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KNOWLEDGE
MANAGEMENT
MODELS
Furious activity is no substitute for understanding. H. H. Williams (1858–1940)
To succeed, a knowledge management initiative must have a robust theo- retical foundation. The major KM activities described in the KM cycle in the previous chapter require a conceptual framework to operate within; otherwise the activities will not be coordinated and will not produce the expected KM benefits. Knowledge management models are presented from Choo (1998), Weick (2001), Nonaka and Takeuchi (1995), Wiig (1993), von Krogh and Roos (1995), Boisot (1998), Beer (1984), and Bennet and Bennet (2004). All the models present different perspectives on the key conceptual elements that form the infrastructure of knowledge management. This chapter describes, compares, and contrasts each model in order to provide a sound understand- ing of the discipline of KM.
LEARNING OBJECTIVES
- Understand the key tenets of the major knowledge management theo- retical models in use today.
- Link the KM frameworks to key KM concepts and the major phases of the KM cycle.
- Explain the complex adaptive system model of KM and how it addresses the subjective and dynamic nature of content to be managed.
INTRODUCTION
In an economy where the only certainty is uncertainty, the one sure source of lasting competitive advantage is knowledge. I. Nonaka (1995)
Although few would argue that knowledge is not important, the overriding problem is that few managers and information professionals understand how to manage knowledge in knowledge-creating organizations. The tendency is to focus on “hard” or quantifiable knowledge, and KM is often seen as some sort of information processing machine. The advent of knowledge management was initially met with a fair degree of criticism, with many people feeling this was yet another buzzword that would quickly pass into history. Instead, KM estab- lished itself credibly as both an academic discipline of study and a professional field of practice, and one reason it was so successful was the work done on theoretical or conceptual models of knowledge management. Early in the devel- opment of KM, more pragmatic considerations about its processes were soon complemented by the need to understand what was happening in organiza- tional knowing, reasoning, and learning. A more holistic approach to KM has become necessary as the complex sub- jective and dynamic nature of knowledge has become a more pressing issue. Cultural and contextual influences further increased the complexity involved in KM, and these factors also had to be taken into account in a model or frame- work that could situate and explain the key KM concepts and processes. Finally, measurements were needed in order to be able to monitor progress toward and attainment of expected KM benefits. This holistic approach encompasses all the different types of content to be managed, ranging from data to information to knowledge, but also from tacit to explicit and back to tacit-knowledge-type conversions. All the KM models presented in this chapter attempt to address knowledge management from a holistic and comprehensive perspective. Davenport and Prusak (1998, p. 2) provide the following distinctions between data, information, and knowledge, which also serve to recap the examples presented in Chapter 1:
Data: A set of discrete, objective facts about events. Information: A message, usually in the form of a document or an audible or visible communication. Knowledge: A fluid mix of framed experiences, values, contextual information, and expert insight that provides a framework for evaluating and incor- porating new experiences and information. It originates and is applied in the minds of knowers. In organizations, it often becomes embed- ded not only in documents or repositories but also in organizational routines, processes, practices, and norms.
Davenport and Prusak (1998) refer to this distinction between data, infor- mation, and knowledge as an operational one, and they argue that we can transform information into knowledge by means of comparison, consequences,
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- They represent a holistic approach to knowledge management (i.e., they are comprehensive and take into consideration people, process, orga- nization, and technology dimensions).
- They have been reviewed, critiqued, and discussed extensively in the KM literature, by practitioners, academics, and researchers alike.
- The models have been implemented and field tested with respect to reli- ability and validity.
This list is not meant either to be exhaustive or a definitive short list, but the models have been selected with a view to providing the widest possible perspective on KM as a whole, combined with a deeper, more robust theoret- ical foundation for explaining, describing, and better predicting the best way to manage knowledge.
The von Krogh and Roos Model of
Organizational Epistemology
The von Krogh and Roos KM model (1995) distinguishes between indivi- dual knowledge and social knowledge, and they take an epistemological approach to managing organizational knowledge: the organizational episte- mology KM model. Whereas the definition of organization has been problem- atic and the term is often used interchangeably with information, a number of issues must be addressed:
■ (^) How and why individuals within an organization come to know. ■ (^) How and why organizations, as social entities, come to know. ■ (^) What counts for knowledge of the individual and the organization. ■ (^) What are the impediments in organizational KM.
The cognitivist perspective (e.g., Varela, 1992) proposes that a cognitive system, whether it is a human brain or a computer, creates representations (i.e., models) of reality and that learning occurs when these representations are manipulated. A cognitive organizational epistemology views organizational knowledge as a self-organizing system in which humans are transparent to the information from the outside (i.e., we take in information through our senses, and we use this information to build our mental models). The brain is a machine based on logic and deduction that does not allow any contradictory propositions. The organization thus picks up information from its environment and processes it in a logical way. Alternative courses of action are generated through information search, and the cognitive competence of an organization depends on the mobilization of individual cognitive resources—a “linear” sum- mation of individuals to form the organizational whole. The connectionist approach, on the other hand, is more holistic than reduc- tionist. The brain is not assumed to sequentially process symbols but to per- ceive “wholeness,” global properties, patterns, synergies, and gestalts. Learning rules govern how the various components of these whole networks are con- nected. Information is not only taken in from the environment but also gen- erated internally. Familiarity and practice lead to learning. Individuals form
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nodes in a loosely connected organizational system, and knowledge is an emer- gent phenomenon that stems from the social interactions of these individuals. In this perspective, knowledge resides not only in the minds of individuals but also in the connections among these individuals. A collective mind is formed as the representation of this network, and it is this that lies at the core of orga- nizational knowledge management. Von Krogh and Roos adopt the connectionist approach. In their organiza- tional epistemology KM model, knowledge resides both in the individuals of an organization and, at the social level, in the relations between the individu- als. Knowledge is said to be “embodied”; that is, “everything known is known by somebody” (von Krogh and Roos, 1995, p. 50). Unlike cognitivism, which views knowledge as an abstract entity, connectionism maintains that there can be no knowledge without a knower. This notion fits nicely with the concept of tacit knowledge, which is very difficult to abstract out of someone and is made more concrete. It also reinforces the strong need to maintain links between knowledge objects and those who are knowledgeable about them—authors, subject matter experts, and experienced users who have applied the knowledge both successfully and unsuccessfully. In 1998, von Krogh, Roos, and Kleine examined the fragile nature of KM in organizations in terms of the mind-set of the individuals, communication in the organization, the organizational structure, the relationship between the members, and the management of human resources. These five factors could impede the successful management of organizational knowledge for innova- tion, competitive advantage, and other organizational goals. For example, if the individuals do not perceive knowledge to be a crucial competence of the firm, then the organization will have trouble developing knowledge-based competencies. If there is no legitimate language to express new knowledge in the individual, contributions will fail. If the organizational structure does not facilitate innovation, KM will fail. If individual members are not eager to share their experiences with their colleagues on the basis of mutual trust and respect, there will be no generation of social, collective knowledge within that organi- zation. Finally, if those contributing knowledge are not highly evaluated and acknowledged by top management, they will lose their motivation to innovate and develop new knowledge for the firm. Organizations need to put knowledge enablers in place that will stimulate the development of individual knowledge, group sharing of knowledge, and organizational retention of valuable knowledge-based content. This approach was further refined (von Krogh, Ichijo, and Nonaka, 2000) to propose a model of knowledge enabling rather than knowledge management. Knowledge enabling refers to the “overall set of organizational activities that positively affect knowledge creation” (p. 4). This typically involves facilitating relation- ships and conversations as well as sharing local knowledge across an organi- zation and across geographical and cultural borders. The connectionist approach appears to be the more appropriate one for underpinning a theoretical model of knowledge management, especially owing to the fact that the linkage between knowledge and those who “absorb” and make use of the knowledge is viewed as an unbreakable bond. The con- nectionist approach provides a solid theoretical cornerstone for a model of
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able, public organizational knowledge. Making personal knowledge available to others in the company is at the core of this KM model. This type of knowl- edge creation process takes place continuously and occurs at all levels of the organization. In many cases, the creation of knowledge happens in an unex- pected or unplanned way. According to Nonaka and Takeuchi, there are four modes of knowledge conversion that
constitute the “engine” of the entire knowledge-creation process. These modes are what the individual experiences. They are also the mechanisms by which indi- vidual knowledge gets articulated and “amplified” into and throughout the organization (p. 57). Organizational knowledge creation, therefore, should be understood as a process that organizationally amplifies the knowledge created by individuals and crystallizes it as a part of the knowledge network of the organization. (p. 59)
Knowledge creation consists of a social process between individuals in which knowledge transformation is not simply a unidirectional process but it is inter- active and spiral. (pp. 62–63)
Knowledge Conversion
There are four modes of knowledge conversion, as illustrated in Figure 3-1:
- From tacit knowledge to tacit knowledge: the process of socialization.
- From tacit knowledge to explicit knowledge: the process of externali- zation.
- From explicit knowledge to explicit knowledge: the process of com- bination.
- From explicit knowledge to tacit knowledge: the process of internaliza- tion.
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F IGURE 3- T HE NONAKA AND T AKEUCHI MODEL OF KNOWLEDGE CONVERSION
Socialization Externalization
Internalization Combination
Tacit Knowledge to Explicit Knowledge
Tacit Knowledge
Explicit Knowledge
from
Source : Nonaka and Takeuchi, 1995, p. 62.
Socialization (tacit-to-tacit) consists of sharing knowledge in face-to-face, natural, and typically social interactions. It involves arriving at a mutual under- standing through the sharing of mental models, brainstorming to come up with new ideas, apprenticeship or mentoring interactions, and so on. Socialization is among the easiest forms of exchanging knowledge because it is what we do instinctively when we gather at the coffee machine or engage in impromptu corridor meetings. The greatest advantage of socialization is also its greatest drawback: because knowledge remains tacit, it is rarely captured, noted, or written down anywhere. It remains in the minds of the original participants. Although socialization is a very effective means of knowledge creation and sharing, it is one of the more limited means. It is also very difficult and time- consuming to disseminate all knowledge using only this mode. Davenport and Prusak (1998) point out that:
Tacit, complex knowledge, developed and internalized by the knower over a long period of time, is almost impossible to reproduce in a document or a database. Such knowledge incorporates so much accrued and embedded learn- ing that its rules may be impossible to separate from how an individual acts. (p. 70)
This means that the process of acquiring tacit knowledge is not strictly tied to the use of language but rather to experience and to the ability to transmit and to share it. This idea must not be confused with that of a simple transfer of information because knowledge creation does not take place if we abstract the transfer of information and of experiences from associated emotions and spe- cific contexts in which they are embedded. Socialization consists of sharing experiences through observation, imitation, and practice. For example, Honda organizes “brainstorming camps” during which detailed discussions take place to solve difficult problems in development proj- ects. These informal meetings are usually held outside the workplace, off-site, where everybody is encouraged to contribute to the discussion and nobody is allowed to refer to the status and qualification of employees involved. The only behavior not admitted during these discussions is simple criticism that is not followed by constructive suggestions. Honda uses brainstorming meetings not only to develop new products but also to improve its managerial systems and its commercial strategies. Brainstorming represents not only occasions for creative dialogue but also a moment when people share experience and, then, tacit knowledge. In this way, they create harmony among themselves, they feel they are a part of the organization, and they feel linked to one another by sharing the same goals. Many other organizations hold similar “Knowledge Days” or “Knowledge Cafés” to encourage this type of tacit-to-tacit knowl- edge sharing. The process of externalization (tacit-to-explicit) gives a visible form to tacit knowledge and converts it to explicit knowledge. It can be defined as “a quin- tessential knowledge creation process in that tacit knowledge becomes explicit, taking the shapes of metaphors, analogies, concepts, hypotheses, or models” (Nonaka and Takeuchi, 1995, p. 4). In this mode, individuals are able to arti- culate the knowledge and know-how and, in some cases, the know-why and
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The last conversion process, internalization (explicit-to-tacit), occurs through diffusing and embedding newly acquired behavior and newly under- stood or revised mental models. Internalization is strongly linked to “learning by doing.” Internalization converts or integrates shared and/or individual experiences and knowledge into individual mental models. Once internalized, new knowl- edge is then used by employees who broaden it, extend it, and reframe it within their own existing tacit knowledge bases. They understand, learn, and buy into the new knowledge, and this is manifested as an observable change; that is, they now do their jobs and tasks differently. For example, General Electric has developed a system of documenting all customer complaints and inquiries in a database that can be accessed by all its employees. This system allows the employees to find answers to new customers’ questions much more quickly because it facilitates the sharing of employees’ experiences in problem solving. This system also helps the workers to inter- nalize others’ experiences in answering questions and solving problems. Knowledge, experiences, best practices, lessons learned, and so on go through the conversion processes of socialization, externalization, and combi- nation, but they cannot halt at any one of these stages. Only when knowledge is internalized into individuals’ tacit knowledge bases in the form of shared mental models or technical know-how does this knowledge become a valuable asset to the individual, to their community of practice, and to the organiza- tion. In order for organizational knowledge creation to take place, however, the entire conversion process has to begin all over again: the tacit knowledge accumulated at the individual level needs to be socialized with other organi- zational members, thereby starting a new spiral of knowledge creation (Nonaka and Takeuchi, 1995, p. 69). When experiences and information are transferred through observation, imitation, and practice, then we are back in the socialization quadrant. This knowledge is then formalized and converted into explicit knowledge, through use of analogy, metaphor, and model, in the externalization quadrant. This explicit knowledge is then systematized and recombined in the combination quadrant, whereupon it once again becomes part of individuals’ experiences. In the internalization quadrant, knowledge has once again become tacit knowledge.
Knowledge Spiral
Knowledge creation is not a sequential process. Rather, it depends on a continuous and dynamic interaction between tacit and explicit knowledge throughout the four quadrants. The knowledge spiral (see Figure 3-2) shows how organizatins articulate, organize and systematize individual tacit knowl- edge. Organizations produce and develop tools, structures, and models to accu- mulate and share knowledge. The knowledge spiral is a continuous activity of knowledge flow, sharing, and conversion by individuals, communities, and the organization itself. The two steps in the knowledge spiral that are the most difficult are those involving a change in the type of knowledge, namely, externalization , which converts tacit into explicit knowledge, and internalization , which converts
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explicit into tacit knowledge. These two steps require a high degree of personal commitment, and they will typically involve mental models, personal beliefs and values, and a process of reinventing yourself, your group, and the organi- zation as a whole. A metaphor is a good way of expressing this “inexpress- ible” content. For example, a slogan, a story, an analogy, or a symbol of some type can encapsulate complex contextual meanings. A metaphor is often used to convey two ideas in a single phrase and may be defined as “accomplishes in a word or phrase what could otherwise be expressed only in many words, if at all” (Sommer and Weiss, 1995, p. vii). All of these vehicles are good models for representing a consistent, systematic, and logical understanding of content without any contradictions. The better and the more coherent the model, and the better the model fits with existing mental models, the higher the likelihood of successful implementation of a knowledge spiral. It is possible to structure metaphors, models, and analogies in an organiza- tional KM design. The first principle is to have built-in redundancy to make sure information overlaps. Redundancy will make it easier to articulate content, to share content, and to make use of it. An example is to set up several competing groups, to build in a rotational strategy so that workers do a variety of jobs, and to provide easy access to company information via a single inte- grated knowledge base. Knowledge sharing and use occurs through the “knowledge spiral,” which, “starting at the individual level and moving up through expanding communi- ties of interaction,... crosses sectional, departmental, divisional and organi- zational boundaries” (Nonaka and Takeuchi, 1995, p. 72). Nonaka and Takeuchi argue that an organization has to promote a facilitating context in which the organizational knowledge-creation process and the individual one can easily take place, acting as a spiral. They describe the following “Enabling Conditions for Organizational Knowledge Creation”:
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F IGURE 3- THE NONAKA AND TAKEUCHI KNOWLEDGE SPIRAL
Externalization
Internalization Combination
Field Building
Dialogue
Learning by Doing
Linking Explicit Knowledge
Socialization
Source : Nonaka and Takeuchi, 1995, p. 71.
damaging the entire system. A human being is “tightly coupled,” whereas the human genome is “loosely coupled.” Loose coupling permits adaptation, evo- lution, and extension. Sense making can be thought of as a loosely coupled system whereby individuals construct their own representation of reality by comparing current with past events. Weick (2001) proposes that sense making in organizations consists of four integrated processes: (1) ecological change, (2) enactment, (3) selection, and (4) retention. Ecological change is a change in the environment that is external to the organization—one that disturbs the flow of information to participants— and triggers an ecological change in the organization. Organizational actors enact their environment by attempting to closely examine elements of the environment. In the enactment phase, people try to construct, rearrange, single out, or demolish specific elements of content. Many of the objective features of their environment are made less random and more orderly through the creation of their own constraints or rules. Enactment clarifies the content and issues to be used for the subsequent selection process. Selection and retention are the phases in which individuals attempt to inter- pret the rationale for the observed and enacted changes by making selections. The retention process in turn furnishes the organization with an organizational memory of successful sense-making experiences. This memory can be reused in the future to interpret new changes and to stabilize individual interpreta- tions into a coherent organizational view of events and actions. These phases
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F IGURE 3- OVERVIEW OF CHOO’S (1998) KNOWLEDGE MANAGEMENT MODEL
Streams of experience Sense Making
Knowledge Creating
Decision New knowledge, Making new capabilities
Shared meanings
Shared meanings
Goal-directed adaptive behavior External information & knowledge Next knowing cycle
also serve to reduce any uncertainty and ambiguity associated with unclear, poorly defined information. Knowledge creating may be viewed as the transformation of personal knowledge between individuals through dialogue, discourse, sharing, and storytelling. This phase is directed by a knowledge vision of “as is” (current situation) and “to be” (future, desired state). Knowledge creation widens the spectrum of potential choices in decision making by providing new knowledge and new competencies. The result feeds the decision-making process with inno- vative strategies that extend the organization’s capability to make informed, rational decisions. Choo (1998) draws upon the Nonaka and Takeuchi (1995) model for a theoretical basis of knowledge creation. Decision making is situated in rational decision-making models that are used to identify and evaluate alternatives by processing the information and knowl- edge collected to date. There are a wide range of decision-making theories such as the theory of games and economic behavior (e.g., Dixit and Nalebuff, 1991; Bierman and Fernandez, 1993), chaos theory, emergent theory, and complex- ity theory (e.g., Gleick, 1987; Fisher, 1984; Simon, 1969; Stewart, 1989; Stacey, 1992). There is even a garbage can theory of decision making (e.g., Daft, 1982; Daft and Weick, 1984; Padgett, 1980). The Garbage Can model (GCM) of organizational decision making was developed in reference to “ambiguous behaviors,” that is, explanations or interpretations of behaviors that at least appear to contradict classical theory. The GCM was greatly influenced by the realization that extreme cases of aggre- gate uncertainty in decision environments would trigger behavioral responses, which, at least from a distance, appear to be “irrational” or at least not in compliance with the total/global rationality of “economic man” (e.g., “act first, think later”). The GCM was originally formulated in the context of the operation of universities and their many interdepartmental communications problems. The Garbage Can model attempted to expand organizational decision theory into the then uncharted field of organizational anarchy, which is characterized by “problematic preferences,” “unclear technology,” and “fluid participation.” “The theoretical breakthrough of the garbage can model is that it disconnects problems, solutions, and decision makers from each other, unlike traditional decision theory. Specific decisions do not follow an orderly process from problem to solution, but are outcomes of several relatively independent streams of events within the organization” (Daft, 1982, p. 139). Simon (1957) identified the principle of bounded rationality as a constraint for organizational decision making: “The capacity of the human mind for for- mulating and for solving complex problems is very small compared with the size of the problems whose solution is required for objectively rational behav- ior in the real world—or even for a reasonable approximation to such objec- tive rationality” (p. 198). Simon suggested that persons faced with ambiguous goals and unclear means of linking actions to those goals seek to fulfill short-term subgoals. Subgoals are objectives that the individual believes can be achieved by allocating resources under his or her control. These subgoals are generally not derived from broad policy goals, but rather from experiences, education, the
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bases (i.e., tacit or explicit knowledge). We first need to know that the knowl- edge is out there. The knowledge may be complete in the sense that all that is available about the subject is there, but if no one knows of its existence and/or availability, they cannot make use of this knowledge. Connectedness refers to the well-understood and defined relations between the different knowledge objects. Very few knowledge objects are totally dis- connected from the others. The more connected a knowledge base is (i.e., the greater the number of interconnections in the semantic network), then the more coherent the content and the greater its value. A knowledge base is said to possess congruence when all the facts, concepts, perspectives, values, judgments, and associative and relational links between the knowledge objects are consistent. There should be no logical inconsisten- cies, no internal conflicts, and no misunderstandings. Most knowledge content will not meet such ideals where congruency is concerned. However, concept definitions should be consistent, and the knowledge base as a whole needs to be constantly “fine-tuned” to maintain congruency. Perspective and purpose refer to the phenomenon through which we “know something” but often from a particular point of view or for a specific purpose. We organize much of our knowledge using the dual dimensions of perspective and purpose (e.g., just-in-time knowledge retrieval or just enough—“on- demand” knowledge). Semantic networks are useful ways of representing different perspectives on the same knowledge content. Figures 3-4 through 3-8 present examples of different perspectives on the same knowledge object (“car”) using semantic networks.
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F IGURE 3- EXAMPLE OF A SEMANTIC NETWORK
Car Maintain
Commute
Vacation
Driving
F IGURE 3- EXAMPLE OF A SEMANTIC NETWORK—“COMMUTE ” VIEW
Car Maintain
Commute
Vacation
Driving
Car pool Traffic jams Gas prices
Wiig’s KM model goes on to define different levels of internalization of knowledge. Wiig’s approach can be seen as a further refinement of Nonaka and Takeuchi’s fourth quadrant, internalization. Table 3-1 briefly defines each of these levels. In general, there is a continuum of internalization, starting with the lowest level, the novice, who “does not know he does not know”—who does not have even an awareness that the knowledge exists—and extending to the mastery level where there is a deep understanding not just of the know- what, but the know-how, the know-why, and the care-why (i.e., values, judg- ments, and motivations for using the knowledge). Wiig (1993) also defines three forms of knowledge: public knowledge, shared expertise, and personal knowledge. Public knowledge is explicit, taught, and routinely shared knowledge that is generally available in the public domain. An example would be a published book or information on a public website.
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F IGURE 3- EXAMPLE OF A SEMANTIC NETWORK—“MAINTAIN ” VIEW
Car
Commute
Maintain
Vacation
Driving
Scheduled maintenance Funny noise Car wash
F IGURE 3- EXAMPLE OF A SEMANTIC NETWORK—“VACATION ” VIEW
Car
Commute
Vacation Maintain
Driving
Book time off Map out trip Sunglasses
F IGURE 3- EXAMPLE OF A SEMANTIC NETWORK—“DRIVING” VIEW
Car
Driving Commute
Vacation Maintain
Driver’s license Optometrist visit Cell phone Weather report
to be managed remains a powerful theoretical model of KM. The Wiig KM model is perhaps the most pragmatic of the models in existence today and can easily be integrated into any of the other approaches. This model enables prac- titioners to adopt a more detailed or refined approach to managing knowledge based on the type of knowledge but goes beyond the simple tacit/explicit dichotomy. Its major shortcoming is the paucity of research and/or practical experience involving the implementation of this model.
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TABLE 3- T HE WIIG KM MATRIX
Form of Type of Knowledge Knowledge
Factual Conceptual Expectational Methodological Public Measurement, Stability, When supply Look for reading balance exceeds demand, temperatures price drops outside the norm Shared Forecast “Market is A little water in Check for past analysis hot” the mix is okay failures Personal The “right” Company has Hunch that the What is the color, texture a good track analyst has it recent trend? record wrong
F IGURE 3- WIIG HIERARCHY OF KNOWLEDGE FORMS
Knowledge
Coded, Accessible
Public Shared (^) Personal
Passive Active Passive^ Active^ Passive^ Active
Coded, Inaccessible (^) Uncoded, Inaccessible
Library books, Manuals
Experts, Knowledge bases
Products, Technologies
Information sytems, Services
Isolated facts, Recent memory
Habits, Skills, Procedural knowledge
The Boisot I-Space KM Model
The Boisot KM model is based on the key concept of an “information good” that differs from a physical asset. Boisot distinguishes information from data by emphasizing that information is what an observer will extract from data as a function of his or her expectations or prior knowledge. The effective move- ment of information goods is largely dependent on senders and receivers sharing the same coding scheme or language. A knowledge good is one that also possesses a context within which it can be interpreted. Effective knowl- edge sharing requires that senders and receivers share the context as well as the coding scheme. Boisot (1998) proposes the following two key points:
- The more easily data can be structured and converted into information, the more diffusible it becomes.
- The less data that has been so structured requires a shared context for its diffusion, the more diffusible it becomes.
Together, they underpin a simple conceptual framework, the Information Space or I-Space KM model. Data is structured and understood through the processes of codification and abstraction. Codification refers to the creation of content categories—the fewer the number of categories, the more abstract the codification scheme. It is assumed that the well-codified abstract content is much easier to understand and apply than the highly contextual content. Boisot’s KM model addresses the tacit form of knowledge by noting that in many situations, the loss of context due to codification may result in the loss of valuable content. This content needs a shared context for its interpretation and implies face-to-face interaction and spatial proximity—which is analogous to socialization in the Nonaka and Takeuchi model (1995). The I-Space model can be visualized as a three-dimensional cube with the following dimensions (see Figure 3-10): (1) codified—uncodified; (2) abstract— concrete; and (3) diffused—undiffused. The activities of codification, abstraction, diffusion, absorption, impacting, and scanning all contribute to learning. Where they take place in sequence— and to some extent they must—together they make up the six phases of a social learning cycle (SLC). These activities are described in Table 3-3. The Boisot model incorporates a theoretical foundation of social learning and serves to link together content, information, and knowledge management in a very effective way. In an approximate sense, the codification dimension is linked to categorization and classification; the abstraction dimension is linked to knowledge creation through analysis and understanding; and the third dif- fusion dimension is linked to information access and transfer. There is a strong potential to make use of the Boisot I-Space KM model as to map and manage an organization’s knowledge assets as the social learning cycle—something that the other KM models do not directly address. However, the Boisot model appears to be somewhat less well known and less accessible, and as a result has not had widespread implementation. More extensive field-testing of this
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