A Taxonomy of Knowledge Maps in Business Application - CiteSeerX

A Taxonomy of Knowledge Maps in Business Application Yu-Hui Taoa＊, Yu-Lung Wub, Jih.-Kun Lic a

National University of Kaohsiung, [email protected] b I-Shou University, [email protected] c ASE Inc., [email protected]

Abstract Knowledge Maps (K-Map), as a format of corporate taxonomies, can be used to increase the efficiency and benefits of organizational Knowledge Management (KM). Nevertheless, K-Map is often mentioned in the literature as an auxiliary concept in KM context such that very few business practice details are available. Moreover, existing descriptions and applications of K-Map are generally high-level and somewhat scattered, leading to a rather diverging and thus confusing impression. This research aims at clarifying the confusing concept of K-Map by forming a taxonomy of business applications based on an in-depth review of K-Map literature. Justification and application of the proposed taxonomy are provided. Suggestions and future work are also discussed alone with the analysis. Key Words: Knowledge Management, Knowledge Map, Literature Review

INTRODUCTION Knowledge Management (KM) is very diverse in theories and applications, such that a taxonomy of seven schools of KM strategies has been proposed (Earl, 2001), and a regional difference in strategic selection of KM has been observed (Chase, 2002). Despite its diversity, KM is highly regarded and discussed in the literature and thus is adequately perceived by the general audience. On the other hand, Knowledge Map (K-Map) as well as other important

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Corresponding author. 1

KM components are less mentioned or often discussed as an auxiliary in KM literature, which concept remains somewhat unclear or confusing in the KM domain. K-Map is brought to researchers’attention by the need to increase transparency and reduce knowledge complexity of KM (Hellström and Husted, 2004). Duffy (2000) clearly stated that K-Map, as a component within the KM structure, integrates a series of product and knowledge near the front end as an interface service. Thus, the contribution of K-Map has been suggested by Lutters et al. (2000) as a feasible tool and method to achieve a goal in rationally allocating and use of organizational resources such as expert capability. From a more comprehensive view of knowledge scope, K-Map was appraised by Arthur Andersen (1998) as a means to “ capture and share explicit knowledge and serve as visual pointers to the holders of implicit knowledge” . Consequently, the importance of K-Map can be seen in many literatures in recent years. For example, Despres and Chauvel (1999) commented that “ knowledge management is the map is the knowledge management” . As a matter of fact, a decade ago Davenport (1994) had already recommended a similar concept called Information Map (I-Map), which is suggested as a solution to companies with inadequate databases while having tremendous data flows inside their organizations, such that no employees know how to find information. Therefore, both K-Map and I-Map imply that there is a missing link between

organizational

information

or

knowledge

and

the

desperate

internal

information/knowledge seekers. Not only K-Map is promoted to be important, its application has spawn from educational experiments (McCagg and Dansereau, 1991; O’ Donnel, 1994; Hall and O’ Donnel, 1996; Bahr and Dansereau, 2001) into various business domains (Smart 1998), including decision making (Brown, Curley and Benson, 1997), healthcare (Birnbaum and Somers, 1998), intellectual capital in manufacturing (Heng, 2001), construction industry (Woo et al., 2004) and tourism (Pyo, 2005). Recently, these K-Map applications have also been extended into the Internet environment (Nagai et al., 2002; Chung, Chen and Nunamaker, 2003), and their 2

representation and access have been evolving from normal 2D maps (Becks, Seeling and Minkenberg, 2002) to 3D maps (Beck, Boyack, Bray and Simens, 1999). Nevertheless, despite of its importance and wide applications, only 30% of the organizations had created a K-Map (KPMG, 2000). The fact that K-Map adoption is second to the last in the 15-item of KM implementation (KPMG, 2000) is an issue worthy of concerns. K-Map practice entails multiple aspects such as the approach for creation, scope of coverage, level of structure, and target of mapping. The literature has contributed to a various extent in each aspect. In the creation aspect, a K-Map can be created via an automatic creation tool from a bottom-up approach (Lin and Hsueh, 2002) or a top-down approach initiated from the organizational vision, missions and strategies (Vail, 1999; Arthur Andersen, 2000). In the scope aspect, K-Map can be designated for a specific domain or department (Terry, 2003), or an organization-wide coverage (D’ Amor e ,Konc ha dyand Obrst, 2000). In the structure aspect, it can be a multi-layered knowledge structure (Leathrum et al., 2001) or a simple one-layer structure (Gordon, 2000). In the mapping aspect, K-Map can target competency maps, concept maps, strategy maps, causal maps, cognitive maps, or a combination of the above (Wexler, 2001). The co-existence of various K-Map aspects in the literature has resulted in a somewhat confusing impression to the general audience. As an important but confusing concept, K-Map needs to be further clarified before it can be appropriately promoted for adoption in KM community. A taxonomy of existing K-Maps like what Earl (2001) has done on KM is a preferred solution because K-Map perception in practice can then be assessed against this taxonomy and K-Map deployment strategies can be formulated. Accordingly, this research attempts to generate a K-Map taxonomy via an in-depth literature review and analysis. The remaining of this paper is organized to present K-Map literature review, K-Map taxonomy, and conclusion.

REVIEW OF K-MAP 3

In order to comprehend existing K-Maps, this review section encompasses K-Map characteristics and roles, types of K-Map, its contents, and its relationship with KM in the literature. Characteristics and Roles of K-Map Gordon (2000) depicted K-Map as the information for supporting knowledge and experiences in order to connect the knowledge representation. In addition, K-Map has also been defined to have knowledge capturing and sharing characteristics, particularly for explicit knowledge (Arthur Andersen, 1998). The interface service of K-Map described by Duffy (2000) shared both views in knowledge representation, capturing and sharing. In addition to the characteristics within the KM process, K-Map has a unique characteristic of spatial visualization. For example, Vail (1999) indicated that K-Map contributes to concrete expression of organizational knowledge as well as visual presentation of information retrieval. So it is called the visual pointer to the holders of implicit knowledge by Arthur Andersen (1998). Although Davenport & Prusak (1998) agreed with the above K-Map characteristics, they explicitly stated that K-Map does not contain the knowledge itself because it is a guide rather than a repository. The major implication of Davenport and Prusak is a broadened candidate pool of K-Map that includes conventions that may have already been in use in organizations, e.g., it can either be “ an actual map, knowledge yellow page or a cleverly constructed database” From the perspective of KM’ s role, Lutters et al. (2000) pointed out that K-Map is a feasible tool and method for achieving two key successful factors of a KM organization reasonable resource allocation and operation. Chen and Yang (2003) affirmed the “ tool” concept by expressing K-Map as an organizational knowledge inventory chart when conducting a knowledge audit. From a higher level of thinking, Tiwana (2000) treated K-Map as a utility for viewing the strategic position within the organization and the industry. Vail (1999) further raised the level from strategies to the organizational vision and missions. In 4

fusion, Chu (2001) agreed with Vail (1999) in that K-Map should be deployed from the higher level of organization vision, missions, and strategies. In particular, a K-Map strategy can be formed by addressing the analyzed knowledge-gap in order to conduct appropriate personal training and knowledge acquisition. In summary, K-Map is beneficial to KM process in knowledge capturing, sharing as well as the visual representation. Because K-Map is a guide instead of a repository, any existing visual representation of knowledge can be a potential candidate, even without a direct link to the knowledge database. Moreover, K-Map can be a top-down strategy deploying tool or method, starting from organizational vision and missions to form appropriate strategies in KM practice.

Type and Structure of K-Map

Figure 1. Static K-Map (Arthur Andersen, 1998)

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Figure 2. Dynamic K-Map (Arthur Andersen, 1998) Among the K-Map references, only a few actually classified K-Map into types. Arthur Andersen (1998) thought knowledge can be shared and learned among different organizational levels and employee backgrounds. Therefore, they classified K-Maps into static and dynamic types, in which a static K-Map, as seen in Figure 1, can be developed by cross-functional managers in one day while the dynamic K-Map, as seen in Figure 2, can be identified to express the relationship of intra-organizational knowledge from the organizational level perspective. The purpose of the dynamic K-Map is to assist the CIO in organizing a consistent framework of various information technology system components. An effective dynamic K-Map not only can control and display the relationship of abstraction levels of knowledge, but also utilize graphical user interface (GUI) to compose more detailed information mapping. In addition, the relationship of knowledge must be displayed electronically to connect to both internal and external knowledge, such as domain experts, as an index. According to Logan & Caldwell (2000), K-Map can be categorized into conceptual, process and competency K-Maps. A conceptual K-Map consists of a few topics or concrete issues to express internal organizational knowledge and the relationship between the knowledge sources while a process K-Map is used to describe activities of organizational 6

schedule or procedures, containing mainly declarative knowledge and procedural knowledge. Competency K-Map, on the other hand, is used to describe the relationship between knowledge and people. Probst, Raub and Romhardt (2000) divided K-Map into topography, assets and source maps depending on different emphases. A knowledge topography map is a tool for defining knowledge, including information such as whom, what and how much. A knowledge asset map utilizes diagrams to record and present the locations as well as to organize, index and classify important knowledge assets for easier access by the employees. A knowledge source map utilizes diagrams to represent the experts with valuable knowledge from external environment and within the organization or its groups for a specific task. For example, Widhalm et al. (2001) applied co-occurrence analysis and visualized 2D K-Map to discover frequent subject and authors in science literature. Eppler (2001) referred five types of K-Maps in business context, including knowledge source maps, asset maps, structure maps, application maps and development maps. The source and asset maps are identical to the above descriptions of Probst, Raub and Romhardt (2000). A knowledge structure map outlines the global architecture of a knowledge domain and how its parts related to one another. A knowledge application map shows which type of knowledge has to be applied at a certain process stage or in a specific business situation as well as the pointers to the location of the specific knowledge. A knowledge development map depicts the necessary stages to develop a certain competence. Other than the K-Map type, there are references defined or described their own ideal K-Map structure, which are presented according to the managerial hierarchy below. Vail (1999) mentioned that all the key elements of a K-Map belong to the organizational intellectual capital. As seen in Figure 3, K-Map has a close relationship with business process and framework. In other words, a K-Map is built upon the business process and framework.

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Figure 3 K-Map Key Components (Vail, 1999) Leathrum et al. (2001) proposed a hierarchical K-Map framework, in which the K-Map builder can setup the K-Map levels based on practical needs. In their Intelligent Questioning Systems, there are three types of nodes as shown in Figure 3, including a “ map node”denoted by a rectangle, a “ question node”denoted by an oval, and a “ null node”denoted by a triangle. The map node defines a new K-map, which is also called a sub-map, and the question node and the null node are defined based on the special demands. Leathrum et al. applied K-Map in education domain to represent digital logic of learning flow with objectives and criteria as seen in Figure 5. For instance, before a test, learning chapters with both logical component and procedural components are needed.

Figure 4 K-Map Illustration (Leathrum et al., 2001)

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Figure 5 K-Map of Digital Logical Subject (Leathrum et al., 2001) In the knowledge network of Gordon’ s work (2000), the knowledge is composed of supporting knowledge and empirical data as presented in Figure 6. The purpose is to explicitly express the relationships between knowledge, supporting knowledge and empirical data with upper-lower level hierarchy, and guidelines of importance and difficulty.

Figure 6 Knowledge Network (Gordon, 2000)

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Figure 7 K-MapComponent (Kim et al., 2003) Kim et al. (2003) thought that a K-Map consists of two components, i.e., diagram and specification as depicted in Figure 7. The node represented by a rectangle indicates the knowledge acquired from business processes and the arrow expresses the interactions between nodes. Another component is used to present a description of a certain node, such as its name, number, author, consultant and brief content.

Figure 8 A Radar Diagram (Tsai, 2003) Tsai (2003) used a radar diagram as shown in Figure 8 to represent the distribution and strength of the knowledge, such that organizational knowledge can be evaluated. A business organization can implement or improve its KM implementation based on the distribution and strength of the radar diagram accordingly. As shown in Figure 9, the Legislative Yuan of 10

Taiwan, R.O.C has a news knowledge management system with a puzzle-like K-Map, which i nc or por a t e s“ s ubj e c tc onc e pt ” ,“ ne wst e r mi nol ogy ”a nd“ r e l a t e di nf or ma t i on”for one-shot news retrieval.

Figure 9 A Puzzle Map (The Legislative Yuan of R. O. C., 2005） Content of K-Map The K-Map by Vail (1999) covers internal and external knowledge, including documents, stories, diagrams, numbers, models and multimedia, and utilizes graphical representations to show complicated knowledge sources, such as models, specific software tools or database. The scope of knowledge sources listed by K-Map can include person, document and database. In particular, K-Map applications can also focus on a specific internal business area, such as the customer call center K-Map by Terry (2003), which is mainly to share the ideas, including information, knowledge, skills, employees and business. Any team member can add ideas to this K-Map that can be accessed by other members.

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C u s to m e rs In fo rm a tio n

K n o w le d g e

A g e n ts P e o p le

C u s to m e r C o n ta c t C e n te rs

B u s in e s s

T e c h n o lo g y C o m p u te r T e c h n o lo g y

T e le p h o n y

N e tw o rk T e c h n o lo g y

Figure 10 Customer Call Center K-Map (Terry, 2003) Comparing to a bigger scope of internal and external business knowledge, many educational K-Map focused on one specific but insightful domain knowledge: Nagai et al. (2002) developed a Web BBS system NakSun to broaden students learning vision, which was evaluated through the math courses. Saad & Zaghloul (2001) poi nt e d outt ha tt oda y ’ s education emphasized on text-based course content, which could not associate all key concepts and skills, and thus adopted a visualized tool to construct an iterative learner-instructor process via a map to present knowledge, concepts and skills. Gordon (2000) represented the gravitational attraction formula in a graphical representation, in which the components are individually presented. Relationship between K-Map and KM Among the five segments of KM defined by Duffy (2000), K-Map falls into the user interface segment, which is also a front-end component for integrating all products and knowledge portal for serving users. From a different perspective, Earl (2001) classified KM strategies into seven research schools. The cartographic school under the technocratic specifically focuses on maps and thus matches the fundamental K-Map concept. However, the systems and engineering under technocratic also match the K-Map spirits because they are based on information or management technologies to support to different knowledge 12

workers in their daily routines. The longest established systems school is to capture specialist knowledge in knowledge base while the cartographic school concerns the mapping of organizational knowledge called “ yellow page” , which targets particularly the knowledgeable people in the organization. The process school, on the other hand, is toward business process reengineering contributed by IT to provide shared databases to all knowledge workers throughout a process. Lawton (2001) referred the knowledge management framework of an European consulting firm OVUM as seen in Figure 11, in which K-Map is built up from a bottom-up approach. By analyzing the information and sources of documents in the bottom levels, the K-Map can be automatically generated for providing knowledge management services of upper levels. Two implementations of this bottom-up OVUM model can be seen in Knowledge Index System (Tornado, Inc., 2003）and SmartKMS (INTUMIT, 2003). Lawton (2001) treated the objects in this framework as the effective software tools for implementing KM. K-Map locates in level four of this framework, which belongs to a taxonomy method or software that can classify organizational knowledge into K-Map based on needs or features. Two observations require some explanations: First of all, although K-Map is not shown at the top level of the framework, the K-Map will present the information to the user via the interface on level six and application layer on level 7. Secondly, compared to the top-down approach of deploying K-Map by Vail (1999) and Tiwana (2000), this bottom-up approach does present a better mode for automatic tools of K-Map implementation. However, there is a need to fuse the K-Map development such that the management perspective determines what K-Map is for and the technology perspective supports how K-Map could be automatically generated. Accordingly, Rosser (1999) suggested that a knowledge map is “ to manually build a high-level structure, guided by enterprise usage and consistent rules or principles, and the use that framework to enable the subsequent classification task to be done through automated 13

means.”

Figure 11 KM Framework (Lawton, 2001) TAXONOMY OF K-MAP This work adopted the definition (Arthur Andersen, 1998) that K-Map is a graphical KM interface to capture explicit knowledge and serve as visual pointers to the holders of implicit knowledge. As concise as it can be, the overall K-Map impression is still a highly abstract and confusing concept. The multiple K-Map aspects each with a single-aspect classification scheme, as seen in Literature Review, further complicate the K-Map concept. Although K-Map is considered as a format of corporate taxonomies (Gilchrist, 2001), it is desirable to have a taxonomy of the K-Map corporate taxonomies, which is more comprehensive in coverage and sophisticated in structure for multiple populations (Woods, 2004). The three-layer taxonomy of K-Map is shown in Table 1, in which the top layer of application scope can be for organizational-wide, cross-functional, or individual applications. That means, deploying a specific K-Map is related to the scale of cohesive employees in a KM organization. In other words, the scale of cohesive employees can be individual level, 14

organization-wide level, or in between. Notably, the cross-functional scope means a team of employees from different functional areas involving in one or more work flows. The second layer contains six K-Map categories is as shown in the second column of Table 1, including association, tool, process, hierarchy, label, and index. These six categories are further divided into nine K-Map types: dynamic association, operational association, top-down tool, bottom-up tool, subject process, hierarchy, analysis and comparison label, directory index and arrangement index. To be more specific, these K-Map types are illustrated by the Figures appeared in Literature Review section in column 4, and to be complete, some K-Map types mentioned in listed references are also added to column 5 as shown in Table 1. As most taxonomies, it is really difficult to sort out a clear-cut layer structure that is pure independent between neighborhood layer items. Therefore, many instances can be found to simultaneous belong to more than two items in the same layer of application scopes, categories or types. A good example in Table 1 is the Competence Map (Logan and Caldwell, 2000) is shown in Dynamic, Hierarchical and Directory types as in the column 5.

Table 1 K-Map Taxonomy Application

Category

Type

Sample K-Map

Scope Organizational

Applicable K-Maps in other Listed References

Association

Dynamic

Figure 2 (Arthur Andersen, 1998) Competence Map (Logan & Caldwell, 2000)

Tool

Operational

Figure 10 (Terry, 2003)

Top-down Approach

Figure 3 (Vail, 1999)

Topology Map (Probst, Raub and Romhardt, 2000)

Cross

Process

Bottom up Approach

Figure 11 (Lawton, 2001)

Subject Process

Figure 5 (Leathrum et al., 2001)

Functional

Source Map (Probst, Raub and Romhardt, 2000); Process Map (Logan & Caldwell, 2000)

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Application

Category

Type

Sample K-Map

Applicable K-Maps in other

Scope

Listed References Hierarchy

Hierarchical

Figure 6 (Gordon, 2000)

Static K-Map (Arthur Andersen, 1998); Competence Map (Logan & Caldwell, 2000); Structure Map (Eppler, 2001)

Label

Analysis and

Figure 8 (Tsai, 2003)

Application Map (Eppler, 2001)

Figure 7 (Kim et al., 2003)

Competence Map (Logan and

Comparison Individual

Index

Directory

Caldwell, 2000); Development Map (Eppler, 2001); Asset Map (Probst, Raub and Ramhardt, 2000) Arrangement

Figure 9 (Legislative Yuan of R. O.C., 2002)

JUSTIFICATION We argue that the proposed K-Map taxonomy exerts four good quality for assisting the understanding of K-Map concept as follows: 1. Hierarchical representation. A taxonomy is basically a hierarchical organization of intended targets. As a result, the hierarchical K-Map taxonomy reenforces the suface meaning of K-Map. Because this layered representation gradually reveals the content of K-Map, the K-Map concept is better illustrated to be easier understood. 2. Multiple aspects. As indicated in Introduction section, K-Map has many aspects in such as approach for creation, scope of coverage, layer of structure, and target of mapping. Unlike some previous studies as surveyed in the Literature Review section that usually contains only single-aspect classification, our taxonomy covers as many as we could enumerate in the references as shown in Table 1. 3. Wide coverage. Although limited by what could be found in the Literature, our taxonomy is as complete as the existing literature. Most K-Map related references listed in this

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paper are covered. 4. Expandable structure. With wide literature coverage and multi-aspect nature, our taxonomy can be expanded as the literature grows. Because the hierarchical-layer and multiple-aspect structure, heterogeious K-Map classifications in future rererences can be encompassed without problems.

CONCLUSION The main objective of our hierarchical and multiple-aspect K-Map taxonomy is to clarify the K-Map definition via concrete theoretical and practical evidence from the literature. With such a K-Map taxonomy, appropriate K-Map deployment strategy can then be developed and assessed in KM organizations. We feel pretty comfortable with such an initial investigation on K-Map taxonomy. Its quality, however, depends heavily on the secondary data collected from the literature. To inspect more comprehensive and generalized propositions or hy pot he s e sf orf ur t he rwor ks uc ha s“ be ne f i t spe r c e i ve dbyt heK-Map tool to f a c i l i t a t eknowl e dges e a r c h” , more efforts are needed. The enhancements can range from more comprehensive data collection methods to a richer variety of data sources. For example, a focus group interview with industrial or consulting experts would be a good supplement to the second data analysis using only the literature. REFRENCES Arthur Andersen 1998. Knowledge mapping: Getting started in knowledge management, Accessed on August 15, 2003 from http://openacademy.mindef.gov.sg. Bahr, G. S., & Dansereau, D. F. 2001. Bilingual knowledge maps in second language vocabulary learning, The Journal of Experimental Education, 70(1): 5-24. Beck, D. F., Boyack, K.W., Bray, O. H. & Siemens, W. D. 1999. Bringing the fuzzy front end into focus, Sandia National Laboratories, Accessed on 2004/12/20 from 17

www.cs.sandia.gov/projects/VxInsight/pubs/chemtech99_prepub.pdf. Becks, A., Seeling, C. & Minkenberg, R. 2002. Benefits of document maps for text access in knowledge, in Proceedings of the 2002 ACM Symposium on Applied Computing, 621-626, Madrid, Spain. Birnbaum, D. & Somers, M. J. 1998. Mapping the terrain of hospital work: technological constraints in work design and redesign, International Journal of Technology Management, 15(3/4/5): 359-380. Brown, G. J., Curley, S. P. & Benson, P. G. 1997. Evoking information in probability assessment: Knowledge maps and reasoning-based directed questions, Management Science, 43(1): 1-14. Chase, R.L. 2000. Global Most Admired Knowledge Enterprises, Accessed on November 30, 2003, from http://www.knowledgebusiness.com. Chen, Z.-A. & Yang, R.-J., K. M. 2003. Technique Information ePaper-Knowledge Map/KM Implementation Column, Industrial Development, the Bureau of Ministry of Economic Affairs,

Taiwan,

R.O.C.

Accessed

on

December

27,

2004

from

http://proj.moeaidb.gov.tw/kmpp/enews/epaper920508.htm. Chung, W., Chen, H. & Nunamaker, J. F. 2003. Business intelligence explorer: A knowledge map framework for discovering business intelligence on the Web, in Proceedings of the 36th Hawaii International Conference on System Sciences. D’ Amor e ,R., Konchady, M. & Obrst, L., 2000. Knowledge mapping aids discovery of organizational information, The MITRE Advanced Technology Newsletter, 4(1): 8-9. Davenport, T. H. & Prusak, L. 1998. Working knowledge: How Organizations Manage What They Know, Boston, Harvard Business School Press. Davenport, T. H. 1994. Sa vi ngI T’ ssoul: human-centered information management, Harvard Business Review, March-April: 119-131. Despres, C., & Chauvel, D.

1999. Knowledge management(s), Journal of Knowledge 18

Management, 3(2): 110-120. Duffy, J. 2000. The KM infrastructure, Information Management Journal, 34:62-66. Earl, M. 2001. Knowledge management strategies: Toward a taxonomy, Journal of Management Information Systems, 18(1): 215-233. Eppler, M. J. 2001. Making knowledge visible through knowledge maps: Concepts, elements, cases, in Proceedings of the 34th Hawaii International Conference on System Sciences. INTUMIT, SmartKMS, accessed on 2003/8/16 from http://www.intumit.com. Gilchrist, A. 2001. Corporate taxonomies: report on a survey of current practice, Online Information Review, 25(2):94. Gordon, J. L. 2000. Creating knowledge maps by exploiting dependent relationships, Knowledge-Based Systems, 13: 71–7. Hall, R. H. & O’ Donne l ,A.M.1996. Cognitive and affective outcomes of learning from knowledge maps, Contemporary Educational Psychology, 21:94-101. Hellström, T., & Husted, K. 2004. Mapping knowledge and intellectual capital in academic environments- A focus group study, Journal of Intellectual Capital, 5(1):165-180. Heng, S. H. M. 2001. Mapping intellectual capital in a small manufacturing enterprise, Journal of Intellectual Capital, 2(1): 53-60. Kim, S., Suh, E., & Hwang, H. 2003. Building the knowledge map: An industrial case study, Journal of Knowledge Management, 7: 34-45. Leathrum, J. F. Jr., Gonzalez, O. R. & Zahorian, S. A. 2001. Knowledge maps for intelligent questioning system in engineering education, in Proceedings of the American Society for Engineering Education Annual Conference & Exposition, June 24-27, Albuquerque, NM. Lin, F.-R., & Hsueh, C.-M. 2003. Knowledge map creation and maintenance for virtual communities of practice, in Proceedings of the 36th Hawaii International Conference 19

on System Sciences, January 6-9, Big Island Hawaii. Logan, D., & Caldwell, F. 2000. Knowledge mapping: five key dimensions to consider, Gartner-Group. Lutters, W. G., Ackerman, M. S., Boster, J., & McDonald, D. W. 2000. Mapping knowledge networks in organizations: Creating a knowledge mapping instrument, in Proceedings of the Americas Conference on Information Systems, August 10-13, Long Beach, CA. McCagg, E. C., & Dansereau, D. F. 1991. A convergent paradigm for examining knowledge mapping as a learning strategy, Journal of Educational Research, 84(6):317-324. Nagai, M., Shiraki, K., Kato, H., & Akahori, K. 2002. The effectiveness of a Web bulletin board enhanced with a knowledge map, in Proceedings of the International Conference on Computers in Education, Auckland, New Zealand. O’ Donnel, A. M. 1994. Learning from knowledge management: the effects of map orientation, Contemporary Educational Psychology, 19: 33-44. Probst, G., Raub, S., & Romhardt, K. 2000. Managing knowledge-Building blocks for success, West Sussex: John Wiley & Sons, Ltd.. Pyo, S. 2005. Knowledge map for tourist destinations-needs and implications, Tourism Management, 26: 583–594. Saad, A., & Zaghloul, A.-R. M. 2002. A knowledge visualization tool for teaching and learning computer engineering knowledge, concepts, and skills, ASEE/IEEE Frontiers in Education Conference, Boston, MA. Smart, G. 1998. Mapping conceptual worlds: Using interpretive ethnography to explore knowledge-making in a professional community, The Journal of Business Communication, 35(1):111-127. Terry, R. D. Consulting services NW/customer facing systems, accessed on October 25, 2003 from http://www.cfsystems.org. The Legislative Yuan of Republic of China, accessed on January 8, 2005 from 20

http://nplnews.ly.gov.tw/index.jsp. Tiwana, A. 2000. The knowledge management toolkit. New Jersey, Prentice Hall. Tsai, C. 2003. Methodology for Constructing Corporate Knowledge Maps, Unpublished master thesis, National Sun Yet-sen University, Taiwan, R.O.C.. Tornado Inc., Knowledge Indexing System of Tornado, accessed on 2003/4/20 from: http://www.tornado.com.tw/b5. Vail, E. F. III. 1999. Knowledge mapping: getting started with knowledge management, Information Systems Management, Fall: 16-23. Wang, S. 2002. Knowledge maps for managing Web-based business, Industrial Management & Data Systems, 102(7):357-364. Wexler, M. N. 2001. The who, what and why of knowledge mapping, Journal of Knowledge Management, 5(3): 249-263. Widhalm, C., Gigler, U., Kopcsa, A., & Schiebel, E. 2001. Co-occurrence and knowledge mapping to identify hot topics and key players in the field of mobility and transport, International Society for Scientometrics and Informetrics, 40: 751-758. Woo, J.-H., Clayton, M. J., Johnson, R. E., Flores, B. E., & Ellis, C. 2004. Dynamic Knowledge Map: r e us i nge xpe r t s ’t a c i tknowl e dg ei nt heAECi ndus t r y , Automation in Construction, 13:203-207. Woods, E. 2004. The corporate taxonomy: creating a new order, KM World, 13(7):8-10.

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