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

By R. Acheampong, Federico Cugurullo. Transportation Research Part F: Traffic Psychology and Behaviour, 2019

295 citations recorded by the source corpus.

Abstract 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.

Subfields: Automotive Engineering, Marketing, Transportation

View Paper: https://www.semanticscholar.org/paper/38cfe64046f712b9ebc029f63a24e4236ade4f54

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Citations in the corpus

A multi-level model on automated vehicle acceptance (MAVA): a review-based study (2019, background): “Acheampong and Cugurullo (2019) found a positive relationship between subjective norm (equivalent to social influence) and the perceived benefits (equivalent to performance expectancy, see Xu et al., 2018; Zhang et al., 2019), and ease of use of automated driving technology (equivalent to effort…”
Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2023, unknown): “[247 e 249]).”
Factors influencing autonomous vehicle adoption: an application of the technology acceptance model and innovation diffusion theory (2020, methodology): “Secondly, some studies use the abstractive PU and PEOU factors as the perceived characteristics of AVs to analyse user acceptance (Acheampong and Cugurullo 2019; Dirsehan and Can 2020).”
Using the UTAUT2 model to explain public acceptance of conditionally automated (L3) cars: A questionnaire study among 9,118 car drivers from eight European countries (2020, background): “Effects of social influence on the other UTAUT2 constructs Acheampong and Cugurullo (2019) revealed positive relationships between subjective norm (i.e., equivalent to social influence) and the perceived benefits of automated vehicles, and the ease of use of automated driving technology, and a…”