How to enhance collaborative innovation resilience? A game-theoretic analysis of triple-helix synergies in high-tech sectors
Yawen Liu et al.
Abstract
In increasingly dynamic and complex innovation ecosystems, the resilience of collaborative innovation systems has become a critical determinant of sustainable technological advancement. This study developed an evolutionary game-theoretic model involving three key actors—government, enterprises, and universities (or research institutions)—to analyze how bounded rationality, policy interventions, cost-benefit structures, and opportunistic behaviors jointly influence the evolution of collaborative innovation resilience (CIR). Unlike previous triple helix studies that primarily focus on synergy measurement or network stability, this work explicitly incorporated collaborative innovation resilience into an evolutionary dynamic framework, thereby linking system stability with adaptive capacity under policy and behavioral uncertainty. The findings indicated that stronger governmental incentives, coupled with well-calibrated supervisory and punitive mechanisms, significantly enhance the willingness of enterprises and universities to collaborate, improving both systemic stability and resilience. Furthermore, reducing collaboration costs, optimizing the distribution of excess returns, and mitigating free-riding behaviors were shown to foster sustainable cooperation and strengthen CIR. To enhance empirical relevance, the model was embedded in a representative high-tech manufacturing region, and numerical simulations using policy-calibrated parameters verified the effects of key strategic variables on evolutionary trajectories and equilibrium states. The results demonstrated that active and adaptive government participation not only stimulates the collaborative motivation of enterprises and universities but also amplifies the overall innovation system's resilience and social benefits. This study extends the triple helix literature by linking dynamic stability and innovation resilience through an evolutionary lens, offering both theoretical insights and practical guidance for policy design in high-tech and other innovation-intensive sectors.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.