The Impact of Generative AI Service Quality on Continued Use Intention: Focusing on the Mediating Effects of Trust and Satisfaction

This study examined the impact of generative AI service quality on users’ continuance intention based on service quality theory and further verified the mediating roles of trust and satisfaction. Generative AI service quality was classified into four dimensions: assurance, reliability, responsiveness, and tangibility. Trust and satisfaction were set as mediating variables, while continuance intention was treated as the outcome variable. Based on these constructs, a research model and related hypotheses were developed. The study targeted users with experience using generative AI services. A total of 255 questionnaires were collected, and after excluding invalid responses, 234 valid samples were used for empirical analysis. SmartPLS 4 was employed to conduct the statistical analysis. The results revealed that assurance and reliability had significant positive effects on trust. In addition, assurance, reliability, responsiveness, and tangibility all had significant positive effects on satisfaction. Furthermore, both trust and satisfaction significantly and positively influenced continuance intention. However, responsiveness and tangibility did not have significant effects on trust, and the effect of trust on satisfaction was also not supported. This study contributes to the expansion of research on generative AI service quality and provides theoretical implications and practical insights for generative AI service platforms seeking to enhance users’ continuance behavior.