Business Question

3

Knowledge Management Models

Furious activity is no substitute for understanding.
—H. H. Williams (1858–1940)

A robust theoretical foundation is required as the basis of any knowledge management
initiative that is to succeed. The major KM activities described in the KM cycle in the
previous chapter must have a conceptual framework to operate within, otherwise the
activities will not be coordinated and will not produce the expected KM benefits. Eight
different knowledge management models are described in this chapter. The models
all present distinct perspectives on the key conceptual elements that form the infrastructure of knowledge management. This chapter describes, compares, and contrasts
each in order to provide a sound understanding of the discipline of KM.
Learning Objectives
1. Understand the key tenets of the major knowledge management theoretical models
in use today.
2. Link the KM frameworks to key KM concepts and the major phases of the KM cycle.
3. Explain the complex adaptive system model of KM and how it addresses the 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 and Takeuchi (1995)

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Although few would argue that knowledge is unimportant, the overriding problem is
that few managers and information professionals understand how to manage knowledge in knowledge-creating organizations. There is a tendency 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—many people felt this was yet another buzzword and bandwagon that
they were expected to jump on. One of the reasons that KM has now established itself
more credibly as both an academic discipline of study and a professional field of
practice is the work that has been done on theoretical or conceptual models of knowledge management. Early on, more pragmatic considerations about the processes of
KM were complemented by the need to understand what was happening in organizational knowing, reasoning, and learning.
A more holistic approach to KM has become necessary as the complex, subjective,
and dynamic nature of knowledge has developed. Cultural and contextual influences
further increased the complexity involved in KM, and these factors also had to be
taken into account in a model or framework that could situate and explain the key
KM concepts and processes. Last but not least, measurements were needed in order to
be able to monitor progress toward and attainment of expected KM benefits.
This holistic approach is one that encompasses all the different types of content to
be managed, from data, to information, to knowledge, but also conversions from tacit
to explicit and back to tacit knowledge types. The KM models presented in this chapter
all attempt to address knowledge management in a holistic and comprehensive
manner.
Davenport and Prusak (1998, 2) provide the following distinctions among data,
information, and knowledge, which recap the examples in chapter 1:
Data

A set of discrete, objective facts about events.

Information

A message, usually in the form of a document or an audible or visible

communication.
Knowledge A fluid mixing of framed experiences, values, contextual information, and
expert insight that provide a framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of those who know.
In organizations, it often becomes embedded not only in documents or repositories,
but also in organizational routines, processes, practices, and norms.
Davenport and Prusak (1998) refer to the distinctions among data, information,
and knowledge as operational, and argue that we can transform information into
knowledge by means of comparison, consequences, connections, and conversation.

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They stress that knowledge-creating activities take place between people and within
each human being, and that we have to consider knowledge to be among the most
important corporate assets.
Since there are many overlapping categories of types of knowledge, it is tempting
to look for the definitive method of knowledge management. While we study many
methods, there is no need to choose one method over another for all of the many
different types of content. Respecting the diversity of types of knowledge, content
management may be a better, more general term than knowledge management.
Nonaka and Takeuchi (1995) provide a more philosophical distinction: starting
from the traditional definition of knowledge as “justified true belief.” They define
knowledge as “a dynamic human process of justifying personal belief toward the truth”
(Nonaka and Takeuchi, 58, emphasis added). They contend that it is necessary to
create knowledge in order to produce innovation. For them, organizational knowledge
creation is: “The capability of a company as a whole to create new knowledge, disseminate it throughout the organization and embody it in products, services, and
systems (p. 58).”
The concept of tacit knowledge, as we saw in chapter 1, has been clarified by Polanyi
(1966) who stresses the importance of the “personal” mode of knowledge construction, affected by emotions and acquired at the end of a process of every individual’s
active creation and organization of the experiences. When a person tacitly knows, he
or she does and acts without distance, uses the body, and has great difficulty explaining in words the rules and algorithms the process he or she is involved in. The act of
tacitly knowing is without distance from things and performances and the knowing
interaction between persons is one of an unaware observation and a social, communitarian closeness.
A thesis of Polanyi is that all knowledge is either tacit or rooted in tacit knowledge.
Tacit knowledge is hard to express in formalized ways, is context-specific, personal,
and difficult to communicate. On the other hand, explicit knowledge is codified,
expressed in formal and linguistic ways, easily transmittable and storable, and expressible in words and algorithms; however, explicit knowledge represents only the tip of
the iceberg of the entire body of knowledge. This definition of the tacit/explicit
concepts makes clear the importance of adequately considering the tacit dimension.
The 80/20 rule appears to apply here—roughly 80 percent of our knowledge is in
tacit form as individuals, as groups, and as an organization. Only 15–20 percent of
valuable knowledge has typically been captured, codified, or rendered tangible and
concrete in some fashion. This is usually in the form of books, databases, audio or
video recordings, graphs or other images, and so forth. The tacit/explicit mobilization

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(in the epistemological dimension) and the individual/group/organizational sharing
and diffusion (in the ontological dimension) have to take place in order to create
knowledge and produce innovation. Each of the KM models presented in the next
section addresses this point in different but complementary ways.
Major Theoretical KM Models
Major theoretical KM models were chosen for this section based on the following
criteria:
•

They represent a holistic approach to knowledge management (i.e., they are com-

prehensive and take into consideration people, process, organization and technology
dimensions).
•

They have been reviewed, critiqued, and discussed extensively in the KM literature—

by practitioners, academics, and researchers.
•

The models have been implemented and field tested with respect to reliability and

validity.
This is not meant to be an exhaustive list 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 theoretical foundation to explain,
describe, and better predict how best to manage knowledge.
The von Krogh and Roos Model of Organizational Epistemology
The von Krogh and Roos KM model (1995) distinguishes between individual knowledge and social knowledge. Von Krogh and Roos take an epistemological approach to
managing organizational knowledge: the organizational epistemology KM model.
While pinning down a definition of organizational has been problematic, and the term
is often used interchangeably with information, there are a number of issues that must
be addressed:
•

How and why individuals within an organization come to know

•

How and why organizations, as social entities, come to know

•

What counts for knowledge of the individual and the organization

•

What are the impediments in organizational KM?
The cognitive perspective (e.g., Varela 1992) proposes that a cognitive system,

whether it is a human brain or a computer, creates representations (i.e., models) of
reality and that learning occurs when these representations are manipulated. A cogni-

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tive organizational epistemology views organizational knowledge as a self-organizing
system in which humans are transparent to the information from the outside (i.e., we
take in information through our senses and use this information to build our mental
models). The brain is a machine based on logic and deduction that does not allow
any contradictory propositions. The organization thus picks up information from its
environment and processes it in a logical way. Alternative courses of action are generated through information search and the cognitive competence of an organization
depends on the mobilization of individual cognitive resources, that is, a linear summation of individuals to form the organizational whole.
The connectionist approach, on the other hand, is more holistic than reductionist
in nature. The brain is not assumed to sequentially process symbols but to perceive
wholeness, global properties, patterns, synergies, and gestalts. Learning rules govern
how the various components of these whole networks are connected. Information is
not only taken in from the environment but also generated internally. Familiarity and
practice lead to learning. Individuals form nodes in a loosely connected organizational
system and knowledge is an emergent phenomenon that stems from the social interactions of these individuals. From this perspective, knowledge resides not only in the
minds of individuals, but also in the connections among these individuals. A collective
mind is formed as the representation of this network; and it is this mind that lies at
the core of organizational knowledge management.
Von Kroch and Roos adopt the connectionist approach. In their organizational
epistemology KM model, knowledge resides in both the individuals of an organization;
and at the social level, in the relations between the individuals. Knowledge is characterized as “embodied” that is, “everything known is known by somebody” (von Krogh
and Roos 1995, 50). Unlike the cognitive perspective, where knowledge is viewed as
an abstract entity, connectionism maintains that there cannot be knowledge without
a knower. This fits nicely with the concept that tacit knowledge is very difficult to
abstract out of someone and make more concrete. It also reinforces the strong need
to maintain links between knowledge objects and those who are knowledgeable about
them—authors, subject matter experts, and experienced users who have applied the
knowledge, successfully and unsuccessfully.
In 1998, von Krogh, Roos, and Kleine examined the fragile nature of KM in organizations. They describe this fragility in terms of the mindset 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 innovation, competitive advantage, and other organizational goals. For example, if individuals do not

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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, then contributions will fail. If the
organizational structure does not facilitate innovation, then KM will fail. If individual
members are not eager to share their experiences with their colleagues on the basis of
mutual trust and respect, then there will be no generation of social, collective knowledge within that organization. Finally, if those contributing knowledge are not evaluated highly 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 who serve to stimulate
individual knowledge development, 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 relationships and conversations as well as sharing local knowledge
across an organization and across geographical and cultural borders.
The connectionist approach appears to be the more appropriate one to underpin a
theoretical model of knowledge management, especially due to the fact that the
linkage between knowledge and those who absorb and make use of the knowledge is
viewed as an unbreakable bond. The connectionist approach provides a solid theoretical cornerstone for a knowledge model and is a component of the models discussed
in this chapter.
The Nonaka and Takeuchi Knowledge Spiral Model
Nonaka and Takeuchi (1995) studied how Japanese companies were successful in
achieving creativity and innovation. They quickly found that it was far from a mechanistic processing of objective knowledge. Instead, they found that organizational
innovation often stemmed from highly subjective insights that can best be described
in the form of metaphors, slogans, or symbols. The Nonaka and Takeuchi model of
KM has its roots in a holistic model of knowledge creation and the management of
“serendipity.” The tacit/explicit spectrum of knowledge forms (the epistemological
dimension) and the individual/group/organizational or three-tier model of knowledge
sharing and diffusion (the ontological dimension) are both needed in order to create
knowledge and produce innovation.
Nonaka and Takeuchi argue that a key factor behind the successful track record in
innovation of Japanese enterprises stems from the more tacit-driven approach to

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knowledge management. They argue that Western culture considers knower and
known as separate entities (harking back to the cognitive approach, which stresses the
importance of communicating and storing explicit knowledge). In contrast, the structural characteristics of the Japanese language and influences such as Zen Buddhism
led the Japanese to consider that there is a oneness of humanity and nature, body and
mind, and self and the other (Nonaka and Takeuchi 1995). It follows that it may be
easier for Japanese managers to engage in the process of indwelling, a term used by
Polanyi (1966) to define the involvement of the individuals with objects through selfinvolvement and commitment, in order to create knowledge. In such a cultural environment, knowledge is principally “group knowledge,” easily converted and mobilized
(from tacit to explicit, along the epistemological dimension) and easily transferred
and shared (from the individual to the group to the organization, in the ontological
dimension).
Nonaka and Takeuchi emphasize the necessity of integrating the two approaches,
from the cultural, epistemological, and organizational points of view, in order to
acquire new cultural and operational tools to better build knowledge-creating organizations. Their construct of the “hypertext organization” is the formalization of the
need for an integration of the traditionally opposed Western and Japanese schools of
thought.
The Knowledge Creation Process Knowledge creation always begins with the individual. A brilliant researcher has an insight that ultimately leads to a patent. A middle
manager has an intuition about market trends that becomes the catalyst for an important new product concept. A shop floor worker draws upon years of experience to
come up with a process innovation that saves the company millions of dollars. In
each of these scenarios, an individual’s personal, private knowledge (predominantly
tacit in nature) is translated into valuable, public organizational knowledge. Making
personal knowledge available to others in the company is at the core of this KM model.
This type of knowledge creation process takes place continuously and it occurs at all
levels of the organization. In many cases, the creation of knowledge occurs in an
unexpected or unplanned way.
According to Takeuchi and Nonaka, there are four modes of knowledge conversion
that:
Constitute the engine of the entire knowledge-creation process. These modes are what the
individual experiences. They are also the mechanisms by which individual knowledge gets
articulated and amplified into and throughout the organization. (p. 57, emphasis added)

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Tacit knowledge to Explicit knowledge

Tacit knowledge

Socialization

Externalization

Internalization

Combination

from
Explicit knowledge

Figure 3.1
The Nonaka and Takeuchi model of knowledge conversion

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 interactive and spiral. (pp. 62–63)

Knowledge Conversion

There are four modes of knowledge conversion, as shown in

figure 3.1:
1. From tacit knowledge to tacit knowledge: process of socialization
2. From tacit knowledge to explicit knowledge: process of externalization
3. From explicit knowledge to explicit knowledge: process of combination
4. From explicit knowledge to tacit knowledge: process of internalization
Socialization (tacit-to-tacit) consists of the sharing of knowledge in face-to-face,
natural, and typically social interactions. This involves arriving at a shared understanding 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

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original participants. Although socialization is a very effective means of knowledge
creation and sharing, it is one of the more limited means. Furthermore, it is difficult
and time-consuming to disseminate all knowledge using only the socialization mode.
Davenport and Prusak (1998, 70) 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 learning that its rules may be impossible to separate from how
an individual acts.”
This means that the process of acquiring tacit knowledge is not strictly tied to the
use of language but to experience and to the ability to transmit and to share it. This
must not be confused with the idea of a simple transfer of information because there
is no knowledge creation if we abstract the transfer of information and experiences
away from the associated emotions and specific contexts in which they are embedded.
Socialization consists of sharing experiences through observation, imitation, and
practice.
For example, Honda organizes “brainstorming camps” during which there are
detailed discussions to solve difficult problems in development projects. These informal meetings are usually held outside the workplace, off-site, where everyone is
encouraged to contribute to the discussion and no one is allowed to refer to the status
and qualification of employees involved. The only behavior not allowed during these
discussions is simple criticism not followed by constructive suggestions. Brainstorming
meetings are used by Honda not only to develop new products, but also to improve
its managerial systems and its commercial strategies. Brainstorming can represent
occasions for creative dialogue. And brainstorming provides a moment of shared
experience, followed by sharing tacit knowledge. During brainstorming, people create
harmony among themselves, they feel engaged as part of a whole, and they feel
themselves allied by the same goal. Many other organizations organize similar “Knowledge Days” or “Knowledge Cafés” to encourage this type of tacit-to-tacit knowledge
sharing.
Externalization (tacit-to-explicit) is a process that gives a visible form to tacit knowledge and converts it to explicit knowledge. It can be defined as “a quintessential
knowledge creation process in that tacit knowledge becomes explicit, taking the shapes
of metaphors, analogies, concepts, hypotheses, or models” (Nonaka and Takeuchi
1995, 4). In this mode, individuals are able to articulate the knowledge and know-how
and, in some cases, the know-why and the care-why. Knowledge that was previously
tacit can somehow be written down, recorded, drawn, or made tangible or concrete
in some manner. An intermediary is often needed at this stage, because it is always

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difficult to transform one type of knowledge into another. A knowledge journalist is
someone who can interview knowledgeable individuals in order to extract, model, and
synthesize in a different way (format, length, level of detail, etc.) in order to increase
its scope (i.e., so that a wider audience can understand and apply this content).
Once externalized, knowledge is now tangible and permanent. It can be shared
more easily with others and leveraged throughout the organization. Good principles
of content management will need to be brought into play in order to make future
decisions about archiving, updating, and retiring externalized knowledge content. It
is particularly important not to lose attribution and authorship information when
tacit knowledge is made explicit. This involves codifying metadata or information
about the content along with the actual content.
For example, Canon decided to design and produce a mini-copier that can be used
occasionally for personal use. This new product was very different from expensive
industrial copiers, which also engendered high maintenance costs. Canon had to
design something that was relatively inexpensive with reasonable maintenance costs.
The Canon mini-copier project members understood that the most frequent problem
was with the drums, so they designed a type of drum that would last through a fair
amount of usage. They then had to be creative and design a drum that did not cost
more than the mini-copier! How did they come up with this innovation? After long
discussions, one day the leader of the unit that had to solve this problem brought
along some cans of beer and as the team was brainstorming, someone noted that beer
cans had low costs and used the same type of aluminum as copier drums did . . . the
rest, as they say, is history.
The next stage of knowledge conversion in the Nonaka and Takeuchi model is that
of combination (explicit-to-explicit), the process of recombining discrete pieces of
explicit knowledge into a new form. Some examples would be a synthesis in the form
of a review report, a trend analysis, a brief executive summary, or a new database to
organize content. No new knowledge is created per se—it is a new combination or
representation of existing or already explicit knowledge. In other words, combination
happens when concepts are sorted and systematized in a knowledge system. Some
examples would be populating a database, when we teach, when we categorize and
combine concepts, or when we convert explicit knowledge into a new medium such
as a computer-based tutorial. For example, in developing a training course or curriculum for a university course, existing, explicit knowledge would be recombined into a
form that better lends itself to teaching and to transferring this content.
Another example is that of Kraft General Foods when they planned and developed
a new point-of-sale (POS) system, one that would track not only items sold but also

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information about the buyers. Their intent was to use this information to plan
new models to sell, new combinations of products, of products and service, of
service, and so on. The POS system collects and analyzes information and then
helps marketing people plan information-intensive marketing programs called
“micro-merchandising.”
Finally, the last conversion process, internalization (explicit-to-tacit) occurs through
the diffusion and embedding of newly acquired behavior and newly understood or
revised mental models. Internalization is very strongly linked to “learning by doing.”
Internalization converts or integrates shared and/or individual experiences and knowledge into individual mental models. Once new knowledge has been internalized, it is
then used by employees who broaden it, extend it, and reframe it within their own
existing tacit knowledge bases. They understand, learn, and buy into the new knowledge and this is manifest as an observable change, that is, they now do their jobs and
tasks differently.
For example, General Electric has developed a system of documenting all customer
complaints and inquiries in a database that can be accessed by all its employees. This
system allows the employees to find answers to new customers’ questions much more
quickly because it facilitates the sharing of employees’ experiences in problem solving.
This system helps the workers to internalize others’ experiences in answering questions and solving problems.
Knowledge, experiences, best practices, lessons learned, and so on go through the
conversion processes of socialization, externalization, and combination. It is crucial
that knowledge is not halted at any one of these stages. The reason is that it is only
when knowledge is internalized into individuals’ tacit knowledge bases in the form
of shared mental models or technical know-how that this knowledge becomes a valuable asset—to the individual, to their community of practice, and to the organization.
In order for organizational knowledge creation to take place, however, the entire
conversion process has to begin all over again: the tacit knowledge accumulated at
the individual level needs to be brought into contact with other organizational
members, thereby starting a new spiral of knowledge creation (Nonaka and Takeuchi
1995, 69). When experiences and information are transferred through observation,
imitation, and practice, then we are back in the socialization quadrant. This knowledge
is then formalized and converted into explicit knowledge, through the use of analogy,
metaphor, and model, in the externalization quadrant. This explicit knowledge is then
systemized and recombined in the combination quadrant—whereupon it once again
becomes part of individuals’ experience. In the internalization quadrant, knowledge
has once again thus become tacit knowledge.

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Knowledge Spiral

Knowledge creation is not a sequential process, but depends on a

continuous and dynamic interaction between tacit and explicit knowledge throughout
the four quadrants. Organizations articulate, organize, and systematize individual tacit
knowledge, produce and develop tools, structures, and models to accumulate it and
share it to create new knowledge through the knowledge spiral as illustrated in figure
3.2. The knowledge spiral is a continuous activity of knowledge flow, sharing, and
conversion by individuals, communities, and the organization itself.
The two steps 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 explicit knowledge into tacit. These two steps
require a high degree of personal commitment and they will typically involve mental
models, personal beliefs, and values, and a process of reinventing oneself, one’s group,
and the organization as a whole. A metaphor is a good way of expressing this “inexpressible” 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 a phrase that “accomplishes
in a word or phrase what could otherwise be expressed only in many words, if at all”
(Sommer and Weiss 1995, vii). All of these vehicles are good models to represent a
consistent, systematic, and logical understanding of content without any 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.
Dialogue
Socialization

Externalization

Linking
explicit
knowledge

Field building

Internalization

Combination

Learning by doing
Figure 3.2
The Nonaka and Takeuchi knowledge spiral

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It is possible to structure metaphors, models, and analogies in an organizational
KM design. The first principle is to have built-in redundancy to make sure that there
is overlapping information. Redundancy will make it easier to articulate content, to
share content, and to make use of it. An example is to set up several competing groups,
to build in a rotational strategy so workers do a variety of jobs, and to provide easy
access to company information via a single integrated knowledge base.
Knowledge sharing and use happens through the knowledge spiral that, “starting
at the individual level and moving up through expanding communities of interaction
[. . .] crosses sectional, departmental, divisional and organizational boundaries”
(Nonaka and Takeuchi 1995, 72). Nonaka and Takeuchi argue that an organization
has to promote a facilitating context in which both the organizational and the individual knowledge-creation processes can easily take place, acting as a spiral. They
describe the following “enabling conditions for organizational knowledge creation”:
Intention

An organization’s aspiration to its goals (strategy formulation in a business

setting)
Autonomy To allow individuals to act autonomously, according to the “minimum
critical specification” principle, and involved in cross-functional self-organized teams
Fluctuation and creative chaos

To stimulate the interaction between the organization

and the external environment and/or create fluctuations and breakdowns by means
of creative chaos or strategic “equivocality”
Redundancy

Existence of information that goes beyond the immediate operational

requirements of organizational members; competing multiple teams on the same issue;
strategic rotation of personnel
Requisite variety Internal diversity to match the variety and complexity of the environment; to provide to everyone in the organization the fastest access to the broadest
variety of necessary information; flat and flexible organizational structure interlinked
with effective information networks
The Nonaka and Takeuchi model has proven to be one of the more robust in the
field of KM and it continues to be applied in a variety of settings. One of its greatest
strengths is the simplicity of the model—both in terms of understanding the basic
tenets of the model and in terms of being able to quickly internalize and apply the
KM model. One of the major shortcomings is that while valid, it does not appear to
be sufficient to explain all of the stages involved in managing knowledge. The Nonaka
and Takeuchi model focuses on the knowledge transformations between tacit and
explicit knowledge, but the model does not address larger issues of how decision
making takes place by leveraging both these forms of knowledge.

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Box 3.1
A vignette: Skidmore, Owings, & Merrill LLP (SOM)
SOM ( is a leading architecture, urban design and planning, engineering, and interior architecture firm in the US (Pulsifer 2008). Founded in 1936, SOM
has completed more than ten thousand projects in over fifty countries. Most architectural
and engineering firms operate in an environment filled with guidelines and regulations
derived from best practices and standards that are often disseminated through the company’s intranet. SOM also has CAD (computer-aided design) libraries, drafting standards,
employee directories, and social networks—in other words, bits and pieces of KM. So why
did they need a KM model in addition to these piecemeal implementations? The model
is necessary in order to have a deeper understanding of how KM contributes to the goals
of the company. In this type of industry, as with many others, tacit knowledge consists
of creative and innovative knowledge—pretty much the polar opposite of such welldocumented explicit knowledge as guidelines and standards. A KM model helps SOM to
harness both types of knowledge in order to perform efficiently, effectively, and competitively. A comprehensive, easy-to-apply KM model can help decision makers and all
employees. With it, they can make the best use of tacit and explicit knowledge and apply
processes to transform knowledge from one form to the other. A KM model, together with
the KM process cycle discussed in the previous chapter, can be used by SOM as a checklist—
to ensure that all key KM components have been addressed—not just addressed well but
also addressed coherently, since KM components are highly interdependent and integrated
with one another. In the absence of a model, the firm can continue implementing KM
pieces in an ad hoc fashion, but will rarely succeed in bringing the pieces together in order
to better attain company goals and objectives.
A good KM model is a framework that positions goals, procedures, and enablers to help
the firm capitalize on their valuable knowledge assets. With a KM model, everyone can
understand what KM is expected to do for SOM, why they should share their knowledge,
how they should share, and how they can assess the costs and benefits that result. The
KM model will help ensure everyone shares the same understanding of the role of KM
throughout their career—from their employee orientation as new hires to their exit interview and knowledge handover at the end of their career. The SOM KM framework helps
ensure that valuable knowledge is not lost when senior employees leave, that information
and knowledge flows among departments, that work is not duplicated, and that errors are
minimized. The company is better able to centrally gather, measure, and analyze how well
they have met their goals. Finally, the KM model helps SOM leadership to better shape
and support the firm’s business strategy. Each group within SOM needs to operate on this
common KM framework in order to promote individual, departmental, and organizational
success.

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The Choo Sense-Making KM Model
Choo (1998) has described a model of knowledge management that stresses sense
making (largely based on Weick 2001), knowledge creation (based on Nonaka and
Takeuchi 1995), and decision making (based on, among others, bounded rationality,
Simon 1957, among others). The Choo KM model focuses on how information elements are selected and subsequently fed into organizational actions. Organizational
action results from the concentration and absorption of information from the external
environment into each successive cycle, as illustrated in figure 3.3. Each of the phases,
sense making, knowledge creation, and decision making, has an outside stimulus or
trigger.
The sense-making stage is the one that attempts to make sense of the information
streaming in from the external environment. Priorities are identified and used to filter

Streams of
experience

Sense
making
Shared
meanings
Shared meanings

Knowledge
creating

New knowledge,
new capabilities

External information
and knowledge

Figure 3.3
Overview of Choo’s (1998) knowledge management model

Decision
making
Goal-directed
adaptive
behavior

Next knowing
cycle

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the information. Common interpretations are constructed by individuals from the
exchange and negotiation of information fragments combined with their previous
experiences. Weick (2001) proposed a theory of sense making to describe how chaos
is transformed into sensible and orderly processes in an organization through the
shared interpretation of individuals. A loosely coupled system is a term used to describe
systems that can be taken apart or revised without damaging the entire system. For
example, a human being is tightly coupled, but the human genome is loosely coupled.
Loose coupling permits adaptation, evolution, and extension. Sense making can be
thought of as a loosely coupled system where individuals construct their own representation of reality by comparing current with past events.
Weick (2001) claims that sense making in organizations consists of four integrated
processes:
•

Ecological change

•

Enactment

•

Selection

•

Retention
Ecological change is a change in the environment that is external to the organiza-

tion—one that disturbs the flow of information to participants. This 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, to rearrange, to single out, or to
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 where individuals attempt to interpret the
rationale for the observed and enacted changes by making selections. The retention
process in turn furnishes the organization with an organizational memory of successful sense-making experiences. This memory can be reused in the future to interpret
new changes and to stabilize individual interpretations into a coherent organizational
view of events and actions. These phases also serve to reduce any uncertainty and
ambiguity associated with unclear, poorly defined information.
Knowledge creating is seen as the transformation of personal knowledge between
individuals through dialog, discourse, sharing, and storytelling. This phase is directed
by a knowledge vision of “as is” (current situation) and “to be” (future, desired state).
Knowledge creation widens the spectrum of potential choices in decision making

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through the provision of new knowledge and new competencies. The result feeds the
decision-making process with innovative strategies that extend the organization’s
capability to make informed, rational decisions. Choo (1998) draws upon the Nonaka
and Takeuchi (1995) model for a theoretical basis of knowledge creation.
Decision making is situated in rational decision-making models that are used
to identify and evaluate alternatives by processing the information and knowledge
collected to date. There are a wide range of decision-making theories such as the
theory of games and economic behavior (e.g., Dixit and Nalebuff 1991; Bierman and
Fernandez 1993), chaos theory, emergent theory, and complexity theory (e.g., Gleick
1987; Fisher 1984; Simon 1969; Stewart 1989; Stacey 1992), and even a garbage can
theory of decision making (e.g., Daft 1982; Daft and Weick 1984; Padgett 1980).
The garbage can model (GCM) of organizational decision making was developed
in reference to “ambiguous behaviors,” that is, explanations or interpretations of
behaviors that at least appear to contradict classical theory. The GCM was greatly
influenced by the realization that extreme cases of aggregate uncertainty in decision
environments would trigger behavioral responses, which, at least from a distance,
appear irrational or at least not in compliance with the total/global rationality of
economic man (e.g., “act 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 tried 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, 139).
Simon (1957, 198) identified the principle of bounded rationality as a constraint
for organizational decision making, stating that “the capacity of the human mind for
formulating and for solving complex problems is very small compared with the size
of the problems whose solution is required for objectively rational behavior in the real
world—or even for a reasonable approximation to such objective rationality.”
Simon suggested that persons faced with ambiguous goals and unclear means of
linking actions to those goals seek to 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 community, and personal needs. Bounded rationality

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theory was first proposed by Simon (1976) as a limited or constrained rationality to
explain human decision-making behavior. When confronted with a highly complex
world, the mind constructs a simple mental model of reality and tries to work within
that model. The model may have weaknesses, but the individual will try to behave
rationally within the constraints or boundaries of that model.
Individuals can be bound in a decisional process by a number of factors,
such as:
•

Limits in knowledge, skills, habits, and responsiveness

•

Availability of personal information and knowledge

•

Values and norms held by the individual that may differ from those of the

organization
This theory has long been accepted in organizational and management sciences.
Bounded rationality is characterized by individuals’ use of limited information analysis, evaluation, and processing, shortcuts and rules of thumb (sometimes called heuristics), and “satisficing” (i.e., a combination of satisfying and sufficing) behavior,
which means it may not be fully optimized, but it is good enough. The 80/20 rule
(e.g., Clemson 1984) is a good example of the application of satisficing behavior—for
example, in a brainstorming session, when the group may not have fully exhausted
all the possibilities but did manage to capture roughly 80 percent of them. Continuing
on would result in the law of diminishing returns—so much more effort would be
required to incorporate the remaining 20 percent—that generally participants would
agree that what they have so far is good enough to proceed with.
One of the strengths of the Choo KM model is the holistic treatment of key KM
cycle processes extending to organizational decision making, which is often lacking
in other theoretical KM approaches. This makes the Choo model one of the more
realistic or feasible models of KM as the model represents organizational actions
with high fidelity. The Choo KM model is particularly well suited to simulations and
hypothesis or scenario-testing applications.
The Wiig Model for Building and Using Knowledge
Wiig (1993) approached his KM model with the following principle: in order for
knowledge to be useful and valuable, it must be organized. Knowledge should be
organized differently depending on what the knowledge will be used for. For example,
in our own mental models, we tend to store our knowledge and know-how in the
form of semantic networks. We can then choose the appropriate perspective based on
the cognitive task at hand.

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Knowledge organized in a semantic network way can be accessed and retrieved
using multiple entry paths that map onto different knowledge tasks to be completed.
Some useful dimensions to consider in Wiig’s KM model include:
•

Completeness

•

Connectedness

•

Congruency

•

Perspective and purpose
Completeness addresses the question of how much relevant knowledge is available

from a given source. Sources may be human minds or knowledge bases (i.e., tacit or
explicit knowledge). We first need to know that the knowledge is out there. The
knowledge may be complete in the sense that all that is available about the subject is
there but if no one knows of its existence and/or availability, they cannot make use
of this knowledge.
Connectedness refers to the well-understood and well-defined relations between
the different knowledge objects. There are very few knowledge objects that are totally
disconnected from the others. The more connected a knowledge base is (i.e., the
greater the number of interconnections in the semantic network), then the more
coherent the content and the greater its value.
A knowledge base is said to be congruent when all the facts, concepts, perspectives,
values, judgments, and associative and relational links between the knowledge objects
are consistent. There should be no logical inconsistencies, no internal 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 where we know something,
but often from a particular point of view or for a specific purpose that we have in
mind. We organize much of our knowledge using the dual dimensions of perspective
and purpose (e.g., just-in-time knowledge retrieval or just enough or “on-demand”
knowledge).
Semantic networks are useful ways of representing different perspectives on the
same knowledge content. Figures 3.4 through 3.8 show examples of different perspectives on the same knowledge object (i.e., a car) using semantic networks.
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 the fourth Nonaka and
Takeuchi quadrant of internalization. Table 3.1 briefly defines each of these levels. In

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Chapter 3

Driving

Commute

Car
Vacation

Maintain

Figure 3.4
Example of a semantic network

Driving

Commute

Carpool
Traffic jams
Gas prices

Car
Vacation

Maintain

Figure 3.5
Example of a semantic network—“commute” view

Driving

Commute

Car
Vacation

Maintain

Scheduled
maintenance
Funny noise
Car wash

Figure 3.6
Example of a semantic network—“maintain” view

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Driving

Commute

Car
Book time off
Map out trip

Maintain

Vacation

Sunglasses
Figure 3.7
Example of a semantic network—“vacation” view

Driver’s license
Optometrist visit

Commute

Driving

Cell phone
Weather report

Car
Vacation

Maintain

Figure 3.8
Example of a semantic network—“driving” view

general, there is a continuum of internalization, starting with the lowest level, the
novice, who “does not know he does not know,” that is, who does not even have
an awareness that the knowledge exists, to the mastery level, where there is a deep
understanding not just of the know-what, but the know-how, the know-why, and the
care-why (i.e., values, judgments, and motivations for using the knowledge).
Wiig (1993) also 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 web site. Shared expertise is proprietary
knowledge assets that are exclusively held by knowledge workers and shared in their
work or embedded in technology. This form of knowledge is usually communicated
via specialized languages and representations. Although he does not use the term,

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Table 3.1
Wiig KM model—degrees of internalization
Level

Type

Description

1

Novice

Barely aware or not aware of the knowledge and how it can be used

2

Beginner

Knows that the knowledge exists and where to get it but cannot
reason with it

3

Competent

Knows about the knowledge, can use and reason with the
knowledge given external knowledge bases such as documents and
people to help

4

Expert

Knows the knowledge, holds the knowledge in memory,
understands where it applies, reasons with it without any outside
help

5

Master

Internalizes the knowledge fully, has a deep understanding with
full integration into values, judgments, and consequences of using
that knowledge

this knowledge form would be common in communities of practice, informal networks of likeminded professionals who typically interact and share knowledge in
order to improve the practice of their profession. Finally, personal knowledge is the
least accessible but most complete form of knowledge. Personal knowledge is typically
more tacit than explicit knowledge, and is used unconsciously in work, play, and
daily life.
In addition to the three major forms of knowledge (personal, public, and shared)
Wiig (1993) defines four types of knowledge (factual, conceptual, expectational, and
methodological). Factual knowledge deals with data and causal chains, measurements,
readings—typically directly observable and verifiable content. Conceptual knowledge
deals with systems, concepts, and perspectives (e.g., concept of a track record, a bull
market). Expectational knowledge concerns judgments, hypotheses, and expectations
held by knowers. Examples are intuition, hunches, preferences, and heuristics that we
make use of in our decision making. Finally, methodological knowledge deals with reasoning, strategies, decision-making methods, and other techniques. Examples would
be learning from past mistakes or forecasting based on analyses of trends.
Together, the three forms of knowledge and the four types of knowledge combine
to yield a KM matrix that forms the basis of the Wiig KM model. Table 3.2 summarizes
the Wiig KM model.
To summarize, Wiig (1993) proposes a hierarchy of knowledge that consists of
public, shared, and personal knowledge forms. Wiig’s hierarchy of knowledge forms
is shown in figure 3.9.

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Table 3.2
Wiig KM matrix
Type of knowledge
Form of
knowledge
Factual

Conceptual

Expectational

Methodological

Public

Measurement,
reading

Stability,
balance

When supply
exceeds demand,
price drops

Look for temperatures
outside the norm

Shared

Forecast analysis

Market is hot

A little water in
the mix is OK

Check for past failures

Personal

The “right”
color, texture

Company has
a good track
record

Hunch that the
analyst has it
wrong

What is the recent
trend?

Knowledge

Public

Shared

Personal

Coded, accessible

Coded, inaccessible

Uncoded, inaccessible

Passive
Library
books,
manuals

Active

Passive

Active

Experts,
knowledge
bases

Products,
technologies

Information
sytems,
services

Figure 3.9
Wiig hierarchy of knowledge forms

Passive
Isolated
facts,
recent
memory

Active
Habits,
skills,
procedural
knowledge

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The major strength of the Wiig model is that despite having been formulated in
1993, the organized approach to categorizing the type of knowledge to be managed
remains a very powerful theoretical model of KM. The Wiig KM model is perhaps the
most pragmatic of the models in existence today and can easily be integrated into any
of the other approaches. This model enables practitioners to adopt a more detailed or
refined approach to managing knowledge based on the type of knowledge, but going
beyond the simple tacit/explicit dichotomy. The major shortcoming is that very little
has been published in terms of research and/or practical experience in implementing
this model.
The Boisot I-Space KM Model
The Boisot KM model is based upon the key concept of an “information good” that
differs from a physical asset. Boisot distinguishes information from data by emphasizing that information is what an observer will extract from data as a function of his or
her expectations or prior knowledge. The effective movement of information goods
is very much dependent on senders and receivers sharing the same coding scheme or
language. A “knowledge good” is a concept that in addition possesses a context within
which it can be interpreted. Effective knowledge sharing requires that senders and
receivers share the context as well as the coding scheme.
Boisot (1998) proposes the following two key points:
The more easily data can be structured and converted into information, the more diffusible it
becomes.
The less data that has been so structured requires a shared context for its diffusion, the more
diffusible it becomes.

Together, they underpin a simple conceptual framework, the information space or
I-Space KM model. The data are structured and understood through the processes of
codification and abstraction. Codification refers to the creation of content categories—
the fewer the number of categories, the more abstract the codification scheme. The
assumption is that well-codified abstract content is much easier to understand and
apply than highly contextual content. Boisot’s KM model does address the tacit form
of knowledge by noting that in many situations, the loss of context due to codification may result in the loss of valuable content. This content needs a shared context
for its interpretation and that implies face-to-face interaction and spatial proximity—
which is analogous to the socialization quadrant in the Nonaka and Takeuchi model
(1995).
The I-Space model can be visualized as a three-dimensional cube with the following
dimensions (refer to figure 3.10):

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Codified

Uncodified
Abstract

Diffused
Concrete

Undiffused

Figure 3.10
The Boisot I-Space KM model

•

Codified—uncodified

•

Abstract—concrete

•

Diffused—undiffused
The activities of coding, abstracting, diffusing, absorbing, impacting, and scanning

all contribute to learning. Where they take place in sequence—and to some extent
they must—together they make up the six phases of a social learning cycle (SLC).
These are described in table 3.3.
The strength of the Boisot model is that it incorporates a theoretical foundation of
social learning. The Boisot model serves to link together content management, information management, and knowledge management in a very effective way. In a very
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 diffusion dimension is linked to information access
and transfer. There is a strong potential to make use of the Boisot I-Space KM model
to map and manage an organization’s knowledge assets as an SLC—something that is
not directly addressed by the other KM models. However, the Boisot model appears
to be somewhat less well known, less accessible, and as a result has not had widespread
implementation. More extensive field-testing of this KM model would provide feedback regarding its applicability as well as provide more guidelines on how best to
implement the I-Space approach.

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Table 3.3
The social learning cycle in Boisot’s I-Space KM model
Phase

Name

Description

1

Scanning

• Identifying threats and opportunities in generally available

2

Problem solving

3

Abstracting

4

Diffusing

5

Absorbing

6

Impacting

but often fuzzy content
• Scanning patterns such as unique or idiosyncratic insights
that then become the possession of individuals or small
groups
• Scanning may be very rapid when the data is well codified
and abstract and very slow and random when the data is
uncodified and context-specific
• The process of giving structure and coherence to such
insights, that is, codifying them
• In this phase they are given a definite shape and much of
the uncertainty initially associated with them is eliminated
• Problem solving initiated in the uncodified region of the
I-Space is often both risky and conflict-laden
• Generalizing the application of newly codified insights to a
wider range of situations
• Involves reducing them to their most essential features, that
is, conceptualizing them
• Problem solving and abstraction often work in tandem
• Sharing the newly created insights with a target population
• The diffusion of well codified and abstract content to a large
population will be technically less problematic than that of
content which is uncodified and context-specific
• Only a sharing of context by sender and receiver can speed
up the diffusion of uncodified data
• The probability of a shared context is inversely proportional
to population size
• Applying the new codified insights to different situations in
a “learning by doing” or a “learning by using” fashion
• Over time, such codified insights come to acquire a
penumbra of uncodified knowledge which helps to guide
their application in particular circumstances
• The embedding of abstract knowledge in concrete practices
• The embedding can take place in artifacts, technical or
organizational rules, or in behavioral practices
• Absorption and impact often work in tandem

Source: Adapted from Boisot (1998).

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Complex Adaptive System Models of KM
The intelligent complex adaptive systems (ICAS) KM theory of the organization views
the organization as an ICAS (e.g., , 1989 1981; Bennet and Bennet 2004). Beer (1981)
was a pioneer in the treatment of the organization as a living entity. In his viable
system model (VSM), a set of functions is distinguished that ensure the viability of
any living system and organizations in particular. The VSM is based on the principles
of cybernetics or systems science that make use of communication and control mechanisms to understand, describe, and predict what an autonomous or viable organization
will do.
Complex adaptive systems consist of many independent agents that interact with
one another locally. Together, their combined behavior gives rise to complex adaptive
phenomena. Complex adaptive systems are said to “self-organize” through this form
of emergent phenomena. There is no overall authority that is directing how each one
of these independent agents should be acting. An overall pattern of complex behavior
arises or emerges as a result of all of their interactions.
The VSM has been applied to a wide range of complex situations, including the
modeling of an entire nation (implemented by President Salvador Allende in Chile in
1972). The model enables managers and their consultants to elaborate policies and to
develop organizational structures with a clear understanding of the recursions in
which they are supposed to operate, and to design regulatory systems within those
recursions that obey certain fundamental laws of cybernetics (e.g., Ashby’s Law of
Requisite Variety). As such, the usefulness of the VSM as a theoretical grounding for
KM becomes quite clear.
A number of researchers have made use of complex adaptive system theories in
deriving a theoretical basis for KM. Snowden (2000, 1) the director of Cynefin, a
research group at IBM, describes his approach as follows: “Complex adaptive systems
theory is used to create a sense-making model that utilizes self-organizing capabilities
of the informal communities and identifies a natural flow model of knowledge creation, disruption and utilization.”
Cynefin is a Welsh word with no direct equivalent in English that can be translated
as “habitat,” or as an adjective, “acquainted” or “familiar.” The Cynefin research
center focuses on action research in organizational complexity and is open to individuals and to organizations. One of the major points emphasized by Snowden (2000)
is that the focus on tacit-explicit knowledge conversion (e.g., the Nonaka and Takeuchi
model, 1995) that has dominated knowledge management practice since 1995 provides a limited, but useful, set of models and tools. The Cynefin model instead proposes the following key types of knowledge: known, knowable, complex, and chaotic.

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Snowden’s Cynefin model is less concerned with tacit-explicit conversions because of
its focus on descriptive self-awareness rather than prescriptive organization models.
Bennet and Bennet (2004) also describe a complex adaptive system approach to
KM but the conceptual roots are somewhat different from the Beer VSM. Bennet and
Bennet believe strongly that the traditional bureaucracies or popular matrix and flat
organizations are not sufficient to provide the cohesiveness, complexity, and selective
pressures that ensure the survival of an organization. A different model is proposed,
one in which the organization is viewed as a system that is in a symbiotic relationship
with its environment, that is, “turning the living system metaphor into reality”
(Bennet and Bennet 2004, 25). The ICAS model is composed of living subsystems that
combine, interact, and coevolve to provide the capabilities of an advanced, intelligent,
technological, and sociological adaptive enterprise. Complex adaptive systems are
organizations that are composed of a large number of self-organizing components,
each of which seeks to maximize its own specific goals but which also operate according to the rules and context of relationships with the other components and the
external world.
In an ICAS, the intelligent components consist of people who are empowered to
self-organize, but who remain part of the overall corporate hierarchy. The challenge
is to take advantage of the strengths of people while getting them to cooperate
and collaborate to leverage knowledge and to maintain a sense of unity of purpose.
Organizations take from the environment, transform those inputs into higher-value
outputs, and provide them to customers and stakeholders. Organizational intelligence
becomes a form of competitive intelligence that helps facilitate innovation, learning,
adaptation, and quick responses to unanticipated situations. Organizations solve problems by creating options, and they use internal and external resources to add value
above and beyond the value of the initial inputs. They must also do this in an effective and efficient manner. Knowledge becomes a valuable resource because it is critical
in taking effective action in a variety of uncertain situations. The actions taken can
be used to distinguish between information management (predictable reactions to
known and anticipated situations) and knowledge management (use existing or
create new reactions to unanticipated situations). Knowledge will typically consist of
experience, judgment, insight, context, and the right information. Understanding and
meaning become prerequisites to taking effective action and they create value by
ensuring the survival and the growth of the organization.
The five key processes in the ICAS KM model can be summarized as:
1. Understanding
2. Creating new ideas

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3. Solving problems
4. Making decisions
5. Taking actions to achieve desired results
Since only people or individuals can make decisions and take actions, the emphasis
of this model is on the individual knowledge worker and his or her competency,
capacity, learning, and so on. These are leveraged through multiple networks (e.g.,
communities of practice) to make available the knowledge, experience, and insights
of others. This type of tacit knowledge leveraged through dynamic networks makes a
broader “highway” available to connect data, information, and people through virtual
communities and knowledge repositories.
To survive and successfully compete, an organization will also require eight emergent characteristics, according to this model:
1. Organizational intelligence
2. Shared purpose
3. Selectivity
4. Optimum complexity
5. Permeable boundaries
6. Knowledge centricity
7. Flow
8. Multidimensionality
An emergent characteristic is the result of nonlinear interactions, synergistic interactions, and self-organizing systems. The ICAS KM model follows along the lines of
the other approaches in that it is connectionist and holistic in nature. The emergent
ICAS characteristics are outlined in figure 3.11. These emergent properties serve to
endow the organization with the internal capability to deal with the future unanticipated environments yet to be encountered.
Organizational intelligence refers to the capacity of the firm to innovate, acquire
knowledge, and apply that knowledge to relevant situations. In the ICAS model, this
property refers to the ability of the organization to perceive, interpret, and respond
to its environment in such a way as to meet its goals and satisfy its stakeholders.
This is very similar to the Choo sense-making model approach. Unity and a shared
purpose represent the ability of the organization to integrate and mobilize resources
through a continuous, two-way communication with its large number of relatively
independent subsystems, much like the VSM. Optimum complexity represents
the right balance between internal complexity (i.e., the number of different relevant

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Organizational intelligence

Shared
purpose

Multidimensionality

Knowledge
centricity

Optimum
complexity

Flow

Selectivity

Permeable boundaries

Creativity

Complexity

Change

Figure 3.11
Overview of ICAS knowledge management model

organizational states) to deal with the external environment without losing sight of
the overall goal and the notion of a “one-firm firm” or common identity. The major
difference here with VSM is the notion of relevant states—not all possible states. This
selectivity is in keeping with the notion of evaluating value of content in KM as
opposed to a more exhaustive warehousing approach.
The process of selectivity consists of the filtering of incoming information from the
outside world. Good filtering requires broad knowledge of the organization, specific
knowledge of the customer, and a strong understanding of the firm’s strategic goals.
Knowledge centricity refers to the aggregation of relevant information from selforganization, collaboration, and strategic alignment. Flow enables knowledge centricity and facilities the connections and the continuity needed to maintain unity and

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give coherence to organizational intelligence. Permeable boundaries are essential if
ideas are to be exchanged and built upon. Finally, multidimensionality represents
organizational flexibility that ensures that the knowledge workers have the competencies, perspectives, and cognitive ability to address issues and solve problems. This is
sometimes seen as being analogous to developing human instinct.
Each of these characteristics must emerge from the nature of the organization. They
cannot be designed by managerial decree—only nurtured, guided, and helped along.
In summary, there are four major ways in which the ICAS model describes organizational knowledge management:
1. Creativity
2. Problem solving
3. Decision making
4. Implementation
Creativity is the generation of new ideas, perspectives, understanding, concepts,
and methods to help solve problems, build products, offer services, and so on. Individuals, teams, networks, or virtual communities can solve problems and they take
the outputs of the creative processes as their inputs. Decision making is the selection
of one or more alternatives that were generated during the problem solving process
and implementation is the carrying out of the selected alternative(s) in order to obtain
the desired results.
Complex-adaptive-system-theory-based KM models are definitely showing both an
evolution and a return to systems-thinking roots in the KM world. All of the models
presented in this chapter are relevant and each offers valuable theoretical foundations
in understanding knowledge management in today’s organizations. What they all
share is a connectionist and holistic approach to better understand the nature of
knowledge as a complex adaptive system that includes knowers, the organizational
environment, and the “bloodstream” of organizations—the knowledge-sharing
networks.
The European Foundation for Quality Management (EFQM) KM Model
The EFQM model (Bhatt 2000, 2001, 2002) looks at the way in which knowledge
management is used to attain the goals of an organization. This model is based on
traditional models of quality and excellence, so there are very strong links between
KM processes and expected organizational results. Figure 3.12 shows the major components of the EQFM KM model.

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Chapter 3

People
Key
performance
Leadership

Policy
and
stategy

results
Processes

(people,
customer,
society)

Partnerships
and
resources

Enablers

Results

Figure 3.12
The key components of the EFQM model

The major components are: leadership, people, policy and strategy, partnerships
and resources, processes, and the ultimate key, performance results. The role of KM
as a whole is thus clearly positioned as an enabler that helps a company achieve
its goals—that is to say, the company’s goals, and not KM-oriented goals. This is an
excellent depiction of the role of KM. One of the major reasons why KM fails occurs
when KM is pursued for the sake of KM itself. This is analogous to producing incomplete sentences when attempting to articulate the justification for KM. For example,
“the objective of the KM program is to promote greater sharing of knowledge” as
opposed to “the objective of the KM program is to promote the greater sharing of
knowledge so that our sales force can collectively benefit from all the best practices
and lessons learned accumulated to date in order to provide faster and better front-line
service.”
The inukshuk KM Model
The inukshuk KM model (Girard 2005) was developed to help Canadian government
departments to better manage their knowledge. This model was developed by both
reviewing existing major models to extract five key enablers (technology, leadership,
culture, measurement, and process) and by conducting quantitative research to

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MEASUREMENT

Tacit knowledge

Explicit knowledge

Socialization

Externalization

Internalization

Combination

LEADERSHIP

TECHNOLOGY

CULTURE

Figure 3.13
Overview of the inukshuk KM model

validate these enablers. The name inukshuk is derived from the human-shaped figures
built by piling stones on one another by the Inuit in the northern part of Canada to
serve as navigational aids. There were three main reasons for choosing this symbol to
represent KM: it is well-recognized in Canada, it emphasizes the key role played by
people in KM, and while all inukshuks are similar they are not identical, reflecting the
variations in KM implemented in different organizations. Figure 3.13 depicts the
major components of the inukshuk KM model.
The process element is directly derived from the SECI model (Nonaka and Takeuchi
1995). Technology and culture represent critical structural elements that help maintain the integrity of the figure. Measurement and leadership are placed at the very top
to represent the importance of the overarching functions of measuring the impact of
KM and providing leadership and support for its implementation. This last model is
a good note to end on, as it represents a good aggregation of the key elements from
most KM models. While there remains diversity in terms of KM models, the major
components are beginning to gain more consensus and acceptance. Few KM researchers and practitioners would argue against including KM measurement, leadership,
technology, culture, and process in a solid KM model.

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Strategic Implications of KM Models
Models help us to put the disparate pieces of a puzzle together in a way that leads to
a deeper understanding of both the pieces and the ensemble that they make up.
Models supplement the concept analysis approach outlined in the first chapter in
order to take our understanding to a deeper level. KM models are still fairly new to
the practice or business of knowledge management, and yet they represent the way
forward. A coherent model of knowledge-driven processes is crucial in order for strategic business goals to be successfully albeit partially addressed by KM initiatives. KM
is not a silver bullet and it will not solve all organizational problems. Those areas of
knowledge-intensive work and intellectual capital development that are amenable to
KM processes, on the other hand, require a solid foundation of understanding what
KM is, what the key KM cycle processes are, and how these fit in to a model that
enables us to interpret, to establish cause and effect, and to successfully implement
knowledge management solutions.
Practical Implications of KM Models
For many years now, KM practitioners have been practicing “KM on the fly.” Many
valuable empirical lessons and best practices have been garnered through experience
with many diverse organizations. However, KM needs to be grounded in more robust,
sound theoretical foundations—something more than “it worked well last time, so
. . .” The key role played by KM models is to ensure a certain level of completeness
or depth in the practice of KM: a means of ensuring that all critical factors have been
addressed. The second practical benefit of a model-driven KM approach is that models
enable not only a better description of what is happening but they help provide a
better prescription for meeting organizational goals. KM models help to explain what
is happening now, and they provide us with a valid blueprint or road map to get
organizations to where they want to be with their knowledge management efforts. Lai
and Chu (2000) reviewed the influence that major KM models have had on KM practice and found that measurement was the most influential component. The next in
terms of level of influence were culture (including reward and motivation components) followed by technology as a strong enabler of KM.

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Key Points
•

Knowledge management encompasses data, information, and knowledge (some-

times referred to collectively as “content”), and it addresses both tacit and explicit
forms of knowledge.
•

The von Krogh and Roos KM model take an organizational epistemology approach

and emphasize that knowledge resides both in the minds of individuals and in the
relations they form with other individuals.
•

The Nonaka and Takeuchi KM model focuses on knowledge spirals that explain the

transformation of tacit knowledge into explicit knowledge and then back again as the
basis for individual, group, and organizational innovation and learning.
•

Choo and Weick adopt a sense-making approach to model knowledge management

that focuses on how information elements are fed into organizational actions through
sense making, knowledge creation, and decision making.
•

The Wiig KM model is based on the principle that in order for knowledge to be

useful and valuable, it must be organized through a form of semantic network that is
connected, congruent, and complete and has perspective and purpose.
•

The Boisot model introduces three key dimensions of knowledge beyond tacit and

explicit; codified, abstract, and diffused knowledge.
•

Complex adaptive systems are particularly well suited to model KM as they view the

organization much like a living entity concerned with independent existence and
survival. Beer and Bennet (1989) and Bennet (1981) have applied this approach to
describe the cohesiveness, complexity, and selective pressures that operate on ICAS.
•

The EFQM model introduces the major components of leadership, people, policy

and strategy, and partnerships and resources, in addition to processes, as being key
enablers of organizational success.
•

The inukshuk model reprises the key enablers that form part of most KM models

and assembles these components in a highly visual and symbolic fashion to depict
the key importance that people play in KM. Canadian government leaders have
applied this model.
Discussion Points
1. Compare and contrast the cognitive and connectionist approaches to knowledge
management. Why is the connectionist approach more suited to the von Krogh KM

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model? What are the strengths of this approach? What are its weaknesses? Use examples to make your points.
2. Describe how the major types of knowledge (i.e., tacit and explicit) are transformed
in the Nonaka and Takeuchi knowledge spiral model of KM. Use a concrete
example to make your point (e.g., a bright idea that occurs to an individual in the
organization).
a. Which transformations would prove to be the most difficult? Why?
b. Which transformation would prove to be fairly easy? Why?
c. What other key factors would influence how well the knowledge spiral model
worked within a given organization?
3. In what ways is the Choo and Weick KM model similar to the Nonaka and Takeuchi
KM model? In what ways do they differ?
a. How does the integration of a bounded rationality approach to decision making
strengthen this model? Give some examples.
b. List some of key triggers that are required in order for the sense-making KM model
approach to be successful.
4. How is the Wiig KM model related to the Nonaka and Takeuchi model? In what
important ways do they differ?
a. List some examples of internalization to illustrate how each of the five levels
differs.
b. How do public, private, and shared knowledge differ? What are the implications
of managing these different types of knowledge according to the Wiig KM model?
5. Outline the general strategy you would use in order to implement the Boisot I-Space
KM model. Where would you expect to encounter difficulties? What would be some
of the expected benefits to the organization of applying this approach?
6. What is the major advantage of a complex adaptive system approach to a KM
model? What are some of the drawbacks?
a. Provide an everyday example of requisite variety. Next, apply this to the management of knowledge in an organization. What are the elements needed in order to
successfully regulate a complex adaptive system? Why?
7. What additional factors do the EFQM and inukshuk KM models introduce?
8. How would you go about selecting a KM model for a given organization? What are
some of the questions you would ask of the employees? Of the senior managers?
Others?

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95

9. How would you justify the need for a KM model?
10. What is the relationship between the KM processes described in chapter 2 and the
KM models outlined in this chapter?
References
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Esperjo and R. Harnden. New York: John Wiley.
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Addison-Wesley.
Boisot, M. 1998. Knowledge assets. Oxford, UK: Oxford University Press.
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Girard, J. 2005. The inukshuk: A Canadian knowledge management model. KMPRO Journal 2
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Gleick, J. 1987. Chaos—Making a new science. Middlesex, UK: Penguin Books Ltd.
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organizations create, represent and use knowledge. Arlington, TX: Schema Press.

4

Knowledge Capture and Codification

If written directions alone would suffice, libraries wouldn’t need to have the rest of the universities attached.
—Judith Martin (1938–)

This chapter addresses the first phase of the knowledge management cycle, knowledge
capture and/or creation. The major approaches, techniques, and tools used to elicit
tacit knowledge, to trigger the creation of new knowledge, and to subsequently organize this content in a systematic manner (codification) are presented. These approaches
represent a multidisciplinary methodology that integrates what we have found to be
successful in a variety of other fields such as knowledge acquisition for the development of expert systems, instructional design techniques for course content creation
and organization, task analysis techniques used in the development of performance
support systems, and taxonomic approaches that originate from library and information studies. Knowledge capture and codification are the primary activities involved
in knowledge retention strategies and the management of strategic human capital.
Learning Objectives
1. Become familiar with the basic terminology and concepts related to knowledge
capture and codification.
2. Describe the major techniques used to elicit tacit knowledge from subject matter
experts.
3. Define the major roles and responsibilities that come into play during the knowledge capture and codification phase.
4. Outline the general taxonomic approaches used in classifying knowledge that has
been captured.

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5. Analyze the type of knowledge to be captured and codified, select the best approach
to use, and discuss its advantages and shortcomings for a given knowledge elicitation
application.
Introduction
The first high-level phase of the knowledge management cycle, as seen in figure 4.1,
begins with knowledge capture and codification. More specifically, tacit knowledge is
captured or elicited and explicit knowledge is organized or coded.
In knowledge capture, a distinction needs to be made between the capture and
identification of existing knowledge and the creation of new knowledge. In most
organizations, explicit or already identified and coded knowledge typically represents
only the tip of the iceberg. Traditional information systems departments primarily
deal with highly structured (records or forms oriented) data that makes up much less
than 5 percent of a company’s information. In knowledge management, we need to
also consider knowledge that we know is present in the organization, which we can
then set out to capture. There remains, however, that interesting area of knowledge
that we do not know about. This as-yet-unidentified knowledge will require additional
steps in its capture and codification. Finally, there is knowledge that we know we do
not have. We will need to facilitate the creation of this new, innovative content (refer
to figure 4.2).
Assess

Knowledge capture
and/or creation

Knowledge sharing
and dissemination

Contextualize

Update
Figure 4.1
An integrated KM cycle

Knowledge acquisition
and application

Knowledge Capture and Codification

99

Information sources

Known

Known

Unknown

Know that
we know

Know that
we don’t know

Don’t know
that we know

Don’t know that
we don’t know

User
awareness
Unknown

Figure 4.2
The known-unknown matrix (Frappaolo 2006)

Capturing the knowledge in an organization is not purely about technology.
Indeed, many firms find that information technology (IT) plays only a small part in
ensuring that information is available to those who need it. The approach needed
depends on the kind of business, its culture, and the ways in which people solve
problems. Some organizations generally deliver standard products and services, while
others are constantly looking for new ways of doing things. Knowledge capture can
therefore span a whole host of activities, from organizing customer information
details into a single database to setting up a mentoring program. We need to capture
both types of knowledge—explicit and tacit. Knowledge about standardized work, for
example, can be described explicitly and is easily captured in writing. On the other
hand, where there is innovation and creativity, people will also need some direct
contact (Moorman and Miner 1997). Knowledge capture cannot, therefore, be a
purely mechanistic “add-on,” because it has to do with the discovery, organization,
and integration of knowledge into the fabric of the organization. Knowledge has to
be captured and codified in such a way that it can become a part of the existing
knowledge base of the organization. Every organization has a history, which provides
a backdrop to the growth and evolution of the organization. Every organization has
a memory. The embodiment of the organizational memory is the experience of its
employees combined with the tangible data and knowledge stores in the organization
(Walsh and Ungson 1991). Bush (1945) envisioned “instruments . . . which, if properly developed, will give man access to and command over the inherited knowledge

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of the ages.” Knowledge that is not captured in this way becomes devalued and
eventually ignored. Knowledge is more than statements, declarations, and observations: it represents an intellectual currency that produces the most value when
circulated. It may have unrealized potential and value, but unless it is spent, its value
is not tested.
In today’s fast-paced economy, an organization’s knowledge base is quickly becoming its only sustainable competitive advantage. As such, this resource must be protected, cultivated, and shared among organizational members. Until recently,
companies could succeed based upon the individual knowledge of a handful of strategically positioned individuals. Increasingly, however, competitive advantage is to be
gained by making individual knowledge available within the organization, which then
becomes organizational knowledge. Organizational knowledge complements individual knowledge and makes it stronger and broader. The full utilization of an organization’s knowledge base, coupled with the potential of individual skills, competencies,
thoughts, innovations, and ideas, will enable a company to compete more effectively
in the future. Competitiveness is becoming increasingly dependent on an organization’s agility or ability to respond to changes in a very timely manner. The major
component of agility lies in the skills and learning abilities of the knowledge workers
within that organization.
There is no doubt that knowledge capture may be difficult, particularly in the case
of tacit knowledge. Tacit knowledge management is the process of capturing the
experience and expertise of the individual in an organization and making it available
to anyone who needs it. The capture of explicit knowledge is the systematic approach
of capturing, organizing, and refining information in a way that makes information
easy to find, and facilitates learning and problem solving. Knowledge often remains
tacit until someone asks a direct question. At that point, tacit can become explicit,
but unless that information is captured for someone else to use again at a later date,
learning, productivity, and innovation are stifled.
Once knowledge is explicit, it should be organized in a structured document that
will enable multipurpose use. The best KM tools enable knowledge creation once and
then leverage it across multiple channels, including phone, e-mail, discussion forums,
Internet telephony, and any new channels that come online. There are a wide variety
of techniques used to capture and codify knowledge and many of these have their
origins in fields other than knowledge management (e.g., artificial intelligence, sociology, instructional design), which are described here.

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Tacit Knowledge Capture
Traditionally, knowledge capture has emphasized the individual’s role in gathering
information and creating new knowledge. The literature shows a lack of consensus on
the role of the individual in knowledge acquisition. Some authors (e.g., Nelson and
Winter 1982) purport that the firm is a learning entity unto itself—that is, it has some
cognitive capabilities that are quite apart from the individuals who comprise it. In
contrast, other authors (e.g., Dodgson 1993) do not believe that organizations per se
can acquire knowledge and learn, only individuals can learn. A middle ground is
needed where individuals in the firm play a critical role in organizational knowledge
acquisition.
Learning at the individual level, however, is widely accepted to be a fundamentally
social process—something that cannot occur without group interaction in some form.
Individuals thus learn from the collective and at the same time the collective learns
from the individuals (e.g., Crossan, Lane, and White 1999). According to Crossan’s 4I
model (see figure 4.3), organizational learning involves a tension between assimilating
new learning (exploration) and using what has been learned (exploitation). Individual,
group, and organizational levels of learning are linked by the social and psychological
Individual

Group

Organization

FEED FORWARD

Individual

FEEDBACK

Intuiting
attending

Interpret

Experimenting

Integrate

Group
Institutionalize
knowledge
Organization
Figure 4.3
The 4I model of organizational learning (Crossan, Lane, and White 1999)

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processes of intuiting, interpreting, integrating, and institutionalizing (the four I’s).
Zietsma et al. (2002) modified this slightly by including the process of attending at
the stage of intuiting and the process of experimenting at the stage of interpreting.
In KM, this knowledge creation or capture may be done by individuals who perform
this role for the organization or a group within that organization, by all members of
a community of practice (CoP) or a dedicated CoP individual—but it is really being
done on a personal level as well. Almost everyone performs some knowledge creation,
capture, and codification activities in carrying out their job. Cope (2000) refers to this
as PKM (personalized KM). Within the firm, individuals share perceptions and jointly
interpret information, events, and experiences (Cohen and Levinthal 1990) and at
some point, knowledge acquisition extends beyond the individuals and is coded into
corporate memory (Inkpen 1995; Spender 1996; Nonaka and Takeuchi 1995). Unless
knowledge is embedded into corporate memory, the firm cannot leverage the knowledge held by individual members of the organization. Organizational knowledge
acquisition is the “amplification and articulation of individual knowledge at the firm
level so that it is internalized into the firm’s knowledge base.” (Malhotra 2000, 334)
The value of tacit knowledge sharing was discovered in a surprising way at Xerox
(Roberts-Witt 2002), which will be discussed later in this chapter.
Many of the tacit knowledge capture techniques described in this chapter stem
from techniques that were originally used in artificial intelligence, more specifically,
in the development of expert systems. An expert system incorporates know-how gathered from experts and is designed to perform as experts do. The term “knowledge
acquisition” was coined by the developers of such systems and referred to various
techniques such as structured interviewing, protocol or talk aloud analysis, questionnaires, surveys, observation, and simulation. Some authors (e.g., Keritsis 2001) even
use the term digital cloning. Knowledge management in business settings is similarly
concerned with knowledge capture, finding ways to make tacit knowledge explicit
(e.g., documenting best practices) or creating expert directories to foster knowledge
sharing through human–human collaboration (Smith 2000). In 1989, for example,
Feigenbaum contrasted traditional libraries as “warehouses of passive objects where
books and journals wait for us to use our intelligence to find them, to interpret them
and cause them finally to divulge their stored knowledge” (p. 122) with a library of
the future where books would interact and collaborate with users.
Tacit Knowledge Capture at the Individual and Group Levels
Knowledge acquisition from individuals or groups can be characterized as the transfer
and transformation of valuable expertise from a knowledge source (e.g., human expert,
documents) to a knowledge repository (e.g., corporate memory, intranet). This process

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involves reducing a vast volume of content from diverse domains into a precise, easily
usable set of facts and rules.
The idea of acquiring knowledge from an expert in a given field for the purpose of designing a
specific presentation of the acquired information is not new. Reporters, journalists, writers,
announcers and instructional designers have been practicing knowledge acquisition for years
. . . system analysts have functioned in a very similar role in the design and development of
conventional software systems. (McGraw and Harrison-Briggs 1989, 8–9)

The approach used to capture, describe, and subsequently code knowledge
depends on the type of knowledge: explicit knowledge is already well described, but
we may need to abstract or summarize this content. Tacit knowledge, on the other
hand, may require much more significant up-front analysis and organization before
it can be suitably described and represented. The ways in which we can tackle tacit
knowledge range from simple graphical representations to sophisticated mathematical
formulations.
In the design and development of knowledge-based systems, or expert systems,
knowledge engineers interviewed subject matter experts, produced a conceptual model
of their critical knowledge and then “translated” this model into a computer executable model such that an “expert on a diskette” resulted (e.g., Hayes-Roth, Waterman,
and Lenat 1983). The global aim of such systems was to extract and render explicit
the primarily procedural knowledge that comprised specialized know-how—typically
in a very narrow field. Procedural knowledge is knowledge of how to do things, how
to make decisions, how to diagnose and prescribe. The other type of knowledge,
declarative knowledge, was used to denote descriptive knowledge or knowing what as
opposed to knowing how. It soon became apparent that certain types of content were
easily extracted and modeled in this manner—anything that was similar to an interactive online manual or help function in such fields as engineering, manufacturing,
decision support, and medicine.
A wonderful by-product of the work in artificial intelligence was the array of innovative knowledge acquisition techniques that were created….
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