IDEAS home Printed from https://ideas.repec.org/a/taf/transp/v43y2020i3p253-278.html
   My bibliography  Save this article

A statistical analysis of consumer perceptions towards automated vehicles and their intended adoption

Author

Listed:
  • Nikhil Menon
  • Yu Zhang
  • Abdul Rawoof Pinjari
  • Fred Mannering

Abstract

While automated vehicle (AV) development continues to progress rapidly, how the public will accept and adopt automated vehicles remains an open question. Using extensive survey data, we apply cluster analysis to better understand consumer perceptions toward potential benefits and concerns related to AVs with regard to factors influencing their AV adoption likelihood. Four market segments are identified – ‘benefits-dominated,’ ‘concerns-dominated,’ ‘uncertain,’ and ‘well-informed.’ A random parameters multinomial logit model is then estimated to identify factors influencing the probability of respondents belonging to one of these four market segments. Among other influences (such as socio-economic and current travel characteristics), it is found that ‘Millennials’ have a higher probability of belonging to the well-informed market segment, ‘Gen-Xers’ with a lower probability to the uncertain market segment, and ‘Baby Boomers’ with a higher probability to the concerns-dominated market (relative to the ‘Great Generation’). We also study the individuals’ expressed likelihood of AV adoption using separate random parameters ordered probit estimations for each of the four market segments. The substantial and statistically significant differences across each AV consumer market segment underscore the potentially large impact that different consumer demographics may have on AV adoption and the need for targeted marketing to achieve better market-penetration outcomes.

Suggested Citation

  • Nikhil Menon & Yu Zhang & Abdul Rawoof Pinjari & Fred Mannering, 2020. "A statistical analysis of consumer perceptions towards automated vehicles and their intended adoption," Transportation Planning and Technology, Taylor & Francis Journals, vol. 43(3), pages 253-278, April.
  • Handle: RePEc:taf:transp:v:43:y:2020:i:3:p:253-278
    DOI: 10.1080/03081060.2020.1735740
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03081060.2020.1735740
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/03081060.2020.1735740?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Koh, Le Yi & Xia, Zhiyang & Yuen, Kum Fai, 2024. "Consumer acceptance of the autonomous robot in last-mile delivery: A combined perspective of resource-matching, perceived risk and value theories," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
    2. Mishra, Sabyasachee & Sharma, Ishant & Pani, Agnivesh, 2023. "Analyzing autonomous delivery acceptance in food deserts based on shopping travel patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    3. Konstantinou, Theodora & Gkritza, Konstantina, 2023. "Are we getting close to truck electrification? U.S. truck fleet managers’ stated intentions to electrify their fleets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    4. Mohammadhossein Abbasi & Amir Reza Mamdoohi & Grzegorz Sierpiński & Francesco Ciari, 2023. "Usage Intention of Shared Autonomous Vehicles with Dynamic Ride Sharing on Long-Distance Trips," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    5. Hassan, Hany M. & Ferguson, Mark R. & Vrkljan, Brenda & Newbold, Bruce & Razavi, Saiedeh, 2021. "Older adults and their willingness to use semi and fully autonomous vehicles: A structural equation analysis11Revised manuscript prepared for publication at the special issue in Journal of Transport G," Journal of Transport Geography, Elsevier, vol. 95(C).
    6. Dai, Jingchen & Wang, Xiaokun Cara & Ma, Wenxin & Li, Ruimin, 2023. "Future transport vision propensity segments: A latent class analysis of autonomous taxi market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    7. Yuen, Kum Fai & Chua, Jessana & Li, Kevin X. & Wang, Xueqin, 2022. "Consumer's adoption of virtual reality technologies for marine conservation: Motivational and technology acceptance perspectives," Technological Forecasting and Social Change, Elsevier, vol. 182(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:transp:v:43:y:2020:i:3:p:253-278. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GTPT20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.