When and how AI personalization drives sustainable purchases: The roles of relevance, privacy, and transparency in eco-friendly advertising

AI-driven personalization uses algorithmic tailoring to deliver content aligned with consumers’ preferences. Although prior research suggests that personalized sustainability messages can influence green behavior by enhancing perceived message relevance, the literature has not systematically examined how this relevance pathway is disrupted by privacy concerns or repaired by AI transparency disclosures. Drawing on the Elaboration Likelihood Model, Self-Congruity Theory, and Privacy Calculus Theory, this research conceptualizes perceived message relevance as the key mechanism through which AI-driven personalization promotes sustainable purchase intention. Across two studies, a scenario-based experiment and a time-lagged field survey in China, this research demonstrates that the effect of AI personalization on sustainable purchase intention is mediated by perceived relevance and conditionally moderated by privacy concern and AI transparency. Specifically, privacy concern weakens, and AI transparency strengthens, the personalization–relevance pathway. The findings are robust across experimental and real-world e-commerce contexts and clarify when AI personalization is likely to succeed or fail in motivating sustainable behavior. In particular, relevance emerges as a persuasion route that depends on the presence of ethical and transparent personalization cues. Managerial implications for designing privacy-respectful, transparency-rich personalization strategies in green advertising are presented.