Abstract
Empowered by large-scale consumer data, Artificial Intelligence (AI) systems act as shopping gurus, serving up highly personalized and expertly curated product recommendations for consumers. Despite their proficiency at inferring what could be the commonly “optimal” choices for the average consumer, AI systems have a limited ability to account for the idiosyncratic factors that uniquely shape each consumer’s purchase decisions, such as one’s hidden motives or complex situations. This inherent limitation, termed as the uniqueness neglect of AI, poses a key challenge to the value of AI-advised decisions. Our research identifies a heightened sense of autonomy as a crucial means to improve the quality of AI-advised decisions through compensating for AI’s uniqueness neglect. Across five laboratory and one field experiments in the context of apparel purchases, we show that fostering the sense of autonomy enhances purchase intention and the quality of purchase decisions. We also delineate the underlying mechanism and demonstrate a managerially actionable solution to effectively promote the sense of autonomy. Our work contributes to the literature by uncovering a distinct mechanism by which the sense of autonomy improves the quality of AI-advised decisions, and by offering a feasible design strategy that leverages personal smartphones to address the uniqueness neglect of AI recommendation systems. Field data on actual product purchases and returns confirm the efficacy of our AI design in a real-world setting. Our findings proffer both theoretical and managerial implications for a wide range of AI-aided decision-making contexts where idiosyncratic factors are important to individual human decision makers, yet are commonly overlooked by today’s AI systems.