A multi-analytical approach to studying customers motivations to use innovative totally autonomous vehicles

McLeay, Fraser ORCID: https://orcid.org/0000-0002-5535-1591, Olya, Hossein, Liu, Hongfei ORCID: https://orcid.org/0000-0001-8539-9054, Jayawardhena, Chanaka ORCID: https://orcid.org/0000-0003-0928-9835 and Dennis, Charles ORCID: https://orcid.org/0000-0001-8793-4823 (2022) A multi-analytical approach to studying customers motivations to use innovative totally autonomous vehicles. Technological Forecasting and Social Change, 174 , 121252. ISSN 0040-1625 [Article] (doi:10.1016/j.techfore.2021.121252)

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Abstract

Increasing technological innovation means level 5 fully autonomous vehicle pods (AVPs) that do not require a human driver are approaching reality. However, the adoption of AVPs continues to lag behind predictions. In this paper, we draw on Mowen's (2000) 3M model taking a multi-analytical approach utilising PLS-SEM and fuzzy set qualitative comparative analysis, to investigate how personality trait sets motivate consumers to adopt AVPs. Based on a survey of 551 US respondents, we identify four necessary traits and five combinations of traits that predict adoption. We contribute to consumer psychology theory by advancing the understanding of the motivational mechanisms of consumers’ adoption of autonomous vehicles that are triggered and operationalised by personality traits and conceptualising innovativeness as a complex multidimensional construct. From a managerial perspective, our findings highlight the significance of incorporating elements that are congruent with target customers’ personality traits, when designing, manufacturing and commercializing innovative products.

Item Type: Article
Research Areas: A. > Business School > Marketing, Branding and Tourism
Item ID: 33957
Notes on copyright: © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
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Depositing User: Jisc Publications Router
Date Deposited: 12 Oct 2021 08:05
Last Modified: 21 Oct 2021 14:12
URI: https://eprints.mdx.ac.uk/id/eprint/33957

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