Literature Review
Knowledge Management
Content
1. Introduction (1)
2.Definition of Knowledge Management (3)
3. Theoretical Perspective (4)
3.1. Source of Knowledge (4)
3.2. Knowledge Accessibility (5)
4. Models of Knowledge Management (7)
4.1 Philosophy-based model of KM (7)
4.2 Cognitive model of KM (7)
4.3 Community of practice model of KM (8)
4.4 Network model of KM (9)
4.5 Quantum model of KM (10)
5. Functions and Benefits of Knowledge Management (11)
6.Application (13)
6. 1 Knowledge Management Process (13)
6.2 Case Study (13)
Conclusions (17)
References (18)
1. Introduction
Knowledge management (KM) comprises a range of strategies and practices used in an organization to identify, create, represent, distribute, and enable adoption of insights and experiences. Such insights and experiences comprise knowledge, either embodied in individuals or embedded in organizations as processes or practices (https://www.doczj.com/doc/af17164499.html,, 2012).
An established discipline since 1991, KM includes courses taught in the fields of business administration, information systems, management, and information sciences. More recently, other fields have started contributing to KM research, these include information and media, computer science, public health, and public policy .etc.
Driven by knowledge (Liao, S-H., 2003) economy and society, knowledge management (KM) becomes increasingly prevalent in both academic literature and practical use. KM technologies and their applications are not only fundamental components of KM operations in organizations, but also basic tools of knowledge workers' daily work. KM technologies reflect various understandings and solutions provided to problems in both academic research and practical work, they are fundamental to the success of KM programs. As KM theories evolve and new technologies emerge, discovering the linkage between them has become a complex proposition. Previous studies have been done on drawing a comprehensive picture of KM technologies. With new technologies constantly emerging, this paper, however, reviews the conceptual foundations of KM technologies, and explores the functions and classifications of KM technologies and applications according to the authors' recent literature survey.
Many large companies (https://www.doczj.com/doc/af17164499.html,, 2012) and non-profit organizations have resources dedicated to internal KM efforts, often as a part of their business strategy, information technology, or human resource management departments. Several consulting companies also exist that provide strategy and advice regarding KM to these organizations.
Knowledge management efforts (https://www.doczj.com/doc/af17164499.html,, 2012) typically focus on organizational objectives such as improved performance, competitive advantage, innovation, the sharing of lessons learned, integration and continuous improvement of the organization. KM efforts overlap with organizational learning, and may be distinguished from that by a greater focus on the management of knowledge as a strategic asset and a focus on encouraging the sharing of knowledge. It is seen as an enabler of organizational learning and a more concrete mechanism than the previous abstract research.
Table 1: Important research contributions to KM KM topics Generation Authors
Explicit, Tacit and Implicit
knowledge
1st Gen Polyani (1966); Nonaka and Takeuchi (1995) KM KM fundamentals
1st Gen Wiig (1993),Liebowitz &Beckman (1998) KM frameworks
2nd Gen Holsapple and Joshi (1997), Rubenstein et al.(2001) KM projects
2nd Gen Davenport et al. (1998) KM and AI
2nd Gen Fowler (2000),Liebowitz (2001) KM and decision support
3rd Gen Courtney (2001),Bolloju et al. (2002) KM software tools 3rd Gen Tyndale (2002),
The fishbone diagram of the article is shown below.
Knowledge Management
Definition
Functions & Benefis Models Theoretical
Perspective Source
Accessibility
Introduction
Philosophy
Network Application Case Study Process Cognitive
Quantum
Practice
2.Definition of Knowledge Management
There are several conflicting definitions and overlapping opinions of KM among researchers. However the central concept is still the same for all of them: managing the knowledge and encouraging people to share the same to create the value adding products and services (Bhatt, 2001; Chorafas, 1987; and Malhotra, 1998). KM is the systematic explicit and explicit management of vital knowledge and its associated processes of creating, gathering, organizing, diffusion, use and exploitation. It has been defined in a number of ways, but in general the idea relates to unlocking and leveraging the knowledge of individuals so that it becomes available to be used as an organizational resource. KM makes knowledge independent from the particular individuals. Different researchers have used different approaches to define KM in their literature. Singh et.al.(2006) classified them with different theoretical perspectives namely need of KM, What KM demands, KM practices, KM and IT, KM processes, and Holistic nature of KM. The present study classifies these KM definitions further into objectives of KM and strategy, KM and Intellectual Capital, and What KM can do.
There are variety of disciplines that have influenced and informed the field of KM thinking and praxis - prominent being philosophy, in defining knowledge; cognitive science (in understanding knowledge workers); social science (understanding motivation, people, interactions, culture, environment); management science (optimizing operations and integrating them within the enterprise); information science (building knowledge-related capabilities); knowledge engineering (eliciting and codifying knowledge); artificial intelligence (automating routine and knowledge-intensive work) and economics (determining priorities).Thus there exists a lot of working definitions of KM and embryonic philosophies circulating in the literature and around corporations of the world.
For some, KM is a "conscious strategy of getting the right knowledge to the right people at the right time and helping people share and put information into action in ways that strive to improve organizational performance" (O'Dell and Jackson, 1998, p.
4). For others, it is "formalization of, and access to, experience, knowledge and expertise that create new capabilities, enable superior performance, encourage innovation and enhance customer value" (Beckman, 1997, pp. 1-6). However, most working definitions in the literature focus on the common idea that KM can incorporate any or all of the following four components: business processes, information technologies, knowledge repositories and individual behaviours (Eschenfelder et al., 1998). With the aim of improving organizational productivity and competitiveness, these four permit the organization to methodically acquire, store, access, maintain and re-use knowledge from different sources. A consistent theme in all espoused definitions of KM is that it provides a framework that builds on past experiences and creates new mechanisms for exchanging and creating knowledg
3. Theoretical Perspective
The theoretical perspective to analyze knowledge management is concerned with defining and describing the fundamentals of KM (Alavi, M. and Leidner, D. ,1999). Because the KM discipline is still so young, we believe that presenting a variety of views is better than trying to describe the subject from just one or two perspectives. Based on the definition of KM, this section begins with the sources of knowledge. Then the characteristics and relationships between knowledge concepts are described (Alavi, M. and Leidner, D., 1999).
3.1. Source of Knowledge
It is important to note that knowledge can only be gained or obtained from outside sources or generated internally. Even though knowledge is available from outside or internal sources, it generally originates within individuals, teams, or organization processes. Once extracted it may be stored in a repository to be accessed and shared by other individuals or groups within an organization. Davenport and Prusak suggested five types of knowledge that correspond to the source of each: Acquired knowledge comes from outside the organization. Dedicated resources are those in which an organization sets aside some staff members or an entire department (usually research and development) to develop within the institution for a specific purpose. Fusion is knowledge created by bringing together people with different perspectives to work on the same project. (Apurva Anand et al. / International Journal of Engineering Science and Technology) (IJEST) Adaptation is knowledge that results from responding to new processes or technologies in the market place. Knowledge networking is knowledge in which people share information with one another formally or informally.
3.1.1. Knowledge Dimensions
There are many aspects around which knowledge can be described. In this paper, several characteristics of knowledge will be discussed such as storage, media, accessibility, hierarchy and difference between data, information and knowledge. In addition, some definitions of KM will be considered for taking a more in depth look.
3.1.2. Knowledge storage media
There are several storage media in which knowledge can reside. The best known can be human mind, organization, document, and computer as shown in figure1. Knowledge in the human mind is often difficult to access; organizational knowledge is often diffuse and distributed; document knowledge can range from free text to well-structured charts and tables; computer knowledge is formalized, sharable, and often well-structured and well-organized.
Difficult
To access Human
Mind Diffuse and
distributed
Organization Formal and
sharable Computer
Free text or well structured
Document
Figure 1: Knowledge storage media and its features
3.2. Knowledge Accessibility
There is the dimension of knowledge accessibility (Apurva Anand, M.D.Singh, 1999).
Nonaka and Takeuchi have divided accessibility into two categories: tacit and explicit.
Y et, in many books it is viewed that there may be three stages of accessibility: tacit,
implicit, and explicit. Accessibility can be mapped to storage media. Knowledge gains
in value as it becomes more accessible and formal.
3.2.1. Tacit Knowledge
Tacit knowledge is knowledge that cannot be expressed (See Figure 2). As ( Michael
Polanyi, 1988) the chemist turned- philosopher who coined the term put it, "We know
more than we can tell." Polanyi used the example of being able to recognize a
person?s face but being only vaguely able to describe how that is done. What we
recognize is the whole or the gestalt and decomposing it into its constituent elements
so as to be able to articulate them fails to capture its essence. Reading the reaction on
a customer?s face or entering text at a high rate of speed using a word processor offer
other instances of situations in which we are able to perform well but unable to
articulate exactly what we know or how we put it into practice. In such cases, the
knowing is in the doing, a point to which we return.
Tacit Knowledge Human Mind And Organization Accessible indirectly
only with difficulty
through knowledge
Elicitation and
observation of
behavior.
Storage Mediate Feature Figure 2: Tacit Knowledge
3.2.2. Implicit Knowledge
Implicit knowledge is knowledge that can be expressed (See Figure 3). Its existence is
implied by or inferred from observable behavior or performance. This is the kind of
knowledge that can often be teased out of a competent performer by a task analyst,
knowledge engineer or other person skilled in identifying the kind of knowledge that
can be articulated but hasn?t. In analyzing the task in which underwriters at an
company processed applications, for example, it quickly became clear that the range
of outcomes for the underwriters? work took three basic forms: they could approve the
application, they could deny it or They could counter offer. Y et, not one of the
underwriters articulated these as boundaries on their work at the outset of the analysis.
Once these outcomes were identified, it was a comparatively simple matter to identify
the criteria used to determine the response to a given application. In so doing, implicit
knowledge became explicit knowledge.
Implicit Knowledge Human Mind
And
Organization Accessible through querying and discussion, but informal knowledge must first
be
located and then
communicated Storage Mediate Feature
Figure 3: Implicit Knowledge
3.2.3. Explicit Knowledge
Explicit knowledge, as the first word in the term implies, is knowledge that has been
expressed and captured in the form of text, tables, diagrams, product specifications
and so on (See Figure 4). In Harvard Business Review article titled "The Knowledge
Creating Company”. Ikujiro Nonaka refers to explicit knowledge as "formal and
systematic" and offers product specifications, scientific formulas and computer
programs as examples. An example ( Aune, B., 1970) of explicit knowledge with
which we are all familiar is the formula for finding the area of a rectangle (i.e., length
time?s width). Other examples of explicit knowledge include documented best
practices, the formalized standards by which a claim is adjudicated and the official
expectations for performance set forth in written work objectives ( Barton- Leonald,
D., 1995).
Explicit
Knowledge Document And Computer Readily accessible, as well as documented into
formal knowledge
sources that are
often
well-organized. Storage Mediate Feature
Figure 4: Explicit Knowledge
4. Models of Knowledge Management
Literatures and praxis reveal that there are as many KM models as there are practitioners and theorists alike. However, a cognitive model of KM is receiving considerable attention in the literature and praxis (Swan and Newell, 2000). Other models, such as network, community (Swan and Newell, 2000), and philosophical are also receiving attention. With advances in quantum physics, the quantum perspective is also emerging. Each model treats knowledge in its own particular way; thus, has different KM approaches (Swan and Newell, 2000). These models are discussed in the following.
4.1 Philosophy-based model of KM
The philosophical model is concerned with the epistemology of knowledge or what constitutes knowledge. The main concern of it is how to collect information about social and organizational reality and focus on objectives (values, abstractions, minds), types (concepts, objects, prepositional) and the sources of knowledge (perception, memory, reason). It is also concerned with the relationship of knowledge to other notions such as certainty, belief justification, causation, doubt and revocability.
The philosophical model of KM is an attempt to think deeper on how one thinks and acts by posing deep-knowledge questions about knowledge within organizations (Murray, 2000). The model provides a high-level strategic overview and creates a valuable framework of understanding, which informs later knowledge initiatives.
Polanyi (1966) treats tacitness and explicitness as two different dimensions of knowledge. Hence, "all knowledge is either tacit or rooted in tacit knowledge" (Polanyi, 1966, p. 7) and, as such, is human activity. The philosophy-based KM model is based on interactive dialogues within a strategic context. Numbers of international research studies conducted by the Cranfield School of Management (Murray and Myers, 1997; Kakabadse and Kakabadse, 1999) show that the philosophy-based model of KM is practised by top teams in learning organizations; where the environment is conducive to an open, quality dialogue. Due to its higher level of operationally - strategic organizational capacity and its focus on dialogue, top teams have a very low dependency on technology. The model holds that KM does not need to be technology intensive or technology driven. It is actor intensive and actor centered actually. It is based on the Socratic definition of knowledge and a searc h for the highest knowledge - wisdom (Plato, 1953).
4.2 Cognitive model of KM
Leading management and organizational theorists summarized the concept of knowledge as a valuable strategic asset because for an organization to remain
competitive it must efficiently and effectively create, locate, capture and share knowledge and expertise in order to apply that knowledge to solve problems and exploit opportunities (Winter, 1987; Drucker, 1991; Kougot and Zander, 1992). For the cognitive model of KM, knowledge is an asset; it is something that needs to be accounted for and a number of efforts are being made to develop procedures for measuring it (Sveiby, 1997; Swan and Newell, 2000). Knowledge is seen as something that needs to be managed (Dodgson, 2000, p. 37). This model builds particularly on definition of knowledge by Schank and Abelson (1977), Holliday and Chandler (1986), and Edvinsson and Malone (1997).
Some researchers argue that the cognitive model of KM probably more applicable to the re-utilization of knowledge; exemplified by instances when a new technology has been effectively adopted by an organization and becomes embedded within organizational practices and routines so that it is an accepted part of the organizational culture (Clark and Staunton, 1989; Swan and Newell, 2000). The organizational focus is to ensure the efficient exploitation of the technology, which is achieved by making explicit the rules, procedures and processes surrounding its use. It makes extensive use of, and is dependent on, databases, group ware, and enterprise and Web-based systems (McKinlay, 2000). Cognitive models of KM are integrative or controlling in approach, operating predominately at the operational level (McKinlay, 2000).
4.3 Community of practice model of KM
One of the oldest models of KM is community of practice (CP), which receives revival and recognition within contemporary organizations. The CP model of KM builds on the sociological and historical perspective. Kuhn (1970, p. 201) argued that scientific knowledge is "intrinsically the common property of a group or else nothing at all". Others expanded this assertion and argued that all knowledge, not just scientific knowledge, is founded in the thinking that circulates in a community (Rorty, 1979; Barabas, 1990). Barabas (1990, p. 61) argues that "there is no universal foundation for knowledge, only the agreement and consensus of the community".
The term "community of practice" was coined in the context of studies of traditional apprenticeship (Lave and Wenger, 1991). A CP model is widely distributed and can be found at work, at home or amongst recreational activities. The model assumes the sense of joint enterprise that brings members together, relationships of mutual engagement that bind members together into a social entity and the shared repertoire of communal resources that members have developed over time through mutual engagement (Wagner, 2000). Members of a community of practice are informally bound by the values they find in learning together and from engaging in informal discussion to help each other solve difficult problems. In organizations, community of practice arises as people address recurring sets of problems together. By participating in a communal manner, they can do the job without having to remember everything
themselves (Wagner, 2000). Because membership is rely on participation rather than on official status, community of practice is not bound by organizational affiliation. Some contend that the CP model is also particularly important for selection and implementation activity which require that explicit knowledge be re-interpreted, re-created and appropriated alongside locally-situated, contextually-specific, often tacit, knowledge about organizational practices and processes (Wilson et al.,1994; Swan and Newell, 2000). These episodes require actors with relevant tacit knowledge and expertise to work together, re-creating and applying transferred information in new and appropriate ways at the local level (Swan and Newell, 2000). However, the engagement of actors with relevant tacit knowledge (Wilson et al., 1994), the development of social cultures and communities of practice, the social construction of new meanings and understandings (Weick, 1995) and the politics of decision making and change (Scarbrough and Corbett, 1992) need to be conducive to the CP approach (Swan and Newell, 2000).
4.4 Network model of KM
The networking perspectives of KM emerge parallel with the theories of the network organization and focus on acquisition, sharing and knowledge transfers. Network organizations are considered to be characterized by horizontal patterns of exchange, interdependent flow of resources and reciprocal lines of communication (Powell, 1990). From the network perspective, the idea of knowledge acquisition and sharing is seen as a primary lever for organizational learning in order for an organization to choose and adopt new practices where relevant (Everett, 1995). The network perspective acknowledges that individuals have social as well as eco nomic motives and that their actions are influenced by networks of relationships in which they are embedded; hence the socialization of knowledge (Swan and Newell, 2000).
Daily sharing of knowledge goes on in and amongst most organizations, of course, and in geographically-dispersed companies some of this has been a practice for many years (Hayes, 2001). Knowledge managing is perceived as collaboration that requires special collaborative and networking skills, with less emphasis on individual achievement and more on teamwork. IT-tools are seen as complementary facilities providing access to other knowledge and/or other databases. In praxis, this model aligns with strategic alliances and IT-networks perspectives (Swan and Newell, 2000). Network models of KM are integrative in approach as they try to develop networks structures and a way to control flow of information. It has the strategic intention of tapping across levels within organization and industry (Swan and Newell, 2000).
4.5 Quantum model of KM
The quantum perspective builds on the work of quantum physics, emergent quantum technology and consequential economy. It assumes that current information and communication technology will fundamentally change when built using quantum principles. Quantum computing will be able to make rational assessment of an almost infinite complexity and will provide knowledge that will largely make sense to people (Tissen et al., 2000). In order to cope with new levels of complexity and decision-making, actors will not just need knowledge but meaningful knowledge or, in Aristotilian terms, wisdom. It allows multiple-reality decision making in business situations where paradoxes prevail and human-level decision making falls short (Tissen et al., 2000).
Quantum model of KM is largely dependent on quantum computing and the assumption that most intellectual work will be performed by IT-based tools. The quantum model of KM is simultaneously integrative and interactive of operations at all levels of organization - hence, solving complex, conflicting and paradoxical problems in a way that is beneficial to shareholders, stakeholders and society.
5. Functions and Benefits of Knowledge Management Technology plays an important role in KM, but they cannot be overemphasized. In the KM process, human beings are at the center and technologies are serving as auxiliary tools (Lu and Liu, 2008). Nevertheless, technologies are still an essential tool for KM implementation and for support of human activities in organizations and enterprises as well.
Basically, KM technologies must provide certain functions in KM life cycle functionality: (1) Acquisition and capture; (2) Organization and storage; (3) Retrieval;
(4) Distribution and presentation; and (5) Maintenance. KM's major objective is to connect people and stimulate collaboration. The overall architecture and functionality must support this at all times. The ability to capture and manage human-added values makes IT particularly suited to dealing with knowledge (Gottschalk, 2005).
Alavi and Leidner have developed a framework to understand functions of IT in KM processes through the knowledge-based view (Alavi and Leidner, 1999). One important implication of this framework is that each of the four knowledge processes of creation, storage and retrieval, transfer, and application can be facilitated by IT. For instance, IT can increase knowledge transfer by extending the individual's reach beyond the formal communication lines and by enabling knowledge workers to share information from various sources; IT can also support knowledge application by embedding knowledge into organizational routines, and enhance the speed of knowledge integration and application by codifying and automating organizational routines.
As to the benefits of KM technologies throughout organizations, Gilbert et al. mentioned four (Gilbert, 2007): (1) Technologies allow users to access knowledge content in context of the situation and process where they need it –to promote fast and easy retrieval; (2) Technologies align processes to support knowledge creation and knowledge use; (3) Technologies automate the knowledge life cycle, and thus they help ensure that content is produced in a timely way and maintain the quality and relevance of the knowledge base over time; and (4) Use the service management system to query data and visualize knowledge-related and knowledge-impacted operational and performance metrics, providing feedback, as relevant, to IT users, management and executives.
In a study on efficiencies from KM technologies in a military enterprise concludes two functions of KM technologies (Schulte, 2006): (1) They help improve performance through increased effectiveness, productivity, quality and innovation; and (2) They increase the financial value of the enterprise by leveraging human capital.
According to Terra, KM has seven dimensions: strategy, culture and organizational values, organizational structure, human resource skills, information systems, measuring and environmental learning (Terra, 2000). Therefore, IT is only one of the dimensions of KM, and technologies do not transform information into knowledge on their own. The ultimate challenge of KM is to increase the chances of innovation through knowledge creation. The role of IT in this context is to exte nd human capacity of knowledge creation through the speed, memory extension and communication facilities of technologies (Zack, 1999).
Studies indicate that KM technologies are adapting their functions in organization shifting from knowledge processing enablers to KM processes enablers, driving towards business enablers
6.Application
6. 1 Knowledge Management Process
Since that KM is complex ( Bassi, L.J.,1997 ), heterogeneous area. Our objective will be precisely to review the different KM process with the aim to understand the different steps involved within it. This study considers a total seven approaches: Wiig, Meyer & Zack, Mc Elory, Bukowitz & Williams, Wong & Aspinwall, Lee et.al. and Dagnfous & Kah. As observed by prior researchers, most small and large organizations practicing any KM would need to participate in each of these KM processes, at least to some extent. Overall KM process can be divided into four main processes and these four processes can be further classified into sub-processes ( Argyris, C., 1993), (See Figure 5).
?Knowledge capture and creation.
?Knowledge organization and retention.
?Knowledge dissemination.
?Knowledge utilization.
Knowledge capture and Creation Knowledge Organization
and Retention Knowledge Dissemination Knowledge Utilization Figure 5: KM Processes
Knowledge capture and creation (Beckman, T., 1999) is a process in which knowledge identification, capture, acquisition, and creation is done. Knowledge organization and retention is a process in which knowledge in tacit form may be codified in an understandable form to the extent possible (Millar et al., 1997). After doing this knowledge needs to be categorized, and stored in repositories in a standard format for later use. Knowledge dissemination is a process which involves knowledge sharing among all within the organization both of tacit and explicit form. A combination of incentives and a cooperative culture are the main supporting factors of knowledge dissemination (Morris & Empson, 1998). Knowledge utilization ( Apurva Anand, M.D.Singh, 1990) is a process of the application and use of knowledge in the organization value-adding process.
6.2 Case Study
The application of knowledge management technologies in education is a typical example. The available knowledge management technologies include data mining, case-based reasoning, information retrieval, topic maps (Li Y ang, 2007). We will
evaluate these technologies in the following with respect to the presented characterization scheme.
6.2.1 Data mining
Han, J.and Kamber, state that data mining is the identification of specific patterns to extract knowledge which is embedded within databases. This view is not fully precise. The consideration of data mining applications like market basket analysis, fraud detection, or risk analysis leads to the thought that data mining functionalities enrich data through the identification of patterns or classes in such a way that a man familiar with the domain is capable of deriving a meaning from the presented results. Hence, specific domain information is generated which can then be combined with other information and knowledge to create new knowledge. So we can call data mining as …data approach?.
With respect to education, students who have to learn how to select information from lots of materials can improve the efficiency of identifying interesting information objects through the skills of classification and association analysis which are basic skills of data mining.
6.2.2 Case-based reasoning
Compared to data mining, Riesbeck, C. K., and Schank, R. C think case-based reasoning is a concept which targets information rather than data. A case provides the solution to some problems which can basically be viewed as providing specific domain information.
Case-based reasoning comprises certain functionalities which allow the emulation of cognitive processes in order to generate solutions. These functionalities make us adapt old cases to suit the needs of new cases and a system enlarges its case base by evaluating and retaining cases which have either been solved, or just provide information about faults. We can put the case-based reasoning approach and these functionalities together, and then apply the general domain knowledge to a model to improve case storage and retrieval, so that we can get a certain context which contains the different cases.
The context has a set of features which are used to index a case and to determine similarity between different cases (Riesbeck, C. K., Schank, R. C., 1989). Thus, features are descriptors of information objects and the corresponding context. On the other hand, there are also case-based reasoning systems which do not have an underlying domain model, like those which use the textual case-based reasoning approach (Aamodt, A., Plaza E., 1994).
Gaines and Shaw (1986) argue that in the case of technology and innovations it seems that the past is not appropriate for predicting the future, but students can learn much more from the old cases. For example, the experience which a student acquires from
an experiment can help him to do excellently in some new experiments.
6.2.3 Information Retrieval
Smolnik, Kremer, and Kolbe, argue that although information itself comprises content and context, the context is interwoven with the content and thus it is difficult to explicate. As a result, technologies which do not include additional explicit contextual information just rely on their content or their representation to be used in search functionalities. Clearly, this is true for most
existing information retrieval conceptual models. Since information retrieval targets raw information, we can call information retrieval as …information approach?.
On the other hand, one can argue that some forms of information retrieval also integrate explicit contextual information into search and retrieval methods. While Smolnik, Kremer, and Kolbe state that “authors have to provide [explicit contextual] information at the time of creation” , the consideration of the concept of aboutness , as introduced by Ingwersen, allows an additional perspective.
6.2.4 Topic Maps
Topic maps provide methods with which to navigate associatively across large amounts of available information in a conscious manner, enabling a systematic identification of information and creation of new knowledge by the users. In this way, it is possible by detaching the information source from the context used to find the information which results in topic maps being “information assets in their own right, irrespective of whether they are actually connected to any information resources or not ” (Ingwersen, P., 1992). Moreover, topic maps support “managing the meaning of the information, rather than just the information” (Rath, H. H., Pepper, S., 1999).
Topic maps provide a good tool for assisting and enhancing many types of thinking and learning. Firstly, with topic maps, students develop the habit of reflective learning on the processes of self-examination. Students reflective what they have learned and write down the main idea which may be a word, a phrase, or an idea.
On the other hand, topic maps can help students to establish the existing cognitive structure, to incorporate the details of the different lessons, and to restructure the students?knowledge and help them learn the details of the lesson (Garshol, L. M., 2002). So the students can have an active process.
Table 2 The roles of information management technologies in education Items Roles in education
Data mining Helping students to select information from a lot of materials;improving the efficiency of identifying interesting information objects
Case-based reasoning Helping students to learn much more from the old cases;doing benefit to students when developing innovative echnologies or learning new knowledge
Information Retrieval Helpin students to retrieve raw information and make information more explicit
Topic Maps Helping students to have reflective learning; being useful for raising questions and analyzing; helping students to master comprehensive knowledge
(Wu Kebao, Dai Junxun, Wuhan, Hubei, 2008
7.Conclusions
Organizations need to resolve conflict between the drive for knowledge management or co-modification of knowledge and learning and generation of knowledge. This conflict also propagates itself into a conflict between "innovation" and "productivity", "change" and "experience" - the "productivity dilemma" (Clark et al., 1987) and acknowledged by economists as a tension between "dynamic" and "allocative" efficiencies and by organizational theorists as a tension between "exploration" and "exploitation" (Dodgson, 1993). Knowledge management is about exploitation whilst "knowledge" is all about exploration. Existing knowledge and cognitive structures it engenders are continually challenged by new knowledge that does not fit in old structures but is, eventually, integrated - creating new cognitive structures and having new impacts.
The knowledge debate is emerging from an individual-knowledge focus in the 1970s and 1980s to a group-knowledge focus in the 1990s and 2000s. Similarly, the debate is moving from the focus about the generation, as opposed to the transfer, o f explicit knowledge which appears to have been overwhelmed by the emphasis on tacit knowledge implied in what has become known as "the action turn" (Reason, 1998). However, this shift of emphasis from explicit knowledge to tacit knowledge overlooks the issue of how tacit and explicit knowledge interact - "the generative dance" (Cook and Brown, 1999).
A purposeful action inquiry into knowledge praxis may draw upon the tools of extant group or individual, tacit or explicit knowledge generated (Cook and Brown, 1999). Hence, the clear constructs of knowledge and knowing action have the potential for wider application and further research. In particular, the need for a enhanced understanding and models of how essential non-transferable knowledge and knowing can be generated within organizations, and how education programs can be re-designed to facilitate subsequent knowledge generation as part of professional practice.
Managing knowledge is not the same as managing human resources - it is more multi-faceted than simply managing people; it also involves managing intellectual property rights and the development and transfer of individual and organizational know-how (Teece, 2000a). In addition, issues such as learning capacity, rooted in education, experiences, social, professional, structural and cultural contexts, equally need to be addressed (Teece, 2000a).
References
[1] Alavi, M. and Leidner, D. (1999).Knowledge Management systems: Emerging views and practices from the field. In International Conference on System Sciences, 239.
[2] Alavi, M., & Leidner, D. (1999). Knowledge Management systems: issues, challenges and benefits. Communications of the Association for Information Systems.
[3] Albena, A, & Elissaveta, G. (2006). Insight into practical utilization
of knowledge management technologies. IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing, 169-174.
[4] Awad, M.A., & Ghaziri. H.M. (2004). Knowledge Management. Pearson Education International, Upper Saddle River, NJ.
[5] Beckman, T. (1999). The current state of Knowledge Management. In Liebowitz, J., Knowledge Management Handbook, CRC Press, FL.
[6] Beckman, T. (1997).Methodology for Knowledge Management..In Harmza,
M.H.(Eds), the IASTED International Conference on Artificial Intelligence and Soft Computing, Banff, IASTED ACT Press, 29-32.
[7] Beijerse R.P.U. (1999). Questions in Knowledge Management: defining and conceptualizing a phenomenon. Journal of Knowledge Management, 94-109.
[8] Bhatt, G.D. (2001). Knowledge Management in organizations: examining the interaction between technologies, techniques and people. Journal of Knowledge Management,68–75.
[9] Carneiro, A. (2001). The role of intelligent resources in Knowledge Management. Journal of Knowledge Management, 5(4), 358-67.
[10] Chase, R. L. (1997). The Knowledge-Based Organization: An International Survey. Journal of Knowledge Management, 1(1), 38-49.
[11] Chua, A. (2003).A framework for Knowledge Management implementation. Journal of Information & Knowledge Management, 2(1), 79-86.
[12] Dalkir, K. (2005). Knowledge Management in Theory and Practice, Burlington: Elsevier Butterworth-Heinemann.
[13] Gilbert, P., Morse, R., & Lee, M. (2007). Enhancing IT support with knowledge management.
[14] Gottschalk, P. (2005). Strategic knowledge management technology, Idea Group Inc. (lGI), 95-98.
[15] Liao, S. (2003), Knowledge Management technologies and applications –literature review from 1995 to 2002. Expert Systems with Applications, 25, 155-64.
[16] Lu, R., & Liu, J. (2008). The research of the knowledge management technology in the education. Proceedings of the 2008 International Symposium on Knowledge Acquisition and Modeling (KAM 2008), 551-554.
[17] Malhotra, Y. (1998). Deciphering the Knowledge Management type. Journal of Quality and Participation, 21(4), 58-60.
[18] Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5, 14-37.