From signals to trust: Multimodal detection and consumer perception of sustainability claims in e-commerce

This study pursues two interrelated objectives. First, it conducts a text-based analysis of consumer product reviews to examine whether sustainability-related language in reviews and product descriptions is associated with product ratings and customer satisfaction. Second, it develops and empirically tests a multimodal AI-based framework for detecting greenwashing in e-commerce environments, identifying misalignments between seller-generated sustainability claims and consumer-verified experience. Drawing on Signaling Theory and the Theory of Planned Behavior as interpretive lenses, the study analyzes data from two large-scale e-commerce platforms: The Amazon Sales Dataset (1465 product listings) and the BaSalam Persian-language marketplace (182,275 product-review pairs). Natural language processing, image-based color analysis, statistical testing, and supervised machine learning are employed. Results indicate that sustainability-related language in consumer reviews is not associated with higher product ratings or customer satisfaction; consumers evaluate products primarily on functional criteria such as performance, usability, and value. The analysis further shows that sustainability-mention frequency differs significantly across discount categories, with the High Discount category exhibiting the highest frequency. The proposed multimodal detection pipeline achieves an overall accuracy of 81.6 % on the Amazon hold-out set and 97 % on the BaSalam dataset, suggesting its potential usefulness for real-time consumer-facing and regulatory screening contexts. Implications for green marketing strategy, platform governance, and sustainability communication policy are discussed.