GenAI-Based Carbon Footprint Feedback as a Decision Context: A Two-Layer Extended TPB Model for Renewable Energy Product Adoption

Carbon footprint information can be a powerful environmental label for encouraging sustainable consumption. However, static environmental labels can be difficult for consumers to interpret during online purchasing decisions. This study examines whether generative artificial intelligence (GenAI)-based carbon footprint feedback (AI-CFB), which transforms static carbon footprint information into decision-relevant feedback, can support consumers’ evaluations and purchase intentions regarding renewable energy-powered products (REPPs). In this context, data from 841 participants in Türkiye were analyzed using PLS-SEM within a two-layer extended TPB model. Results from the first layer confirm AI-CFB as an antecedent of TPB dimensions, which, in turn, are associated with purchase intention toward renewable energy-powered products, with environmental concern and technological self-efficacy serving as motivating factors. The second layer reveals that AI-CFB functions as a decision-support mechanism, while consumer trust strengthens the relationship between AI-CFB and REPP purchase intention. This study contributes a validated AI-TPB model that explains how GenAI-based carbon footprint information is associated with consumer evaluations and moderates the relationships with purchase intention, extending the sustainable consumption literature by integrating GenAI-based systems in e-commerce. The findings offer practical recommendations for policymakers, e-commerce platforms, and carbon footprint experts to encourage low-carbon consumption and reduce CO2 emissions in Türkiye, while providing a foundation for future research at the intersection of sustainable consumption and AI-assisted decision-making.