Predicting intentions to adopt AI-based transport management systems for sustainable mobility in an uncertain world: An extended UTAUT model
Research in Transportation Economics, vol.117, 2026 (SSCI, Scopus)
- Publication Type: Article / Article
- Volume: 117
- Publication Date: 2026
- Doi Number: 10.1016/j.retrec.2026.101787
- Journal Name: Research in Transportation Economics
- Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, EconLit
- Keywords: Artificial intelligence, Compatibility, Environmental concern, R40, R41, Sustainable transport, Trust, Urban resilience
- Azerbaijan State University of Economics (UNEC) Affiliated: Yes
Abstract
This study extends the Unified Theory of Acceptance and Use of Technology2 by incorporating image, trust, environmental concern and compatibility as additional predictors of artificial intelligence-based transport management system (AITMS) adoption. Also, the study introduces compatibility as a moderator of key relationships in the model, revealing how perceived fit with existing infrastructure and lifestyle shapes the intentions to adopt AITMS in Pakistan. Data were collected from 684 AI application users by adopting a structured questionnaire and analyzed using partial least squares structural equation modeling. The findings reveal that performance expectancy, environmental concern, effort expectancy, hedonic motivation, trust, and compatibility positively influence, while image has a negative influence on the intentions to adopt AITMS. Social influence and facilitating conditions are found insignificant. Further, compatibility significantly and negatively moderates the effort expectancy-intentions relationship and positively moderates the trust-intentions relationship. Theoretically, compatibility performs a dual function by dampening the influence of effort expectancy while strengthening the influence of trust on intentions to adopt AITMS. Practically, the importance-performance map analysis identifies compatibility and trust as high-importance, low-performance variables that policymakers and system designers should prioritize by enhancing system compatibility and fostering user trust to accelerate AI transport adoption in developing economies.