How recommendation conciseness shapes customers’ attitudes toward the AI chatbot

Purpose This study aims to use the commensal scene model and feelings-as-information theory to examine how the conciseness of AI chatbot recommendations (concise vs verbose) affects consumers’ decision comfort and attitudes toward the hospitality chatbot. It also investigates how these effects may vary depending on the consumption context and social distance. Design/methodology/approach Two experiments were conducted to investigate consumers’ opinions about AI chatbot recommendation conciseness (concise vs verbose) and its interaction with consumption context (social vs solitary) and social distance (close vs distant). Findings The findings indicate that the consumption context and social distance each moderate the effect of verbose (vs concise) recommendations generated by AI chatbots. Specifically, the positive impacts of verbose AI recommendations are more pronounced in the social consumption context, especially when the social distance is distant (vs close). Moderated mediation analyses confirm that decision comfort explains the proposed effects. Practical implications This research instructs AI developers to design AI concierge chatbots with recommendation conciseness in mind to facilitate customers’ decision comfort. In consumption contexts such as business lunches and professional networking events, frequented by groups with distant relationships, AI chatbots should provide verbose recommendations. Recommendation conciseness does not matter for solitary consumers. Originality/value By integrating the notions of commensality and social distance, the authors expand the comprehension of how recommendation conciseness facilitates individuals’ appraisals of AI chatbot recommendations under complex social conditions. The authors also highlight decision comfort as a pivotal factor in people’s attitudes toward AI chatbot-provided recommendations.