Knowledge Management


Knowledge Management

Critical Dimensions of Knowledge Management. The Benchmarking Structure


Over the last decade or so, Knowledge Management (KM) has received significant attention in the management literature and in management practice. Within a world marked by increasing globalization and rapid technological change –and obsolescence– knowledge is seen as one of the few remaining ways to achieve sustainable competitive advantage (Eisenhardt & Santos, 2001). But this knowledge needs to be actively managed. The creation of processes to transform tacit into more explicit knowledge and vice versa (Nonaka et al., 2005) is just an example of such approach. KM is not only important for the private sector. Public sector organizations have also turned increasingly towards an active way of managing their knowledge as a way to improve their service delivery and reduce its costs.

From a financial standpoint private companies can be easily evaluated and benchmarked one another by using various financial ratios. Nevertheless, it would be worthwhile to benchmark the organizations from a different perspective that includes for example elements that refer to the intangible assets of an organization. KM is just one of the proposed elements that may be worthwhile to consider since it includes both tangible and intangible organizational dimensions. Thus, the idea of this study is to propose and develop a conceptual framework that will facilitate the comparison of the KM practices that exist in organizations. The main questions of such endeavor refer to what building blocks should be selected out of the vast KM body of knowledge and why they are relevant.

The definition of knowledge is still a contentious issue even though an effort in this sense was initiated since ancient times. The sensitivity of knowledge evaluation and management is rooted in its bi-dimensional characteristics: tacit and explicit. Depending on the level of analysis, individual or organizational, the tacit aspect is addressed by organizational and behavioural sciences. The explicit aspect is normally addressed by IT science since it deals primarily with codified knowledge that can be straightforwardly stored and transferred using modern IT technologies. This bi-dimensional aspect of knowledge commands a systematic approach of KM that must include both dimensions.

In fact, one of the most salient fallacies in KM is the fracture that exists between these two broad bodies of knowledge. Sometimes, too much emphasis is put on technology as the sole enabler of KM whereas the structural and behavioural building blocks are marginalized. In contrast, other KM approaches focus exclusively on the organizational and individual dimensions of the KM.

The second fallacy is represented by the credo that KM practices can be standardized and applied widely and equally in all industries and public sectors. This is not the case since a clear differentiation should be made between the KM needs of knowledge-intensive organizations versus labour or capital intensive ones.

Some benchmarking frameworks have been developed in the past (e.g Hiebler, 1995). They are well construed and include both aspects of the knowledge. However, they suffer from too much generality. For example, technology is considered a KM enabler and a dimension of the benchmarking model however not too much is said about what types of technologies are used in the model. Currently, it is clear that Web 1.0 differs from Web 2.0 technology; however, this aspect is not captured by the existing benchmarking models.

At least in the public health sector specifically, no such benchmarking study has been performed so far. It is expected that such study will provide the executives with the right information to better leverage the existing knowledge and design KM strategies.

To determine the most relevant dimensions and sub-dimensions of the proposed KM benchmarking framework, the level of analysis will focus on technology, individual and organizational, as well as process facets of the KM. A literature overview of the fundamental theories in the KM area will be also employed. The technology component will be addressed …

The employed methodology will include the literature overview of KM paradigms based on peer-reviewed articles and public conferences. The analysis of the Intranets and Extranets of the involved organizations will also provide the evidence required to support the benchmarking process.


Chapter I: Knowledge – From Data to Enlightenment


While Knowledge Management (KM) seems to emerge as a relatively novel discipline, knowledge per se is not at all a novel concept. Plato for example suggested two millennia ago that human beings should distinguish between understanding and belief. In his view, understanding includes both knowledge and thought and is associated with being. Similarly, belief includes opinion and imagination and it is associated with becoming. In a remarkable dialectic approach Plato further asserts that as being is to becoming so understanding is to belief; and as understanding is to belief, so knowledge is to opinion and thought to imagination. Although occasionally an opinion may be well founded, Plato deemed that knowledge supported by reason, is the underlying factor of the highest intellectual insights and ultimately, the truth[1].In Phaedo, Plato also defined knowledge as the “justified true belief” thus associating knowledge with truth. Fascinatingly, Plato highly valued the discussion, and specifically collective discussion or dialog as a means of examining and answering questions.


Knowledge – Definition


As the world progressed over time, knowledge became an important intangible asset for all societal entities from large public institutions and corporations to small business enterprises. Its definition and application however, became more refined and sophisticated, and to some extent, elusive.

In its public glossary, the World Health Organization provides a broad definition of knowledge[2] emphasizing the know-how (applied information) and the actionable capability of knowledge. NASA accentuates knowledge’s function to provide the fabric for decision-making process[3]. Davenport and Prusak (1995), the two researchers who actually coined the KM term, provide a working definition of the knowledge itself: 


Knowledge is a fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of knowers. In organizations, it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices, and norms[4].


In essence, it can be highlighted that knowledge is a mix of experience, contextual information, and expert insight that enables the rapid absorption of new knowledge. Its actionable dimension stimulates the decision-making process.


From Data to Wisdom and Enlightenment


According to Ackoff (1989) we can basically differentiate among data[5], information[6], knowledge[7], understanding[8], and wisdom[9] (i.e. DIKW hierarchy). Ackoff’s hierarchy of characteristic processes of the human mind can be visualized as a knowledge pyramid, where each level is an essential precursor to the levels above, with data at its base and wisdom at its apex. Zeleny (cf. Rowley, 2006) adds the enlightenment as another cognitive layer on top on the DIKW hierarchy (See Figure 1 below).




Figure 1: DIKW Hierarchy


Similar to Plato’s association of knowledge and truth, Zeleny associates enlightenment to the sense of truth as well. In addition, enlightenment implies social recognition, respect and sanction of the respective truth (See Table 1).


Table 1: Zeleny versus Ackoff – DIKW Hierarchy






Know nothing









Know what

Data are processed to be useful; provides answers to



who, what, where, and when questions





Know how

Application of data and information; answers how question






Appreciation of why





Know why

Evaluated understanding





Attaining the sense of truth, the sense of right



and wrong, and having it socially accepted, respected



and sanctioned



Source: The Wisdom Hierarchy: Representations of the DIKW Hierarchy (Rowley, 2006)


By adopting the time dimension in the DIKW hierarchy model, Lachica (2005) suggests that data, information, and knowledge elements relate to the past (See Figure 1). According to this time continuum, knowledge is the closest element to present, and connects the past to the present and future through its predictability potential. The sole exception in this model is wisdom which relates to the future and embodies visionary capabilities.




Figure 1: DIKW – Including Time Dimension





An evolution of the perspective proposes that understanding is not a separate level in itself, but the catalyst facilitating the transition from stage to stage (Bellinger, Castro, and Mills, 2004).

Regardless of the theoretical debate on the role of understanding and enlightenment, DIKW model represents a robust conceptual framework and context for further understanding and analyzing knowledge and its attributes.


Knowledge Taxonomy


            Knowledge can be classified using an extremely varied portfolio of criteria, which requires us to distinguish between different forms of knowledge. Common sense or “received knowledge” is culturally determined, is often associated with perceptions and impressions[10], and is also referred to as “folk knowledge”. In a medical setting it often constitutes the fundament of the every-day thinking, and practice processes for ordinary people and even for scientists (Bate & Robert, 2002). Some hold that this type of knowledge is rather naïve and should not be a substitute for evidence-based, expert or academic knowledge[11]. However, because common sense intervenes almost naturally during human thinking, it too should be leveraged and included as a key aspect in any KM endeavor.

            Another important distinction that should be made is between tacit and explicit knowledge (Nonaka, 1994).  Tacit knowledge is highly personal and deeply rooted in individuals’ actions and experience, as well as in their ideals, values, and emotions. Tacit knowledge can be described on two dimensions: cognitive and technical. The cognitive dimension refers to one’s mental map, beliefs, and view-points, whereas the technical dimension includes one’s crafts and skills. The personal nature of tacit knowledge makes it hard to formalize and therefore communicate with others.  As such, many organizations that experienced massive re-engineering processes (dumbsizing),  put their knowledge at risk by laying off people, as most of their existing tacit knowledge was lost in the process (Baskerville & Dulipovici, 2006).  In contrast, explicit knowledge includes the articulated and codified knowledge that is easily expressed in symbolic or language forms and applied to particular situation. Explicit knowledge can be expressed in words and numbers, and is readily transferred between individuals.

            As well, there is a considerable difference between individual and collective knowledge. Individual knowledge surfaces as a result of an individual’s effort and diligence, such as the inherent expertise gained after completing a particular task or project. Collective knowledge by distinction results from group or social actions. It includes for instance the development of formal and informal norms for inter-group communication during or after a collective action (Nonaka, 2005).

Within each dichotomy (i.e. commonsense-scientific, tacit-explicit, and individual-collective), the seemingly opposing parameters should be seen as complementary and interrelated rather than conflicting and separated.

            The three classes of knowledge just presented above are extensively used in the KM area. There are though other criteria that help to classify knowledge. Causal criterion for instance would provide the “know-why” dimension of the knowledge. Likewise, the conditional criterion would specify a “know-when” knowledge class. (See Table 2).  


Table 2: Knowledge Taxonomy


Knowledge Types




Knowledge is rooted in actions, experience, and involvement in specific context

Best means of dealing with specific customers.

Cognitive Tacit

Mental models

Individual’s belief in cause-effect relationships

Technical Tacit

Know-how applicable to specific work

Surgery skills


Articulated, generalized knowledge

Knowledge of major customers in a region


Created by and inherent in the individual

Insights gained from completed projects


Created by and inherent in collective actions of a group

Norms for inter-group communication



What drug is appropriate for an illness



How to administer a particular drug



Understanding why the drug works



Understanding when to prescribe the drug



Understanding how the drug interacts with other drugs


Useful knowledge for an organization

Best practices, business frameworks, experiences, engineering drawings, market reports


Source: Alavi M. &  Leidner D.E. (2001). Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundation and Research Issues. MIS Quarterly25(1), 163-201. 



Key Concepts: understanding, belief, opinion, imagination, knowledge, thought, being, becoming, dialog, DIKW hierarchy, wisdom, enlightenment, tacit knowledge, explicit knowledge, individual knowledge, collective knowledge, knowledge taxonomy


Chapter II: Theoretical Foundation of Knowledge Management


Knowledge Management – Definition


There are almost as many definitions of KM as the number of academics, specific journals, and interested organizations. WHO holds that KM is a set of principles, tools and practices that enable people to create knowledge, and to share, translate and apply what they know to create value and improve effectiveness[12]. A simple, however extremely useful KM definition for practitioners is provided by Holm (2001) “KM is getting the right information to the right people at the right time, helping people create knowledge and sharing and acting on information”.  

As suggested in the precedent chapter, the KM term was coined in 1995 by Davenport and Prusak. These authors make available another definition that emphasizes the systematic effort that an organization must spend in order to share and renew the tacit and explicit knowledge through KM process:


KM is managing the corporation’s knowledge through a systematically and organizationally specified process for acquiring, organizing, sustaining, applying, sharing and renewing both the tacit and explicit knowledge of employees to enhance organizational performance and create value.


Knowledge Management – Theoretical Foundation


As stated before in this study knowledge has been known and analyzed since ancient times. In contrast, KM terminology and practice are relatively novel concepts. In 1997, KM was included as a real practice by HR managers. Executives also begun to recognize that their central responsibility was to leverage organizational knowledge (Benson, 1997; Ruggles, 1998; cf. Baskerville & Dulipovici, 2006). Generally, KM evolved from a large group of theories[13] and practices, however, considering the scope of this paper, five major paradigms are relevant: 1) Intellectual Capital and Intellectual Property; 2) Knowledge Networks and Clusters. Communities of Practice; 3) Organizational Culture; 4) Organizational Structure and 5) Organizational Behaviour and Learning (Baskerville & Dulipovici, 2006). The first two paradigms draw from information economics science, whereas the following three, as their names suggest, relate closely with Organizational Culture, Structure, and Behaviour sciences.


Intellectual Capital and Intellectual Property


The intellectual capital[14] theory stresses the fact that, recently, intangible assets, or goodwill, became more important than physical assets, at least in the service sector. Thus, assets like trademarks, structure, corporate culture, IT style, and employees’ knowledge and personal networks are viewed at least as important as, for example, buildings and factories (Baskerville & Dulipovici, 2006).

Inpken and Tsang (2005) links the social capital to networks and defines it as “the aggregate of resources embedded within, available through, and derived from the network of relationships possessed by an individual or organization”. Notably, this definition includes both the individual (private) and organizational (public) good perspectives of the social capital. Considered as a private good originated and possessed by individuals, the social capital brings benefits to respective individuals only (Belliveau,O’Reilly, & Wade, 1996; Burt, 1997; Useem & Karabel, 1986; cf. Inkpen & Tsang, 2005). In contrast, when deemed as a public good, the social capital benefits not only the individuals who created it but also the entire group or network in which the respective individuals are embedded (Bourdieu, 1986; Coleman, 1988; Putnam, 1993; cf. Inkpen & Tsang, 2005).

Social capital affects knowledge flows through organizations and networks. Within this context it can be defined on three dimensions: 1) Structural; 2) Cognitive; and 3) Relational. The structural dimension can be further divided into three components such as network ties, network configuration, and network stability. By the same token, the cognitive dimension includes the shared goals and shared culture components while the third one refers mostly to trust (Inkpen & Tsang, 2005).

Intellectual property includes the ethical and legal aspects of the intellectual capital[15] and thus generally refers to patents and copyrights (Baskerville & Dulipovici, 2006).


Knowledge Networks and Clusters. Communities of Practice (CoP)


While all are relevant, the Knowledge Networks and Clusters paradigm seems to be the most significant for the purpose of this research. In general, organizations’ participation and membership in various networks and clusters creates the premises for superior knowledge acquisition and exchange (Inkpen & Tsang, 2005). Such clusters stimulate members’ competitiveness because knowledge spreads very fast within. In fact, these clusters facilitate not only the creation and dissemination of knowledge capital, but also the development of learning capital which supports the rapid upgrade and adaptation of existing skills and capabilities (Baskerville & Dulipovici, 2006). Nevertheless, inside such networks, knowledge sharing declines as the size of the network increases: i.e. the periphery of the network is less likely to share a substantial amount of knowledge with the centre (Hansen, 2002).




Three major network categories are selected as follows: 1) Intracorporate Network; 2) Strategic Alliance; and 3) Industrial District. Within this classification, intracorporate networks are the most structured entities. An intracorporate network is a classically designed organization that includes a headquarter and many other sub-entities (e.g. branches). It is rather a non-unitary, dispersed organization since these sub-entities are physically, functionally, and culturally distant to certain degrees one to another. It also resembles a hierarchy where each sub-entity has different levels of decision-making independence.

Strategic alliances are temporary efforts initiated by two or more organizations (alliance constellations) to achieve a specific goal. These networks are not as structured as intracorporate networks however they are more structured than industrial districts.

The most representative industrial districts are Silicon Valley and City of London. These districts are co-located and un-structured networks comprising independent organizations, operating in the same or related markets, and benefiting from external economies of scale and scope (Brown & Hendry, 1998; cf. Inkpen & Tsang, 2005). Usually, major universities located in such industrial districts provide the highly skilled labour and research support to various actors operating within the network.

As opposed to skeptical economists[16] who rather neglect the tacitness of knowledge and call it a “quasi-mystical notion”, the theorists of CoP’s hold that actually tacitness is the key component of knowledge. The interpretation of information and facts does not depend solely on the text (i.e. explicit knowledge) but on the nature of the community making the interpretation (Fish, 1994; cf. Duguid, 2005). Membership in the CoP offers context, identity, and content as well. CoP then is the crucible where an aspiring practitioner learns to be a practitioner. He or she receives know how and the art of practice (much of it lies tacit in a CoP) and ultimately becomes a practitioner. Learning about means the accumulation of the explicit knowledge (knowing that) only: “it confers the ability to talk a good game, but not necessarily to play one” (Duguid, 2005).


Social Capital and Knowledge Transfer within Networks


Social capital determines the quality of the knowledge transfer among and within the members of the three types of networks outlined above (Inkpen & Tsang, 2005). For instance, for an intracorporate network personnel transfer between members, decentralization of authority, and low personnel turnover are all key structural social capital determinants for knowledge transfer (See Table 3 below). Personnel transfers create new social ties that strengthen the already existing formal ties. These additional social interaction ties provide new channels for knowledge exchange and transfer thus increasing the diffusion of knowledge within the organization. Decentralization facilitates the development of lateral ties without seeking approval from upper layers of management and thus increases knowledge sharing. Organizational learning depends on individuals’ memories and their ability to learn. When individuals leave they take with them a substantial portion of organization. They exchange and share knowledge through not only official channels but also informal ones like friendship and rapport. Hence, organizational stability meaning a low personnel turnover is desirable in order to maintain knowledge exchange and sharing.


Table 3: Conditions Facilitating Knowledge Transfer


Social Capital

Intracorporate Network

Strategic Alliance

Industrial District





Network ties

Personnel transfer between

Strong ties through

Proximity to other


network members

repeated exchanges







Decentralization of authority

Multiple knowledge

Weak ties and boundary


by headquarters

connections between





to maintain relationship




with various cliques





Network stability

Low personnel turnover

Noncompetitive approach

Stable personal


organization wide

to knowledge transfer










Shared goals

Shared vision and

Goal clarity

Interaction logic derived


collective goals


from cooperation





Shared culture

Accommodations for local

Cultural diversity

Norms and rules to


or national cultures


govern informal knowledge









Relational: Trust

Clear and transparent

Shadow of the future

Commercial transactions


reward criteria to


embedded in social ties


reduce mistrust among




network members




Source: Social Capital, Networks, and Knowledge Transfer (Inkpen & Tsang, 2005).


Organizational Culture


As knowledge is a cognitive process, inseparable from the whole human thinking and feeling mechanisms, knowledge processes appear to be highly sensitive to cultural values and beliefs. The existing values and beliefs are actually the invisible and underlying driving forces of human attitudes and behaviours, and ultimately, human actions. In an organizational setting and considering the knowledge aspect, the extent to which actors share common values and beliefs can influence the ratio of tacit to explicit (articulated) knowledge and the rate and mode of conversion of these two critical types of knowledge (See Table 4). These modes of conversions can be viewed as acts of knowledge creation, articulation being the “quintessential knowledge creation process” (Nonaka & Takeuchi, 1995; cf Baskerville & Dulipovic, 2006). In a culture when cultural values are shared intensively through the process of socialization the tacit knowledge is easily transferred through sympathy; similarly, the conversion of tacit to articulated knowledge seems to happen rather organically.


Table 4: Four modes of knowledge conversion (adapted from Nonaka and Takeuchi, 1995; Nonaka et al., 2005; cf. Baskerville and Dulipovic, 2006)




Tacit Knowledge

Articulated knowledge

Tacit Knowledge

Socialization (creates sympathized knowledge)

Externalization (creates conceptual knowledge

Articulated knowledge

Internalization (creates operational knowledge)

Combination (creates systemic knowledge)


Johnson (1998; cf Baskerville & Dulipovic, 2006) sees the organizational culture as a web of multiple elements such as symbols, power structure, organizational structures, control systems, rituals, and myths.

Knowledge culture in particular is merely a form of organizational culture that values and understands KM. To create a knowledge culture the support and commitment of the top management to KM represents a vital condition. For example, by reducing bureaucratic barriers management can create the setting where spontaneity, creativity, and learning elements are highly encouraged. In addition, the creation of a balanced environment in terms of power, control, and trust is another essential condition for building a knowledge-oriented culture. In power organizations where “knowledge is power” no employees intend to share it. Similarly, in organizations where trust is not a deeply embedded organizational value people tend to keep their knowledge secreted as well.  However, the effort of top management only is not sufficient to build a knowledge culture. Employees should avoid taking a passive stance; they should be committed as well to internalize, reflect, and articulate knowledge.


Organizational Structure


            Knowledge organizations should always develop a knowledge strategy that guides the organizational philosophy about its goals and strategy. Organizational structure enables the formulation and implementation of knowledge management strategy and practices across its divisions. There are many types of organizational structures however the differentiation between M-form and N-form structures is salient from a KM standpoint. An M-form organization is essentially a classic hierarchy in which communication flows vertically and top management plays a crucial role. On the contrary, in an N-form organization (or network) communication flows laterally and middle management plays the critical role. Middle management is the center of the professional knowledge creation, transformation, and articulation. It also resolves the contradictions between grand strategies and limitations given by day-to-day realities by rationalizing top management’s plans and directions (Baskerville & Dulipovic, 2006).

            However, M- and N-form organizations can not be simply compared in terms of their KM performance. There is no evidence showing that a hierarchy is inferior or superior to a network. Moreover, sometimes hierarchies perform better than networks. For example, in the case of rapid infusion and diffusion of radical knowledge, hierarchies perform better than any other types of organizations[17]. On the other hand, middle-management concept is hierarchical per se even though it is neither a top-down nor bottom-up but rather a “middle-up-down” one (Nonaka & Takeuchi, 1995; cf. Baskerville 7 Dulipovic, 2006). From this perspective, a knowledge organization should be designed as a balanced hybrid between a hierarchy and network: this form is called heterarchy (Hedlund, 1999; cf. Baskerville & Dulipovic, 2006).


Organizational Behaviour and Learning


A behavioural approach moves the focus of KM towards knowledge creation. Knowledge creation implies overcoming the barriers of uniformity and social conformity leading to out-of-the-box thinking. A reasonable level of formal management corroborated with a certain degree of constructive chaos can both spur creativity and innovation (Inkpen, 1996). The implementation of systems that reward new ideas is another mechanism that impulses and motivates creativity. Similarly, creativity values can be created and maintained by providing creativity training and triggering cultural shifts (Baskerville & Dulipovici, 2006).

Organizational learning is an important factor in the KM field in the sense that managing knowledge implies also managing organizational learning. According to the theory of double-loop learning (Argyris & Schon, 1978; cf. Baskerville and Dulipovic, 2006) individual’s behaviour is adapted as soon as the respective individual learns in a single loop. Similarly, “organizational behaviour changes as the individuals adapt to others in a double loop”. Thus, organizational learning is in fact related to organizational adaptation and change. Organizational learning is a form of knowledge creation and its success relies heavily on actions that motivate the learning process (Baskerville and Dulipovic, 2006). Senge (1990; cf. Baskerville and Dulipovic, 2006) advances a new theory that links organizational learning to human communities, and general systems concepts. Three elements are peculiar to this theory: 1) A set of practices for generative conversation and coordinated action (double-loop learning); 2) An organizational culture that values humility, compassion, and wonder; and 3) Managerial capacity to understand and work within a human system.

Based on the theories and concepts presented above the structural benchmarking framework has been created (See Exhibit 1). This framework includes two major sub-divisions o dimensions: 1) Organizational and 2) Individual.


Exhibit 1 – Structural Benchmarking Framework





Key Concepts: intellectual capital, intellectual property, knowledge networks, knowledge clusters, knowledge capital, learning capital, intracorporate network, strategic alliance, industrial districts, social ties, lateral ties, cultural values, moders of knowledge conversion, knowledge culture, M-form and N-form organizational structures, heterarchy, organizational learning, creativity training, cultural shift, organizational change.


Chapter III: Knowledge Management Process, Infrastructure and Performance


Knowledge Management – Process


A review of the literature suggests that four major phases constitute the foundation of KM process: 1) generation (creation) and development; 2) transformation and transfer; 3) storage and codification; and 4) utilization (See Figure 2). Knowledge generation refers to the idea creation; knowledge development represents the subsequent step of converting the idea into a valuable product or service (Zaim, Tatoglu & Zaim, 2007). The most important mission of the knowledge transformation and transfer phase is to make the intellectual capital and knowledge resources accessible and intelligible across organizational boundaries. Codification, classification, and, ultimately, storage of the scattered knowledge represent another central function of KM process. The most important outcome of the KM process is to create value for the organization out of knowledge resources. In other words, the proper and facile utilization of knowledge by various categories of users should lead to positive changes in the overall practices and policies of the respective organization. Therefore, by activating the actionable dimension of knowledge, any organization should aim to become more efficient, effective and ultimately, refine its core competencies and gain the sustainable competitive advantage in its field.


Figure 2: Path Model



Source: Performance of Knowledge Management Practices: a Causal Analysis (Zaim,  Tatoglu & Zaim, 2007).


            For the purpose of this paper only the KM process will be used for the benchmarking analysis presented in part II of this study as follows:


Exhibit 2 – Process Benchmarking Framework




Knowledge Management – Infrastructure


KM Infrastructure is another key variable to consider, especially in regards to KM performance measurement (Zaim, Tatoglu & Zaim, 2007). Infrastructure includes organizational culture and technology. Since KM is highly dependent on context and social milieu, it is mandatory to create a knowledge-friendly and knowledge-aware cultural setting (Zaim, Tatoglu & Zaim, 2007). Core and supporting technology infrastructure can be differentiated. In general, core technology is specially designed to be used solely for KM purposes, whereas supporting technology can be used generally in many other fields.


Knowledge Management – Performance


Studies indicate that an effective application of KM determines the overall performance of the organization (Hasan & Al-Hawari, 2003; Claycomb, et al., 2002). Moreover, the appropriate evaluation of KM performance is in itself a factor contributing to organizational effectiveness (Tarim, 2003). In this respect, the evaluation of KM performance can be done in four steps: 1) Identify the goals and objectives; 2) Identify the knowledge capabilities and visualize the value creation pathways; 3) Define metrics[18]; and 4) Analyze and determine the gap between actual and planned performance. Certainly, this evaluation can be performed at the strategic, functional, and individual level. As a conclusion, KM performance should be measured and evaluated methodically and consistently in correlation with KM processes and infrastructure.


Key Concepts: knowledge creation, knowledge transfer, knowledge storage, knowledge utilization, KM infrastructure, Core and supporting technology, KM performance.


Chapter IV: Web 2.0 versus Web 1.0





Web 2.0 Overview



Web 1.0 Overview


            Generally, one of the key differences between the Web 1.0 and Web 2.0 approaches is given by the manner the content is generated and used. The content of the first generation of technologies is generated by its owners and, from a user perspective, is read-only. In contrast, the Web 2.0 content is user-generated. The encyclopedia-wikipedia antinomy is just a classic example of the difference between the two approaches.

            All of the studied organizations have many Web 1.0 technologies in place. Obviously, classic tools like Intranets, Extranets, e-mails, or shared folders have been all adopted and used in the past decade by these organizations. Thus, there is no need to include such tools as dimensions of the proposed benchmarking framework. However, the following Web 1.0 tools are introduced in the benchmarking framework: 1) Glossary of Terms; 2) Acronym List; 3) Virtual library; 4) File Management; 5) Templates; 6) E-learning; and 7) Business Intelligence Repository. The rationale behind the usage of this second group of Web 1.0 dimensions is twofold: 1) they are relatively more atypical (e.g. rarer) than classic tools; and 2) they are more related to KM in the sense they contribute to the effective transfer and dissemination of (explicit) knowledge within organizations (e.g. templates help to avoid work duplication, e-learning supports directly the uptake of explicit knowledge).    

Based on the technology evaluation presented in this chapter the benchmarking frameworks shown in Exhibit 3 and Exhibit 4 have been created. They will be used in the second part of this study.


Exhibit 3 – Web 2.0 Benchmarking Framework



Exhibit 4 – Web 1.0 Benchmarking Framework



Key Concepts: Web 1.0, Web 2.0, glossary of terms, acronym list, virtual library, file management, templates, e-learning, business intelligence (BI) repository, blog, discussion forum, microblog, RSS, wiki, social bookmarks, tag clouds, mashup, social networking, podcast, video sharing.




The mandate of the KM is to enhance organizational performance and create value by systematically managing both the tacit and explicit knowledge. In fact, the recognition of the bi-dimensional form of knowledge, tacit and explicit, makes KM different from other pure functions like IS (IT)          and HR.

Historically, KM emerged as a practice in the mid 1990’s within the HR function. In a global and interconnected world where technology started to play a primordial role it was soon realized that intangible assets like social and intellectual capital are at least as important as tangible assets for the organizational performance. Social networks, clusters, and CoP’s also became preeminent subjects of analysis since, within and among these entities, knowledge is created and disseminated superiorly.

The organizational culture influences both the rapport between the tacit and explicit knowledge the mode of conversion between these two principal forms of knowledge. The success of formulation and implementation of KM strategies depends significantly on the chosen organizational structure. The selection of an M-form, N-form, or heterarchy should follow a careful analysis of the overall context (i.e. internal and external environment), and the degree of alignment with organizational philosophy and scope. Creativity trainings can help organizations to augment their level of originality and inventiveness. As a form of knowledge creation, organizational learning depends on the underlying motivational factors of the learning process.

An integrative KM includes four major processes: 1) Creation; 2) Storage; 3) Transfer; and 4) Utilization. The creation of an adequate cultural and technological setting contributes significantly to KM performance and overall organizational effectiveness.


            Based on the theoretical and technological foundation of KM four benchmarking frameworks have been created: Web 2.0; 2) Web 1.0; 3) Organizational and individual; and 4) Process. They will be used in the second section of this study where the actual benchmarking will be performed.


Eventually, the key aspects of KM are summarized below:


  1. Knowledge has two key characteristics: tacit and explicit.
  2. KM is a combination of people-centric and techno-centric orientations.
  3. In a networked environment (e.g. CoP) knowledge is created and disseminated effectively
  4. Organizational structure, culture, and behaviour are key determinants of KM practices and KM performance
  5. Web 2.0 technologies represent an important vehicle for (explicit) knowledge dissemination and transfer.





[1] Plato’s Republica

[2] In organizational terms, knowledge is generally thought of as being ’know how’, ’applied information’, ’information with judgment’ or ’the capacity for effective action’ (1). Knowledge is information transformed into capabilities for effective action. In effect, knowledge is action (2). Source:

[3] Fluid mix of experience, values, intelligence, insight, and inspiration that provides a framework for decision-making. Source:

[5] Data are defined as symbols that represent properties of objects, events and their environment.They are the products of observation. But are of no use until they are in a useable (i.e. relevant) form. The difference between data and information is functional, not structural (cf. Rowley, 2006).

[6] Information is contained in descriptions, answers to questions that begin with such words as

who, what, when and how many. Information systems generate, store, retrieve and process data. Information is inferred from data (cf. Rowley, 2006).

[7] Knowledge is know-how, and is what makes possible the transformation of information into instructions. Knowledge can be obtained either by transmission from another who has it, by instruction, or by extracting it from experience.

[8] Ackoff included understanding in his hierarchy, but more recent commentators have disputed that understanding is a separate level” (Rowley, 2006). Bellinger, Castro, and Mills, (2004) refer to understanding as the appreciation of „why” – requires diagnosis and prescription. Source:

[9] Wisdom is the ability to increase effectiveness. Wisdom adds value, which requires the mental function that we call judgement. The ethical and aesthetic values that this implies are inherent to the actor and are unique and personal (cf. Rowley, 2006).

[10] John Locke (1690) in An Essay Concerning Human Understanding also holds that common sense is opposed to judgement.

[11]Albert Einstein stated: „Common sense is the collection of prejudices acquired by age eighteen.”

[13] 1) Intellectual Capital. 2) Intellectual Property. 3) Knowledge Economy. 4) Knowledge Assets. 5) Knowledge Clusters and Networks. 6) Knowledge Spillovers. 7) Continuity Management. 8) Core Competencies. 9) Dynamic Capabilities. 10) Dumbsizing. 11) Knowledge Alliances. 12) Knowledge Strategy. 12) Knowledge Marketplaces. 13) Knowledge Capability.  (Baskerville and Dulipovici, 2006) 

[14] Intellectual capital term was coin earlier in 1969 by John Kenneth Galbraith (Bontis & Serenko, 2004).

[15] Copyrights, patents, etc. (Baskerville and Dulipovici, 2006)

[16] Generally skeptical economists argued that knowledge can be reduced to explicit knowledge only (Duguid, 2005).

[17] E.g. Japanese electronic manufacturers, Sony especially, are hierarchies which are  leaders in breakthrough innovations.

[18] Tangible-Intangible, Financial-Non Financial, etc. (Hasan and Al-Hawari, 2003; Claycomb et al., 2002




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