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.
Citation: Yawen Liu, Jia-Hui Meng, Fang Ji, Jian Wang. How to enhance collaborative innovation resilience? A game-theoretic analysis of triple-helix synergies in high-tech sectors[J]. Journal of Industrial and Management Optimization, 2026, 22(3): 1539-1570. doi: 10.3934/jimo.2026057
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.
| [1] |
X. Duan, P. F. Sun, X. Wang, B. S. Zhan, Evolutionary game analysis of industry-university-research cooperative innovation in digital media enterprise cluster based on GS algorithm, Wirel. Commun. Mob. Comput., (2022), 1–10. https://doi.org/10.1155/2022/5701917 doi: 10.1155/2022/5701917
|
| [2] |
N. Su, Z. Q. Shi, X. Q. Zhu, Y. S. Xin, An evolutionary game model of collaborative innovation between enterprises and colleges under government participation of China, Sage Open, 11 (2021), 1–16. https://doi.org/10.1177/2158244021994854 doi: 10.1177/2158244021994854
|
| [3] |
B. J. Hou, J. Hong, H. Y. Wang, C. Y. Zhou, Academia-industry collaboration, government funding and innovation efficiency in Chinese industrial enterprises, Technol. Anal. Strateg. Manag., 31 (2019), 692–706. https://doi.org/10.1080/09537325.2018.1543868 doi: 10.1080/09537325.2018.1543868
|
| [4] |
X. Y. Qi, G. Mei, Network Resilience: Definitions, approaches, and applications, King Saud Univ. - Comput. Inf. Sci., 36 (2024), 101882. https://doi.org/10.1016/j.jksuci.2023.101882 doi: 10.1016/j.jksuci.2023.101882
|
| [5] |
H. G. Bo, Y. G. Zheng, Y. F. Li, S. Q. He, Manufacturing resource outsourcing and matching: Service mode selection and equilibrium evolution, J. Oper. Res. Soc., 76 (2025), 772–789. https://doi.org/10.1080/01605682.2024.2392797 doi: 10.1080/01605682.2024.2392797
|
| [6] |
X. M. Xie, M. G. Wang, Firm' digital capabilities and green collaborative innovation: The role of green relationship learning, J. Innov. Knowl., 10 (2025), 100663. https://doi.org/10.1016/j.jik.2025.100663 doi: 10.1016/j.jik.2025.100663
|
| [7] |
J. H. Meng, J. Wang, Y. W. Liu, How is government embedded in innovation process for breakthroughs? A meta-synthesis of qualitative case studies, Technol. Forecast. Soc. Change, 194 (2023), 122735. https://doi.org/10.1016/j.techfore.2023.122735 doi: 10.1016/j.techfore.2023.122735
|
| [8] |
L. Hollands, L. Haensse, N. Lin-Hi, The how and why of organizational resilience: A mixed-methods study on facilitators and consequences of organizational resilience throughout a crisis, J. Appl. Behav. Sci., 60 (2024), 449–493. https://doi.org/10.1177/00218863231165785 doi: 10.1177/00218863231165785
|
| [9] |
C. G. Schmidt, S. M. Wagner, Blockchain and supply chain relations: A transaction cost theory perspective, J. Purch. Supply Manag., 25 (2019), 100552. https://doi.org/10.1016/j.pursup.2019.100552 doi: 10.1016/j.pursup.2019.100552
|
| [10] |
T. Togoontumur, N. S. Cooray, Does collaboration matter: The effect of university-industry R&D collaboration on economic growth, J. Knowl. Econ., 15 (2023), 9482–9496. https://doi.org/10.1007/s13132-023-01469-5 doi: 10.1007/s13132-023-01469-5
|
| [11] |
T. Clauss, T. Kesting, M. Franco, Innovation generation through formalisation and fairness in university-industry collaboration, Technovation, 134 (2024), 103049. https://doi.org/10.1016/j.technovation.2024.103049 doi: 10.1016/j.technovation.2024.103049
|
| [12] |
G. Melis, S. McCabe, M. Atzeni, G. D. Chiappa, Collaboration and learning processes in value co-creation: A destination perspective, J. Travel Res., 62 (2023), 699–716. https://doi.org/10.1177/00472875211070349 doi: 10.1177/00472875211070349
|
| [13] |
Y. Y. Xu, Q. Q. Liang, X. C. Lyu, X. Y. Zang, An evolution analysis of collaborative innovation network considering government subsidies and supervision, Math. Probl. Eng., 2019 (2019), 2906908. https://doi.org/10.1155/2019/2906908 doi: 10.1155/2019/2906908
|
| [14] |
D. Q. Gao, S. S. Li, C. Guo, Top management team career experience heterogeneity, digital transformation, and the corporate green innovation: A moderated mediation analysis, Front. Psychol., 14 (2023), 1276812. https://doi.org/10.3389/fpsyg.2023.1276812 doi: 10.3389/fpsyg.2023.1276812
|
| [15] |
Y. L. Wan, Y. C. Gao, Y. M. Hu, Blockchain application and collaborative innovation in the manufacturing industry: Based on the perspective of social trust, Technol. Forecast. Soc. Change, 177 (2022), 121540. https://doi.org/10.1016/j.techfore.2022.121540 doi: 10.1016/j.techfore.2022.121540
|
| [16] |
S. Sindakis, S. Showkat, G. Showkat, Enhancing resilience and embracing antifragility: The role of innovation in collaborative networks, Eur. J. Innov. Manag., 2025. https://doi.org/10.1108/ejim-12-2023-1129 doi: 10.1108/ejim-12-2023-1129
|
| [17] |
L. Cheng, J. Zhang, Is tourism development a catalyst of economic recovery following natural disaster? An analysis of economic resilience and spatial variability, Curr. Issues Tour., 23 (2020), 2602–2623. https://doi.org/10.1080/13683500.2019.1711029 doi: 10.1080/13683500.2019.1711029
|
| [18] |
A. Mangan, A. M. Ward, Cooperative resilience: Toward a heuristic model of collective action in a crisis, Financ. Account. Manag., 40 (2024), 308–325. https://doi.org/10.1111/faam.12382 doi: 10.1111/faam.12382
|
| [19] |
K. Medel, R. Kousar, T. Masood, A collaboration-resilience framework for disaster management supply networks: A case study of the Philippines, J. Humanit. Logist. Supply Chain Manag., 10 (2020), 509–553. https://doi.org/10.1108/jhlscm-09-2019-0066 doi: 10.1108/jhlscm-09-2019-0066
|
| [20] |
R. Prijadi, A. S. Santoso, T. E. Balqiah, H. Jung, P. M. Desiana, P. Wulandari, Enhancing resilience in digital multi-sided platform start-ups: An exploration of entrepreneurial logic and open innovation strategies, Entrep. Bus. Econ. Rev., 12 (2024), 35–53. https://doi.org/10.15678/eber.2024.120103 doi: 10.15678/eber.2024.120103
|
| [21] |
X. Y. Wang, Y. X. Zhang, S. H. Zhang, Dynamic advertising, pricing, and the optimal elongation timing of the supply chain, Int. J. Prod. Res., (2025), 1–22. https://doi.org/10.1080/00207543.2025.2553833 doi: 10.1080/00207543.2025.2553833
|
| [22] |
H. Etzkowitz, L. Leydesdorff, The dynamics of innovation: From national systems and "Mode 2" to a triple helix of university-industry-government relations, Res. Policy, 29 (2000), 109–123. https://doi.org/10.1016/S0048-7333(99)00055-4 doi: 10.1016/S0048-7333(99)00055-4
|
| [23] |
G. Linton, Triple helix dynamics and hybrid organizations: An analysis of value creation processes, J. Knowl. Econ., 15 (2024), 20797–20822. https://doi.org/10.1007/s13132-024-01911-2 doi: 10.1007/s13132-024-01911-2
|
| [24] |
M. Ranga, H. Etzkowitz, Triple helix systems: An analytical framework for innovation policy and practice in the knowledge society, Ind. High. Educ., 27 (2013), 237–262. https://doi.org/10.5367/ihe.2013.0165 doi: 10.5367/ihe.2013.0165
|
| [25] |
N. Testi, A triple helix model for the diffusion of industry 4.0 technologies in firms in the Marche region, Open Res. Eur., 3 (2023), 89. https://doi.org/10.12688/openreseurope.15706.2 doi: 10.12688/openreseurope.15706.2
|
| [26] |
Y. Z. Cai, H. Etzkowitz, Theorizing the triple helix model: Past, present, and future, Triple Helix, 7 (2020), 189–226. https://doi.org/10.1163/21971927-bja10003 doi: 10.1163/21971927-bja10003
|
| [27] |
Y. Z. Cai, M. Amaral, The triple helix model and the future of innovation: A reflection on the triple helix research agenda, Triple Helix, 8 (2021), 217–229. https://doi.org/10.1163/21971927-12340004 doi: 10.1163/21971927-12340004
|
| [28] |
E. G. Carayannis, D. F. Campbell, E. Grigoroudis, Helix trilogy: The triple, quadruple, and quintuple innovation helices from a theory, policy, and practice set of perspectives, J. Knowl. Econ., 13 (2022), 2272–2301. https://doi.org/10.1007/s13132-021-00813-x doi: 10.1007/s13132-021-00813-x
|
| [29] |
M. M. Schocair, A. A. Dias, S. V. R. Galina, M. Amaral, The evolution of the triple helix thematic: A social networks analysis, Triple Helix, 9 (2023), 325–368. https://doi.org/10.1163/21971927-bja10037 doi: 10.1163/21971927-bja10037
|
| [30] |
L. Leydesdorff, The triple helix model and the study of knowledge-based innovation systems, Int. J. Contemp. Sociol., 42 (2005), 12–27. https://doi.org/10.48550/arXiv.0911.4291 doi: 10.48550/arXiv.0911.4291
|
| [31] |
H. Yin, J. X. Liu, N. M. Zeng, How to promote the digital business model innovation of high-tech SMEs through government venture capital? An evolutionary game perspective, Manag. Decis. Econ., 45 (2024), 3193–3216. https://doi.org/10.1002/mde.41855 doi: 10.1002/mde.41855
|
| [32] |
W. W. Liu, J. N. Yang, The evolutionary game theoretic analysis for sustainable cooperation relationship of collaborative innovation network in strategic emerging industries, Sustainability, 10 (2018), 4585. https://doi.org/10.3390/su10124585 doi: 10.3390/su10124585
|
| [33] |
X. Y. Wang, Y. X. Zhang, S. H. Zhang, Dynamic order allocation in a duopoly hybrid workforce of competition: A machine learning approach, Eur. J. Oper. Res., 315 (2024), 668–690. https://doi.org/10.1016/j.ejor.2023.12.026 doi: 10.1016/j.ejor.2023.12.026
|
| [34] |
A. Zan, Y. H. Yao, H. H. Chen, University-industry collaborative innovation evolutionary game and simulation research: The agent coupling and group size view, IEEE Trans. Eng. Manag., 68 (2021), 1406–1417. https://doi.org/10.1109/tem.2019.2908206 doi: 10.1109/tem.2019.2908206
|
| [35] |
N. Yuan, M. J. Li, Research on collaborative innovation behavior of enterprise innovation ecosystem under evolutionary game, Technol. Forecast. Soc. Change, 206 (2024), 123508. https://doi.org/10.1016/j.techfore.2024.123508 doi: 10.1016/j.techfore.2024.123508
|
| [36] |
X. Y. Wang, Y. L. Li, S. H. Zhang, The interplay between green product production and advertising investment under green reputation, IEEE Trans. Eng. Manag., 72 (2025). https://doi.org/10.1109/TEM.2025.3582257 doi: 10.1109/TEM.2025.3582257
|
| [37] |
X. D. Liu, Corruption culture and corporate misconduct, J. Financ. Econ., 122 (2016), 307–327. https://doi.org/10.1016/j.jfineco.2016.06.005 doi: 10.1016/j.jfineco.2016.06.005
|
| [38] |
W. Z. Sun, S. N. Chen, Y. Q. Jiao, X. Feng, How does ESG constrain corporate earnings management? Evidence from China, Finance Res. Lett., 61 (2024), 104983. https://doi.org/10.1016/j.frl.2024.104983 doi: 10.1016/j.frl.2024.104983
|
| [39] |
M. X. Li, X. M. Chi, J. L. Su, H. Z. Sun, O. A. Anaba, A. U. Khan, Evolutionary game theory in patent alliance dynamics: Strategy and policy implications, Technol. Anal. Strateg. Manag., 37 (2024), 1–16. https://doi.org/10.1080/09537325.2024.2361424 doi: 10.1080/09537325.2024.2361424
|
| [40] | W. H. Sandholm, Population games and evolutionary dynamics, MIT Press, Cambridge, 2010. |
| [41] |
X. S. Peng, S. Y. Wang, C. Zhao, Government subsidy policies for industrial internet adoption: A tripartite evolutionary game analysis, Manag. Decis. Econ., 46 (2025), 1392–1404. https://doi.org/10.1002/mde.4440 doi: 10.1002/mde.4440
|
| [42] |
H. P. Young, Commentary: John Nash and evolutionary game theory, Games Econ. Behav., 71 (2011), 12–13. https://doi.org/10.1016/j.geb.2008.12.007 doi: 10.1016/j.geb.2008.12.007
|
| [43] |
R. Viale, V. Cucchiarini, Behavioral triple helix for innovation, Triple Helix, 2025, 1–23. https://doi.org/10.1163/21971927-bja10065 doi: 10.1163/21971927-bja10065
|
| [44] |
H. Etzkowitz, Innovation in innovation: The triple helix of university-industry-government relations, Soc. Sci. Inf., 42 (2003), 293–337. https://doi.org/10.1177/05390184030423002 doi: 10.1177/05390184030423002
|
| [45] |
E. K. Wang, C. M. Chen, S. M. Yiu, M. M. Hassan, M. Alrubaian, G. Fortino, Incentive evolutionary game model for opportunistic social networks, Future Gener. Comput. Syst., 102 (2020), 14–29. https://doi.org/10.1016/j.future.2019.07.034 doi: 10.1016/j.future.2019.07.034
|
| [46] |
O. Nazih, N. Benamar, M. Younis, InAn evolutionary bargaining-based approach for incentivized cooperation in opportunistic networkst. J. Commun. Syst., 33 (2020), e4377. https://doi.org/10.1002/dac.4377 doi: 10.1002/dac.4377
|
| [47] |
D. Friedman, On economic applications of evolutionary game theory, J. Evol. Econ., 8 (1998), 15–43. https://doi.org/10.1007/s001910050054 doi: 10.1007/s001910050054
|
| [48] |
E. A. Lindquist, The digital era and public sector reforms: Transformation or new tools for competing values? Can. Public Adm., 65 (2022), 547–568. https://doi.org/10.1111/capa.12493 doi: 10.1111/capa.12493
|
| [49] |
N. Zaharia, University-industry knowledge transfer: Channels of sport research interaction, Int. J. Bus. Manag., 12 (2017), 1. https://doi.org/10.5539/ijbm.v12n9p1 doi: 10.5539/ijbm.v12n9p1
|
| [50] | Pingyang county people's government office, Several policy opinions of Pingyang county on accelerating technological innovation to promote high-quality development in the manufacturing industry, 2025. Available from: https://www.zjpy.gov.cn/art/2022/5/24/art_1229519551_1999086.html. |
| [51] | Hangzhou municipal people's government, Notice of the Hangzhou municipal people's government on issuing several policies to further promote high-quality economic development, 2025. Available from: https://www.hangzhou.gov.cn/art/2024/3/1/art_1229783229_7800.html. |
| [52] | Ningxia hui autonomous region department of science and technology, R&D post-subsidy support provided to more than 1,700 enterprises in Ningxia, 2024. Available from: https://www.most.gov.cn/dfkj/nx/zxdt/202409/t20240919_191726.html. |
| [53] | J. Sterman, System dynamics: Systems thinking and modeling for a complex world, McGraw-Hill, 2002. http://hdl.handle.net/1721.1/102741 |
| [54] |
B. Walker, C. S. Holling, S. R. Carpenter, A. Kinzig, Resilience, adaptability and transformability in social-ecological systems, Ecol. Soc., 9 (2004). https://doi.org/10.5751/ES-00650-090205 doi: 10.5751/ES-00650-090205
|
| [55] |
S. Duchek, Organizational resilience: A capability-based conceptualization, Bus. Res., 13 (2020), 215–246. https://doi.org/10.1007/s40685-019-0085-7 doi: 10.1007/s40685-019-0085-7
|
| [56] |
I. D. Noni, L. Orsi, F. Belussi, The role of collaborative networks in supporting the innovation performances of lagging-behind European regions, Res. Policy, 47 (2018), 1–13. https://doi.org/10.1016/j.respol.2017.09.006 doi: 10.1016/j.respol.2017.09.006
|
| [57] |
A. Saz-Carranza, S. M. Ospina, The behavioral dimension of governing interorganizational goal-directed networks—Managing the unity-diversity tension, J. Public Adm. Res. Theory, 21 (2011), 327–365. https://doi.org/10.1093/jopart/muq050 doi: 10.1093/jopart/muq050
|
| [58] |
X. L. Liu, C. J. Xue, F. J. Xie, The impact of policy mix characteristics of multi-level governance on innovation output of the new energy vehicle industry in China, Technol. Anal. Strateg. Manag., 37 (2024), 1–14. https://doi.org/10.1080/09537325.2024.2304729 doi: 10.1080/09537325.2024.2304729
|
| [59] |
N. I. Okeke, O. A. Bakare, G. O. Achumie, Integrating policy incentives and risk management for effective green finance in emerging markets, Int. J. Frontiers Sci. Technol. Res., 7 (2024), 76–88. https://doi.org/10.53294/ijfstr.2024.7.1.0050 doi: 10.53294/ijfstr.2024.7.1.0050
|
| [60] |
C. Rosário, C. Varum, A. Botelho, The role of public incentives in promoting innovation: An analysis of recurrently supported companies, Economies, 12 (2024), 148. https://doi.org/10.3390/economies12060148 doi: 10.3390/economies12060148
|
| [61] |
M. Tripsas, S. Schrader, M. Sobrero, Discouraging opportunistic behavior in collaborative R&D: A new role for government, Res. Policy, 24 (1995), 367–389. https://doi.org/10.1016/0048-7333(93)00771-k doi: 10.1016/0048-7333(93)00771-k
|
| [62] |
J. Y. You, Y. Q. Chen, W. Q. Wang, C. X. Shi, Uncertainty, opportunistic behavior, and governance in construction projects: The efficacy of contracts, Int. J. Project Manag., 36 (2018), 795–807. https://doi.org/10.1016/j.ijproman.2018.03.002 doi: 10.1016/j.ijproman.2018.03.002
|
| [63] |
A. Peterman, A. Kourula, R. Levitt, Balancing act: Government roles in an energy conservation network, Res. Policy, 43 (2014), 1067–1082. https://doi.org/10.1016/j.respol.2013.12.007 doi: 10.1016/j.respol.2013.12.007
|
| [64] |
S. Nambisan, M. Wright, M. Feldman, The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes, Res. Policy, 48 (2019), 103773. https://doi.org/10.1016/j.respol.2019.03.018 doi: 10.1016/j.respol.2019.03.018
|
| [65] | O. E. Williamson, The economic institutions of capitalism: Firms, markets, relational contracting, Free Press, New York, 1985, 61–75. https://doi.org/10.1007/978-3-8349-9320-5_6 |
Figures(6) / Tables(3)
Yawen Liu, Jia-Hui Meng, Fang Ji, Jian Wang. How to enhance collaborative innovation resilience? A game-theoretic analysis of triple-helix synergies in high-tech sectors[J]. Journal of Industrial and Management Optimization, 2026, 22(3): 1539-1570. doi: 10.3934/jimo.2026057
DownLoad: