Do AI traits shape value-in-use across the retail customer journey?

초록

PurposeThis study aims to investigate how consumers' perceptions of artificial intelligence (AI) traits - warmth and competence - influence value-in-use across the customer decision journey (CDJ). Integrating the stereotype content model (SCM), cognitive fit theory (CFT) and the heuristic-systematic model (HSM), it examines the cognitive and emotional mechanisms shaping responses to AI at different decision stages.Design/methodology/approachThree experiments using a 2 & times; 3 factorial design (AI type & times; CDJ stage) were conducted in online grocery-shopping scenarios. Study 1 tested the direct effects of AI traits on value-in-use. Study 2 assessed perceived fit as a mediator. Study 3 examined heuristic versus systematic processing as a moderator. A robustness check used alternative stimuli.FindingsCompetence-based AI enhanced value-in-use during pre-purchase and purchase, whereas warmth-based AI was more effective post-purchase. Perceived fit mediated these effects, and processing mode moderated them: competence was more effective under systematic processing, while warmth performed better under heuristic processing.Originality/valueThis research extends SCM, CFT and HSM to AI-mediated retail by showing that the effects of AI traits depend on CDJ stages and processing conditions. It offers a framework for tailoring AI strategies to maximise value-in-use across the journey.

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