Human–robot interaction attributes at the restaurants: will it enhance revisit intentions?

Purpose This study aims to investigate human–robot interactions (HRI) in hospitality, examining how perceived intelligence, social presence and social interactivity influence customer attitudes, trust, rapport and revisit intentions in robotic service restaurants. Design/methodology/approach A purposive sampling method was used to explore customer perceptions of restaurant service robots. Data were collected via the Prolific platform through a structured questionnaire from 500 US restaurant customers. The study used partial least squares structural equation modeling to assess relationships between HRI attributes, trust, rapport and revisit intentions. Findings Service robots significantly influence customer experiences and revisit intentions by fostering perceived intelligence, social presence and interactivity. Trust and rapport emerge as key determinants of service robot acceptance. Positive HRI increase the likelihood of customers returning, demonstrating that robots can enhance operational efficiency while maintaining the emotional engagement necessary for customer retention. Practical implications Findings provide strategic insights for restaurant owners, managers and stakeholders integrating service robots. Enhancing social interaction and trust-building features can make robotic services more appealing. Restaurants should focus on advanced AI capabilities that personalize interactions, remember customer preferences and deliver emotionally engaging experiences. Originality/value This study contributes to hospitality and tourism literature by providing empirical evidence on service robots’ role in shaping customer behavior. It expands social exchange theory and the technology acceptance model by incorporating trust, rapport and social interactivity into customer–robot interactions. The findings offer practical guidance for improving service efficiency while ensuring a humanized robotic dining experience that meets evolving customer expectations.