Trapped by AI Recommendation: How Identity Concerns Reduce Variety‐Seeking Behavior

Zelin Tong, Huilin Liu, Jingdan Feng, Wei Wang, Huizhi Wu and Jilv Xu

Managerial and Decision Economics, 2025, vol. 46, issue 5, 3200-3211

Abstract: This research explores the impact of AI recommendations on consumers' variety‐seeking behavior. Through three studies, we examine how AI recommendations in different contexts, such as music and shopping apps, influence consumers' variety‐seeking behavior. The results consistently show that AI recommendation significantly reduces variety‐seeking behavior among consumers with strong identification due to heightened concerns about AI misclassification. In contrast, consumers with weak identification remain unaffected. These findings reveal a potential dark side of AI recommendation, where consumers' desire to maintain a consistent identity leads them to engage less in diverse explorations, thereby intensifying the creation of information cocoons. Our research contributes to the literature by highlighting the psychological mechanisms underlying consumer responses to AI recommendations and underscores the need for a balanced approach in AI personalization strategies.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:wly:mgtdec:v:46:y:2025:i:5:p:3200-3211

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