Balancing codification and personalization for knowledge reuse: a Markov decision process approach

Abstract

Purpose – This paper aims to provide a systematic framework for organizations to analyze their knowledge reuse processes, and balance codification and personalization within their knowledge strategy according to cost/benefit analysis. Design/methodology/approach – This paper divides knowledge reuse process into a sequence of five stages, and accordingly analyzes costs/benefits under codification and personalization strategies. Markov decision process, a mathematical framework for multi-stage decision-making, is employed to optimize a mixed strategy for knowledge reuse processes within an organization. Findings – Organizations need to consider factors such as the number of reusable knowledge items, reuse patterns, and intra-organizational interest alignment which are critical to determine their optimal mix between codification and personalization. Companies should determine a knowledge strategy based on their knowledge reuse contexts instead of following success cases blindly. Research limitations/implications – This paper presents an illustrative example to show how this framework might be applied by an organization. However, the validity and reliability of strategic decision-making also depends on the accuracy of the model's parameter values. Firms can adopt many methods as surveys, Delphi method, to determine the parameter values. Practical implications – The proposed framework offers an opportunity for firms to gain insights by setting the model's parameters to their own reuse contexts/characteristics and conducting what-if analysis. Originality/value – This paper proposes a formal framework for analyzing knowledge reuse processes and offers organizations guidelines about decision-making of knowledge strategies.