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

Autonomous Vehicles (AVs) have the potential to make motorized transport safer and more sustainable, by integrating clean technologies and supporting flexible shared-mobility services. Leveraging this new form of transport to transform mobility in cities will depend fundamentally on public acceptance of AVs, and the ways in which individuals choose to use them, to meet their daily travel needs. Empirical studies exploring public attitudes towards automated driving technologies and interest in AVs have emerged in the last few years. However, within this strand of research there is a paucity of theory-driven and behaviourally consistent methodologies to unpack the determinants of user adoption decisions with respect to AVs. In this paper, we seek to fill this gap, by advancing and testing four conceptual frameworks which could be deployed to capture the range of possible behavioural influences on individuals’ AV adoption decisions. The frameworks integrate socio-demographic variables and relevant latent behavioural factors, including perceived benefits and perceived ease of use of AVs, public fears and anxieties regarding AVs, subjective norm, perceived behavioural control, and attitudinal factors covering the environment, technology, collaborative consumption, public transit and car ownership. We demonstrate the utility and validity of the frameworks, by translating the latent variables into indicator items in a structured questionnaire, and administering it online to a random sample of adult individuals (n = 507). Using the survey data in confirmatory factor analyses, we specify and demonstrate scale reliability of indicator items, and convergent and discriminant validity of relationships among latent variables. Ultimately, we advance four measurement models. These theory-grounded measurement models are intended for application in research aimed at understanding and predicting (a) AV interest and adoption intentions, and (b) user adoption decisions regarding three different AV modes: ownership, sharing and public transport.