The influence of artificial intelligence chatbot problem solving on customers’ continued usage intention in e-commerce platforms: an expectation-confirmation model approach

The rapid advancement of artificial intelligence (AI) technology has led to its widespread application across various fields, including e-commerce platforms. AI chatbots, powered by large data models, have emerged as a crucial tool for enhancing customer service and user experience. This study aims to investigate the impact of AI chatbot problem-solving capabilities on users’ intention to continue using these services within e-commerce platforms. By integrating elements from the Expectation-Confirmation Model (ECM) and the Technology Acceptance Model (TAM), a comprehensive research model is proposed to examine the relationships between problem-solving, confirmation, perceived ease of use, satisfaction, trust, and continued usage intention. The study employs structural equation modelling (SEM) to analyse data collected from 315 participants through an online questionnaire. The findings reveal that problem-solving abilities positively influence users’ confirmation, which in turn positively affects satisfaction, perceived ease of use, and trust. Furthermore, perceived ease of use exhibits a positive impact on satisfaction, trust, and continued usage intention. Satisfaction and trust have a positive impact on continued usage intention, with satisfaction showing a stronger influence than both problem-solving ability and ease of use. The study contributes to the theoretical understanding of user perceptions and behaviours in AI-driven interactions within e-commerce platforms. It highlights the importance of problem-solving capabilities, perceived ease of use, satisfaction, and trust in driving users’ continued engagement with AI chatbots. Practical implications for AI technology developers and e-commerce companies are discussed, emphasizing the need to focus on enhancing chatbots’ problem-solving proficiency and user-friendliness to foster long-term user retention. Future research directions are proposed, addressing the study’s limitations and exploring the generalizability of the findings beyond the e-commerce context.