Gift giving in the age of AI: The role of social closeness in using AI gift recommendation tools

Yuxin Fu, David B. Dose, Radu Dimitriu
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Abstract

Firms increasingly employ artificial intelligence (AI) gift recommendation tools to assist consumers with their gift choices. Yet, a notable gap exists in understanding consumers’ responses to AI recommendations in a gift giving context. Through five studies, we found that social closeness between the giver and recipient significantly affects the use of AI gift recommendation tools, driven by two underlying mechanisms: expected relational signaling and preference matching. In addition to establishing these effects, this research identifies relevant boundary conditions. Our findings reveal that self‐oriented perfectionism increases preference matching for gifts to distant friends, while revealing the giver's identity boosts AI tool use for close friends by reducing relational signaling expectations. Additionally, AI tools capable of turn‐taking enhance preference matching and AI tool use, especially for gifts to close friends. Our findings advance the understanding of how and why social closeness influences givers’ utilization of AI gift recommendation tools and offer valuable insights for practitioners on designing these tools more effectively, considering the nuances of social relationships.
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人工智能时代的礼品赠送:社交亲密度在使用人工智能礼品推荐工具中的作用
企业越来越多地采用人工智能(AI)礼品推荐工具来帮助消费者选择礼品。然而,在了解消费者在送礼时对人工智能推荐的反应方面还存在明显差距。通过五项研究,我们发现送礼者和收礼者之间的社会亲密度会显著影响人工智能礼品推荐工具的使用,其驱动力来自两个基本机制:预期关系信号和偏好匹配。除了确定这些影响之外,这项研究还确定了相关的边界条件。我们的研究结果表明,以自我为导向的完美主义会增加向远方朋友赠送礼物时的偏好匹配,而揭示赠送者身份则会通过降低关系信号预期来促进对亲密朋友使用人工智能工具。此外,能够轮流使用的人工智能工具会增强偏好匹配和人工智能工具的使用,尤其是在向亲密朋友赠送礼物时。我们的研究结果加深了人们对社交亲密程度如何以及为什么会影响送礼者使用人工智能礼物推荐工具的理解,并为从业者提供了宝贵的见解,帮助他们更有效地设计这些工具,同时考虑到社交关系的细微差别。
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