Love–hate tango: exploring the efficacy of human–machine interaction in service robots

Purpose This study contributes to explore interactions between human service providers and service robots, focusing on their impact on customer satisfaction and sustained intention for human–machine collaboration. By adopting a resource-based view (RBV), the research examines these interactions from both the customer's and employee's perspectives to uncover the dynamics of intellectual capital in the context of service delivery. Design/methodology/approach The study utilizes fuzzy set qualitative comparative analysis (fsQCA), an asymmetric data analysis method, to identify sufficient conditions for achieving high customer satisfaction and sustained human–machine interaction. It employs purposive sampling and collects data through anonymous online surveys targeting consumers and employees experienced with service robots. Findings The fsQCA results revealed four causal configurations (Paths A1, A2, A3 and A4) sufficient for high customer satisfaction and three configurations (Paths B1, B2 and B3) sufficient for sustained intention of human-machine interaction. Originality/value This study offers an integration of the RBV and intellectual capital theory to examine human–machine interaction in service settings. While prior research has applied fsQCA to explore consumers' intention to use service robots, this study extends that approach by focusing on the interplay between technological and human resources. By investigating the substitutive and complementary roles of service robots and human service providers, this research contributes theoretical insights and practical implications for optimizing service delivery in the age of artificial intelligence.