<p>AI chatbots are increasingly embedded in e-commerce platforms to provide automated and real-time customer service in business-to-consumer settings. Grounded in expectation confirmation theory, this study examined how online shoppers constructed expectations, evaluated their experiences with AI chatbot services, and perceived effective collaboration between AI chatbots and human agents. A mixed-methods research design was adopted, beginning with a repeated-measures experimental approach. Data were collected from 30 participants, yielding 120 data points for quantitative analysis. This was complemented by World Café focus group discussions to further explore customer perspectives on collaboration between AI chatbots and human agents. The findings indicated that AI chatbot performance in handling complaints and problems, managing customer expectations and satisfaction, and influencing customer churn intention significantly affected customers’ electronic word-of-mouth (eWOM) behaviors. Satisfied users were more likely to share positive online feedback, whereas dissatisfaction increased negative reviews and customer churn intentions. Accordingly, improving service quality through an AI chatbot is critical for an e-commerce platform to facilitate customer retention and a firm’s online reputation. Particularly, training empathetic chatbots and creating user-friendly interfaces help mitigate customer-perceived impersonality of robots and foster stronger customer engagement. When AI chatbot service failures occur, well-designed service recovery mechanisms, including timely referral to human agents, can effectively reduce customer dissatisfaction and negative eWOM behaviors.</p>