Research on general AI in e-commerce intelligent customer sat-isfaction

This study focuses on the satisfaction of Intelligent Customer Service (ICS) with the application of generative AI technology in e-commerce. In the context of the rapid development of information technology, e-commerce platforms actively use and explore innovative applications of ICS to improve user shopping experience. Exploring the user's acceptance and satisfaction of ICS is still an important topic in current research and practice. This study combined Customer Satisfaction Barometer (CSB) and Technology Acceptance Model (TAM), collected data through questionnaire survey, and then used K-means clustering algorithm to cluster data to identify different behaviors and preference patterns of users when using ICS. Then, CHAID algorithm is used in each cluster to dig deeply into the key factors affecting users' acceptance of ICS, and then obtain the key factors of user satisfaction and their interaction relationship. Research data showed that Cronbach's Alpha=0.874 and KMO=0.8. The results show that a stable, reliable, accurate, efficient, intuitive understanding and caring ICS system is the key to improving user satisfaction.