Data mining for exploring hidden patterns between KM and its performance

Authors:
Wei-Wen Wu;Yu-Ting Lee;Ming-Lang Tseng;Yi-Hui Chiang
Affiliations:
Department of International Trade, Ta Hwa Institute of Technology, 1, Ta Hwa Road, Chiung-Lin, Hsin-Chu 307, Taiwan;Department of International Trade, Ta Hwa Institute of Technology, 1, Ta Hwa Road, Chiung-Lin, Hsin-Chu 307, Taiwan;Department of Business Administration, Ming-Dao University, 369 Wen-Hwa Rd., Peetow Township, Changhua County, Taiwan;Department of International Trade, Ta Hwa Institute of Technology, 1, Ta Hwa Road, Chiung-Lin, Hsin-Chu 307, Taiwan
Venue:
Knowledge-Based Systems
Year:
2010

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Abstract

A large volume of works have addressed the importance of Knowledge management (KM). However, there are increasingly numerous concerns about whether the KM efforts can be fairly reflected and transformed into the business performance. Even though the KM contribution is qualitative and hard to measure, some works using statistical methods declare that a specific KM style may produce a better corporate performance. Statistical methods attempt to summarize yesterday's success rules, while data mining techniques aim to explore tomorrow's success clues. This study challenges the issue of what the hidden patterns between KM and its performance are, and whereby identifies the reality of whether a better performance is resulted from a special KM style. The analysis results using Bayesian network classifier and rough set theory show that it is not easy to support that a special KM style would produce a similar performance.