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Journal of Industrial and Management Optimization

2026, Volume 22, Issue 11: 5692-5731. doi: 10.3934/jimo.2026193
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Tripartite stochastic evolutionary game analysis of data sharing in the sports equipment manufacturing industry under the risk of technology supply disruption

  • 1.
    Physical education institute, Shanxi University, Taiyuan 030006, China
  • 2.
    School of Information, Shanxi University of Finance and Economics, Taiyuan 030006, China
  • Published: 15 October 2026

  • Primary: 91A80

  • Effective data-sharing in the sports equipment manufacturing industry is central to breaking down data silos, yet multiparty collaboration is still hindered by the risks of technological supply disruptions and exposure of private data. We construct a tripartite stochastic evolutionary game model involving core manufacturers, digital transformation platforms, and third-party software developers, incorporating Gaussian white noise to capture exogenous random disturbances. Evolutionary stability analysis and numerical simulations are used to assess how key parameters and stochastic shocks shape the system's trajectories. The results indicate that supply disruption risks and high privacy exposure costs deter core manufacturers from high-quality data-sharing. Strict platform data governance, strong accountability mechanisms, and the mitigation of external noise, on the other hand, help move the ecosystem toward a stable, system-optimal cooperative state. This study contributes to the stochastic evolutionary game literature on industrial data-sharing and provides a theoretical framework with guidelines for the sports manufacturing sector to mitigate disruption risks and achieve collaborative innovation.

    Citation: Yutong Bai, Jian Yang, Jin Dong, Xufeng Lv. Tripartite stochastic evolutionary game analysis of data sharing in the sports equipment manufacturing industry under the risk of technology supply disruption[J]. Journal of Industrial and Management Optimization, 2026, 22(11): 5692-5731. doi: 10.3934/jimo.2026193

    Related Papers:

  • Effective data-sharing in the sports equipment manufacturing industry is central to breaking down data silos, yet multiparty collaboration is still hindered by the risks of technological supply disruptions and exposure of private data. We construct a tripartite stochastic evolutionary game model involving core manufacturers, digital transformation platforms, and third-party software developers, incorporating Gaussian white noise to capture exogenous random disturbances. Evolutionary stability analysis and numerical simulations are used to assess how key parameters and stochastic shocks shape the system's trajectories. The results indicate that supply disruption risks and high privacy exposure costs deter core manufacturers from high-quality data-sharing. Strict platform data governance, strong accountability mechanisms, and the mitigation of external noise, on the other hand, help move the ecosystem toward a stable, system-optimal cooperative state. This study contributes to the stochastic evolutionary game literature on industrial data-sharing and provides a theoretical framework with guidelines for the sports manufacturing sector to mitigate disruption risks and achieve collaborative innovation.



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