Modeling and optimizing the motivation of workers and managers for knowledge-sharing: a game-theoretic analysis

Abstract

Knowledge management (KM) is especially difficult to implement for project-based work. Tacit knowledge gained during projects diffuses when project teams disband and reform. Organizations are increasingly focusing on managing their knowledge flows, developing or investing in KM systems. Knowledge-sharing (KS) behaviors are the main input to KM systems, so we aim to optimize knowledge input to magnify the usefulness of KM systems. We adopt game-theoretic models to analyze workers' knowledge-sharing dynamics under different KM strategies, taking managers' viewpoints. We will solve Nash Equilibria by identifying associated conditions on each equilibrium path, and derive implications from these condition for KM strategies. We will explore: (1) types of knowledge that deserve sharing; (2) types of employees that possess different knowledge; (3) incentives that cause the right individuals to share useful knowledge; and (4) costs and benefits associated with alternative company KM strategies. We plan to validate these theoretical results via an empirical study. This paper lays out the problem, discusses the key points of departure, and presents our initial version of a game tree for employees' decisions about knowledge sharing. Interviews will subsequently be conducted to validate the game tree, Nash Equilibria, and implications for knowledge management in project-based firms.