DOI: 10.1155/atr/8212512 ISSN: 0197-6729

Determinants of Mobility‐As‐a‐Service Adoption Intentions: A UTAUT2‐Based Comparative Analysis of Car and Public Transport Users in the Greater Accra Area

Eugene Sogbe, Susilawati Susilawati, Graham Currie

As urban areas in low‐ and middle‐income countries face rising car dependence, congestion, and fragmented transport systems, Mobility‐as‐a‐Service (MaaS) is increasingly proposed as an integrated mobility solution. However, empirical evidence remains limited on MaaS adoption in preimplementation contexts, particularly whether these factors differ between traveler groups. This study examines the determinants of MaaS adoption intention and its behavioral implications among car and public transport users in Greater Accra, Ghana. Drawing on data from a structured survey ( n  = 1243), structural equation modeling was employed to estimate and compare UTAUT2‐based adoption models for private car and public transport users. The results indicate that performance expectancy, effort expectancy, hedonic motivation, and price value significantly influence MaaS adoption intentions. Effort expectancy is the strongest predictor among car users, whereas the price value was the most influential predictor for public transport users; formal multigroup comparisons further indicated that the effects of effort expectancy and price value differed significantly between the groups, highlighting distinct decision‐making priorities. Mann–Whitney U test and one‐way ANOVA further show that gender, age, and household size significantly shape adoption relationships among public transport users, suggesting a more heterogeneous response pattern, while no significant differences were observed among car users. By examining MaaS adoption in a preimplementation setting, this study advances understanding of behavioral heterogeneity between private car and public transport users while providing context‐specific evidence from an underrepresented emerging urban environment. The findings offer practical insights for designing inclusive and scalable MaaS strategies in cities where such systems are yet to be implemented.

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