Is high realism more effective in addressing customer responses of humanoid robot? – The mediation role of perceived trust

Abstract

Purpose Realism of humanoid robots refers to human-like characteristics in these robots, which makes them resemble humans in appearance and behavior. Humanoid robots with varying degrees of realism not only influence customer acceptance but also serve as a crucial consideration in optimizing their design for commercial applications. This study aims to examine how realism of humanoid robots and service context influence customer responses. Design/methodology/approach This research tests the model using data collected from 1,269 subjects in four scenario-based experimental studies which varies in six different service contexts: museum, restaurant, bank vs. hotel and school library vs. school coffee shop. Findings Results demonstrated that a higher degree of realism and a better match between form and behavior effectively improve customer responses toward humanoid robots. However, mismatches in form and behavioral realism in intermediate states could lead to varying effects. These effects are driven by customers’ perceived trust and are more effective in utilitarian-dominated service context. Research limitations/implications This research advocates the significant role played by humanoid robot realism, in explaining the effect of robot realism on customer usage intention and behavioral choices. The underlying mechanism played by perceived trust and the boundary effect of service context are also examined. Practical implications The findings have important implications for designing future robots. Firms must consider service context to align robot design with customer expectations to foster trust and ultimately positively influence customer responses of humanoid robots. Originality/value This research introduces a holistic realism perspective. It comprehensively examines how the realism of humanoid robots influence customer usage intention and behavioral attitude, addressing critical gaps in previous research and provides insights for creating humanoid robots tailored to specific service context.