How to Optimize Data Sharing in Logistics Enterprises: Analysis of Collaborative Governance Model Based on Evolutionary Game Theory

Data, as a key production factor in modern logistics systems, plays a crucial role in enhancing industry efficiency and promoting supply chain coordination. To address challenges in data sharing among logistics enterprises—such as conflicts of interest, unequal risk allocation, and insufficient security governance—this study develops a tripartite evolutionary game model involving logistics enterprises, data partners, and supervisory institutions. The payoff matrix incorporates prospect theory to account for risk attitudes, loss–gain perceptions, and subjective judgments. Stable equilibrium points are derived using the Jacobian matrix, and numerical simulations examine strategic evolution under varying parameters. Results indicate that increased returns for data partners reduce their motivation to provide truthful data, while higher enterprise profits suppress logistics enterprises’ willingness to share. Compensation levels have limited impact, whereas excessively high supervision subsidies weaken participation and oversight across all parties. Stronger penalties and higher-level enforcement significantly promote compliance and positive system evolution. Enterprise investment positively correlates with data-sharing behavior, and risk preferences of all parties accelerate convergence to stable equilibria. Conversely, excessively low risk preference in supervisory institutions may lead to an unstable “sharing–false data–non-regulation” pattern. These findings provide theoretical support and policy guidance for designing a dynamic governance mechanism that balances incentives, constraints, and collaboration, thereby facilitating secure and effective logistics data sharing and informing the development of the data factor market.