AI memory expression: mitigating AI aversion through perceived uniqueness

Abstract

Purpose This study aims to investigate how artificial intelligence (AI) memory expression influences customer attitudes during service interactions. Drawing on uniqueness theory, it examines whether AI’s ability to recall customer preferences enhances perceived uniqueness and reduces AI aversion. Additionally, it explores whether embarrassing service contexts moderate these effects. Design/methodology/approach Three scenario-based experiments were conducted. Study 1 used a 2 × 2 between-subjects design (memory expression × agent type) to examine whether memory expression attenuates AI aversion. Study 2 examined the mediating role of perceived uniqueness using moderated mediation analysis. Study 3 tested the boundary condition of embarrassment using a 2 × 2 × 2 between-subjects design. Findings The results show that AI memory expression improves customer attitudes by enhancing perceived uniqueness, thereby reducing AI aversion. However, in embarrassing service contexts, memory expression produces the opposite effect, leading to lower customer evaluations regardless of agent type. Practical implications Service providers can leverage memory expression to personalize customer experiences and mitigate AI aversion. However, in embarrassing contexts, memory expression should be used cautiously withheld to maintain customers’ psychological comfort. Originality/value This study introduces memory expression as a novel dimension of AI behavior and demonstrates its dual role in shaping customer evaluations. While memory expression enhances customer attitudes by increasing perceived uniqueness, it can also trigger negative reactions in embarrassing service contexts. These findings extend theoretical understanding of AI aversion and provide practical insights for managing AI-enabled service interactions.

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