Data-driven mechanisms for network freight platforms: An evolutionary game perspective - Chan He; Qun Xu; Xu Xu; Feng Kong; Feng Shen (PLoS ONE, 2025)
This paper examines the impact of data-driven mechanisms in network freight platforms. The main objective is to understand how these mechanisms can improve operational efficiency and encourage cooperation among key stakeholders, including a risk-neutral shipper, a loss-averse carrier and network freight platform. The study uses evolutionary game theory to model the interactions between these parties. Numerical simulations are conducted to evaluate the effects of initial conditions and important parameters on cooperation. The results show that consistent implementation of data-driven mechanisms fosters stable and honest cooperation. Key factors, such as subsidies, penalties for dishonest behavior, the likelihood of detecting dishonesty, and incentives for honest actions, motivate carriers and shippers to participate in fair transactions. Additionally, specific costs are identified as deterrents to dishonest practices. These findings contribute to our understanding of digital transformation and provide valuable insights for enhancing resilience and collaboration within network freight platforms. The risk appetite can obviously influence the decision of the three parties. The study also highlights important implications for policymakers and industry practitioners, emphasizing the importance of effective data governance and the strategic use of information to shape the future of freight logistics.