Consumer Responses to Service Robots - From Pre-Interaction to Post-Interaction

Abstract

Although robots are increasingly being deployed in service industries, the field of research pertaining to consumer responses to service robots remains insufficient. This dearth of knowledge and understanding has the potential to hinder the widespread adoption of service robots and the successful implementation of robotic service (r-service). This dissertation answers the following research question: How do consumers respond to service robots regarding their emotion, cognition, and behavioral intention from pre-interaction to post-interaction with these robots? Paper 1 focuses on how perceived comfort with robots penetrates consumers' implicit social decision-making (trust) and affects consumer responses. This study is motivated by a scarcity of knowledge on user pre-interaction perception and reaction to service robots. To bridge this gap, this paper cartographically investigates the effect of the mechano-humanness degree of robots on users' perceived comfort with robots, disentangling the underlying mechanism of human–robot trust. The findings provide tools for future studies on social–psychological and affective factors that could inform the design of socially competent robots. Whereas studies regarding what drives consumers to use service robots have offered fragmented results, Paper 2 launches a conceptual framework to comprehend the literature and obtain an in-depth understanding of individual attitudes and intentions to use service robots. Drawing on a triangulation of three perspectives on end-users in adoption research, this framework adopts technology acceptance theories, service quality, and expectancy-value theory to set up the skeleton of the framework. The antecedents of service robot acceptance are subdivided into robot design, consumer-oriented, relational components, and exogenous factors. The paper not only elaborates on the present situation of service robot acceptance research but also promotes the literature by developing a comprehensive framework regarding the effect factors. Paper 3 is motivated by a lack of well-developed studies sorting out the antecedents that affect consumer evaluation of r-service. This paper seeks to develop an r-service quality scale. I conducted a systematic literature review on r-service quality evaluation, thereby identifying the indicators of r-service dimensions and potential methodological issues of developing measurement instruments. The deliverables are strategically relevant for business operations of r-services. Drawing from the view of functional adaptivity, Paper 4 investigates how service heterogeneity affects consumer post-interaction responses to r-service by differentiating satisfying and dissatisfying service situations. This paper delineates the effects of different service providers and the inclusion of prior r-service experience on r-service heterogeneity. The findings offer theoretical and practical implications by answering the call for more research on r-service, broadening the understanding of the business value of artificial intelligence innovations and their relation to human responses.