When Robots Feel Human: Rethinking Guest Experience in Smart Hospitality

Abstract

This study examines how service robot design shapes customer experiences in the hospitality sector and responds to the accelerating shift toward automation in technologically advanced contexts such as Hangzhou, China. Despite the rapid adoption of service robots, a critical problem persists: existing technology acceptance models emphasize functional efficiency while overlooking the emotional and relational dimensions essential to hospitality experiences. Moreover, researchers have paid limited attention to how anthropomorphism and perceived emotional intelligence (PEI) jointly influence customer emotions, and to how perceived risk of service failure may alter these relationships. To address these gaps, this study aims to achieve three objectives. First, it evaluates the effects of anthropomorphism and PEI on customer emotional responses. Second, it analyzes how these emotional responses mediate overall service experience. Third, it tests whether perceived risk of service failure moderates these relationships. Drawing on the Service Robot Acceptance Model (SRAM), Anthropomorphism Theory, and Cognitive Appraisal Theory (CAT), the study develops a moderated-mediation framework that integrates both design attributes and customer perceptions. The study adopts a quantitative approach and collects data through a cross-sectional survey of 319 hotel guests in Hangzhou who have interacted with service robots. It analyzes the data using a two-step structural equation modeling (SEM) approach, conducting preliminary analysis in SPSS 26.0 and hypothesis testing in SmartPLS 4.0. It evaluates reliability, validity, mediation, and moderation systematically to ensure robustness. The findings show that anthropomorphism and PEI significantly enhance positive customer emotional responses, which in turn improve evaluations of the overall service experience. The results also confirm that emotional responses fully mediate these relationships, underscoring their central role in human–robot interaction. However, the analysis does not support the moderating effect of perceived risk of service failure, indicating that emotional engagement remains strong even when customers perceive potential risks. This study advances theory by extending technology acceptance frameworks to incorporate emotional and relational dynamics and by challenging assumptions about the role of perceived risk. In practice, it highlights the importance of hospitality managers designing emotionally intelligent, culturally sensitive robots that enhance guest satisfaction and service quality.