Consumer perceived disrespect in chatbot-based complaint handling: A mixed-methods study

AI-based chatbot usage in services has diversified, and complaint handling is no exception. While existing literature explores responses such as satisfaction, eWOM, and loyalty intentions, it overlooks the role of dignity and negative social emotions in high-stakes human–chatbot-interactions. This study introduces ‘perceived disrespect’, a moral-emotional response to unfair treatment, as a key mechanism explaining user frustration during chatbot-induced service failures. Adopting a sequential mixed-methods design, the study identifies antecedents: lack of empathy, uniqueness neglect, and poor human integration, and key outcomes: perceived disrespect and frustration. The hypotheses were subsequently tested using structural equation modelling. The findings reveal that uniqueness neglect, poor human integration (and not lack of empathy) directly lead to frustration. Instead, the impact of lack of empathy is fully mediated by perceived disrespect, suggesting that emotionless responses are punished only when users interpret them as an interpersonal slight. The study establishes a new theoretical construct in human–chatbot interactions, and also offers practical insights for designing AI-based service chatbots that respect consumer dignity in sensitive service encounters.