Knowledge Management and Its Role in Education

Literature Review

Today, knowledge is no longer just another resource in a list that includes labor, capital, and land (Drucker 1994). It has become the resource of knowledge workers. Sharing is the sole method for creation of knowledge. In the new era of knowledge economy, traditional organizations, such as educational institutes, are no longer considered as ‘social organizations’. As Nonako (1994) states:

Get your customized and 100% plagiarism-free paper on any subject done
with 15% off on your first order

“The interaction between knowledge of experience and rationality enables individuals to build their own perspectives on the world. Yet these perspectives remain personal unless they are articulated and amplified through social interaction. One way to implement the management of organizational knowledge creation is to create a “field” or “self-organizing team” in which individual members collaborate to create a new concept.” (p. 22)

Thus, the precondition to creation of knowledge is sharing of knowledge between individuals. Literature has various theories regarding the creation of knowledge (Nonako 1994), knowledge sharing and barriers which causes hindrance in creation and sharing of knowledge in traditional environment viz. traditional higher education system. The literature review will enunciate different definitions of knowledge and creation of knowledge. How knowledge has been managed in traditional higher education set up and what are the possible options that recent development of knowledge management hold. Then it will discuss the literature which deals with the cultural and organizational barriers to knowledge sharing in organizations.

Knowledge

Knowledge can be defined as a justified belief that increases an organization’s capacity for effective action (Huber 1991; Nonaka 1994). Knowledge may be viewed from several perspectives (1) a state of mind, (2) an object, (3) a process, (4) a condition of having access to information, or (5) a capability. Knowledge has been described as a state or fact of knowing with knowing being a condition of understanding gained through experience or study; the sum or range of what has been perceived, discovered, or learned (Schubert et al. 1998). Some researchers believe that sharing of knowledge is expanding one’s personal knowledge and apply it to the organization’s needs. A second view defines knowledge as an object (Carlsson et al. 1996; McQueen 1998; Zack 1998a). The later proponents on knowledge believe that it can be stored and manipulated (i.e., an object). Alternatively, knowledge can be viewed as a process of simultaneously knowing and acting (Carlsson et al. 1996; McQueen 1998; Zack 1998a).

The process perspective focuses on the applying of expertise (Zack 1998a). The fourth view of knowledge is that of a condition of access to information (McQueen 1998). According to this view, organizational knowledge must be organized to facilitate access to and retrieval of content. This view may be thought of as an extension of the view of knowledge as an object, with a special emphasis on the accessibility of the knowledge objects. Finally, knowledge has been viewed as a capability with the potential for influencing future action (Carlsson et al. 1996). Watson (1999) extends upon the capability view by suggesting that knowledge is not so much a capability for specific action, but the capacity to use information; learning and experience result in an ability to interpret information and to ascertain what information is necessary in decision making. Due to the diverse views of knowledge there arose different perceptions of knowledge management (Carlsson et al. 1996).

Drawing on the work of Polanyi (1962, 1967), Nonaka (1994) explicated two dimensions of knowledge in organizations: tacit and explicit. Rooted in action, experience, and involvement in a specific context, the tacit dimension of knowledge (henceforth referred to as tacit knowledge) is comprised of both cognitive and technical elements (Nonaka 1994). The cognitive element refers to an individual’s mental models consisting of mental maps, beliefs, paradigms, and viewpoints. The technical component consists of concrete know-how, crafts, and skills that apply to a specific context. The explicit dimension of knowledge (henceforth referred to as explicit knowledge) is articulated, codified, and communicated in symbolic form and/or natural language. An example is an owner’s manual accompanying the purchase of an electronic product. The manual contains knowledge on the appropriate operation of the product.

Our academic experts can deliver a custom essay specifically for you
with 15% off for your first order

Knowledge can also be viewed as existing in the individual or the collective (Nonaka 1994). Individual knowledge is created by and exists in the individual whereas social knowledge is created by and inherent in the collective actions of a group. Both Nonaka and others (e.g., Spender 1992, 1996a, and 1996b) rely heavily on the tacit explicit, individual-collective knowledge distinction but do not provide a comprehensive explanation as to the interrelationships among the various knowledge-types. One potentially problematic aspect in the interpretation of this classification is the assumption that tacit knowledge is more valuable than explicit knowledge; this is tantamount to equating an inability to articulate knowledge with its worth. Few, with the exception of Bohn (1994), venture to suggest that explicit knowledge is more valuable than tacit knowledge, a viewpoint that if accepted might favor a technology enabled knowledge management process (technology being used to aid in explicating, storing, and disseminating knowledge).

Whether tacit or explicit knowledge is the more valuable may indeed miss the point. The two are not dichotomous states of knowledge, but mutually dependent and reinforcing qualities of knowledge: tacit knowledge forms the background necessary for assigning the structure to develop and interpret explicit knowledge (Polyani 1975). The inextricable linkage of tacit and explicit knowledge suggests that only individuals with a requisite level of shared knowledge can truly exchange knowledge: if tacit knowledge is necessary to the understanding of explicit knowledge, then in order for Individual B to understand Individual A’s knowledge, there must be some overlap in their underlying knowledge bases (a shared knowledge space) (Ivari and Linger 1999; Tuomi 1999).

The tacit-explicit knowledge classification is widely cited, although sundry other knowledge classifications exist that eschew the recondite subtleties of the tacit-explicit dimension. Some refer to knowledge as declarative (know-about or knowledge by acquaintance [Nolan Norton 1998]), procedural (know-how), causal (know-why), conditional (know-when), and relational (know with) (Zack 1998b). A pragmatic approach to classifying knowledge simply attempts to identify types of knowledge that are useful to organizations.

An understanding of the concept of knowledge and knowledge taxonomies is important because theoretical developments in the knowledge management area are influenced by the distinction among the different types of knowledge. Furthermore, the knowledge taxonomies discussed here can inform the design of knowledge management systems by calling attention to the need for support of different types of knowledge and the flows among these different types.

Knowledge Management

Knowledge can be defined as (Awad and Ghaziri, 2004) the understanding that is obtained through the process of experience or appropriate study. The Knowledge management principles if applied to management education will enhance the quality of academic learning process. The term “Knowledge Management” (KM) is used to describe everything from the application of new technology to harnessing of the intellectual capital of an organization (Sallis and Jones, 2002). Rowley, (2000) describes the term KM as follows:

We’ll deliver a high-quality academic paper tailored to your requirements

“Knowledge management is concerned with the exploitation and development of the knowledge assets of an organization with a view to furthering the organization’s objectives. The knowledge to be managed includes both explicit, documented knowledge, and tacit, subjective knowledge. Management entails all of those processes associated with the identification, sharing, and creation of knowledge. This requires systems for the creation and maintenance of knowledge repositories, and to cultivate and facilitate the sharing of knowledge and organizational learning. Organizations that succeed in knowledge management are likely to view knowledge as an asset and to develop organizational norms and values, which support the creation and sharing of knowledge” (Rowley, 2000).

KM practitioners apply many different approaches to develop the type of culture that builds the desire for teamwork and a collaborative working environment as described by (Senge, 1990; Nonaka and Takeuchi, 1995).

Knowledge Sharing

Knowledge sharing is central to the success of all knowledge management strategies. According to Nonaka (1994) knowledge grew with sharing. Effective knowledge sharing practices enable reuse and regeneration of knowledge at individual and organizational level. In recent years there has been considerable emphasis on the need to create a culture in organizations that is pro knowledge sharing and implement strategies that are more knowledge friendly. Organizations worldwide have been trying to undertake initiatives for introducing effective knowledge management by embedding knowledge sharing practices in their work processes.

Chang and Ng (2003) stated as most of the proponents of KM were from the West, the organizations which practiced KM were in the west, with exceptions being Japan and Singapore. They studied the influence of social culture on the practice of KM. they found that the Asian cultural traditions of respecting knowledge and passing wisdom through ancestral clans as positive influences in knowledge sharing practices but pointed out several challenges that also arise from cultural traditions such as hierarchy consciousness, saying things nicely (politeness), and emphasis on memorization in the examination systems.

Various knowledge concepts: from data to knowledge

Many writers have addressed the distinctions among data, information and knowledge (Allee 1997). Suurla, Markkula and Mustajarvi (2002) believe that “data refers to codes, signs and signals that do not necessarily have any significance as such” (p.35). It means that data are raw facts that have no context or meaning of their own. Organizations collect summaries and analyse data to identify patterns and trends. Most of the data thus collected is associated with functional processes of the organization. On the other hand, information as a concept takes up different meanings, depending on the context in which is discussed. Data becomes information when organised, patterned, grouped, and or categorized; thus increasing depth of meaning to the receiver. Through learning and adoption, information can be changed into knowledge (Suurla, Markkula & Mustajarvi, 2002). It is evident from literature that knowledge is an intrinsically ambiguous term, and therefore, defining it precisely is difficult. It is because different disciplines use the term to denote different things. Despite the difficulties in defining knowledge, it is well agreed that, “knowledge is the expertise, experience and capability of staff, integrated with processes and corporate memory” (Abell and Oxbrow 2001, p.73).

Knowledge is always bound to persons and validated in the context of application. A well-known distinction in this respect is that between explicit and tacit knowledge, a distinction first elaborated by Michael Polanyi. Polanyi (1966) stated that “personal or tacit knowledge is extremely important for human cognition, because people acquire knowledge by the active creation and organization of their own experience” (cited in Beijerse 1999, p.99). This implies that most of the knowledge is tacit and becomes explicit when shared. Tacit knowledge is personal, context-specific (Allee, 1997) and therefore hard to formalize and communicate. It resides in the brains of the people. Explicit or “codified” knowledge, on the other hand, refers to knowledge that is transmittable in formal, systematic language (Nonaka and Takeuchi, 1995). In other words, explicit knowledge is expressed as information in various formats that include published materials and manuals of rules, routines and procedures.

Knowledge and management of knowledge appear to be regarded as increasingly important features for organizational survival (Martensson, 2000). In addition, knowledge is a fundamental factor, whose successful application helps organizations deliver creative products and services. Today organizations are fundamentally different as compared to organizations existed in one or two decades ago in terms of their functions, structures and style of management. Yu (2002) pointed out that organizations put more emphasis on understanding, adapting and managing changes and competing on the basis of capturing and utilizing knowledge to better serve their markets. The central argument around which knowledge management revolves is that people hold a wealth of knowledge and experience that represents a significant resource for an organization. Most of this knowledge is represented in a wide variety of organizational processes, best practices and know-how (Gupta, Iyer & Aronson 2000). However, knowledge is diffused, and mostly unrecognized. It is important for organizations to determine who knows what in an organization and how that knowledge can be shared throughout the organization.

Managing knowledge in organizations has become an issue of utmost priority today. Knowledge management is purported to increase innovativeness and responsiveness (Hackbarth 1998).

According to Davenport and Prusak (1998), most knowledge management projects have one of three aims: (1) to make knowledge visible and show the role of knowledge in an organization; (2) to develop a knowledge-intensive culture by encouraging and aggregating behaviors such as knowledge sharing (as opposed to hoarding) and proactively seeking and offering knowledge; (3) to build a knowledge infrastructure not only a technical system, but a web of connections among people given space, time, tools, and encouragement to interact and collaborate.

Knowledge management is largely regarded as a process involving various activities. Slight discrepancies in the delineation of the processes appear in the literature, namely in terms of the number and labeling of processes rather than the underlying concepts. At a minimum, one considers the four basic processes of creating, storing/retrieving, transferring, and applying knowledge. These major processes can be subdivided, for example, into creating internal knowledge, acquiring external knowledge, storing knowledge in documents versus storing in routines (Teece 1998) as well as updating the knowledge and sharing knowledge internally and externally.

Knowledge Creation

Organizational knowledge can be created through social and collaborative processes as well as an individual’s cognitive processes (e.g., reflection), knowledge is created, shared, amplified, enlarged, and justified in organizational settings (Nonaka 1994). This model views organizational knowledge creation as involving a continual interplay between the tacit and explicit dimensions of knowledge and a growing spiral flow as knowledge moves through individual, group, and organizational levels. Four modes of knowledge creation have been identified: socialization, externalization, internalization, and combination (Nonaka 1994). The socialization mode refers to conversion of tacit knowledge to new tacit knowledge through social interactions and shared experience among organizational members (e.g., apprenticeship). The combination mode refers to the creation of new explicit knowledge by merging, categorizing, reclassifying, and synthesizing existing explicit knowledge (e.g., literature survey reports). The other two modes involve interactions and conversion between tacit and explicit knowledge.

Externalization refers to converting tacit knowledge to new explicit knowledge (e.g., articulation of best practices or lessons learned). Internalization refers to creation of new tacit knowledge from explicit knowledge (e.g., the learning and understanding that results from reading or discussion). The four knowledge creation modes are not pure, but highly interdependent and intertwined. That is, each mode relies on, contributes to, and benefits from other modes. For example, the socialization mode can result in creation of new knowledge when an individual obtains a new insight triggered by interaction with another. On the other hand, the socialization mode may involve transferring existing tacit knowledge from one member to another through discussion of ideas. New organizational knowledge per se may not be created, but only knowledge that is new to the recipient. The combination mode in most cases involves an intermediate step that of an individual drawing insight from explicit sources (i.e., internalization) and then coding the new knowledge into an explicit form (externalization). Finally, internalization may consist of the simple conversion of existing explicit knowledge to an individual’s tacit knowledge as well as creation of new organizational knowledge when the explicit source triggers a new insight.

Figure 1 illustrates the interplay among Nonaka’s knowledge creation modes, and hence may be useful in interpreting relationships between the four modes. In Figure 1, each arrow represents a form of knowledge creation. The arrows labeled A represent externalization; the arrows labeled B represent internalization; the arrows labeled C represent socialization; and the arrows labeled D represent combination.

Knowledge creation modes.
Figure 1: Knowledge creation modes.

It may be useful to consider the conditions and environments that facilitate new knowledge creation. Nonaka and Konno (1998) suggest that the essential question of knowledge creation is establishing an organization’s ba (defined as a common place or space for creating knowledge). Four types of ba corresponding to the four modes of knowledge creation discussed above are identified: (1) originating ba, (2) interacting ba, (3) cyber ba, and (4) exercising ba (Nonaka and Konno 1998). Originating ba entails the socialization mode of knowledge creation and is the ba from which the organizational knowledge creation process begins. Originating ba is a common place in which individuals share experiences primarily through face-to-face interactions and by being at the same place at the same time. Interacting ba is associated with the externalization mode of knowledge creation and refers to a space where tacit knowledge is converted to explicit knowledge and shared among individuals through the process of dialogue and collaboration. Cyber ba refers to a virtual space of interaction and corresponds to the combination mode of knowledge creation. Finally, exercising ba involves the conversion of explicit to tacit knowledge through the internalization process. Thus, exercising ba entails a space for active and continuous individual learning. Understanding the characteristics of various ba and the relationship with the modes of knowledge creation is important to enhancing organizational knowledge creation.

KM in Higher Education Systems

Any organization that dynamically deals with a changing environment should not only to process information efficiently but also create information and knowledge (Nonaka 1994). Knowledge management as it evolved in the business sector is slowly gaining acceptance in the academic sector. Oosterlink and Leuven (2002) pointed out that, “in our era of knowledge society and a knowledge economy, it is clear that universities have a major role to play”. In other words, universities are faced with a challenge to better create and disseminate knowledge to society. However, Reid (2000) argued “traditionally, universities have been the sites of knowledge production, storage, dissemination and authorization”.

Higher education institutions are an ever transforming organization. Universities are the main instruments of society for the constant pursuit of knowledge. Knowledge management in educational settings should provide a set of designs for linking people, processes, and technologies and discuss how organizations can promote policies and practices that help people share and manage knowledge (Petrides & Nodine, 2003). Colleges and universities are social organizations where workers transform resources for use by consumers through the functions of teaching, research, and service. Also created is a growing amount of transactional information in databases, knowledge embedded in processes and documentation as well as explicit and implicit knowledge in the heads of the workers. That is why librarians can play a key role in the knowledge management process (Cronin 1998; Nonaka 1994; Keonig 1996; Pinchot and Pinchot 1998).

There is an increased need to change even in traditional higher education systems. Increases in organizational information and change have created a great need to manage knowledge to ensure effectiveness. All approaches to KM essentially look at the methods to manage the human interactions better. The KM approach is conscious integration of all human resources involved, all the academic processes and the technological advancements involved in designing, capturing and implementing the intellectual infrastructure of any management institution. The approach supports in shaping and managing the academic rigor to learn by balancing among various entities in an academic environment. (Fermie et al, 2003) examines the issues on engaging the individual in any approach at sharing knowledge as the notion of knowledge can not be separated from the user. However the emphasis has to be on KM at the institutional level, not at the individual level. Studies have shown that technology tools alone can not be used to address discordant organizational information (Telem, 1996).

Libraries and KM

In colleges and universities, libraries are a source of knowledge management (Townley, 2001). Knowledge management techniques are being implemented in traditional academic libraries. Higher education is in the midst of major change as accountability, technology, faculty aging, distance education, and many other pressures come to bear. Knowledge management offers an opportunity to manage some of these issues and achieve institutional goals by using organizational knowledge. Libraries can bring specific skills in the selection and organization of knowledge, training, and user support to cross-functional teams. By doing so, they can create increased interest and support for their other missions. In the apocryphal words of the old sage, it is a risk that most libraries cannot afford not to take (Katz 1999; Santos, Heitor and Caraca 1998).

The Changing Role of Universities

As organizations (recognized to be in the knowledge business), universities face similar challenges that many other non-profit and for-profit organizations face (Rowley, 2000). Among these challenges are financial pressures, increasing public scrutiny and accountability, rapidly evolving technologies, changing staff roles, diverse staff and student demographics, competing values and a rapidly changing world (Naidoo, 2002).

Universities seek to share information and knowledge among the academic community within the institution. Knowledge management has become a key issue in universities due to changes in knowledge cultures. Oosterlink and Leuven (2002) argued that:

Universities are no longer living in splendid isolation. They have their own place in society, and they have a responsibility to society, which expects something in return for privileges it has granted.

In other words, universities do not exist as single entities. They are part of society through engaging in teaching, research and community service. Therefore, the knowledge created in universities through research and teaching should be relevant to the labor market. It may be noted that the university is concerned with the conservation of knowledge and ideas; teaching, research, publication, extension and services and interpretation (Budd, 1998; Ratcliffe-Martin, Coakes & Sugden, 2000).

As a result, promoting knowledge as the business of the university should be the major focus of higher education institutions. Similar to corporate organizations there are forces that are driving the changes in the way universities operate. Nunan (1999) argued that “higher education is undergoing transformations due to a range of external forces such as market competition, virtualization and internationalization, giving rise to new ways of understanding the role and function of the university”. This implies that the present day economic, social and technological context is bringing about changes to which universities must also adapt (CRUE, 2002). Universities compete against each other due to a great number of people who have access to higher education. Furthermore, the competitive pressures universities are now experiencing also result from changes in financial support, increasing costs of education and demand for educational services. Again, the present speed of knowledge transfer has generated an increasing demand from professionals and businesses for continuing education (CRUE, 2002). This shift to a market orientation will alter the form in which knowledge is disseminated. That is, the focus of universities is moving away from the autonomy professionals and toward an integrated sharing of knowledge.

Abell and Oxbrow (2001) pointed out that, “as with all organizations, academic institutions have recognized that their strength in the market may in future hinge on their ability to build collaborative and strategic partnerships” (p.230). These demands require the development of partnerships between universities and curricula customized to meet students’ needs. It can be noted that universities are complicated environments, incorporating a variety of very different kinds of work. As is true of all organizations, the universities have their own political structures and their own cultures (Budd, 1998). In addition to that, they have their own ways of responding to the society. Another challenge that universities face, is demographic changes and that affects the institution’s delivery of education. It is a challenge that requires universities to restructure their services to meet the needs of the academic community.

It is suggested that institutions of higher education need to prepare for a massive increase in the demand for educational services (Stoffle, 1996). Hawkins (2000) highlighted that collaboration requires the actual commitment and investment of resources, based on a shared vision. As a result, universities may be required to pool their resources in terms of human expertise, skills and competencies to achieve their goals. These challenges which occur as a result of change and transformation demands that universities come to grips with the notion that collaboration is one of the means of competitive survival (Hawkins, 2000).

In addition, the universities’ market demands are changing in terms of improving student learning outcomes. Some of the changes taking place in higher education have a direct impact on the library and its services. These include alterations in institution’s curricula, demographic changes in student bodies and additions to the media used in the classroom and in support of research (Budd, 1998). This translates to a demand that cannot be met with current resources, present bureaucratic structures and traditional methods for delivering services. Reid (2000) pointed out that this causes universities to measure their teaching programs, at least to some extent, as a market commodity that is aimed to meet the needs of the customer. In addition, universities will be required to re-examine all traditional methods and frameworks for a university education. In doing so, the discussion about this re-examination of the university will move into the same kind of paradigm shifting as that about libraries (Stoffle, 1996). It is also a challenge to academic libraries to support the needs of students for virtual learning. Due to these challenges, it is clear that academic libraries are turning to be “libraries without walls” and the information they deal with is now multi-format.

Furthermore, emerging information and communication technologies (ICTs) allow for the virtualization of teaching and learning (Reid, 2000). The use of ICTs in universities makes it possible for courses, modules and training programs that are interactive and multimedia based to be delivered on any time any place basis (Stoffle, 1996). This has created competition between universities in terms of delivering higher education services to the academic community.

In addition, universities have been influenced by the modes of organizing that dominated the corporate world and institutions. The upshot of the foregoing is that universities are facing the need for massive change in organizational structure, organizational culture in order to facilitate and integrate the sharing of knowledge within the university community. Commitment to change and learning together is important in that it combines to turn the universities into learning organizations.

Role of KM in Universities

As organizations grow ever complex, the organizational structures reflect specialization in knowledge and expertise. Budd (1998) argued that higher education, as it grew, took an organizational characteristic of these other institutions, because there was increasing organizational complexity, that is, the level of knowledge and expertise in an organization. As a result, today many educational institutions are seeking better ways to transform that knowledge into effective decision-making and action (Petrides & Nodine, 2003). The focus of universities, is based on making individual knowledge reusable for the achievement of the missions of the university.

However, Ratcliffe-Martin, Coakes and Sugden (2000) argued that:

Universities do not generally manage information well. They tend to lose it, fail to exploit it, duplicate it, do not share it, do not always share it, do not always know what they know and do not recognize knowledge as an asset.

In order for universities to achieve their institutional mission, that is, education, research and service to society, they need to be consciously and explicitly managing the processes associated with the creation of knowledge. Academic institutions exist to create knowledge, and thus, they have a role to play.

Knowledge management should have significance in higher education institutions. Sallis and Jones (2002) pointed out that education ought to find it easier to embrace knowledge management ideas, processes and techniques than many other organizations. Oosterlink and Leuven (2002) emphasized that with a suitable and multifaceted approach to knowledge management; universities can guarantee their own survival and at the same time prove that they are essential to modern society.

Knowledge management is an appropriate discipline for enabling a smooth integration of these new needs that have arisen from the present economic, social and technological context, into higher education. The application of knowledge management should aim at both internal reorganization of resources and improving teaching and research. It is clear in the era of a knowledge society and a knowledge economy that universities have a major role to play.

Barriers to KM

The most important reason for KM failure is the lack of connecting it to organizational goals (McDermott and O’Dell 2001). One prime problem in the failure of KM in organizations is the approach towards KM which is primarily dependent on the organizational culture and structure.

Culture plays an important role in establishing successful KM (McDermott and O’Dell 2001). They identified organizational cases wherein the KM process failed because, “people believed they were already sharing well enough, that senior managers did not really support it, or that, like other programs, it too would blow over.” (p.77) They believe that the organizational culture should not be changed to fit in KM, on the contrary, KM should be built around the culture.

The word ‘culture’ stems from the Latin “colere”, translated as to build on, to cultivate, to foster. Leibnitz, Voltaire, Hegel, von Humbold, Kant, Freud, Adorno, Marcuse, all have reflected on the meaning of the word in different versions of its use. Culture can be defined as the shared values, beliefs and practices of the people in the organization (Schein 1985). Another view of culture identifies culture as a set of values and attributes of a given group, and the relation of the individual to the culture, and the individual’s acquisition of those values and attributes. Hofstede (1991) refers to this as the “collective programming of the mind.”

Knowledge advancement is fundamentally a socio-cultural process, enhanced by cultures of innovation. Bakhtin (1986) uses the term “intertextuality” to indicate how the voices of others are integrated into what we think, write, and say. “Standing on the shoulders of giants” is a rough approximation. Cultures of innovation provide a broader base, making productive use of diverse contributions and allowing innovation to become the cultural norm (Drucker, 1985).

How KM should be implemented depends on the structure and the formal information system flow system of the organization (Guptara 1999). An organization with a top-down hierarchical structure will find it easier to implement a common data storage system (for all it will take is the top management pronouncing the implementation of the same and the need to abide to it) than in a flat, project oriented structure. The formal structure of most companies prevents KM from operating. Most companies are organized by function, region, division or business unit, each complete with its own recruitment, induction, and reward systems based on its “own” bottom line (Guptara 1999).

Culture weakens KM for various reasons. First, if an organizational structure is hierarchical, then the culture will also be layered. The nature of hierarchies is to militate against communication and internal relationships. If KM is to be implemented in a company, constant dialogue is required especially with employees lower down in the hierarchy (Cabrera 2002). Even if senior management feels that the organizational culture is very open to a KM process, it is actually the front line employees who can definitively say if the organization is genuinely open to radical new ideas.

Several scholars (e.g. Davenport et al 1998; Davenport and Prusak 1998; Nevis et al 1995; DeLong and Fahey 2000) have argued that creating a culture that values creativity, continuous improvement and the sharing of ideas is necessary for knowledge management initiatives to succeed. For an organization to manage its knowledge assets effectively, it needs to have employees who are motivated to explore new market opportunities, new work procedures or new products, and who are willing to apply new ideas to their own work; it needs structures and work systems that are flexible enough to admit innovative changes, and job definitions that grant employees a fair level of autonomy; and, very importantly, it needs to set up mechanisms by which new ideas are shared (Gupta and Govindarajan 2000). The sharing of ideas among employees is only one of several processes underlying collective knowledge within an organization, but it is a key process without which a company may not be able to leverage its most valuable asset (Wasko and Faraj 2000; Jarvenpaa and Staples 2000; Nahapiet and Ghoshal 1998).

Motivational forces are derived from two factors which are the employees’ personal belief structure (Szulanski 1996) and institutional factors which include values, norms and accepted practices which are instrumental in shaping individual’s belief structure (DeLong and Fahay 2000).

Sharing knowledge has certain cast of participation which can be measured in terms of time and effort (Gibbert and Krause 2002). But this creates a dilemma in the mind of knowledge sharer (Barry and Hardin 1982; Marwell and Oliver 1993). A knowledge asset contributed for the good of the organization can be used by others regardless of whether or not they make a contribution in return (Dawes 1980; Thorn and Connolly 1987). This dilemma is intensified when expertise (i.e., personal reputation) is highly valued in an organization but mentoring or assisting others is not (Leonard and Sensiper 1998). Further, in a knowledge economy, employees face an inherent fear of sharing knowledge with fears lest they loose their unique value within the organization, but any knowledge shared that is subsequently judged to be unsound or irrelevant can damage his/her reputation. Consequently, due to the lack of sufficient extrinsic and/or intrinsic rewards to compensate individuals for the costs of sharing knowledge becomes a common barrier to knowledge sharing (Constant et al. 1994, 1996; Huber 2001).

Recent research on knowledge sharing suggest that the relative lack of attention paid to the role of motivational factors that influence knowledge sharing behaviors (Kalling and Styhre 2003).

Thus an understanding of the literature on knowledge sharing in organizations surfaces the following salient motivational factors which researchers believe reflects three levels of motivational forces among knowledge workers.

  • Individual benefit, i.e., self-interest, personal gain, etc. (Constant et al. 1994, 1996; Tampoe 1996; Wasko and Faraj 2000)
  • Group benefit, i.e., reciprocal behaviors, relationships with others, community interest, etc. (Constant et al. 1994, 1996; Kalman 1999; Wasko and Faraj 2000)
  • Organizational benefit, i.e., organizational gain, organizational commitment, etc. (Kalman 1999)

Research shows that the organizational culture which fosters a climate of sharing and understanding among individuals provides an impetus for knowledge sharing (Hinds and Pfeffer 2003), an open climate with free-flowing information (Dixon 2000; Gibbert and Krause 2002; Hinds and Pfeffer 2003; Jarvenpaa and Staples 2000; Leonard and Sensiper 1998), a climate that is tolerant of well reasoned failure (Leonard and Sensiper 1998), and a climate infused with pro-social norms (Constant et al. 1994, 1996; Hinds and Pfeffer 2003; Wasko and Faraj 2000).

Conclusion

Universities in the Caribbean lack communication with the environment (Alleyne, et al. 2006). Using examples of Japanese organizations Nonako (1994) has exemplified the need to share knowledge with and from suppliers as well as customers, in the case of universities the students, the faculty and the industry who hire the students. Further, the University of West Indies has not made use of modern technologies to communicate. Further there was a lack of publications/magazines through which knowledge could be shared (Alleyne, et al. 2006). Nonako (1994) stresses on the creation of self-organizing teams which he believes helps in sharing of experience as well as creates vision for future development. He stresses on “cross-functional team in which experience sharing and continuous dialogue are facilitated by the management of interaction rhythms serves as the basic building block for structuring the organization knowledge creation process.” (p.24)

The interview shows that there is a distinct lack of knowledge management strategy in the Caribbean universities at the institutional level. What the management fosters as strategy is individual strategy wherein the staffs themselves make their personal knowledge strategy. Hence we see that there is no formal or organizational stress on knowledge management in the higher education system of the Caribbean. The culture that is developed in the universities is that of individual research and knowledge enhance wherein the institute does not interfere.

Another problem that comes out as a barrier to knowledge sharing among faculty members is the structure of the organization. From the focus group interview it is apparent that a change in the higher education system is absolutely necessary for a change to set in. There is need for new policies which can set the change process rolling. But how this is possible is not clear to the management of the Caribbean universities. In the present culture which does not encourage open collaboration among peers is a potent hurdle which makes it difficult to get staffs to share knowledge. Even though the universities are well equipped with technological tools to enhance knowledge sharing there is no optimum use of them in a closed environment which does not support knowledge sharing.

To motivate the faculty members to engage in knowledge creation grants and funds are provided to do research. This in turn brings in an environment of competition for the same funds provided by the higher education authorities. This extrinsic motivational tool does not increase knowledge sharing for it ignites competitiveness among educators who, instead of sharing knowledge, start hiding it from peers to get the fund for their individual research agenda.

Another difficulty is the unionization of staffs, wherein all salary and benefits are previously discussed. So a performance linked pecuniary benefit for knowledge sharing has to be pre-prescribed which is not a very effective idea and will not promote an environment of collaboration. Hence the interview results stresses on the development of a proper culture for knowledge sharing.

The tendency of knowledge workers to engage in individual research is imminent. There is lack of desire among faculty to engage in a project which requires teamwork. Though there are plans to reward those who engage in team based research, it is still not final.

The research work that is done is documented only in traditional libraries. But there are no other forms where these are stored for public view. Though seminars are held among staff members regarding the research they are engaging, the outcome or agenda of these seminars seldom becomes public knowledge. There are no forums for research discussions on informal basis, for the staff seldom get time out of their teaching and administrative duties.

To sum up the findings of the interview we see that the barriers that are imminent in the Caribbean universities are:

  1. Staff reluctant to share knowledge with peers both formally as well as informally due to presence of a competitive environment.
  2. Institutional structure does not create an environment for knowledge sharing.
  3. No motivational tool employed to work as a catalyst in the knowledge sharing process.

Hypothesis

In the traditional teaching scenario in universities, previous studies have observed that there exists no internal drive for the educators to change the methodology of their knowledge delivery (Toffler 1985). As Nonaka (1994) states that there should be an exchange of knowledge between teacher and students. Organizationas need to foster exchange of knowledge among its employees to leverage its most valuable asset, i.e. knowledge (Wasko and Faraj 2000; Jarvenpaa and Staples 2000; Nahapiet and Ghoshal 1998). Personal beliefs regarding knowledge sharing can be a deciding factor for the faculty member’s unwillingness to share knowledge. Knowledge is the property of the individual. Till the time the individual is willing to share these with others, it cannot be shared or transferred. Szulanski (1996) suggests that motivational forces derive from one of two bases: employees’ personal belief structures and institutional structures, i.e., values, norms and accepted practices which are instrumental in shaping individuals’ belief structures (Delong and Fahey 2000). Thus from the institutional factor we see that due to the lack of any enforcement of rule on faculty member to share their knowledge has made knowledge sharing and management in universities difficult. So from this we derive our first hypothesis.

Hypothesis 1

Staff is not obliged to share knowledge as this may impact on intellectual freedom, academic practices, norms and expected behavior.

Organizational culture has been viewed by scholars as a salient feature influencing individuals’ tendencies toward knowledge sharing. They believe that a culture in which individuals are highly trusting of others and of the organization (Hinds and Pfeffer 2003), an open climate with free-flowing information (Dixon 2000; Gibbert and Krause 2002; Hinds and Pfeffer 2003; Jarvenpaa and Staples 2000; Leonard and Sensiper 1998), a culture that is tolerant of well reasoned failure (Leonard and Sensiper 1998), and a culture infused with pro-social norms (Constant et al. 1994, 1996; Hinds and Pfeffer 2003; Wasko and Faraj 2000). Further, it is believed that a sense of self-worth influences individuals’ behaviors in directions congruent with the prevailing group and organizational norms (Huber 2001). The reference group’s norms become the internalized standard against which individuals judge themselves (Gecas 1982; Kelman 1961). This brings us to our second hypothesis which tries to relate faculty’s tendency to hesitate to share knowledge fearing judgment of peers.

Hypothesis 2

Faculty is hesitant to share knowledge as this would impact on the way they are viewed by the colleagues.

Sharing knowledge consumes both time and effort (Gibbert and Krause 2002), but doing so in an organizational setting results in the classic public good dilemma (Barry and Hardin 1982; Marwell and Oliver 1993): a knowledge asset contributed for the good of the organization can be used by others regardless of whether or not they make a contribution in return (Dawes 1980; Thorn and Connolly 1987). This dilemma is intensified when expertise (i.e., personal reputation) is highly valued in an organization but mentoring or assisting others is not (Leonard and Sensiper 1998). Not only does an individual choosing to share knowledge stand to lose his/her unique value within the organization, but any knowledge shared that is subsequently judged to be unsound or irrelevant can damage his/her reputation. Consequently, the lack of sufficient extrinsic and/or intrinsic rewards to compensate individuals for the costs of sharing knowledge becomes a common barrier to knowledge sharing (Constant et al. 1994, 1996; Huber 2001). Kalling and Styhre (2003) comment on the relative lack of attention paid to the role of motivational factors that influence knowledge sharing behaviors. Individual benefit, i.e., self-interest, personal gain, etc. (Constant et al. 1994, 1996; Tampoe 1996; Wasko and Faraj 2000). Thus, faculty members need a motivational impetus to shre knowledge that they have. As knowledge sharing does not come without participant costs, individuals expect a price for the knowledge that they deliver. This arouses the need for motivating the knowledge deliverer through certain intrinsic means. Anticipated extrinsic rewards are posited to encourage more positive attitudes toward knowledge sharing, leading to the third hypothesis.

Hypothesis 3

Faculty will be more open to sharing knowledge if they perceive that the organization will recognize and reward them for doing so.

Benefits of Research

It is hoped that results of the study would provide senior management with incentives to formulate human resource policy and guidelines and to provide encouragement and tangible resources (e.g. information technology and infrastructure) to support knowledge sharing activities such as networking and cross faculty collaboration in all areas of academic life. In the long run such action would serve to propel the University as a learning organization and enable it actively work towards building its organizational capital.

Methodology

Location of Research

The study was conducted at the St. Augustine Campus of the University of the West Indies, Trinidad and Tobago which is one of three island campuses of the University comprising five faculties: Engineering, Humanities and Education, Medical Sciences, Science and Agriculture and Social Sciences.

Survey Sample

The sample selected represented faculty who have been actively engaged in research in the recent past, the rationale being that this group are the chief knowledge creators in the institution. The sample was extracted from University and Faculty Annual Reports.

The investigation was done in two phases by means of (1) a focus group and (ii) a qualitative survey. The survey population was made up of cross-campus faculty.

Phase I: Focus Group

The focus group comprised 7 educators as follows:

  1. Coordinator of Campus Graduate Studies and Research
  2. 2 Deputy Deans, Research
  3. 2 heads of department
  4. 2 faculty engaged in teaching and research

After a series of postponements, the focus group was convened on July 10th 2008 and lasted four (4) hours from 9.30am to 12.30p.m. Questions were asked and responses and notes were taken using a laptop computer. Some difficulty was experienced in recording answers as at intervals, faculty tended to be addressing one another rather than the interviewer and had to be redirected to focus on the topic in question. However, it is the interviewer’s view that the responses recorded represent a true record of responses received.

The result of this focus group formed the basis of questionnaire prepared to solicit feedback from cross campus faculty on the topics of culture and organization. (See Appendix A)

Phase II – Questionnaire

A total of fifty (50) cross-campus faculties were approached by e-mail (See Appendix …) to participate in the survey. Responses were received by 38 persons and dates and times confirmed (by e-mail) for conducting the survey. At the last minute, eight persons declined to participate for various reasons. From the onset, the research gleaned that respondents were willing to dedicate only minimum time to the exercise. Hence a very structured questionnaire (Appendix B ) was formulated. Interviews lasted from 5 to 50 minutes each using very structured questions and were done over a period of five days July 21st to July 25th 2008.

Equipment

A laptop computer, required software (MS Office Professional, SPSS) and internet utilities owned by the student was used for all communication (for example e-mail) and for Web searching.

Data Analysis

Interview Analysis

The responses show there is an extensive pool of intellectual capital in the universities of Caribbean but no proper instrument to utilize or control it. The interviews portray that the faculty are involved in numerous research work but sharing these with the university or with their peers is lacking. This confirms the findings of Ratcliffe-Martin, Coakes and Sugden (2000) who believed that universities do not manage knowledge efficiently. The responses to the questions asked during the interview can be summarized in the following manner.

The interview confirms that there exists an air of competition among peers which becomes a barrier for knowledge sharing. They compete for the funding for research which will ultimately increase their personal knowledge and not that of the institution. Clearly shows that there is lack of trust and cooperation among faculty members which is due to the culture of competitiveness in the Caribbean universities. Hence the faculty members are either conscious of sharing their knowledge for possible loss of unique knowledge or position.

The interview shows that there is a distinct lack of knowledge management strategy in the Caribbean universities at the institutional level. What the management fosters as strategy is individual strategy wherein the staffs themselves make their personal knowledge strategy. Hence we see that there is no formal or organizational stress on knowledge management in the higher education system of the Caribbean. The culture that is developed in the universities is that of individual research and knowledge enhance wherein the institute does not interfere.

Another problem that comes out as a barrier to knowledge sharing among faculty members is the structure of the organization. From the focus group interview it is apparent that a change in the higher education system is absolutely necessary for a change to set in. There is need for new policies which can set the change process rolling. But how this is possible is not clear to the management of the Caribbean universities. In the present culture which does not encourage open collaboration among peers is a potent hurdle which makes it difficult to get staffs to share knowledge. Even though the universities are well equipped with technological tools to enhance knowledge sharing there is no optimum use of them in a closed environment which does not support knowledge sharing.

To motivate the faculty members to engage in knowledge creation grants and funds are provided to do research. This in turn brings in an environment of competition for the same funds provided by the higher education authorities. This extrinsic motivational tool does not increase knowledge sharing for it ignites competitiveness among educators who, instead of sharing knowledge, start hiding it from peers to get the fund for their individual research agenda.

Another difficulty is the unionization of staffs, wherein all salary and benefits are previously discussed. So a performance linked pecuniary benefit for knowledge sharing has to be pre-prescribed which is not a very effective idea and will not promote an environment of collaboration. Hence the interview results stresses on the development of a proper culture for knowledge sharing.

The tendency of knowledge workers to engage in individual research is imminent. There is lack of desire among faculty to engage in a project which requires teamwork. Though there are plans to reward those who engage in team based research, it is still not final.

The research work that is done is documented only in traditional libraries. But there are no other forms where these are stored for public view. Though seminars are held among staff members regarding the research they are engaging, the outcome or agenda of these seminars seldom becomes public knowledge. There are no forums for research discussions on informal basis, for the staff seldom get time out of their teaching and administrative duties.

To sum up the findings of the interview we see that the barriers that are imminent in the Caribbean universities are:

  1. Staff reluctant to share knowledge with peers both formally as well as informally due to presence of a competitive environment.
  2. Institutional structure does not create an environment for knowledge sharing.
  3. No motivational tool employed to work as a catalyst in the knowledge sharing process.

Survey Analysis

From the above discussion we derive the survey questionnaire which deals with questions directed towards the faculty members to ascertain their perception of the current knowledge sharing situation in universities and the hindrances that exist in individual and institutional level to sharing knowledge. The research study was based on qualitative interviews where university management was asked open ended questions. With the help of these structured interviews, we derived our questionnaire for the survey. The questionnaire was made to determine individual and organizational barriers to knowledge sharing among faculty members in the university.

The data of the survey was conducted in two methods: first, where the respondents had to answer dichotomous questions and questions that required them to provide specific ratings which were analyzed by calculating descriptive statistics for the sample. Then we employ factor analysis to test the hypothesis.

Considering the initial few questions of the questionnaire we see that most of the staff do not coordinate or share knowledge with peers informally. For this we group in the responses as low, medium and high level of interaction. Responses between 0-9% and 10-19% are grouped in as low, 20-29% and 30-39% as medium, and 40-49% and 50+% as high level of interaction. As figure 2 shows only 3 percent staff interact with peers informally which can be considered as high interaction. But most importantly 67 percent staffs say that their level of interaction is low with their peers informally. This shows that the level of informal interaction among staff members at the university is low.

Informal Communication with Peers
Figure 2.

In terms of motivational influences to influence or enhance collaboration among staffs the survey shows that there exists almost no pecuniary or motivational impetus for knowledge sharing. Figure 2 shows this fact.

Considering the descriptive statistics we see get the following table (see Table 1). Here we have codified each response as 1 for 0-9% to 6 for 50+ %. The mean and modal value is 2, which shows that most of the staff members engage in informal communication for 10-19 % times. The confidence level at 95 percent confidence is.34. This implies that around 66 percent of the population engages in informal knowledge sharing with peers’ 10-19 percent times.

Table 1.

% Informal Communication with Peers
Mean 2
Standard Error 0.168382
Median 2
Mode 2
Standard Deviation 0.9222661
Sample Variance 0.8505747
Kurtosis 1.3663258
Skewness 0.9524132
Range 4
Minimum 1
Maximum 5
Sum 70
Count 30
Confidence Level(95.0%) 0.34438
Percent of Faculty Rewarded due to Knowledge Shaining
Figure 3.

The above fire shows that there are no financial benefits or promotion given for research. So knowledge creation in universities is not linked to performance evaluation of the staff, which in turn is linked to promotion and financial reward. 17 percent respondents say that there is special recognition for knowledge sharing and maximum number of respondents says that there is availability of funds for research work i.e. knowledge creation.

A descriptive analysis done on this question after codifying the responses as: Special recognition (5), Promotion (4), Financial rewards (3), Increased funding for research (2) and No reward (1). The descriptive results are shown in the following table.

Table 2.

Faculty Reward due to Knowledge Sharing
Mean 4
Standard Error 0.24
Median 4
Mode 4
Standard Deviation 1.33
Sample Variance 1.77
Kurtosis 0.87
Skewness -1.42
Range 4.00
Minimum 1.00
Maximum 5.00
Sum 113.00
Count 30.00
Confidence Level(95.0%) 0.50

The mean and mode are both 4. This implies that majority of the sample responded that the reward that is provided for knowledge sharing and creation is increased funds for research. The sample is negatively skewed. From the descriptive analysis we can be 95 percent sure that 50 percent of the population believe that the reward that the university would provide for sharing knowledge is only increase funding for research work.

The graphical display of the responses (Figure 4) that training to create more knowledge is provided on demand by the staff members. More than fifty percent of the respondents believe this.

Organization Provides Training on Demand
Figure 4.

The responses are codified as 4, 3, 2, and 1 for yes, no, some of the time and all of the time. From the table below we see that modal value sample is 4 which mean that most of the respondents said “yes” regarding the training that is provided by the universities when demanded by the employees. From the descriptive data analysis we can say that almost 55 percent of the population thinks that training is provided when demanded for.

Table 3.

Organization Provides with Training on Demand
Mean 3
Standard Error 0.22
Median 4
Mode 4
Standard Deviation 1.21
Sample Variance 1.47
Kurtosis -1.06
Skewness -0.82
Range 3.00
Minimum 1.00
Maximum 4.00
Sum 93.00
Count 30.00
Confidence Level(95.0%) 0.45

Question six of the survey questionnaire has six parts. Each is related to the individual factors that influence knowledge sharing. We codified the responses for yes, no and no response as 3, 2 and 1 respectively. The table below shows the descriptive analysis of the survey data. This shows that the modal values being 3 i.e. ‘yes’ for questions Q4, 5, 6, and 9. This implies that most of the staff in the sample feels that they would like to shares their knowledge with peers for they like share knowledge, can receive more funding through shared research, and sharing knowledge would help their peers in their research endeavors. The standard deviations for all these questions are more than 0.5 which implies that the responses are widely dispersed. The responses to question 7 and 8 shows that most of the respondents said “no” when asked if knowledge sharing would increase their chances of getting promoted and knowledge sharing will positively affected their performance evaluation. The standard deviation for these two questions is low.

Table 4.

Q4 Q5 Q6 Q7 Q8 Q9
Mean 3 2.9 2.8 1.3 1 2.3
Standard Error 0 0.05 0.11 0.09 0 0.17
Median 3 3 3 1 1 3
Mode 3 3 3 1 1 3
Standard Deviation 0 0.25 0.61 0.47 0 0.94
Sample Variance 0 0.06 0.37 0.22 0 0.89
Kurtosis #DIV/0! 12.21 6.31 -1.24 #DIV/0! -1.69
Skewness #DIV/0! -3.66 -2.81 0.92 #DIV/0! -0.58
Range 0 1.00 2.00 1.00 0 2
Minimum 3 2.00 1.00 1.00 1 1
Maximum 3 3.00 3.00 2.00 1 3
Sum 90 88.00 84.00 39.00 30 68.00
Count 30 30.00 30.00 30.00 30 30.00
Confidence Level(95.0%) 0 0.09 0.23 0.17 0 0.35

We then do a graphical analysis using a stacked bar graph (Figure 5). The figure below shows the motivating factors as shown in question 6 which are motivating factors that the organization uses to influence staffs to share their knowledge. From the figure it is clear that all the respondents said that they believe that it does not influence their performance evaluation positively which is an organizational barrier. They said they enjoy sharing experience with their peers. They say that they do not feel that they have greater chance of getting promoted because of sharing knowledge with colleagues. But they believe that they have a greater chance of getting funds due to research.

Reasons for Individuals Shaining Knowledge
Figure 5.

To analyze question 7 of the questionnaire we codified the response options as 1,-1 and 0 for yes, no and no response respectively. First doing a descriptive analysis of the data we get the table below.

Table 5. One-Sample Statistics.

N Mean Std. Deviation Std. Error Mean
Q4 30 3.00 .000(a) .000
Q5 30 2.93 .254 .046
Q6 30 2.8000 .61026 .11142
Q7 30 1.30 .466 .085
Q8 30 1.00 .000(a) .000
Q9 30 2.27 .944 .172

a t cannot be computed because the standard deviation is 0.

One-Sample Test.

Test Value = 3
t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference
Lower Upper
Q5 -1.439 29 .161 -.067 -.16 .03
Q6 -1.795 29 .083 -.20000 -.4279 .0279
Q7 -19.977 29 .000 -1.700 -1.87 -1.53
Q9 -4.253 29 .000 -.733 -1.09 -.38

The above table (see Table 5) shows the one sample t-test values. We see that for Q4, 5, 6, 7 and 9 only Q6 does not give a significant result. Rest of the p-values is above the confidence interval. The t-values for Q4 and 8 could not be computed as they have zero standard deviation. This means in the case of Q4 the mean is same as the expected mean for all the samples and that in case of Q8 is not. So 100 percent of the sample shows that that Q8 is not possible implying, all respondents said that sharing knowledge does not influence their performance positively.

To the question asked during the survey if the lessons they learn during the course of their research are passed on to their colleagues gives us the following response (see Figure 6). Most of the faculty members say that they always help their colleagues with the knowledge they have gathered in the course of their research which helps in knowledge sharing.

To the question asked during the survey if the lessons they learn during the course of their research are passed on to their colleagues gives us the following response
Figure 6.

A simple analysis using bar graphs gives us figure 7 for questions 10 to 17.

To the question asked during the survey if the lessons they learn during the course of their research are passed on to their colleagues gives us the following response
Figure 7.

The above graph shows that for most of the individual factors are not significant barriers when we consider staff perception regarding knowledge sharing. Most of the respondents who answered the survey believe that barriers like lack of time, or not knowing what other colleagues are doing sharing not being a job requirement as hindrances to knowledge sharing. Further, concerns like fear of losing job or threatened by peers or sharing revealing their incompetence are false. This shows that the individual factors, according to the respondents, are not barriers to sharing knowledge.

Table 6.

Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17
Mean 0 0 -1 -1 -1 0 0 1
Standard Error 0.18 0.16 0.14 0.13 0.09 0.18 0.18 0.03
Median 1 1 -1.00 -1.00 -1.00 1.00 -1.00 1.00
Mode 1 1 -1.00 -1.00 -1.00 1.00 -1.00 1.00
Standard Deviation 0.98 0.90 0.76 0.73 0.51 0.99 1.00 0.18
Sample Variance 0.96 0.81 0.59 0.53 0.26 0.99 0.99 0.03
Kurtosis -1.78 -0.82 1.15 0.17 12.21 -2.06 -1.95 30.00
Skewness -0.58 -1.11 1.72 1.26 3.66 -0.21 0.43 -5.48
Range 2 2 2.00 2.00 2.00 2.00 2.00 1.00
Minimum -1 -1 -1.00 -1.00 -1.00 -1.00 -1.00 0.00
Maximum 1 1 1.00 1.00 1.00 1.00 1.00 1.00
Sum 8 14 -19.00 -16.00 -26.00 3.00 -6.00 29.00
Count 30 30 30.00 30.00 30.00 30.00 30.00 30.00
Largest(1) 1 1 1.00 1.00 1.00 1.00 1.00 1.00
Smallest(1) -1 -1 -1.00 -1.00 -1.00 -1.00 -1.00 0.00
Confidence Level(95.0%) 0.37 0.34 0.29 0.27 0.19 0.37 0.37 0.07

We cluster the questions 7 (a, b, c, d, e, f, g, and h) to see if they have any particular trend of relationship. We find the correlation of the seven questions. To do this we codified the responses as follows: True = 1, False = -1 and No Response = 0. We found the correlation of the questions which asked questions relating to the staff’s awareness, time constraint, staff’s apprehensions of sharing knowledge and the absence of sharing as a job requirement. We found that there existed positive relationship between Q17 and Q10, 11, 14, 15, 16 which confirms that knowledge sharing not being a job criteria is the reason for which they do not get time to share knowledge with peers, their unawareness of their peers’ research area, fear of sharing knowledge which may expose their ignorance, job security, and the fear of losing credit.

Table 7.

Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17
Q10 1
Q11 0.7925271 1
Q12 0.2790033 0.1937817 1
Q13 -0.0834913 -0.2379543 0.7325413 1
Q14 0.2033553 0.1611646 0.5804612 0.5707354 1
Q15 0.7849997 0.6011155 0.403321 0.0759418 0.2459224 1
Q16 0.6212607 0.492366 0.506668 0.2274294 0.3273268 0.7513055 1
Q17 0.2440515 0.3079409 -0.1563852 -0.137931 0.0496292 0.0189854 0.1516196 1

The table (Table 8) below gives the descriptive statistics of the questions which asked the organizational factors such as if the staff feels good to share knowledge because his/her peers respect his or her knowledge, the organization has an open environment for knowledge sharing and it supports collaboration to meet common goal. The descriptive statistics shows that there are the modal value for peer support for knowledge sharing got the maximum responses as “hardly ever” which implies there is a lack of peer making the faculty member feel good about knowledge shared. For the rest of the questions, the modal value was 3 which implied that most of the respondents felt “yes, always” when it came to respect from colleagues, open door for collaboration and all members work towards a common goal. The standard deviation for Q18, 19, and 21 are high indicating scattered responses. Whereas that of Q20 is low, indicating more concentrated response.

The figure below shows the stacked bar diagram for questions 8, 9, 10 and 11 of the survey questionnaire. The figure shows that most of the respondents believe that peers respect the staff’s knowledge, there exists an open door policy in the universities and the universities try to work towards a common goal. Its only in the case of colleagues making staffs good about sharing knowledge that we get responses tending more towards “hardly ever”.

that most of the respondents believe that peers respect the staff’s knowledge, there exists an open door policy in the universities and the universities try to work towards a common goal.
Figure 8.

Table 8.

Q18 Q19 Q20 Q21
Mean 1.97 2.67 2.83 2.23
Standard Error 0.17 0.12 0.07 0.16
Median 2 3 3 3
Mode 1 3 3 3
Standard Deviation 0.93 0.66 0.38 0.90
Sample Variance 0.86 0.44 0.14 0.81
Kurtosis -1.90 2.05 1.66 -1.61
Skewness 0.07 -1.82 -1.88 -0.50
Range 2 2 1 2
Minimum 1 1 2 1
Maximum 3 3 3 3
Sum 59 80 85 67
Count 30 30 30 30
Confidence Level(95.0%) 0.35 0.25 0.14 0.34

The above table with the descriptive statistics of Q18 to 21 shows that the mean value for the markings in Q18 is below 2 which stands for “some of the time” but rest are for above 2, tending towards the rating “always” which has been codified as 3. The modal value for Q18 is 1 which clearly shows that most of the respondents answered that their colleagues do not make them feel good about sharing knowledge. To check if this gives a significant finding, we do a t-test on Q18.

Table 9. One-Sample Test.

Test Value = 3
t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference
Lower Upper
Q18 -6.100 29 .000 -1.033 -1.38 -.69

The t-test result on Q18 is shown in the above tables. We see that the mean difference from the expected mean is 3 which states that colleagues makes them feel good when they share knowledge. But when we consider the p-value at 5 percent level of confidence we see that it is higher than the upper confidence interval which means that the result is significantly different and shows that colleagues not making staffs good about sharing knowledge can be a potential barrier to knowledge sharing.

The next analysis is done for the responses to the question which asked if the staff thought that their university had any of the features of a ‘learning organization’ as was described by Peter Senge. The responses were taken in a three-point scale as yes, no and no response. Following the previous practice, we quantified the responses as 3, 1 and 2 respectively. The stacked bar diagram for the frequencies of the sample is shown in the following figure (Figure 9). The figure shows that the respondents believe that their university possessed the characteristics like systems thinking, personal mastery, mental model and building shared vision. But majority of the respondents said that they lacked team learning, i.e. knowledge sharing environment.

Are Senge's Five Disciplines Practiced at University?
Figure 9.

Given this holistic view we see that most of the individual factors which are supposed to be barriers to knowledge sharing have been negated by the respondents as hindrances to share knowledge. In the next section we test the hypothesis using t-test of significance.

Hypothesis 1

To analyze the first hypothesis we will have to look at the factors that may affect all the options provided in the hypothesis. If we break up hypothesis 1 into 4 distinct hypotheses we get the following.

H1a: Staff is not obliged to share knowledge as this may impact on intellectual freedom. The first hypothesis tries to relate the university policy of not putting knowledge sharing as a job requirement of staffs for they believe that it may infringe their individual freedom to possess that knowledge. To test this hypothesis we do a paired t-test on two questions, first we ask the question if the staffs are required to share knowledge and if they consider it as their job requirement. We pair this with staffs’ response to if they know what their colleagues are doing. This will provide us the dependency and the staffs’ point of view regarding knowledge sharing as a job criteria and their personal intellectual independence. Doing a paired t-test on the two variables we get the following table.

Table 10. Paired Samples Statistics.

Mean N Std. Deviation Std. Error Mean
Pair 1 Q17 2.93 30 .365 .067
Q25 3.00 30 .000 .000
Pair 2 Q10 2.20 30 .997 .182
Q7 1.70 30 .466 .085

Paired Samples Correlations.

N Correlation Sig.
Pair 1 Q17 & Q25 30 . .
Pair 2 Q10 & Q7 30 .802 .000

Paired Samples Test.

Paired Differences t df Sig. (2-tailed)
Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference
Lower Upper
Pair 1 Q17 – Q25 -.067 .365 .067 -.203 .070 -1.000 29 .326
Pair 2 Q10 – Q7 .500 .682 .125 .245 .755 4.014 29 .000

The above table shows that the paired mean difference of Q17 and Q25 is.067 and that of Q10 and Q7 is 0.5. The p-value for the first pair is.326 which is higher than the confidence interval at 5 percent significance level. That of the second pair is 0 which lies within the interval. This clearly shows that we cannot accept nor reject the hypothesis.

H1b: Staff is not obliged to share knowledge as this may impact on academic practices. The academic practices include taking classes, seminars, research work etc. it is believes that the faculty members will fall back on their duties as an educator in trying to share knowledge with peers. But as Nonaka (1994) said knowledge sharing is the source to create of knowledge so an open policy towards knowledge sharing will help in the creation of more knowledge. To ascertain this point we will run a paired t-test on two of the questions asked regarding knowledge sharing as a job requirement and the personal mastery which evaluates academic knowledge according to Senge.

Table 11. Paired Samples Statistics.

Mean N Std. Deviation Std. Error Mean
Pair 1 Q17 2.93 30 .365 .067
Q23 2.23 30 .898 .164

Paired Samples Correlations.

N Correlation Sig.
Pair 1 Q17 & Q23 30 .260 .166

Paired Samples Test.

Paired Differences t df Sig. (2-tailed)
Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference
Lower Upper
Pair 1 Q17 – Q23 .700 .877 .160 .373 1.027 4.372 29 .000

The above table returns the analysis which gives a mean difference of.7 and the standard deviation of.877. The t-value is 4.37 and the p-value is.00 at 5 percent significance level. The p-value does not lie between the confidence interval which shows that the test result is significantly different. Hence we may reject the hypothesis that if staffs are made to share knowledge it will affect their academic practices.

H1c: Staff is not obliged to share knowledge as this may impact on norms.

Now if we consider the five disciplines of Peter Senge and the responses the staff had regarding what is present and what is not in their university will give a vivid idea as to if knowledge sharing is not mandatory for staff as it will affect the norms and culture of the organization. We again do a one sample t-test.

Table 12. One-Sample Statistics.

N Mean Std. Deviation Std. Error Mean
Q21 30 2.83 .379 .069
Q23 30 2.23 .898 .164
Q27 30 2.23 .898 .164

One-Sample Test.

Test Value = 1
t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference
Lower Upper
Q21 26.492 29 .000 1.833 1.69 1.97
Q23 7.526 29 .000 1.233 .90 1.57
Q27 7.526 29 .000 1.233 .90 1.57

The organizational norms are considered in terms of the organization’s systems thinking, its stress and acceptance of knowledge sharing and creation of a knowledge sharing environment. We hypothesized that knowledge sharing lacks in the Caribbean universities as because there are barriers in terms of norms. The hypothesis shows that the sample means for Q21, 23 and 27 are greater than the expected mean. Further, the p-value lies within the confidence interval measured at 5 percent level. Hence the test shows non-significant result and the hypothesis is not rejected.

H1d: Staff is not obliged to share knowledge as this may impact on expected behavior.

To ascertain if this hypothesis holds we will consider questions asked in the questionnaire that is, knowledge sharing is not a job requirement and that the universities follow Senge’s mental model which stresses on looking inward to change those things about the individual that make it difficult to communicate in a learning environment. We do a paired t-test on both the arguments to see if they hold true.

Table 13. Paired Samples Statistics.

Mean N Std. Deviation Std. Error Mean
Pair 1 Q17 2.93 30 .365 .067
Q24 2.63 30 .490 .089

Paired Samples Correlations.

N Correlation Sig.
Pair 1 Q17 & Q24 30 .244 .194

Paired Samples Test.

Paired Differences t df Sig. (2-tailed)
Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference
Lower Upper
Pair 1 Q17 – Q24 .300 .535 .098 .100 .500 3.071 29 .005

The mean difference for both the pairs is 0.3 which is very small. Further, the t value of the distribution is 3.071 and the p-value at 5 percent level significance is.005 which is less than the lower confidence interval. This clearly rejects the hypothesis.

Since all the subparts of hypothesis 1 are rejected then we may conclude that hypothesis 1 which states “Staff is not obliged to share knowledge as this may impact on intellectual freedom, academic practices, norms and expected behavior” can be rejected.

Hypothesis 2

To test the hypothesis, we consider the questions which may link to the hypothesis and test them for paired sample t-test. The questions which may link to the hypothesis are: “I feel as though seeking out knowledge may imply a lack of competence on my part” and “Sharing my knowledge may expose my lack of knowledge in my field.” Here the t-test results are shown below.

Table 14. Paired Samples Statistics.

Mean N Std. Deviation Std. Error Mean
Pair 1 Q12 -.63 30 .765 .140
Q14 -.83 30 .531 .097

Paired Samples Correlations.

N Correlation Sig.
Pair 1 Q12 & Q14 30 .609 .000

Paired Samples Test.

Paired Differences t df Sig. (2-tailed)
Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference
Lower Upper
Pair 1 Q12 – Q14 .200 .610 .111 -.028 .428 1.795 29 .083

The mean for both the sets are -.63 and -.83 with a standard deviation of.765 and.531. The difference in the means is 0.2. The t value is 1.7 at 29 degree of freedom. As the p value for the pair is.083 which is less than the upper interval and more than the lower interval at 5 percent level of significance. This holds the null hypothesis true. Thus, we can say that faculty is hesitant to share knowledge as this would impact on the way they are viewed by the colleagues.

Hypothesis 3

From the interview we found that faculty is reluctant to share knowledge as they feel there are no benefits attached to sharing knowledge. Moreover, the university policy of funding researches actually makes staff relation more competitive for all the staffs try for the same group of funding. So to ascertain our third hypothesis we will have to consider the HR policies of the University along with the perception the staffs hold regarding the benefits they may get from the university as motivational impetus to share knowledge. So we consider the staffs’ perception regarding knowledge sharing when it came to university policies and promotion, performance evaluation and funding. To analyse this, we have set quantitative values to the qualitative responses and done a paired t-test.

Table 15. Paired Samples Test.

Paired Differences t df Sig. (2-tailed)
Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference
Lower Upper
Pair 1 Q22 – Q6 -1.03333 .49013 .08949 -1.21635 -.85031 -11.547 29 .000
Pair 2 Q22 – Q7 .467 .860 .157 .145 .788 2.971 29 .006
Pair 3 Q22 – Q8 .767 .430 .079 .606 .927 9.761 29 .000

The results of the paired t-test for the perception of staffs regarding the university’s HR policies and their perception about how knowledge sharing may reward them in terms of promotion, fund or positive performance appraisal. The difference in mean for Q22 and Q6 is -1.033, that of Q22 and Q7 is.46 and that of Q22 and Q8 is -.767. The t values are -11.547, 2.971 and 9.761 respectively where in all the cases the degree of freedom is 29. All the three pair’s p-value does not fall in the confidence interval. The result of the test shows that the HR policies influence the staff’s perception of promotion. They would feel more open to sharing knowledge if they knew that promotion was an HR policy associated to knowledge sharing. But in case of funding and positive feedback in the performance evaluation are negated. This brings us to the conclusion that we cannot conclusively say that the staffs would have share more knowledge if there was a policy of providing pecuniary benefit for knowledge sharing. Clearly we may reject the hypothesis.

Summary

From the above discussion it is apparent that the barriers to knowledge sharing as is experienced in Caribbean universities are not individual problems. The analysis of the interview and that of the survey data has shown a lot of discrepancy. The similarities that have been found are regarding the organizational barriers. Analysis of the data collected through survey questionnaire we see that the staffs are eager to share knowledge. This is contradicting our finding through the survey and that of the interview where we are told that the faculty are reluctant to share knowledge. What our findings show that the greatest barriers to knowledge sharing at the universities in the Caribbean are organizational barriers. The reasons that affect knowledge sharing most in the universities are the lack of knowledge sharing environment, structure and culture or the organization, but the barriers and HR policies. The respondents say that they do not get access to any of the knowledge resources of the institution. The result is indicative that the institute does not have any policy to reward knowledge sharing among staff. On the contrary the environment does not help in open collaboration among staff members which can lead to knowledge sharing. Further the interview showed time as a factor that staffs complains about which prevents them from sharing knowledge. But the survey shows that the staffs do not think that paucity of time is a barrier to knowledge sharing. Clearly what the university is trying to do is promote is individual mastery of knowledge, which according to Fermie et al. (2003) should not be promoted at individual level. Our finding shows that the universities do not utilize its resources properly for knowledge creation or sharing purposes. This supports findings of Ratcliffe-Martin et al. (2000) where they said that universities fail to recognize knowledge and utilize it effectively.

As McDermott and O’Dell (2001) found, culture plays a significant barrier to knowledge management has been found to hold true even in the case of Caribbean universities. We saw that cultural factors like open collaborative process or team learning is not supported by the universities. Further as discussed by Guptara (1999) structure and policy play a vital role in effective sharing of knowledge in organizations, has been found to hold true in our case. There is lack of communication among employees that prevents for proper knowledge sharing in the universities. This has been said by Cabrera (2002) that if KM is to be implemented in a company, constant dialogue is required especially with employees lower down in the hierarchy. There is lack of sharing of ideas among employees which is prevented due to the company policy and becomes a significant hindrance to knowledge sharing. This finding supports the research findings of Wasko and Faraj (2000), Jarvenpaa and Staples (2000), and Nahapiet and Ghoshal (1998). The university failed to provide any individual motivation in terms of increased salary or positive performance appraisal.

To sum up the barriers that has been observed in the Caribbean universities are:

  • Individual: Lack of benefits to employees, fear of personal knowledge being misused by peers, and fear of being reprimanded by colleagues.
  • Organizational: Lack of collaborative environment, absence of team learning, lack of motivational impetus to promote knowledge sharing, and failure to use resources to the optimum.

Clearly there are more organizational barriers to knowledge sharing than individual factors. The institutional factors at the universities especially the lack of collaborative environment and culture for knowledge sharing. There are other factors such as lack of motivation being provided to staffs to engage in knowledge sharing activities. Further, due to lack of open environment for knowledge sharing staffs have a fear of malpractice with their knowledge by their peers. This can be removed from the staffs’ mindset only through a collaborative environment.

Appendix A: Focus Group Questionnaire

  1. It has been argued that the university has the largest pool of specialists and experts in the Caribbean and that there is an abundance of intellectual capital. Would you say that your University’s strategic plan has adequately addressed management of its intellectual capital?
  2. Does your University have a knowledge strategy?
  3. Do you believe that there is need for a knowledge management plan at your university?
  4. Do you believe that staff should be rewarded for sharing their knowledge? It has been said that the dominant culture and practices of traditional universities work contrary to the requirements of a true learning organization that recognize and reward individual rather than group achievements. Can you Comment?
  5. Does your university have systems to collect and share information about on-going research?

Appendix B: Survey Questionnaire

  1. What percent of time do you engage in informal communication with your counterparts?
    • 0-9 %
    • 10-19
    • 20-29
    • 30-39
    • 40-49
    • 50+
  2. How are you rewarded by your University for sharing knowledge
    • Special recognition
    • Promotion
    • Financial rewards
    • Increased funding for research
    • No reward
  3. Does your organization provide you with training on demand?
    • Yes.
    • No
    • Some of the time
    • All of the time
  4. Do you have easy access to the University’s pool of knowledge?
    • I am aware of what exists in my department
    • I search the intranet
    • I ask the HR department
    • I check the library
    • I ask my assistant to search for me
  5. Do you take steps to ensure that lessons learned in the course of your research activities are passed along to your colleagues doing similar tasks?
    • Yes, always
    • No, never
    • Sometimes
  6. Answer Yes or No to the following: I share knowledge because,
    • I enjoy the sharing experience
    • I feel a sense of empowerment
    • I feel that I have a better chance of receiving funding if work on collaborative projects
    • I feel that I have a greater chance of being promoted
    • It influences my performance evaluation positively
    • It supports my colleagues in their research and teaching
  7. State whether the following are true or false:
    1. I generally do not have the time to share knowledge with colleagues
    2. I generally do not know what my colleagues are doing so I am unable to support them
    3. I feel as though seeking out knowledge may imply a lack of competence on my part
    4. Students and faculty challenge my knowledge
    5. Sharing my knowledge may expose my lack of knowledge in my field.
    6. I feel that sharing may jeopardize my job security
    7. I feel that colleagues may misuse my knowledge or take unjust credit for it
    8. Sharing is not a job requirement
  8. Do your colleagues make you feel good about sharing knowledge?
    • All the time
    • Some of the time
    • Hardly Ever
  9. Do your colleagues respect you as well as your knowledge?
    • All the time
    • Some of the time
    • Hardly Ever
  10. Do you share knowledge as a way to open the doors for collaboration ?
    • All the time
    • Some of the time
    • Hardly Ever
  11. Does your University organization work to enhance the capacity of all members to work productively towards common goals?
    • All the time
    • Some of the time
    • Hardly Ever
  12. Do your HR policies encourage knowledge sharing?
    • Yes
    • No
    • No Response
  13. According to Peter Senge’s model of business, the dimensions that distinguish the learning environment from more traditional organizations is the master of the five disciplines as follows:
    1. Systems thinking: Looking at the whole picture.
    2. Personal mastery: Individual knowledge.
    3. Mental models: Looking inward to change those things about the individual that make it difficult to communicate in a learning environment.
    4. Building Shared Vision: A genuine shared vision of the future.
    5. Team learning: Knowledge sharing environment.
  14. Which of the five disciplines listed above do you believe would most benefit your University?
    1. Systems thinking: Looking at the whole picture.
    2. Personal mastery: Individual knowledge.
    3. Mental models: Looking inward to change those things about the individual that make it difficult to communicate in a learning environment.
    4. Building Shared Vision: A genuine shared vision of the future.
    5. Team learning: Knowledge sharing environment.
    6. All of the Above

Reference

Abell, A. and Oxbrow, N. (1999). Skills for the knowledge economy. Web.

Abell, A. and Oxbrow, N. (2001). Competing with knowledge: the information professional in the knowledge management age. London: Library Association.

Allee, V. (1997). The knowledge evolution: expanding organizational intelligence.Boston: Butterworth-Heinemann.

Awad, M., Elias, Ghaziri, M. and Hassan. (2004) Knowledge Management, Pearson Education Inc., Prentice Hall, pp.1-9.

Bakhtin, M. M. (1986). The problem of speech genres. In C. Emerson & M. Holquist (Eds.), Speech Genres and Other Late Essays (pp. 60-102). Austin: University of Texas Press.

Barry, B., and Hardin, R. 1982. Rational Man and Irrational Society?, Sage Publications, Newbury Park, CA.

Beijerse, R. P. u. (1999): Questions in knowledge management: defining and conceptualizing a phenomena in: Journal of Knowledge Management, Vol. 3, No. 2, pp. 94-109.

Bohn, R. Measuring and Managing Technological Knowledge,. Sloan Management Review, Fall 1994, pp. 61-73.

Budd, J. M. (1998). The academic library: its context, its purpose and its operation.Englewood, Colorado: Libraries Unlimited.

Budd, J. M. (1998). The academic library: its context, its purpose and its operation. Englewood, Colorado: Libraries Unlimited.

Cabrera, Ángel 2002, “Knowledge-Sharing Dilemmas”, Organization Studies, 23 (5), pp. 687-710.

Carlsson, S. A., El Sawy, O. A., Eriksson, I., and Raven, A. 1996. “Gaining Competitive Advantage Through Shared Knowledge Creation: In Search of a New Design Theory for Strategic Information Systems,” in Proceedings of the Fourth European Conference on Information Systems, J. Dias Coelho, T. Jelassi, W. König, H. Krcmar, R. O.Callaghan, and M. Sääksjarvi (eds.), Lisbon.

Chan, S. and Ng, B. 2003. “KM and Asian Cultures: Are there advantages and disadvantages when sharing knowledge in the East?” Knowledge Management.

Constant, D., Kiesler, S., and Sproull, L. 1994. “What’s Mine is Ours, or Is it? A Study of Attitudes about Information Sharing,” Information Systems Research (5:4), pp. 400-421.

Constant, D., Kiesler, S., and Sproull, L. 1996. “The Kindness of Strangers,” Organization Science (7:2), pp. 119-135.

Cronin, Blaise 1998 “Information Professionals in the Information Age,” International Informationand Library Review 30, pp. 37-50.

CRUE. 2002. The university and knowledge management. Web.

Davenport, T. H., and Prusak, L. 1998 Working Knowledge, Harvard Business School Press, Boston.

Davenport, T. H., De Long, D. W. and Beers, M. C. 1998 ‘Successful knowledge management projects’. Sloan Management Review 39/2: 43-57.

Dawes, R. 1980. “Social Dilemmas,” Annual Review of Psychology (31), pp. 169-193.

De Long, D. W. and Fahey, L. 2000 ‘Diagnosing cultural barriers to knowledge management’. Academy of Management Executive 14/4: 113-127.

DeLong, D. W., and Fahey, L. 2000. “Diagnosing Cultural Barriers to Knowledge Management,” Academy of Management Executive (14:4), pp. 118-127.

Dixon, N. M. 2000 Common Knowledge: How Companies Thrive by Sharing What They Know, Harvard Business School Press, Boston.

Drucker, P. 1985. Innovation and entrepreneurship: Practice and principles. New York: Harper and Row.

Drucker, P. 1994. “The Age of Social Transformation,” The Atlantic Monthly.

Fermie, S., Green, S.D., Weller, S.J. and Newcombe, R. (2003) ‘Knowledge Sharing Context: confusion and controversy’, International Journal of Project Management, Vol.21, No.3, pp.177-187.

George A. O. Alleyne, Keva J. Bethel, Kenneth A. Bobb, Dennis J. Gayle, Lois S. Graham, Elsa A. Leo-Rhynie, Neville V. Nicholls, and Joseph R. Pereira 2006. “Report Of The Chancellor’ Task Force On Governance Of The Uwi”, The University of the West Indies.

Gibbert, M., and Krause, H. 2002. “Practice Exchange in a Best Practice Marketplace,” in Knowledge Management Case Book: Siemens Best Practices, T. H. Davenport and G. J. B. Probst (Eds.), Publicis Corporate Publishing, Erlangen, Germany, pp. 89-105.

Gupta, Anil K. and Vijay Govindarajan 2000 ‘Knowledge management’s social dimension: Lessons from Nucor Steel’. Sloan Management Review 42/1: 71-80.

Gupta, B., Iyer, L. S. and Aronson, J. E. 2000. “Knowledge management: practices and challenges”. Industrial Management and Data Systems, 100(1), 17-21.

Guptara, Prabhu, 1999. “Why Knowledge Management Fails”, Knowledge Management Review, Issue .

Hackbarth, G. 1998 “The Impact of Organizational Memory on IT Systems,” in Proceedings of the Fourth Americas Conference on Information Systems, E. Hoadley and I Benbasat (eds.), 1998, pp. 588-590.

Hawkins, B. 2000. Libraries, knowledge management, and higher education in an electronic environment. Australia: ALIA Conference.

Hinds, P. J., and Pfeffer, J. 2003. “Why Organizations Don’t ‘Know What They Know’: Cognitive and Motivational Factors Affecting the Transfer of Expertise,” in Sharing Expertise: Beyond Knowledge Management, M. Ackerman, V. Pipek, and V. Wulf (Eds.), MIT Press, Cambridge, MA, pp. 3-26.

Hofstede, G. 1991 Culture and Organizations Intercultural Cooperation and Its Importance for survival- Software of the Mind. McGraw-Hill, London.

Huber, G. 1991. “Organizational Learning: The Contributing Processes and the Literatures,” Organization Science (2:1), pp. 88-115.

Huber, G. P. 2001. “Transfer of Knowledge in Knowledge Management Systems: Unexplored Issues and Suggested Studies,” European Journal of Information Systems (10), pp. 72-79.

Ivari, J., and Linger H. 1999.Knowledge Work as Collaborative Work: A Situated Activity Theory View,. in Proceedings of the Thirty-Second Annual Hawaii International Conference on Systems Sciences, IEEE Computer Society Press, Los Alamitos, CA.

Jarvenpaa, S. L., and Staples, D. S. 2000. “The Use of Collaborative Electronic Media for Information Sharing: An Exploratory Study of Determinants,” Journal of Strategic Information Systems (9), pp. 129-154.

Jarvenpaa, Sirkka L. and D. Sandy Staples 2000 ‘The use of collaborative electronic media for information sharing: An exploratory study of determinants’. Journal of Strategic Information Systems 9/1: 129-154.

Kalling, T., and Styhre, 2003. A. Knowledge Sharing in Organizations, Copenhagen Business School Press, Copenhagen, Denmark.

Katz, Richard N. and Associates, ed., 1999. Dancing with the Devil: Information Technology and the New Competition in Higher Education, San Francisco: Jossey-Bass.

Keonig, Michael E. D. 1996 “Intellectual Capital and Knowledge Management,” IFLA Journal 22 : 299-301.

Leonard, D., and Sensiper, S. 1998. “The Role of Tacit Knowledge in Group Innovation,” California Management Review (40:3), pp. 112-132.

Martensson, M. 2000. “A critical review of knowledge management as a management tool”. Journal of Knowledge Management, 4(3), 204-216.

Marwell, G., and Oliver, P. 1993. The Critical Mass in Collective Action: A Micro-social Theory, Cambridge University Press, New York.

McDermott, R. and O’Del, C. 2001. “Overcoming cultural barriers to sharing knowledge”, Journal of Knowledge Management, Volume 5 Number 1, pp. 76-85.

McQueen, R. 1998.”Four Views of Knowledge and Knowledge Management,”. in “Proceedings of the Fourth Americas Conference on Information Systems,” E. Hoadley and I. Benbasat (eds.), pp. 609-611.

Nahapiet, Janine and Sumantra Ghoshal 1998 ‘Social capital, intellectual capital and the organizational advantage’. Academy of Management Review 23/2: 242-267.

Naidoo, V. 2002. Organisational culture and subculture influences on the implementation and outcomes of aspects on internal quality assurance initiatives. HERDSA.

Nevis, Edwin C., Anthony J. DiBella, and Janet M. Gould 1995 ‘Understanding organizations as learning systems’. Sloan Management Review 36/2: 73-85.

Norton, N. 1998 Institute..Putting the Knowing Organization to Value,. White Paper.

Nonaka, I. / Takeuchi, H. 1995. The Knowledge-Creating Company; How Japanese Companies Create the Dynamics of Innovation, Oxford University Press.

Nonaka, I., 1994. “A Dynamic Theory of Organizational Knowledge Creation”. Organization Science. Vol. 5, No. 1:14-37.

Nonaka, I., and Konno, N 1998 “The Concept of.Ba.: Building a Foundation for Knowledge Creation,” California Management Review (40:3), pp. 40-54.

Nunan, T. 1999 ‘Redefining Worthwhile Knowledge in Higher Education’ AARE Conference Melbourne.

Oosterlink, A. and Leuven, K. U. 2002. Knowledge management in post-secondary education: universities. Web.

Petrides, L. A. & Guiney, S. Z. 2002. Knowledge management in education: Defining the landscape.

Petrides, L.A. and Nodine, T. R. 2003. Knowledge management in education: defining the landscape. Web.

Pinchot, Gifford and Pinchot, Elizabeth. 1998. “The Intelligent Organization,” in The Infinite Resource: Creating and Leading the Knowledge Enterprise, ed. William E. Halal San Francisco: Jossey-Bass: 191-212.

Polanyi, M. 1962. Personal Knowledge: Toward a Post-Critical Philosophy, Harper Torchbooks, New York.

Polanyi, M. 1967. The Tacit Dimension, Routledge and Keoan Paul, London.

Polanyi, M.1975. “Personal Knowledge, in Meaning,” M. Polanyi and H. Prosch (eds.), University of Chicago Press, Chicago, pp. 22-45.

Reid, I. C. 2000. The web, knowledge management and universities. Web.

Rowley, J. 2000a ‘From learning organization to knowledge entrepreneur’, Journal of Knowledge Management, Vol 4, pp.7-14.

Rowley, J. 2000b. “Is higher education ready for knowledge management?” The International Journal of Education Management, 14(7), 325-333.

Sallis, E. and Jones, G. 2002 Knowledge Management in Education, Kogan Page, London.

Santos, Filipe, Heitor, Manuel V. and Caraca, Joao. 1998. “Organizational Challenges for the University,”. Higher Education Management 10 (Nov.): 87.107.

Schein, E. 1985, Organizational Culture and Leadership, Jossey-Bass, San Francisco, CA.

Schubert, P., Lincke, D., and Schmid, B.A Global Knowledge Medium as a Virtual Community: The Net Academy Concept,. in Proceedings of the Fourth Americas Conference on Information Systems, E. Hoadley and I. Benbasat.

Senge, P. 1990, The Fifth Discipline: The Art and Practice of the Learning Organization. New York: Doubleday Currency.

Spender, J. C. 1992 “Strategy Theorizing: Expanding the Agenda,”. in Advances in Strategic Management, P. Shrivastava, A. Huff, and J. Dutton (eds.), JAI Press, Greenwich, CT, , pp. 3-32.

Spender, J. C. 1996a “Making Knowledge the Basis of a Dynamic Theory of the Firm,” Strategic Management Journal (17), Special Issues, pp. 45-62.

Spender, J. C. 1996b. “Organizational Knowledge, Learning, and Memory: Three Concepts in Search of a Theory,” Journal of Organizational Change Management (9), 1996b, pp. 63-78.

Stoffle, C. J. 1996. The emergence of education and knowledge management as major functions of the digital library. Web.

Suurla, R., Markkula, M. and Mustajarvi, O. 2002. Developing and implementing knowledge management in the Parliament of Finland.

Szulanski, G. 1996 “Exploring Internal Stickiness: Impediments to the Transfer of Best Practice within the Firm,” Strategic Management Journal (17), pp. 27-44.

Tampoe, M. 1996. “Motivating Knowledge Workers – The Challenge for the 1990s,” in Knowledge Management and Organizational Design, P. S. Myers (Ed.), Butterworth-Heinemann, Boston, pp. 179-189.

Teece, D. 1998 “Capturing Value from Knowledge Assets: The New Economy, Markets for KnowHow, and Intangible Assets,” California Management Review (40:3), pp. 55-79.

Telem, M. 1996 ‘MIS implementation in Schools: A Systems Socio-Technical Framework.’, Computers and Education, Vol.27, No.2, pp.85-93.

Thorn, B. K., and Connolly, T. 1987. “Discretionary Data Bases: A Theory and Some Experimental Findings,” Communication Research (14:5), pp. 512-528.

Toffler, A. 1985. The Adaptive Corporation. New Yrk: McGraw-Hill.

Townley, C.T. 2001. “Knowledge Management and Academic Libraries”, Colleges and research Libraries: 44-56.

Tuomi, I. 1999. “Data is More Than Knowledge: Implications of the Reversed Hierarchy for Knowledge Management and Organizational Memory,” in Proceedings of the Thirty-Second Hawaii International Conference on Systems Sciences, IEEE Computer Society Press, Los Alamitos, CA.

Wasko, M. M., and Faraj, S. 2000. “It is What One Does: Why People Participate and Help Others in Electronic Communities of Practice,” Journal of Strategic Information Systems (9), pp.155-173.

Wasko, Molly McLure and Samer Faraj 2000 ‘“It is what one does”: why people participate and help others in electronic communities of practice’. Journal of Strategic Information Systems 9/1: 155-173.

Watson, R. T. 1999Data Management: Databases and Organizations (2nd ed.), John Wiley, New York.

Yu, C. M. 2002. “Socialising knowledge management: the influence of the opinion leader” Journal of Knowledge Management Practice.

Zack, M. 1998a. “An Architecture for Managing Explicated Knowledge,” Sloan Management Review.

Zack, M. 1998b “What Knowledge-Problems Can Information Technology Help to Solve,” in Proceedings of the Fourth Americas Conference on Information Systems, E. Hoadley and I. Benbasat (eds.), Baltimore, MD, pp. 644-646.

Knowledge Management and Its Role in Education
The following paper on Knowledge Management and Its Role in Education was written by a student and can be used for your research or references. Make sure to cite it accordingly if you wish to use it.
Removal Request
The copyright owner of this paper can request its removal from this website if they don’t want it published anymore.
Request Removal

Cite this paper

Select a referencing style

Reference

YourDissertation. (2022, June 5). Knowledge Management and Its Role in Education. Retrieved from https://yourdissertation.com/dissertation-examples/knowledge-management-and-its-role-in-education/

Work Cited

"Knowledge Management and Its Role in Education." YourDissertation, 5 June 2022, yourdissertation.com/dissertation-examples/knowledge-management-and-its-role-in-education/.

1. YourDissertation. "Knowledge Management and Its Role in Education." June 5, 2022. https://yourdissertation.com/dissertation-examples/knowledge-management-and-its-role-in-education/.


Bibliography


YourDissertation. "Knowledge Management and Its Role in Education." June 5, 2022. https://yourdissertation.com/dissertation-examples/knowledge-management-and-its-role-in-education/.

References

YourDissertation. 2022. "Knowledge Management and Its Role in Education." June 5, 2022. https://yourdissertation.com/dissertation-examples/knowledge-management-and-its-role-in-education/.

References

YourDissertation. (2022) 'Knowledge Management and Its Role in Education'. 5 June.

Click to copy
Copied