E-Collaboration-Based Knowledge Refinement as a Key Success Factor for Knowledge Repository Systems

E-Collaboration-Based Knowledge Refinement as a Key Success Factor for Knowledge Repository Systems

T. Rachel Chung (University of Pittsburgh, USA) and Kwangsu Cho (University of Missouri, Columbia, USA)
Copyright: © 2008 |Pages: 7
DOI: 10.4018/978-1-59904-000-4.ch035
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Abstract

Electronic knowledge repository systems are fundamental tools for supporting knowledge management (KM) initiatives (Alavi, 2000; King, Marks, & McCoy, 2002). The KPMG Consulting Knowledge Management Research Report 2000 (KPMG, 2000) shows 61% of 423 firms surveyed in the United States and Europe have either implemented or expected to implement repository systems. A follow-up KPMG survey (KPMG, 2003) shows that more than 70% of the firms have either implemented knowledge repositories in the last 2 years or planned to implement them in the next 2 years. Compared to other IT systems for KM, repositories are one of the most widely implemented and used KM tools (KPMG, 2000).

Key Terms in this Chapter

Knowledge Source: An individual who provides content to a knowledge repository.

Knowledge User: An individual who accesses documents stored in a knowledge repository and applies the knowledge to his or her tasks.

Expertise Gap: The discrepancy between the knowledge source and user in their levels of expertise.

Decentralized Knowledge Refinement: A knowledge refinement approach that involves both experts and nonexperts in quality judgment and improvement processes.

Direct Refinement: A knowledge refinement process in which multiple participants refine and edit a codified document directly.

Indirect Refinement: A knowledge refinement process where reviewers refine the document indirectly by providing qualitative or quantitative feedback to the author.

Expert-Centralized Knowledge Refinement: A knowledge refinement approach that involves experts only in quality judgment and improvement processes.

Knowledge Refinement: The process of evaluating, analyzing and optimizing knowledge to be stored in a repository.

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