What drives support for self-driving car technology in the United States?
Graham Dixon,
P. Sol Hart,
Christopher Clarke,
Nicole H. O’Donnell and
Jay Hmielowski
Journal of Risk Research, 2020, vol. 23, issue 3, 275-287
Abstract:
Recent advances in automotive technology have made fully automated self-driving cars technologically feasible. Despite offering many benefits such as increased safety, improved fuel efficiency, and greater disability access, public support for self-driving cars remains low. While previous studies find that demographic factors such as age and sex influence self-driving car support, limited research has examined variables that are well known to predict public attitudes toward emerging technology. Using self-report data from a quota sample of American adults (N = 1008), we find that age and sex are not significantly associated with support for self-driving car policies when controlling for these other variables. Instead, significant predictors of support included trust in automotive institutions and regulatory bodies, recognition of self-driving car benefits, positive affect toward self-driving cars, and a greater perception that human-driven cars are riskier than self-driving cars. Importantly, we also find that individualism is negatively associated with support. That is, people who value personal autonomy and limited government regulation may perceive policies encouraging self-driving car use as threatening to their worldviews. Altogether, our results suggest strategies for encouraging greater public support of self-driving vehicles while also forecasting potential barriers as this technology emerges as a fixture in transportation policy.
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://hdl.handle.net/10.1080/13669877.2018.1517384 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:jriskr:v:23:y:2020:i:3:p:275-287
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RJRR20
DOI: 10.1080/13669877.2018.1517384
Access Statistics for this article
Journal of Risk Research is currently edited by Bryan MacGregor
More articles in Journal of Risk Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().