ENHANCING AI-ASSISTED PURCHASE DECISIONS: THE ROLE OF THE SENSE OF AUTONOMY

• 成果类型: Article
• 署名作者: Hou, Jinghui (Jove); Yang, Shuai; Xiong, Guiyang; Pavlou, Paul A.
• 署名单位: University of Houston System; University of Houston; Donghua University; Syracuse University; University of Miami
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• DOI: 10.25300/MISQ/2025/17607
• 发表日期: 2026-06
• 关键词: Artificial intelligence; uniqueness neglect of AI; sense of autonomy; AI-assisted decision-making; DECISION QUALITY; purchase decision; product return; self-determination; psychological ownership; consumers; TECHNOLOGY; INFORMATION; PERSPECTIVE; complexity; reactance; BEHAVIOR; thinking

摘要

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 AI's uniqueness neglect, 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 ofAI-advised decisions through compensating for AI's uniqueness neglect. Across five laboratory experiments and one field experiment in the context of apparel purchases, we show thatfostering 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 ofAI-advised decisions and by offering a feasible design strategy that leverages personal smartphones to address the uniqueness neglect ofAI recommendation systems. Field data on actual product purchases and returns confirm the efficacy of ourAI design in a real-world setting. Our findings offer 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.