1. Information Knowledge Systems Management 5 (2005/2006) 153–169 153
IOS Press
Knowledge management and knowledge
sharing: A review
Cynthia T. Smalla and Andrew P. Sageb
a Information Technology Center, The MITRE Corporation, McLean, VA, USA
b Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA
22039, USA
Abstract: Knowledge Management is one of the major issues in the management of contemporary organizations and enterprises.
A review of the knowledge management (KM) literature reveals many different definitions and perspectives on knowledge and
knowledge management. Here, we provide an overview of some of this discourse along with descriptions of KM models and
frameworks that can be used to guide KM initiatives. Knowledge sharing, critical to creation of knowledge and organizational
performance, is often addressed under the umbrella of KM. We provide a survey of recent literature and progress in both of
these areas.
1. Introduction: knowledge and knowledge management
A review of the knowledge management (KM) literature reveals many different definitions and per-
spectives on knowledge and knowledge management. Here, we provide an overview of some of this
discourse. Knowledge, as defined by Plato and accepted by most Western philosophers, is “justified true
belief.” Information is a closely related term and is generally assumed to be data that is of potential value
in decision making. According to Brown and Duguid [11], there are at least three important distinctions
between information and knowledge: knowledge entails a knower; knowledge is much harder to detach,
transfer, and share than information; and knowledge is much harder to assimilate and understand than
information.
Nonaka and Takeuchi [52] describe differences in how Westerners and Japanese often view knowledge.
They espouse that Japanese view knowledge as being primarily tacit, something not easily seen or
expressible. Western culture has a strong focus on explicit knowledge, which can be expressed in words
and numbers and is more easily communicated than tacit knowledge. They describe the contrast between
these perceptions on knowledge as being rooted in culture. In the Western culture, there has been a long
history of separating knowledge from the knower, whereas this is not the situation in Japanese traditions.
Nonaka and Takeuchi [52] adopt a traditional definition of knowledge as “justified personal belief.”
Belief is critical to this concept of knowledge because it is closely tied to an individual’s, or group’s,
values and beliefs. Knowledge originates, from this perspective, in the minds and bodies of individuals.
Very important to the concept of knowledge is that of knowing. Knowing and learning capture the
dynamic aspects of knowledge. A knower, one who is knowing, can be said to possess “actionable
knowledge.” Miller and Morris [48] suggest that knowledge is gained when theory, information, and
experience are integrated. Cook and Brown [17] contend that innovation is the result of a generative
dance between knowledge and knowing.
1389-1995/05/06/$17.00 © 2005/2006 – IOS Press and the authors. All rights reserved

2. 154 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review
Wisdom
Learning
Knowledge
Theory Experience
Information
Contextual
Contextual
Filter
Data Filter
Fig. 1. Knowledge: A Derivative of Theory, Information, and Experience.
Most discussions and definitions of knowledge distinguish between two types of knowledge: tacit and
explicit. Explicit knowledge is knowledge that can be codified. It is more formal and systematic and
is often found in books, enterprise repositories, databases, and computer programs. Tacit knowledge,
which is highly personal, is difficult to articulate and is rooted primarily in our contextual experiences.
The definition of tacit knowledge originated with Polanyi’s [57] concept of tacit knowing. In Polanyi’s
discussion of human knowledge, he states, “we know more than we can tell” and provides an example of
face recognition to illustrate this. While the human can recognize a face, we can not articulate precisely
how we do it. Nonaka [51] expands on the concept of tacit knowledge and describes tacit knowledge
as consisting partly of technical skills and also as having a cognitive dimension that consists of mental
models, beliefs, and ingrained perspectives.
Enterprise or Organizational Knowledge is also a very important concept. Many discussions of
enterprise knowledge are contained in the works of Polanyi [57]; Nonaka and Takeuchi [52]; Cook and
Brown [17]; Miller and Morris [48]; Leonard [39]; Leonard and Strauss [40]; Davenport and Prusak [19].
Enterprise knowledge is generally said to be a dynamic mix of individual, group, organizational and
inter-organizational experiences, values, information, and expert insights. It originates in the minds
of the individual knowledge worker and emerges as individual knowledge workers interact with other
knowledge workers and the environment.
Most discussions of knowledge distinguish between data, information, and knowledge. Miller and
Morris [48], for example, define knowledge as the intersection of information, experience, and theory.
This can be extended to include wisdom, which might be defined as successfully applied knowledge and
which will often be tacit in nature. Their concept of knowledge is shown in Fig. 1.
Cook and Brown [17] distinguish organizational knowledge from organizational knowing. They refer
to the concept that knowledge is something that is processed by the individual as the “epistemology
of possession.” Critical to their theory is that the tacit/explicit dimension and the individual/group
dimension yields four types of knowledge that are each distinct and that, none is subordinate to or made
up of any of the others. Additionally, they contend that there is an element of knowledge not captured
by these types of knowledge. An individual can have knowledge of why or what it means to ride a
bike, but not necessarily be able to personally ride a bike, which requires knowledge that is rooted in
practice. Knowing, as action, calls for an “epistemology of practice.” Figure 2 depicts these four types
of knowledge that interact with knowing and provides an example of each. It is through this interaction,
which Cook and Brown describe as a generative dance, that new knowledge is created.

3. C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 155
Individual Group
Knowledge Knowledge
Concepts Stories
Explicit
Knowledge
Knowing
(As Action)
Tacit
Knowledge Skills Genres
Fig. 2. Interaction of Knowing and Types of Knowledge.
Fig. 3. Nonanaka and Takeuchi Based Four Stages of Knowledge Creation.
Tacit and explicit knowledge are each critical to the Nonaka and Takeuchi [52] theory of organizational
knowledge creation. As shown in Fig. 3, the interaction of tacit knowledge and explicit knowledge forms
the four stages of knowledge conversion (socialization, externalization, combination, and internalization)
identified by these authors and which results in different knowledge content. Individual and group
knowledge are not distinct here, but are captured in the theory as the ontological dimension that relates
to the knowledge creation entities.
Enterprise Knowledge Management is also a very important concept, as we have noted. Most discourse
regarding the management of knowledge comes from two primary schools of thought: one that focuses
on existing, explicit knowledge and a second that focuses on the building or creation of knowledge.
Some KM studies focus almost entirely upon information technology tools, whereas others focus on KM
as a transdisciplinary subject with major behavioral as well as technology concerns. Definitions and
studies found in the computer science and artificial intelligence literature generally focus on tools and
technology. For example, O’Leary [55] defines enterprise KM as the formal management of knowledge
resources to facilitate access and reuse of knowledge that is generally enabled by advanced information
technology. Knowledge resources vary from enterprise to enterprise, but usually include manuals,
letters, customer information, and knowledge derived from work processes. To this end, Alavi and
Leidner [1]) define knowledge management as the “systemic and organizationally specified process for

4. 156 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review
acquiring, organizing, and communicating both tacit and explicit knowledge . . .”. Other works of interest
that discuss primarily the information systems technologies efforts in knowledge management include
Malhotra [45,46], Maier [44], Tiwana [73,74], and Srikantaiah and Koenig [68].
The works of Nonaka and Takeuchi [52] and Leonard [39] are well-known works concerning the
management of knowledge which focus on generation and creation of knowledge. There is a major
environmental context associated with this “knowledge” and an appropriate definition of knowledge
is that it is information imbedded in environmental context such that the information can be used
successfully for decision related purposes. A not inappropriate definition of knowledge management
is that it is the management of the context and environment for knowledge acquisition, representation,
transformation, sharing, and use.
Many contemporary organizations, with the objective of enhanced organizational performance, have
initiated knowledge management programs and related activities [63] to enable the sharing (exchange)
and integration of knowledge. Knowledge which is created in the mind of the individuals is generally
of little value to an enterprise unless it is shared. Organizations are rapidly learning that, just because
appropriate knowledge technology exists, knowledge will not necessarily flow freely throughout an
organization. Cultural issues are regularly cited as one of the concerns of those implementing KM
initiatives. The cultural issues that concern managers as reported by Alavi and Leider [1] were the
implications of change management, and the ability to convince organizational entities (individuals and
business units) to share their knowledge. In many organizations, a major cultural shift would be required
to change the employee’s attitude toward knowledge sharing. Holtshouse [30] identified two knowledge
research issues that are related to knowledge sharing: 1) the exchange of tacit knowledge, and 2) the flow
of knowledge. While not using the term knowledge sharing explicitly, knowledge sharing is very implicit
in each of these activities. There seems generally uniform agreement among these authors and many
others, such as the work of Thomas et al. [69], that the benefit of knowledge management initiatives will
not be realized unless the cultural, management, human, social, and organizational elements or factors are
aligned appropriately. Of course, appropriate attention needs to be paid to the many technology facets [62]
that enable successful knowledge management as well. There have been many recent efforts to provide
integration and synthesis of knowledge management efforts. In a recent bibliometric analysis [24], no
less that 1407 knowledge management publications were noted. Another recent work by Nonaka and
Peltokorpi [54] attempts present a review and categorization of what the authors describe as the “twenty
most influential knowledge management publications in management journals”.
2. Existing KM models and frameworks
A model is a representation of reality. Casti [14] defines a taxonomy of models that include exper-
imental, logical, mathematical/computational, and theoretical. Most KM models are theoretical in the
sense that they are an imagined mechanism, or process that has been developed to account for observed
phenomena. Theoretical models are based on hypothesized relationships among factors. Within this
taxonomy, models are further categorized by their purpose:
Predictive – enables us to predict what a system’s behavior will be.
Explanatory/descriptive – provides a framework in which past observations can be understood as part
of an overall process. These models are also called descriptive because they are explicit descriptions that
capture and organize information.
Prescriptive – provides a picture of the real world as it will be if certain postulates (prescriptions) or
formal axiomatic rules of behavior are applied.

5. C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 157
A survey of the literature finds many descriptive KM models and frameworks. Many of the frameworks
have been developed by large consulting firms and have been used both for internal and external KM
improvement.
Apostolou and Mentzas [6] distinguish four groups of KM frameworks: those that focus on knowledge
generation, those that focus on knowledge processes, those that focus on technology, and those that are
holistic. They identify and provide an overview of models in each group. The model developed by
Nonaka and Takeuchi [52] and the framework proposed by Leonard [39] are included in the knowledge
generation group. The knowledge processes group frameworks include those of the APQC [2–4] and
Romhardt and Probst [58]. Within IBM’s Knowledge Management Framework [31], the primary business
goals that can be improved through knowledge management are highlighted: innovation, responsiveness,
productivity, and competency. Holistic frameworks emphasize the interdisciplinary nature of KM and
explicitly include technology, processes, organizational structures, and cultural issues. The holistic
frameworks include those of Coopers and Lybrand [36] and the Intellectual Capital Framework (ICM)
of IBM [31]. Based on an analysis and adaptation of these frameworks, Apostolou and Mentzas [5]
adopted a KM framework that included six elements: context, goals, strategy, culture, KM processes,
and technological and organizational infrastructure. The framework was used to perform a comparative
analysis of KM efforts.
Holsapple and Joshi [28] provide a description and comparative analysis of ten descriptive KM
frameworks and models. Each of these frameworks or models attempts to explain one or more aspects
of the KM phenomena. They analyze the frameworks in five areas: 1) the focus, 2) roots/origin, 3)
knowledge resources, 4) knowledge manipulation activities, and 5) influences on the conduct of KM.
The first two areas include the context which describes the objective and development process of the
KM framework. The other areas address the conduct of KM within an organization. Findings and
observations of the analysis include the following: KM frameworks are being approached from a variety
of perspective and methodologies,minimum attention has been given to knowledge resources, no common
way of characterizing knowledge manipulation activities or influences on the conduct of KM is being
used, and no individual KM framework subsumes the others. They conclude that a more comprehensive
KM framework is needed in order to more fully describe knowledge manipulation activities and their
relationships.
Arthur Andersen and APQC [7] developed a KM Assessment Tool TM (KMAT) to promote discussion
about organizational KM and to facilitate benchmarking. This tool is built around an organizational
KM model that consists of the KM processes (Apply, Share, Create, Identify, Collect, Adapt, and
Organize) and its enablers (Leadership, Measurement, Technology, and Culture). This tool is used to
characterize the current state of the processes and to assess how well the enablers within an organization
are supporting the KM processes. Liebowitz [42] also discusses a variety of issues and some tools for
knowledge management.
Bukowitz and Williams [12] present a KM Process Framework that includes both the tactical and
strategic processes of managing knowledge assets. They espouse managing both the tactical and strategic
elements together to ensure that the right mix of knowledge assets and the capability to access them
are available. In this model, the tactical components of KM consist of the knowledge management
processes that knowledge workers exercise as they carry out day-to-day work activities: get, use, learn,
and contribute. According to Bukowitz and Williams [12], the get and use elements of the KM framework
are process elements that organizations have generally been performing for a long time. The learn and
contribute process elements, described as being relatively new to organizations, are indicated to be the
most challenging. A brief description of these process elements, which are tactical in nature, follows.

6. 158 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review
Get – This is the process step a knowledge worker uses to find information to solve a problem.
Knowledge workers are familiar with seeking information. The challenge for the organization is how to
do it efficiently with the glut of information available today.
Use – This process step is often associated with innovation. Organizations are interested in knowledge
workers using and combining information in ways not previously thought of to create more innovative
solutions.
Learn – This process step is generally new from the perspective that organizations are now formally
examining learning as a way of generating and keeping competitive advantage.
Contribute – This is the process step of getting knowledge workers to contribute to organizational
knowledge bases. It will also be new for most organizations. Technology often exists to support some
of this, such as to help organize and post information. The challenge is getting employees to believe
that there is some benefit in contributing knowledge for them. This element is one facet of enterprise
knowledge sharing; however, knowledge sharing is a broader concept and encompasses sharing of both
tacit and explicit knowledge at the individual, group, and enterprise level.
There is also a strategic component of the framework, and the goal of the strategic component of
the KM Process Framework is to ensure that knowledge strategy is aligned with business strategy. The
strategic process steps, which include assess, build/sustain, and divest, are performed by KM leadership
and groups and are defined as follows:
Assess – This process step assesses how well the current knowledge assets fulfills current knowledge
needs. It includes developing metrics that link the investments in knowledge bases to the company
benefits.
Build and Sustain – This process step entails the design and maintenance of knowledge bases with the
goal of ensuring that the organization remains viable.
Divest – This process step examines the organizational knowledge bases in terms of opportunity costs
and alternative sources of value. Knowledge bases are assessed to determine whether they are enough to
justify continued maintenance.
The KM Process Framework is used as the foundation of the diagnostics and improvements guidance
provided in the Knowledge Management Fieldbook of Bukowitz and Williams [12].
As noted, Nonaka and Takeuchi [52] present a theory of knowledge creation that consists of four
knowledge conversion phases: socialization, externalization, combination, and internalization. The
conversion phase takes place in five steps: sharing of tacit knowledge, creating concepts, justifying
concepts, building an archetype, and cross-leveling knowledge. Critical to this theory is the concept
of levels of knowledge: individual, group, organizational, and inter-organization. Knowledge sharing
primarily occurs during the socialization, externalization, and combination phases. It does not generally
occur during internalization. The importance of sharing in the creation of knowledge is captured in the
concept of ‘redundancy.’ Those concepts created by an individual or group will often need to be shared
by other individuals who may not need the concept initially or immediately. During the socialization
stage, sharing occurs primarily at the individual and group levels. In the externalization stage, knowledge
is codified and shared at the group and organizational levels. In the cross-leveling knowledge phase, an
enterprise shares knowledge both intra- and inter-organizationally. The relationship of knowledge sharing
to the enterprise knowledge-creation process, as adapted from Nonaka and Takeuchi’s organizational
knowledge-creating process, is depicted in Fig. 4.
Knowledge creation is a natural phenomenon; however, within the context of an enterprise, there are
often practices that are embedded in organizational culture, processes, and strategies that inhibit this
process. In addition, there may be insufficient technological support to enable knowledge sharing, even
when other organizational support is present, although this would represent an uncommon occurrence.

7. C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 159
Fig. 4. Knowledge Sharing and Enterprise Knowledge-Creation Model of Nonaka and Takeuchi.
KM Ontology
Knowledge Resources Knowledge Manipulation KM Influences
Activities
Managerial Resource
Schema Content Internalizing
Acquiring
Culture Artifacts Selecting Using Environmental
Strategy
Participants Knowledge
Purpose
Infrastructure
Human Computer-based
Computer-
Fig. 5. Knowledge Management Ontology.
Holsapple and Joshi [29] developed a knowledge management (KM) ontology using a collaborative
methodology based on a study of international practitioners and researchers. The design of the KM
ontology is based on Knowledge Management Episodes (KMEs) which consist of activities that occur
from the time a knowledge need is recognized until the time the knowledge need is satisfied. During a
KME, knowledge resources are manipulated in KM activities by knowledge participants, which are gov-
erned by KM influences. Examples of KMEs include making a decision, solving a problem, developing
a prototype, or servicing a customer. The major components of the KM ontology are basic knowledge
manipulation activities that occur with KMEs, major influences on KM episodes, and knowledge re-
sources. A brief description of the KM ontology components and subcomponents, depicted in Fig. 5, is
of interest.
The knowledge manipulation component of the ontology consists of four major activities: acquiring,
selecting, internalizing, and using knowledge. Each of these activities is further decomposed into
sub-activities as follows:

8. 160 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review
Acquiring knowledge – Refers to the activity of identifying knowledge in the environment and
transforming it to knowledge that can be used. Sub-activities include: identifying, capturing,
organizing, and transferring knowledge.
Selecting Knowledge – Refers to the activity of identifying knowledge needs within an organization.
Sub-activities include: identifying, capturing, organizing, and transferring knowledge.
Internalizing Knowledge – Includes the activities that make knowledge part of an organization.
Sub-activities include: assessing, targeting, structuring, and delivering. Delivering involves storing,
updating, disseminating, and sharing knowledge.
Using Knowledge – Incorporates the activities that apply existing knowledge to generate new knowl-
edge or externalization (make available outside the organization). Sub-activities include: generating
and externalizing knowledge.
The influence component of the KM ontology includes factors that influence the success of KM
initiatives in an organization. The influence factors are categorized into three major types of influences:
managerial, resource, and environmental. The sub-factors of these three influences are:
Managerial – includes leadership, coordination, control, and measurement.
Resource – includes financial, knowledge manipulation skills, material, human, and knowledge
resource.
Environmental (external to organization) – includes competition, fashion, markets, and technology.
The KM ontology resource component includes the major knowledge resources that should be available
to an organization during a KME. The taxonomy of ingredients in this component includes content
knowledge resources and schema knowledge resources. Content knowledge resources are tangible
(useable) representations of knowledge and can be either of two types: participant knowledge and
artifacts. Participant knowledge resources, which can be either human or material resources, have
knowledge processing capabilities, whereas artifacts do not. Examples of material knowledge resources
are decision support systems, expert systems and performance support systems.
Schematic knowledge, as defined by Holsapple and Joshi [29], is knowledge that is embedded in the
workings of an organization. While this type of knowledge resource can be captured in artifacts, it exists
independently. The schematic knowledge resources identified in the KM ontology are as follows:
Culture – an organization’s values, norms, and unwritten rules.
Infrastructure – the knowledge that structures the participants in the organization based on role,
relationships, and policies that govern the relationships.
Purpose – defines the reason an organization exists. It can include mission, vision, purpose, and
objectives.
Strategy – defines how an organization plans to achieve its purpose.
All of these are needed ontological components in a knowledge management process.
There are a relatively large variety of related efforts. Of special note are the works of Pfeffer and
Sutton [56], Stewart [69], Morey et al. [50], Davis et al. [20], Dalkir [18], Garavelli et al. [23], Mertins
et al. [47], and Von Krogh [76,77].
Wong and Aspinwall [80] also review knowledge management frameworks and place particular em-
phasis upon identifying suitable KM implementation frameworks. Based on their studies they suggest
five guidelines for developing an implementation framework:
1. Incorporate a clear structure within the framework to enable construction and organization of the
to-be-identified KM tasks.

9. C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 161
2. Address the different knowledge resources or types of knowledge to be managed.
3. Include the KM processes that will be needed to manipulate the knowledge.
4. Identify and include significant influences that will affect performance of KM efforts.
5. Provide balanced and integrated technological and cultural, social and behavioral perspectives.
These authors suggest that their studies indicate that none of the currently available frameworks are in
complete accord with these guidelines. We believe that this is potentially capable of realization through
development of a knowledge management process architectural framework (KMPAF) and a knowledge
management process architecture development process (KMPADP) that can be used to instantiate the
KMPAF such as to result in an appropriate knowledge management process architecture that ultimately
leads to an enterprise or organizational knowledge management process.
Back et al. [9] provide a framework and methodology for managing knowledge in networks. They
espouse managing knowledge in networks needs to integrate various disciplines such as human resource,
organization development, change management, strategy, information technology, sociology, and net-
work theory. The network-based approach differs from other KM frameworks in that it attempts to
integrate these diverse disciplines into a holistic framework. It also addresses both explicit and im-
plicit knowledge and where and how knowledge is being created and transferred. Knowledge work
processes, knowledge network architecture, and facilitating conditions are important building blocks for
the methodology.
There are also several works by Rouse and Sage [59–61,65,66] that focus strongly on the role of
information systems frontiers and contemporary information technology in supporting systems engi-
neering and systems management, including a very recent one [60] that is much concerned with effective
enterprise management.
3. Enterprise knowledge sharing
Enterprise knowledge sharing can occur in many forms. While a survey of the literature yields
numerous KM articles, frameworks and models, and assessment tools, few are targeted specifically at
knowledge sharing. Enterprise knowledge sharing is often described in the literature as being critical to
the performance of knowledge creation and in the leveraging of knowledge [75].
Ives et al. [33] describe knowledge sharing as a human behavior that must be examined in the context of
human performance. Human performance is described as a complex activity that is influenced by many
factors. They describe a human performance model that includes the business context and organizational
and individual factors. Organizational performance factors include: structure and roles, processes,
culture, and physical environment. Individual performance factors include: direction, measurement,
means, ability, and motivation. These inter-related factors each contribute to successful knowledge
sharing and can not be effective alone. A description of these factors and how they contribute to
knowledge sharing is of interest.
1. Business Context – Employees are more likely to share knowledge if the behavior is linked to
business goals. These authors emphasize the need for the business strategy to be communicated
to employees.
2. Organizational Structure and Roles – Supporting knowledge sharing is encouraged by means of
a two-part organizational structure: 1) a dedicated KM staff who owns the knowledge processes,
templates, and technologies; and 2) knowledge sponsors and integrators from the business units
who “own” the knowledge content.

10. 162 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review
3. Organizational Processes – Knowledge processes need to be built into the daily work process, and
well-defined knowledge capture processes should exist. Knowledge processes should depend on
the type and level of knowledge.
4. Organizational Culture – In addition to stressing the importance of organizational culture to
Knowledge-sharing (KnS) behavior, the authors also emphasize the importance of understanding
the cultural differences between individual knowledge workers. Steps to achieving a KnS culture
include setting KnS priorities, strong KnS leadership, KnS investment support, and modeling by
senior leadership (i.e., visible advocacy of KnS behavior).
5. Physical Environment – Many organizations are beginning to recognize the need to create envi-
ronments (e.g., quiet space, informal environments, relaxed physical environments enhanced by
technology) that are appropriate for knowledge sharing.
6. Direction – Knowledge sharing is a new behavior to many organizations, so guidance is needed
to achieve enhanced value. Guidance for knowledge sharing is therefore needed in terms of the
contextual awareness abstractions of what to share, when to share, and how to share, as well as
why share, what to share and who to share with. Guidance of this sort that is given in the context
of the daily work processes is especially useful to knowledge workers.
7. Measurement – Human performance measurement is becoming increasingly more important as
knowledge-based organizations begin to recognize that the organization’s greatest resource is
comprised of its people. How a KnS proficiency has been established and measured at the authors
company is described. KnS expectations are communicated and translated into actions that can be
documented in a performance review. Individual and team KnS metrics provide definition to KnS
behavior and communicate that the organization places a value on it. Documenting the mission
impact (outcome metrics) of KnS behavior is important to obtaining and keeping senior leadership
support.
8. Means – Effective enterprise knowledge sharing can not be done without information technology
(IT). The existing knowledge management infrastructure (i.e., e-mail, internet, intranet, group-
ware, and web technologies) can be extended to support KnS processes. Videoconferencing,
application sharing, and electronic meeting support are KnS enablers. Many organizations focus
on the IT component of knowledge sharing because it is the most tangible; however, it is im-
portant to provide the means to accomplish this within the context of the various organizational
performance attributes.
9. Ability – KnS behavior within a corporate environment needs ongoing support and training. It
is important to coordinate or integrate KnS training with the entire array of training initiatives.
Knowledge workers need training prior to job performance, knowledge support during job exe-
cution, and time to reflect on lessons learned to improve individual learning and to contribute to
organizational learning.
10. Motivation – There are individual and cultural differences that drive KnS behavior. Knowledge
sharing is best supported by intrinsic rewards (e.g., saving work time, participating in useful and
interesting dialog, or professional pride in being recognized as an expert). External rewards must
be selected carefully because what motivates in one organization may be a barrier in another. The
importance of employee care and trust is also emphasized. KnS motivation factors cited include:
being a normal part of the job, being related to career growth, receiving thanks and recognition,
knowing how others used their contributions, and knowing it is expected behavior.
For many companies, getting employees to share knowledge and to contribute knowledge to organi-
zational repositories is the focus of their knowledge management programs. Liebowitz and Chen [43]

11. C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 163
espouse the view that establishing a KnS proficiency can help to jump start and build a KnS culture. They
define a KnS proficiency as “an attribute that allows the creation of knowledge to take place through an
exchange of ideas, expressed either verbally or in some codified way.” Their investigation of existing
KnS assessment instruments found several assessment instruments that broadly cover the area of KM, but
few if any that explicitly addressed knowledge sharing. Recognizing this void, Liebowitz and Chen [43]
developed a KnS effectiveness inventory that consists of 25 questions covering four areas:
1. Communications flows – assesses how knowledge and communication exchanges are captured
and disseminated throughout the organization.
2. KM environment – examines internal cultural factors.
3. Organizational facilitation – assesses the sophistication of the KM infrastructure and KnS capa-
bility.
4. Measurement – assesses the likelihood of knowledge sharing and KM being successful within the
organization.
The effectiveness inventory was designed to assess how well an organization is performing KnS
activities. The results of the inventory instrument allow an organization to pinpoint potential areas of
improvement.
APQC conducted a benchmarking study to determine what best practice firms do to develop a KnS
culture. This study [3] examined culture on three levels:
1. Company’s espoused philosophy, values, structures, and systems
2. Behavior of people’s peers and managers
3. Deeper core company values.
This study found that several factors influenced and/or enabled a KnS culture to varying degrees.
The factors included: link between knowledge sharing and business strategy; fit with overall culture
of the organization; fit with daily work; role of leaders and managers; role of human networks; and
institutionalization of learning disciplines. This study provided no insight into the extent of the influence
of each factor. It did conclude, however, “. . . what draws people to share is different in various
organizations and matches the company’s core values as well as the look and feel of other organizational
processes.”
Managing knowledge sharing efforts is very important. Huysman and de Wit [32] identify the set of
KnS practices that facilitate and structure knowledge sharing for knowledge workers. They conducted
research on KnS practices with ten large (more than 1000 employees each) companies. They identify
three primary reasons for sharing knowledge: knowledge acquisition, knowledge reuse, and knowledge
creation. They identify the following traps: the information and communication technology (ICT) trap,
the management trap, and the local learning trap. The authors assert that the second wave of KM will put
knowledge connections center stage and will link the idea of social capital (social networks that create
opportunities) with KM. Social capital has three dimensions: structural (network ties), cognitive (shared
codes and languages); and relational (mutual trust and norms). While the authors describe the first wave
rather negatively, they conclude by recognizing the significance and importance of managing knowledge
sharing in the second wave.
Liao et al. [41] assert that knowledge sharing in business is strongly related to behavioral factors. They
conducted a case study of a Taiwanese finance and securities firm in order to investigate employee attitudes
and intentions regarding knowledge sharing in the context of employee relationships. The premise of
this research is that by managing employee relations an organization could have a positive impact on
knowledge sharing. The variables examined in the study include the working environment, conditions of

12. 164 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review
Systems Planning Research, System Acquisition,
and Development, Test and Production, or
Marketing Evaluation Procurement
Conversion
Knowledge Perspectives
Conversion
Sharing Knowledge Principles
Sharing
Knowledge Practices
Learning over Time
Fig. 6. Knowledge Sharing and Conversion across SE Processes.
respect, conditions of support, justice perception, relationships with superiors, self-satisfaction, and self-
learning. The study found that conditions of respect, justice perception, and relationships with superiors
could affect attitudes toward knowledge sharing in a major way. The study found that employees with
good relationships with their firm would generally share knowledge voluntarily and unconditionally,
while employees with not so good relationships with their firm were reluctant to share knowledge and
experiences with colleagues. The authors conclude that organizations should devote much attention to
managing employee relationships because of the impact they can have on the resulting KnS behavior.
In another notable work, Styrhre and Kailing [71] describe different knowledge sharing programs at
two large international corporations in the paper and pharmaceuticals industry.
The ability to acquire, create, and make actionable the knowledge needed to achieve business goals
is critical to enterprises that engage professionally in systems engineering. Both strategic and tactical
knowledge are needed to remain competitive. Systems engineering consists of three primary lifecy-
cles [64]: Systems Planning and Marketing; Research, Development, Test and Evaluation; and System
Acquisition, Production and Procurement. As illustrated in Fig. 6, knowledge is created in each of these
phases and is shared and used by other phases. This results in proactive and interactive learning.
In this work, knowledge perspectives represent the strategic knowledge about future directions. This
knowledge is used primarily by the Systems Planning and Marketing lifecycle. Knowledge principles are
formal problem solving methods and are used primarily during the Research, Development, and Test and
Evaluation lifecycle. Knowledge practices enable systems acquisition based upon generally proven and
low risk approaches. In order for knowledge to flow properly from one life cycle to the other, knowledge
conversion and knowledge sharing are each needed.
In order to improve enterprise knowledge sharing, effective ways of measuring KnS behavior are
needed. As previously discussed, there are two types of knowledge: tacit and explicit. Lee [38]
investigates KnS measurement from the perspective of the four stages of knowledge conversion as
described by Nonaka and Takeuchi [52]: tacit to tacit, tacit to explicit, explicit to explicit, and explicit
to tacit. He contends that most KnS metrics focus on the tacit to explicit or explicit to tacit knowledge
conversion. Examples of metrics for the tacit to explicit knowledge conversion process include:
– Number of shared documents published.

13. C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 165
– Number of suggestions for improvement.
– Corporate directory coverage.
– Number of patents issued.
– Number of presentations made.
Examples of explicit to tacit knowledge conversion process metrics include:
– Number of hits on document repository.
– Subscriptions to journals.
– Attendance at group presentations.
– Size of discussion data bases.
Lee contends that tacit to tacit knowledge sharing contributes to 90% of total knowledge sharing.
Emphasizing the importance of tacit knowledge sharing, Lee [38] proposes KnS “in process” metrics
for the tacit to tacit knowledge conversion process. “In process” metrics measure the processes that can
lead to the outcome metrics found on the Balanced Scorecard [35]. Given the nature of tacit knowledge,
the author suggests that measuring social interactions can provide a workable proxy for measuring the
degree of tacit to tacit knowledge sharing. Adapting the Social Network Analysis techniques, Lee
developed KnS metrics for tacit to tacit knowledge transfer based on the number and perceived quality of
relationships. Lee [38] indicated that the Global Maintenance Network (GMN) was established by BHP,
a global resource company headquartered in Australia, to enable sharing of best maintenance practices
worldwide. A case study using the adapted Social Network Analysis technique was conducted at BHP.
The tacit to tacit KnS metrics included the following:
– Number of links per respondent.
– Frequency of advice seeking.
– Individual with highest number of nominations for being an expert in a given area.
– Ratio of internal to external links.
– Proportion of total contacts that are inward.
– Proportion of total contacts that are outward.
These metrics are intended to complement the traditional Balanced Scorecard metrics captured by the
organization.
The MITRE Corporation [67] developed a KM Measurement framework that includes two goals related
to knowledge sharing: enable and motivate knowledge sharing and actually share knowledge. Using the
Balanced Scorecard [35]. methodology, indicators for the achievement of the KnS goals were identified.
Indicators for explicit and tacit knowledge sharing included: demographics of work product capture,
demographics of knowledge exchanges, strength of communities of practice, and breadth of knowledge
capture. Indicators for enabling and motivating knowledge sharing include: reward and recognition;
alignment with business strategy; alignment with culture; effective KnS tools; and support structure for
communities of practice (CoPs). Measures were identified and captured for each of the indicators. In
another recent and useful work, Brauner and Becker [10] discuss issues associated with the management
of knowledge sharing systems. These authors suggest that it is explicit and unshared expertise, rather
than implicit and shared knowledge, which is truly the most valuable for organizations. They propose
knowledge management as an instrument of organizational learning since a major objective is managing
the organizational accessibility of this knowledge. In this sense, knowledge management as a social
process is stressed, and not just knowledge management and sharing as a technical process.

14. 166 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review
4. KM assessment and improvement models
The KM literature identifies several KM maturity models [34,37,49] that are used to assess or improve
the maturity of the KM process. These models generally leverage the work of the several Carnegie Mellon
University (CMU) Software Engineering Institute (SEI) Capability Maturity Models (CMMs) [13].
Kochikar [37] leveraged the work of the SEI CMM in the development of the knowledge management
maturity (KMM) model. The KMM model has five levels: 1- Default, 2- Reactive, 3- Aware, 4-
Convinced, and 5- Sharing. The knowledge lifecycle has three stages: acquisition, sharing/dissemination,
and reuse. The state of the three key result areas (process, people, and technology) is used to assess
the KMM level. The Systems Engineering Capability Maturity Model (SE-CMM) identifies seventeen
process areas that are critical to systems engineering. Each of these process areas consists of multiple
base practices. While the SE-CMM does not explicitly discuss knowledge management, the capture
activity is explicit in many of the base practices.
5. Summary – the missing pieces
Many descriptive KM representations exist. They differ in their focus and purpose. These repre-
sentations serve to provide a foundation for understanding KM and potential initiatives that can result
in an enhanced state of KM within an organization, but they generally provide minimum support for
prescriptive and predictive study and assessment. Additionally, most KM representations lack automated
simulation-based support that allows empirical experimentation.
Enterprise knowledge sharing is a critical aspect of the leveraging and transmission of knowledge, and
of the enterprise knowledge creation process. Enterprises are as diverse as the knowledge workers that
comprise them. KM leadership and practitioners need enhanced tools to help them better understand
what influences knowledge workers to share. Knowledge sharing is a human behavior that is influenced
by both the KnS environment and other knowledge workers in the environment. Knowledge workers are
diverse and heterogeneous. The KM models and tools that exist today do not address the heterogeneous
attributes of the knowledge workers and pay minimal attention to the interaction between knowledge
workers. To improve the KnS process, the interaction of the knowledge worker within the environment as
well as the interactions among knowledge workers must be addressed. A complex adaptive system based
enterprise KnS model may well speak effectively to these concerns and are addressed in a companion
paper.
In this survey paper we have attempted to present an overview of contemporary knowledge management
issues. While we have discussed a number of relevant works, there are a number of value [8,25–27,30,
50,53,69,70,77–79] that we have not specifically discussed here.
References
[1] M. Alavi and D. Leider, Knowledge Management Systems: Emerging Views and Practices from the Field, (Vol. Track 7),
Proceedings for the 32nd Hawaii International Conference on on Systems Sciences, 5–8 Jan. 1999, 8.
[2] American Productivity & Quality Center (APQC). 1997. Using Information Technology to Support Knowledge Manage-
ment. Consortium Benchmarking Study – Best-Practice Report.
[3] American Productivity & Quality Center (APQC). 1999. Creating a Knowledge-Sharing Culture. Consortium Bench-
marking Study – Best-Practice Report.
[4] American Productivity & Quality Center (APQC). 2000. Successfully Implementing Knowledge Management. Consor-
tium Benchmarking Study – Final Report, 2000.

15. C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 167
[5] D. Apostolou and G. Mentzas, Towards a Holistic Knowledge Leveraging Infrastructure: The KNOWNET Approach,
Proceeding of the 2nd International Conference on Practical Aspects of Knowledge Management (PAKM98) Basel,
Switzerland, 29–30 October 1998, 3–1 through 3–8.
[6] D. Apostolou and G. Mentzas, Managing Corporate Knowledge: A Comparative Analysis of Experiences in Consulting
Firms, Part 1. Knowledge and Process Management 6(3) (1999), 129–138.
[7] Arthur Anderson and APQC. 1996. The Knowledge Management Assessment ToolTM : External Benchmarking Version.
[8] E.M. Awad and H.M. Ghazin, Knowledge Management, Prentice Hall, Englewood Cliffs NJ, 2003.
[9] A. Back, G. von Krogh, A. Seufert and E. Enkel, Putting Knowledge Networks into Action: Methodology, Development,
Maintenance, Springer-Verlag, Heidelberg, 2005.
[10] E. Brauner and A. Becker, Beyond Knowledge sharing: The Management of Transactive Knowledge Systems, Knowledge
and Process Management 13(1) (2006), 62–71.
[11] J.S. Brown and P. Duguid, The Social Life of Information, Harvard Business School Press, Boston, MA, 2000.
[12] W.R. Bukowitz and R.L. Williams, The Knowledge Management Fieldbook, Financial Times Prentice Hall, London,
1999.
[13] Carnegie Mellon University/Software Engineering Institute (SEI). 1995. The Capability Maturity Model: Guidelines for
Improving the Software Process. Addison-Wesley Publishing Company, Inc.
[14] J.L. Casti, Would-Be Worlds: How Simulation Is Changing the Frontiers of Science, John Wiley & Sons, Inc., New York,
NY., 1997.
[15] C.W. Choo, The Knowing Organization: How Organizations Use Information to Construct Meaning, Create Knowledge
and Make Decisions, International Journal of Information Management 16(5) (1996), 329–340.
[16] R.E. Cole, ed., Special Issue on Knowledge and the Firm, California Management Review 40(5) (1998).
[17] S.D. Cook and J.S. Brown, Bridging Epistemologies: The Generative Dance Between Organizational Knowledge and
Organizational Knowing, Organization Science 10(4) (July-August 1999), 381–400.
[18] K. Dalkir, Knowledge Management in Theory and Practice, Butterworth-Heinemann, 2005.
[19] T.H. Davenport and L. Prusak, Working Knowledge: How Organizations Manage What They Know, Harvard Business
School Press, Boston MA, 1998.
[20] J. Davis, E. Subrahmanian and A. Westerberg, eds, Knowledge Management: Organizational and technological Dimen-
sions, Verlag, Heidelberg, 2005.
[21] N.M. Dixon, Common Knowledge: How Companies Thrive by Sharing What They Know, Harvard Business School
Press, Boston MA, 2000.
[22] D.P. Ford, Trust and Knowledge Management: The Seeds of Success, in: Handbook on Knowledge Management 1:
Knowledge Matters, C.W. Holsapple, ed., Springer-Verlag, 2003.
[23] C. Garavelli, M. Gorgoglione and B. Scozzi, Knowledge Management Strategy and Organization: A Perspective of
Analysis, Knowledge and Process Management 11(4) (2004), 273–282.
[24] Y. Gu, Global Knowledge Management Research: a Bibliometric Analysis, Scientometrics 61(2) (2004), 171–190.
[25] Harvard Business Review on Knowledge Management, Harvard Business School Press, 1998.
[26] C.W. Holsapple, ed., Handbook on Knowledge Management 1: Knowledge Matters, Springer-Verlag, 2003.
[27] C.W. Holsapple, ed., Handbook on Knowledge Management 2: Knowledge Directions, Springer-Verlag, 2003.
[28] C.W. Holsapple and K.D. Joshi, Description and Analysis of Existing Knowledge Management Frameworks, (Vol. Track1),
Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences, 5–8 Jan. 1999, 15.
[29] C.W. Holsapple and K.D. Joshi, A Knowledge Management Ontology, in: Handbook on Knowledge Management 1:
Knowledge Matters, C.W. Holsapple, ed., Springer-Verlag, 2003, 89–124.
[30] D. Holtshouse, “Knowledge Research Issues” in Special Issue on Knowledge and the Firm, California Management
Review 40(3) (Spring 1998), 277–280.
[31] K.T. Huang, Capitalizing Collective Knowledge for Winning, Execution and Teamwork, Journal of Knowledge Manage-
ment 1(2) (February 1997), 149–156.
[32] M. Huysman and D. de Wit, Practices of Managing Knowledge Sharing: Towards a Second Wave of Knowledge
Management, Knowledge and Process Management 11(1) (2004), 81–92.
[33] W. Ives, B. Torrey and C. Gordon, Knowledge Sharing is Human Behavior, in: Knowledge Management: Classic and
Contemporary Works, D. Morey, M. Maybury and B. Thuraisingham, eds, MIT Press, Cambridge MA, 2003.
[34] M. Kaner and R. Karni, A Capability Maturity Model for Knowledge-Based Decisionmaking, Information, Knowledge,
and Systems Management 4(4) (2004), 225–252.
[35] R.S. Kaplan and D.P. Norton, The Balanced Scorecard, Harvard Business School Press, Boston, Massachusetts, 1996.
[36] M.E. Knapp, Knowledge Management: The Key to Success in the 21st Century, Proceedings European Business
Information Conference, Lisbon, 18 March, 1998.
[37] V. Kochikar, The Knowledge Management Maturity Model – A Staged Framework for Leveraging Knowledge, Proceedings
of the KM World 2000 Conference, September 2000.

16. 168 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review
[38] L.L. Lee, Knowledge Sharing Metrics for Large Organizations, in: Knowledge Management: Classic and Contemporary
Works, W. Ives, B. Torrey and C. Gordon, eds, The MIT Press, Cambridge, MA, 2003.
[39] D. Leonard, Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation, Harvard Business School
Press, Boston, MA, 1995.
[40] D. Leonard and S. Straus, “Putting Your Company’s Whole Brain to Work, Harvard Business Review on Knowledge
Management. Boston: Harvard Business School Press, Boston, MA, 1998.
[41] S.H. Liao, J.C. Chang, S.C. Cheng and C.M. Kuo, Employee Relationship and Knowledge Sharing: A Case Study of a
Taiwanese Finance and Securities Firm, Knowledge Management Research & Practice 2(1) (2004), 24–34.
[42] J. Liebowitz, ed., Knowledge Management Handbook, CRC Press, Boca Raton FL, 1999.
[43] J. Liebowitz and Y. Chen, Knowledge Sharing Proficiencies: The Key to Knowledge Management, in: Handbook on
Knowledge Management 1: Knowledge Matters, C.W. Holsapple, ed., 2003, pp. 409–424.
[44] R. Maier, Knowledge Management Systems: Information and Communication Technologies for Knowledge Management,
Springer, Berlin, 2001.
[45] Y. Malhorta, ed., Knowledge Management and Virtual Organizations, Idea Group Publishing, Hershey PA, 2000.
[46] Y. Malhorta, ed., Knowledge Management and Business Model Innovation, Idea Group Publishing, Hershey PA, 2001.
[47] K. Mertins, P. Heisig and J. Vorbek, eds, Knowledge Management: Best Practices in Europe, Springer, Heidelberg, 2001.
[48] W.L. Miller and L. Morris, Fourth Generation R&D: Managing Knowledge, Technology, and Innovation, Wiley, Hoboken
NJ, 1999.
[49] M. Maybury, Knowledge Management at MITRE, in: Case Studies in Knowledge Management Practices. Leading With
Knowledge: KM Practices in Global InfoTech Companies, M. Rao, ed., New Delhi: Tata McGraw-Hill, 2003.
[50] D. Morey, M. Maybury and B. Thuraisingham, eds, Knowledge Management: Classic and Contemporary Works, MIT
Press, Cambridge, 2002.
[51] I. Nonaka, 1991, The Knowledge-Creating Company, Harvard Business Review on Knowledge Management, Harvard
Business School Press, 1998.
[52] I. Nonaka and H. Takeuchi, The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of
Innovation, Oxford University Press, New York, 1995.
[53] I. Nonaka and T. Nishiguchi, eds, Knowledge Emergence: Social, Technical, and Evolutionary Dimensions of Knowledge
Creation, Oxford University Press, 2001.
[54] I. Nonaka and V. Peltokorpi, Objectivity and Subjectivity in Knowledge Management: A Review of 20 Top Articles,
Knowledge and Process Management 13(2) (2006), 73–82.
[55] D.E. O’Leary, Enterprise Knowledge Management, IEEE Computer 31(3) (March 1998), 54–61.
[56] J. Pfeffer and R.I. Sutton, The Knowing Doing Gap: How Smart Companies Turn Knowledge into Action, Harvard
Business School Press, Cambridge MA, 2000.
[57] M. Polanyi, 1966, The Tacit Dimension, Doubleday & Company Inc, Reprinted 1983.
[58] K. Romhardt and G. Probst, Building Blocks of Knowledge Management – A Practical Approach, Input-Paper for the
seminar: Knowledge Management and the European Union-Towards a European Knowledge Union, 12–14 May 1997.
[59] W.B. Rouse, Need to Know – Information, Knowledge, and Decision Making, IEEE Transactions on Systems, Man, and
Cybernetics – Part C: Applications and Reviews 32(4) (2002), 282–292.
[60] W.B. Rouse, A Theory of Enterprise Transformation, Systems Engineering 8(4) (2005), 279–295.
[61] W.B. Rouse and A.P. Sage, Information Technology and Knowledge Management, in: Chapter 30 in Handbook of Systems
Engineering and Management, A.P. Sage and W.B. Rouse, eds, John Wiley and Sons, New York, 1999, pp. 1175–1209.
[62] R.L. Ruggles, ed., Knowledge Management Tools, Butterworth-Heinemann, Boston, 1997.
[63] R.L. Ruggles, “The State of the Notion: Knowledge Management in Practice” Special Issue on Knowledge and the Firm,
California Management Review 40(3) (Spring 1998), 80–89.
[64] A.P. Sage, Systems Management for Information Technology and Software Engineering, John Wiley & Sons, Inc, New
York, 1995.
[65] A.P. Sage and W.B. Rouse, Information Systems Frontiers in Knowledge Management, Information Systems Frontiers
1(3) (1999), 205–219.
[66] A.P. Sage, “Knowledge Management,” McGraw Hill Yearbook of Science and Technology, 2003, 210–212.
[67] C. Small and J. Tatalias, Knowledge Management Model Guides KM Process, The EDGE, The MITRE Corporation,
Bedford, April 2000.
[68] T.K. Srikantaiah and M.E.D. Koenig, eds, Knowledge Management for the Information Professional, American Society
for Information Science, Medford NJ, 2000.
[69] A.S. Thomas, The Wealth of Knowledge: Intellectual Capital and the Twenty-first Century Organization, Doubleday,
New York, 2000.
[70] M. Stankosky, ed., Creating the Discipline of Knowledge Management: The Latest in University Research, Butterworth-
Heinemann, 2005.
[71] A. Styhre and T. Kailing, Knowledge Sharing in Organizations, Copenhagen Business School Press. Copenhagen, 2003.

17. C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 169
[72] K.E. Sveiby, The New Organizational Wealth: Managing and Measuring Knowledge-Based Assets, Berret-Koehler
Publications, San Francisco, 1997.
[73] A. Tiwana, The Essential Guide to Knowledge Management, Prentice Hall, Englewood Cliffs NJ, 2001.
[74] A. Tiwana, The Knowledge Management Toolkit: Orchestrating IT, Strategy, and Knowledge Platforms, Prentice Hall,
Englewood Cliffs NJ, 2002.
[75] G. Von Krogh, K. Ichijo and I. Nonaka, Enabling Knowledge Creation, Oxford University Press, Oxford, 2000.
[76] G. Von Krogh, J. Roos and D. Kleine, eds, Knowing in Firms: Understanding, Managing, and Measuring Knowledge,
Sage Publications, London, 1998.
[77] K. Wiig, Knowledge Management Foundations, Schema Press, Arlington VA, 1993.
[78] K. Wiig, Knowledge Management, Schema Press, Arlington VA, 1994.
[79] K. Wiig, Knowledge Management Methods, Schema Press, Arlington VA, 1995.
[80] K.Y. Wong and E. Aspinwall, Knowledge Management Implementation Frameworks: A Review, Knowledge and Process
Management 11(2) (2004), 93–104.
Cynthia Taylor Small is the Associate Department Head of the Information Management Department
at The MITRE Corporation. She received a BA from The College of William and Mary, a MS
in Technology Management from American University, and a PhD in Information Technology from
George Mason University. She has held numerous positions, providing system engineering and IT
support, and knowledge management (KM) for a host of government agencies. She participates in a
variety of academic, industry, and government forums, authoring articles and presentations in the area
of knowledge management. Her research interests include knowledge engineering, knowledge sharing,
knowledge governance, KM measurement, and complex adaptive systems. E-mail: csmall@mitre.org.
Andrew P. Sage received the BSEE degree from the Citadel, the SMEE degree from MIT and the
Ph.D. from Purdue, the latter in 1960. He received honorary Doctor of Engineering degrees from
the University of Waterloo in 1987 and from Dalhousie University in 1997. He has been a faculty
member at several universities including holding a named professorship and being the first chair of the
Systems Engineering Department at the University of Virginia. In 1984 he became First American Bank
Professor of Information Technology and Engineering at George Mason University and the first Dean
of the School of Information Technology and Engineering. In May 1996, he was elected as Founding
Dean Emeritus of the School and also was appointed a University Professor. He is an elected Fellow of
the Institute of Electrical and Electronics Engineers, the American Association for the Advancement of
Science, and the International Council on Systems Engineering. He is editor of the John Wiley textbook
series on Systems Engineering and Management, the INCOSE Wiley journal Systems Engineering and is coeditor of Information,
Knowledge, and Systems Management. He edited the IEEE Transactions on Systems, Man, and Cybernetics from January 1972
through December 1998, and also served a two year period as President of the IEEE SMC Society. In 1994 he received the
Donald G. Fink Prize from the IEEE, and a Superior Public Service Award for his service on the CNA Corporation Board
of Trustees from the US Secretary of the Navy. In 2000, he received the Simon Ramo Medal from the IEEE in recognition
of his contributions to systems engineering and an IEEE Third Millennium Medal. In 2002, he received an Eta Kappa Nu
Eminent Membership Award and the INCOSE Pioneer Award. He was elected to the National Academy of Engineering in
2004 for contributions to the theory and practice of systems engineering and systems management. His interests include
systems engineering and management efforts in a variety of application areas including systems integration and architecting,
reengineering, engineering economic systems, and sustainable development.