Differential Game Analysis of Strategic Emerging Industry Convergence Cluster Innovation Strategy Considering Technology Integration Capabilities and Technology Heterogeneity

Abstract

Driven by strategic direction, strategic emerging industry convergence clusters are a complex evolutionary process of increasing technological innovation levels and optimizing industrial structure through cross-sectoral organizational collaboration and cooperation among various stakeholders. Given the dynamic and long-term nature of this process, alongside factors such as technological heterogeneity and integration capabilities, we use a differential game approach to compare the optimal innovation strategies across three scenarios: centralized decision-making, Stackelberg leader–follower, and Nash noncooperative game models. This analysis explores how different innovation entities within strategic emerging industry clusters can coordinate and cooperate to achieve converged cluster development. The results indicate that (1) innovation levels in convergence clusters and the returns of individual actors are lowest under the Nash noncooperative game model, followed by the Stackelberg leader–follower model. The optimal strategy for convergence cluster development is centralized decision-making and collaborative development. (2) While technological heterogeneity inhibits the benefits of innovation entities, technological integration capabilities increase them. Additionally, the growth of convergence clusters is more strongly impacted by technical heterogeneity, with higher levels of heterogeneity having a negative impact on their development. (3) Under centralized decision-making, government subsidies have the strongest incentive effect; nevertheless, as compared to other characteristics, their influence on increasing convergence cluster returns is weaker. Findings here may provide theoretical support for enhancing innovation efficiency and promoting strategic emerging industry convergence clusters.