How Behavioral and Normative Characteristics Influence User Satisfaction with Gen-AI Chatbots: The Mediating Roles of Emotional Empathy and Proficiency Evaluation

With the rapid diffusion of generative AI (Gen-AI) chatbots in service settings, understanding how their interactional characteristics are associated with user satisfaction has become increasingly critical. Although prior research has primarily emphasized system performance, relatively limited attention has been devoted to the underlying psychological mechanisms through which specific interactional features influence user experience. To address this gap, the present study develops a dual-path mediation model that differentiates between affective and cognitive processes, focusing on the mediating roles of emotional empathy and proficiency evaluation. Empirical data were collected via an online survey from 550 users with prior chatbot interaction experience. Structural equation modeling was employed to examine the effects of six chatbot characteristics on user satisfaction. These characteristics were classified into two categories: behavioral interaction characteristics (perceived initiative, perceived responsibility, and perceived timeliness) and normative interaction characteristics (perceived standardization, perceived impartiality, and perceived transparency). The results indicate that all characteristics, except perceived timeliness and perceived impartiality, exert significant positive effects on emotional empathy. Meanwhile, all characteristics exert significant positive effects on proficiency evaluation. The mediation analyses further indicate that emotional empathy and proficiency evaluation mediate some of the relationships between chatbot interactional characteristics and user satisfaction. By disentangling these mechanisms, this study advances current understanding of human–AI interaction from a dual-process perspective and clarifies the distinct roles of affective and cognitive pathways. It also offers practical guidance for the design of Gen-AI systems, suggesting ways to better align system features with users’ emotional expectations and task-oriented needs in real service settings.