Acceptance towards advanced driver assistance systems (ADAS): A validation of the unified model of driver acceptance (UMDA) using structural equation modelling

Abstract

Advanced Driver Assistance Systems (ADAS) are linked with enhanced transportation system efficiency and safety. However, prior research highlights the indispensable role of drivers’ acceptance in realizing these benefits. Several models have been introduced to investigate drivers’ acceptance of ADAS. This study examined the predictive validity of the Unified Model of Driver Acceptance (UMDA) using Structural Equation Modelling (SEM), expanding beyond multiple regression utilized in the past and taking the full conceptual Model of Driver Acceptance proposed by Rahman et al. (2018) as the theoretical starting point. Specifically, we focused on Belgian car drivers’ acceptance towards a retrofitted ADAS bundle, encompassing forward collision warning, headway monitoring and warning, and lane-keep assist. Because of the new European law, the provision of certain ADAS technologies in vehicles will be mandatory, and this will accelerate the retrofitting of ADAS into older vehicles. Therefore, knowing the determinants of drivers’ acceptance of retrofitted systems is important. Data was collected through an online structured questionnaire based on the UMDA framework. The final dataset consisting of responses from 322 participants underwent Principal Component Analysis (PCA) to identify the factor structure, Confirmatory Factor Analysis (CFA) to verify it, and path analysis to reveal determinants of ADAS acceptance. Additionally, the study employed independent sample t-test and ANOVA to assess how driver-related variables moderated the model’s constructs. Descriptive findings indicated moderate overall acceptance, with participants generally favouring system-specific characteristics. PCA and CFA identified and confirmed four distinct factors: endorsement, attitude, usability, and affordability. SEM path analysis unveiled that attitude played a dual role— mediating the impact of affordability and endorsement on behavioural intention to use ADAS as well as exerting a direct effect on behavioural intention. Other variables directly impacting behavioural intention included endorsement and usability. Demographic and driving-related variables moderated the influence of system-related factors on intention to use ADAS. To conclude, the study identified a factor structure distinct from the original UMDA hypothesis. Moreover, the application of SEM provides novel insights into ADAS acceptance that could be helpful, specifically in the EU context where ADAS retrofitting into vehicles is under consideration. The findings suggest that policymakers should prioritize strategies encouraging ADAS adoption and use. This prioritization should extend beyond shaping decision-making processes alone.