Journal of Retailing and Consumer Services · 2023 · 130 citations · 55 references
ComputingComputer SciencePsychologyArtificial IntelligenceConsumer BehaviorRecommender SystemsConversational RecommendationEvaluation Of Recommender SystemsPreference ElicitationRecommendation EvaluationRecommendation SystemsRecommendation System EffectsUser Preferences
This research examines how individuals respond differently to recommendation options generated by ChatGPT, an AI-powered language model, in five studies. In contrast to previous research on choice overload, Studies 1 and 2 demonstrate that people tend to respond positively to a large number of recommendation options (60 options), revealing diverse consumer perceptions of AI-generated recommendations. Studies 3 and 4 further illustrate the moderating effect of recommendation agents and indicate that choice overload elicits distinct patterns of consumer reactions depending on whether the recommendations are from a human or AI agent. Lastly, Study 5 directly measures consumer preferences for recommendation agents, revealing a general preference for ChatGPT, particularly when a large number of options are available. These findings have significant implications for recommendation system design and user preferences regarding AI-powered recommendations.
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Using collaborative filtering to weave an information tapestry
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