Research Paper INNOVATION IN INFORMATION AND KNOWLEDGE MANAGEMENT

Contents ix

Contents

Preface v

Part I Introduction: Exploring the Role of Technology in Knowledge Management

Chapter 1 The Rise of Knowledge Management: In Pursuit of Excellence 3 1.1 Introduction 4 1.2 Drivers of KM 5 1.3 Outcomes of KM 11 1.4 KM Frameworks 13 1.5 Conclusions 17

References 17

Chapter 2 Managing Knowledge with Technology: Mission Possible 21 2.1 Introduction 22 2.2 Technology and Integrated KM 23 2.3 Categorisation of Technology Roles 25 2.4 Issues and Challenges for Practice and Research 32 2.5 Conclusions 35

References 36

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C o p y r i g h t 2 0 0 4 . W o r l d S c i e n t i f i c .

A l l r i g h t s r e s e r v e d . M a y n o t b e r e p r o d u c e d i n a n y f o r m w i t h o u t p e r m i s s i o n f r o m t h e p u b l i s h e r , e x c e p t f a i r u s e s p e r m i t t e d u n d e r U . S . o r a p p l i c a b l e c o p y r i g h t l a w .

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x KM: Through the Technology Glass

Part II Codification Technologies: Supporting Knowledge Storage and Finding

Chapter 3 Web-based Knowledge Records: Empowering Societies 41 3.1 Introduction 42 3.2 The World Wide Web 43 3.3 The Concept of Knowledge Record 44 3.4 Case study: Exploring Australian Web Sites 45 3.5 Issues and Challenges 50 3.6 Australian Standard Guidelines 52 3.7 Conclusions 53

References 54

Chapter 4 Structured Knowledge Repositories: Building Corporate Memories 57 4.1 Introduction 58 4.2 Concept of Knowledge Repository 59 4.3 Facilitating Knowledge Extraction from

Repositories 60 4.4 Empirical Study 63 4.5 Lessons Learned 67 4.6 Conclusions 70

References 70

Chapter 5 Knowledge Maps: Locating and Acquiring Expert Advice 73 5.1 Introduction 74 5.2 Understanding Knowledge Maps 75 5.3 Competency Map Description 78 5.4 Empirical Test 79 5.5 Conclusions 85

References 86

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Contents xi

Chapter 6 Knowledge Discovery Tools: Application in Associations Analysis 89 6.1 Introduction 90 6.2 Knowledge Discovery from Data 91 6.3 Empirical Study 93 6.4 Lessons Learned 98 6.5 Conclusions 101

References 102

Part III Personalisation Technologies: Supporting Knowledge Creation and Sharing

Chapter 7 Interactive Idea Generator: Stimulating Creative Thinking 109 7.1 Introduction 110 7.2 Creativity in Decision-Making 111 7.3 Tool Description 115 7.4 Tool Evaluation 118 7.5 Conclusions 122

References 123

Chapter 8 Electronic Mentor: Fostering Knowledge Development 125 8.1 Introduction 126 8.2 Learning from Feedback and Guidance 127 8.3 Empirical Study 130 8.4 Conclusions 135

References 136

Chapter 9 Knowledge Sharing Technology: To E-talk Or Not to E-talk? 141 9.1 Introduction 142 9.2 Technologies for Knowledge Sharing 143 9.3 Empirical Study 145 9.4 Conclusions 150

References 151

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xii KM: Through the Technology Glass

Chapter 10 Virtual Reality Model: Visualising Social Networks 153 10.1 Introduction 154 10.2 Visualisation 154 10.3 Virtual Reality Model Description 156 10.4 Tool Application 161 10.5 Conclusions 166

References 167

Part IV Complete KM Solutions: Integrated Systems and Technologies

Chapter 11 Web Course Technology: Creating Virtual Knowledge Spaces 171 11.1 Introduction 172 11.2 A Conceptual Model of Virtual k-Space 173 11.3 k-Space Design Features 175 11.4 Empirical Evaluation 180 11.5 Conclusions 186

References 187

Chapter 12 Simulation Game: Adventures in Knowledgeland 189 12.1 Introduction 190 12.2 Contingency Factors 190 12.3 Game Description 198 12.4 Lessons Learned 201 12.5 Conclusions 203

References 204

Chapter 13 Internet Portals: Supporting Online Communities of Practice 207 13.1 Introduction 208 13.2 Portal Technology 209 13.3 Case Study: ActKM Community of Practice 210 13.4 Conclusions 218

References 218

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Contents xiii

Chapter 14 Total Knowledge Management System: Combining Technological and Social Aspects 221 14.1 Introduction 222 14.2 Social Aspects of KMS 223 14.3 Empirical Study 228 14.4 Conclusions 232

References 233

Part V Issues and Challenges: Present and Future

Chapter 15 Towards Knowledge Management Practice: How to Get There? 239 15.1 Introduction 240 15.2 Review of Leading KM Frameworks 240 15.3 Guidelines for Conducting KM in Organisations 244 15.4 Illustrative Cases 246 15.5 Conclusions 250

References 251

Chapter 16 The Future of Knowledge Management: What Is on the Horizon? 255 16.1 Introduction 256 16.2 Automation: Intelligent Systems That Apply

Knowledge 257 16.3 Integration: Merging of E-commerce and

Knowledge Management 261 16.4 The Final Word 265

References 266

Index 269

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PART I

Introduction: Exploring the Role of

Technology in Knowledge Management

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The Rise of Knowledge Management 3

CHAPTER 1

The Rise of Knowledge Management:

In Pursuit of Excellence

Necessity is the mother of all inventions — Proverb

This chapter provides an overarching introduction to the field of knowledge management (KM). It examines the emerging context and rationale for KM, the implications and benefits of KM for organisations, and the current understanding of the KM concept itself. The aim is to provide a broad theoretical basis for exploring the role of technology in KM and to set the scene for the remaining chapters of the book.

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4 KM: Through the Technology Glass

1.1 Introduction

The growing interest in knowledge management has been fuelled by a number of development trends: globalisation with the increasing intensity of competition; virtualisation or digitalisation enabled by advances in information and communication technology; and the transformation to knowledge based economy together with changing organisational structures, new worker profiles, preferences and predispositions (Raich 2000; Hall, 2003). This new emerging world is variously referred to as third wave, information age, knowledge-based or knowledge economy or society. Regardless of the terminology, these names, and others, refer to the transition that is taking place in the business environment.

As organisations move towards becoming more knowledge-based, their business success will increasingly depend on how successful knowledge workers are at developing and applying knowledge productively and efficiently. The ability to identify and leverage key knowledge plays a critical role in organisational survival and advancement. Consequently, the companies are facing the need to improve the management of their knowledge.

The knowledge economy demands that organisations integrate their activities, processes and systems in order to exploit their resources more efficiently and subsequently gain economies of scope and access to and from new markets (Burnes, 2000). Those organisations that are unable to change or choose not to adapt in a timely manner are likely to become vulnerable and unable to compete in the future.

The basic assumption of KM is that organisations that manage organisational and individual knowledge better will deal more successfully with the challenges of the new business environment. More specifically, knowledge management is considered to be central to achieving process and product improvement, executive decision making and organisational adaptation and renewal (Earl, 2001). The central task of those concerned with knowledge management is to determine ways to better cultivate, nurture and exploit knowledge at different levels and in different contexts.

However, while there is a widespread agreement of the importance of knowledge with respect to the struggle for economic success, there are

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The Rise of Knowledge Management 5

differences among researchers and practitioners alike in what constitutes useful knowledge and the ways in which it should be managed (Holsapple and Joshi, 1999). There are major disagreements as to whether it should be considered a technical issue, a human resources issue, a procedural issue or a part of strategic management (Handzic and Hasan, 2003). The variation between different schools of thoughts on knowledge management are an indication of the many problems faced. To gain a greater understanding of the KM phenomena, the following sections will examine major drivers, outcomes and models of KM, and introduce an integrated KM framework as a basis for understanding the role of technology in KM.

1.2 Drivers of KM

We are currently experiencing a period of major change in the world economy. This is characterised by increased complexity, uncertainty and surprises. Some analysts think of it as a period of living in the centre of the “Bermuda Triangle” (Raich, 2000) where individuals and organisations have to deal with the increasing turbulence and speed of change. This change is brought about by the mega-trends of globalisation, digitalisation (or virtualisation) and transformation to a knowledge-based economy.

Knowledge Economy

The transformation from the old economy to a new, knowledge-based economy, is driven largely by the recognition that knowledge rather than financial capital, land or labour is the major source of continued economic growth, value and improved standards of living. Figure 1.1 shows that while every economy relies on knowledge to some extent as its base, in the “knowledge economy” knowledge itself is for sale and ideas are the main output or product of the economic institutions.

Tiwana (2001) identifies major characteristics of the new economy in terms of knowledge centricity, increasing returns, network effects,

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6 KM: Through the Technology Glass

accelerated clockspeed, transparency, customer loyalty, innovation, ad-hoc alliances, and products as experiences. He notes that knowledge centricity is typically demonstrated in the increasing dependence of services and non-physical as well as physical goods on knowledge for their production and distribution.

Tiwana further notes that knowledge-based offerings have increasing returns. Once the first unit is produced at a significant cost, additional units can be produced at a near-zero incremental cost (e.g., piece of software). Network effects are evidenced in the positive correlation between the market size and the value of the knowledge offering. Thus, companies trying to capture as much market as possible do so even at an initial loss (e.g., offering free software). Rapid and unpredictable changes dominate. To cope, business must have adequate organisational and technological mechanisms to support speedy adaptation and knowledge application. As businesses become increasingly networked with others (e.g., customers, partners) their knowledge becomes more transparent and potent. Firms are differentiated by differences in levels of assimilation and mobilisation of their knowledge.

Success in the new economy requires intimate knowledge of the company customer base. Such knowledge can help businesses provide

Capital

Industrial

Labor

Land

Intellect

Figure 1.1 Shifting economy

Knowledge

Agricultural

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The Rise of Knowledge Management 7

tailored products and services and thus attract and retain their customer’s loyalty. Success often requires inventing new business processes, new industries and new customers rather than re-arranging old ones. With the rapidity of knowledge obsolesce, new knowledge must be integrated fast. This can be done through the formation of temporary collaborations between partners and members on an as-needed basis. Finally, in the knowledge economy, products and services are increasingly perceived as experiences. Accordingly, organisations act as knowledge integrators, finding out and offering customers individualised experiences they want and need.

Knowledge Organisation

Economic progress throughout history has been driven by commerce and business organisations. These organisations have internal structures that mediate roles and relationships among people working towards some identifiable goal. Their existence is the result of a successful balance between the forces in their environment and their own creativity and adaptivity (Bennet and Bennet, 2003). Currently, at the forefront of organisational performance are the organisations which recognised that information, knowledge and their intelligent application are the essential factors of success in the new economy, and take advantage of information technology to achieve high level of efficiency and effectiveness. Various metaphors used to describe a knowledge organisation include: agile production system, living organism, complex adaptive system, self- organising system and virtual organisation.

The knowledge organisation can be best viewed as an intelligent complex adaptive system. It is complex because the system is composed of a large number of individual specialists called intelligent agents, who have multiple and complex relationships with the system and environment. It is adaptive because these intelligent agents direct and discipline their own performance through organised feedback from colleagues, customers and headquarters. It has been suggested in the literature (Bennet and Bennet, 2003) that a successful knowledge organisation exhibits the following characteristics: high performance, customer-driven, improvement-driven, high flexibility and adaptiveness,

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8 KM: Through the Technology Glass

high levels of expertise and knowledge, high rates of learning and innovation, innovative IT-enabled, self-directed and managed, proactive and futurist, valuing expertise and sharing knowledge.

One of the ways to achieve effective knowledge creation, transfer and utilisation within an organisation is through communities of practice (Wenger, 1998). This approach to organisational structuring advocates the formation of centres of expertise for each knowledge domain, discipline or subject matter speciality. The alternative approach suggests organising around projects and related activities. Information and communication technology can be the catalyst to form and sustain heterogeneous communities. With the support of the intranet or internet, these communities can include diverse people from different space and time zones of the globe (Hasan and Crawford, 2003).

While information technology is not necessary to create a knowledge organisation, the use of advanced technologies can transform the way the whole business works. The concept of “cybercorp” has been heralded as the new business revolution (Martin, 1996). It is envisaged as a totally virtual organisation based on the capabilities of the modern communication, i.e., the internet and the mobile phone. Typically, a virtual organisation consists of three fundamental parts: knowledge professionals and workers who possess core competencies; relationships and networks of people including partners, suppliers and customers grouped around a common brand; and a culture based on co-operation and collaboration and sitting in the centre of global networks linked electronically (Raich, 2000).

A knowledge organisation must of necessity become a learning organisation, so that the entire firm will learn while it works and be able to adapt quickly to market changes and other environmental perturbations. It has been suggested that the building blocks of a learning organisation are systematic problem solving, experimentation, learning from past experience, learning from others and transferring knowledge (Garvin, 1998). The way to build it is to first foster the environment that is conducive to learning, then open up boundaries to stimulate the informal exchange of ideas and finally create formal learning forums and programmes with explicit learning goals tailored to business needs.

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The Rise of Knowledge Management 9

Learning organisations have also been described as places “where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning how to learn together” (Senge, 1990). To achieve these ends these organisations use systems thinking, personal mastery, mental models, shared vision and team learning. Furthermore, knowledge creating companies are characterised as places where “inventing new knowledge is not a specialised activity, but the way of behaving, indeed a way of being, in which everyone is a knowledge worker”. The way to achieve this is to use metaphors, encourage dialogue, and make tacit ideas explicit.

Knowledge Work and Workers

In knowledge based organisations, the largest part of their workforce is engaged in knowledge work (Schultze, 2003). The following paragraphs summarises different perspectives on this new phenomenon. The economic perspective emphasises how knowledge work differs from other types of work in the nature of knowledge possessed and produced by workers. Knowledge work assumes the possession of mostly abstract, theoretical and esoteric knowledge gained through formal education. It also suggests that workers have to produce new knowledge rather than just manipulate existing knowledge. The labour process perspective concerns itself with the formation and composition of a new class of white-collar workers between the proletariat and bourgeoisie who perform managerial, professional and clerical tasks. Their work is characterised by scientific base, formal education, autonomy, ethical rules, culture, client orientation, social sanction and authorisation.

The work practice perspective focuses on the work that workers do and classifies it into knowledge production and knowledge reproduction. Re-production includes transfer and application. Specific processes and practices that form part of knowledge work include generating new knowledge, interpreting and representing it, as well as expressing, monitoring, translating and networking. For example, software engineering qualifies as knowledge work based on the presence of

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10 KM: Through the Technology Glass

creativity and problem-solving aspects of the work; co-location which allows work remote from the employing firm; and “gold collar” conditions of employment, including exceptional remuneration and benefits packages (Edwards, 2003).

Given that all work requires the application of knowledge, the emergence of knowledge work as a separate category of work has been criticised. However, it does offer a possibility of categorising groups of workers by highlighting their similarities and differences. Generally speaking, a knowledge worker is any worker who performs knowledge work (as described in the previous section) in every element of the economy. It covers various managerial, professional and clerical occupations. Examples of occupations that qualify as knowledge workers include executives, legislators, engineers, scientists, administrators and counselors. According to Australian statistics (ABS, 2003) there has been a significant increase in the percentage of knowledge workers in the country’s labour force over the last couple of years, this being consistent with the country’s transition to a knowledge economy.

Of particular interest for KM is a special category of knowledge workers, KM professionals, who make knowledge management in an organisation work. KM professionals are a new phenomenon and there is still no clear picture about what roles they should play in an organisation and what competencies and skills they need to have to play these roles. Currently there is a wide range of KM related job titles and roles found in organisations. Examples include titles such as Chief Knowledge Officer (CKO), Knowledge Asset Manager, Knowledge Officer, Web Master, etc.

From this variety three distinct categories can be recognised: knowledge manager, knowledge engineer and knowledge scientist (Weidner, 2003). The knowledge manager is expected to be primarily concerned with the knowledge needs of the enterprise. The knowledge engineers, with various specialisations, are perceived as advisors on what can be done given the current “state of the art”. Knowledge scientists are seen as showing them what is possible if they were willing to try. A snapshot of actual CKOs portrays them as highly educated and experienced individuals, generally satisfied with their position, freedom and latitude it affords (McKeen

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The Rise of Knowledge Management 11

and Staples, 2003). At present, their primary goal is to raise awareness of KM, and they have little direct authority and effect changes through persuasion, negotiation and communication.

1.3 Outcomes of KM

The importance of KM for organisational performance has been widely recognised and acknowledged in the management literature. In general, KM is assumed to create value for organisations from applying their accumulated knowledge to their products and services outputs. These ensure organisational survival or advancement. KM can impact organisational performance in a number of different ways, these can be grouped into three broad categories: risk minimisation, efficiency improvement and innovation (Von Krogh et al., 2000).

Risk Minimisation

Risk minimisation is closely linked to identifying and holding onto the core competencies that the company has. In most organisations, people have been recognised as key holders of valuable knowledge. KM can minimise the risk of losing valuable knowledge by identifying, locating and capturing what is known by individuals and groups of organisational employees that is of critical importance for organisational survival. Frank (2002) offers five tips to reduce knowledge loss: do not let people leave, mentor and coach, share best practices, share lessons learned and document. Indeed, documented project management knowledge, expertise and skills accumulated in the construction industry were found to benefit both employees and the public at large (Land et al., 2002). At another level, society’s knowledge records are preserving the cultural capital of nations (Handzic, 2003).

KM can also impact people’s learning, adaptability and job satisfaction (Becerra-Fernandez et al., 2004). For example, KM can facilitate employees’ creativity and group effectiveness through informal and formal socialisation (Handzic and Chaimungkalanont, 2003). Socialisation forms

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12 KM: Through the Technology Glass

a vital component of Nonaka’s (1998) knowledge creation model. It enables tacit knowledge to be transferred between individuals through shared experience, space and time. Examples include spending time, working together or informal social meetings. More importantly, socialisation drives the creation and growth of personal tacit knowledge bases. By seeing other people’s perspective and ideas, a new interpretation of what one knows is created.

Efficiency and Effectiveness Improvement

In today’s complex economy, businesses are constantly confronted with the need to operate more efficiently in order to stay competitive and satisfy increasing market demands. Organisations are under increasing pressure from customers to deliver solutions and services faster and cheaper. KM can improve organisational efficiency by transferring experiences and best practices throughout the organisation in order to avoid unnecessary duplication and to reduce cost. Technology is often an important part of achieving efficiency improvements. For example, a best practice replication program at Ford (Rollo and Clarke, 2001) achieved process improvements in plants around the globe, and nine- figure cost savings from a simple intranet-based KM system for knowledge sharing.

KM can also help organisations become more effective by helping them select and perform the most appropriate processes and make the best possible decisions. KM can help organisations to avoid repeating past mistakes, foresee potential problems and reduce the need to modify plans (Becerra-Fernandez, 2004). For example, The Australian Government responded to increasing community expectations of better social services and access to empowering information sources by integrating historically separate health, housing and community services via a virtual corporate environment (Rollo and Clarke, 2001). The outcome is that various community and service providers have been given equitable and wide-spread access to expert knowledge and can directly contact the right people for service delivery.

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The Rise of Knowledge Management 13

Process and Product Innovation

There is a growing belief that knowledge can do more than improve efficiency and effectiveness. KM can impact process innovations, value- added products and knowledge-based products (Becerra-Fernandez, 2004). Innovation of products, processes and structures have been assessed as a critical component in the success of new-age firms. The new products and services resulting from the interaction of knowledge and technology bring profound changes in the way businesses operate and compete in the new economy.

Typically, innovative organisations focus on both new knowledge and on knowledge processes. They constantly engage and motivate people, creating the overall enabling context for knowledge creation. These organisations take a strategic view of knowledge, formulate knowledge visions, tear down knowledge barriers, develop new corporate values and trust, catalyse and coordinate knowledge creation, manage various contexts involved, develop conversational culture and globalise local knowledge (Nanaka and Nishiguchi, 2001).

The unifying thread among various theoretical views is the perception that innovation is the key driver of an organisation’s long-term economic success. According to Von Krogh (2000) the greatest challenge for organisations is to move in knowledge -enabling direction by consciously and deliberately addressing knowledge management. Pfizer is one good example of such an organisation (Rollo and Clarke, 2001). This company uses KM primarily to beat the industry average. Its main approach to management of the research process involves the “mining” of scientific publications to make its researchers aware of the progress and projects of others. This approach has resulted in the discovery of the well-known Viagra drug.

1.4 KM Frameworks

There have been a number of recent efforts at developing KM frameworks to better understand KM phenomena. In order to make sense of the

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14 KM: Through the Technology Glass

variety of existing KM frameworks, some form of categorisation or grouping is needed. One way to group them is into partial and integrated models or ontologies.

Partial KM Frameworks

Partial KM frameworks encompass a broad range of issues, methods and theories that differ in scope and focus. Some are knowledge oriented, like the intellectual capital models of McAdam and McCreedy (1999) and the economic school in Earl’s (2001) taxonomy. The list of types and perspectives elaborated in Alavi and Leidner’s (2001) review also fits this category. Knowledge-orientated models are well known in the business environment. The HR literature relies heavily on this grouping of KM models and frameworks, as does the Accounting discipline’s work on intangible assets. From this perspective, KM focuses on hiring, retaining, training of personnel, i.e., “intellectual assets”, and organisational knowledge is often defined as the sum of the knowledge of its personnel. However, in the broader view of KM this is just one aspect that would be included in an integrated approach.

Other models, like Nonaka’s (1998) knowledge spiral and Earl’s (2001) behavioural school are process orientated. Process orientated frameworks are perhaps the most frequently quoted and used category in the knowledge management literature. The knowledge creation spiral of Nonaka views organisational knowledge creation as a process involving a continual interplay between explicit and tacit dimensions of knowledge. Four levels of carriers of knowledge in the organisation area are assumed, namely individual, group, organisational and inter-organisational. The spiral model describes a dynamic process in which explicit and tacit knowledge are exchanged and transformed through four modes: socialisation, combination, externalisation and internalisation.

Several frameworks emphasise the dependence of knowledge on socio- technological influences. Nonaka and Konno’s (1998) model of ‘ba’ suggests four types of ba (originating, interacting, cyber and exercising) that act as promoters of knowledge processes (socialising, externalising, combining, and internalising) respectively. Earl’s (2001) technocratic

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The Rise of Knowledge Management 15

school that supports and structures IS work in KM also belongs to this category. Much KM work within the field of Information Systems, makes the distinction between knowledge as an object that can be stored in a computerised system, and knowledge embedded in people. This group of models or frameworks address issues of complexity and change in areas of organisational culture and learning, change and risk management and the support of communities of practice. Frameworks in this KM grouping also emphasise the dependence of knowledge on context.

Integrated KM Frameworks

There have been a number of attempts to bring together this diversity of partial approaches and propose more comprehensive and integrated frameworks in order to provide holistic views and common ground for KM research, and improved methods for KM practice (for review see Handzic and Hasan, 2003). Among some of the most recent developments is Handzic’s (2003) integrated KM framework. The KM framework presented in Figure 1.2 is an extended version of the original model. It illustrates various components involved in the conduct of knowledge management and their relationships. This framework is used as a basis

Figure 1.2 Extended framework of KM

External Environment

KM

Technological infrastructure

Organisational environment

Knowledge processes

Knowledge stock

Measurement

O U T C O M E S

D R I V E R S

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16 KM: Through the Technology Glass

for examining the role of technology in KM in the later chapters of this book.

The core KM model suggests two types of organisational factors: organisational environment (e.g., leadership, culture, structure, etc.) and technological infrastructure (e.g., information and telecommunication technologies) as major enablers that facilitate knowledge processes (e.g., creation, transfer, utilisation) and foster the development of knowledge stocks (e.g., explicit and tacit, know what and how). The model also suggests that organisational environment governs the choice and implementation of the technological infrastructure that supports knowledge processes. Finally, the core model incorporates a feedback loop to suggest the need for continuous knowledge measurement and potential adjustment of strategies over time. The extended KM model includes two additional components: KM drivers and outcomes. This model suggests that various KM drivers (e.g., changes in external environment) trigger KM initiatives (i.e., specific configurations of knowledge processes and enablers that act upon knowledge stocks) that, in turn, lead to various KM outcomes (e.g., improved performance, innovation).

In essence, the Handzic framework synthesises human and object perspectives of knowledge by adopting a two-dimensional model of organisational knowledge, with explicit and tacit know-what and know- how dimensions. This model adapts and extends the original work by Polanyi (1966) and Nonaka (1998). The integrated framework further considers knowledge management as a complex multidimensional concept that includes three essential and inter-related components: knowledge stocks, processes and enablers. In this way, it provides a missing link between different partial perspectives discussed earlier in the chapter. Furthermore, it recognises knowledge management as both a social and technological phenomenon, a view strongly emphasised in the opinions of KM academics and practitioners in a recent survey (Edwards et al., 2003). Finally, it suggests the evolutionary and context dependent nature of KM. This is consistent with the view that the main objective of KM is to help the organisation realise the best value from its knowledge assets (Bollinger and Smith, 2001).

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The Rise of Knowledge Management 17

1.5 Conclusions

This chapter examines the main drivers, outcomes and conceptualisations of KM in order to set the scene and provide theoretical foundation for exploring the role of technology in KM. The chapter recognises that KM is fuelled by the changing nature of the business environment. The emerging environment is identified as global, directly based on the production, distribution and use of knowledge in the development and distribution of products and services, and heavily reliant on information and communication technology.

This chapter also recognises that KM contributes to organisational performance in many ways. It impacts people, processes, products and structures in attempting to minimise risk, improve efficiency and effectiveness, and create innovative processes or products. In this way, KM provides sustainable competitive advantages that ensure the organisation’s survival or advancement.

Finally, this chapter promotes the view of KM as a dynamic phenomenon with an emphasis on knowledge processes in expanding cycles of knowledge growth. KM is considered as a socio-technical undertaking enabled and facilitated by a variety of social, organisational and technical factors. which must be considered in any KM initiative. Finally, KM is recognised as being severely dependent on context so that there is no ‘one size fits all’ solution.

References

ABS (2003), “Science and Technology Statistics Update”, Australian Bureau of Statistics Bulletin, No. 9, December.

Alavi, M. and Leidner, D.E. (2001), “Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues”, MIS Quarterly, 25(1), 107–136.

Becerra-Fernandez, I., Gonzales, A. and Sabherwal, R. (2004), Knowledge Management: Challenges, Solutions, and Technologies, Pearson Education, New Jersey.

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18 KM: Through the Technology Glass

Bennet, D. and Bennet, A. (2003), “The Rise of the Knowledge Organisation”, chapter 1 in Holsapple, C.W. (ed.), Handbook on Knowledge Management, Vol 1, Springer, Berlin, pp. 5– 20.

Burnes, B. (2000), Managing Change — A Strategic Approach to Organisational Dynamics, Pearson Education, Harlow.

Bollinger, A.S. and Smith, R.D. (2001), “Managing Organisational Knowledge as a Strategic Asset”. Journal of Knowledge Management 5(1), 8 – 18.

Earl, M. (2001), “Knowledge Management Strategies: Toward a Taxonomy”, Journal of Management Information Systems, 18(1), 215– 233.

Edwards, J., Handzic, M., Carlsson, S. and Nissen, M. (2003), “Knowledge Management Research and Practice: Visions and Directions”, Knowledge Management Research & Practice, 1(1), 49– 60.

Edwards, J.S. (2003), “Managing Software Engineers and Their Knowledge”, chapter 1 in Aurum et al. (eds.), Managing Software Engineering Knowledge, Springer, Berlin, pp. 5– 27.

Frank, B. (2002), “Five Tips to Reduce Knowledge Loss”, in Thought & Practice, The Journal of the KM Professional Society (KMPro), December, pp. 1– 3.

Garvin, D.A. (1998), “Building a Learning Organisation”, Harvard Business Review on Knowledge Management, HBS Press, Boston, pp. 47– 80.

Hall, R. (2003), Knowledge Management in the New Business Environment, Acirrt Report, University of Sydney.

Handzic, M. and Hasan, H. (2003), “The Search for an Integrated KM Framework”, chapter 1 in Hasan H. and Handzic M. (eds.), Australian Studies in Knowledge Management, UOW Press, Wollongong, pp. 3– 34.

Handzic, M. (2003), “An Integrated Framework of Knowledge Management”, Journal of Information and Knowledge Management, 2(3), September.

Handzic, M. and Chaimungkalanont, M. (2003), “The Impact of Socialisation on Organisational Creativity”, in Proceedings of the European Conference on Knowledge Management (ECKM 2003), Oxford, September 18 – 19.

Hasan, H. and Crawford, K. (2003), “Distributed Communities of Learning and Practice”, chapter 5 in Hasan H. and Handzic M. (eds.), Australian Studies in Knowledge Management, UOW Press, Wollongong.

Holsapple, C.W. and Joshi, K.D. (1999), “Description and Analysis of Existing Knowledge Management Frameworks”, in Proceedings of the 32nd Hawaii International Conference on System Sciences, p. 11.

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The Rise of Knowledge Management 19

Land, L.P.W., Land, M. and Handzic, M. (2002), “Retaining Organisational Knowledge: A Case Study of an Australian Construction Company”, Journal of Information & Knowledge Management, 1(2), 119 –129.

Martin, J. (1996), Cybercorp: The New Business Revolution, AMACOM, Washington.

McAdam, R. and McCreedy, S. (1999), “A Critical Review of Knowledge Management Models”, The Learning Organisation, 6(3), 91–100.

McKeen, J.D. and Staples, D.S. (2003), “Knowledge Managers: Who They Are and What They Do”, in Holsapple, C.W, (ed.), Handbook on Knowledge Management, Vol 1, Springer, Berlin, pp. 21– 41.

Nanaka, I. and Nishiguchi, T. (2001), Knowledge Emergence, Oxford University Press, New York.

Nonaka, I. (1998), “The Knowledge-Creating Company”, Harvard Business Review on Knowledge Management, HBS Press, Boston, pp. 21– 45.

Nonaka, I. and Konno, N. (1998), “The Concept of Ba: Building a Foundation for Knowledge Creation”. California Management Review, 40(3), 40– 54.

Polanyi, M. (1966), “The Logic of Tacit Inference”, Philosophy, 41(1), 1– 18.

Raich, M. (2000), Managing in the Knowledge Based Economy, Raich, Zurich.

Rollo, C. and Clarke, T. (2001), International Best Practice: Case Studies in Knowledge Management. Standards Australia International Limited.

Senge, P. (1990), The Fifth Discipline, the Art and Practice of the Learning Organisation, Century Business, London.

Shultze, U. (2003), “On Knowledge Work”, chapter 3 in Holsapple, C.W. (ed.), Handbook on Knowledge Management, Vol. 1, Springer, Berlin, pp. 43 – 58.

Tiwana, A. (2001), The Essential Guide to Knowledge Management, Prentice Hall, New Jersey.

Von Krogh, G., Ichijo, K. and Nonaka, I. (2000), Enabling Knowledge Creation, Oxford University Press, New York.

Weidner, D. (2003), “The Education of the Knowledge Professions — Meeting the Challenge”, in Thought & Practice, Vol. 1, December.

Wenger, E. (1998), Communities of Practice, Cambridge University Press, Cambridge.

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Managing Knowledge with Technology 21

CHAPTER 2

Managing Knowledge with Technology:

Mission Possible

It is not computers that make the difference, but what people do with them.

� Paul Strassmann

This chapter attempts to provide an understanding of the role of technology in KM. It identifies some promising ways in which technology can be employed in organisational knowledge management. In general, technology is perceived as a tool in enabling and facilitating processes of knowledge development, transfer and utilisation. The focus of the chapter is on different categories of technologies and their roles in facilitating knowledge sharing, representation and transformation, as well as improving people’s ability to acquire and create knowledge.

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22 KM: Through the Technology Glass

2.1 Introduction

The role of technology in KM is the source of major disagreement within the KM community (Holsapple, 2003; Edwards, 2004). At one extreme of the spectrum of views are those who consider that KM has nothing (or very little) to do with KM. The frequent quote heard in this camp is “a good KM solution is 10% technology and 90% culture” (Snowden, 2003). At the other extreme are those that see KM as being all (or mostly) about technology. The first view is largely driven by the interests of those wishing to privilege the role of people in organisations, the second by those wishing to sell KM tools and systems (Swan, 2003).

The proponents of the view that technology is incidental to KM argue that knowledge is a uniquely human concept. It exists only in the context of human interpretation and processing. What is represented and processed by computers is called data or information. From this perspective, computer-based technology has no key role in the KM field. Instead, it deals with data and information that become knowledge only upon human interpretation. The goal of KM is to create a connected environment for knowledge exchange. It allows knowledge seekers to identify and communicate with knowledge sources, i.e., experts (Handzic and Hasan, 2003).

In contrast, the proponents of the view that technology is a cornerstone of KM see knowledge as an object that can be separated from its source. It can be codified and then stored in a computer-based system to be made available on demand (Handzic and Hasan, 2003). From this perspective, computer-based technology serves as a means for representing and processing knowledge. The technology camp also predicts that breakthroughs in KM will be technological. These will continue to change the nature of knowledge creation, publication and sharing, and will have social and managerial implications (Holsapple, 2003). Thus, organisations that ignore or minimise technology in the conduct of KM may lose the chance of success.

More recently there have been attempts to bridge this artificial divide between technology and people orientated perspectives of KM. The argument is that human culture is at least in part formed by our capacity

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Managing Knowledge with Technology 23

to create and use tools. Therefore ignoring technology would be foolish, as would be denying human complexity (Snowden, 2003). One consequence of the on-going polemic is the creation of an integrated view that considers KM as a socio-technological phenomenon with both technology and people playing an important role. Another consequence is the generation of a contingent view of KM that links the relative emphasis on either people or technology to the nature of business context. Essentially, the contingency view suggests that no one approach is best under all circumstances (Handzic and Hasan, 2003).

Taking the view that technology does have a role to play in KM, the objective of this chapter is to explore major opportunities and circumstances in which various information and communication technologies may help to support the KM effort in organisations. The focus is on those technologies suggested by the literature as potentially valuable and convenient (Alavi and Leidner, 2001).

2.2 Technology and Integrated KM

This book promotes an integrated view of KM presented in Chapter 1 which synthesises human and object perspectives of knowledge; considers knowledge management as a complex concept that includes three essential and inter-related components: knowledge stocks, processes and enablers; considers KM as both social and technological phenomenon; suggests the dynamic evolutionary nature of KM; and finally recognises its context dependent drivers and outcomes.

Within this integrated framework, technology is clearly placed among major influencing factors on knowledge processes. More specifically, technology is perceived as a catalyst that enables and facilitates the development, transfer and application of knowledge, and thus contributes to organisational learning, improvement and innovation. The framework also recognises that social influences (e.g., organisational leadership and culture) impact the choice and implementation of KM technologies.

Various knowledge processes can be supported by technology. According to Becerra-Fernandez et al. (2004), technology can facilitate

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24 KM: Through the Technology Glass

sharing as well as growth of knowledge. For example, communication technology can allow the movement of information at a greater speed and efficiency than ever before. Furthermore, information technology can capture and quickly manipulate data from measurements of natural phenomena to improve our understanding of those phenomena. Thus, technology can increase the speed at which knowledge and ideas proliferate. It can also enable the measurement of increasingly complex processes at a lower cost.

A wide range of commercial software products and tools are available to address various aspects of knowledge management (Tsui, 2003). Such wide availability has been attributed to the dynamic progress in the field of ICT. A number of new technologies have been created, and some of the existing ones renamed as KM tools to support KM activities. It is argued that all types of information and communication technologies can be viewed to a greater or lesser extent as KM tools as they support processes through which knowledge is moved or modified.

According to Tsui (2003) the two most dominant approaches to deploying technology-based KM initiatives in organisations are codification and personalisation. The proponents of codification approach show a central preoccupation with explicit knowledge. They favour greater emphasis on the use of information technology, especially organisational databases, search engines and discovery tools. On the other hand, the proponents of personalisation seem to be more interested in tacit knowledge and sharing. They often focus more on people and cultural issues in the attempt to establish virtual groups or knowledge communities. Locating and connecting people of common interest is the prime goal here. Communication technology, such as e-mails and discussion forums are examples of technologies that can facilitate socialisation and knowledge exchange.

It is not unusual for organisations to adopt a combination of the two approaches in deploying KM initiatives. Some authors argue that such a holistic approach to KM is the only possible way to realise the full power of knowledge (Davenport and Prusak, 1998). Others, like Hansen et al. (1999) emphasise that trying to pursue the wrong approach or both at the same time can waste time and money and even undermine

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Managing Knowledge with Technology 25

business success. They propose that the codification approach is more suited for situations where work tasks are similar and existing knowledge assets can be reused. In contrast, they suggest that the personalisation approach is more suited for situations where the tasks are fairly unique and knowledge largely tacit.

In order to better understand and appreciate the potential roles of various technologies in supporting knowledge processes, the discussion below has been arranged in four thematic categories: (i) building knowledge repositories, (ii) promoting virtual socialisation and collaboration, (iii) facilitating knowledge search and discovery and (iv) stimulating creativity and complex problem solving. Categories 1 and 3 support “codification”, and categories 2 and 4 “personalisation” strategies.

2.3 Categorisation of Technology Roles

Building Knowledge Repositories

Knowledge has been widely recognised as a critical organisational resource for competitive advantage in the new economy. One of the important objectives of knowledge management is to capture, codify, organise and store relevant organisational knowledge for later use by organisational members (Hansen et al., 1999). A knowledge management framework (Hahn and Subramani, 2000) suggests that the availability of a KM system such as a codified knowledge repository should lead to increased organisational knowledge and result in improved performance. The Interim Australian KM standard (Standards Australia, 2003) proposes a number of technologies including databases, textbases, data warehouses and data marts as useful in building organisational knowledge repositories. It describes these technologies in the following terms:

Databases and Textbases. Electronic data generated by daily transactions are usually recorded in business documents and notes or in transaction records. These are typically stored in structured database systems and constitute a part of the

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26 KM: Through the Technology Glass

organisational memory. In addition to data and text, multimedia systems organise and make available to users their knowledge assets in a variety of other representational forms, including images, audio and video formats.

Data Warehouses and Data Marts. Unlike organisational databases that typically store current data related to specific business functions, a data warehouse stores data that retains historical and cross-functional perspectives. Data is extracted daily from the business transaction systems and from any other systems deemed relevant. Compared to data warehouses which combine databases across an entire enterprise, data marts are usually smaller and focus on a particular subject or department.

Currently, there is ample evidence to show that organisations do implement these technologies as part of their best KM practices (AA, 1998). However, there is little empirical evidence regarding the impact of these knowledge repositories on organisational performance (Alavi and Leidner, 2001). Some researchers point out that our ability to accumulate and store knowledge artefacts has by far surpassed our ability to process them, and warn of the danger that vast institutional memories may easily become tombs rather than wellsprings of knowledge (Handzic and Bewsell, 2003). Thus, it is argued here that one of the most challenging roles for technology with respect to building effective organisational knowledge repositories is to make stored knowledge more visible and accessible. The next section addresses this issue in more detail.

Facilitating Knowledge Search and Discovery

Many enterprise systems acquire large volumes of knowledge artefacts from multiple and often remote sources. The complexity of interrelated knowledge artefacts stored in these repositories make it often difficult for people to locate relevant artefacts or comprehend and interpret their meaning. Locating relevant knowledge in corporate memories is one of

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Managing Knowledge with Technology 27

the important objectives of developing and deploying knowledge management systems in organisations. Researchers in the areas of artificial intelligence and information retrieval have been particularly influential in directing the development and evolution of such systems. Search engines and intelligent agents are increasingly becoming evident in the market where they comprise a considerable proportion of the available commercial KM software (Tsui, 2003). The Interim KM Standard (Standards Australia, 2003) describes these technologies in the following way:

Search Engines. Commercial search engines provide a standard interface for text searching and enable access to the unstructured information on the internet. In this way they allow organisations to gather external third-party information. Search engine utilities also perform a similar function and provide access to large knowledge repositories on intranets and extranets.

Intelligent Agents. Intelligent agents are software programmes that act as personal or communication assistants to their users and carry out some sets of operations on their behalf with some degree of independence or autonomy. Intelligent agents that grew from expert systems and artificial intelligence research learn from data input during the course of their performance and modify their behaviour accordingly. Examples of the tasks that agents can do include: retrieving documents, conducting a user-initiated search activity, maintaining profile on behalf of their users, or learning and deducing from user specified profiles and assisting in the formalisation of a query or target search.

Although knowledge can be embedded in a computerised system using rules or heuristics, it is argued here that many knowledge-based systems, expert systems, case-based reasoning systems and software agents may not qualify as knowledge management systems. The position of this text is that knowledge management systems should not be viewed as

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28 KM: Through the Technology Glass

automating expert tasks, but rather informing about such tasks. As cognitive overload increasingly chokes the effective utilisation of codified knowledge in organisations, scholars are pointing to some promising alternative knowledge technologies. Knowledge maps or K-maps are seen as a particularly feasible method of coordinating, simplifying, highlighting and navigating through complex silos of knowledge artefacts (Wexler, 2001). Knowledge maps point to knowledge but they do not contain it. They are guides, not repositories. Typically, they point to people, documents and repositories. The main purpose of knowledge maps is to direct people where to go when they need certain expertise. In addition to the guiding function, knowledge maps may also identify strengths to exploit and knowledge gaps to fill.

Considering that knowledge workers often must rely on enterprise systems for their work activities, it is critical that they understand better the knowledge available in these systems. The proposition made here is that an appropriate knowledge discovery tool may help organisational members to improve their task performance by enhancing their understanding of the patterns hidden in the stored knowledge artefacts. By definition, knowledge discovery involves uncovering previously unknown valid and useful patterns in data for description and prediction purposes (Fayyad et al., 1996). The uncovered patterns in the form of relationships, categories, clusters or trends are described and presented in a mode understandable by humans to help them better predict future behaviour of interest. The Interim KM Standard (Standards Australia, 2003) describes the following technologies as useful in knowledge discovery and representation:

Data Mining and Visualisation Tools: Data mining and knowledge discovery are terms used to describe applications that look for hidden patterns in groups of data to discover previously unknown trends or relationships. These applications often use complex and sophisticated algorithms to discover knowledge. Visualisation tools are intended to assist people in analysing complex data sets by mapping physical properties to the data. Visualisation can map expertise, links between people across the organisation and

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Managing Knowledge with Technology 29

identify missing knowledge areas. Making organisational knowledge visible can support improvements and changes to the way knowledge is used, shared and transferred. Some of the characteristics often used to help visualise data include light effects, colour, direction and size of shadows, relative sizes and distances between objects, speed, curvature and transparency.

Promoting Virtual Socialisation and Collaboration

In the personalisation knowledge management approach knowledge is tied to the people who develop it and it is shared through person-to- person interaction (Hansen et al., 1999). The spiral model of knowledge creation (Nonaka and Takeuchi, 1995) recognises the crucial importance of socialisation in developing and transferring tacit knowledge in an organisation. The main aim of technology is seen in enabling and facilitating interaction among people for the purpose of knowledge sharing and collective learning (Handzic, 2001). The Interim Australian Standard (Standards Australia, 2003) recommends several types of technologies for consideration by organisations when developing KM solutions that support virtual socialisation. These are summarised as follows:

Communication and Collaboration Technologies: Various applications have been developed that use ICT networks to facilitate peer-to-peer communication and knowledge sharing. Examples include e-mail, bulletin boards, chat- rooms, whiteboards, audio and video conferencing. They also include various specialised groupware applications. The term refers to a particular type of application focused on collaborative processes among people. Communication and collaboration technologies may be classified by the features offered to support group activities from facilitating communication, through to process and task structuring, to regulating interaction. Alternative approaches involve classification by technology application context, or by time

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30 KM: Through the Technology Glass

and place factors. Examples include computer supported meetings (same place and time), video conferencing (different place, same time), mailboxes (same place, different time) or bulletin boards (different place and time).

Portals, Intranets and Extranets: A portal is an interface that provides a single point of access to multiple sources of knowledge. Corporate portals usually point to numerous sources of internal and external knowledge in structured and searchable directories and databases, collaborative groupware tools and utilities to manage e-mail, discussion group materials, reports, memos and meeting minutes. They also improve work efficiency by providing single gateway to personalised content. Intranets and extranets provide connectivity that can support knowledge sharing inside and outside the organisation. An intranet is an internal corporate network that allows authorised personnel electronic access to documents, forms and web-applications. An extranet extends selected resources of the intranet to outside groups of customers, suppliers, business partners or employees in remote locations. Management of content is necessary to ensure its relevancy and currency. The extension of knowledge resources to external stakeholders means that authentication and privacy standards are critical factors.

A comprehensive survey of best KM practices (AA, 1998), reveals that most organisations implement some kind of technology to connect people and enable their interaction and collaboration. However, there are differences among researchers regarding the value of virtual (technology-mediated) interaction in comparison with real (face-to-face) interaction in knowledge management. Some researchers warn that technologies lack the emotional richness and depth of real, live, in- person interaction (Santosus, 2001), and are an unsuitable vehicle to fully develop relationships and to gain an understanding of complex situations (Bender and Fish, 2000). Others argue that communication

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Managing Knowledge with Technology 31

mediated by technology is no less effective than face-to-face communication (Warkentin et al., 1997). More and more cyber- communities are also beginning to challenge traditional ideas about communities’ needs for a physical presence.

Stimulating Creativity and Complex Problem Solving

There is a widespread recognition in the knowledge management literature of the importance of creativity and innovation for organisational success in a changing environment (Drucker, 1985). The evolutionary principle proposes that those who are innovative will survive, while those who are not will become extinct. An organisation’s adaptive response may be a choice between development and decay. Recent surveys show that creativity and innovation are among the top priorities for senior executives in industry (BW, 1998).

It is argued that great innovations need creative thinking and ideas. While some theorists believe that creativity is reserved for the gifted, others (and us) see creativity as a skill that can be learned (Ford, 1996) and stimulated by technology (Aurum et al., 2001). Thus, organisations’ needs for creativity and innovation require an appropriate response from various parties including the education sector and IT industry. An interesting observation is that the Interim Australian Standard (Standards Australia, 2003) does not include any creative software products in their list of useful KM technologies. However, other literature mentions a variety of technologies that can be used to stimulate creativity (Handzic and Cule, 2002).

Mind Games: This group of technologies is focused on fostering creativity and innovative problem solving. Most systems are designed to stimulate creative thinking based on the principles of associations, memory retrieval and the use of analogy and metaphor. In multi-participant settings, it is also assumed that generation of creative ideas will be stimulated though participants’ interaction where one idea leads to another and the process tends to build upon itself.

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32 KM: Through the Technology Glass

Virtual Reality: Virtual reality technology enables an individual to become actively immersed in a simulated environment. It can have a dramatic impact on a number of areas including manufacturing, education and training, medical interventions, military preparedness and entertainment. Virtual reality offers a tool that enables people to learn more easily through experiential exercise rather than through memorising rules.

A simulation game approach may also be helpful in solving complex problems. For example, in a conventional management game, one or more players have to make managerial decisions in a simulated world (Casimir, 1986). Typically in such a game, a number of players make decisions for organisations modelled after industrial companies in a competitive environment, or a single player tries to minimise/maximise certain results in a simulated world. The game is played in a discrete number of rounds or periods. Once the players enter their decisions the results are computed and reported back to them. Normally, the players can interfere with the simulated world at any time during a round or a period, and adjust their future strategies based on what they have learnt from past experience and feedback.

2.4 Issues and Challenges for Practice and Research

The preceding section illustrates several major classes of technologies and their related roles in supporting knowledge processes. These are summarised in Figure 2.1. Readers should note that these tools and roles are not mutually exclusive and organisations may adopt any combination of them to tackle their particular problems or support particular motives. If the prime reason for knowledge management is minimising the risk of losing valuable knowledge, the response may involve identifying and holding onto the core competencies that the company has. Thus, risk minimisation is closely related to knowledge initiatives and technologies specifically aimed at locating and capturing valuable company knowledge (Von Krogh et al., 2000).

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Managing Knowledge with Technology 33

In today’s complex economy, businesses are constantly confronted with the need to operate more efficiently in order to stay competitive and satisfy increasing market demands. Seeking efficiency usually relates to knowledge initiatives for transferring experiences and best practices throughout the organisation in order to avoid unnecessary duplication and to reduce cost. Technology is often an important part of achieving efficiency improvements (Von Krogh et al., 2000). Therefore, researchers and practitioners in the field of KM need to turn their attention to new approaches and tools for improving knowledge transfer, as a possible means for achieving enhanced efficiency and sustaining competitive advantage for knowledge intensive firms.

There is a growing belief that knowledge can do more than improve efficiency. The new products and services resulting from knowledge and technology may bring profound changes in the way businesses operate and compete in the new economy. The unifying thread among various theoretical views is the perception that innovation is the key driver of

Figure 2.1 Types of KM technologies and roles

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34 KM: Through the Technology Glass

an organisation’s long-term economic success. Innovation of products, processes and structures have been assessed as a critical component in the success of new-age firms. Typically, innovative organisations focus on new knowledge and creation processes (Nanaka and Nishiguchi, 2001). Technology may help in nurturing creative thought, enabling idea sharing and use, getting knowledge out of individual minds into social environment, and by turning individual creativity into collective innovativeness.

Despite the existing substantial theoretical support for the role of technology in KM, there is still a large gap in the body of knowledge in this area. The ultimate challenge for KM is to determine the best strategies to improve the development, transfer and use of organisational knowledge at individual and collective levels. Hahn and Subramani (2000) identify a number of issues and challenges related to the utilisation of ICT for knowledge management support in phases of deployment. In the planning of long term effects, the issue is balancing knowledge exploitation and exploration. In the setup phase, the issue is balancing information overload and potentially useful content. In the maintenance phase, the challenge is balancing additional workload and accurate content. Finally, in the development phase, the issues are high context dependence of knowledge, and the need for flexibility, evolutionary development and user acceptance of the knowledge system.

Alavi and Leidner (2001) propose five major research questions concerning the application of IT in KM initiatives. These include the questions about: (i) consequences of increasing the breath and depth of knowledge via information technology for organisational performance; (ii) ways of ensuring that knowledge captured via technology is effectively modified where necessary prior to application, (iii) ways of ensuring that ICT captures modifications to knowledge along with the original knowledge, (iv) development of trust in knowledge captured via technology, and (v) factors related to the quality and usefulness of IT systems applied to KM initiatives. These questions together with the ones discussed earlier in this chapter form a good basis for empirical research in this area.

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Managing Knowledge with Technology 35

In summary, the idea that modern KM is inseparable from a consideration of computer-based technology (Holsapple, 2003) provides justification for research that improves technologies of KM (e.g., enabling and facilitating knowledge flows, supporting manupulation activities); and provides better understanding of the technology use and users (e.g., what works and under what conditions) and the outcomes of that use (e.g., enhancing productivity, innovation, reputation, etc.). The following chapters report the results of a series of empirical studies undertaken to address some of these and other research opportunities of interest to the author.

2.5 Conclusions

The integrated KM framework from Chapter 1 provides a basis for understanding the role of technology in KM. It places technology among major influencing factors in KM that enable and facilitate knowledge processes and thus contribute to organisational learning, improvement and innovation. All types of technologies outlined in the current chapter can be viewed to a greater or lesser extent as KM tools that support organisational knowledge development by encouraging those processes through which knowledge is moved or modified. This chapter has mainly focused on categories of technologies that may play important roles in facilitating knowledge sharing, representation and transformation, as well as improving people’s ability to acquire and create knowledge.

Among specific categories of technologies that may play important roles in building knowledge repositories are � databases, textbases, datawarehouses and datamarts; in promoting virtual socialisation and collaboration � groupware, portals, intranets, extranets and internets; in facilitating knowledge search and discovery� �� search engines, knowledge maps, and data mining tools; and in stimulating creativity and complex problem solving � electronic brainstorming, virtual reality and simulation games. The integrated view presented here may help make sense of the diverse field of KM and guide organisations in choosing technologies that best suit their knowledge needs and activities. It also provides an agenda for future research.

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36 KM: Through the Technology Glass

References

AA (1998), Best Practices in Knowledge Management, Arthur Andersen.

Alavi, M. and Leidner, D.E. (2001), “Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues”, MIS Quarterly, 25(1), 107�136.

Aurum, A., Handzic, M., Cross, J. and Van Toorn, C. (2001), “Software Support for Creative Problem Solving”, in Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT ’2001), 6�8 August, Madison, USA.

Becerra-Fernandez, I., Gonzales, A. and Sabherwal, R. (2004), Knowledge Management: Challenges, Solutions, and Technologies, Pearson Education, New Jersey.

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Edwards, J.S. (2004), “Supporting Knowledge Management with IT”, (forthcoming).

Edwards, J.S., Handzic, M., Carlsson, S. and Nissen, M. (2003), “Knowledge Management Research and Practice: Visions and Directions”, Knowledge Management Research & Practice, 1(1), 49�60.

Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. (1996), “Knowledge Discovery and Data Mining: Towards a Unifying Framework”, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD-96, Oregon.

Ford, C.M. (1996), “Theory of Individual Creative Action in Multiple Social Domains”, Academy of Management Review, 21(4), 1112�1142.

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Hahn, J. and Subramani, M.R. (2000), “A Framework of Knowledge Management Systems: Issues and Challenges for Theory and Practice”, in Proceedings of the International Conference on Information Systems, ICIS’2000, Brisbane, Australia, pp. 302�312.

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PART II

Codification Technologies:

Supporting Knowledge Storage and Finding

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Web-based Knowledge Records 41

CHAPTER 3

Web-based Knowledge Records:

Empowering Societies

The new source of power is not money in the hands of a few, but information in the hands of many.

� John Naisbitt

In this chapter we explore the role of web technology in managing knowledge records. We present a view of knowledge records as society’s tools for establishing evidence, protecting human rights, supporting the rule of law, preserving cultural capital and providing knowledge services. A sample of selected Australian web sites is analysed to demonstrate how the web is used to facilitate storage and access to knowledge that empowers justice and helps governments to achieve a wiser and fairer society. The chapter concludes with a set of guidelines for developing and implementing quality knowledge records systems.

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3.1 Introduction

New knowledge is expanding at a pace that makes it an immense management task to keep up with. The Internet and the web technologies can help with this problem. Web technology provides virtually unlimited storage as part of huge server farms that may be located all around the world. With blinding speed, the Internet can link knowledge workers to mountains of digital records stored on the web all over the world, otherwise too expensive and too difficult to tap (Laudon and Laudon, 1998). It is therefore not surprising that individuals and collectives are increasingly using these technologies to store and gain easy access to their key knowledge resources.

The growing number of knowledge workers in the new economy requires easy access to all kinds of knowledge. For example, investors require lists of possible investments, stock prices and changes in stock prices to help them make better decisions. Scientists need to quickly obtain photographs taken by space-craft for research purposes. Internet library access is vital to students and teachers for locating relevant articles, papers, books and conference reports. Many hundreds of library catalogues are already on-line through the Internet. In addition, users can access many thousands of databases that have been opened to the public by corporations, governments and non-profit organisations (Laudon and Laudon, 1998). Individuals can gather knowledge on almost any conceivable topic and get empowered by the wealth of knowledge from these vast storehouses.

Various benefits for users deriving from the use of the Internet and the web technologies have been suggested in the literature. These range from reducing communication costs, to enhancing communication, accelerating the distribution of knowledge, and facilitating knowledge service delivery (Laudon and Laudon, 1998). Of particular interest to this chapter is the role of the Web in facilitating the storage of vast amounts of knowledge records. This chapter introduces the web as an important type of knowledge repository and examines its application in the public sector’s knowledge records management. The emphasis is on benefits to a knowledge society in terms of wisdom and justice.

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Web-based Knowledge Records 43

3.2 The World Wide Web

The world wide web (“the web”) is at the heart of the explosion in the business and government uses of the Internet. It is a system with a universally accepted set of standards for storing, retrieving, formatting and displaying information in a networked environment (Laudon and Laudon, 1998). The web represents a giant knowledge depository linked by the Internet. It makes available to the requesting public, knowledge artifacts in the form of documents, files, photos, drawings, videos, sound and other various holders of knowledge (Becerra-Fernandez et al., 2004).

The invention of the Internet together with its web capability has been compared to Gutenberg’s invention of the printing technology in the fifteenth century. The web handles all types of digital records and links knowledge resources that span multiple web servers. It accelerates the distribution of knowledge. It also facilitates document publishing and distribution, thus assisting the sharing of accumulated knowledge across individuals, organisations and societies.

Knowledge artifacts are stored and displayed on the web in the form of electronic pages. Web pages are hypermedia documents that often express the content in an artistic and dynamic fashion using stylish typography, colourful graphics, push-button interactivity, sound and video. These pages can be linked electronically to other pages regardless of where they are located and can be viewed by any type of computer. By clicking on highlighted words or buttons on a web page, one can link to related pages to find additional content of interest, or links to other points on the web (Laudon and Laudon, 1998, Becerra-Fernandez et al., 2004).

The web pages created by an individual, business organisation or a government agency are called a web site. For an entity to establish a presence on the web it must set up a web site of one or more pages. The default page for that website is a home page. A home page is a text and graphical screen display that usually welcomes the user and explains the entity that has established the page. The home page will lead the user to other static and dynamic web pages. Essentially, web technology supports the integrating school of thought on KM (Edwards, 2004) by

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placing focus on a single system (the web site) to provide all knowledge needs to the users.

Typically, web sites are created to widely disseminate product and service knowledge, broadcast advertising and messages to customers, to collect orders and customer data, and to co-ordinate organisations on a global scale. Appropriate, substantial and up-to-date knowledge records are considered crucial for the success of any web site (Lowe, 2003).

3.3 The Concept of Knowledge Record

The term “knowledge record” is a derivative of two terms: “knowledge” and “record”. The term knowledge has different meanings for different people. From the cognitive perspective, for example, knowledge is perceived as externally justified beliefs, based on formal models, universal and explicit, that operate through cognitive processes. On the other hand, from the constructivist perspective, knowledge is viewed as acts of construction or creation, creative arts, not universal, beliefs that depend on personal sense making (Van Krogh, 1998). Often, knowledge is defined in terms of relationships between data and information. In theory, knowledge is described as deeper and richer information (Davenport and Prusak, 1998); information combined with experience, context, interpretation and reflection (Davenport et al., 1998); valuable information in action (Grayson and O’Dell, 1998); and information that has been internalised by a person to the degree that he or she can make use of it (Devlin, 1999). However, in practice, the terms data, information and knowledge are often used interchangeably (Huang et al., 1999).

Knowledge is usually classified as either explicit or tacit (Nonaka and Takeuchi, 1995, Nonaka, 1998). Explicit knowledge is described as formal, systematic knowledge that can be expressed or communicated without vagueness or ambiguity. It can be stored in books, manuals, databases and in other ways. Tacit knowledge, on the other hand, is considered as highly personal know-how that is derived from experience and beliefs and usually hard to articulate and communicate. Such knowledge exists in the individual minds of people (Polanyi, 1966).

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Web-based Knowledge Records 45

Furthermore, some taxonomies make a distinction between declarative, procedural, inferential and motivational forms of knowledge (Quinn et al., 1996), as well as conditional, relational and pragmatic types (Alavi and Leidner, 2001). Other schemes recognise individual and artifact loci, varying degrees of knowledge structure, and individual and collective levels of knowledge (Hahn and Subramani, 2000). A pragmatic approach to classifying knowledge simply attempts to identify knowledge useful to organisations. In summary, knowledge is a complex and multi-faceted concept.

Knowledge resides in different locations or reservoirs, including people, artifacts and structures. Electronic knowledge repositories such as data warehouses and websites represent a way of storing knowledge in artifacts. This requires externalising knowledge into explicit forms such as words, concepts and visuals (Nonaka, 1998). We use the word “record” to denote any kind of explicit form that is created and kept as part of the knowledge externalisation process. Electronic records can be further grouped into structured fielded records such as those found in databases and data warehouses, and unstructured or semi-structured digital documents such as those found on the web containing text, graphics, animations, sound and video.

Digital records represent an increasingly important part of an entity’s integrated knowledge base. With embedded multimedia objects, they have the potential to be highly expressive. However, they often make knowledge search and discovery difficult. Mechanisms such as the web are playing an increasingly important role in facilitating storage and distribution of these knowledge records. The main objective of the following case study is to examine a sample of the Australian government websites and their role in publishing and disseminating useful and timely knowledge to their public. The text presented here is derived from two recent papers (Handzic, 2003, 2004).

3.4 Case Study: Exploring Australian Web Sites

The wealth of a society is linked to its knowledge capital. Many nations, including Australia, are transforming themselves into knowledge

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46 KM: Through the Technology Glass

economies and societies (Edvinsson, 2003). Knowledge management plays a major role in this transformation. However, our current understanding of the level of penetration and impact of KM in societies is very limited. Preliminary empirical evidence from Australia reveals a relatively high level of awareness, combined with a low level of implementation of knowledge management in academia (Handzic and VanToorn, 2002); some KM activities in major public sector agencies (Stephens, 2001); and some efforts focused on delivering better e-government services to the public (NOIE, 2002).

There is also widespread recognition that different societies treat their knowledge in different ways. Eastern cultures seem to value more “tacit” knowledge that is kept in people’s heads and transferred through socialisation. Western cultures, on the other hand, appear to focus more on “explicit” knowledge, captured and preserved in collective and codified repositories (Nonaka and Takeuchi, 1995). Based on this distinction, a widely adopted broad classification of knowledge management strategies comprises two classes: personalisation and codification (Hansen et al., 1999). The personalisation strategy assumes that tacit knowledge is shared through interpersonal communication. Codification assumes that knowledge can be effectively extracted and codified. In this approach, knowledge artifacts are stored and indexed in databases for later retrieval and use. As such, it can serve as evidence and proof of an idea, decision or action taken by individuals, organisations and governments.

Following the principles of codification, Pederson (2002) proposed a comprehensive framework for building a society’s cumulative “explicit” knowledge base. This framework suggests that the process starts with identifying critical documentable knowledge. This may include individual and group ideas, actions, decisions and transaction worth preserving. Once identified as such, these documentable “acts” are then codified, organised, stored and kept together with relevant meta-knowledge for as long as required. Typically, only a small portion of recorded knowledge has long term significance and becomes part of the society’s cumulative memory and of concern to public authorities.

Pederson (2002) provides numerous examples of knowledge records from the world’s public archives available on the Internet. Such

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Web-based Knowledge Records 47

web-based collections of digital content represent an exciting development full of promise for users for research, education and practice purposes (Henninger, 2003). The following sections illustrate some notable Australian web sites as examples that are particularly significant for empowering justice and attaining wisdom in the society.

Achieving a Fairer Society

Accumulated knowledge records can be viewed as an important societal tool for establishing facts and a way to validate human memory. Typically they include personal documents, corporate knowledge bases, industry and government reports and statistics, technical and specialist literature, databases and internet resources, academic journals, scholarly and reference books, general knowledge compilations, popular books and magazines, and info-tainment. Founding Documents is an Australian project in social history realised by a partnership of eight government archives, for the Centenary of Federation celebration. The project is available at the URL http://www.foundingdocs.gov.au/. Since documented knowledge often serves as proof of an idea, decision or action taken by individuals, organisations and governments, it is of vital importance that records are constructed with care, and that they are complete, reliable and unchanging.

Knowledge records are also important in ensuring the protection of individual rights. One of the first and most important protective documents in this category is The Universal Declaration of Human Rights proclaimed by the newly formed United Nations after World War II. Thirty clauses of the declaration can now be easily accessed and read from the United Nations website by all those who may wish to remind themselves or teach others about human rights, and ensure their fulfillment. On a different level, personal documents are especially crucial to individuals seeking to establish their identity and ensure their entitlements. The intentional destruction of one’s vital personal records can be extremely disruptive and often life threatening to those affected. The stories of Kosovars found at web pages of the United Nations High Commissioner for Refugees, and various government councils give a

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48 KM: Through the Technology Glass

graphic insight into the experiences and deprivation suffered by people whose identity documents were systematically destroyed and who were forced to become refugees. The Australian Government provides instruction and help to refugees through The Refugee Council of Australia, available at the URL http://www.refugeecouncil.org.au/.

Progressive societies demand that governments and individuals demonstrate great responsibility and accountability in the conduct of their public and personal affairs. Typically, governments enact laws and regulations which define the structures and sets of rules governing the relations and activities of all legal entities within their jurisdictions. Written laws and regulations represent infrastructural archives that support the rule of law and enable society to hold people and institutions accountable for their actions. They also help to achieve improved stability and fairer distribution of society’s resources. Despite all these measures, keeping those in charge honest still represents a major challenge for democratic societies. The Australian Government has recently awarded a major research grant to the Records Continuum Research Group at Monash University to investigate the issue of accountability in public services (URL http://rcrg.dstc.edu.au/publications/recordscontinuum/ smoking.html). The group’s web site contains numerous reports and analyses of recent crises, scandals and risks faced by the Australian public sector that all have their roots in inadequate knowledge record management.

Achieving a Wiser Society

The capacity to construct and transfer culture has always been considered as an essential social function. Recorded knowledge has an important cultural value too. It links us to the ideas and activities which have lasting importance for symbolic or concrete reasons. Bodies of recorded knowledge constitute a society’s “cultural capital”. Cumulative layers of evidence legitimise and witness the development of significant ideas and activities within a society over time. When cultural treasures do not survive, it is hard to reclaim the lost wisdom and skills and the ability of the society to progress is hindered. It gradually loses its memory and

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Web-based Knowledge Records 49

capacity to sustain itself. Stories of recent attempts to destroy cultural treasures, as well as international conventions and safeguarding measures designed to protect them can be found at UNESCO’s web site. One of the recent Australian contributions to preserving society’s achievements is Bright Sparcs, an Australian Science Archives Project, available at the URL http://www.asap.unimelb.edu.au/bsparcs/bsparcshome.htm. It is a register of over 3,000 people involved in the development of science, technology and medicine in Australia. The site also includes references to scientists’ materials and bibliographic resources.

Governments represent an important source of knowledge needed to facilitate citizen and business orientated services. The Australian Government Entry Point http://www.fed.gov.au shown in Figure 3.1 is a comprehensive government web site that currently signposts over 700 Australian Government web sites and over 1 million pages of text. It is the Australian Government’s aim to provide efficient access to its expertise, and the variety of access approaches available on this site

Figure 3.1 The Australian Government Website

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50 KM: Through the Technology Glass

allow users to choose the method that best suits their needs. Customised links are provided for different types of users including individuals, students, businesses and non-residents. For example, the site points to relevant places where best advice on benefits and payments may be obtained for individuals, education related information for students, taxation help for businesses, and immigration tips for non-residents.

The public administration and government work is often delivered in the form of strategic knowledge products. The Australian Bureau of Statistics (ABS), Australia’s official statistical organisation http:// www.abs.gov.au assists and encourages informed decision-making, research and discussion within governments and the community, by providing a high quality, objective and responsive national statistical service on its web site. ABS organises its statistical products around the broad themes including: economy, environment and energy, industry, people and regional statistics. Each of these is further divided into more specific categories. For example, industry theme includes agriculture and rural, building and construction, information technology, manufacturing, mining, retail, science and innovation, service industries, tourism and transport statistics. At the next level are related data and publications. By dividing its statistical expertise into logical blocks, ABS helps the user in finding, comprehending and interpreting it.

3.5 Issues and Challenges

Due to the power of recorded knowledge as a resource of wisdom and justice in society, knowledge management must ensure that government’s knowledge repositories are of high quality and integrity and also ensure their availability to the right people, at the right time, and in the right form.

Shanks and Tansley (2002) define quality in terms of “fitness for purpose”. This means that the available knowledge records must be usable and useful to users and support their work effectively. Some desirable quality dimensions include accuracy, reliability, importance, consistency, precision, timeliness, understandability, conciseness and usefulness (Ballou and Pazer, 1985; Wand and Wang, 1996). More complete perspectives

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Web-based Knowledge Records 51

on quality are provided by a number of proposed frameworks that organise and structure the concept of quality.

For example, the framework by Strong et al., (1997), based on a survey of opinions of expert practitioners, suggests four quality dimensions: intrinsic, contextual, representational and accessibility. The framework of Shanks and Darke (1998) is based on semiotic theory and Bunge’s ontology and consists of quality goals and measures for consumer stakeholders. Quality goals include syntactic, semantic, pragmatic and social levels. Syntactic quality concerns the form, semantic quality concerns the meaning, pragmatic quality concerns the usage, and social quality the shared understanding. Other frameworks include Wand and Wang (1996), which is also based on Bunge’s ontology, and Kahn et al., (1997), which is based on product and service quality theory.

The Australian standard for records management (Standards Australia, 2002) lists several important characteristics that records should have in order to ensure society’s need for evidence, accountability and information. First, a record’s content should correctly reflect an idea that was communicated, or a decision that was made or an action that was taken. In addition to the content, the record should also contain the metadata describing its structure, context and links to other relevant records. Furthermore, the record should be authenticated, that is proven to be what it purports to be, shown to have been created or sent by the person purported to have created or sent it, and verified as having been created or sent at the time purported. The record should also be reliable, so that its contents can be trusted as a complete and accurate representation of knowledge. The integrity of a record should be preserved by protection from an unauthorised alteration. Finally, it should be useable, easy to locate, retrieve, present and interpret.

The failure to address the issues of quality and integrity of records in government’s knowledge repositories may lead to serious impairment of functioning of society and its institutions, the loss of evidence of the rights of people as citizens, the inability of societal watchdogs to call to account governments and individuals, the loss of collective and individual identity and memory, and the inability to authenticate and source critical knowledge. These issues pose a great challenge to governments. One possible way forward is in standardisation of relevant policies and

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52 KM: Through the Technology Glass

procedures. Such standards may ensure that appropriate attention and protection is given to all records and that the evidence and information they contain can be retrieved more efficiently and effectively. The Australian government’s response to this challenge is described in the following section.

3.6 Australian Standard Guidelines

Australian Standard provides a methodology that specifically aims at facilitating the implementation of knowledge records management in organisations (Standards Australia, 2002). One of the first recommendations to institutions intending to implement the knowledge records system is to define and document relevant policies. A policy statement can be understood as a statement of intentions. It also sets out programmes and procedures that will achieve those intentions. The standard describes a step-by-step procedure for designing and implementing records systems, that includes (1) preliminary investigation, (2) analysis of activities, (3) identification of requirements, (4) assessment of existing systems, (5) identification of strategies, (6) systems design, (7) systems implementation and (8) post-implementation review.

The purpose of the first step is to provide an understanding of relevant administrative, legal and social contexts. The next step involves building of a conceptual model of institutional activities. The purpose of the third step is to identify requirements to create, receive and keep knowledge records. The following steps involve reviewing of the existing system; determining the most appropriate policies, procedure, standards, tools and other tactics; and converting these strategies into a plan that fulfils requirements and remedies deficiencies identified in earlier steps; and putting in place an appropriate mix of strategies to implement the designed plan. The purpose of the final step is to evaluate and maintain the effectiveness of the system, and to establish a monitoring regime for the duration of the life of the system.

Another Standard recommendation specifies required operational processes and controls. Theoretically, these processes include a linear

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sequence of capture, registration, record classification, access and security classification, identification of disposition status, storage, use and tracking, and implementation of disposal activities. In practice, however, some operations may take place simultaneously. Certain operations may depend on the existence of controls. Controls include the instruments needed for different operations, and factors that may affect these operations. The principal instruments are classification schemes, the disposition authority and the security and access scheme. Additional instruments include specific tools such as a thesaurus and a glossary of terms, as well as regulatory and risk frameworks, formalised delegation of authority and the registry of permissions.

Finally, the Standard specifies the need to define responsibilities and authorities. The overriding objective of this recommendation is to establish and maintain the regime that meets the needs of all stakeholders. The likely authorities and responsibilities of senior managers include ensuring the success of the overall knowledge records management programme. Specialised professionals have primary responsibility for the implementation of policies, procedures and standards. Other knowledge workers may have a variety of specific duties, including responsibility for security, responsibility for design and implementation of information and communication technology based systems, or creation, receipt and storage of knowledge records.

By establishing proper management of knowledge records, governments may prevent serious impairment of the functioning of society and its institution, avoid the loss of evidence of the rights of people as citizens, avert the inability of societal watchdogs to call to account governments and individuals, stop the loss of collective and individual identity and memory, and hinder the inability to authenticate and source critical knowledge, and create a wiser and fairer society.

3.7 Conclusions

This chapter addresses the role of web technology in facilitating the preservation and dissemination of society’s accumulated knowledge to

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the public. Recorded and documented knowledge is seen as an important societal tool for establishing facts, and a way to validate human memory, protect human rights, support the rule of law, and preserve culture. The case study of the selected Australian government web sites further highlights the importance of the availability of high quality and timely knowledge records to their seekers in order to ensure a better and fairer society. A systematic approach to managing knowledge records is essential to protect and preserve evidence, support decision-making and ensure accountability to present and future stakeholders.

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Edwards, J. (2004), Supporting Knowledge Management with IT, (paper submitted to DSS 2004 conference).

Grayson, C.J. and O’Dell, C. (1998), “Mining Your Hidden Resources”, Across the Board, 35(4), 23�28.

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Hahn, J. and Subramani, M.R. (2000), “A Framework of Knowledge Management Systems: Issues and Challenges for Theory and Practice”, in Proceedings of the International Conference on Information Systems, ICIS’2000, Brisbane, Australia, pp. 302�312.

Handzic, M. (2004), “The Role of Knowledge Mapping in Electronic Government”, in Wimmer, M.A. (ed.), in Proceedings of the 5th IFIP International Working Conference (KMGov 2004), Krems, Austria, May 17�19 (forthcoming).

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Quinn, J.B., Anderson, P. and Finkelstein, S. (1996), “Managing Professional Intellect: Making the Most of the Best”, Harvard Business Review, March� April, pp. 71�80.

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Structured Knowledge Repositories 57

CHAPTER 4

Structured Knowledge Repositories:

Building Corporate Memories

We’re drowning in information and starving for knowledge. — Rutherford D. Rodgers

This chapter investigates ways of building more effective knowledge repositories and tests empirically the impact of a massaging technique on people’s ability to process and use stored knowledge in a judgmental decision-making task context. The main findings indicate that knowledge massaging in the form of aggregation had a significant positive effect on people’s knowledge assimilation and utilisation. This, in turn, led to enhanced decision accuracy. These findings have important implications for practice as they point to a proven way to enhance the effectiveness of corporate memories in organisations, but warn of the dangers of overdependence on tools.

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4.1 Introduction

Knowledge has been widely recognised as a critical organisational resource for success in the new economy. Many organisations are trying to capitalise on their organisational knowledge in order to maintain their competitive advantage. This requires mobilising the collective assemblage of all intelligences that contribute towards building a shared vision, renewal process and direction for the organisation (Liebowitz, 2000).

In Chapter 1, we identified that one of the important objectives of knowledge management is to capture, codify, organise and store relevant organisational knowledge. Capturing and storing knowledge into knowledge repositories is an important part of building organisational memory. The assumption is that tacit knowledge needs to be made explicit and formilised to be shared and used more easily by organisational members. By capturing experiences, anecdotes, war stories, case studies, lessons learnt, best practices, failures and success, heuristics and valuable relationships organisations also begin to evolve into true knowledge organisations.

Technology can be used as an enabler to support these KM efforts via building computer-based knowledge repositories. The Interim Australian KM standard (Standards Australia, 2003) proposes a number of technologies including databases, textbases, data warehouses and data marts as useful in building organisational knowledge repositories. A knowledge management systems framework (Hahn & Subramani, 2000) suggests that the availability of a KM system, such as a codified knowledge repository, should lead to an increase in organisational knowledge and result in improved performance. Other researchers argue that knowledge repositories contribute to knowledge and performance by facilitating knowledge processes such as assimilation (O’Leary, 2003).

Currently, there is ample of evidence to show that organisations do implement various storage technologies as part of their best KM practices (AA, 1998). However, there is little empirical evidence regarding the impact of these initiatives on organisational performance (Alavi and Leidner, 2001). The existing KM research is mainly limited to anecdotal stories and descriptive case studies. Some researchers point out that our ability to accumulate and store knowledge artefacts has by far surpassed

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our ability to process them, and warn of the danger that vast institutional memories may easily become tombs rather than wellsprings of knowledge (Fayyad & Uthurusamy, 2002). Thus, we argue here that one of the most challenging research questions with respect to building effective knowledge repositories for organisations is to find technologies (tools and methods) that make stored knowledge more effective. The purpose of this chapter is to address the issue through a series of laboratory experiments. The specific aim is to determine those technologies that best facilitate knowledge extraction and assimilation.

4.2 Concept of Knowledge Repository

A knowledge repository can be viewed as a form of organisational memory, that is a set of stored artifacts that organisations acquire and retain, and to bear on their present activities in order to avoid future mistakes. A knowledge repository can be studied from two perspectives. The “content” perspective focuses on the knowledge that is captured and the context in which it is used. The “repository” perspective focuses on how knowledge is stored and retrieved (Jennex and Olfman, 2003).

In general, repositories store two types of knowledge: 1) structured concrete information and knowledge in databases, documents and artifacts (e.g., standards, rules), and 2) the representation of unstructured abstract information and knowledge of human actors (e.g., conceptual lenses, frameworks). They serve two basic functions: representation — presenting the knowledge for a given context, or interpretation — providing the frames of reference and guidelines for knowledge application. With the aggressive rate of growth of disk storage in the last decade, organisations are increasingly relying on computer-based repositories, as opposed to more traditional paper based repositories or human memory. Computer- based repositories incorporate a variety of knowledge forms ranging from data and text-based documents and models, to digital images, video and audio-recordings.

Some researchers anticipate that applying corporate knowledge repositories will result in improved organisational effectiveness. Jennex and Olfman (2002) put forward a number of propositions to define the

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60 KM: Through the Technology Glass

beneficial outcomes of the use of organisational memories. These range from improved decisions, through reduced decision resistance, to more successful change efforts. However, these may or may not happen. Some researchers (O’Leary, 2003) argue that while knowledge may be gathered, created and converted, if it is not assimilated, the organisations will not be able to take action on that knowledge or actualise its potential value. As a result, corporate memories will have only limited impact on an organisation.

Other researchers (Fayyad and Uthurusamy, 2002) warn that the increasing ability to capture and store data may produce a phenomenon called the “data tombs”. These are effectively write-only stores where knowledge artifacts are deposited to merely rest in peace never to be accessed again. Such stores represent missed opportunities to support exploration in a scientific activity or commercial exploitation by a business organisation. If knowledge captured in organisational stores stays unused, most opportunities to discover, profit, improve service or optimise will be lost.

Given that much of the previous research on knowledge management has ignored the issues associated with extraction and internalisation of knowledge from corporate stores, this chapter will particularly focus on factors that promote effective use of corporate knowledge repositories. The challenge is to find ways to turn them from tombs into wellsprings of knowledge (Handzic and Bewsell, 2003).

4.3 Facilitating Knowledge Extraction from Repositories

The prime function of a knowledge repository is to store captured artifacts in forms that can be retrieved and applied effectively. O’Leary (2003) suggested a number of technologies for assimilation that may assist in achieving this goal. They include knowledge storage, massaging, organising, linking, filtering and navigating. In this chapter, we will take a closer look at storage, massaging and filtering approaches. The common characteristic of these three approaches is that they all deal with different ways of “structuring” knowledge content in repositories. The other three

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approaches, namely organising, linking and navigating deal more with the issues of “guiding” users towards knowledge stored in repositories. This issue will be addressed in more detail in the following chapter.

Structuring Knowledge Content

According to O’Leary, knowledge storage and thus availability provides a basis to facilitate knowledge assimilation. Knowledge can be stored in a variety of forms: documents, rules, cases, diagrams etc. For decision making, the value of stored environment, organisation and cause-effect knowledge may be seen primarily in its ability to explain past and anticipate future changes in the behaviour of the variable of interest. This enables the decision maker to deal more competently with his or her decision task.

O’Leary (2003) suggested that massaging knowledge into an appropriate format by a KM system could help the user better understand and use knowledge. One approach is to provide knowledge in a summarised rather than in a detailed format. For decision making purposes, aggregated knowledge can be produced by modeling systems that combine existing knowledge into single integrated responses. It is assumed that processing of smaller amounts of task information induces lower demands on the mental resources of the decision maker and reduces the complexity of the decision problem. This in turn may have a positive effect on performance.

Furthermore, knowledge can be filtered to facilitate assimilation. Filtering tries to get the right knowledge to the right people at the right time. One possible approach is to package single workgroups’ critical knowledge into smaller knowledge marts as opposed to storing collective knowledge across functional teams, departments, divisions and subsidiaries in enterprise wide knowledge warehouses. The assumption is that filtering prevents the negative effect of irrelevant information on human judgment. Filtering can be done manually, using human experts who determine if knowledge is relevant, and take responsibility for filtering it for others. Alternatively, technology can be used to help users directly monitor and determine high quality content from a broader knowledge base.

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62 KM: Through the Technology Glass

Providing Guidance to Knowledge

Alternative ways to facilitating extraction and assimilation of knowledge from repositories is by providing guidance to relevant knowledge by making it more visible and accessible to the user. O’Leary (2003) suggests that one possible way of achieving improved assimilation is by developing knowledge taxonomies or ontologies. An ontology is defined as an explicit specification of a concept. Linking knowledge to other knowledge or people is another way suggested to improve assimilation. Technology can be used to determine who knows who or what. Finally, knowledge navigation may facilitate assimilation by helping the user better visualise the world. Many different approaches for assisting knowledge navigation have been suggested including hyperbolic browsing, table lens, and intelligent agents. Knowledge maps or k-maps are alternative terms used to denote the above ideas (Wexler, 2001). They will be addressed in a greater detail in Chapter 5.

To summarise, all of the above approaches have been suggested as methods to improve knowledge extraction and assimilation. However, there has been very little empirical evidence to support such propositions. Recent findings indicate that both storage and filtering are beneficial. One paper reported that people managed to extract between 40% and 60% of the knowledge stored in codified repositories depending on quantity, quality and diversity of the content available (Handzic and Bewsell, 2003). As a result they improved the quality of their decisions compared to those without such repositories, but failed to achieve what was theoretically possible. In another study Handzic and Parkin (2000) found out that providing people with a knowledge mart instead of a knowledge warehouse resulted in improved knowledge use and performance.

The main objective of the current study was to examine the impact of one knowledge massaging technique (aggregation) on the effectiveness of knowledge absorption from repositories, and its subsequent effect on people’s performance in a decision making task. The knowledge repository in this study was a prototype data warehouse. This is relatively new and immature, but promising KM technology (Finnegan and Sammon, 2002). It was hoped that the current research would provide a deeper insight

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Structured Knowledge Repositories 63

into the potential and limitations of the knowledge massaging approach to improving the effectiveness of knowledge repositories in supporting decision making.

4.4 Empirical Study

Experimental Task

The experimental task in the current study was a simple production planning activity in which subjects made decisions regarding daily production of fresh ice cream. The participants assumed the role of Production Manager for a fictitious dairy firm that sold ice cream from its outlet at Bondi Beach in Sydney, Australia. The fictitious company incurred equally costly losses if production was set too low (due to loss of market to the competition) or too high (by spoilage of unsold product). The participants’ goal was to minimise the costs incurred by incorrect production decisions. During the experiment, participants were asked at the end of each day to set production quotas for ice cream to be sold the following day. Subjects were required to make thirty production decisions over a period of thirty consecutive days. Before commencing the task, participants had an opportunity to make five trial decisions (for practice purposes only).

From pre-experiment discussions with actual store owners at Bondi Beach, three factors emerged as important in determining local demand for product: the ambient air temperature, the amount of sunshine and the number of visitors/tourists at the beach. This knowledge was presented to the subjects in two different forms. One half of the participants received it in a massaged form as one aggregated cue (i.e., index), and another half received it in a raw form as three separate contextual cues (i.e., temperature, sunshine, visitors).

The task provided a challenge because it did not stipulate exactly how this knowledge should be translated into specific judgment. The participants were provided with a meaningful task context, sequential historic information of task relevant variables to provide some clues to causal relationship, and their forecast values to suggest future behaviour.

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64 KM: Through the Technology Glass

However, they were not given any explicit analysis of the quality of their knowledge artifacts, or rules they could use to apply the available knowledge.

At the beginning of the experiment, task descriptions were provided to inform subjects about the task scenario and requirements. The given text differed only with respect to the form of knowledge presented. In addition, throughout the experiment instructions and feedback were provided to each participant to analyse earlier performance and to adjust future strategies.

Experimental Design and Variables

A laboratory experiment with random assignment to treatment groups was used, since it allowed greater experimental control. This made it possible to draw stronger inferences about causal relationships between variables due to high controllability. The only independent variable was knowledge form (raw vs massaged).

The raw knowledge form was operationalised by providing the participants with three cues of decision relevant contextual information (e.g., temperature, sunshine and visitors) in a computerised database. The massaged knowledge form was operationalised in terms of one aggregated cue (e.g., sales index). All information cues were equally predictive irrespective of the form to control for the potential confounding effects of quality and diversity.

Individual performance was evaluated in terms of processing efficiency of the available knowledge and decision accuracy achieved as a result. Processing efficiency was operationalised by a relative to optimal error (ROE) and was calculated as a ratio of an absolute error of a person’s decision to the corresponding error of the “optimal strategy”. The optimal strategy was modeled using stepwise regressions with three (or one) cues as independent variables and sales data as the dependent variable. R-square values were 0.87 (F(3,26)=56.82, p=0.000) for the raw form, and 0.66 (F(1,28)=55.62, p=0.000) for the massaged form. The optimal response integrated individual variables into a single response using regression weights and produced the best possible performance given the

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Structured Knowledge Repositories 65

available cues. ROE were calculated to assess how much of the maximum knowledge potential was extracted and used by the subjects. Scores equal to 1 indicate maximal, while scores greater than 1 indicate suboptimal processing efficiency.

Decision accuracy was operationalised by a relative to naive error (RNE) and was calculated as a ratio of an absolute error of a person’s decision to the corresponding error of the “random walk” (naive) strategy (for details see Amstrong and Collopy, 1992). A naive strategy is one that simply determines the next day sales as equal to the current day’s sales. Such strategy makes no use of any contextual knowledge and typically produces poor performance. RNE was used to assess improvement in the quality of decisions due to knowledge use. Scores equal to 1 indicated no improvement, while scores smaller than one indicated improved accuracy.

Subjects and Procedure

The subjects were 28 graduate students enrolled in the Master of Commerce course at the University of New South Wales, Sydney. Subjects participated in the experiment on a voluntary basis and received no monetary incentives for their performance. Graduate students are generally considered to be appropriate subjects for this type of research (Ashton and Kramer, 1980; Remus, 1996; Whitecotton, 1996). The experiment was conducted in a microcomputer laboratory. On arrival, subjects were assigned randomly to one of the treatment groups by picking up a diskette with an appropriate version of the research instrument to be used. The instrument was specifically developed by the author in Visual Basic. Subjects were briefed about the purpose of the study, read the case descriptions and performed the task. The session lasted about one hour.

Results

The analysis of collected data was performed using a series of T-tests to examine the effects of knowledge form on two dependent variables of

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66 KM: Through the Technology Glass

interest (processing efficiency and decision quality). The results are presented graphically in Figures 4.1 and 4.2. They were all significant at p=0.05.

The results of the analysis shown in Figure 4.1 indicate a significant positive effect of knowledge massaging on processing efficiency. The mean ROE of the subjects provided with knowledge in the aggregated form was significantly lower than that of the subjects presented with knowledge in a raw form (2.70 vs 4.00). Mean values greater than 1 indicated that subjects assimilated knowledge less efficiently than they could have irrespective of variations in knowledge form.

Figure 4.1 Decision efficiency graph

Figure 4.2 shows a significant positive effect of knowledge massaging on decision accuracy. The subjects with the aggregated knowledge form had a significantly lower mean RNE than their counterparts with the raw knowledge form (0.94 vs 1.15). Mean values less than 1 indicate marginal improvement in decision accuracy compared to naive strategy due to information utilisation.

1.00

raw aggregated Form

Mean processing efficiency (ROE) by knowledge form

Actual

Optimal

ROE

4.00

3.00

2.00

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Structured Knowledge Repositories 67

Figure 4.2 Decision accuracy graph

4.5 Lessons Learned

Main Findings

The main findings of this study provide important empirical support for the proposition that knowledge massaging will enhance knowledge extraction and assimilation, and improve performance in a decision- making task context. The study demonstrated that knowledge massaging had a beneficial effect on processing efficiency. Subjects provided with aggregated knowledge tended to assimilate their available knowledge relatively more efficiently compared to those with raw knowledge. This was demonstrated by significantly smaller processing errors found among the subjects from the aggregated than from raw knowledge form groups. Consistent with a substantially improved processing efficiency, the study revealed that knowledge massaging resulted in enhanced decision accuracy. This was evidenced by significantly lower decision errors found among the subjects with aggregated than raw knowledge forms.

Naive

Actual

RNE

2.00

1.00

raw aggregated Form

Mean Decision quality (RNE) by knowledge form

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68 KM: Through the Technology Glass

In general, these findings are consistent with the theoretical expectations from the KM literature (O’Leary, 2003) that suggest a positive effect of knowledge massaging approach on assimilation. They also agree with the earlier work done in the psychology field on the impact of cognitive load (Schroder et al., 1967) and task complexity (Beach and Michell, 1978; Christensen-Szalanski, 1978; Payne, 1982; Wood, 1986) on performance. Finally, they confirm our own earlier empirical evidence of enhanced efficiency and accuracy (Handzic, 1997) with reduced volumes of stored artifacts.

With respect to overall performance, the study revealed relatively inefficient use of the available knowledge across both treatments, which consequently resulted in little or no real improvement in decision accuracy over naive strategy. The relatively poor overall performance could be potentially attributed to the lack of monetary incentives. Other Handzic (1997) study provided participants with a substantial monetary reward for their performance. It is possible that without monetary incentives, the subjects in this study were not as motivated to use the full potential of their information to improve decisions. This is offset by the assumption that graduates chosen from the pool of students attending an advanced Master’s level course should be motivated to do their best on the task by their intrinsic interest in the subject matter.

Alternatively, poor overall performance could be attributed to the characteristics of the task information and the task performers. Computerised knowledge repositories available to participants in the current study contained only factual knowledge with little analysis, and had no procedural information. Conversion of explicit factual knowledge into personal tacit knowledge was insufficiently achieved to perform well on the task. The participants also needed analytical information such as an evaluation of the predictive validity of each contextual factor, as well as the relevant know-how to integrate factual information into a decision response. This crucial information was assumed to be a part of an individual’s “tacit” knowledge. However, non-expert participants appear not to have had high levels of the required “tacit” knowledge to perform well on the task without prior training or experience.

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Structured Knowledge Repositories 69

Limitations and Implications

While the current study provides a number of interesting findings, some caution is necessary regarding their generalisability due to a number of limiting factors. One of the limitations refers to the use of a laboratory experiment that may compromise external validity of research. Another limitation relates to artificial generation of information that may not reflect the true nature of real business. The subjects chosen for the study were students and not real life decision makers, although the fact that they were mature graduates may mitigate the potential differences. Also, no incentives were offered to the subjects for their effort in the study. Consequently, subjects could have found the study tiring and unimportant and may not have tried as hard as possible. Most decisions in real business settings have significant consequences which contributes to motivate the decision makers.

Although limited, the findings of the current study may have some important implications for organisational KM strategies. They indicate that aggregation as a form of knowledge massaging is a very useful means of improving decision performance in organisations. Some researchers predict that the adoption of aggregation and other similar model-based tools in organisations will increase over time (Snowden, 2003). The main danger is seen in promoting an over-reliance on the tools at the cost of human judgment. One of the distinguishing characteristics of humans from other life forms is their ability to create means to hold knowledge and organise the world. Overdependence on tools can destroy these human capabilities by breeding in conformity. This is of major concern to KM that seeks to empower and not to enslave human judgment.

Filtering and storage approaches are also not without shortcomings. Filtering requires reliance on other people or tools for selection of relevant knowledge, and involves a substantial cost and time to realise. Uncontrolled storage, on the other hand, may produce an overload effect. These concerns call for devising alternative measures aimed at improving the depth of understanding as well as knowledge consumption in KM. Further research is necessary to address the issue.

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70 KM: Through the Technology Glass

4.6 Conclusions

The main objective of this chapter is to examine ways of building more effective knowledge repositories. It described an empirical test of the effect of knowledge massaging on people’s ability to process and use stored knowledge to improve performance in a specific decision-making context. In summary, the findings of the study indicate that an aggregated knowledge form had a beneficial effect on both processing efficiency and decision accuracy of individual decision makers. These findings have important implications for practitioners, as they point to one proven way of enhancing performance in organisations by alleviating the detrimental effect of cognitive overload. However, further research is required that would look at alternative ways to improve performance through knowledge management.

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Finnegan, P. and Sammon, D. (2002), “Fundamentals of Implementing Data Warehousing in Organisations”, in Barnes, S. (ed.), Knowledge Management Systems: Theory and Practice, Thomson Learning, London, pp. 195 – 209.

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Standards Australia (2003), AS5037(Int)-2003, Interim Australian Standard, Knowledge Management, Standards Australia International Limited, Sydney.

Wexler, M.N. (2001), “The Who, What and Why of Knowledge Mapping”, Journal of Knowledge Management, 5(3), 249 – 263.

Whitecotton, S.M. (1996), “The Effects of Experience and a Decision Aid on the Slope, Scatter, and Bias of Earnings Forecasts”, Organisational Behaviour and Human Decision Processes, 66(1), 111–121.

Wood, R.E. (1986), “Task Complexity: Definition of the Construct”, Organisational Behaviour and Human Decision Processes, 37(1), 60 – 82.

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CHAPTER 5

Knowledge Maps:

Locating and Acquiring Expert Advice

Without geography, you are nowhere. — Jimmy Buffet

This chapter focuses on knowledge mapping technology. First, it examines the concept and types of knowledge maps. Then it reports the development effort and results of an empirical examination of the usefulness of a competency map in locating and acquiring expert knowledge to support decision-making. Results indicate that the expert competency map was quite useful in enhancing user’s knowledge of the decision task and led to improved decision performance. Subjects tended to perform significantly better with the map irrespective of expert group, but needed less time to do the task with a group of similar experts.

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74 KM: Through the Technology Glass

5.1 Introduction

According to some analysts, the capacity of digital storage in the last decade has increased worldwide at twice the rate predicted for the growth of computing power (Fayyad and Uthurusamy, 2002). The gap between the two trends represent an interesting pattern in the state of technological evolution. Our ability to capture and store data has by far outpaced our ability to process and utilise it. The proliferation of knowledge artefacts in organisational stores creates an overload that is threatening to inhibit the efficient functioning of these organisations. As more artefacts are added to an organisational store, it becomes clear that there need to be some sort of mechanism to help organise and search for useful knowledge from these stores. Otherwise it may remain invisible and unused. This poses a major challenge for knowledge management (KM).

Some authors point to “knowledge mapping” as a feasible KM method to coordinate, simplify, highlight and navigate through complex webs of knowledge possessed by institutions (Wexler, 2001). Knowledge maps or k-maps point to knowledge but they do not contain it. They are guides, not repositories (Davenport and Prusak, 1998). One of the main purposes of k-maps is to locate important knowledge in an organisation and show users where to find it (Kim et al., 2003). Effective k-maps should point not only to people but to documents and databases as well. K-maps should also locate actionable information, identify domain experts, and facilitate organisation-wide learning (Eppler, 2003). They should also trace the acquisition and loss of knowledge, as well as map knowledge flows throughout the organisation (Grey, 1999).

Knowledge mapping can offer many benefits including economic, cultural, structural and knowledge returns (Wexler, 2001). Indeed, empirical findings indicate that knowledge mapping has been successfully used in education to facilitate students’ learning (Chung et al., 1999). Knowledge mapping tools have also been used in the medical field (Miller, 1999) and aerospace industry (Despres and Chauvel, 1999, Gordon, 2000). Despite its many possible beneficial applications in industry, a recent survey shows that knowledge mapping is a relatively rarely used

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Knowledge Maps 75

knowledge management method in business organisations (Stanford, 2001).

The purpose of this chapter is to analyse how knowledge mapping can be used to facilitate the visibility of and access to organisational knowledge resources required by its employees. In addition, results of an empirical study will be presented to illustrate the level of use and benefits achievable from a specific competency map application in a decision- making task.

5.2 Understanding Knowledge Maps

A review of the literature reveals a variety of definitions and categories of knowledge maps proposed and used by industry and academia. Most definitions circle around the idea of tools or processes that help users navigate the silos of artefacts that reside in an organisation, while determining meaningful relationships between knowledge domains (Grey, 1999; Speel et al., 1999; Wexler, 2001). For the purpose of this paper, knowledge map or k-map is understood as the visual display of knowledge and relationships using text, stories, graphics, models or numbers (Eppler, 2003; Vail, 1999a,b).

K-map examples provided by Eppler (2003) include knowledge application, knowledge structure, knowledge source, knowledge asset and knowledge development maps. Wexler (2001) identifies concept, competency, strategy, causal and cognitive maps. Plumley (2003) suggests that knowledge maps can be procedural, concept, competency and social network maps. A more abstract set of categories focusing primarily on cognitive maps is used by Huff (1990). The analysis of similarities and differences among various types of maps mentioned in the KM literature led us to the following three-class categorisation: concept, competency and process based k-maps.

Concept Based k-maps

The group of concept-based k-maps or taxonomies includes conceptual k-maps (Plumley, 2003) and knowledge structure maps (Eppler, 2003).

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76 KM: Through the Technology Glass

Both these maps provide a framework for capturing and organising domain knowledge of an organisation around topical areas. They represent a method of structuring and classifying content in an hierarchical manner. Concept based maps also allow for internal experts’ knowledge to be made explicit in a visual, graphical representation that can be easily understood and shared. Mind maps (Wexler, 2001) as special forms of concept or cognitive maps provide further ability to express and organise a person’s thoughts about a given topic.

Concept maps improve both the visibility and usability of organisational knowledge. The visibility is typically enhanced by the structure of the concept maps and the use of the visual symbols. The visual symbols can be quickly and easily recognised, while the minimum use of text makes it easy to scan for a particular word or phrase. In short, visual representation allows for development of a more holistic understanding of the domain that words alone cannot convey. Concept maps also improve the usability of knowledge as they organise knowledge artefacts around topics rather than functions. Thus, they provide the ability to cross functional boundaries.

Competency Based k-maps

Competency based k-maps cover a group of similar maps including competency k-maps (Plumley, 2003), knowledge source and knowledge assets maps (Eppler, 2003). These maps provide an overview of expertise that resides in the organisation along with the identification of entities that possess such expertise. They act as “yellow pages” or directories which enable people to find needed expertise; visually qualify the existing stock of knowledge of an individual, team or whole organisation; and document the skills, positions and career paths. Essentially, they are simple graphic balance sheets of a company’s intellectual capital (Eppler, 2003).

One of the major benefits of competency based k-maps is that they make the human capital of the organisation highly visible. They can be used to profile a company’s workforce across a number of criteria such as domains of expertise, proximity, seniority or regional distribution. They can also be used to depict the stages of development of a certain

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Knowledge Maps 77

competence. This can be used to help project managers in assessing the available knowledge for projects, jobs as well as to make decisions about personal development and training (Eppler, 2003). Competency based maps can also greatly improve the usability of intellectual capital within the organisation. When converted into “yellow pages” and directories these maps can enable employees to easily find needed expertise within an organisation (Plumley, 2003).

Process Based k-maps

Process based k-maps are one of the most commonly used types of knowledge maps in organisations. They include procedural maps (Plumley, 2003) and knowledge application maps (Eppler, 2003). They are similar in that they both focus on work/business processes. Essentially, process based k-maps present business processes with related knowledge sources in auditing, consulting, research and product development. Any type of knowledge that drives these processes or results from execution of these processes can be mapped. For example, this could include tacit knowledge in people, explicit knowledge in databases, and customer of process knowledge (Plumley, 2003).

Process k-maps have several benefits. They help to improve the visibility of knowledge in organisations by showing which type of knowledge has to be applied at a certain process stage or in a specific business situation. These maps also provide pointers to locate that specific knowledge (Eppler, 2003). Process based k-maps help to improve the usability of knowledge in organisations by forcing participants to identify key knowledge areas that are critical to their business. The analysis of the knowledge map generates ideas for sharing and leveraging knowledge most suited to the organisation and the business context. Finally, the clear and simple visual format is easy to update and evolve over time (Plumley, 2003).

Research Objectives

While researchers have suggested that all three types of knowledge maps improve knowledge access and visibility, there has been very little

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78 KM: Through the Technology Glass

empirical research to support such claims. One recent study found out that providing people with a concept map or ontology resulted in improved weather forecasting. Similarly, a study on the effectiveness of a procedural map in sales forecasting revealed less frequent use of heuristics and more accurate forecasting with such a map (Handzic, 2004). The current study builds on recent research by Handzic and Li (2003) and examines competency maps.

In view of the general lack of empirical evidence, the main objective of the study was to develop and examine the impact of a competency map on locating and accessing advisors’ expertise, and its subsequent impact on users’ performance in a decision-making task. It was hoped that the current research would provide a deeper insight into the potential and limitations of knowledge mapping technology in managing-knowledge for decision making purposes.

5.3 Competency Map Description

A computer-based competency map was devised as a KM tool to facilitate decision-makers’ access to and evaluation of the available expertise. Several features have been implemented in the simulation software to aid users in locating and accessing different advisors’ opinions, and subsequently making their predictions as accurate as possible. These features were based on two concepts: regression analysis and graphical visualisation. In addition, a simple questionnaire was included in the simulation software, asking primarily about the tool usefulness.

One of the features involved line curves. Time series of sales events for twenty days was initially provided in order to guide the decision- making process. The series data were presented in the form of a line curve. With the line curve described above, the users had a reasonable amount of knowledge to make fairly accurate decisions. However, in a knowledge-based economy, “fairly good” is not enough. Therefore an extra K-map was provided in one software version to help users locate and access experts knowledgeable on the subject matter.

Since different advisors in our simulation had different opinions about the next day’s sales, it was important to know their forecasting

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Knowledge Maps 79

ability. To do so, the linear regression modeling technique was employed. A new graph was produced containing several components. The first was the three regression lines calculated from historic figures recorded in a repository. Only lines of best fit and no scatter graphs were presented. The second component in the graph was three dotted lines extended from the three regression lines. These dotted lines extended to both x and y axes. The value cut at the x axis refers to estimated values by the three advisers, while the value cut at the y axis refers to the best estimated values based on the advisors’ past accuracy. The third component included sample correlation coefficients for all independent variables. These three components jointly provided users with a very good idea of how good different advisors were at sales forecasting task.

5.4 Empirical Test

Experiment Description

We tested the competency map developed in a simulated forecasting exercise. As part of the forecasting task, the participants were instructed to assume the Role of the Sales Manager for a fictitious Dream Cream dairy company. They were required to make a series of predictions of the expected product sales over a given period of thirty simulated days. The company was assumed to incur substantial costs from inaccurate forecasts, therefore the goal of the exercise was to minimise forecast errors and save cost.

Subjects performed the task with or without a competency map, so that any difference in performance due to the tool could be determined. In addition, subjects had available a group of similar or diverse advisors to ask for help. The first group was composed of three advisors with similarly moderate levels of forecasting expertise. The second group was composed of advisors with unequal forecasting expertise, one with very high and two with very low levels. The intention was to create varying degrees of uncertainty and complexity in the business environment in order to examine the potential contingent effect of a competency map upon the context.

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80 KM: Through the Technology Glass

Experimental research design was used for this investigation as it allowed us to have greater control and made possible drawing of stronger inferences about causal relationships among the variables examined. We carried out the experiment with sixty-eight student-subjects from the Business Intelligence Systems course at UNSW. These subjects participated in the exercise on a voluntary basis, and received no monetary incentives.

The effectiveness of the competency map was evaluated in terms of its users’ accomplishment. To explore what the competency map users could actually accomplish with the tool, the total cost of errors incurred by each subject was calculated. It was obtained as a sum of absolute difference between subjects’ forecasts and actual sales over a period of thirty trials (Makridakis, 1993), and expressed in dollar terms. In addition, the total time taken to complete the task was calculated for each subject to evaluate the efficiency effect of the map.

Results

Mean performance scores (total cost, total decision time) of four experimental groups are presented in Table 5.1. The collected data were further analysed statistically by a t-test. The analysis found some significant results at p = 0.1 or better.

Table 5.1 Summary results for total cost and decision time

Dependent Similar Advisors Diverse Advisors

Variable Without Map With Map Without Map With Map

Cost $9,455.18 $8,160.18 $12,628.19 $9,321.53

(Std.Dev.) (2,332.71) (2,877.45) (7,544.68) (5,028.91)

Time 12.38 min 8.19 min 10.75 min 10.61 min

(Std.Dev.) (4.72) (4.84) (4.52) (4.87)

With respect to the total cost incurred, the results of the analysis performed indicate significant differences in error scores between subjects

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Knowledge Maps 81

in different k-map groups. Subjects with the k-map tended to make significantly smaller forecast errors than their counterparts without the k-map irrespective of the advisor group ($8,741 < $11,042). Smaller errors indicated better knowledge and greater improvement in the quality of actual forecasts over naive forecasts.

With respect to the total decision time spent, the results of the analysis indicated different effects of k-map in different advisor groups. Subjects with the k-map tended to take a shorter decision time than their counterparts without the k-map only in a similar advisor group (8.2 min vs. 12.4 min). Shorter time indicated faster knowledge acquisition in addition to greater improvement in the quality of actual forecasts. However, the availability of the k-map made no significant difference in the decision time spent by subjects with diverse advisor group (10.6 min vs. 10.7 min).

Effectiveness Impacts

It was predicted that the use of a competency k-map such as the regression graph applied in the experiment above would improve the ability of decision makers to locate and acquire experts’ opinions and increase the performance of a decision task.

Table 5.1 supports the predicted hypothesis in two ways. Firstly, the average sales cost of subjects working on the simulation with a k-map tool is much less than those working without such a tool: by 13.70% for the equally competent advisors and 26.18% for the unequally competent advisors. The effects are quite significant when taking into account that the k-map tool was quite simple. Another important fact that can be observed from these figures is that the positive effect of a k-map increases with the increased diversity in the advisor set (26.18% > 13.70%).

An additional observation from Table 5.1 is the much smaller standard deviation of total sales cost for k-map subjects with diverse set of advisors. The smaller standard deviation value means that most subjects consistently out-perform those who worked without the aid of the k-map. It implies that k-maps help to increase both performance accuracy and consistency of users, the latter is particularly evident in a more complex diverse advisor situation that is hard for users to deal with.

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82 KM: Through the Technology Glass

Frequency distribution graphs presented in Figure 5.1 provide further support for the above analysis. The graphs reveal that more subjects with the map showed improved performance irrespective of the advisor set.

Efficiency Impacts

As stated earlier, the time consumed to make decisions is also an important factor when considering the effectiveness of a k-map. It is predicted that with the aid of a tool, the time required to locate and acquire needed knowledge should be less than the time required without the tool. The correctness of this hypothesis can be justified by Table 5.1.

Figure 5.1 Total cost with and without map for (a) equally and (b) unequally competent advisors

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Knowledge Maps 83

Similar to the case for sales cost, the average time required to finish the task was 33.89% and 1.36% less for subjects who worked with the simulation accompanied by k-map, with similar and diverse advisor sets, respectively. The difference is significant for the similar advisor set, but not for the diverse one.

These results are further supported by the frequency distribution graphs presented in Figure 5.2. These graphs show that more subjects required less time to complete the task for the similar advisor set only.

The explanation for this phenomenon may lie in the different level of effort required to interpret the mixed information provided by the regression based k-map tool in different cases. In the case of similar advisors, the three sales figures suggested by the set were usually quite

Figure 5.2 Total time with and without map for (a) equally and (b) unequally competent advisors

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84 KM: Through the Technology Glass

close to one another. This enabled subjects to make quick decisions. On the other hand, in the case of diverse advisors, the three values were usually quite different since the three advisors differed in their level of competency and tended to predict the sales of ice-cream differently. Thus, extra time had to be spent on considering both the suggested values and different relative competency of the three advisors. While the time required to complete the task in both cases was comparable, at the same time, the quality of the decision making outcome was higher with k-map use. Thus, k-map still improved the overall process of decision-making.

Other Issues

The present study has several aspects that limit the generalisability of its results. First of all, the investigation took place in a laboratory experiment. The main reason for using a laboratory experiment design was to accomplish a high degree of control over the independent variables. High control maximises the internal validity of the research and enables drawing of stronger inferences about causal relationships (Huck et al., 1974; Judd et al., 1991). However, it may compromise external validity. Data were artificially generated from statistical processes. This was done to allow internal control of different advisor characteristics, such as their predictive ability of the variable of interest, but makes it difficult to generalise to more realistic settings. Finally, the subjects in the study were students. Their lack of real-life experience on decision making tasks implies that different strategies may be chosen when comparing with decision makers in industry domains. They also had to learn how the system works. Despite making a serious effort to ensure its ease of use, it is possible that five practice trials were not sufficient to completely familiarise subjects with the system and allow optimal performance.

Due to the above limitations, the present study should be considered exploratory and its conclusions tentative. Replications using real experts in their domain as subjects in applied settings are needed to gain deeper insight into people’s knowledge acquisition process. Additional insight may also be obtained by using real world data. Several further extensions

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Knowledge Maps 85

to the current research are also possible. This study has examined the tool effectiveness in noisy but stable data patterns. In addition, the current study has investigated only the extreme situations when all available advisors were either very similar or very different. Further studies should incorporate less extreme situations. In particular, one may examine whether moderate contrasts would dilute the effect of a k-map on performance.

With respect to tool presentation, the present study used only two- dimensional (regression line) graphs for displaying different advisor competencies. Future research may investigate whether multi-dimensional graphs would facilitate faster and better understanding of the situation and possibly further help to improve performance. A preliminary study conducted by Stephens and Handzic (2004) indicates that a virtual reality tool may be useful for visualising social networks and identifying paths to right people. Finally, decision performance analysis was employed as a primary method to evaluate the effectiveness of a k-map. Although this method is unobtrusive and robust, other techniques, such as verbal protocols or eye fixation movement, may provide deeper insight into the users’ knowledge acquisition processes. The results may also be utilised to further validate inferences made in this study.

5.5 Conclusions

The study showed the ability of a competency k-map to improve people’s ability to locate and acquire expert knowledge and enhance performance of a forecasting task. From our research findings one may conclude that using k-maps can effectively enhance decision makers’ working knowledge and performance while at the same time reduce the time required to finish the task. However, the study is not without limitations. Different technologies will have advantages and disadvantages under different situations, and thus careful consideration of different combinations of technologies is necessary. Further empirical research is recommended that would address some of these issues.

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