This study investigates key factors influencing user adoption of Generative Artificial Intelligence (Generative AI) by extending the Information System Success Model (ISSM) with the construct of trust. Based on 290 valid responses from Taiwan and analyzed using Structural Equation Modeling (SEM), the study examines the effects of system quality, information quality, and service quality on trust, user satisfaction, and intention to reuse. Findings show that information and service quality significantly enhance both trust and satisfaction, while system quality mainly affects satisfaction but not trust. Trust and satisfaction are strong predictors of reuse intention, indicating a shift in emphasis from system stability to content accuracy, responsiveness, and personalization. The study contributes theoretically by refining ISSM for the Generative AI context and offers practical guidance for organizations to prioritize output quality and tailored services over technical performance. Limitations regarding geographic scope are noted, suggesting the need for cross-national and longitudinal research to further validate the model.