As artificial intelligence (AI) technology evolves, there is a growing concern about excessively deferring decision-making authority to AI systems. To address this concern, we investigate the potential impacts of AI-driven recommendations on users’ heteronomy inducing their impulsive buying behaviors. We further investigate the role of explainable AI (XAI) in alleviating the counterproductive impacts of AI recommender systems. Using the heuristic-systematic and reflective-impulsive models, we conduct an experiment exposing 77 participants to varied online shopping experiences. Results indicate a positive association between AI recommendations and user heteronomy, leading to impulsive buying. XAI explanations were found to negatively moderate this relationship, suggesting the potential to maintain user self-determination in the AI recommender system. This study provides strong evidence on user inner mechanisms of AI over-reliance, highlighting the importance of addressing user heteronomy. Our findings offer insights for future research on AI system design, emphasizing the significance of XAI in mitigating over-reliance.