knowledge moment

Week 5

Managing Organizational Knowledge, Learning and Intellectual Capital II

• Experimentation strategies for knowledge integration

Knowledge integration is the process of synthesizing multiple knowledge models (or

representations) into a common model (representation).

Compared to information integration, which involves merging information having different

schemas and representation models, knowledge integration focuses more on synthesizing the

understanding of a given subject from different perspectives.

For example, multiple interpretations are possible of a set of student grades, typically each from

a certain perspective. An overall, integrated view and understanding of this information can be

achieved if these interpretations can be put under a common model, say, a student performance

index.

The Web-based Inquiry Science Environment (WISE), from the University of California at

Berkeley has been developed along the lines of knowledge integration theory.

Knowledge integration has also been studied as the process of incorporating new information

into a body of existing knowledge with an interdisciplinary approach. This process involves

determining how the new information and the existing knowledge interact, how existing

knowledge should be modified to accommodate the new information, and how the new

information should be modified in light of the existing knowledge.

A learning agent that actively investigates the consequences of new information can detect and

exploit a variety of learning opportunities; e.g., to resolve knowledge conflicts and to fill

knowledge gaps. By exploiting these learning opportunities the learning agent is able to learn

beyond the explicit content of the new information.

The machine learning program KI, developed by Murray and Porter at the University of Texas

at Austin, was created to study the use of automated and semi-automated knowledge integration

to assist knowledge engineers constructing a large knowledge base.

A possible technique which can be used is semantic matching. More recently, a technique useful

to minimize the effort in mapping validation and visualization has been presented which is

based on Minimal Mappings. Minimal mappings are high quality mappings such that i) all the

other mappings can be computed from them in time linear in the size of the input graphs, and ii)

none of them can be dropped without losing property i).

• Knowledge transfer

Knowledge transfer refers to sharing or disseminating of knowledge and providing inputs

to problem solving. In organizational theory, knowledge transfer is the practical problem of

transferring knowledge from one part of the organization to another. Like knowledge

management, knowledge transfer seeks to organize, create, capture or distribute knowledge and

ensure its availability for future users. It is considered to be more than just

a communication problem. If it were merely that, then a memorandum, an e-mail or a meeting

would accomplish the knowledge transfer. Knowledge transfer is more complex because:

• knowledge resides in organizational members, tools, tasks, and their

subnetworks and

• much knowledge in organizations is tacit or hard to articulate.

The subject has been taken up under the title of knowledge management since the 1990s. The

term has also been applied to the transfer of knowledge being transferred at the international

level.

In business, knowledge transfer now has become a common topic in mergers and acquisitions. It

focuses on transferring technological platform, market experience, managerial expertise,

advance corporate culture, and other intellectual capital that can improve the companies'

competence. Since technical skills and knowledge are very important assets for firms'

competence in the global competition, unsuccessful knowledge transfer will have a negative

impact to the corporations and leads to the expensive and time-consuming M&A not creating

values to the firms.

Background

Argote & Ingram (2000) define knowledge transfer as "the process through which one unit (e.g.,

group, department, or division) is affected by the experience of another" (p. 151). They further

point out the transfer of organizational knowledge (i.e., routine or best practices) can be

observed through changes in the knowledge or performance of recipient units. The transfer of

organizational knowledge, such as best practices, can be quite difficult to achieve.

Szulanski's doctoral dissertation ("Exploring internal stickiness: Impediments to the transfer of

best practice within the firm") proposed that knowledge transfer within a firm is inhibited by

factors other than a lack of incentive. How well knowledge about best practices remains broadly

accessible within a firm depends upon the nature of that knowledge, from where (or whom) it

comes, who gets it, and the organizational context within which any transfer occurs.

"Stickiness" is a metaphor that comes from the difficulty of circulating fluid around an oil

refinery (including effects of the fluid's native viscosity). It is worth noting that his analysis

does not apply to scientific theories, where a different set of dynamics and rewards apply.

Three related concepts are "knowledge utilization", "research utilization" and "implementation",

which are used in the health sciences to describe the process of bringing a new idea, practice or

technology into consistent and appropriate use in a clinical setting. The study of knowledge

utilization/implementation (KU/I) is a direct outgrowth of the movement toward evidence-based

medicine and research concluding that health care practices with demonstrated efficacy are not

consistently used in practice settings.

Knowledge transfer within organisations and between nations also raises ethical considerations

particularly where there is an imbalance in power relationships (e.g. employer and employee) or

in the levels of relative need for knowledge resources (such as developed and developing

worlds).

Knowledge transfer includes, but encompasses more than, technology transfer.

Knowledge transfer mechanisms

Two kinds of knowledge transfer mechanisms have been noticed in

practice: Personalization and Codification. Personalization refers to the one-to-one transfer of

[knowledge] between two entities in person. A very good example of this is the act of teaching a

person how to ride a bicycle. On the other hand, codification refers to the act of converting

knowledge into knowledge artifacts such as documents, images and videos that are consumed

by the knowledge recipients asynchronously.

Personalized knowledge transfer results in better assimilation of knowledge by the recipient

when knowledge tacitness is higher and/or when information content in a knowledge object is

high. On the other hand, codification is driven by the need to transfer knowledge to large

number of people and results in better knowledge reuse. Entropy of the knowledge objects can

provide a measure of their information content or tacitness.

Between public and private domains

With the move of advanced economies from a resource-based to a knowledge-based

production, many national governments have increasingly recognized "knowledge" and

"innovation" as significant driving forces of economic growth, social development, and job

creation. In this context the promotion of 'knowledge transfer' has increasingly become a subject

of public and economic policy. However, the long list of changing global, national and regional

government programmes indicates the tension between the need to conduct 'free' research – that

is motivated by interest and by private sector 'short term' objectives - and research for public

interests and general common good.

The underlying assumption that there is a potential for increased collaboration between industry

and universities is also underlined in much of the current innovation literature. In particular the

Open Innovation approach to developing business value is explicitly based on an assumption

that Universities are a "vital source for accessing external ideas". Moreover, Universities have

been deemed to be "the great, largely unknown, and certainly underexploited, resource

contributing to the creation of wealth and economic competitiveness."

Universities and other public sector research organisations (PSROs) have accumulated much

practical experience over the years in the transfer of knowledge across the divide between the

domains of publicly produced knowledge and the private exploitation of it. Many colleges and

PSROs have developed processes and policies to discover, protect and exploit intellectual

property (IP) rights, and to ensure that IP is successfully transferred to private corporations, or

vested in new companies formed for the purposes of exploitation. Routes to commercialization

of IP produced by PSROs and colleges include licensing, joint venture, new company formation

and royalty-based assignments.

Organisations such as AUTM in the US, the Institute of Knowledge Transfer in the

UK, SNITTS in Sweden and the Association of European Science and Technology Transfer

Professionals in Europe have provided a conduit for knowledge transfer professionals across the

public and private sectors to identify best practice and develop effective tools and techniques for

the management of PSRO/college produced IP. On-line Communities of Practice for knowledge

transfer practitioners are also emerging to facilitate connectivity (such as The Global Innovation

Network and the knowledge Pool).

Business-University Collaboration was the subject of the Lambert Review in the UK in 2003.

In the knowledge economy

In the knowledge-based economy, learning becomes extremely important in determining the

fate of individuals, firms and national economies.

Knowledge transfer can also be achieved through investment programme, both intentionally and

unintentionally in the form of skills, technology, and ‘tacit knowledge’

including management and organisational practices. For example, foreign investment in African

countries have shown to provide some knowledge transfer.

In landscape ecology

By knowledge transfer in landscape ecology, means a group of activities that increase the

understanding of landscape ecology with the goal of encouraging application of this knowledge.

Five factors will influence knowledge transfer from the view of forest landscape ecology: the

generation of research capacity, the potential for application, the users of the knowledge, the

infrastructure capacity, and the process by which knowledge is transferred (Turner, 2006).

Types of knowledge

Knowledge is a dominant feature in our post-industrial society, and knowledge workers are

important in many enterprises. Blackler expands on a categorization of knowledge types that

were suggested by Collins (1993):

• Embrained knowledge is that which is dependent on conceptual skills and

cognitive abilities. We could consider this to be practical, high-level knowledge,

where objectives are met through perpetual recognition and revamping. Tacit

knowledge may also be embrained, even though it is mainly subconscious.

• Embodied knowledge is action oriented and consists of contextual practices. It is

more of a social acquisition, as how individuals interact in and interpret their

environment creates this non-explicit type of knowledge.

• Encultured knowledge is the process of achieving shared understandings through

socialization and acculturation. Language and negotiation become the discourse of

this type of knowledge in an enterprise.

• Embedded knowledge is tacit and resides within systematic routines. It relates to

the relationships between roles, technologies, formal procedures and emergent

routines within a complex system. In order to initiate any specific line of business

knowledge transition helps a lot.

• Encoded knowledge is information that is conveyed in signs and symbols (books,

manuals, data bases, etc.) and decontextualized into codes of practice. Rather than

being a specific type of knowledge, it deals more with the transmission, storage and

interrogation of knowledge.

• Value-of-ownership models

What Is the Total Cost of Ownership?

The total cost of ownership (TCO) is the purchase price of an asset plus the costs of operation.

Assessing the total cost of ownership represents taking a bigger picture look at what the product

is and what its value is over time.

When choosing among alternatives in a purchasing decision, buyers should look not just at an

item's short-term price, known as its purchase price, but also at its long-term price, which is its

total cost of ownership. The item with the lower total cost of ownership is the better value in

the long run.

KEY TAKEAWAYS

• The total cost of ownership, or TCO, includes the purchase price of a particular asset,

plus operating costs over the asset's lifespan.

• Looking at the total cost of ownership is a way of assessing the long-term value of a

purchase to a company or individual.

• Corporations use the total cost of ownership as a means of analyzing business deals,

while individuals look at the total cost as a way of assessing potential purchases.

Total value of ownership

Total value of ownership (TVO) or total value of opportunity, is a methodology of

measuring and analyzing the business value of IT investments. Gartner Group designed this

methodology in 2003.

TVO differs from total cost of ownership (TCO) in that TVO considers the benefits of

alternative investments. It is a comparative measurement that evaluates the TCO and any

additional benefits, such as the mobility of laptops when compared to desktop computers.