Unveiling the complexity of consumers’ intention to use service robots: An fsQCA approach

Abstract

Despite the growing body of research exploring factors associated with service robot adoption, the existing comprehension of this emerging technology remains largely fragmented. Previous studies have largely focused on the “net effect” between variables, leaving the complexity of consumer behavior uninvestigated. Building on relevant literature and complexity theory, this study intends to consolidate the fragmented views of service robot adoption literature by examining how human-likeness (i.e., anthropomorphism and perceived intelligence), technology-likeness (i.e., performance expectancy, hedonic motivation, and privacy risks), and consumer personalities (i.e., extraversion and openness to experience) combined as causal configurations to explain the behavioral intention to use service robots. Fuzzy set qualitative comparative analysis (fsQCA) was employed to analyze data from a sample of 566 Taiwanese consumers. The results from fsQCA results suggest that multiple, distinct, and equally effective combinations of human-like, technology-like, and consumer features exist to achieve high intention to use service robots. Four solutions are presented that lead to high adoption intention. This study contributes to the artificial intelligence literature by adopting a novel methodological approach to unveil the complexity behind the adoption of service robots. It also offers practical guidance for robot manufacturers and service managers to optimize the combination of human-likeness and technology-likeness in correspondence to consumer personalities for a successful service robots’ implementation.