Validating the Mediating Role of User Satisfaction in Artificial Intelligence Adoption within Abu Dhabi’s Public Transportation System

Abstract

Artificial Intelligence (AI) is increasingly recognized as a transformative tool for improving efficiency, sustainability, and decision-making in public transportation. However, its adoption is shaped not only by technical system availability but also by behavioural and user-related factors, highlighting the importance of user satisfaction in ensuring successful implementation. This study develops and validates a comprehensive framework that examines the mediating role of user satisfaction in AI adoption within Abu Dhabi’s public transportation sector. A quantitative research design was employed, with data collected through a structured questionnaire distributed to employees of the Abu Dhabi Department of Transport and affiliated public transportation entities. Using a convenience non-probability sampling technique, 325 valid responses were obtained from full-time employees across operational, technical, supervisory, and managerial roles. The framework was validated using Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS. The analysis included reliability and validity testing, measurement and structural model assessment, path analysis, predictive relevance, model fit evaluation, and mediation analysis. The results confirm that system availability significantly influences both user satisfaction and behavioural intention to adopt AI, with satisfaction partially mediating this relationship. The validated model explained 73.9% of the variance in behavioural intention, underscoring the importance of reliable, accessible, secure, and user-friendly AI systems supported by appropriate training and responsive support. This user-centred framework provides practical insights for policymakers and transport authorities, positioning satisfaction as a critical driver of sustainable AI adoption.