This study focuses on analyzing the core contradiction that while AI investment reduces consumers' misfit costs, it also exacerbates the risk of privacy leakage. By analyzing the interplay between government subsidies and penalties, this research aims to elucidate how regulatory designs can effectively steer platforms' privacy-protection behavior and suppliers' AI investment toward a socially optimal outcome.
This study constructs a Stackelberg game involving a supplier, an e-commerce platform, and consumers, integrating government regulatory instruments of subsidies and penalties, the supplier's AI investment decision, and the platform's privacy protection choice. We compare four scenarios to derive optimal strategies for all parties and to analyze the effects of key parameters.
The findings reveal that government regulation should prioritize subsidies with supplementary penalties to best incentivize platforms' privacy protection. Furthermore, the supplier's AI investment depends on the stringency of government regulation. Finally, well-matched government regulatory designs can reduce privacy risks and achieve mutually beneficial outcomes, balancing privacy protection with intelligent upgrading of the e-commerce supply chain.
This study contributes to research on consumer privacy protection, AI investment in e-commerce supply chain management, and government regulation strategies. We incorporate AI-induced consumer privacy breach risk as an externality into suppliers' investment analysis, explaining how such risk offsets AI-driven demand growth. In addition, we compare subsidy and penalty policies, and propose risk-matched regulation to balance privacy protection, innovation incentives, and social welfare.
