Greenwashing in supply chain networks is shaped by heterogeneous enterprise relationships, uncertain payoffs, and decentralized strategic interactions. This study develops a network-based evolutionary game model integrating a Barabási–Albert scale-free network, triangular fuzzy payoff representations, degree-dependent strategic inertia, and an inertia-modified Fermi imitation mechanism. A homogeneous mean-field approximation is derived as an analytical benchmark, while repeated Monte Carlo simulations examine the initial strategy's placement, payoff uncertainty, stochastic sampling mechanisms, social learning, network connectivity, and payoff evaluation methods. The baseline mean-field model yields an attracting interior equilibrium at a greenwashing density of 0.8889 under the weighted defuzzified payoffs, which does not directly predict the finite network's outcomes. Numerical results indicate that the initial strategy's placement substantially affects greenwashing diffusion: Assigning 30 initial greenwashing strategies to high-degree, randomly selected, and low-degree enterprises produces mean terminal densities of 0.9609, 0.7744, and 0.3609, respectively. Extreme payoff boundary adjustments significantly increase terminal greenwashing density relative to the baseline, whereas mean-controlled interval width variations do not produce statistically detectable differences. The intensity of social learning and strategic inertia exhibit significant overall effects, but their interaction is not statistically significant under the tested configurations. Furthermore, degree-normalized payoff evaluation attenuates the differences associated with network connectivity, while mechanism ablation experiments indicate that the initial placement's effects depend on payoff evaluation and behavioral assumptions. Overall, the findings highlight the conditional roles of network position, payoff uncertainty, and strategic inertia in the evolution of supply chain greenwashing, providing insights for network-sensitive governance.
Citation: Xianrong Hu, Haiping Ren, Fan Zhang. Conditional effects of network position, payoff uncertainty, and strategic inertia on supply chain greenwashing evolution[J]. Journal of Industrial and Management Optimization, 2026, 22(12): 6166-6212. doi: 10.3934/jimo.2026208
Greenwashing in supply chain networks is shaped by heterogeneous enterprise relationships, uncertain payoffs, and decentralized strategic interactions. This study develops a network-based evolutionary game model integrating a Barabási–Albert scale-free network, triangular fuzzy payoff representations, degree-dependent strategic inertia, and an inertia-modified Fermi imitation mechanism. A homogeneous mean-field approximation is derived as an analytical benchmark, while repeated Monte Carlo simulations examine the initial strategy's placement, payoff uncertainty, stochastic sampling mechanisms, social learning, network connectivity, and payoff evaluation methods. The baseline mean-field model yields an attracting interior equilibrium at a greenwashing density of 0.8889 under the weighted defuzzified payoffs, which does not directly predict the finite network's outcomes. Numerical results indicate that the initial strategy's placement substantially affects greenwashing diffusion: Assigning 30 initial greenwashing strategies to high-degree, randomly selected, and low-degree enterprises produces mean terminal densities of 0.9609, 0.7744, and 0.3609, respectively. Extreme payoff boundary adjustments significantly increase terminal greenwashing density relative to the baseline, whereas mean-controlled interval width variations do not produce statistically detectable differences. The intensity of social learning and strategic inertia exhibit significant overall effects, but their interaction is not statistically significant under the tested configurations. Furthermore, degree-normalized payoff evaluation attenuates the differences associated with network connectivity, while mechanism ablation experiments indicate that the initial placement's effects depend on payoff evaluation and behavioral assumptions. Overall, the findings highlight the conditional roles of network position, payoff uncertainty, and strategic inertia in the evolution of supply chain greenwashing, providing insights for network-sensitive governance.
| [1] |
Y. Gao, M. Zhang, M. A. Nasir, Y. Yan, J. Wu, L. Zhang, ESG greenwashing behaviour in the electric vehicle supply chain: Insights from evolutionary game theory, Int. J. Prod. Econ., 289 (2025), 109798. https://doi.org/10.1016/j.ijpe.2025.109798 doi: 10.1016/j.ijpe.2025.109798
|
| [2] |
C. Liu, X. Gao, S. Dong, Supply chain green gap and firm greenwashing: An institutional isomorphism perspective within the supply chain field, Supply Chain Manag., 30 (2025), 799–817. https://doi.org/10.1108/SCM-03-2025-0226 doi: 10.1108/SCM-03-2025-0226
|
| [3] |
Y. Wei, J. Tang, H. He, C. Wu, M. Lin, H. Xie, Customer concentration and corporate greenwashing, Res. Int. Bus. Financ., 79 (2025), 103090. https://doi.org/10.1016/j.ribaf.2025.103090 doi: 10.1016/j.ribaf.2025.103090
|
| [4] |
R. Su, J. Du, S. Jin, Green transformation or speculative greenwashing: Strategy choice of high-carbon enterprises from the perspective of financial support, Enterp. Econ., 42 (2023), 139–149. https://doi.org/10.13529/j.cnki.enterprise.economy.2023.12.014 doi: 10.13529/j.cnki.enterprise.economy.2023.12.014
|
| [5] |
M. Zhou, Z. Y. Zhou, Q. Liu, Green funds and corporate greenwashing: Purifying or fueling the fire, Finance Account. Mon., 45 (2024), 75–83. https://doi.org/10.19641/j.cnki.42-1290/f.2024.14.012 doi: 10.19641/j.cnki.42-1290/f.2024.14.012
|
| [6] |
X. Y. Lu, H. Y. Pan, F. Xin, Motivation and governance mechanisms of corporate greenwashing based on peer imitation in ESG disclosure, China Popul. Resour. Environ., 35 (2025), 116–128. https://doi.org/10.12062/cpre.20241116 doi: 10.12062/cpre.20241116
|
| [7] |
Y. Chen, D. Liu, H. He, L. Li, Peer effects of ESG greenwashing within board networks, Int. Rev. Financ. Anal., 106 (2025), 104540. https://doi.org/10.1016/j.irfa.2025.104540 doi: 10.1016/j.irfa.2025.104540
|
| [8] |
N. Guo, D. H. Chen, J. Zhang, Can the digital coupling of banking and enterprises curb corporate ESG greenwashing, Guizhou Soc. Sci., 12 (2024), 122–132. https://doi.org/10.13713/j.cnki.cssci.2024.12.010 doi: 10.13713/j.cnki.cssci.2024.12.010
|
| [9] |
W. N. Zhao, Y. Chen, T. K. Zhang, The emission permit system and corporate ESG greenwashing, Macroeconomics, 4 (2025), 112–127. https://doi.org/10.16304/j.cnki.11-3952/f.2025.04.005 doi: 10.16304/j.cnki.11-3952/f.2025.04.005
|
| [10] |
H. Zhang, M. D. Jiang, Z. T. Wu, How does digital environmental monitoring affect corporate greenwashing: An analysis based on dual internal and external governance mechanisms, Ind. Econ. Res., 2 (2026), 129–142. https://doi.org/10.13269/j.cnki.ier.2026.02.004 doi: 10.13269/j.cnki.ier.2026.02.004
|
| [11] |
J. Xue, R. Li, Y. Li, L. Zhao, A dynamic evolutionary game to discourage enterprise greenwashing, J. Clean. Prod., 477 (2024), 143823. https://doi.org/10.1016/j.jclepro.2024.143823 doi: 10.1016/j.jclepro.2024.143823
|
| [12] |
X. Hao, T. Tian, L. Dong, C. W. Y. Wong, K. Lai, Unmasking greenwashing in ESG disclosure: Insights from evolutionary game analysis, Ann. Oper. Res., 356 (2026), 667–695. https://doi.org/10.1007/s10479-025-06538-3 doi: 10.1007/s10479-025-06538-3
|
| [13] |
P. Majumdar, B. C. Giri, Investment decisions in blockchain and big data information services for mitigating greenwashing in closed-loop supply chains: A tripartite evolutionary game model, Appl. Soft Comput., 188 (2026), 114439. https://doi.org/10.1016/j.asoc.2025.114439 doi: 10.1016/j.asoc.2025.114439
|
| [14] |
A. L. Barabási, R. Albert, Emergence of scaling in random networks, Science, 286 (1999), 509–512. https://doi.org/10.1126/science.286.5439.509 doi: 10.1126/science.286.5439.509
|
| [15] |
F. C. Santos, J. M. Pacheco, Scale-free networks provide a unifying framework for the emergence of cooperation, Phys. Rev. Lett., 95 (2005), 098104. https://doi.org/10.1103/PhysRevLett.95.098104 doi: 10.1103/PhysRevLett.95.098104
|
| [16] |
M. A. Nowak, R. M. May, Evolutionary games and spatial chaos, Nature, 359 (1992), 826–829. https://doi.org/10.1038/359826a0 doi: 10.1038/359826a0
|
| [17] |
G. Szabo, G. Fath, Evolutionary games on graphs, Phys. Rep., 446 (2007), 97–216. https://doi.org/10.1016/j.physrep.2007.04.004 doi: 10.1016/j.physrep.2007.04.004
|
| [18] |
Z. Zhou, T. Li, The co-evolution of information sharing behavior and motivation: A resilience perspective on complex supply chain networks, Chaos Solit. Fractals, 208 (2026), 118246. https://doi.org/10.1016/j.chaos.2026.118246 doi: 10.1016/j.chaos.2026.118246
|
| [19] |
X. Wu, E. Shu, D. Ye, S. Zhou, H. Sun, Study on green packaging diffusion in logistics enterprise clusters based on complex network evolutionary game, J. Clean. Prod., 499 (2025), 145166. https://doi.org/10.1016/j.jclepro.2025.145166 doi: 10.1016/j.jclepro.2025.145166
|
| [20] |
X. Chen, W. Yang, R. Zhang, Green innovation diffusion based on complex network evolutionary game, Manag. Decis. Econ., 45 (2024), 1215–1229. https://doi.org/10.1002/mde.4060 doi: 10.1002/mde.4060
|
| [21] |
T. Yang, C. Ma, X. Mi, Blockchain-enabled green supply chain development from a complex network evolutionary perspective, Comput. Ind. Eng., 197 (2024), 110548. https://doi.org/10.1016/j.cie.2024.110548 doi: 10.1016/j.cie.2024.110548
|
| [22] |
D. Dubois, H. Prade, Possibility theory, probability theory and multiple-valued logics: A clarification, Ann. Math. Artif. Intell., 32 (2001), 35–66. https://doi.org/10.1023/A:1016740830286 doi: 10.1023/A:1016740830286
|
| [23] |
N. Khalil, I. Leyva, J. A. Almendral, I. Sendiña-Nadal, Deterministic and stochastic cooperation transitions in evolutionary games on networks, Phys. Rev. E, 107 (2023), 054302. https://doi.org/10.1103/PhysRevE.107.054302 doi: 10.1103/PhysRevE.107.054302
|
| [24] |
L. Cheng, M. Zhang, K. Wang, M. Yuan, Z. Liu, J. Wang, et al., Evolutionary smart contracts for virtual power plant trading: Integrating prospect theory and multi-stage negotiation in cross-regional energy markets, Int. J. Electr. Power Energy Syst., 173 (2025), 111453. https://doi.org/10.1016/j.ijepes.2025.111453 doi: 10.1016/j.ijepes.2025.111453
|
| [25] |
L. Cheng, M. Li, C. Tan, P. Huang, M. Zhang, R. Sun, Computational game-theoretic models for adaptive urban energy systems: A review, Arch. Comput. Methods Eng., 33 (2026), 2037–2114. https://doi.org/10.1007/s11831-025-10364-y doi: 10.1007/s11831-025-10364-y
|
| [26] |
K. Wang, L. Cheng, M. Yin, K. Zhang, R. Wang, M. Zhang, et al., Evolutionary game theory in energy storage systems: A systematic review, Sustainability, 17 (2025), 7400. https://doi.org/10.3390/su17167400 doi: 10.3390/su17167400
|
| [27] |
C. Liu, W. Li, L. Chang, Q. Ji, How to govern greenwashing behaviors in green finance products: A tripartite game approach, Financ. Innov., 10 (2024). https://doi.org/10.1186/s40854-023-00549-4 doi: 10.1186/s40854-023-00549-4
|
| [28] |
L. Wang, Z. Y. Li, Y. F. Sha, Synergy governance or collusion fraud: Heterogeneous institutional co-ownership and corporate greenwashing, Collect. Essays Finance Econ., 8 (2023), 70–80. https://doi.org/10.13762/j.cnki.cjlc.2023.08.005 doi: 10.13762/j.cnki.cjlc.2023.08.005
|
| [29] |
J. X. Wang, Z. M. Cao, Can digital transformation restrain greenwashing behavior of enterprises, J. Hunan Univ. Soc. Sci., 38 (2024), 58–67. https://doi.org/10.16339/j.cnki.hdxbskb.2024.02.008 doi: 10.16339/j.cnki.hdxbskb.2024.02.008
|
| [30] |
S. Y. Yan, J. H. Tan, S. X. Zhang, Green credit interest subsidy policy and the governance of greenwashing in heavy-polluting enterprises, Finance Econ., 12 (2025), 31–45. https://doi.org/10.27041/j.cnki.cjkx.2025.12.004 doi: 10.27041/j.cnki.cjkx.2025.12.004
|
| [31] |
J. Zeng, T. Huang, X. Wu, T. Liu, Authentic or spurious: Evolutionary game of manufacturing greenwashing under dynamic government regulation, Humanit. Soc. Sci. Commun., 12 (2025), 1307. https://doi.org/10.1057/s41599-025-05630-0 doi: 10.1057/s41599-025-05630-0
|
| [32] |
H. L. Han, The impact of independent directors' environmental background on corporate greenwashing behavior, Chin. Certif. Public Account., 5 (2026), 49–58. https://doi.org/10.16292/j.cnki.issn1009-6345.2026.05.017 doi: 10.16292/j.cnki.issn1009-6345.2026.05.017
|
| [33] |
J. Y. Cui, S. Q. Xu, X. Na, L. M. Pan, L. Y. Lü, Supply chain research based on complex network theory, Acta Phys. Sin., 73 (2024), 198901. https://doi.org/10.7498/aps.73.20240702 doi: 10.7498/aps.73.20240702
|
| [34] |
F. Ding, H. Chen, Supply chain network modeling research from the perspective of complex networks, J. North Univ. China Nat. Sci. Ed., 39 (2018), 120–127. https://doi.org/10.3969/j.issn.1673-3193.2018.02.003 doi: 10.3969/j.issn.1673-3193.2018.02.003
|
| [35] |
Z. Zhao, G. Zhou, R. Pan, Study on local world evolution model of weighted complex supply chain network based on location attraction, Comput. Sci., 45 (2018), 71–76. |
| [36] |
W. B. Cao, X. Xiong, Local-world evolution model of complex supply chain networks under edge benefits, Appl. Res. Comput., 1 (2016), 75–77. https://doi.org/10.3969/j.issn.1001-3695.2016.01.017 doi: 10.3969/j.issn.1001-3695.2016.01.017
|
| [37] |
R. Zhang, L. Li, Research on evolutionary game and simulation of information sharing in prefabricated building supply chains, Sustainability, 15 (2023), 9885. https://doi.org/10.3390/su15139885 doi: 10.3390/su15139885
|
| [38] |
X. J. Wang, Overview of evolutionary games and their learning mechanisms and evolutionary dynamics on complex networks, Yuejiang Acad. J., 13 (2021), 70–84. https://doi.org/10.13878/j.cnki.yjxk.20210521.003 doi: 10.13878/j.cnki.yjxk.20210521.003
|
| [39] |
G. Wu, X. Chen, X. Niu, Complex network evolutionary games of green technology innovation incorporating prospect theory, Manag. Decis. Econ., 46 (2025), 1774–1791. https://doi.org/10.1002/mde.4468 doi: 10.1002/mde.4468
|
| [40] |
Y. Duan, J. Huang, J. Zhang, Public goods games with long-term payoff mechanisms in heterogeneous networks, Chaos Solit. Fractals, 174 (2023), 113862. https://doi.org/10.1016/j.chaos.2023.113862 doi: 10.1016/j.chaos.2023.113862
|
 jimo-22-12-208-s001.pdf |
 |
Figures(14) / Tables(20)
Xianrong Hu, Haiping Ren, Fan Zhang. Conditional effects of network position, payoff uncertainty, and strategic inertia on supply chain greenwashing evolution[J]. Journal of Industrial and Management Optimization, 2026, 22(12): 6166-6212. doi: 10.3934/jimo.2026208
Schematic diagram of a scale-free supply chain network's topology
Impact of hub nodes' initial strategies on the evolutionary path of the system's greenwashing density
Evolution of network strategy snapshots under three initial hub strategy scenarios
Evolution of global greenwashing density under different fuzziness levels
Comparison of the evolution of network strategy snapshots under different fuzziness levels
Evolutionary trajectories of greenwashing density under extreme boundary scenarios
Network snapshots of average greenwashing frequency under extreme boundary scenarios
Evolutionary trajectories of greenwashing density under different mean-controlled payoff interval widths
Evolutionary trajectories of greenwashing density under different learning intensities and inertia configurations
Network snapshots of average greenwashing frequency under different learning intensities and inertia configurations
Evolutionary trajectories of greenwashing density under different BA attachment parameters
Evolutionary trajectories under different initial placements of greenwashing and behavioral mechanisms
Evolutionary trajectories of greenwashing density under different initial ratios
Network snapshots of average greenwashing frequency under different initial ratios
DownLoad: