Research on the strategy of knowledge sharing among logistics enterprises under the goal of digital transformation

Abstract

Accelerating the digital transformation of the traditional logistics industry is an important step toward the high-quality development of China's economy, a fundamental task to ensure the smooth operation of the entire industrial chain, and a necessary path to building the Digital-China. Carrying out knowledge sharing can help promote knowledge integration and absorption among logistics enterprises, optimize the allocation of enterprise resources and enhance their business capabilities. To explore the knowledge sharing strategy selection mechanism of logistics enterprises in the process of digital transformation, this paper establishes a dynamic game model of knowledge sharing among logistics enterprises based on evolutionary game theory, and further demonstrates the reliability of the model by combining enterprise data with Matlab2022a for case analysis. The results show that: (1) the higher the cost of knowledge sharing in the digital transformation of logistics, the lower the willingness of enterprises to share; (2) when a single enterprise provides too much knowledge, it may trigger the breach of trust of the other party to “hitchhiking”; (3) appropriately increasing the amount of penalty for breach of trust helps promote both parties to reach a knowledge sharing strategy; (4) The benefit distribution coefficient significantly affects the knowledge sharing strategies of both parties, and a reasonable benefit distribution coefficient can prompt both parties to reach a stable strategy quickly; (5) Government financial incentives positively promote enterprises' willingness to share knowledge, and both parties can obtain higher revenue when a sharing strategy is reached. Thus, this paper explores the knowledge sharing strategy and evolution mechanism in the process of digital transformation of the logistics industry from the micro perspective of enterprises, which is of great practical significance and reference value.