34. ALGORITHMIC TRANSPARENCY, TRUST IN ARTIFICIAL INTELLIGENCE AND INTENTION TO USE SMART FINANCIAL SERVICES IN MOROCCO: THE ROLES OF PERCEIVED RISK AND SATISFACTION

This study investigates the effect of algorithmic transparency on trust in artificial intelligence (AI), perceived risk, user satisfaction and intention to use smart financial services in Morocco. AI is spreading rapidly in digital banking through robo-advisors, recommendation tools, chatbots and automated detection systems, yet the psychological mechanisms through which transparency shapes the sustained intention to use these services remain insufficiently explored. Based on a survey of 256 Moroccan users of smart financial services, the study adopts a positivist, cross-sectional approach with multi-item measures on five-point Likert scales. The analysis combines descriptive statistics, reliability and validity tests, common method bias tests, correlation analysis, multiple regression and mediation analysis. The results show that algorithmic transparency increases trust, reduces perceived risk and enhances satisfaction. Trust and satisfaction positively influence intention to use, whereas perceived risk has a negative influence. Although algorithmic transparency has no significant direct effect on intention to use, its influence operates indirectly through trust and satisfaction, which act as relational and perceptual mediators. The findings contribute to the growing literature on the acceptance of AI-based smart financial services by showing, from an emerging-market perspective, the theoretical and practical value of algorithmic transparency. They also underline the importance of intelligible transparency embedded in the user experience, rather than formal disclosures alone.