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

Author

Listed:
  • Zelin Tong
  • Huilin Liu
  • Jingdan Feng
  • Wei Wang
  • Huizhi Wu
  • Jilv Xu

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.

Suggested Citation

  • Zelin Tong & Huilin Liu & Jingdan Feng & Wei Wang & Huizhi Wu & Jilv Xu, 2025. "Trapped by AI Recommendation: How Identity Concerns Reduce Variety‐Seeking Behavior," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 46(5), pages 3200-3211, July.
    • Handle: RePEc:wly:mgtdec:v:46:y:2025:i:5:p:3200-3211
    DOI: 10.1002/mde.4524
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    References listed on IDEAS

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    1. Li, Feng & Liu, SiTong & Wang, Huanzhang, 2026. "Collaborative filtering or content-based recommendation? The effects of digital platform recommendation type on consumer's intention," Journal of Retailing and Consumer Services, Elsevier, vol. 90(C).

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