilities: Acting in an advisory/facilitative role to allied knowledge management
groups. Designing architectures, processes, and infrastructure for libraries, databases, and
intranet. Editing content of library home pages. Conducting analysis and producing
reports of KM surveys.
• Education: Degree level qualifi cation in library and information science or a related
discipline
• Experience: Two to four years of professional experience in a business environment.
Experienced in managing, indexing, and abstracting knowledge.
• Skills: Indexing and classifi cation. Excellent written and oral skills. Good project manage-
ment skills. Research and analytical skills. Strong IT and database skills including MS
Offi ce, Lotus Notes, and the Internet.
• Personality: Good judgment, fl exibility, and resourcefulness. Highly analytical. Strongly
motivated.
Knowledge Coordinator
• Purpose/objectives: To manage the provision of value added research to sales
departments
• Department: Providing added value research information to sales departments within
the company
• Responsibilities: Maintaining reference library. Liaising with other departments to assess
availability and adequacy of material. Building an industry and issues library to support
sales and marketing. Focused on database searching and market/competitor/business issues
analysis.
• Education: Degree level qualifi cation in business or marketing or accountancy or infor-
mation science
• Experience: Two plus years experience in business-to-business research or market analy-
sis. Experience in researching databases.
• Skills: Computer literate with report writing and presentations skills
• Personality: Highly organized, self starter, good communicator
Box 12.3 (continued)
416 Chapter 12
Knowledge Administrator
• Purpose: To manage the acquisition and provision of external business information
• Objectives: To identify and maintain links with corporate sources of business
information
• Department: Servicing the business information needs of the entire company in the UK
• Responsibilities: Management of external resources. Serials management. Journal and
report circulation. Acquisitions, maintaining records, providing invoicing service, shelving
and fi ling and general offi ce duties. Maintaining links with knowledge administrators in
business support departments.
• Education: At least “ A ” Level standard. Experience: Six months to a year administrative
experience.
• Skills: General offi ce or library administration skills, networking and communication
skills
• Personality: Initiative, confi dence, and sense of humor
Box 12.3 (continued)
supports a set of values centering on personal success, power, and popularity, and
tends not to care about the means by which they are achieved.
The fi eld of ethics, also called moral philosophy, involves systematizing, defending,
and recommending concepts of right and wrong behavior (The Internet Encyclopedia
of Philosophy, http://www.iep.utm.edu/e/ethics.htm.) Philosophers today usually
divide ethical theories into three general subject areas:
• Metaethics investigates where our ethical principles come from, and what they
mean. Are they merely social inventions? Do they involve more than expressions of
our individual emotions? Meta-ethical answers to these questions focus on the issues
of universal truths, the will of God, the role of reason in ethical judgments, and the
meaning of ethical terms themselves.
• Normative ethics takes on a more practical task, which is to arrive at moral standards
that regulate right and wrong conduct. This may involve articulating the good habits
that we should acquire, the duties that we should perform, or the consequences of
our behavior on others.
• Applied ethics involves examining specifi c controversial issues, such as environmen-
tal concerns, how whistleblowers will be treated, and so on. By using the conceptual
tools of metaethics and normative ethics, discussions in applied ethics try to resolve
these controversial issues.
The KM Team 417
McElroy (2002) discusses recent accounting scandals that highlight the dangers of
allowing dysfunctional knowledge processing in a corporate context. He points
out that knowledge management can help generate a greater sense of openness in
managerial decision making. KM can promote ethics by enhancing transparency in
management where transparency is defi ned as openness with respect to knowledge
and knowledge processes. In this way, it becomes possible to identify dysfunctional
knowledge processes and bad practices or ideas. KM deals explicitly with the
manner in which organizational knowledge is produced and integrated into practice.
Openness should contribute not only to more ethical business practices but also to
innovation
KM is the one management discipline that concerns itself with managing the
quality and complexion of knowledge processing. KM, and no other body of man-
agement practice, deals explicitly with the manner in which organizational knowl-
edge is produced and integrated into practice. The transparency problem in business
is fundamentally a knowledge management problem, because bad practice is nothing
more than bad knowledge in use, and bad knowledge in use is the product of dys-
functional knowledge processing. Separately, we can see that a move toward
more openness or transparency in organizations not only has an impact on illicit
behaviors but also serves to enhance innovation through greater inclusiveness in
knowledge processing. By involving higher proportions of stakeholders in knowledge
production and integration, organizations can avail themselves of both more quality
control over knowledge in use and more stakeholder participation in the process,
thereby adding to the depth and breadth of organizational creativity. Openness is,
at once, a prescription for enhancing both corporate responsibility and business
innovation.
It is also clear that knowledge management is uniquely well equipped to assist
organizations in making the transition from relative states of closure to greater open-
ness in knowledge processing, primarily because KM is a management discipline that
seeks to enhance knowledge processing. The targets of its interventions are always
knowledge processing behaviors, not just their outcomes. This is often referred to as
the transparency of an organization ( Tapscott and Ticoll 2003 ).
In terms of knowledge processing behaviors, ethics in KM consists of valuing
human beings. Ethics is often considered to be a simple matter, whereas it most
defi nitely is not. Much of ethics can be distilled down to boundaries — boundaries
that can help employees of an organization stay on the correct side of organizational
policy and can help clarify ethical issues ( Groff and Jones 2003 ). Some examples of
boundaries are landmarks, fences, and DMZs (demilitarized zones). A landmark is a
418 Chapter 12
high-level ethical guideline often built upon the company ’ s culture (e.g., value the
demonstration of social responsibility among their employees, promote recycling,
donating to local charities, paying employees to work on community events) and these
can often be conveyed through good stories. Fences are explicit boundaries that show
exactly where an important ethical line lies (e.g., offi cial company policies on ethics).
These should be ubiquitous as policies defi ne the fence; the procedures defi ne operat-
ing within the limits of the ethical fence. DMZs are concerned with active compliance
monitoring (e.g., monitoring of software licenses). They defi ne exactly where the
ethical line is and prevent employees from crossing the ethical line in order to monitor
and report any violations.
Managing ethical liabilities involves four major processes:
Prevention Using codes of conduct, standard operating practices ,and providing land-
marks, fences, and DMZs
Detection Using automated systems to enforce and monitor ethical compliance and
to verify appropriate use of company assets
Reporting Where employees able to report unethical behaviors (whistleblowers)
without suffering any retaliation
Investigations Often require outside assistance in order to be thorough, fair, and
neutral
The challenge is, once again, a question of establishing and maintaining a dynamic
balance — too much monitoring and regulation can lead to a lack of innovation. Orga-
nizations must be able to continue rewarding and motivating innovative and creative
behaviors but this cannot be at the expense of cutting corners so drastically that ethical
values become compromised.
What is needed is a KM code of ethics to help govern the professional practice of
knowledge management work. A number of good examples exist that can serve as a
basis or starting point and a great deal of work is being done on this issue by the
KMCI (Knowledge Management Certifi cation Institute, http://www.kmci.org/). A good
illustration is the code of ethics developed for health science librarians (http://www
.mlanet.org/about/ethics.html) shown in table 12.2 .
Another good example exists in the U.S. Federal Government, particularly in the
forestry sector. A list of key questions is used to assess and monitor the ethical health
of the organization, such as, do senior leaders generate high levels of motivation
and commitment in the workforce and promote ethical behavior through modeling,
communication, training, accountability systems, and disclosure mechanisms?
Some performance indicators that are used include the promotion of teamwork, con-
The KM Team 419
Table 12.2 Sample code of ethics from Medical Libraries Association (MLA)
Goals and principles for ethical conduct
The health sciences librarian believes that knowledge is the sine qua non of informed decisions in health care, education, and research. The health sciences librarian serves society, clients, and the institution by working to ensure that informed decisions can be made.
Society The health sciences librarian promotes access to health information for all and creates and maintains conditions of freedom of inquiry, thought, and expression that facilitate informed health care decisions.
Clients The health sciences librarian works without prejudice to meet the client ’ s information needs, respects the privacy of clients, protects the confi dentiality of the client relationship, and ensures that the best available information is provided to the client.
Institution The health sciences librarian provides leadership and expertise in the design, development and ethical management of knowledge-based information systems that meet the information needs and obligations of the institution.
Profession The health sciences librarian advances and upholds the philosophy and ideals of the profession, advocates and advances the knowledge and standards of the profession, conducts all professional relationships with courtesy and respect, and maintains high standards of professional integrity.
tinual feedback, and whistleblower rights and employee protection if they report
wrongdoing.
Morris (1997) emphasizes that the business world does not exist in isolation. The
way people think and act in clearly business contexts fi lters into all other social con-
texts as well. How can we overcome short-term, bottom-line thinking in order to do
the right thing? Ethical decision making emerges when we emerge from self-centered-
ness to inclusion. Why are ethical rules of conduct not enough? Because we can never
have enough rules, rules have exceptions, rules can confl ict, and rules require inter-
pretation. The Golden or Universal Rule: Treat others the way you would want to be
treated in their place.
Key Points
• A number of studies have been undertaken to better describe the knowledge, skills,
capabilities, and attitudes that good KM professionals require.
420 Chapter 12
• KM skills span the range from business awareness and experience, management
skills, learning abilities, communication, and interpersonal skills, as well as informa-
tion management and information technology expertise.
• In general, KM professionals should be profi cient in retrieving information, evaluat-
ing/assessing information, organizing and analyzing content, presenting content,
ensuring the security of content, and collaborating around valuable content.
• Major types of KM roles include knowledge manager, knowledge journalist, KM
champion, KM navigator, knowledge synthesizer, content editor, knowledge pub-
lisher, coach or mentor, and help desk activities. More senior roles are chief learning
offi cer and chief knowledge offi cer.
• CKOs ensure that KM goals are in line with organizational strategies and
objectives.
• CLOs ensure that the organization acts like a learning organization, improving over
time with the help of accumulated best practices and lessons learned.
• Wide ranges of organizations employ KM professionals, including private, academic,
and public sector companies.
• The KM profession is an emerging one and is in the process of examining the ethics
that KM professionals should be espousing in their work. As with all professions, KM
must be practiced in an ethical fashion. A KM code of ethics should be formulated
and shared with key stakeholders for all KM projects.
Discussion Points
1. What are some of the major types of KM roles or jobs that exist in organizations
today? Describe the types of tasks that each would be expected to carry out.
2. How would you devise a training program or a course curriculum to train KM
professionals in the critical job skills they will need in the workplace?
3. What types of competencies should be present in a good KM team? What is the
contribution of each skill set?
4. List some of the major types of organizations that offer KM positions and discuss
why they need these KM skills.
5. Compare and contrast professional KM training courses with academic degree
programs that integrate KM within their curricula.
6. What core skills will KM professionals need in the next fi ve years? Why do you feel
these will be important in the future?
The KM Team 421
7. In your opinion, what are the three critical ethical issues facing KM? Why have
you selected these as being critical?
8. Draft a sample code of ethics for KM professionals. Explain/justify each element in
your proposed code. What would be the best way of publicizing this? How would you
make sure that KM professionals practice KM in an ethical fashion?
Note
1. http://www.chcoc.gov/About.aspx .
References
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June 4, 2010).
Bonner , D. 2000 . Enter the chief knowledge offi cer. Training & Development 54 ( 2 ): 36 –
40 .
Crumpacker, M ., and J . Crumpacker 2004 . Elevating, integrating, and institutionalizing strategic
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4. 2010).
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Groff , T. , and T. Jones . 2003 . Introduction to knowledge management . Boston : Butterworth-
Heineman .
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McElroy , M. 2002 . Ethics, innovation and the open enterprise, http://www.kmmagazine.com/
xq/asp/sid.0/articleid.20C04C34-11CE-45AE-8815-CAAB07EE516D/qx/display.htm (accessed
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13 Future Challenges for KM
The gem cannot be polished without friction.
— Chinese proverb
Knowledge management objectives are ambitious and almost always involve change —
change at the level of the individual, the group, and the organization as a whole. As
a result, they are almost never easy or straightforward. A number of critical challenges
must be successfully addressed in order to obtain the maximum value for KM invest-
ments — both in terms of budget but also in terms of time and human resources. This
chapter explores some issues facing knowledge management such as political issues
regarding Internet search engines, the shift to knowledge-based assets, intellectual
property, and how to provide incentives for knowledge sharing to successfully incor-
porate KM into organizations.
Learning Objectives
1. Discuss the politics of information seeking and what this implies for successful
knowledge management applications. Be able to outline how this would impact the
design of an organizational memory management system.
2. Describe the fi ve major types of information politics models and how knowledge-
sharing activities would take place in each of them. Be able to evaluate each model
with respect to goodness of fi t with KM requirements.
3. Defi ne the paradox of value and explain how this impacts on the design of KM
solutions. Describe the ways in which this impact can be minimized.
4. Compare and contrast the different ways incentives can be provided for knowledge
sharing.
424 Chapter 13
5. Understand and critically debate where KM stands today, particularly with respect
to how well initial expectations of KM have been met.
6. Outline the major reasons why KM may be perceived as a success or a failure and
discuss how you would improve upon ROI measures for KM.
7. Describe the key areas of research in the fi eld of KM today and make educated
guesses about how these new developments will impact KM.
8. List the key challenges KM faces today and in the near future and provide some
recommended approaches to best address them.
9. Summarize the history of KM to date and predict some directions that the fi eld
may take with respect to the profession, the education of KM professionals, and the
types of KM implementations that will be undertaken in organizations.
Introduction
The major challenges facing KM include people or cultural issues, an overemphasis
on technology, conducting KM in isolation from business goals, ignoring the dynamic
aspects of content, and opting for quantity of content over quality. While this is not
an exhaustive list, there does appear to be a fairly good consensus on the most impor-
tant challenges that are facing KM. These can be found as recurring themes in KM
discussion groups, conferences, and publications (e.g., Firestone and McElroy 2003 ;
Tannenbaum and Alliger 2000 ).
The major problems that occur in KM usually result from companies ignoring
the people and cultural issues. In an environment where an individual ’ s knowledge
is valued and rewarded, establishing a culture that recognizes tacit knowledge
and encourages employees to share it is critical. The challenge of selling the KM
concept to employees should not be underestimated; after all, in many cases employ-
ees are being asked to surrender their knowledge and experience — the very traits
that make them valuable as individuals. One way companies motivate employees
to participate in KM is by creating an incentive program. However, there is the
danger that employees will participate solely to earn incentives without regard to
the quality or relevance of the information they contribute. The best KM efforts are
as transparent to employees ’ workfl ow as possible. Ideally, participation in KM
should be its own reward. If KM does not make life easier for employees, it will fail.
This is why the role of organizational culture is so important together with any
cultural change that needs to take place in order to better accommodate any KM
initiatives.
Future Challenges for KM 425
KM is not a technology-based concept. All-inclusive KM solutions, despite vendor
claims to the contrary, simply do not exist. Companies that implement a centralized
database system, electronic message board, web portal, or any other collaborative tool
in the hope that they have established a KM program are wasting both their time and
money. While technology can support KM, it is not the starting point of a KM
program. KM decisions should be based on who/whom (people), what (knowledge),
and why (business objectives). You should save the how (technology) for last. In other
words, successful KM begins with a sound KM strategy combined with a fostering
organizational culture that enables and rewards the sharing of valuable knowledge.
A KM program should never be divorced from a business goal. For example, while
sharing best practices is a commendable idea, there must be an underlying business
reason to do so. Without a solid business case, KM is a futile exercise. Knowledge is
also not static. Since knowledge can get stale fast, the content in a KM program should
be constantly updated, amended, and deleted. What is more, the relevance of knowl-
edge at any given time changes, as do the skills of employees. Therefore, there is no
endpoint to a KM program. Like product development, marketing and R & D, KM is a
constantly evolving business practice. Finally, companies diligently need to be on the
lookout for information overload. Quantity rarely equals quality, and KM is no excep-
tion. Indeed, the point of a KM program is to identify and disseminate knowledge
gems from a sea of information.
The key critical issues are discussed in this chapter:
• Access issues: What are the political issues factors governing Internet information
seeking?
• Organizational issues: What is the political context of the organization and how
does this affect KM?
• Accounting issues: What is the impact of a shift from resource-based assets to
knowledge-based assets (i.e., from tangible, measurable assets to intangible ones)?
• How do copyright (and “ copyleft ” ) and other intellectual property issues impact
KM? How can knowledge be shared without losing attribution and without false
attribution?
Political Issues Regarding Internet Search Engines
Googlewhacking is a term that has entered our language recently. Googlewhacking
refers to the “ challenging pursuit of searching the popular Google search engine with a
two-word or more search argument that will produce exactly (no less and no more
426 Chapter 13
than) one result. That is, only one web page in the world (at least as indexed by Google)
will happen to have the combination of words you have entered in the search box ”
(http://www.googlewhack.com/). Some examples of past Googlewhacks that have
been successful include word pairs such as: comparative unicyclist, maladroit wheezer,
blithering clops, and demurrable insuffi ciencies. Both the term and the occupation of
Googlewhacking are the inventions of Gary Stock, Chief Innovation Offi cer, Nexcerpt,
Inc. (http://www.googlewhack.com/ and http:/www.unblinking.com).
The raison d ’ ê tre of this phenomenon lies with the information overload issue: the
number of hits that are returned for a given search term is incredible and yet not
particularly useful. For example, what results from typing “ knowledge management ” ?
It is interesting to compare the results to the concept analysis technique that was
presented in chapter 1. For example, Weinberger (1998) used the keywords human,
user, change management, knowledge worker, and person and kept a tally of the
number of hits returned using those key terms. This can then be compared to the hits
obtained when technology-related key terms are used such as processor, RAID, mouse,
Internet, repository. The number of hits obtained with KM technology terms far
exceeds the number of hits obtained with nontechnology terms. This is partially due
to the fact that they are possibly more technology publications, but it illustrates that
the “ human ” is often the last thing considered as organizations change their technol-
ogy. This is a key reason why many technology initiatives result in failure: neglecting
the human element.
To make matters worse, there is common misconception that the commercial
search engines perform an objective and exhaustive search of all things digital and
that the hits are ranked — that is, the fi rst hit is the most relevant to what you were
looking. Nothing, of course, could be further from the truth. Introna and Nissenbaum
(2000) argue that search engines raise not merely technical issues but also political
ones. Their study of search engines suggests that they systematically exclude (in some
cases by design and in some by accident) certain sites and certain types of sites in
favor of others, systematically giving prominence to some at the expense of others.
Such biases would lead to a narrowing of the web ’ s functioning in society, run counter
to the basic architecture of the web, as well as to the values and ideals that have fueled
widespread support for its growth and development. It is doubtful that the market
mechanism could serve as an acceptable corrective.
There are political as well as technical issues associated with search engines that
exlude certain sites systematically in favor of others (by accident or design). Users are
largely ignorant of what goes on under the hood and this is compounded by unusu-
ally high degree of trust in what the computer says. Lawrence and Giles (1999) con-
Future Challenges for KM 427
ducted a study and found that NONE of the search engines individually indexed more
than 16 percent of the total indexable content of the web. Taken together, they index
about 42 percent of the available content. Search engines are only partially effective
at fi nding things; a great deal of the web remains hidden. This is not, however, simply
due to technological constraints as is popularly believed. The politics of information
seeking must be taken into account with organizational knowledge management
systems in order to ensure that the best possible (i.e., the most relevant, valid, and
up-to-date) content is found, retrieved, and made available to the organization ’ s
knowledge workers.
The Politics of Organizational Context and Culture
KM must address not only the information itself, but also the business practices and
processes that generate the information. This means that the politics of organizational
context and culture must also be taken into consideration. For example, at Dow
Chemical, managers believe there should be a common set of fi nancial processes
around the world to create common measures of fi nancial performance whereas at
IBM, they rely on more traditional measures such as customer satisfaction, time to
market, cost evaluation, and so on. The organizational context will thus affect KM
implementation, and the evaluation of how successful this implementation was.
Five models of information politics can be used to characterize the politics of the
organizational context and culture ( Klein 1999 ; Davenport, Eccles, and Prusak 1992 ).
They are:
• Technocratic utopianism
• Anarchy
• Feudalism
• Monarchy
• Federalism
In technocratic utopianism, a heavily technical approach is taken to information
and knowledge management, stressing categorization and the modeling of an
organization ’ s full information assets (often in the form of an exhaustive inventory).
There is heavy reliance on emerging technologies and content tends to be driven by
information systems.. The focus is in detailed corporate data rather than knowledge.
The underlying assumption is that technology will resolve all problems with the
consequence that little attention paid to content and its use. Data are perceived
as a corporate asset.
428 Chapter 13
In the anarchy model, there is an absence of overall information management
policy. Individuals are left to their own devices to obtain and manage their own
information that is made possible by the introduction of the personal computer.
Anarchy models are often seen in early stages of start-ups. They stand at the opposite
end of the spectrum from technocratic model because no amalgamation of corporate
information is possible (e.g., of revenues, costs, customer order levels, etc.). This model
rarely represents a conscious choice but instead tends to evolve into some sort of order
with time.
The feudalism model is based on the management of information by individual
business units or functions, which defi ne their own information needs and report only
limited information to the overall corporation. This is the most commonly encoun-
tered model with its emphasis on “ the control of information ” and “ knowledge is
power. ” The “ king ” decides on content, language, format, distribution list, and the
analysis. Key organizational and environmental information often ignored. It is quite
diffi cult to make informed decisions.
In the monarchy, the fi rm ’ s leaders create the defi nition of information categories
and reporting structures: they may or may not share the information willingly after
having collected it. The CEO or someone empowered by the CEO dictates the
rules for how information will be managed. This model represents an extreme top-
down model that is commonly found in entrepreneurial profi les, small business
owner, and micro-managers. This model is appropriate when consensus cannot be
reached.
A constitutional monarchy can evolve directly from feudalism or monarchies. There
is a document (Magna Carta) that is an information management charter that states
the monarch ’ s limitations. This document identifi es what information will be col-
lected, rules, processes, platforms, common vocabulary, and so on.
Finally, the federalism model emphasizes an approach to information management
based on consensus and negotiation on the organization ’ s key information elements
and reporting structures. This is the preferred model for most intellectual capital
management applications as it makes extensive use of negotiation to bring potentially
competing and noncompeting parties together. People with different interests work
out among themselves a collective purpose and a means of achieving it. Federalism
requires strong (but not too strong) central leadership and a culture of trust, coopera-
tion, and learning. It is important to understand the value of information itself as well
as that of the technology that stores, manipulates, and distributes it. Federalism
encourages the use of cooperative information resources to create a shared information
vision for genuine leveraging of fi rm ’ s knowledge assets in the form of data marts not
Future Challenges for KM 429
exhaustive data warehouses. As a result, this model is also a very good fi t with com-
munities of practice.
It is important to critically assess an organization and to identify the type of poli-
tical model that is in place so that potential KM barriers can be better anticipated.
Shift to Knowledge-Based Assets
The paradox of value ( Boisot 1998 ) lies in the fact that the easier it is to extract the
knowledge, the less value it actually embodies. That is to say, the more the knowledge
is tacit, the greater its value (see fi gure 13.1 ).
Knowledge assets are a source of competitive advantage for fi rms that possess them.
Yet the way the possession of knowledge translates into a competitive advantage is
not well understood. Of course, this does not happen automatically — a fi rm has to
know how to extract value from knowledge assets. There are also defi nite costs
incurred in managing knowledge assets ( Boisot 1998 ). These are:
• Moving knowledge costs incurred by data processing and data transmission.
• Codifi cation costs due to searching and selections made under uncertainty.
• Abstraction costs arising from generalizing knowledge over wider problem spaces.
• Diffusion costs incurred when communicating with potentially large audiences in
ways that can be understood and can lead to effective responses.
• Absorption costs of getting potential recipients of new knowledge to internalize it
and familiarize themselves with it.
• Impacting costs of applying internalized knowledge in a variety of concrete
situations.
Idiosyncratic
Rare
Tacit
Codified
Common
Explicit
Cost/
Benefit
Figure 13.1 The value of a knowledge asset
430 Chapter 13
Classical theories of value focus on resource-based, largely renewable nature ’ s
bounty with little concern with the role of information or knowledge. Labor power is
put into the equation but is largely unadulterated by knowledge, skills, or expertise.
Technical advances were made on behalf of all individuals — not a property of any
single individual. Land, labor, and physical factors of production constituted the basis
of this traditional approach. However, we need to consider the value of information
goods more closely. In the second half of the nineteenth century, value ceased to be
regarded as an intrinsic property of the energy inputs required for production. Instead,
value became relational and contingent. The focus was still primarily physical goods
but with knowledge playing a supporting role. Information goods cannot be inspected
prior to purchase. In fact, the value of a knowledge asset is derived from the utility of
services it renders over time and the fact that it offers a competitive advantage over
those who do not possess it. This lies at the core of the defi nition of an intellectual
asset as discussed in chapter 10.
This leads to another paradox of a knowledge asset: knowledge transfer does
not require physical contiguity. It does require codifi cation and abstraction. There
is cost involved with this, therefore only select information with potential value
and utility will justify the time and effort required. Yet the more transferable we
make knowledge, the less scarce it becomes. We therefore need reliable ways of
measuring intangibles in valuing intellectual capital. An excellent overview of the
major measures and techniques used to assess intellectual capital can be found in
Sveiby (2001) .
In general, most approaches concur that there are three different types of intel-
lectual capital (IC) to be considered:
Human capital The ability of individuals and teams to apply solutions to customer
needs, competencies, mind-sets.
Organizational capital The codifi ed knowledge, culture, values, norms.
Customer capital The strength of customer relationships, superior customer-perceived
value, and customized solutions.
The intellectual capital model is thus the relationship between human capital, cus-
tomer capital, and organizational capital that maximizes the organization ’ s potential
to create value (see fi gure 13.2 ).
Measurement success stories in a number of companies such as Skandia,
Dow Chemical, Buckman Labs, the World Bank, and CIBC are outlined in the
Knowledge Management of Internal best practices report, available at http://www
.bestpracticedatabase.com. A brief summary is provided here.
Future Challenges for KM 431
Human capital
Organizational capital
Intellectual capital model Relationship between human, customer
and organizational capital that maximizes
the organization’s potential to create value
Ability of individuals
and teams to apply
solutions to customer
needs, competencies,
mind-sets.
Strength of customer relationship
Superior customer-perceived value
Customized solutions
Codified knowledge,
culture, values, normsCustomer capital
Figure 13.2 The intellectual capital model
In 1993, before the terms intellectual capital and knowledge management became
industry buzzwords, Dow Chemical began to realize that its database of over 29,000
patents represented a gold mine in underutilized intellectual capital. Over time, the
company ’ s database had become little more than a dusty, neglected fi ling cabinet. To
combat this neglect, Gordon Petrash was hired to direct Dow ’ s intellectual asset man-
agement. Once in offi ce, Petrash took immediate action to identify and index all of
Dow ’ s patents. His initial review revealed that less than one half of Dow ’ s patents were
being utilized. Understanding the value waiting to be discovered, Petrash worked to
develop patent portfolios for each of Dow ’ s business units. All unused patents were
indexed and checked for royalty opportunities. This included:
• Projected costs until expiration
• Percentage of annual intellectual asset management costs of R & D budget
• Percentage of competitive samples analyzed that initiate business actions by purpose
• Percentage of business using
• Percentage that business will use . . .
432 Chapter 13
Dow credits Petrash ’ s actions for saving more than $1 million in patent maintenance
fees within the fi rst eighteen months. Petrash estimates that in addition to an esti-
mated $50 million in tax savings, Dow increased its annual licensing and patent
revenue to $125 million in the year 2000. In effect, Dow expects to reap a benefi t of
$175 million dollars by better managing its most obvious intellectual assets.
The Skandia Navigator (Edvinsson and Malone 1997) makes use of four types of
dimensions:
Financial focus Gross premium income, insurance result
Customer focus Satisfi ed customer index, customer loyalty, market share
Human focus Number of employees, average age, empowerment index
Process focus Operating expense ratio, premium income/salesperson, net claims ratio
In addition, renewal and development is assessed in terms of training expense, employ-
ees, and sales-oriented operations. In all, twenty-one indicators are used to measure
IC with nine indicators used to measure effi ciency of use of IC.
At Buckman Labs, the following metrics are used:
• Percentage of company effectively engaged with customer (target = 80 percent).
• Percentage of revenues invested in knowledge transfer system.
• Number of college graduates.
• Sales of new products less than fi ve years old as a percentage of total sales.
The World Bank emphasizes the creation of knowledge, public expenditure on
education relative to GNP, and public expenditure on education absolute. They also
look at the assimilation of knowledge through such metrics as:
• Gross enrollment rate
• Secondary education
• Tertiary education
• Literacy — newspaper readership
• Adult literacy rate
• Mean years of schooling
Finally, at the CIBC, three major dimensions are taken into consideration:
human capital consisting of the skills individuals need to meet customer demands,
structural capital consisting of the information required to understand specifi c markets,
and customer capital which consists of essential data about the bank ’ s customer
base.
Future Challenges for KM 433
Similarly, Sveiby ’ s Intangible Asset Monitor ( Sveiby 1997 ) focuses on external struc-
ture, internal structure, and the competences of people. External structure contains
customers, suppliers and other “ external ” stakeholders. One selects the ones that are
relevant. In most private companies, this will be the customer and in the public sector
organizations will use other stakeholders, such as community members. Many com-
panies have such valuable alliances with their suppliers that they must be included
too. Internal departments will have internal “ customers ” that will form their external
structure. Tobin ’ s q ( Tobin 1998 ) is a metric that looks at the ratio between the market
value stock price multiplied by the outstanding shares and replacement value of physi-
cal assets. It serves to quantify the value of knowledge on an objective basis at the
global level.
In order to complete the cycle, it is also extremely important to know when to
divest knowledge assets. We need to understand why, when, where, and how to for-
mally divest parts of the knowledge base. After having invested so much — how can
we throw it away! An opportunity cost analysis should be carried out to identify which
knowledge assets are no longer contributing to competitive advantage. Examples of
divesting knowledge would include:
• Selling, licensing, donating a patent
• Spinning off or selling a business unit
• Outsourcing a function of the operating process
• Terminating a training program
• Retaining, relocating, or fi ring individuals with obsolete or ill-fi tted skills
• Replacing or upgrading information technology systems
• Terminating partnerships, alliances, and contracts
Figure 13.3 summarizes the different types of intellectual assets and the relative ease
with which their value can be extracted.
Intellectual Property Issues
At fi rst glance, intellectual property issues may appear to make knowledge implemen-
tation quite problematic. However, two dimensions need to be considered for KM
applications. The fi rst is that when discussion occurs around intellectual property and
authorship, even ownership, of content to be posted and shared company-wide, con-
cerns need to be further elucidated. Most practitioners have found that the concerns
expressed by knowledge workers revolve around attribution and unwanted attribution.
434 Chapter 13
Attribution refers to the discomfort over the possibility of a knowledge resource — a
best practice, a template — may sever the link between the creator and the actual
knowledge. If KM takes appropriate steps to ensure that attribution — or author cred-
its — are always connected and therefore move with the knowledge, then most of the
concerns have been allayed. The second issue is a related but is almost the exact
opposite of attribution Authors are also very concerned that once the knowledge is
out of their hands, it will be modifi ed, watered down, invalidated, or otherwise modi-
fi ed and still attributed to them. Authors and creators feel that they cannot control
the changes and adaptations and therefore they can no longer attest to the validity
and quality of the knowledge. One of the best means of mitigating such circumstances
is to follow tried-and-true document management and version control best practices.
Knowledge resources should be tracked just as seriously with contact names associated
for those knowledgeable about the resource, such as experienced users, subject matter
experts, authors, and any subsequent authors of modifi ed versions. Most of this type
of knowledge history (analogous to document or report history) can be captured in
the metadata as well as being clearly indicated in the corporate memory system.
A second development may also aid the KM cause: the evolution of a “ copyleft
revolution ” or reaction against some of the restraints imposed by copyright
laws. Copyleft is more formally known as the Creative Commons (see http://search
Customer
relationships
Owned
Borrowed
Easy to extract value Difficult to extract value
Business
processes
Organizational
design
Joint ventures
Strategic alliances
Employee innovation
Employee commitment
Individual knowledge
Culture
Norms
Values
Products and services
IT infrastructure
Intellectual property
Physical workspace
Figure 13.3 The intellectual capital matrix
Future Challenges for KM 435
.creativecommons.org/) and refers to a more customized approach to author rights
than the one size fi ts all approach of more traditional copyright rules. The polar
opposite of copyright would be the removal of all restrictions, for example, open
source software or any publicly available content. Copyleft does not venture this far
but does remove some of the copyright restrictions, making it easier for others to use,
modify. and otherwise adapt their original works. A typical creative commons license
would read as follows (excerpt from http://en.wikipedia.org/wiki/Creative_Commons):
There are four major permissions that are contained in creative commons licenses:
• Attribution (by) requires users to attribute a work ’ s original author. All Creative
Commons licenses contain this option, but some now-deprecated licenses did not
contain this component.
• Authors can either not restrict modifi cation or use Share-alike (sa), which is a
copyleft requirement that requires that any derived works be licensed under the same
license; or
• No derivatives (nd), which requires that the work not be modifi ed.
• Noncommercial (nc) requires that the work not be used for commercial purposes.
As of the current versions, all Creative Commons licenses allow the core right to
redistribute a work for noncommercial purposes without modifi cation. The creative
commons license has become quite popular in the academic world and has a good
potential to be applied to knowledge content in organizational KM systems.
How to Provide Incentives for Knowledge Sharing
KM practitioners often neglect the crucial management issues of organizational learn-
ing, motivation, and culture when formulating a knowledge management strategy.
Knowledge workers need to have a climate in which knowledge sharing is encouraged
and they need a reason for sharing the knowledge. Incentives remain one of the more
important challenges facing KM today. An incentive is a reward or some form of posi-
tive feedback given when a desired behavior is exhibited. Since human beings are
purposeful creatures who would tend to continue behaviors associated with positive
rewards and avoid those behaviors that lead to negative consequences, it seems rea-
sonable to expect that incentives for knowledge sharing should lead to more sharing
of knowledge. This being said, the situation is, as always, not so clear-cut.
Incentives can be quite tricky to get right because others may see as an insult what
some perceive as a reward. An example is the system of recognition. In one company,
the public posting of a “ knowledge-sharer of the month ” serves to motivate employees
to share more knowledge. In another context, employees feel that as highly educated
436 Chapter 13
professionals, they should not be reduced to something that reminds them of a plaque
used by fast food companies to motivate their staff. De gustibus non disputatum — of
some tastes there is no disagreeing. In other words, the reward should fi t the person
being rewarded — personalization is very important. At a minimum, employees should
be allowed to choose their reward from a list of possibilities. At Buckman Labs, this
problem was resolved by polling the employees. The top choice turned out to be a
fully equipped laptop computer to be conferred to the top KM citizens, fl own in to
headquarters for a public remittance of the prize by the President himself.
It may be helpful to look at how incentives can be classifi ed according to the dif-
ferent ways in which they motivate agents to take a particular course of action. The
common and useful taxonomy developed by Callahan (2004) divides incentives into
three broad classes:
• Remunerative incentives (or fi nancial incentives) are said to exist where an agent
can expect some form of material reward — especially money — in exchange for acting
in a particular way.
• Moral incentives are said to exist where a particular choice is widely regarded as the
right thing to do, as particularly admirable, or where the failure to act in a certain
way is condemned as indecent. A person acting on a moral incentive can expect a
sense of self-esteem, approval, or even admiration from her community; a person
acting against a moral incentive can expect a sense of guilt, condemnation, or even
ostracism from the community.
• Coercive incentives are said to exist where a person can expect that the failure to
act in a particular way will result in physical force being used against him or her (or
her loved ones) by others in the community — for example, by punishment, imprison-
ment, fi ring, or by confi scating or destroying their possessions.
These categories are not an exhaustive list of all types of incentives. For example,
personal incentives are related to preferences and personal objectives that may moti-
vate actions of individual people. The reason for setting these sorts of incentives to
one side is not that they are less important to understanding human action. Personal
incentives are essential to understanding why a specifi c person acts the way he or she
does, but social analysis has to take into account the situation faced by any individual
in a given position within a given society, which means mainly examining the prac-
tices, rules, and norms established at a social, rather than a personal, level.
Quite intuitively, if there is no economic, social, or personal incentive for any
individual to do work, it will not get done. Therefore, a society must provide incentive
for the work necessary for its own maintenance. Likewise, a company or organization
Future Challenges for KM 437
will have better results if it provides incentives for its members to improve said insti-
tution. One that provides no or little incentive will suffer from weak morale.
Incentive is very much a double-edged sword. For example, corporate policies with
the goal of encouraging productivity — especially of the extreme incentive variant
popular during the 1990s — may not have the intended effect. For example, stock
options, intended to boost CEO productivity by tying CEO compensation to company
performance, have been blamed for many of the falsifi ed earnings reports and public
statements in the late 1990s and early 2000s. Throughout the 1990s and 2000s, many
corporations have sought to increase individual incentives by increasing the sizes of
bonuses (to the point where they exceed salaries, sometimes by a factor as high as
ten) for star performers while also laying off large proportions of their workforce,
hoping to cultivate fear-factor-related gains. The most extreme version of this is forced
ranking, a scheme by which workers are annually ranked and a set proportion (usually
between 10 and 15 percent) automatically fi red. The results of these programs are
mixed, but in extreme cases, usually negative.
While competition among fi rms often has benefi cial results, lowering prices and
encouraging competition within fi rms has almost uniformly negative results. Designed
to encourage production, extreme incentive schemes actually create a cutthroat
working environment where offi ce politics dominate and actually overshadow the
productive goals of the company. An example of this is the now-deceased Enron
Corporation. According to Callahan (2004) , the environment at that company was so
cutthroat (as a result of extreme incentive management) that employees feared leaving
their computer terminals, worried that co-workers might steal information for their
own purposes.
There are obviously some issues with KM as it is applied in many organizations.
Care needs to be taken so that the application of this effective approach is accepted
and supported. It is NOT the information collection but the processes and systems
that must be acceptable to those involved. Business issues as well as people issues are
involved and a simple framework might be helpful in understanding and rolling
forward. Remember, nobody ever washes a rental car, so address issues of ownership
and involvement as you progress.
Denning (2000) points out that since knowledge sharing usually entails a change
in the way the business of an organization is conducted — often, it entails a shift from
vertical “ look up and yell down ” modes of behavior to horizontal knowledge-sharing
behaviors — relevant behaviors should be refl ected in whatever incentive systems are
in place in the organization. It is important that the value of knowledge sharing be
refl ected in the on-going personnel evaluation, periodic merit review, or pay bonuses
438 Chapter 13
of the organization, so that managers and staff can see that knowledge sharing is one
of the principal behaviors that the organization encourages and rewards.
Knowledge sharing should be designated as one of a small number of core behaviors
that are rewarded in the performance review system. Getting agreement across a large
organization to focus on knowledge sharing as one of a small number of core behav-
iors is not easy, and even when accomplished, does not have any instant effect. In
the short run, there is often cynicism and posturing, but the experience of organiza-
tions, particularly the large consulting fi rms, is that over time such a change sends an
unmistakable signal throughout the organization, which does accelerate the intended
behavioral change.
In practice, informal incentives in the form of recognition by management and
visibility within the organization can often be more powerful incentives than the
formal incentive system. While the establishment of formal incentives is important
for the long-run sustainability of a knowledge management program, it is easy to
over-estimate the value of incentives. The absence of formal incentives in the early
days of knowledge sharing can become a pretext for not implementing the program.
The establishment of rewards for individual knowledge-sharing activities can signal
the importance of knowledge sharing but also runs the risk of creating expectations
of rewards for behavior that should be part of the normal way of conducting the busi-
ness of the organization.
In the long term however, the establishment of incentives through the regular
personnel and reward system of the organization can establish a clear value framework
that confi rms that knowledge sharing is not a mere management fad, but rather part
of the permanent fabric of the organization.
Stevens (2000) discusses how organizations use a variety of incentives to show that
they ’ re serious about sharing knowledge. For example, some have rewards and recogni-
tion programs for knowledge sharers; these range from kudos in the company newslet-
ter to substantial pay bonuses. Other companies evaluate employees for raises,
advancement, and even extra vacation time partly on how much they participate in
knowledge-sharing activities. Government departments are beginning to focus on
social or group incentives over individual incentives by rewarding team projects or
exemplary success in mentoring or otherwise sharing valuable knowledge. Buckman
Labs invites top knowledge sharers to visit the headquarters to personally receive a
state-of-the-art laptop as recognition. This incentive was chosen by surveying employ-
ees to ascertain what they felt a good reward for being a good knowledge sharer should
be. Given that value is in the eye of the beholder, asking employees to suggest rewards
Future Challenges for KM 439
they would like to receive is probably the best way to proceed. What is considered a
reward may not necessarily be perceived in the same light as it was intended. In a
science and technology group, for example, being named “ top knowledge aharer ” was
perceived as being slightly insulting (someone explained it was too much like
“ employee of the month ” at a fast food restaurant). In a multinational consulting
company, a $50 bonus was offered for each contribution made to the organization ’ s
knowledge base. Again, this was perceived as slightly embarrassing by the members
of the organization, yet this type of reward was quite welcome in a similar albeit
smaller consulting company located in the same European country. Instead of trying
to guess and risk sabotaging the incentive scheme, a representative needs assessment
survey of the target group is by the far the preferred option.
Traditional incentives, such as pay bonuses, are not always enough to change
behavior. Stevens (2000) surveyed seven organizations about their efforts to encourage
knowledge sharing. The following list is adapted from the best practices identifi ed in
the survey.
• Hire people who will share to encourage knowledge sharing from the beginning and
to catalyze the necessary cultural change. Having current employees participate in the
hiring process can do this.
• Develop trust. At Buckman Labs, a code of ethics is formally posted and deals with
how to treat fellow employees properly with respect and to recognize and reward all
contributions.
• Vary motivations by providing different types of incentives at different levels within
the organization in order to better reward executives, department heads, and
individuals.
• Show public recognition via plaques, newsletters as well as adding mentions to
employees ’ permanent fi les.
• Reorganize for sharing to leverage the fact that people naturally share knowledge
with others in their own team and/or community of practice. Formalize natural incli-
nations to group around certain projects, themes, or professional skills.
• Encourage, support, and sustain communities to promote the sharing of expertise,
skills, technical knowledge, or even just professional interest in a particular subject
matter. Enlarge the network of contacts that each employee has and thus enlarge the
scope of knowledge sharing that is possible.
• Develop leaders and role models, as even a small group of KM enthusiasts within a
company can be a powerful catalyst for knowledge sharing.
440 Chapter 13
Future Challenges for KM
What lies ahead for KM? There is one camp that predicts no future for KM, citing a
number of failures to deliver. However, this gloomy forecast can be mitigated some-
what. It is true that, as with all innovation, initial expectations were on the unrealistic
side. One of the reasons for this was underestimating the people component of KM
together with an overemphasis on the role of KM technology in KM solutions. As
Pollard (2003) notes, the reason for this failure was the unrealistic expectation that
human organizational behavior could be easily and rapidly changed. Of course, behav-
Gale (2002) describes the case of Siemens Medical Solutions and how they decided to
change their knowledge is power culture into one in which knowledge sharing was the
norm. The company wanted employees to have easy access to information and expertise
across business units so that they could do their jobs better and faster without reinventing
the wheel. The problem was that many employees associated sharing knowledge with
losing power. Busy employees also perceived taking the time to share information or to
coach someone in a new skill as a burden. Employees saw no value in this activity. In
order to change that attitude, employees had to see an immediate and personal advantage
to sharing information. To support the new environment, the company built three web-
based knowledge-sharing tools through which employees can collect and disseminate
useful information to the rest of the company. The fi rst, “ People of Med, ” is an online
database of employee profi les that includes each member ’ s contact information, experi-
ence, areas of expertise, and photograph. The second, “ Communities of Practice, ” is an
online meeting place where employees volunteer to host forums on specifi c topics, such
as ISO 9001 certifi cation challenges. Any employee interested in that topic can register
and participate in conversations and share materials that may be of value to the group.
The third knowledge-sharing tool is the “ Knowledge Square, ” an online database fi lled
with presentations, web sites, technical papers, specs, and any other materials that might
be of value to the company. Employees can search the database to quickly fi nd informa-
tion related to their area of interest. To encourage employees to take advantage of the
knowledge-sharing opportunities, they receive bonus points every time they use one of
the three tools. These can be used to purchase items from a gift catalog that includes
everything from T-shirts to vacations. Whether they store their profi les in People of Med,
participate in a community, or download information from the Knowledge Square, they
get rewarded. Community leaders are also encouraged to throw parties for their members
where they can share the stories of successful knowledge-tool users in company newslet-
ters, marketing materials, and broadcast e-mails.
Box 13.1 An example: Siemens Medical Solutions
Future Challenges for KM 441
ioral change at the individual level and cultural change at the organizational level are
two very diffi cult and lengthy processes. The KM “ quick fi x ” was therefore vastly
misleading.
The return on KM investments should not be exclusively perceived as short-term
gains but long-term process of people and organizationalimprovements. Unfortu-
nately, people only change their behavior when there is an overwhelmingly compel-
ling argument to do so (not the “ leap of faith ” on which much of KM was predicated),
or where there is simply no alternative. Skyrme (2002) , for example, discusses some
of the cornerstones of KM as summarized in table 13.1 .
Before KM, the way in which people shared knowledge was person-to-person, just
in time, and in the context of solving a specifi c business problem. With the ever-
increasing widespread adoption of KM, knowledge management processes such as
knowledge creation/capture, knowledge sharing/dissemination and knowledge acqui-
sition/application begin to form part and parcel of the how organizations conduct
their core business and how knowledge workers go about conducting their work activi-
ties in an effi cient and effective manner.
Another way of looking at what lies ahead for KM is to inventory the types of
research that are being conducted on KM issues.
Table 13.1 Summary of KM cornerstones
1. Steady and pervasive growth — into almost every business function and geographic location
2. The holistic perspective of people, processes, and technology — as many organizations still fi nd out to their cost — you cannot simply put in KM technical solutions and leave the realization of business benefi ts to chance
3. The knowledge cycle — from creation to identifying, gathering, classifying, storing, accessing, exploiting, and protecting (and many activities in between)
4. Conducting of information audits and development of knowledge maps
5. The classifi cation of intellectual capital into customer capital, structural (organizational) capital, and human
6. The need for KM to demonstrate its value to the organization ’ s bottom line
7. Communities of practice and the importance of nurturing and not trying to manage or control them
8. The Internet as an infrastructure for communication, collaboration, and information sharing
9. The need to root knowledge into its environment and context
442 Chapter 13
KM Research
Some examples of research being conducted in the area of KM include Thomas,
Kellogg, and Erickson (2001) who are exploring the role of social and cognitive factors
in knowledge codifi cation. The simple picture of knowledge management as getting
the right information to the right people at the right time is wrong. Knowledge man-
agement is not just a matter of managing information. It is deeply social in nature
and must be approached by taking human and social factors into account. As the fi eld
of knowledge management develops, and more widespread and varied experience with
different approaches to KM is gained, it will become clearer how all the pieces fi t
together to create a rich picture of social and intellectual capital within organizations.
Certainly, looking toward the future of work, as it becomes more centered in virtual
relationships and spaces both within and across organizations, creating and maintain-
ing knowledge and its social context will only become more vital.
One of the most important aspects of a knowledge management system is that it
becomes what Thomas, Kellogg, and Erickson (2001) termed a “ knowledge commu-
nity ” : a place within which people discover, use, and manipulate knowledge, and can
encounter and interact with others who are doing likewise. They discuss two approaches
for supporting knowledge communities, namely social computing and knowledge
socialization. A fundamental characteristic of a knowledge community is that it
includes conversation and other forms of narrative, for example, stories and/or
unguarded discussion among people who know one another, who share professional
interests, and who understand the contexts within which their remarks are being
made. The authors outline a variety of specifi c techniques that can contribute to a
realistic and effective approach to knowledge management, including supporting new
forms of group interaction (e.g., Bohm Dialogue, stories), methods for enhancing
creativity (e.g., the use of metaphor), and support for expressive communication.
When such techniques are incorporated into knowledge communities, they result in
organizational opportunities to build social capital, including trust and cooperation
among colleagues.
The notion of a knowledge management environment as a trusted place is an
interesting and challenging one for system designers and for organizations. How
technically, socially, and organizationally can we balance the need for a safe and
trusting place within which so much knowledge creation and social capital building
takes place with the organizational imperative to share information more broadly? A
greater understanding of how to design socially translucent systems that permit social
mechanisms to come into play will help developers of technological systems to negoti-
Future Challenges for KM 443
ate such issues. Similarly, understanding better how to socialize knowledge through
techniques such as storytelling and scenarios will offer organizations greater mastery
and scope in creating, sharing, and reusing the knowledge that is critical to survival
in the twenty-fi rst century.
Others, such as Bouthillier and Shearer (2002) undertook survey research to inves-
tigate whether KM is an emerging discipline or just a new label for information man-
agement (IM). The authors gathered empirical evidence of how KM is practiced in
several types of organizations demonstrating the variety of organizational approaches
that are used and the processes that are involved. Based on an exploratory study of
KM practices, they presents a typology of methodologies that are employed in various
organizations to illustrate what may be considered as the particular nature of KM to
show potential differences with IM.
The fi eld of knowledge management is fairly new. This explains why its research
base is still under development. Despite the vagueness of KM, its potential overlaps
with IM, and its weak theoretical base, KM is practiced in many organizations. Exam-
ining empirical evidence is certainly a valid approach for identifying building blocks
of theories and concepts to support the development of new scientifi c fi elds. Indeed,
scientifi c knowledge is often rooted in practice: culture and society existed before we
had anthropology and sociology. The empirical evidence that was gathered for this
study shows that KM involves human/soft and technical/hard aspects (Hlupic,
Pouloudi, and Rzevski, 2002). KM seems to be made of various organizational practices
requiring changes in policies, work routines, and organizational structures. More spe-
cifi cally, these authors found the following general principles:
• Knowledge, in practice, is most often defi ned as tacit knowledge in spite of the
conceptual problems mentioned above. Explicit knowledge was included only in those
initiatives where the focus was converting tacit knowledge into explicit knowledge.
• Knowledge management, as it is practiced, really means facilitating the sharing of
tacit knowledge. Despite the fact that other processes were part of the KM projects,
sharing was the primary emphasis of all case studies.
• There are slight differences in the practices between private and public sector knowl-
edge management. Private sector organizations use KM for internal knowledge sharing
targeted in specifi c areas of the organization. The KM initiatives are most often con-
cerned with managing business and administrative knowledge. Public sector organiza-
tions use KM for both internal and external knowledge sharing throughout the
organization and the KM initiatives are most often concerned with managing product-
related knowledge.
444 Chapter 13
• KM practices could benefi t from the skills already held by information professionals.
These skills include the identifi cation of knowledge needs, helping to distinguish
between information and knowledge to help facilitate a broader and more inclusive
KM initiative.
One can claim that the ontological and epistemological aspects of knowledge are
still so ill defi ned and poorly understood that KM cannot be an emergent discipline.
And, indeed, although the concepts of tacit and explicit knowledge, knowledge
sharing, and knowledge technologies are often used, they are not clearly defi ned.
However, the question remains why do large private and public organizations bother
to use unclear terminologies? The reason arises from a lack of consensus or use of
standardized terms across organizations rather than a lack of clarity. The IM commu-
nity cannot continue to claim that it has addressed for years the same issues addressed
now by KM experts. Dismissing KM as simply a management fad could be a missed
opportunity to understand how knowledge is developed, gained, and used in organiza-
tions and ultimately in society. New labels can be misleading but they can also force
some refl ections. There continues to be a need to examine why there is such an inter-
est for KM in both the academic, business communities, and governments.
Researchers have also begun to study KM technologies. For example, Studt (2003)
found that drug discovery is one of a handful of technologies that create value by
transforming vast amounts of data into knowledge that is then used to create useful
products. in this case drugs for human health. Unfortunately, the creation of that data
in the drug area is growing at a faster rate than researchers are being able to manage
it. Genomics, proteomics and the biotech industry based on them have turned the
traditional, mostly linear fl ow of information, into a dynamic, iterative loop.
Along with new types of biotech data, however, information capture throughout
the development process has also become more critical. Decisions to advance and
prioritize targets and potential leads require the integration and capture of whole new
types of information using new research technologies.
The use of knowledge management tools is becoming critical to reduce develop-
ment times and costs and to improve the overall success rate of testing new com-
pounds. Understanding the different components of knowledge management and
how they interact in a drug development environment is the fi rst step in implement-
ing a workable system. A knowledge management process consists of the creation,
collection, interpretation, storage, and interaction with data. A number of pharma-
ceutical and biotechnology companies have reported signifi cantly improved
R & D productivities with the implementation of knowledge management initiatives.
Future Challenges for KM 445
Bristol-Myers Squibb ’ s SMART-IDEA, for example, incorporates data repositories, data
integration technologies, data visualization, and data mining tools, as well as having
decision support functionalities. Finally, there are a growing number of doctoral theses
that address KM themes.
Some sample KM research topics include:
• What are the exact mechanisms by which knowledge and learning are institutional-
ized and embedded in the corporate memory?
• How can communities of practice support and enhance professional education?
• When do stories work best and why? Is there a best practice for creating and telling
stories?
• What drives employees to share their knowledge with each other or hoard it? What
can management do to increase knowledge sharing among employees?
• How can blogs be used in KM research? What types of data can be collected and
how can they be analyzed?
• Evaluation of knowledge generation methods within companies, information
sharing in multinational companies, reward structures, and commitment in global
teams, cultural differences (cross cultural communication problems) within global
teams, defi nitions of knowledge (e.g., cultural relativity of defi nitions, temporal insta-
bility of defi nitions), effects of organizational identity on defi nition of core knowledge
competences
• Language (genre-specifi c analyses of stories, argumentation, conversation, computer-
mediated) as used in organizations and practical applications or theoretical contri-
butions of strategy development, language coherence, language contingency,
organizational rhetoric, impression management, language and organizational
identity
• The gap between theory and implementation of knowledge management systems
and principles
• Do top-down knowledge management initiatives meet bottom-up organizational
learning?
• Business ethics as it relates to the use of IT; methods of effectively assessing ethical
quality in business systems and processes
• Evaluating and improving the facilitation of group workshops
• The implementation of strategy (or change programs), and the adaptability of
strategy to new pressures
• Search engine use patterns. How do employees interact with intranet search engines?
446 Chapter 13
• Metadata: Hype or help? Does the use of metadata actually improve information
fi nding?
As Schulz & Jobe (2001) point out, empirical research in the corporate knowledge
management world is limited. Many opportunities exist for further detailed empirical
research.
A Postmodern KM
Weinberger (2001) introduced the term “ postmodern KM ” to distinguish from tradi-
tional KM which he views as having traditionally suffered from the belief that we can
discover ultimate truths and organize the world according to rational principles using
clever code. The idea was that we should capture and organize bits of knowledge in
central databases. The people involved were relevant only as donors to the common
ontology or as empty vessels into which knowledge could be poured. Postmodernism
holds that the lenses of individual subjectivity and group power dynamics always warp
our concept of reality. Therefore, postmodern KM cannot be about management at
all because management implies external control of some defi nable resource. Its goal
is simpler yet deeper: leveraging people. Postmodern KM operates within and on the
basis of existing behavior patterns, mining conversation streams, and relationships
automatically to incorporate structure and context into the information human users
already manipulate. It fosters human intelligence and interaction rather than trying
to replace them.
Concretely, that means things like automatically parsing e-mail messages and other
internal content to draw out useful context and associations (an approach being
pursued by Lotus and a bevy of others including Tacit Knowledge Systems, Abridge,
EcoCap, Krypteian, and Neomeo); mining discussion content and user feedback on
intranets (Newknow); adding workfl ow directly into e-mail messages (Zaplet); and
building on weblogs as a powerful web-native tool for knowledge sharing (Onclave
and Slashdot derivatives). In other words, tools to help manage knowledge.
Miller and Morris (1999) discuss the impending transformation of R & D from its
historical, product-centric past to its emerging knowledge-centric future. In addition,
their focus on discontinuous and fusion innovation promises to lead the way for
industry, in general, whose R & D functions typically produce less than one new
product innovation per decade and whose new products, when they are produced,
tend to fail in under four years. The authors explicit embrace of knowledge manage-
ment is also welcome, as the value of most companies now tends to rest more on the
Future Challenges for KM 447
weight of their intellectual assets than on so-called “ hard ” assets. The focus is on
distributed, enterprise-wide innovation that signals the tearing down of R & D ’ s overly
centralized and compartmentalized profi le in most fi rms and offers strong support for
the view that innovation should be structured as a distributed, whole-fi rm social
process, not an administrative one.
Critical KM issues are often the reason why applications of KM fail. A KM strategy
enables an organization to act proactively (acting before the problem occurs) than
reactively (acting after a crisis has arisen). This means trying to anticipate potential
problems, potential areas of resistance to organizational change, the lack of incentives
for knowledge sharing, and the very thorny ethical issues that are associated with KM
applications. Some good practices and lessons learned from organization ’ s experiences
with KM to date could help guide us in being proactive. Some recommendations would
include:
• Improving access to information and knowledge — covering the availability, acces-
sibility and affordability of information (especially of scientifi c information in devel-
oping countries)
• Promoting knowledge sharing through learning circles and vertical/horizontal
coalitions, peer-to-peer technology, communities of practice, infomediaries, help
desks, e-learning, and better interaction/mutual learning with target groups (the poor)
• Networking international and regional cooperation — covering networking models,
digital solidarity, collaboration tools like portals and common terminology (thesau-
rus), network effectiveness, strengthening existing structures and resource centers
• Other issues include the development of local content in local languages and
dissemination channels besides Internet, capacity building, and quality control/
standards
• Avoid weak incentives. A weak incentive is an incentive that does not encourage
maximization of an objective because it is ambiguous. For example, payment of weekly
wages is a weak incentive since by construction it does not encourage maximum
production, but rather the minimal performance of showing up every workday. This
can be the best kind of incentive in a contract if the buyer does not know exactly
what he wants or if output is not straightforwardly measurable.
Concluding Thought
The Gartner Group (1998) has stated that knowledge management “ will be the stan-
dard way of running a business. ” In a short-term perspective, knowledge management
448 Chapter 13
does contribute to improved exploitation of the information and knowledge resources
available to the company. In a longer-term perspective, knowledge management
builds the new foundation for improved business advantages and strengthens the
capabilities for a sustainable future.
Key Points
• Knowledge management is a complex undertaking, one that involves people and
cultural issues, not just technology-related decisions.
• Information seeking, particularly on the World Wide Web, should not always be
taken at prima facie — there are political, commercial infl uences in addition to techni-
cal constraints and these will all affect the type and volume of content that can be
easily retrieved.
• Organizational knowledge repositories should ensure information seeking is both
objective and optimized, if not to each individual user at least to the different thematic
groups or CoPs that exist within the company.
• The type of organizational culture will often prove to be a KM barrier — this profi le
needs to be assessed and characterized in order to allow for proactive actions to be
taken.
• The paradox of the value of an intellectual or knowledge asset is one of the
major issues facing KM today. Human, structural, and customer capital will need
to be codifi ed to some extent and their sharing promoted actively throughout the
organization.
• One of the most important challenges in ensuring success of KM applications is
putting into place the appropriate rewards and punishments to motivate knowledge
workers to share knowledge. This means there has to be “ something in it for me ” as
well as for the CoP and the organization.
• KM has enjoyed a steady and pervasive growth into many business functions and
the future of KM lies in KM becoming part of the how knowledge workers carry out
their professional tasks.
• There continues to be a need for KM to be able to demonstrate its value.
• KM requires a holistic perspective, one that encompasses business goals, people,
processes, technologies, and organizational context.
• KM requires a comprehensive approach, one that addresses each step in the KM
cycle.
Future Challenges for KM 449
• KM must rest on solid theoretical foundations. Current research studies will add to,
complete, and complement KM theoretical models.
• Knowledge capture and codifi cation will evolve as knowledge taxonomy develop-
ment methods and tools are increasingly available.
• Knowledge sharing will be leveraged throughout the organization via communities
of practice that acts as a two-way bridge between individual and organizational
learning.
• Knowledge application in the future will be increasingly based on organizational
memory management systems that will contain valuable lessons learned and best
practices.
• Organizational cultures will continue to transform and be guided to offer environ-
ments that are more conducive to effective knowledge management.
• KM continues to evolve as a profession. This is attested to by the fact that there is
more empirical research being undertaken, professionals can attend academic KM
programs, KM skill sets are being more clearly identifi ed, and a new wave of KM-related
doctoral theses are well on their way.
Discussion Points
1. What are some of the critical issues facing the successful implementation of KM
applications? How do they play out in your organization?
2. What do we mean when we refer to the politics of information seeking? Why would
this be a potential risk for KM?
3. What are the fi ve major types of organizational cultures? Critically evaluate their
strengths and weaknesses. How would you analyze or identify these organizational
profi les? Where does your organization lie?
4. The paradox of value is one of the greatest challenges facing KM today. Do you
agree with this statement? Why or why not? Provide illustrative examples to support
your arguments.
5. KM often fails to live up to its ideal goals of knowledge sharing due to a lack of
incentives. How would you set up a system of rewards and censures to motivate
knowledge workers to share knowledge? What are some typical obstacles that
you would expect to encounter? How would you address these obstacles? Outline an
incentive strategy and describe how you would evaluate its success.
450 Chapter 13
6. Much of the expected benefi ts of KM stem from being able to deliver the “ right
information to the right person at the right time in the right format. ” What are the
implications of this on issues of privacy of information?
7. If after six months of effort, you fi nd your KM project is still not making headway.
What actions would you take? What information would you seek in order to decide
the best course of action to take? How and when would you assess progress again?
8. Provide a brief history of the fi eld of KM and describe where you feel it is today
and where it is heading.
9. What do you feel are the key priorities to be addressed in order for KM to continue
to evolve and become better embedded in critical business processes?
10. Describe some research themes in the fi eld of KM. What do you see as the Next
Big Thing in KM? What breakthroughs would be needed before KM could make a
quantum leap in its evolution?
References
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14 KM Resources
Libraries are not made; they grow.
— Augustine Birrell (1850 – 1933)
In this fi nal chapter, a wide variety of additional knowledge management resources
are provided to help grow your own KM library. Note that these are in addition to the
references found in each preceding chapter. 1
The Classics
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454 Chapter 14
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success . Burlington, MA : Butterworth-Heinemann .
KM for Specifi c Disciplines Bixler , C. 2002 . Knowledge management: A practical solution for emerging global security
requirements. KM World 11 ( 5 ): 18 – 28 .
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neers. Chemical Engineering Magazine August 14:50 – 52.
KM Resources 455
Institute for the Study of Knowledge Management in Education (ISKME) . n.d. Available at http://
www.iskme.org/ .
Liebowitz , J. 2004 . Will knowledge management work in the government? International Journal
of Electronic Government 1 ( 1 ): 1 – 7 .
Luen , T. , and S. Al-Hawamdeh . 2001 . Knowledge management in the public sector: Principles
and practices in police work. Journal of Information Science 27 ( 5 ): 311 – 318 .
Muscholl , M. , and K. Maximini . 2005 . Knowledge management in medicine (KMM). Professional
knowledge management. Third Biennial Conference, Revised Selected Papers, edited by K.-D. Althoff.
Berlin: Springer, 316 – 383.
Parson , M. 2004 . Effective knowledge management for law fi rms . Oxford, UK : Oxford University
Press .
Shaw , J. , C. Subramaniam , G. Tan , and M. Welge . 2001 . Knowledge management and data
mining for marketing. Decision Support Systems 31 : 127 – 137 .
Soliman , F. , and K. Spooner . 2000 . Strategies for implementing knowledge management: Role of
human resources management. Journal of Knowledge Management 4 ( 4 ): 337 – 345 .
Vasconcelos , J. , P. Seixas , P. Lemos , and C. Kimble . 2005. Knowledge management in non-
governmental organisations: A partnership for the future. In Proceedings of the 7th International
Conference, Enterprise Information Systems (ICEIS), Miami, USA, May 24 – 28, 2005 . Available at:
http://129.3.20.41/eps/dev/papers/0506/0506002.pdf/
International KM Alavi , M. , and D. Leidner . 1999 . Knowledge management systems: Issues, challenges, and ben-
efi ts . Communication of the Association forInformation Systems ( 1 ): 1 – 28 .
Holden , N. 2002 . Cross-cultural management: A knowledge management perspective . Boston : Pearson
Education .
Inkpen , A. , and A. Dinur . 1998 . Knowledge management processes and international ventures.
Organization Science 9 ( 4 ): 454 – 468 .
Mertens , K. , P. Heisig , and J. Vorbeck . 2000 . Knowledge management best practices in Europe . New
York : Springer-Verlag .
O ’ Riordan , J. 2005 . A review of knowledge management in the Irish civil service . Ireland : Institute of
Public Administration .
KM Journals
• E-Journal of Organizational Learning and Leadership
• Electronic Journal of KM
456 Chapter 14
• IBM Systems Journal
• Interdisciplinary Journal of Information, Knowledge and Management
• International Journal of Knowledge, Culture and Change Management
• International Journal of Knowledge Management
• International Journal of Knowledge Management Studies
• International Journal of Nuclear Knowledge Management ( IJNKM)
• Interdisciplinary Journal of Information, Knowledge and Management ( IJIKM)
• Interdisciplinary Journal of Storytelling Studies
• Journal of Information and Knowledge Management ( JIKM)
• Journal of Intellectual Capital
• Journal of Knowledge Management
• Journal of KM Practice
• Journal of Managerial and Organizational Learning
• Journal of Organizational Change Management
• Knowledge Management
• Knowledge Management for Development Journal
• Knowledge Management Research and Practice
• Knowledge Management Review
• Knowledge and Process Management
Key Conferences
• APQC KM Conferences, http://www.apqc.org/km2008call.
• Conference on Knowledge, Culture and Change in Organizations ,http://m08
.cgpublisher.com.
• ECKM — European Conference on Knowledge Management, http://www
.academic-conferences.org/eckm.
• ICICKM — International Conference on Intellectual Capital, Knowledge Manage-
ment and Organizational Learning, http://www.academic-conferences.org/icickm.
• ICKM — International Conference on Knowledge Management, http://www.ickm08
.com.
• KM World, http://www.kmworld.com.
KM Resources 457
• OKLC — Organizational Knowledge and Learning Conference, http://www.feweb.
vu.nl/olkc2009/olkc.html.
Key Web Sites
• APQC KM Edge, http://www.kmedge.org.
• The community of practice on communities of practice, CPsquare, http://cpsquare
.org/.
• Dave Gurteen, http://www.gurteen.com/.
• Seth Earley ’ s community of practice on knowledge organization, taxonomies and
content management, http://thecontentwrangler.ning.com/.
• The eLearning Guild (a CoP for e-learning), http://www.elearningguild.com/.
• ICASIT, http://www.icasit.org/km/.
• Krebs, V. V. Krebs, Knowledge Networks, http://www.orgnet.com/.
• KM for Development, http://www.km4dev.org/.
• The Knowledge Management Resource Centre, http://www.kmresource.com/.
• KM Resources, http://www.skyrme.com/resource/kmres.htm.
• Knowledge Praxis, http://www.media-access.com/resources.html.
• KnowledgeBoard, http://www.knowledgeboard.com/.
• National Library of Health (NLS), http://www.library.nhs.uk/
KnowledgeManagement/.
• Organizational Learning and KM Resources, http://carbon.cudenver.edu/~mryder/
itc_data/org_learning.html.
• Organizational storytelling resources, http://www.creatingthe21stcentury.org/. and
http://www.stephendenning.
• Virtual KM Library, http://www.kmnetwork.com/.
KM Glossaries
• ICASIT, http://www.icasit.org/km/intro/glossary.htm.
• Knowledge Point, http://www.knowledgepoint.com.au/starting_out/glossary.html.
• NLH, http://www.library.nhs.uk/knowledgemanagement/Page.aspx?pagename=
GLOSSARY.
458 Chapter 14
KM Case Studies and Examples
KM Case Studies
• Australian Public Service Case Studies, http://www.developmentgateway.com.au/
jahia/Jahia/pid/7037.
• Buckman, R. 2004. Building a knowledge-driven organization. New York: McGraw Hill.
• Community of practice case studies available from Etienne Wenger, http://www
.ewenger.com/pub/index.htm.
• Davenport, T., and G. Probst. 2002. Knowledge management case book: Siemens best
practices . New York: John Wiley and Sons.
• Harvard Business Online for Educators, http://harvardbusinessonline.hbsp.harvard
.edu/b01/en/academic/edu_interactive.jhtml.
• Jennex, M., Ed. 2005. Case studies in knowledge management . Hershey, PA: Idea Group.
• The Knowledge Management Advantage, http://www.providersedge.com/kma/km_
articles_case_studies.htm.
• Knowledge Management at the World Health Organization (WHO), http://www
.who.int/kms/en/.
• Liebowitz, J. 2002. A look at NASA Goddard Space Flight Center ’ s knowledge man-
agement initiatives. IEEE Software 19 (3):40 – 42.
• McCormick, J. 2007. Five companies that got KM right. CIO Insight. Available
online at: http://www.cioinsight.com/c/a/Case-Studies/5-Big-Companies-That-Got-
Knowledge-Management-Right/.
• The National School of Government has case studies on Hewlett-Packard, Buckman
Labs, Ford Motor Company, Allen and Overy, and Scandia, http://www.library.nhs
.uk/KnowledgeManagement/ViewResource.aspx?resID=286800/.
• Rao, M. 2003. Leading with knowledge: Knowledge management practices in global info-
tech. New York: McGraw Hill.
• Rao, M. 2004. Knowledge management tools and techniques: Practitioners and experts
evaluate KM solutions. Boston: Elsevier Butterworth-Heinemann.
• Ross, M., and W. Schulte. 2005. Knowledge management in a military enterprise: A
pilot case study of the space and warfare systems command. In Creating the discipline
of knowledge management: The latest in university research , edited by M. Stankosky.
London: Elsevier/Butterworth-Heinemann, 157 – 170.
• Step Two Designs, http://www.steptwo.com.au/category/papers/case-studies.
KM Resources 459
• U.S. Navy, http://www.about-goal-setting.com/KM-Library/knowledge-management-
case-study-us-navy.html.
KM Examples
• A lessons learned database developed for the U.S. National Firefi ghters, http://www
.fi refi ghternearmiss.com.
• A storytelling site for management and leadership best practices and lessons learned,
http://fi ftylessons.com.
• The World Bank KM site, http://web.worldbank.org/WBSITE/EXTERNAL/WBI/
0,,contentMDK:20939032~menuPK:204788~pagePK:209023~piPK:207535~theSitePK
:213799,00.html/.
• The World Bank lessons learned database, http://www4.worldbank.org/afr/ikdb/
search.cfm.
• KM for development agencies, http://www.u4.no/themes/km/examples.cfm
KM Wikis
• http://kmwiki.wikispaces.com/.
• http://knowledge-management.wikia.com/wiki/Knowledge_Management_Wiki.
• http://it.toolbox.com/wiki/index.php/CommunityTopic?a=Knowledge+Management.
KM Blogs
• Librarian perspective, http://urlgreyhot.com/personal/taxonomy/term/159.
• Consulting perspective, http://blogs.forrester.com/information_management/.
• Joe Firestone, http://kmci.org/alllifeisproblemsolving/archives/km-20-and-knowledge-
management-part-seven/.
• Knowledge Jolt, http://blog.jackvinson.com/archives/2005/11/30/pollard_on_pkm
.html.
• KM tutorial videos highlighted in the Talking KM Blog, http://talkingkm.blogspot
.com/2007/08/video-tutorials.html.
460 Chapter 14
Visual Resources
YouTube
Most of these are available for use through the Creative Commons License.
• Great visual introduction to knowledge developed by the KM program at Natural
Resources Canada, http://ca.youtube.com/watch?v=9vm77Ge2Kxs.
• Another attention-grabbing introduction to the major components of KM, http://
ca.youtube.com/watch?v=f_x78XLBBVM
• Excellent visual explanation of Web 2.0 by Michael Wesch, http://youtube.com/
watch?v=NLlGopyXT_g.
• A series of people attending a KM conference were videotaped when asked to answer:
how would you defi ne KM? http://www.gurteen.com/gurteen/gurteen.nsf/id/wiskm.
Other Visual Resources
• Leonard, D. (2003). Knowledge management at JPL, http://www.harvardbusines-
sonline.org.
• KM at the World Bank, http://web.worldbank.org/WBSITE/EXTERNAL/WBI/0,,cont
entMDK:20212624~menuPK:575902~pagePK:209023~piPK:207535~theSitePK
:213799,00.html.
• Stephen Denning talking about storytelling, http://www.stevedenning.com/
WatchAVideo.htm
Some Useful Tools
• The mindmapping tool is very useful for building knowledge models, for document-
ing knowledge acquisition sessions, for depicting mental models, and for taxonomy
building, http://www.mindjet.com.
Other Visual Mapping Tools
• The Brain, http://www.thebrain.com
• Inspiration, http://www.inspiration.com
• Viso, http://offi ce.microsoft.com/en-ca/viso/default/aspx .
Note
1. Please consult the book web site for more updated KM resources.
Glossary
Absorption costs Costs incurred when recipients of knowledge content understand and inter- nalize the knowledge in order to be able to apply it.
Absorptive capacity The individual and/or organizational openness to change and innovation and the capability or preparedness to integrate it.
Abstraction costs Costs incurred when knowledge context is generalized over a wider scope.
After action review An assessment that is conducted after a project or major activity to allow employees and leaders to discover what happened and why (popularized by the U.S. Army); a
professional discussion of an event that enables participants to understand what worked well,
what did not and what they learned from the experience. An AAR need not be performed at the
end of a project or activity as it can also be performed after each identifi able event or milestone,
thus becoming a live learning process to help support a learning organization.
Artifact Material objects manufactured by people to facilitate culturally expressive activities. The signs and symbols by which the organization is recognized by. The events, behaviors, and
people that embody a culture.
Anarchy An organizational political model where there is an absence of any information/ knowledge management policy.
Applied ethics The examination of specifi c controversial issues to try to resolve them, to fi nd a standard or accepted way of proceeding with respect to the specifi c issue.
Audit trail A documented history of a piece of knowledge in the knowledge base from knowl- edge acquisition/capture source to subsequent use and reuse.
Balanced scorecard The balanced scorecard is a measurement and management system that enables organizations to clarify their vision and strategy and translate them into action. It pro-
vides feedback around both the internal business processes and external outcomes in order to
continuously improve strategic performance and results.
Belief An idea with emotional or spiritual appeal that has not been tested and/or is not con- sidered accepted knowledge.
462 Glossary
Benchmarking The search for industry-wide best practices that lead to superior performance. A study of similar companies to see how things are done best in order to adapt these methods for
their own use.
Best practice An improvement in a particular process, approach, technique, or subject matter knowledge that is good enough to replace existing practices and general enough to merit being
disseminated widely throughout an organization. A “ good work practice ” or innovative approach
that is captured and shared to promote repeat applications.
Blog A blog is basically a journal that is available on the web. The activity of updating a blog is “ blogging, ” and someone who keeps a blog is a “ blogger. ” Blogs are typically updated daily
using software that allows people with little or no technical background to update and maintain
the blog. Postings on a blog are almost always arranged in chronological order with the most
recent additions featured most prominently. An online diary or journal, typically documenting
the day-to-day life of an individual. Often very personal.
Boundary A boundary separates a system and its environment. Just as there is a subjective element in defi ning a system, there is a subjective element in choosing a boundary. Defi ning a
boundary is tantamount to defi ning the thing that is to be considered a “ system ” and those other
things that are to be considered a system ’ s “ environment. ”
Brainstorming A commonly used group problem-solving technique whose goal is to generate as many solutions to a problem as possible.
Censure Harsh criticism or disapproval. To rebuke formally to blame, criticize adversely, or express disapproval. If you are censured for something you have done, someone in authority
tells you that they strongly disapprove of it.
Change An event that occurs when something passes from one state or phase to another. A relational difference between states; especially between states before and after some event.
Change management Activities involved in (1) defi ning and instilling new values, attitudes, norms, and behaviors within an organization that support new ways of doing work and overcome
resistance to change; (2) building consensus among customers and stakeholders on specifi c
changes designed to better meet their needs; and (3) planning, testing, and implementing all
aspects of the transition from one organizational structure or business process to another.
Chief human capital offi cer (CHCO) Title of the person who integrates strategic workforce planning, aligns with overall organizational mission, responsible for policy on recruitment and
retention of workforce, leads workforce planning.
Chief knowledge offi cer (CKO) Title of the person who is responsible for managing intellectual capital and is custodian of KM practices in an organization.
Chief learning offi cer (CLO) An enterprise-level position that typically reports to the chief executive offi cer (CEO) of a corporation. The overall goal of a CLO is to improve organizational
effectiveness and effi ciency by facilitating increased knowledge and skill profi ciency in individu-
Glossary 463
als, teams, and the enterprise as a whole. Ultimately, the goal of the CLO is to transform an
enterprise into a learning organization.
Chunking A chunk can be a letter, syllable, word, phrase, or even a sentence. Chunking is defi ned as the organization of blocks of content that are conceptually related. The amount of
information that is processed as a chunk depends on the learner ’ s ability, maturity, motivation,
and prior knowledge related to the content being processed. For example, to a poor or beginning
reader, a chunk may be a letter. Good readers generate chunks in the form of words. S-t-u-d-y
becomes study. The effect of prior knowledge on processing speed is obvious when we try to
read a complex article outside of our area of expertise. Short-term memory can usually handle
only about seven chunks.
Climate The prevailing psychological state (e.g., “ the climate of opinion, ” “ the national mood had changed radically since the last election ” ).
Closed questions Questions that set limits on the type, level, and amount of information a respondent provides, often used to validate content and can be answered by a fi nite number of
responses such as yes/no (e.g., is it true that this project was initiated by yourself?).
Cluster analysis Generic term for a set of statistical analysis techniques that elicit or produce classifi cations from seemingly unordered data.
Codifi cation costs Costs incurred in rendering tacit knowledge explicit.
Coercive incentive Failure to act in the desired manner brings about some form of punish- ment — physical force, fi ring, disbarment, and so on.
Cognitive maps Theoretical representations of how humans organize and process some type of knowledge.
Collaboration A coalition of diverse people with diverse values and expectations working together at the community level to solve problems. A social skill involving working together
with two or more persons. Collaboration is the process of shared creation: two ore more individu-
als with complementary skills interacting to create a shared understanding that none had previ-
ously possessed or could have come to on their own.
Combination The reassembling of existing explicit knowledge into new, systematically orga- nized forms such as a database, a summary document, or a trend analysis.
Community of practice (CoP) An affi nity group or information network that provides a forum where members can exchange tips or generate ideas; a group of professionals who
try to face common problems to solve and who strive to improve their profession and
thereby themselves. An informal network or forum where tips are exchanged and ideas generated.
A group of professionals, informally bound to one another through exposure to a common
class of problems or in a common pursuit of solutions, and thereby themselves embodying
a store of knowledge. A group of practitioners held together by shared practices and common
beliefs.
464 Glossary
Complex adaptive systems Organizations that are composed of a large number of self-organiz- ing components, each of which seeks to maximize its own specifi c goals but which also operate
according to the rules and context of relationships with the other components and the external
world.
Concept analysis A technique used to clarify the meaning of subjective, value-laden terms such as “ democracy. ” Derived from science education and philosophy, the technique explicitly
distinguishes between related terms to pinpoint the boundaries of the concept, and lists
examples and nonexamples of the concept in order to extract a set of “ necessary and suffi cient ”
attributes that the a defi nition must have in order to adequately refl ect the meaning of the
concept.
Concept clustering A methodology for organizing and summarizing domain data by producing an abstraction of the domain based on the analysis of clusters.
Concept dictionary A conceptual analysis technique that provides a mechanism to visualize an abstraction of the primary concepts in a domain and the terminology used to label them.
Concept hierarchy A structural taxonomy or arrangement of the associations that make up a concept.
Concept sorting A psychological paradigm that can be used to tap into the way in which a subject matter expert has organized key concepts.
Content management The processes and workfl ows involved in organizing, categorizing, and structuring information resources so that they can be stored, published, and reused in multiple
ways. A content management systems (CMS) is used to collect, manage, and publish content,
storing the content either as components or whole documents, in such a way as to maintain the
links between components. “ Content ” in this context generally refers to computer-based infor-
mation such as the content of a web site or a database. Content management is about making
sure that content is relevant, up-to-date, accurate, easily accessible, well organized, and so on,
so that quality information is delivered to the user.
Content steward Person responsible for improving the management of an organization ’ s knowledge assets, driving new processes and promoting behaviors for creating higher quality
information and sharing knowledge.
Continuous process improvement An ongoing effort to incrementally improve how products and services are provided and internal operations are conducted.
Core competency Set of skills that confer a competitive advantage on an organization; required to carry out the mission-critical business of the organization.
Core or key process Business processes that are vital to the organization ’ s success and survival.
Corporate memory All the information, data, and know-how that a company possesses; accumulation of historical events and experiences. The knowledge and understanding embedded
Glossary 465
in an organization ’ s people, processes, and products or services, along with its traditions and
values. Organizational memory can either assist or inhibit the organization ’ s progress.
Corporate yellow pages Also called expertise location systems . Detection, discovery, and manage- ment of human knowledge resources, including subject matter experts. An expertise directory
provides a map to subject matter experts in an organization or “ virtual ” organization (as in com-
munities of practice). Expertise directories usually exist as part of a knowledge-management
software environment, sometimes as a fall- back resource for computer-based knowledge retrieval
systems.
Cultural assumptions Beliefs about the internal workings and external environment of an orga- nization which, having worked well in the past, have gradually come to be taken for granted,
and which provide the basis for group consensus about common events and circumstances.
Cultural assumptions function as the unifying themes of organizational culture.
Culture A people ’ s ways of being, knowing, and doing. All the knowledge and values shared by a cohesive group or organization. The attitudes and behavior that are characteristic of a particular
social group or organization. The accumulated habits, attitudes, and beliefs of a group of people
that defi ne for them their general behavior and way of life; the total set of learned activities of
a people. The beliefs, traditions, habits, and values controlling the behavior of the majority of
the people in a social-ethnic group. These include the people ’ s way of dealing with their problems
of survival and existence as a continuing group.
Custom A usage or practice that is common to a group of people or to a particular place. Accepted or habitual practice.
Cybrarian One of many new terms being used to defi ne a “ virtual librarian. ” Others include electronic services librarian, digital librarian, and Internet information specialist.
Data Directly observable or directly verifi able facts.
Decision tree A technique for organizing knowledge that divides sets of elements into subsets such that each node has only one “ parent ” based on discriminating evidence provided by attri-
butes and their values.
Data mining An information extraction activity whose goal is to discover hidden facts con- tained in databases. Using a combination of machine learning, statistical analysis, modeling
techniques, and database technology, data mining fi nds patterns and subtle relationships in data
and infers rules that allow the prediction of future results. Typical applications include market
segmentation, customer profi ling, fraud detection, evaluation of retail promotions, and credit
risk analysis.
Demilitarized zone (DMZ) Demilitarized zones serve to prevent employees from breaching ethical boundaries. They monitor compliance and report any violations.
Diffusion costs Costs incurred in the dissemination and distribution or publishing of knowledge.
466 Glossary
Digital library A collection of a very large number of digital objects, composed of all types of material and media, that are stored in distributed information repositories and accessed through
national computer networks. Digital libraries can include reference material or resources acces-
sible through the World Wide Web. Digitized portions of a library ’ s collection or original material
produced for the web can also be included in a digital library.
Environment Those variables whose changes affect the system and that are in turn affected by the system ’ s behavior. Things outside a system that are important to it. Understanding the sys-
tem ’ s behavior usually requires some understanding of its context or environment.
Epistemology The scientifi c study of knowledge. Knowledge science.
EPSS Any computer software program or component that improves employee performance by reducing the complexity or number of steps required to perform a task, providing the perfor-
mance information an employee needs to perform a task, or providing a decision support system
that enables an employee to identify the action that is appropriate for a particular set of
conditions.
Ethics The “ science of morality. ” In philosophy, ethical behavior is that which is “ good. ” The philosophical study of the moral value of human conduct and of the rules and principles that
ought to govern it; moral philosophy. A social, religious, or civil code of behavior considered
correct, especially that of a particular group, profession, or individual. The moral fi tness of a
decision, course of action, and so on.
Expectation Belief about (or mental picture of) the future. The anticipation of what is to happen next (e.g., curiosity and suspense), what a character is like, or how he or she will develop, what
the theme or meaning of the story will prove to be, and so on.
Expertise locator system See Corporate yellow pages
Explicit knowledge Knowledge that has been rendered visible (usually through transcription into a document or an audio/visual recording); typically, captured and codifi ed knowledge.
Expressive culture Refl ects emotions, feelings, and aspirations of the organizations ’ personnel.
Externalization The conversion of tacit knowledge into explicit knowledge — rendering previ- ously unarticulated, undocumented, and uncaptured content into a visible, tangible, and con-
crete form (e.g., recording a meeting, writing up minutes of a meeting).
Facilitation A collaborative process used to help parties discuss issues, identify and achieve goals, and complete tasks in a mutually satisfactory manner. This process uses an impartial third party,
the facilitator, who focuses on the processes and procedures of dispute resolution and decision
making. The facilitator is impartial to the issues being discussed, rarely contributes substantive
ideas, and has no decision-making authority.
Federalism An organizational political model where information/knowledge management is approached using negotiation processes to reach a consensus.
Glossary 467
Fence Explicit ethical boundaries that show exactly where the important ethical lines lie, typi- cally encapsulated in formal policy statements or laws.
Feudalism An organizational political model where individual business units act fairly autono- mously in defi ning their information/knowledge needs.
Googling The use of the Google search engine (http://www.google.com) to locate content and information about people.
Googlewhacking Searching the popular Google search engine with a two-word or more search argument that will produce exactly (no less and no more than) one result.
Groupware Software that enables a group of users to collaborate on a project by means of network communications. Software which supports collaborative work. It may include conferenc-
ing, shared fi les, or facilities to allow several people to work on one document. Software that
enables members of a network work group to communicate and collaborate through e-mail,
scheduling, bulletin boards, conferencing, project management, fi le sharing, and other means.
Heuristic A set of instructions for searching out an unknown goal by exploration, which con- tinuously or repeatedly evaluates progress according to some known criterion. A method of
achieving a goal where the exact means of doing so cannot be precisely specifi ed: we know what
it is but not where it is. General rules and guidelines, but not prescribing a specifi c route to the
goal (antonym: algorithm).
Ideal Model of excellence or perfection of a kind; one having no equal. Conforming to an ultimate standard of perfection or excellence; embodying an ideal. Constituting or existing only
in the form of an idea or mental image or conception.
Incentive A reward for a specifi c behavior, designed to encourage that behavior. Also called inducement. In economics, an incentive in anything that provides a motive for a particular
course of action that counts as a reason for preferring one choice to the alternatives.
Information Analyzed data. Facts that have been organized in order to impart meaning.
Information literacy A set of abilities requiring individuals to recognize when information is needed and have the ability to locate, evaluate, and effectively use the needed information.
Information resource management (IRM) An emerging discipline that helps managers assess and exploit their information assets for business development. It draws on the techniques of
information science (libraries) and information systems (IT related). It is an important foundation
for knowledge management, in that it deals systematically with explicit knowledge. Knowledge
centers often play an important part in introducing IRM into an organization.
Innovation Innovation is a new idea applied to initiating or improving a product, process, or service. All innovations involve change, but not all changes necessarily involve new ideas or
lead to signifi cant improvements. The concept of innovation encompasses new production
process technologies, new structures or administrative systems, and new plans or programs per-
taining to organizational members. The creation of something new or different; the conversion
468 Glossary
of knowledge and ideas into a new benefi t, such as new or improved processes or services. An
improvement of an existing technological product, system, or method of doing something.
Organizational innovation is the process by which new products or new methods of production
are introduced, including all the steps from the inventor ’ s idea to bringing the new item to
market.
Intellectual asset/capital Intellectual assets generally refer to an organization ’ s recorded infor- mation (and, increasingly, human talent itself), where such information is typically either inef-
fi ciently warehoused or simply lost, especially in large, physically dispersed organizations. An
asset is a claim to future benefi ts (value, cash fl ows). An intangible asset can be defi ned as a
nonphysical claim to future value or benefi ts. Intangibles, intangible assets, knowledge assets,
and intellectual capital are more or less synonyms. All are widely used — intangibles specifi cally
in the accounting literature, knowledge assets by economists, and intellectual capital predomi-
nantly in the management literature.
Intelligent agent Also called an Internet agent. Most commonly found on web sites, this mini- program is designed to retrieve specifi c information automatically. Agents rely on cookies to keep
track of the user ’ s preferences, store bookmarks, and deliver news through push technology.
Intelligent agents cannot perform their duties if the user ’ s browser rejects cookies, and some
web pages (especially online ordering sites) will not function properly without the agent ’ s
information.
Internalization The conversion of explicit knowledge into tacit knowledge. Understanding of new knowledge and its integration into existing mental models. Accepting that this new knowl-
edge is valuable and acting accordingly.
Invisible college An informal communication network, typically consisting of scholars or researchers working around a common theme.
Jargon A characteristic language of a particular group (as among thieves); “ they don ’ t speak our lingo. ” The technical language of an occupation or group. The informal or technical language
used by members of the same profession or industry.
Job analysis An analytical technique that entails structuring the major responsibilities of a job and high-level description of the key tasks encompassed by that job.
Knowledge Subjective and valuable information that has been validated and that has been organized into a model (mental model); used to make sense of our world; typically originates
from accumulated experience; incorporates perceptions, beliefs, and values.
Knowledge acquisition The process of extracting, transforming, and transferring expertise from a knowledge source.
Knowledge audit A qualitative evaluation, essentially a sound investigation into an organiza- tion ’ s knowledge “ health. ” The knowledge audit provides an evidence-based assessment of where
the organization needs to focus its knowledge management efforts. It can reveal the organiza-
tion ’ s knowledge management needs, strengths, weaknesses, opportunities, threats, and risks.
Glossary 469
Knowledge base The fundamental body of knowledge available to an organization, including the knowledge in people ’ s heads, supported by the organization ’ s collections of information and
data. An organization may also build subject-specifi c knowledge bases to collate information on
key topics or processes. The term knowledge base is also sometimes used to describe a database of
information.
Knowledge broker A person who facilitates the creation, sharing, and use of knowledge in an organization. Many organizations have created knowledge broker roles such as “ knowledge
coordinator. ” The term knowledge broker is also sometimes used to describe companies or indi-
viduals that operate commercially as knowledge traders or provide knowledge-related services.
Knowledge center (KSO, knowledge support offi ce) A place where knowledge is gathered and stored and can be accessed and used by other people. It may be a physical place like a library, a
“ virtual ” place like an interactive web site or an online discussion board, or a place where people
gather such as a caf é or an informal meeting room or discussion area created to encourage
knowledge sharing. A focal point for collection, structuring, and disseminating information. That
does not mean they do it all themselves. They set the framework and structures, develop the
good practice guides, and provide information management expertise. A central services group
that consists of information specialists who manage content and provide services to the organiza-
tion ’ s members.
Knowledge codifi cation The process of producing a knowledge or intellectual artifact — any- thing that allows knowledge to be communicated independently of its holder (e.g., a document,
a picture, a sound recording, a fi lm, or a video).
Knowledge elicitation The process of interacting with experts using techniques to stimulate the articulation of the expertise — to convert tacit knowledge into explicit knowledge.
Knowledge management Knowledge management is the deliberate and systematic coordina- tion of an organization ’ s people, technology, processes, and organizational structure in order to
add value through reuse and innovation. This is achieved through the promotion of creating,
sharing, and applying knowledge as well as through the feeding of valuable lessons learned and
best practices into corporate memory in order to foster continued organizational learning.
Knowledge management assessment A systematic analysis of an organization ’ s current knowl- edge management capabilities. It assesses current performance against world-class practice and
identifi es critical areas for applying knowledge management.
Knowledge management system Centralized databases in which employees enter information about their jobs and from which other employees seek answers. Often rely on groupware tech-
nologies, which facilitate the exchange of organizational information but emphasize identifying
knowledge sources, knowledge analysis, and managing the fl ow of knowledge within an organi-
zation — all the while providing access to knowledge stores. A system or tool that manages the
sum of all knowledge within the organization as its “ intellectual assets. ”
Knowledge manager A role with developmental and operational responsibility for promoting and implementing knowledge management principles and practices.
470 Glossary
Knowledge researcher Individual who is responsible for searching, retrieving, and delivering knowledge that is in explicit or codifi ed form.
Knowledge repository A place to store and retrieve explicit knowledge. A low-tech knowledge repository could be a set of fi le folders. A high-tech knowledge repository might be based on a
database platform.
Knowledge steward Individual whose responsibility is to convert tacit knowledge to explicit knowledge that can be more easily codifi ed. Person who interviews a project team and then
captures and summarizes the learnings from that session.
Knowledge taxonomy A scheme that partitions a body of knowledge and defi nes the relation- ships among the pieces; used for classifying and better understanding the body of knowledge.
Knowledge worker Term coined by Peter Drucker to refer to professionals who are relatively well educated and who create, modify, and/or synthesize knowledge as a fundamental part of
their jobs. Someone whose primary job focus is the accumulation, processing, or analysis of data
and information, as opposed to physical goods.
Landmark A high-level ethical guideline often built upon tenets of an organization ’ s culture and often conveyed through stories.
Learning organization An organization that possesses the practices, systems, and culture that actively promotes sharing of experiences and lessons learned to encourage quality performance
and continuous improvement.
Legitimate peripheral participation Formerly referred to as “ lurking, ” this refers to a quite dif- ferent kind of learning theory, situated learning, which is primarily social rather than psychologi-
cal. It is legitimate because all parties accept the position of “ unqualifi ed ” people as potential
members of the community of practice. It is peripheral because they hang around on the edge
of the important stuff, do the peripheral jobs, and gradually get entrusted with more important
ones. It is participation because the person is learning.
Lesson learned Knowledge that results from a postmortem or after-the-fact analysis of a project, a new technique, or the application of new knowledge; lessons learned are the “ opposites ” of
best practices — they are caveats, hard-earned experiences of unsuccessful endeavors that should
be disseminated widely throughout an organization in order to prevent the same mistakes from
being made again or to ensure that valuable innovations are not lost. A work practice or experi-
ence that is captured and shared to avoid a recurrence.
Likert scale A scale developed by Rensis Likert for the purpose of measuring a person ’ s degree of agreement or disagreement with a set of carefully constructed statements.
Maturity The state of being fully developed. Attainment of a desired goal when growth and progress toward that goal has been successfully completed.
Media richness The ability of a given medium or channel to carry content with respect to metadata, speed of feedback, diversity of cues, and ability to convey emotion.
Glossary 471
Mental model Mental models are the result of internal psychological representations of peoples ’ interactions with the world. One purpose of these representations is that they allow us to solve
problems and use artifacts such as computer systems and the like. An individual ’ s existing under-
standing and interpretation of a given concept, which is formed and reformed on the basis of
experiences, beliefs, values, socio-cultural histories, and prior perceptions. Mental models are
representations in the mind of real or imaginary situations. Scientists sometimes use the term
“ mental model ” as a synonym for “ mental representation. ”
Metaethics Investigation of origins of ethical principles and their meaning.
Metaknowledge Knowledge about knowledge — conscious knowledge about what is known. A process of self-assessment about knowledge levels and abilities while planning, changing strate-
gies, and evaluating/revising throughout task completion.
Model A model is a representation of the essential features of a system from the perspective of the observer or participant in that system. It can be as simple as a mental picture or as complex
as a computer simulation or model of the world (e.g., Club of Rome).
Monarchy An organizational political model that is an extreme top-down hierarchical model, where information is controlled at the very top.
Moral incentive When a particular alternative is widely regarded as the right thing to do.
Myth A dramatic narrative of imagined events usually used to explain the origins of a transfor- mation. An unquestioned belief about the practical benefi ts of certain behaviors; techniques not
supported by demonstrated facts.
Needs assessment The process of determining or isolating needs to develop a KM initiative that meets specifi c objectives.
Nominal group technique A group problem-solving technique that reduces the negative effects that may be triggered by face-to-face interaction among members of a group or team.
Nonrefl ective skills Behaviors that initiate, guide, or transition communication (e.g., conversa- tional ice-breakers, attentive silence).
Nonverbal communication Communication that takes place through media other than talking (e.g., gestures, observation of a demonstration).
Norm Expectation of how a person or persons will behave in a given situation based on established protocols, rules of conduct, or accepted social practices. A way of behaving
or believing that is normal for a group or culture. All societies have their norms; they are
simply what most people do. Deviants break norms. Some norms are enshrined in law and
society punishes those who deviate from them. Breaches of unwritten norms are unoffi cially
punished.
Normative culture A set of formal rules, norms, prescriptions, positions, and hierarchies. A culture that emphasizes compliance with the rules.
472 Glossary
Normative ethics The attempt to arrive at moral standards to regulate what is right and wrong, to ensure compliance.
Ontology An explicit formal specifi cation of how to represent the objects, concepts, and other entities that are assumed to exist in some area of interest and the relationships that hold among
them. A formal, explicit specifi cation of a shared conceptualization. Conceptualization refers to
an abstract model of phenomena in the world by having identifi ed the relevant concepts of those
phenomena. Explicit means that the type of concepts used, and the constraints on their use, are
explicitly defi ned. Formal refers to the fact that the ontology should be machine readable. Shared
refl ects that ontology should capture consensual knowledge accepted by the communities.
Open questions Broad questions that impose few restrictions on the respondent and encourage free response (e.g., what do you think about this project?).
Open space technology (OST) A large-group facilitation process that consists of the setting of an agenda by all members present, self-organization into smaller groups, conveners who report
each group ’ s fi ndings into a proceedings, which are then distributed to all participants. The
cultural approach to open space technology serves to create an environment for innovation,
teamwork, and rapid change.
Organizational knowledge A complex network of knowledge and knowledge sets held by an organization consisting of declarative and procedural rules (validated knowledge claims).
Organizational learning A process involving human interaction, knowledge claim formulation, and validation by which new organizational knowledge is created. The ability of an organization
to learn from past behavior and information and improve as a result. The capture and use of
organizational knowledge to make organizational decision making more effi cient and effective.
In organizational learning, working and learning become increasingly collaborative activities
based on the limitations of the individual human mind. Individual learning needs to be comple-
mented by organizational learning. Repositories (such as organizational intranets) can support
organizational learning by their function as organizational and artifact memories.
Organizational memory Knowledge is the key asset of the knowledge organization. Organiza- tional memory extends and amplifi es this asset by capturing, organizing, disseminating, and
reusing the knowledge created by its employees. Also called a knowledge repository or corporate
memory .
Participant observation A fundamental method of research used in cultural anthropology. It involves a researcher, or researchers, living within a given culture for an extended period
of time, to take part in its daily life in all its richness and diversity. The anthropologist in
such an approach tries to experience a culture “ from within, ” as a person native to that culture
might do.
Personalization/profi ling Using continually adjusted user profi les to match content or services to individuals. Personalization includes determining a user ’ s interest based on his or her prefer-
ences or behavior, constructing business rules to select relevant content based on those prefer-
ences or behaviors, and presenting the content to the user in an integrated, cohesive format. For
Glossary 473
example, the process that occurs upon a page request to a web server and is handled by either
(a) a general application server, (b) a specialized one-to-one application server, or (c) a specifi c
personalization engine; or, the capability for electronic library users to choose the information
to be “ pushed ” or delivered directly to them through the e-library.
Portal A grand and imposing entrance “ the portals of the cathedral ” ; A site that the owner positions as an entrance to other sites on the Internet; a gateway whose purpose is to be the
major starting point for users when they connect to the web.
Process tracing Any of a set of techniques that enables the determination of an individual ’ s train of thought while he or she completes a task or reaches a conclusion.
Productivity paradox Standard measures of labor productivity in the United States suggest that computers, at least until 1995, were not improving productivity. The productivity paradox is the
question: why, then, were U.S. employers investing more and more heavily in computers and
information technologies?
Protocol analysis A method used to discern an individual ’ s general problem-solving approach and the specifi c operations used to move from one knowledge state to another.
Protocols Verbal reports or transcripts that are typically the result of a process-tracing or inter- view session to acquire/code knowledge.
Refl ective listening Listening behaviors that provide feedback that the message was communi- cated (e.g., paraphrasing, clarifying, summarizing).
Remunerative incentive A fi nancial reward, when money is exchanged for acting in a particular, desired way.
Requisite variety The Law of Requisite Variety (formulated by Ross Ashby, a specialist in cyber- netics) shows that regulation can be measured. The maximum possible effectiveness of a regulator
will be directly measurable by a comparison between the variety (number of possible states) of
the regulator and that which is being regulated. In other words, only variety can absorb variety.
If a thermostat is to control temperature over a range, it must have more than two settings (on/
off). Management must similarly fi nd ways to increase variety through the use of models that
present decision makers with the required information.
Repertory grid A psychological technique for eliciting and analyzing a model of the expert ’ s world so that similarities and differences among objects can be represented in a grid.
Retrospective verbalization A variation on the process tracing technique that asks the expert to verbalize his or her reasoning process after completing the task being investigated.
Reuse Multiple individuals are able organize meaningful activities around shared and reusable artifacts to achieve specifi c goals, typically within the context of distributed work and expertise.
These artifacts may be any number of knowledge objects. Knowledge objects may be executable
procedures, procedures, sections of text, or audiovisual “ sound bites. ” The artifacts may include
474 Glossary
the use of previously-used material in the same or different process. Organizational reuse aims
to make additional use of standard parts or components such as reusable code, designs, archi-
tectures, test cases, templates, references, and other valuable knowledge-based components.
Reward An act performed to strengthen approved behavior. Act or give compensation in rec- ognition of someone ’ s behavior or actions to reinforce good behavior. Money or anything else
of value usually given in exchange for a good or service.
Rite Relatively elaborate, dramatic, planned sets of activities that consolidates various forms of cultural expressions into one event, which is carried out through social interactions, usually for
the benefi t of an audience.
Ritual A standardized, detailed set of techniques and behaviors that manage anxieties but seldom produce intended consequences of practical importance.
Semantic networks Cognitive models that illustrate associations among elements. A semantic network is a graph structure in which nodes (or vertices) represent concepts, while the arcs
between these nodes represent relations among concepts. From this perspective, concepts
have no meaning in isolation, and only exhibit meaning when viewed relative to the other
concepts to which they are connected by relational arcs. In semantic networks, structure is
everything.
Social capital The value created when a community or society collaborates and cooperates (through such mechanisms as networks) to achieve mutual benefi ts. The value of social networks
that people can draw on to solve common problems. The benefi ts of social capital fl ow from the
trust, reciprocity, information, and cooperation associated with social networks.
Social constructivism Emphasizes the importance of culture and context in understanding what occurs in society and constructing knowledge based on this understanding. Social constructivists
believe that reality is constructed through human activity and that knowledge is also a human
product that has been socially and culturally constructed. Learning is a social process in which
individuals create meaning through their interactions with each other and with the environment
they live in.
Social network analysis The mapping and measuring of relationships and fl ows between people, groups, organizations, computers, or other information/knowledge processing entities.
Social presence The degree to which an individual perceives he or she is communicating with another human being using a given medium. The degree to which the other participant is judged
to be a “ real ” person. The extent to which one feels he or she is communicating with another
person and not with a technological medium.
Sociogram A diagram that shows interaction patterns between people; for instance, a diagram with a node to represent each individual and lines drawn between individuals to indicate that
they interact frequently. These diagrams can be used to study work fl ows, the clustering of groups,
communication needs, and ineffi ciencies in work processes.
Glossary 475
Structured interview An interview that is organized, planned, and appropriate for the sessions that require specifi c information.
Symbol An arbitrary sign (written or printed) that has acquired a conventional signifi cance. Something visible that by association or convention represents something else that is invisible;
“ the eagle is a symbol of the United States. ”
System A set of interrelated elements. A system is an entity that is comprised of at least two elements and a relation that holds between each of the elements and at least one other in the
set. A system is a holistic or gestalt — it cannot be understood by simple reductionist inquiry
because “ the whole is greater than the sum of the parts. ”
Tacit knowledge From the Latin tacitare, which refers to something that is very diffi cult to articulate, to put into words or an image; typically highly internalized knowledge such as
knowing how to do something or recognizing analogous situations.
Task analysis The process of determining or describing the nature of a task, job, or procedure by breaking it into its primitive components. Analyzes what a user is required to do in terms of
actions and/or cognitive processes to achieve a task.
Task model User-centered representation of goals and actions a user needs to perform in the context of information processing. A task model helps to characterize tasks that might be fruit-
fully supported by current or future systems and therefore is a promising aid for a deeper under-
standing of user activities in certain application domains.
Taxonomy Basic classifi cation system that enable the conceptual identifi cation of concept hierarchies and dependencies. A hierarchical structure used for categorizing a body of informa-
tion or knowledge, allowing an understanding of how that body of knowledge can be broken
down into parts, and how its various parts relate to each other. Taxonomies are used to organize
information in systems.
Technocratic utopianism An organizational political model where the emphasis is on technol- ogy and corporate data.
Thesaurus An organized language used to describe synonyms, that predefi nes the relationships between terms and concepts used in its vocabulary.
Transparency The quality of being clear and transparent. Evolving global standard for state institutions and international organizations, requiring open processes according to general rules
subject to monitoring; regarded as basis of accountability, diminishing corruption. Sharing infor-
mation and acting in an open manner. Transparent systems have clear procedures for public
decision making and open channels of communication between stakeholders and offi cials, and
make a wide range of information accessible.
Trust Certainty based on past experience. The trait of trusting; of believing in the honesty and reliability of others. Complete confi dence in a person or plan.
Unstructured interview Interviews that have the goal of exploring an issue, used primarily in early stages of knowledge acquisition/capture.
476 Glossary
User model The user model defi nes the types of users of the interface and the relevant attributes of those users. Its main purpose is to infl uence interface generation. It is not designed to be a
model of the mental state of the user at a particular time during the interaction.
Value An ideal accepted by some individual or group. The quality (positive or negative) that renders something desirable or valuable.
Variety The total number of possible states of a system or an element of a system. It is a measure of the complexity of the system. The total number of distinguishable states, that is, dependent
on the observational powers of a given observer. A useful managerial measure that conveys the
amount of requisite variety that will be required to model the system (and to base decisions on).
Virtualness “ As-if reality, ” an object that has an effect and shows behavior without physically existing in reality.
Virtual organization Structure in which organization members in different locations work together using e-mail, phone, fax, and other communication methods; a cluster of organizations
united by a series of electronic linkages.
Weak incentive A weak incentive is an incentive that is does not encourage maximization of an objective, because it is ambiguous or lends itself to “ satisfi cing ” instead of optimizing.
Wiki Wiki wiki is a Hawaiian term meaning “ quick ” or “ super fast, ” and wiki became a term for a web site or other hypertext document collection that gives users the ability to add content, as
on an Internet forum, but also allows this content to be edited by other users. The term can also
refer to collaborative software used to create such a web site.
XML eXtensible markup language. A subset of SGML constituting a particular text markup language for interchange of structured data. The unicode standard is the reference character set
for XML content. XML is a trademark of the World Wide Web Consortium. A fl exible way to
create standard information formats and share both the format and the data on the World Wide
Web.
Index
Note : t refers to a table and f refers to a fi gure
AAR. See After action review
Absorptive capacity, 259 – 260
Accenture, 345 – 347, 350
Acquisition and application tools. See
Technology, knowledge acquisition and
application
Ad hoc sessions, 113
Adaptive systems, 208 – 209
Adaptive technologies, 302 – 303
AES, 332
Aesop ’ s fables, for capturing knowledge, 110
Affective domain, Bloom ’ s Taxonomy, 191,
195 – 196
After action review, 55, 287, 314, 348, 378
Albridge Solutions, 446
Al-Hawamdeh, Suleiman, 382, 398, 412
Analysis
gap, 322 – 325
market basket, 273
Anarchy model. See Information politics
Andriessen, D., 344
Anklam, P., 150
Application of knowledge. See Knowledge
application
APQC model of best practice evolution,
165 – 166
Argyris, Chris, 43, 120, 249, 370
Army ’ s After Action Review. See After action
review
ARPANET, 17, 156
Artefacts. See Culture
Artifi cial intelligence, 32, 297
Assessing knowledge management. See
Evaluating knowledge management
Assessing organizational learning. See
Evaluation of organizational learning
Attributes of knowledge management, 13 – 15
Balanced scorecard method, 345 – 353
Beer, Stafford, 68, 85 – 86, 93
Benchmarking, 345 – 351
Bennet, Alex, 85 – 86
Bennet, D., 85 – 86
Best practices, 2 – 4, 11, 16, 22, 24 – 25, 35, 40,
54, 69, 72, 90, 92, 102, 105, 109, 112 – 120,
160 – 165, 185 – 186, 192, 195, 198, 213, 231,
250 – 255, 278 – 279, 287, 294 – 295, 304, 366,
368, 378, 379, 381
Bhatt, G., 89
Blogs, 274 – 275
Blood, Rebecca, 274
Bloom, T., 188, 191 – 198, 211
Bloom ’ s taxonomy, 191 – 198
Boisot, M., 19, 82 – 84, 429
Boisot I-Space KM model, 82 – 84
Bounded rationality principle, 73, 75 – 76
Bouthillier, France, 131, 443
Brainstorming, 231, 252
478 Index
Bristol-Myers Squibb, 445
British Petroleum, 155, 287
British Telecommunications, 208
Broadband communication, 208
Brown Seely, John, 289
BSC. See Balanced scorecard method
Buckman Labs, 247 – 249, 332, 345, 432, 437
Bukowitz and Williams, KM cycle, 38 – 42
Bukowitz, W., 33, 38 – 42, 52
Bush, V, 99
Business intelligence, 131, 133
Canadian International Development Agency,
163 – 164
Canadian Treasury Board, 356
Canon Inc., 68
Capability Maturity Model, 238, 242
Capacity, absorptive. See Absorptive
capacity
Capital. See Intangible assets
Capturing knowledge. See also Tacit
knowledge
ad hoc sessions, 113
Aesop ’ s fables, 110
defi ned, 99 – 100
e-learning, 115 – 117, 214 – 215
interviewing experts, 104 – 107
knowledge engineering, 103
learning by being told, 107 – 111
learning by observation, 113
learning histories, 115
at the organizational level ( see
Organizational knowledge capture)
road maps, 113 – 114
storytelling, 107 – 110
Carnegie Mellon Software Engineering
Institute, 238
CBT. See Computer-based training
Central Intelligence Agency (CIA), 291
Chapparal Steel, 16
Characteristics of knowledge, 2
Chat rooms, 271, 281 – 285
Chevron, 287, 345 – 347
Chief Knowledge Offi cer. See Knowledge
management team, roles
Chief Learning Offi cer. See Knowledge
management team, roles
Choo, Chun Wei, 73 – 76, 87, 302, 381 – 382,
384 – 385, 389
Choo Sense-making KM model, 73 – 76, 87
CIBC, 432
CIDA. See Canadian International
Development Agency
CKO. See Chief Knowledge Offi cer
CLO. See Chief Learning Offi cer
Cluster analysis, 127 – 129, 271
CMM. See Capability Maturity Model
CMMI. See Carnegie Mellon Software
Engineering Institute
CMS. See Content Management System
Coca-Cola Company, 333
Codifying, explicit knowledge, 100, 121
cognitive maps, 121 – 123
decision trees, 123 – 124
knowledge taxonomies, 124 – 131
Cognitive domain, Bloom taxonomy of,
191 – 193
Cognitive maps, 121 – 123
Cohen, W. M., 260
Coleman, J., 170
Collaboration technologies, 281 – 285
Collaboration tools. See Collaboration
technologies
Colleges, invisible. See Invisible colleges
Communication systems, 232
Communication technologies. See
Communication systems
Community of practice (CoP)
APQC model of best practice evolution,
165 – 166
defi ned, 145
evaluation of, 359 – 360
history of, 154 – 157
knowledge broker, 163
Knowledge services, KSO (Knowledge
Support Offi ce), 163
Index 479
learning in, 175
maturity model, 244 – 245
roles and responsibilities, 160 – 163
sharing knowledge in, 145 – 147, 154 – 157
technologies, 164 – 165
types of, 158 – 160
virtual, 166
yellow pages ( see Yellow Pages)
Competitive intelligence, 131, 133
Complex adaptive system KM model, 85 – 89
Complex adaptive systems, 85 – 89
Computer-based learning. See Computer-
based training
Computer-based training, 297
Concept analysis, 11 – 15
Concept mapping, 122
Connectionism, 63
Content creation tools, 269 – 271, 276
Content management tools, 276 – 277
Continuity management. See Knowledge
continuity
CoP. See Community of practice
Corporate amnesia, 23, 366, 372, 376, 390
Corporate memory, 16 – 17, 35, 185, 366 – 368,
372 – 377, 382 – 384
Corporate wikis. See Wikis
Corporate yellow pages. See Yellow Pages
Creation of knowledge
in the Boisot model, 82 – 85
in the Choo model, 73 – 76
in the Complex adaptive system model,
85 – 89
in the European Foundation for Quality
Management model, 89 – 90
in the integrated KM cycle, 51 – 54
in the Inukshuk model, 90 – 91
in the Nonaka and Takeuchi model, 61 – 72
Process, 2, 7, 48, 51 – 54
in the Von Krogh and Roos model, 62 – 64
in the Wiig model, 76 – 82
Creative Commons Agreement, 434 – 435
Crossan, M., 101, 384
Crossan, Lane, and White ’ s 4I model, 101
Culture,
analysis of, 229 – 232
barriers to change of, 259 – 260
communication systems, 232
defi ned, 225 – 227
effects of on individuals, 235 – 237
knowledge-sharing, 229, 232 – 234, 240,
246 – 256
history of, 224 – 225, 232
impact of a merger on, 256 – 257
impact of virtualization on, 258
norms, 226 – 227, 230 – 233, 235 – 236, 246,
249, 258, 260
organizational analysis, 229 – 232
politics of organizational culture, 427 – 429
transformation into knowledge-sharing
culture, 246 – 250
transparency, 253
types of, 227 – 229
workplace design and redesign, 250 – 252
Cultural change needed to succeed in KM,
224 – 227
Customer capital. See Social capital
Cybernetics. See Viable System Model
Data, defi ned, 60
Data mining and knowledge discovery,
271 – 274
Data mining tools, 271 – 274
Davenport, Tom, 2, 3, 6, 12, 60, 67, 214, 293,
344 – 345, 382, 407, 427
Decision making
defi ned, 71 – 72
in the Choo model, 73 – 76
in the Wiig model, 80 – 89
Decision support systems, 297 – 299
Decision trees, 123 – 124
Dell Inc., 408
Denning, Steven, 15, 107, 109, 386, 437
Dow Chemical, 431 – 432
Drucker, Peter, 2, 16, 289
Duguid, P., 145, 147, 157, 289
Dynamic profi ling. See Personalization
480 Index
E-learning, 115 – 117, 214 – 215
Edvinsson, Leif, 19, 335, 432
Electronic Performance Support Systems,
201 – 206
Emergence, 303
Employee loyalty, 174
Enterprise portals. See Portals
EPSS. See Electronic Performance Support
Systems
Ericsson Inc, 146
Ethics, 413 – 419
European Foundation for Quality
Management (EFQM) KM model, 89 – 90
Evaluating communities of practice. See
Community of practice, evaluating
Evaluating knowledge management
balanced scorecard method of, 345 – 353
benchmarking method of, 345 – 351
house of quality method of, 354 – 356
intangible asset monitor, Sveiby, 345, 433
process of, 345
results-based method of, 356 – 358
Skandia Navigator, 344 – 345, 432
strategy for, 340 – 343
Evaluating organizational learning, 369 – 370
Expertise locator system. See Yellow Pages
Explicit knowledge
codifi cation, 98 – 100, 102 – 104
defi ned, 3
sharing, 143, 145, 163
tacit vs., 9 – 11
Extensible Markup Language, 276
Fables, for capturing knowledge, 101
Facilitators, community. See Community of
practice roles and responsibilities
Federalism model. See Information politics
Feudalism model. See Information politics
Filtering tools, intelligent, 298 – 302
Folksonomies. See Social tagging
Ford Company, 330 – 331
Forrester Group KM Model, 242 – 243
Fragmented cultures. See Culture
Frappalo, C., 99
Fujitsu Organizational Maturity Model. See
Maturity models
Future challenges for KM, 440 – 441
Gap analysis, 322 – 325
GE. See General Electric
Gemeinschaft, defi ned, 122
General Electric, 69, 254
Gery, Gloria, 7, 201
Gesellschaft, defi ned, 122
Girard, John, 90, 395
Gladwell, Malcolm, 250, 252
Granovetter, M., 170, 289
Ground truth, 32
Groupware. See Collaboration Technologies
Hardin, G., 159
Hill and Knowlton, 261
History, of KM, 15 – 19, 24 – 25
Holistic approach to KM, 60
Honda Motor Company Ltd., 67
House of Quality, 354 – 356
HSC. See Hughes Space and Communications
Huber, J., 120
Hughes Space and Communications, 199
Human capital, 170, 335
Iaccoca Institute, 2 – 3
IBM Corporation, 85 – 86, 111, 290
ICAS. See Intelligent Complex Adaptive
Systems model
ICL Ltd, 146, 256
Importance of KM today, 22 – 24
Incentives, 248, 259
for knowledge sharing, 169, 173, 435 – 440
Information
cycle, 35 – 37
defi ned, 60
digital, 32
overload, 23 – 24
politics, models of, 427 – 429
Information literacy, 39, 142
Index 481
Information politics, models of, 427 – 429
Information resources management, 318
Information seeking, 142 – 144
Infosys KM Maturity Model, 239 – 242
Infrastructure capital, 317
Inkpen, A., 102, 120
Innovation. See KM objectives
Intangible assets, 19 – 20, 333 – 336, 429 – 433
Intangible Asset Monitor, Sveiby, 345
Integrated KM cycle, 51 – 54
Intellectual assets. See Intangible assets
Intellectual capital. See Intangible assets
Intellectual capital approach vs. KM, 3
Intellectual capital management, 3, 19 – 20
Intellectual property, 433 – 435
Intelligence, artifi cial. See Artifi cial
intelligence
Intelligent agents, 298 – 302
Intelligent Complex Adaptive Systems Model,
(ICAS), 85 – 89
Intelligent fi ltering tools, 298 – 302
Interdisciplinary nature of KM, 8 – 9
International Data Corporation, 110
Internet communities. See Community of
Practice
Interviewing experts, 104 – 107
Interviews, stakeholders, 317
Inukshuk KM Model, 90 – 91
Inventory of intellectual assets. See
Knowledge audit
Invisible colleges, 156
IRM. See Information Resources Management
I-space KM model, Boisot. See Boisot I-Space
KM Model
Jet Propulsion Lab (JPL), 148
J. P. Morgan Chase, 212
KAO Company, 332
Kaplan, R., 16 – 17, 351
KM. See Knowledge Management
KMCI Certifi cate in Knowledge and
Information Management, 412, 418
Knowledge
assets ( see Intangible assets)
characteristics, 2
continuity, 117, 133 – 134, 385 – 390
engineering, 103
social context of, 114 – 115
tacit and explicit forms of, 9 – 11, 61
Knowledge acquisition. See Technology,
knowledge acquisition and application
Knowledge application. See also Technology,
knowledge acquisition and application
assessing with Bloom ’ s taxonomy, 191 – 198
at group and organizational levels, 207 – 211
Knowledge assets. See Intangible assets
Knowledge audit, 318 – 322
Knowledge brokers. See Community of
practice
Knowledge capital. See Intangible assets
Knowledge capture. See Capturing knowledge
Knowledge continuity, 117, 133 – 134, 385 – 390
Knowledge creation. See Creation of
knowledge
Knowledge discovery and data mining, See
Data mining and knowledge discovery
Knowledge engineering, 103
Knowledge hoarding, 250, 254, 260
Knowledge intermediary, 211
Knowledge Management
attributes, 13 – 15
cultural change needed to succeed, 224 – 227
defi ned, 3 – 6
future challenges, 440 – 441
history, 15 – 19, 24 – 25
holistic approach, 60
importance of, today, 22 – 24
intellectual capital approach vs., 3
milestones. See History of KM
multidisciplinary nature of, 8 – 9
myths, in KM, 224
objectives, 4, 184, 312
research in, 442 – 446
skills, 399 – 401
technology ( see Technology)
482 Index
Knowledge management 2.0. See Social
networking
Knowledge management challenges, issues,
424 – 425, 440 – 441
Knowledge management cycles
Bukowitz and Williams, 38 – 42
integrated, 51 – 54
McElroy, 42 – 45
Meyer and Zack, 33 – 38
Wiig, 45 – 51
Knowledge management ethics. See Ethics
Knowledge management models
Boisot I-Space, 82 – 85
Choo sense-making, 73 – 76
complex adaptive system, 85 – 89
European Foundation for Quality
Management (EFQM), 89 – 90
Inukshuk, 90 – 91
Nonaka and Takeuchi knowledge spiral KM
model, 64 – 71
Von Krogh and Roos, 62 – 64
Wiig, 76 – 82
Knowledge management organizations,
410 – 412
Knowledge management profession, 412 – 413
Knowledge management research, 442 – 446
Knowledge management skills. See Knowledge
management team
Knowledge management metrics. See
Evaluating knowledge management
Knowledge management strategy
defi ned, 315
gap analysis, 322 – 325
knowledge audit, 318 – 322
major components of, 316 – 317
road map, 325 – 328
Knowledge management systems. See
Technologies
Knowledge management teams, roles, 400,
402 – 403, 410 – 411
CHCO, 403 – 405
CKO, 403, 406 – 407
CLO, 403, 407 – 410
in a community of practice, 160 – 163
senior management roles, 403 – 410
Knowledge management tools. See
Technology, Knowledge acquisition and
application
Knowledge mapping. See Cognitive maps
Knowledge objects, 184 – 185
Knowledge Process Quality Model (KPQM), 242
Knowledge repositories. See Repositories
Knowledge reuse. See Reuse
Knowledge Service Centre, KSO, Knowledge
Support Offi ce, 163
Knowledge sharing, 11, 24
culture, 229, 232 – 234, 240, 246 – 256
incentives for, 169, 173, 435 – 440
in communities of practice, 145 – 147,
154 – 157
obstacles, 167 – 168
in virtual CoPs, 163 – 168, 171
Knowledge Spiral Model, Nonaka and
Takeuchi. See Nonaka and Takeuchi
Knowledge Spiral KM Model
Knowledge Support Offi ce, 163
Knowledge taxonomy, 124 – 131
Knowledge worker, 2, 16, 142
Kotter, J., 231
KPMG International, 296
Kraft General Foods, 68 – 69
Kransdorff, Arnold, 366, 383
KSO. See Knowledge Support Offi ce
L. L. Bean Clothing, 348
Lambe, Patrick, 126 – 127
Lane, W., 101
Lave, J., 145, 147
Learning
by being told, 107 – 111
in a community of practice, 175
computer-based, 297
histories, 115
by observation, 113
organizational ( see Organizational learning)
social nature of, 145, 147, 149
Index 483
Learning hierarchy. See Bloom ’ s taxonomy
Learning histories, 115
Learning organization, 370 – 372
Lesser, Eric, 170, 174, 360, 381
Lessons learned, 3 – 4, 11, 22, 25, 163 – 165,
366 – 368, 378 – 379
Lev, B., 343
Liebowitz, Jay, 131, 243, 292, 322 – 325
Lotus Corporation, 466
Mapping knowledge. See Cognitive maps
Market basket analysis, 273
Markus, M., 383
Mashups, 275 – 276
Maturity models,
Capability Maturity Model (CMM), 238 – 239
Community of Practice, CoP, model,
244 – 245
Forrester Group KM Model, 242 – 243
history of, 238
Infosys model, 239 – 242
Knowledge Process Quality Model (KPQM),
242
McDermott, R., 147, 175, 360
McElroy, M., 33, 42 – 46, 52, 417
McElroy KM cycle, 42 – 45
McKinsey and Company, 345
Mead ’ s Lexis/Nexis System, 34
Media richness, 166 – 167
Mental model, 249
Mercedes-Benz, 304
Metadata, 130, 210, 276 – 278
Meyer, M., 33 – 38, 42, 52
Meyer and Zack KM cycle, 33 – 38
Microsoft Corporation, 334 – 335
Milestones in KM. See History of KM
Milgram, S., 174
Miller, G., 202
Models. See Knowledge Management Models,
Organizational maturity models
Monarchy model. See Information politics
Multidisciplinary nature of KM, 8 – 9
Myths, in the KM fi eld, 224
National Science Digital Library (NSDL), 215
National Aeronautic and Space
Administration (NASA), 366 – 367, 380
Network analysis. See Social network
analysis
Networking technologies, 292 – 296
Networks, Semantic, 189 – 191, 203
Nokia, 251
Nonaka, I., 61, 64 – 72, 73 – 74, 77, 82, 85, 91,
102, 121, 184, 195, 361
Nonaka and Takeuchi Knowledge Spiral
Model, 64 – 72
Norms, 226 – 227, 230 – 233, 235 – 236, 246,
249, 258, 260
Northrop-Grumman, 319 – 320
Norton, D., 16 – 17, 351
Objectives of KM, 4, 184, 312
Obstacles to knowledge sharing, 167 – 168
O ’ Dell, Carla, 5, 16, 24, 378
Online communities. See Community of
practice, virtual
OpenText Corporation, 287
Oracle Systems, 204 – 205
Organizational capital. See Infrastructure
capital
Organizational culture. See Culture
Organizational epistemology. See von Krogh
and Roos model
Organizational knowledge capture,
118 – 121
Organizational learning, 101, 368 – 369,
377 – 378
Choo ’ s model of, 384
Crossan and White model of, 384
models of, 379 – 384
Organizational maturity models. See Maturity
models
Organizational memory. See Corporate
memory
Organizational stories. See Storytelling
Organizations, virtual. See Virtual
organizations
484 Index
Personalization, 186 – 189
Personal knowledge management (PKM),
279 – 280
Polanyi, Michael, 9, 14, 61, 65
Politics of organizational culture, 427 – 429
Politics of search engines, 425 – 427
Portals, 276, 280, 292 – 296, 302
Postmodern KM, 446 – 447
PriceWaterhouseCoopers, 294
Principle of bounded rationality, 60 – 61
Productivity paradox, 142 – 144
Profi ling, 192, 204 – 205, 210
Prusak, Larry, 2 – 4, 12, 60, 67, 145, 170, 293,
381 – 382, 407
Putnam, Robert, 173
PWC. See PriceWaterhouseCoopers
Repertory grid, 127
Repositories, knowledge, 13, 15, 35 – 38, 185,
188, 195, 205, 209, 213 – 214, 216, 293 – 294
Research in KM, 442 – 446
Reuse, 211 – 213, 216 – 217. See also KM
Objectives
Reward and censure. See Incentives
Reward systems. See Incentives
Road maps, 113 – 114
Rollet, H., 269, 280
Roos, G., 62 – 64, 344
Roos, J., 62 – 64, 344
SABRE reservation system, 20
Schein, E., 229 – 232
Schon, D., 43, 120, 249, 370
Seely Brown, J., 145, 147, 157
Semantic mapping, 12
Semantic networks, 189 – 191, 203
Senge, Peter, 17, 207, 368, 370, 387
Senior management roles in KM, 403 – 410
Sense making in organizations, 73 – 76
Sense-making model, Choo, 73 – 76
Sharing, knowledge. See Knowledge sharing
Shearer, K., 131, 143
Siemens AG ShareNet, 130
Siemens Medical Solutions, 440
Sigma organization, 257
Simon, Herbert, 73, 75 – 76
Skandia Inc., 344 – 345
Skidmore, Owings and Merrill LLP (SOW), 72
Snowden, David, 85 – 86, 110
Social bookmarking. See Social tagging
Social capital, 170 – 173
Social context of knowledge, 114 – 115
Social network analysis, SNA, 149 – 152, 359
Social networking, 288 – 292
Social presence, 166
Social tagging, 277 – 279
Sociograms, 149 – 152
Stankosky, Michael, 5, 12
Stewart, Tom, 3 – 4, 17, 20, 254 – 255, 334,
406 – 407
Storytelling, 107 – 110
Structural capital, 335
Sun Microsystems. See Oracle Systems
Sveiby, Karl, 19, 172, 233, 313, 344 – 345, 430,
433
Systems science. See Viable System model
Tacit, and explicit forms of knowledge, 9 – 11,
61
Tacit knowledge, 9 – 11, 35, 39, 42
Tacit Knowledge Systems Inc., 446
Takeuchi, H., 61, 64 – 72, 73 – 74, 77, 82, 85,
91, 102, 121, 184, 195, 361
Tapscott, D., 253, 417
Task analysis, 200 – 201
Taxonomies. See Knowledge taxonomy
Teamware Group, 286
Technology
adaptive technologies, 302 – 303
artifi cial intelligence, 32, 297
broadband communication, 208
capture and creation, 270 – 280
chat rooms, 271, 281 – 285
cluster analysis, 127 – 129, 271
collaboration technologies, 281 – 285
communication systems, 232
Index 485
in a community of practice, 164 – 165
computer-based training, 297
content creation tools, 269 – 271, 276
content management tools, 276 – 277
data mining tools, 271 – 274
decision support systems, 297 – 299
electronic performance support systems,
201 – 206
intelligent agents, 298 – 302
intelligent fi ltering tools, 298 – 302
knowledge acquisition and application,
297 – 303
knowledge sharing and dissemination,
280 – 296
networking technologies, 292 – 296
portals, 276, 280, 292 – 296, 302
repositories, 13, 15, 185, 188, 195, 205, 209,
213 – 214, 216
twitter, 284
types of, 269 – 270
Texaco, 155
TFPL Company, 398 – 401
Thomas and Betts Corporation, 148 – 149
3M, 332
Tools. See Technologies
Trace analysis. See Usage analysis
Tragedy of the Commons, 158 – 159
Transparency of an organization. See
Organization transparency
Transport Canada, 388 – 390
Twitter, 284
Undernet, the, 169 – 179
Usage analysis, 189 – 190, 206
U.S. Army, 287
User-centric profi ling. See Profi ling
User modeling. See Personalization; Usage
analysis
Valuation of intellectual capital. See
Evaluating knowledge management
Viable System Model, 85
Viant Consulting Company, 255
Virtual communities. See Community of
practice, virtual
Virtual organizations, 258
Von Krogh, G., 62 – 64
Von Krogh and Roos Organizational
epistemology Model, 62 – 64
VSM. See Viable System Model
Web 2.0. See Social networking
Web logs. See Blogs
Weick, Karl, 73 – 75
Wenger CoP lifecycle maturity model,
244 – 245
Wenger, Etienne, 145, 147, 158, 160, 175,
386
White, R., 101
Wiig, K., 6 – 7, 17, 19, 21 – 22, 33, 45 – 52,
76 – 82, 381
Wiig KM cycle, 45 – 52
Wiig KM model, 76 – 82
Wiki, 285 – 288
Wikipedia, 286
Williams, R., 33, 38 – 42, 52
World Bank, 314 – 315
Xerox Business Systems, 111, 256, 348
XML. See Extensible Markup Language
Xref, 254
Yellow Pages, 35, 69, 152 – 154, 284
Zack, M., 33 – 38, 42, 52, 214, 322
- Cover
- Contents
- Foreword
- 1 Introduction to Knowledge Management
- 2 The Knowledge Management Cycle
- 3 Knowledge Management Models
- 4 Knowledge Capture and Codification
- 5 Knowledge Sharing and Communities of Practice
- 6 Knowledge Application
- 7 The Role of Organizational Culture
- 8 Knowledge Management Tools
- 9 Knowledge Management Strategy
- 10 The Value of Knowledge Management
- 11 Organizational Learning and Organizational Memory
- 12 The KM Team
- 13 Future Challenges for KM
- 14 KM Resources
- Glossary
- Index
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De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR <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> /PTB <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> /SUO <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> /SVE <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> /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.) >> /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ << /AsReaderSpreads false /CropImagesToFrames true /ErrorControl /WarnAndContinue /FlattenerIgnoreSpreadOverrides false /IncludeGuidesGrids false /IncludeNonPrinting false /IncludeSlug false /Namespace [ (Adobe) (InDesign) (4.0) ] /OmitPlacedBitmaps false /OmitPlacedEPS false /OmitPlacedPDF false /SimulateOverprint /Legacy >> << /AddBleedMarks false /AddColorBars false /AddCropMarks false /AddPageInfo false /AddRegMarks false /ConvertColors /ConvertToCMYK /DestinationProfileName () /DestinationProfileSelector /DocumentCMYK /Downsample16BitImages true /FlattenerPreset << /PresetSelector /MediumResolution >> /FormElements false /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles false /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /DocumentCMYK /PreserveEditing true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling /UseDocumentProfile /UseDocumentBleed false >> ] >> setdistillerparams << /HWResolution [2400 2400] /PageSize [612.000 792.000] >> setpagedevice
<< /ASCII85EncodePages false /AllowTransparency false /AutoPositionEPSFiles true /AutoRotatePages /None /Binding /Left /CalGrayProfile (Dot Gain 20%) /CalRGBProfile (sRGB IEC61966-2.1) /CalCMYKProfile (U.S. Web Coated \050SWOP\051 v2) /sRGBProfile (sRGB IEC61966-2.1) /CannotEmbedFontPolicy /Error /CompatibilityLevel 1.4 /CompressObjects /Off /CompressPages true /ConvertImagesToIndexed true /PassThroughJPEGImages true /CreateJDFFile false /CreateJobTicket false /DefaultRenderingIntent /Default /DetectBlends false /DetectCurves 0.0000 /ColorConversionStrategy /LeaveColorUnchanged /DoThumbnails false /EmbedAllFonts true /EmbedOpenType false /ParseICCProfilesInComments true /EmbedJobOptions true /DSCReportingLevel 0 /EmitDSCWarnings false /EndPage -1 /ImageMemory 1048576 /LockDistillerParams false /MaxSubsetPct 100 /Optimize false /OPM 1 /ParseDSCComments true /ParseDSCCommentsForDocInfo true /PreserveCopyPage true /PreserveDICMYKValues true /PreserveEPSInfo true /PreserveFlatness true /PreserveHalftoneInfo false /PreserveOPIComments false /PreserveOverprintSettings true /StartPage 1 /SubsetFonts false /TransferFunctionInfo /Apply /UCRandBGInfo /Preserve /UsePrologue false /ColorSettingsFile (None) /AlwaysEmbed [ true ] /NeverEmbed [ true ] /AntiAliasColorImages false /CropColorImages true /ColorImageMinResolution 300 /ColorImageMinResolutionPolicy /OK /DownsampleColorImages false /ColorImageDownsampleType /Average /ColorImageResolution 1200 /ColorImageDepth -1 /ColorImageMinDownsampleDepth 1 /ColorImageDownsampleThreshold 1.50000 /EncodeColorImages false /ColorImageFilter /DCTEncode /AutoFilterColorImages true /ColorImageAutoFilterStrategy /JPEG /ColorACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /ColorImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000ColorACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000ColorImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 300 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages false /GrayImageDownsampleType /Average /GrayImageResolution 1200 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages false /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /GrayImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000GrayACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000GrayImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 1200 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages false /MonoImageDownsampleType /Average /MonoImageResolution 1200 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages false /MonoImageFilter /None /MonoImageDict << /K -1 >> /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (None) /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False /Description << /CHS <FEFF4f7f75288fd94e9b8bbe5b9a521b5efa7684002000410064006f006200650020005000440046002065876863900275284e8e9ad88d2891cf76845370524d53705237300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c676562535f00521b5efa768400200050004400460020658768633002> /CHT 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<FEFF9ad854c18cea306a30d730ea30d730ec30b951fa529b7528002000410064006f0062006500200050004400460020658766f8306e4f5c6210306b4f7f75283057307e305930023053306e8a2d5b9a30674f5c62103055308c305f0020005000440046002030d530a130a430eb306f3001004100630072006f0062006100740020304a30883073002000410064006f00620065002000520065006100640065007200200035002e003000204ee5964d3067958b304f30533068304c3067304d307e305930023053306e8a2d5b9a306b306f30d530a930f330c8306e57cb30818fbc307f304c5fc59808306730593002> /KOR <FEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020ace0d488c9c80020c2dcd5d80020c778c1c4c5d00020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002e> /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR <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> /PTB <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> /SUO <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> /SVE <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> /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.) >> /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ << /AsReaderSpreads false /CropImagesToFrames true /ErrorControl /WarnAndContinue /FlattenerIgnoreSpreadOverrides false /IncludeGuidesGrids false /IncludeNonPrinting false /IncludeSlug false /Namespace [ (Adobe) (InDesign) (4.0) ] /OmitPlacedBitmaps false /OmitPlacedEPS false /OmitPlacedPDF false /SimulateOverprint /Legacy >> << /AddBleedMarks false /AddColorBars false /AddCropMarks false /AddPageInfo false /AddRegMarks false /ConvertColors /ConvertToCMYK /DestinationProfileName () /DestinationProfileSelector /DocumentCMYK /Downsample16BitImages true /FlattenerPreset << /PresetSelector /MediumResolution >> /FormElements false /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles false /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /DocumentCMYK /PreserveEditing true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling /UseDocumentProfile /UseDocumentBleed false >> ] >> setdistillerparams << /HWResolution [2400 2400] /PageSize [612.000 792.000] >> setpagedevice
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<FEFF9ad854c18cea306a30d730ea30d730ec30b951fa529b7528002000410064006f0062006500200050004400460020658766f8306e4f5c6210306b4f7f75283057307e305930023053306e8a2d5b9a30674f5c62103055308c305f0020005000440046002030d530a130a430eb306f3001004100630072006f0062006100740020304a30883073002000410064006f00620065002000520065006100640065007200200035002e003000204ee5964d3067958b304f30533068304c3067304d307e305930023053306e8a2d5b9a306b306f30d530a930f330c8306e57cb30818fbc307f304c5fc59808306730593002> /KOR <FEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020ace0d488c9c80020c2dcd5d80020c778c1c4c5d00020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002e> /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR <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> /PTB <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> /SUO <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> /SVE <FEFF0041006e007600e4006e00640020006400650020006800e4007200200069006e0073007400e4006c006c006e0069006e006700610072006e00610020006f006d002000640075002000760069006c006c00200073006b006100700061002000410064006f006200650020005000440046002d0064006f006b0075006d0065006e007400200073006f006d002000e400720020006c00e4006d0070006c0069006700610020006600f60072002000700072006500700072006500730073002d007500740073006b00720069006600740020006d006500640020006800f600670020006b00760061006c0069007400650074002e002000200053006b006100700061006400650020005000440046002d0064006f006b0075006d0065006e00740020006b0061006e002000f600700070006e00610073002000690020004100630072006f0062006100740020006f00630068002000410064006f00620065002000520065006100640065007200200035002e00300020006f00630068002000730065006e006100720065002e> /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.) >> /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ << /AsReaderSpreads false /CropImagesToFrames true /ErrorControl /WarnAndContinue /FlattenerIgnoreSpreadOverrides false /IncludeGuidesGrids false /IncludeNonPrinting false /IncludeSlug false /Namespace [ (Adobe) (InDesign) (4.0) ] /OmitPlacedBitmaps false /OmitPlacedEPS false /OmitPlacedPDF false /SimulateOverprint /Legacy >> << /AddBleedMarks false /AddColorBars false /AddCropMarks false /AddPageInfo false /AddRegMarks false /ConvertColors /ConvertToCMYK /DestinationProfileName () /DestinationProfileSelector /DocumentCMYK /Downsample16BitImages true /FlattenerPreset << /PresetSelector /MediumResolution >> /FormElements false /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles false /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /DocumentCMYK /PreserveEditing true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling /UseDocumentProfile /UseDocumentBleed false >> ] >> setdistillerparams << /HWResolution [2400 2400] /PageSize [612.000 792.000] >> setpagedevice
<< /ASCII85EncodePages false /AllowTransparency false /AutoPositionEPSFiles true /AutoRotatePages /None /Binding /Left /CalGrayProfile (Dot Gain 20%) /CalRGBProfile (sRGB IEC61966-2.1) /CalCMYKProfile (U.S. Web Coated \050SWOP\051 v2) /sRGBProfile (sRGB IEC61966-2.1) /CannotEmbedFontPolicy /Error /CompatibilityLevel 1.4 /CompressObjects /Off /CompressPages true /ConvertImagesToIndexed true /PassThroughJPEGImages true /CreateJDFFile false /CreateJobTicket false /DefaultRenderingIntent /Default /DetectBlends false /DetectCurves 0.0000 /ColorConversionStrategy /LeaveColorUnchanged /DoThumbnails false /EmbedAllFonts true /EmbedOpenType false /ParseICCProfilesInComments true /EmbedJobOptions true /DSCReportingLevel 0 /EmitDSCWarnings false /EndPage -1 /ImageMemory 1048576 /LockDistillerParams false /MaxSubsetPct 100 /Optimize false /OPM 1 /ParseDSCComments true /ParseDSCCommentsForDocInfo true /PreserveCopyPage true /PreserveDICMYKValues true /PreserveEPSInfo true /PreserveFlatness true /PreserveHalftoneInfo false /PreserveOPIComments false /PreserveOverprintSettings true /StartPage 1 /SubsetFonts false /TransferFunctionInfo /Apply /UCRandBGInfo /Preserve /UsePrologue false /ColorSettingsFile (None) /AlwaysEmbed [ true ] /NeverEmbed [ true ] /AntiAliasColorImages false /CropColorImages true /ColorImageMinResolution 300 /ColorImageMinResolutionPolicy /OK /DownsampleColorImages false /ColorImageDownsampleType /Average /ColorImageResolution 1200 /ColorImageDepth -1 /ColorImageMinDownsampleDepth 1 /ColorImageDownsampleThreshold 1.50000 /EncodeColorImages false /ColorImageFilter /DCTEncode /AutoFilterColorImages true /ColorImageAutoFilterStrategy /JPEG /ColorACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /ColorImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000ColorACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000ColorImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 300 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages false /GrayImageDownsampleType /Average /GrayImageResolution 1200 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages false /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /GrayImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000GrayACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000GrayImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 1200 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages false /MonoImageDownsampleType /Average /MonoImageResolution 1200 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages false /MonoImageFilter /None /MonoImageDict << /K -1 >> /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (None) /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False /Description << /CHS <FEFF4f7f75288fd94e9b8bbe5b9a521b5efa7684002000410064006f006200650020005000440046002065876863900275284e8e9ad88d2891cf76845370524d53705237300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c676562535f00521b5efa768400200050004400460020658768633002> /CHT 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<FEFF9ad854c18cea306a30d730ea30d730ec30b951fa529b7528002000410064006f0062006500200050004400460020658766f8306e4f5c6210306b4f7f75283057307e305930023053306e8a2d5b9a30674f5c62103055308c305f0020005000440046002030d530a130a430eb306f3001004100630072006f0062006100740020304a30883073002000410064006f00620065002000520065006100640065007200200035002e003000204ee5964d3067958b304f30533068304c3067304d307e305930023053306e8a2d5b9a306b306f30d530a930f330c8306e57cb30818fbc307f304c5fc59808306730593002> /KOR <FEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020ace0d488c9c80020c2dcd5d80020c778c1c4c5d00020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002e> /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR <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> /PTB <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> /SUO <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> /SVE <FEFF0041006e007600e4006e00640020006400650020006800e4007200200069006e0073007400e4006c006c006e0069006e006700610072006e00610020006f006d002000640075002000760069006c006c00200073006b006100700061002000410064006f006200650020005000440046002d0064006f006b0075006d0065006e007400200073006f006d002000e400720020006c00e4006d0070006c0069006700610020006600f60072002000700072006500700072006500730073002d007500740073006b00720069006600740020006d006500640020006800f600670020006b00760061006c0069007400650074002e002000200053006b006100700061006400650020005000440046002d0064006f006b0075006d0065006e00740020006b0061006e002000f600700070006e00610073002000690020004100630072006f0062006100740020006f00630068002000410064006f00620065002000520065006100640065007200200035002e00300020006f00630068002000730065006e006100720065002e> /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.) >> /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ << /AsReaderSpreads false /CropImagesToFrames true /ErrorControl /WarnAndContinue /FlattenerIgnoreSpreadOverrides false /IncludeGuidesGrids false /IncludeNonPrinting false /IncludeSlug false /Namespace [ (Adobe) (InDesign) (4.0) ] /OmitPlacedBitmaps false /OmitPlacedEPS false /OmitPlacedPDF false /SimulateOverprint /Legacy >> << /AddBleedMarks false /AddColorBars false /AddCropMarks false /AddPageInfo false /AddRegMarks false /ConvertColors /ConvertToCMYK /DestinationProfileName () /DestinationProfileSelector /DocumentCMYK /Downsample16BitImages true /FlattenerPreset << /PresetSelector /MediumResolution >> /FormElements false /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles false /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /DocumentCMYK /PreserveEditing true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling /UseDocumentProfile /UseDocumentBleed false >> ] >> setdistillerparams << /HWResolution [2400 2400] /PageSize [612.000 792.000] >> setpagedevice
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<FEFF9ad854c18cea306a30d730ea30d730ec30b951fa529b7528002000410064006f0062006500200050004400460020658766f8306e4f5c6210306b4f7f75283057307e305930023053306e8a2d5b9a30674f5c62103055308c305f0020005000440046002030d530a130a430eb306f3001004100630072006f0062006100740020304a30883073002000410064006f00620065002000520065006100640065007200200035002e003000204ee5964d3067958b304f30533068304c3067304d307e305930023053306e8a2d5b9a306b306f30d530a930f330c8306e57cb30818fbc307f304c5fc59808306730593002> /KOR <FEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020ace0d488c9c80020c2dcd5d80020c778c1c4c5d00020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002e> /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR <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> /PTB <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> /SUO <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> /SVE <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> /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.) >> /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ << /AsReaderSpreads false /CropImagesToFrames true /ErrorControl /WarnAndContinue /FlattenerIgnoreSpreadOverrides false /IncludeGuidesGrids false /IncludeNonPrinting false /IncludeSlug false /Namespace [ (Adobe) (InDesign) (4.0) ] /OmitPlacedBitmaps false /OmitPlacedEPS false /OmitPlacedPDF false /SimulateOverprint /Legacy >> << /AddBleedMarks false /AddColorBars false /AddCropMarks false /AddPageInfo false /AddRegMarks false /ConvertColors /ConvertToCMYK /DestinationProfileName () /DestinationProfileSelector /DocumentCMYK /Downsample16BitImages true /FlattenerPreset << /PresetSelector /MediumResolution >> /FormElements false /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles false /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /DocumentCMYK /PreserveEditing true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling /UseDocumentProfile /UseDocumentBleed false >> ] >> setdistillerparams << /HWResolution [2400 2400] /PageSize [612.000 792.000] >> setpagedevice
<< /ASCII85EncodePages false /AllowTransparency false /AutoPositionEPSFiles true /AutoRotatePages /None /Binding /Left /CalGrayProfile (Dot Gain 20%) /CalRGBProfile (sRGB IEC61966-2.1) /CalCMYKProfile (U.S. Web Coated \050SWOP\051 v2) /sRGBProfile (sRGB IEC61966-2.1) /CannotEmbedFontPolicy /Error /CompatibilityLevel 1.4 /CompressObjects /Off /CompressPages true /ConvertImagesToIndexed true /PassThroughJPEGImages true /CreateJDFFile false /CreateJobTicket false /DefaultRenderingIntent /Default /DetectBlends false /DetectCurves 0.0000 /ColorConversionStrategy /LeaveColorUnchanged /DoThumbnails false /EmbedAllFonts true /EmbedOpenType false /ParseICCProfilesInComments true /EmbedJobOptions true /DSCReportingLevel 0 /EmitDSCWarnings false /EndPage -1 /ImageMemory 1048576 /LockDistillerParams false /MaxSubsetPct 100 /Optimize false /OPM 1 /ParseDSCComments true /ParseDSCCommentsForDocInfo true /PreserveCopyPage true /PreserveDICMYKValues true /PreserveEPSInfo true /PreserveFlatness true /PreserveHalftoneInfo false /PreserveOPIComments false /PreserveOverprintSettings true /StartPage 1 /SubsetFonts false /TransferFunctionInfo /Apply /UCRandBGInfo /Preserve /UsePrologue false /ColorSettingsFile (None) /AlwaysEmbed [ true ] /NeverEmbed [ true ] /AntiAliasColorImages false /CropColorImages true /ColorImageMinResolution 300 /ColorImageMinResolutionPolicy /OK /DownsampleColorImages false /ColorImageDownsampleType /Average /ColorImageResolution 1200 /ColorImageDepth -1 /ColorImageMinDownsampleDepth 1 /ColorImageDownsampleThreshold 1.50000 /EncodeColorImages false /ColorImageFilter /DCTEncode /AutoFilterColorImages true /ColorImageAutoFilterStrategy /JPEG /ColorACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /ColorImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000ColorACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000ColorImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 300 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages false /GrayImageDownsampleType /Average /GrayImageResolution 1200 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages false /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /GrayImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000GrayACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000GrayImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 1200 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages false /MonoImageDownsampleType /Average /MonoImageResolution 1200 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages false /MonoImageFilter /None /MonoImageDict << /K -1 >> /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (None) /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False /Description << /CHS <FEFF4f7f75288fd94e9b8bbe5b9a521b5efa7684002000410064006f006200650020005000440046002065876863900275284e8e9ad88d2891cf76845370524d53705237300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c676562535f00521b5efa768400200050004400460020658768633002> /CHT 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<FEFF9ad854c18cea306a30d730ea30d730ec30b951fa529b7528002000410064006f0062006500200050004400460020658766f8306e4f5c6210306b4f7f75283057307e305930023053306e8a2d5b9a30674f5c62103055308c305f0020005000440046002030d530a130a430eb306f3001004100630072006f0062006100740020304a30883073002000410064006f00620065002000520065006100640065007200200035002e003000204ee5964d3067958b304f30533068304c3067304d307e305930023053306e8a2d5b9a306b306f30d530a930f330c8306e57cb30818fbc307f304c5fc59808306730593002> /KOR <FEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020ace0d488c9c80020c2dcd5d80020c778c1c4c5d00020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002e> /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR <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> /PTB <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> /SUO <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> /SVE <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> /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.) >> /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ << /AsReaderSpreads false /CropImagesToFrames true /ErrorControl /WarnAndContinue /FlattenerIgnoreSpreadOverrides false /IncludeGuidesGrids false /IncludeNonPrinting false /IncludeSlug false /Namespace [ (Adobe) (InDesign) (4.0) ] /OmitPlacedBitmaps false /OmitPlacedEPS false /OmitPlacedPDF false /SimulateOverprint /Legacy >> << /AddBleedMarks false /AddColorBars false /AddCropMarks false /AddPageInfo false /AddRegMarks false /ConvertColors /ConvertToCMYK /DestinationProfileName () /DestinationProfileSelector /DocumentCMYK /Downsample16BitImages true /FlattenerPreset << /PresetSelector /MediumResolution >> /FormElements false /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles false /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /DocumentCMYK /PreserveEditing true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling /UseDocumentProfile /UseDocumentBleed false >> ] >> setdistillerparams << /HWResolution [2400 2400] /PageSize [612.000 792.000] >> setpagedevice
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<FEFF9ad854c18cea306a30d730ea30d730ec30b951fa529b7528002000410064006f0062006500200050004400460020658766f8306e4f5c6210306b4f7f75283057307e305930023053306e8a2d5b9a30674f5c62103055308c305f0020005000440046002030d530a130a430eb306f3001004100630072006f0062006100740020304a30883073002000410064006f00620065002000520065006100640065007200200035002e003000204ee5964d3067958b304f30533068304c3067304d307e305930023053306e8a2d5b9a306b306f30d530a930f330c8306e57cb30818fbc307f304c5fc59808306730593002> /KOR <FEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020ace0d488c9c80020c2dcd5d80020c778c1c4c5d00020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002e> /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR <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> /PTB <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> /SUO <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> /SVE <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> /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.) >> /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ << /AsReaderSpreads false /CropImagesToFrames true /ErrorControl /WarnAndContinue /FlattenerIgnoreSpreadOverrides false /IncludeGuidesGrids false /IncludeNonPrinting false /IncludeSlug false /Namespace [ (Adobe) (InDesign) (4.0) ] /OmitPlacedBitmaps false /OmitPlacedEPS false /OmitPlacedPDF false /SimulateOverprint /Legacy >> << /AddBleedMarks false /AddColorBars false /AddCropMarks false /AddPageInfo false /AddRegMarks false /ConvertColors /ConvertToCMYK /DestinationProfileName () /DestinationProfileSelector /DocumentCMYK /Downsample16BitImages true /FlattenerPreset << /PresetSelector /MediumResolution >> /FormElements false /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles false /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /DocumentCMYK /PreserveEditing true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling /UseDocumentProfile /UseDocumentBleed false >> ] >> setdistillerparams << /HWResolution [2400 2400] /PageSize [612.000 792.000] >> setpagedevice
<< /ASCII85EncodePages false /AllowTransparency false /AutoPositionEPSFiles true /AutoRotatePages /None /Binding /Left /CalGrayProfile (Dot Gain 20%) /CalRGBProfile (sRGB IEC61966-2.1) /CalCMYKProfile (U.S. Web Coated \050SWOP\051 v2) /sRGBProfile (sRGB IEC61966-2.1) /CannotEmbedFontPolicy /Error /CompatibilityLevel 1.4 /CompressObjects /Off /CompressPages true /ConvertImagesToIndexed true /PassThroughJPEGImages true /CreateJDFFile false /CreateJobTicket false /DefaultRenderingIntent /Default /DetectBlends false /DetectCurves 0.0000 /ColorConversionStrategy /LeaveColorUnchanged /DoThumbnails false /EmbedAllFonts true /EmbedOpenType false /ParseICCProfilesInComments true /EmbedJobOptions true /DSCReportingLevel 0 /EmitDSCWarnings false /EndPage -1 /ImageMemory 1048576 /LockDistillerParams false /MaxSubsetPct 100 /Optimize false /OPM 1 /ParseDSCComments true /ParseDSCCommentsForDocInfo true /PreserveCopyPage true /PreserveDICMYKValues true /PreserveEPSInfo true /PreserveFlatness true /PreserveHalftoneInfo false /PreserveOPIComments false /PreserveOverprintSettings true /StartPage 1 /SubsetFonts false /TransferFunctionInfo /Apply /UCRandBGInfo /Preserve /UsePrologue false /ColorSettingsFile (None) /AlwaysEmbed [ true ] /NeverEmbed [ true ] /AntiAliasColorImages false /CropColorImages true /ColorImageMinResolution 300 /ColorImageMinResolutionPolicy /OK /DownsampleColorImages false /ColorImageDownsampleType /Average /ColorImageResolution 1200 /ColorImageDepth -1 /ColorImageMinDownsampleDepth 1 /ColorImageDownsampleThreshold 1.50000 /EncodeColorImages false /ColorImageFilter /DCTEncode /AutoFilterColorImages true /ColorImageAutoFilterStrategy /JPEG /ColorACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /ColorImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000ColorACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000ColorImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 300 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages false /GrayImageDownsampleType /Average /GrayImageResolution 1200 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages false /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /GrayImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000GrayACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000GrayImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 1200 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages false /MonoImageDownsampleType /Average /MonoImageResolution 1200 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages false /MonoImageFilter /None /MonoImageDict << /K -1 >> /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (None) /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False /Description << /CHS <FEFF4f7f75288fd94e9b8bbe5b9a521b5efa7684002000410064006f006200650020005000440046002065876863900275284e8e9ad88d2891cf76845370524d53705237300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c676562535f00521b5efa768400200050004400460020658768633002> /CHT 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<FEFF9ad854c18cea306a30d730ea30d730ec30b951fa529b7528002000410064006f0062006500200050004400460020658766f8306e4f5c6210306b4f7f75283057307e305930023053306e8a2d5b9a30674f5c62103055308c305f0020005000440046002030d530a130a430eb306f3001004100630072006f0062006100740020304a30883073002000410064006f00620065002000520065006100640065007200200035002e003000204ee5964d3067958b304f30533068304c3067304d307e305930023053306e8a2d5b9a306b306f30d530a930f330c8306e57cb30818fbc307f304c5fc59808306730593002> /KOR <FEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020ace0d488c9c80020c2dcd5d80020c778c1c4c5d00020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002e> /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR <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> /PTB <FEFF005500740069006c0069007a006500200065007300730061007300200063006f006e00660069006700750072006100e700f50065007300200064006500200066006f0072006d00610020006100200063007200690061007200200064006f00630075006d0065006e0074006f0073002000410064006f0062006500200050004400460020006d00610069007300200061006400650071007500610064006f00730020007000610072006100200070007200e9002d0069006d0070007200650073007300f50065007300200064006500200061006c007400610020007100750061006c00690064006100640065002e0020004f007300200064006f00630075006d0065006e0074006f00730020005000440046002000630072006900610064006f007300200070006f00640065006d0020007300650072002000610062006500720074006f007300200063006f006d0020006f0020004100630072006f006200610074002000650020006f002000410064006f00620065002000520065006100640065007200200035002e0030002000650020007600650072007300f50065007300200070006f00730074006500720069006f007200650073002e> /SUO <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> /SVE <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> /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.) >> /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ << /AsReaderSpreads false /CropImagesToFrames true /ErrorControl /WarnAndContinue /FlattenerIgnoreSpreadOverrides false /IncludeGuidesGrids false /IncludeNonPrinting false /IncludeSlug false /Namespace [ (Adobe) (InDesign) (4.0) ] /OmitPlacedBitmaps false /OmitPlacedEPS false /OmitPlacedPDF false /SimulateOverprint /Legacy >> << /AddBleedMarks false /AddColorBars false /AddCropMarks false /AddPageInfo false /AddRegMarks false /ConvertColors /ConvertToCMYK /DestinationProfileName () /DestinationProfileSelector /DocumentCMYK /Downsample16BitImages true /FlattenerPreset << /PresetSelector /MediumResolution >> /FormElements false /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles false /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /DocumentCMYK /PreserveEditing true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling /UseDocumentProfile /UseDocumentBleed false >> ] >> setdistillerparams << /HWResolution [2400 2400] /PageSize [612.000 792.000] >> setpagedevice
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geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR <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> /PTB <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> /SUO <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> /SVE <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> /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing. 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false /CropGrayImages true /GrayImageMinResolution 300 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages false /GrayImageDownsampleType /Average /GrayImageResolution 1200 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages false /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /GrayImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000GrayACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000GrayImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 1200 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages false /MonoImageDownsampleType /Average /MonoImageResolution 1200 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages false /MonoImageFilter /None /MonoImageDict << 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