A tripartite evolutionary game analysis of stakeholder decision-making behavior in the internet of vehicles data supply chain

The Internet of Vehicles, as a new generation of information infrastructure that integrates multiple industries such as automotive, information communication, and transportation, is currently in a rapid development stage. However, its data supply chain involves numerous stakeholders and faces severe challenges in terms of data sharing, security, and regulation. To address this issue, this paper utilizes evolutionary game theory, setting key variables such as the strategy set, probability combination, and game behavior of each stakeholder to construct a tripartite evolutionary game model and its replicator dynamic equations, involving the Internet of Vehicles data sharing platform, vehicle manufacturers, and sellers. We studied the equilibrium solutions of this model and conducted an in-depth analysis of the local stability of the equilibrium state. Through simulation analysis, we explored the interference factors and their mechanisms of action in the interaction and dynamic changes during the evolutionary process and analyzed the impact of different parameters on the system’s evolution. The experimental results show that compensation mechanisms and the risk of information leakage have a significant impact on decision-making behavior; enhancing the security technology of the data-sharing platform and the construction of the data governance system, as well as implementing corresponding incentive and punitive measures, can promote the system to reach a stable state. The results of this study provide a scientific and reasonable decision-making basis for core enterprises in the Internet of Vehicles data supply chain, helping them to more effectively supervise and coordinate the data sharing behavior of downstream enterprises, thereby enhancing the collaborative effect of the entire supply chain system and improving the overall competitiveness and stability of the supply chain.