AI in the Aisle: Understanding Consumer Usability of Visual AI Assistants for In-Store Product Selection

Abstract

As consumers increasingly enlist visual artificial intelligence during in?store purchase episodes, established theories of consumer decision making and service encounters require substantive refinement. This paper proposes a parsimonious, testable framework that delineates boundary conditions, antecedents, processes, and multi?level outcomes for AI?mediated visual shopping assistance. The framework identifies situational and individual antecedents that drive consultation, including resourcefulness, perceived vulnerability, knowledge gaps, human service failure, time pressure, and perceived AI affordances. At its core is an AI?mediated service triad in which consumers, the retailer service-scape, and external AI platforms form parallel advisory streams that interact through information exchange, trust formation, and a reinforcement loop shaping subsequent use. Cognitive and affective mediators such as decision confidence, perceived information sufficiency, empowerment, and anxiety transmit the influence of AI consultation to immediate behaviors and longer?term relationships, yielding compliance, hybrid choice strategies, platform dependency, cross?category generalization, and retailer loyalty effects. The paper advances theory by reconceptualizing AI as a hybrid decision agent, expanding service encounter logic to accommodate external digital actors, and isolating trust mechanisms specific to visual analysis. It concludes by outlining measurement, experimental, field, design, managerial, and policy research priorities to facilitate rigorous empirical testing and responsible implementation as AI?enabled shopping practices diffuse.
Keywords: Visual Artificial Intelligence, Consumer behavior, Trust, Product Selection