Title: Knowledge management and knowledge sharing: A review

Authors: Small, Cynthia T., Andrew P.

Subject: Knowledge Management

Publish: 2006

Status: full text

Source: Information Knowledge Systems Management; 2005-2006, Vol. 5 Issue 3, p153-169

Preparation: Scientific Database Management Articles www.SYSTEM.parsiblog.com

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 deions 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. --Download Article--

  
1. Introduction: knowledge and knowledge management 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. Knowledge, as defined by Plato and accepted by mostWestern 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 howWesterners and Japanese often viewknowledge.
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.

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.

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 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 ive 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 experimental, 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/deive – provides a framework in which past observations can be understood as part of an overall process. These models are also called deive because they are explicit deions that capture and organize information.
Preive – provides a picture of the real world as it will be if certain postulates (preions) or formal axiomatic rules of behavior are applied. A survey of the literature finds many deiveKMmodels 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 KMframeworks: 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 deion and comparative analysis of ten deive 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 ive 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.
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Fig. 4. Knowledge Sharing and Enterprise Knowledge-Creation Model of Nonaka and
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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 deion 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.
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 environments
(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, groupware,
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 important
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 execution,
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 organizational
repositories is the focus of their knowledge management programs. Liebowitz and Chen [43]
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 capability.
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.
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Fig. 6. Knowledge Sharing and Conversion across SE Processes.
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