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PurposeThe 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.Design/methodology/approachThe 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.FindingsA 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/implicationsExtensive empirical analyses in the real world business environment would be helpful to verify and generalize this approach.Practical implicationsThe 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.Originality/valueAs 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.

This paper suggests an improved formulation for the multiperiod network topology and capacity expansion problem and proposes new lower bounding schemes based on it. It differs from earlier formulations and solution methods in that entirely new and different subproblems are solved and a number of lower bound tightening schemes are added within the framework of a Lagrangian relaxation. Dual ascent and multiplier adjustment procedures are suggested for the Lagrange multiplier updating procedure. Computational results are reported to demonstrate the tightness of the bounds generated by the suggested procedures. Heuristics based on converting the dual information obtained from the Lagrangian procedure into primal feasible solutions are tested. The tests show that the Lagrangian-based heuristics generate solutions superior to solutions generated by other heuristics proposed in the literature.

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