A principal‐agent model for incentive design in knowledge sharing

Purpose – The purpose of this paper is to explore effective incentive design that can address the information asymmetry in knowledge sharing processes and variability of the intangible nature of knowledge. Design/methodology/approach – A principal‐agent model is first developed to formulate the asymmetry of information in knowledge sharing. Then, a set of optimal incentive solutions are derived from the principal‐agent model for knowledge types with specific levels of intangibility. Findings – For knowledge with low level of intangibility (e.g. data), a target payment scheme is optimal. For knowledge with medium level of intangibility (e.g. expressible tacit knowledge), the optimal incentive solution is a function of management's ability to infer employees' effort from knowledge sharing results. For knowledge with high level of intangibility (e.g. inexpressible tacit knowledge), there is no payment scheme that can be derived from the principal‐agent model to encourage employees to share knowledge. Research limitations/implications – The principal‐agent model developed by this study complements the previous game theoretic models and market mechanisms in incentive design. The applicability of the findings can be improved by further empirical analysis. Practical implications – There is no one‐size‐fits‐all incentive solution. The better the management can infer the effort level of employees from the reusability of the shared knowledge, the more effective the incentive schemes are. Knowledge management technologies can facilitate the application of the incentive design. Originality/value – This paper explicitly addresses the problem of information asymmetry in incentive design. It aligns a schedule of incentive schemes with the classification of knowledge based on intangibility. The schedule of incentive schemes leads to better understanding of the value of technologies in supporting knowledge sharing activities.