Delegating the Purchase: Consumer Trust and Adoption of Agentic AI Shopping Assistants
DOI:
https://doi.org/10.65419/albahit.v5i2.146Keywords:
Agentic AI, AI shopping assistant, Autonomous agents, Technology adoption, UTAUT2, Trust in AI, Privacy concern, Purchase delegation, PLS-SEMAbstract
Autonomous “agentic” AI shopping assistants—systems that search, decide and purchase on a consumer's behalf—are moving from concept to deployment, yet what drives consumers to adopt them remains unexplored. Existing adoption research addresses recommendation engines and chatbots rather than AI that executes purchases, and rarely models the trust mechanism specific to delegating decisions to an autonomous agent. Drawing on an extended UTAUT2 enriched with agent-specific antecedents, this study models how performance expectancy, effort expectancy, social influence and hedonic motivation, together with perceived agent competence, perceived autonomy risk and privacy concern, shape trust in the AI agent and ultimately adoption intention. A survey of 412 online consumers analysed with partial least squares structural equation modelling (PLS-SEM) explains 48% of the variance in adoption intention and 38% in trust. Perceived agent competence is the strongest driver of trust (β=0.49), while privacy concern and perceived autonomy risk significantly erode it; trust in turn is a key driver of adoption (β=0.33) and partially mediates the effects of competence and privacy concern. All measurement criteria are satisfied (loadings>0.80, CR 0.89–0.93, AVE 0.68–0.78, HTMT<0.85). The findings give marketers a trust-centred roadmap for deploying purchase-executing AI agents.
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