Beyond Rule-Based Interaction: How Generative AI Chatbot Attributes Shape Customer Trust, Satisfaction, and Continuance Intention

With increasing deployment of generative AI (GenAI) chatbots in organizational settings, understanding the determinants of customer trust, satisfaction, and continuance intention has become a critical research priority. Unlike traditional rule-based and deterministic systems, GenAI chatbots generate adaptive, context-aware, and socially expressive responses that transform human-GenAI interactions. Existing service quality, and social response theories are mostly applicable to static interactions and do not fully account for the probabilistic and relational dynamics introduced by GenAI chatbots. This study proposes a theoretical framework examining how functional attributes (reliability, responsiveness, privacy) and relational attributes (empathy, assurance) affect trust and satisfaction, and continuance intention. By extending post-adoption theory to the context of GenAI chatbots, this research reinterprets service attributes as dynamic and interaction-dependent, providing new insights into trust formation and its role in enhancing the customer satisfaction and long-term engagement.