Diffusion dynamics of smartphone trade-in strategies under government intervention: A complex network evolutionary game approach incorporating prospect theory

The rapid expansion of the smartphone industry and the surge in e-waste highlight the need for effective Trade-in ( TI ) programs. To examine the diffusion of TI strategies among smartphone enterprises, this study develops a complex network evolutionary game model that integrates government intervention, enterprise pricing, consumer demand, and network topology. The baseline model is further extended by incorporating prospect theory to capture bounded rationality, comparing four canonical network structures, conducting external benchmarking with aggregate public evidence, and performing joint parameter sensitivity analyses. Regulatory intervention generally promotes TI diffusion, although a conditional diffusion ceiling may persist under certain parameter settings, suggesting that stricter enforcement alone may not ensure full market saturation. Government subsidies and enterprise social reputation further facilitate early-stage diffusion, but their marginal effects gradually weaken. Meanwhile, a larger share of green consumers and a broader network scale support TI adoption, although their effects remain bounded, whereas pricing strategies create a competitive trade-off between TI and Non-Trade-in ( NTI ) adopters. The prospect theory extension shows that loss aversion emerges as an influential behavioral factor, whereas psychological discounting weakens the effect of abstract social reputation. Comparisons across four canonical network topologies indicate that long-run adoption remains broadly stable, while external benchmarking and joint sensitivity analyses further support the plausibility and robustness of the main findings. Overall, large-scale TI adoption may benefit from coordinated regulatory enforcement, adaptive subsidies, and behavior-oriented signals. This study provides model-dependent implications for improving smartphone recycling systems and resource-efficient industrial development.