Abstract & Overview
Purpose This study investigated the factors that influence customer satisfaction with AI-driven services by focusing on chatbot agents. The conceptual model included psychological and social factors, such as trust, perceived social presence, competence perception, social-oriented communication style, warmth perception, subjective norms and attachment anxiety. Design/methodology/approach A quantitative methodology was employed utilising a survey conducted among 525 consumers who interacted with chatbot services. The data were analysed using structural equation modelling (Smart-PLS 4.0) to test the proposed hypotheses. Findings The study revealed that social-oriented communication, perceptions of competence and warmth, trust and subjective norms significantly enhanced customer satisfaction with chatbots. Trust was critical in fostering satisfaction, whereas perceived social presence and attachment anxiety had minimal impact. The findings suggest that despite the emphasis on social presence, its influence on satisfaction may depend on contextual factors that were not captured in this study. Originality/value This study extended the Technology Acceptance Model and Stereotype Content Model by integrating factors such as perceived social presence, trust, competence perception, social-oriented communication style, warmth perception, subjective norm and attachment anxiety. Challenging conventional assumptions on the role of social presence and attachment anxiety, the study provides new insights into the complex dynamics of human–chatbot interactions, offering practical implications for improving chatbot design and enhancing user experience that emphasise the importance of trust, competence and social-oriented communication in customer satisfaction.