When Privacy Concerns Don’t Deter: How Brand Trust Enables Data Sharing in AI-Driven Green Marketing.

While privacy concerns are traditionally assumed to deter data sharing, this study challenges that consensus within the context of value-aligned green marketing. Drawing on privacy calculus theory and trust-building frameworks, we surveyed 1,136 consumers to investigate the mechanisms driving engagement with AI-driven sustainability initiatives. Following a rigorous attrition analysis to ensure data quality (final N = 482), structural equation modeling reveals a complementary mediation effect: brand trust mediates the relationship between privacy concern and data-sharing willingness (indirect effect = 0.068), yet a significant positive direct effect persists (\beta = .109, p = .010). This finding suggests that privacy-conscious consumers do not exhibit paradoxical behavior but rather exercise privacy self-efficacy—engaging in informed, strategic selectivity where high concern correlates with high competence in managing risks. Furthermore, the study addresses the “Green-AI Paradox”—the tension between AI’s environmental utility and its substantial carbon footprint. We propose a new framework of “Impact Transparency” and introduce the AI Data Usage Efficiency (ADUE) metric for integration into the EU Digital Product Passport. These findings offer a roadmap for marketers and policymakers to foster trust through authentic, outcome-based transparency.