AI Transparency and User Behavior in Human–AI Collaboration: Evidence from E-Commerce Recommendation Systems

The growing reliance on artificial intelligence (AI)-based recommendation systems is transforming e-commerce into a space where decision-making is increasingly co-constructed between users and intelligent systems. However, it remains insufficiently understood how the transparency of these systems influences users’ trust and purchasing decisions within human–AI collaboration contexts. Addressing this gap, the study develops a conceptual model that explains the role of cognitive mechanisms in the relationship between AI transparency and consumer behavior. Specifically, algorithmic understanding and fairness perception are conceptualized as cognitive processes through which users evaluate AI-generated recommendations, while perceived control is positioned as a key link between these evaluations and trust formation. The model is empirically tested using partial least squares structural equation modeling (PLS-SEM) based on data collected from 312 users of recommender systems. The results highlight the role of cognitive mechanisms and perceived control in explaining the effects of AI transparency on trust and, indirectly, on purchase intention. AI literacy also shapes how users interpret the information provided by the system. The present research provides an integrated perspective on human–AI collaboration in e-commerce, with relevant implications for the design of recommender systems and the optimization of user experience.