Capturing the behavioural determinants behind the adoption of autonomous vehicles: Conceptual frameworks and measurement models to predict public transport, sharing and ownership trends of self-driving cars

Acheampong, Ransford A.; Cugurullo, Federico · 2019 · OpenAlex-citations

DOI: 10.1016/j.trf.2019.01.009

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Summary

This paper addresses the lack of theory-driven methodologies for understanding the behavioral determinants of autonomous vehicle (AV) adoption. While AVs promise safer and more sustainable transport, existing research often relies on narrow metrics that fail to capture the complex interplay of socio-economic, cultural, and psychological factors influencing user decisions. The authors aim to fill this gap by developing and validating comprehensive conceptual frameworks to predict public acceptance and adoption trends across three distinct AV modes: ownership, sharing, and public transport. To achieve this, the authors synthesized principles from the Theory of Planned Behavior, Socio-Ecological Models, Technology Acceptance Model, and Technology Diffusion Theory. They constructed four interconnected conceptual frameworks (CM-1 through CM-4). CM-1 serves as a baseline model for general AV interest, incorporating latent variables such as perceived benefits, ease of use, subjective norms, and fears. CM-2, CM-3, and CM-4 extend this baseline to address specific adoption modes by integrating attitudes toward collaborative consumption, public transit, and car ownership, respectively. The researchers translated these latent variables into a structured questionnaire using 7-point Likert scales. After a pilot study refined the instrument from 99 to 54 items, the final survey was administered online to a random sample of 507 adults in the Greater Dublin Area. The study employed Confirmatory Factor Analysis (CFA) to test the validity and reliability of the proposed measurement models. The authors evaluated model fit using indices such as the Comparative Fit Index and Root Mean Square Error of Approximation. They assessed internal consistency via Cronbach’s Alpha, convergent validity through Average Variance Explained, and discriminant validity using Maximum Shared Variance. The results demonstrated that the indicator items possessed sufficient scale reliability and that the hypothesized relationships among latent variables exhibited convergent and discriminant validity. Consequently, the authors successfully specified four robust measurement models that accurately reflect the theoretical constructs of AV adoption behavior. The significance of this work lies in providing the transportation research field with validated, theory-grounded tools to analyze AV diffusion. By moving beyond simple financial or safety metrics, these frameworks allow researchers to unpack the nuanced behavioral influences—such as environmental concerns and collaborative consumption ethos—that drive adoption decisions. The resulting models are designed to predict not only general interest in self-driving cars but also specific user choices regarding whether to own, share, or use AVs as public transport, thereby supporting more accurate forecasting of future mobility trends.

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• acceptance adoption

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• Empirical Findings: self report data, observational prevalence