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Product and process knowledge in the performance‐oriented knowledge management approach

Suk‐Gwon Chang (Professor of MIS and Telecommunications at Hanyang University. He received his PhD in Management Science from KAIST (Korea Advanced Institute of Science and Technology). He had held many academic positions in the area of MIS and Management Science. He is now serving as the Dean of the Institute of Information and Communication of Hanyang University. His current research interests include the development of KM framework, KMS implementation, information systems strategy, internet industry analysis, and telecommunications and e‐business collaboration.)
Jae‐Hyeon Ahn (Associate Professor at the Korea Advanced Institute of Science and Technology (KAIST) in Seoul, Korea. He received his BS and MS degrees from Seoul National University South Korea in 1984 and 1986 respectively, and PhD degree in Decision Sciences from Stanford University in 1993. After graduation, he worked as a senior researcher at AT&T Bell Labs from 1993 to 1998. He is one of the active members of the Knowledge Management Research Lab at KAIST. His current research interests are focused on implementation of KMS, incentive system for knowledge management, knowledge measurement in health‐care industry, new telecommunications service development strategy, and customer loyalty in e‐business.)

Journal of Knowledge Management

ISSN: 1367-3270

Article publication date: 1 August 2005




The paper proposes first, to understand how and how much knowledge contributes toward explicit business performance improvement and, second, through the understanding of knowledge contribution, to provide a guiding principle for the effective knowledge management activities.


The authors use a Cobb‐Douglas type production function to model the relationship between knowledge and performance. Then, regression analysis is used to estimate the knowledge elasticity of performance. Finally, a laboratory experiment is used to demonstrate the whole process.


A performance‐oriented knowledge management approach was developed. Through the analysis of knowledge‐intensive production function, it is shown that the knowledge elasticity of performance for each knowledge entity (product knowledge and process knowledge) can be estimated and can be used with great managerial implications.

Research limitations/implications

Extensive empirical analyses in the real world business environment would be helpful to verify and generalize this approach.

Practical implications

The paper demonstrates the specific process of how to measure the contribution of knowledge to performance, and provide a guiding principle for the effective knowledge management activities.


As far as the authors understand, this is the first systematic and complete approach to analyze and estimate the contribution of knowledge to performance. Using the production function approach, it was possible to estimate the knowledge elasticity of performance, which provides valuable insight on the resource allocation for knowledge management activities.



Chang, S. and Ahn, J. (2005), "Product and process knowledge in the performance‐oriented knowledge management approach", Journal of Knowledge Management, Vol. 9 No. 4, pp. 114-132.



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Copyright © 2005, Emerald Group Publishing Limited