Understanding Consumer Responses to Robotic Cafés: A Person–Environment Interaction Perspective

Abstract

Technological advancements in automation and artificial intelligence are transforming the coffee shop experience, with a major industry shift driven by robot baristas whose capabilities differ from those of existing self-service technologies and other hospitality operations. However, understanding consumers’ adoption and experiences remains unclear. To bridge this gap, two studies were conducted based on South Korea as the empirical setting (N = 670). Specifically, Study 1 examined how different personality traits influence consumers’ affective responses and word-of-mouth behaviors using Latent Profile Analysis, and Study 2 investigated how the social servicescape affects customer experience and behavioral loyalty in robotic service models with Structural Equation Modeling. Data from consumers who have experienced either of the different robotic coffee shop models reveal significant differences in affective experiences and word-of-mouth behaviors across the two personality profiles. Servicescape dimensions are significant predictors of affective experiences and word-of-mouth behaviors. These findings contribute to a deeper understanding of consumer behavior in automated service environments, offering valuable insights for businesses seeking to optimize customer experience and adoption strategies in the coffee shop industry.