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<p>This study focuses on the collaborative innovation among the university and the enterprise in the R&amp;D of General Purpose Technologies (GPTs). To explore the impact of decision-making in the two stages of resource sharing and technological R&amp;D on innovation performance, a dynamic model based on differential game theory is constructed. The study systematically analyzes the optimal decisions of universities and enterprises and the role of government subsidy policies under three cooperation models: Nash non-cooperative, Stackelberg leader–follower, and coordinated cooperation. The results show that: (1) University-enterprise co-innovation is the Pareto optimal strategy for GPTs, outperforming Stackelberg and Nash models by enhancing collaboration and achieving higher innovation outputs; (2) Enterprise cost-sharing significantly boosts university investment in both resource-sharing and R&amp;D phases, improving resource levels, technological capacity, and overall system performance; (3) External technology embargoes hinder GPTs R&amp;D, but cost-sharing effectively mitigates these impacts by stimulating university efforts; (4) Co-innovation maximizes the effectiveness of government subsidies, driving higher R&amp;D investment and delivering the greatest social returns. This study provides valuable theoretical and practical insights for policymakers and stakeholders, emphasizing the importance of cost-sharing, refined cooperation models, and strategic government subsidies to enhance the efficiency and resilience of GPTs innovation systems.</p>

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Differential Game Analysis of University-Enterprise Co-innovation in General Purpose Technologies Innovation Based on Resource Complementation and Collaborative R&D

  • 期刊论文

  • 发表日期：

    2025年9月18日

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  • Yucai Jia,
  • Xiaohu Zhou,
  • Guiyang Zhang,
  • Yue Sui,
  • Lixin Li

摘要

This study focuses on the collaborative innovation among the university and the enterprise in the R&D of General Purpose Technologies (GPTs). To explore the impact of decision-making in the two stages of resource sharing and technological R&D on innovation performance, a dynamic model based on differential game theory is constructed. The study systematically analyzes the optimal decisions of universities and enterprises and the role of government subsidy policies under three cooperation models: Nash non-cooperative, Stackelberg leader–follower, and coordinated cooperation. The results show that: (1) University-enterprise co-innovation is the Pareto optimal strategy for GPTs, outperforming Stackelberg and Nash models by enhancing collaboration and achieving higher innovation outputs; (2) Enterprise cost-sharing significantly boosts university investment in both resource-sharing and R&D phases, improving resource levels, technological capacity, and overall system performance; (3) External technology embargoes hinder GPTs R&D, but cost-sharing effectively mitigates these impacts by stimulating university efforts; (4) Co-innovation maximizes the effectiveness of government subsidies, driving higher R&D investment and delivering the greatest social returns. This study provides valuable theoretical and practical insights for policymakers and stakeholders, emphasizing the importance of cost-sharing, refined cooperation models, and strategic government subsidies to enhance the efficiency and resilience of GPTs innovation systems.