Unraveling the influence mechanism: how context sensitivity affects consumers’ choice of artificial intelligence recommendations

Abstract

Purpose Businesses are increasingly using artificial intelligence (AI) to provide recommendations to consumers. However, in many domains, consumers prefer human customer service and demonstrate aversion to AI-driven interactions. This study aims to examine consumers' willingness to accept AI vs human recommendations based on the sensitivity of the personal information needed to access services. Design/methodology/approach We simulated scenarios that consumers frequently encounter – namely, movie ticket purchasing, financial services, online shopping and fitness and/or weight management. Study 1 primarily examined the main effect (i.e. how contextual sensitivity influences users’ willingness to accept different types of recommendations) and verified the mediating role of self-awareness. Study 2 investigated how privacy concern and need for uniqueness moderate the relationship between contextual sensitivity and recommender type when shaping users’ acceptance intentions. To enhance the robustness of the findings, Study 2 also retested the main and mediating effects of the model. Findings In high-sensitivity contexts, consumers tend to favor AI agents over human ones, but the opposite is true for low-sensitivity scenarios. Furthermore, this study investigates the moderating effects of consumer characteristics (i.e. the need for uniqueness and privacy concerns) and the mediating role of (public and private) self-awareness in shaping these preferences. Originality/value The findings offer valuable insights and carry significant implications for corporate decision-making regarding the deployment of AI technologies in consumer-facing services.

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