AI-Driven E-Commerce data

This study examines how algorithmic transparency influences consumer purchase intention in AI-driven e-commerce environments by uncovering its underlying psychological mechanisms. Drawing on signaling theory, organizational justice theory, and trust theory, this research develops a process-based framework in which perceived fairness and consumer trust jointly mediate the relationship between transparency and consumer behavior. Using survey data from 416 online consumers and structural equation modeling, the results show that algorithmic transparency significantly enhances perceived fairness, consumer trust, and purchase intention. The findings further reveal a significant sequential mediation effect, indicating that transparency operates through a cognitive–affective process in which fairness perceptions foster trust and subsequently drive purchase intention. In addition, the direct effect of algorithmic transparency on purchase intention remains significant, suggesting partial mediation. Furthermore, multi-group analysis demonstrates that the strength of structural relationships varies across e-commerce platforms, highlighting the presence of cross-platform heterogeneity. This study contributes to the literature by providing a process-based explanation of algorithmic transparency, integrating multiple theoretical perspectives into a unified framework, and emphasizing the importance of contextual differences in digital environments. The findings also offer practical implications for e-commerce platforms seeking to enhance consumer trust and engagement through transparent algorithmic design, highlighting the presence of cross-platform heterogeneity in consumer responses.