Optimizing AI-Powered Service Quality for User Satisfaction and Continuous Usage

Abstract

Mobile health (mHealth) applications are increasingly integrating artificial intelligence (AI), transforming digital health technologies by making them more convenient, accessible, and personalized. This research addresses the gap in understanding how AI functionalities influence user behavior, guiding the design of effective mHealth solutions. This study examines the correlation between AI-powered service quality, user satisfaction, and continuous usage, using the Sehhaty app in Saudi Arabia as a case study. We collected data via an online survey and analyzed it using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test seven hypotheses. Results revealed that system quality significantly enhances both user satisfaction (β=0.462, p<0.05) and continuous usage (β=0.344, p<0.05). Interaction quality strongly influences user satisfaction (β=0.753, p<0.05) but not continued usage (β=0.165, p>0.05), while information quality negatively affects satisfaction (β=-0.324, p<0.05) and does not directly impact continued usage (β=-0.216, p>0.05). User satisfaction emerged as a crucial predictor of continued usage (β=0.587, p<0.05). These findings emphasize the need for user-centric design in mHealth apps to enhance satisfaction and sustain long-term usage. For developers, healthcare organizations, and policymakers, this research underscores the importance of balancing system efficiency, interaction quality, and information relevance to maximize the potential of AI-powered mHealth solutions. Further research is needed to explore how these dimensions collectively shape long-term usage.