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

Author

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  • 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
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    Cited by:

    1. 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).
    2. 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).
    3. 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.
    4. 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).
    5. 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).
    6. 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).

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