Purpose This research aims to investigate when and how restaurants can cross the innovation chasm in adopting service robots in both front-of-house and back-of-house operations. Specifically, it explores customer responses to varying levels of robot deployment, ranging from fully human-operated to fully automated services, across different restaurant types based on cuisine and thematic elements. Design/methodology/approach It employs two experiments to evaluate customer perceptions of authenticity, quality, fit and patronage intention under different service configurations. These configurations are examined within the context of local cuisine restaurants, fast-food establishments and futuristic-themed dining settings. The research sample consists of adults from the United States, ranging in age from 18 to 83. Multivariate analysis of variance (MANOVA) was conducted to analyze the data. Findings The results reveal that local cuisine restaurants receive higher ratings in authenticity, quality, fit and patronage intention with fully human-operated services. In contrast, fast-food and futuristic-themed restaurants achieve comparable fit evaluations across human-operated, robot-involved and fully automated service configurations. Within futuristic-themed contexts, human-operated and robot-involved services receive comparable ratings for authenticity and patronage intention. However, when robots are responsible for cooking or when service is fully automated, human-operated services are perceived as higher in quality, with some advantage also observed in authenticity and patronage intention. Practical implications The findings guide restaurant operators in optimizing service robot integration strategies to align with customer expectations across diverse dining contexts. Social implications The research sheds light on the evolving interplay between technology and human interaction in dining, contributing to broader discussions on automation’s societal impact. Originality/value This research addresses the gap in understanding when and how to deploy service robots to facilitate customer adoption. It provides insights into optimal deployment levels to bridge the ‘innovation chasm’ in service robot adoption across different restaurant contexts.
Hu et al. (Thu,) studied this question.