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

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. •This study adopts a holistic and unifying approach to understand service robot adoption.•We employed a novel analytical method (fSQCA) and complexity theory to unveil the complexity behind the adoption decision.•The fsQCA results show that none of the factors are necessary conditions for achieving a high level of behavioral intention.•Four combinations of the human-like, technology-like, and consumer features can lead to a high level of behavioral intention.•We offer guidance for managers on how to tailor robot characteristics in consideration of consumer personalities.