Abstract
Automated vehicles (AVs) have garnered increasing attention since they have the potential to dramatically reshape our lives in the near future. At the same time, people are concerned about various risks associated with the new technologies. Thus, people’s attitudes toward AVs pose a major challenge to the wider adoption of them. Previous studies examined the effect of benefit/risk perception on people’s acceptance of AVs, but they did not address the multidimensionality of benefit/risk perception. We conducted a survey (n = 840) to reveal the underlying dimensions of how people construe the benefits and risks of conditionally/fully automated vehicles. Our results showed that there were two dimensions underlying benefit perception (i.e., the perception that AVs would increase convenience and reduce harm) and three dimensions underlying risk perception (i.e., the perception of risk to physical safety and comfort, cybersecurity, and ease of use). The perception that AVs would reduce harm positively impacted people’s intention to use both fully automated vehicles and conditionally automated vehicles. The perception that AVs would increase convenience and the perception that AVs would pose a risk to ease of use had a positive and negative effect, respectively, on intention to use fully automated vehicles. This study makes theoretical contributions by questioning the assumption that benefit/risk perception is a one-dimensional factor that impacts people’s acceptance of AVs. This study also has practical implications as it suggests an effective method for automobile manufacturers and policymakers to communicate with the public regarding the new technologies and diffuse them safely.
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Data availability
We uploaded data as supplementary material. We described the detail of the questionnaire in the manuscript, and the original version (written in Japanese) is available upon request.
Code availability
We uploaded R code as supplementary material.
Notes
Some researchers suggest that AVs might increase traffic congestion and fuel consumption due to an increase in travel demand (Chen et al. 2019; Greenwald and Kornhauser 2019; Kellett et al. 2019). Although considering such side effects is important, we focused on immediate effects in the present study. See Chapter 4 for further discussions.
References
Bansal, P., Kockelman, K.M., Singh, A.: Assessing public opinions of and interest in new vehicle technologies: an Austin perspective. Transp. Res. Part c Emerg. Technol. 67, 1–14 (2016). https://doi.org/10.1016/j.trc.2016.01.019
Bearth, A., Siegrist, M.: Are risk or benefit perceptions more important for public acceptance of innovative food technologies: a meta-analysis. Trends Food Sci. Technol. 49, 14–23 (2016). https://doi.org/10.1016/j.tifs.2016.01.003
Bogost, I.: Can you sue a robocar? The Atlantic. (2018) Retrieved from https://www.theatlantic.com/technology/archive/2018/03/can-you-sue-a-robocar/556007/
Brell, T., Philipsen, R., Ziefle, M.: sCARy! Risk perceptions in autonomous driving: the influence of experience on perceived benefits and barriers. Risk Anal. 39(2), 342–357 (2019). https://doi.org/10.1111/risa.13190
Cangur, S., Ercan, I.: Comparison of model fit indices used in structural equation modeling under multivariate normality. J. Mod. Appl. Stat. Methods 14(1), 152–167 (2015). https://doi.org/10.22237/jmasm/1430453580
Chen, Y., Gonder, J., Young, S., Wood, E.: Quantifying autonomous vehicles national fuel consumption impacts: a data-rich approach. Transp. Res. Part a Policy Pract. 122, 134–145 (2019). https://doi.org/10.1016/j.tra.2017.10.012
Choi, J.K., Ji, Y.G.: Investigating the importance of trust on adopting an autonomous vehicle. Int. J. Hum. Comput. Interact. 31(10), 692–702 (2015). https://doi.org/10.1080/10447318.2015.1070549
Continental, A.G.: The 2018 mobility study: Mobility of tomorrow – where will the road take us? (2018) Retrieved from https://www.continental.com/en/press/initiatives-surveys/continental-mobility-studies/mobility-study-2018/the-2018-mobility-study-148298
Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35(8), 982–1003 (1989). https://doi.org/10.1287/mnsc.35.8.982
Dinno, A.: Exploring the sensitivity of Horn’s parallel analysis to the distributional form of simulated data. Multivar. Behav. Res. 44(3), 362–388 (2009). https://doi.org/10.1080/00273170902938969
Fagnant, D.J., Kockelman, K.: Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp. Res. Part a Policy Pract. 77, 167–181 (2015). https://doi.org/10.1016/j.tra.2015.04.003
Fukunaka, K.: Multiple-group analysis. In Toyota, H. (Ed.), Covariance Structure Analysis for R, pp. 101–112 (2014)
Governors Highway Safety Association: Autonomous vehicles meet human drivers: Traffic safety issues for states. (2017) Retrieved from https://www.ghsa.org/sites/default/files/2017-01/AV%202017%20-%20FINAL.pdf
Greenwald, J.M., Kornhauser, A.: It’s up to us: policies to improve climate outcomes from automated vehicles. Energy Policy 127, 445–451 (2019). https://doi.org/10.1016/j.enpol.2018.12.017
Harper, C.D., Hendrickson, C.T., Mangones, S., Samaras, C.: Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions. Transp. Res. Part c Emerg. Technolo. 72, 1–9 (2016). https://doi.org/10.1016/j.trc.2016.09.003
Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? The influence of emotions across different age groups. Transp. Res. Part a Policy Pract. 94, 374–385 (2016). https://doi.org/10.1016/j.tra.2016.09.022
Horn, J.L.: A rationale and test for the number of factors in factor analysis. Psychometrika 30, 179–185 (1965). https://doi.org/10.1007/BF02289447
Institute for Electrical and Electronics Engineers. (n.d.): New Level 3 autonomous vehicles hitting the road in 2020. IEEE Innovation at Work. Retrieved May 2, 2020, from https://innovationatwork.ieee.org/new-level-3-autonomous-vehicles-hitting-the-road-in-2020/
Japan’s Ministry of Land, Infrastructure, Transport and Tourism: Guideline regarding safety technology for automated vehicles in Japan. (2018) Retrieved from https://www.mlit.go.jp/common/001253665.pdf
Kellett, J., Barreto, R., Hengel, A.V.D., Vogiatzis, N.: How might autonomous vehicles impact the city? The case of commuting to central Adelaide. Urban Policy Res. 37(4), 442–457 (2019). https://doi.org/10.1080/08111146.2019.1674646
Kock, N., Lynn, G.: Lateral collinearity and misleading results in variance-based SEM: an illustration and recommendations. J. Assoc. Inf. Syst. 13(7), 546–580 (2012)
Kolodny, L., Schoolov, K., & Evers, A.: Take a peek inside Lyft’s lab where 400 engineers are working on self-driving cars. CNBC. (2019) Retrieved from https://www.cnbc.com/2019/11/05/lyft-is-developing-self-driving-cars-at-its-level-5-lab-in-palo-alto.html
König, M., Neumayr, L.: Users’ resistance towards radical innovations: the case of the self-driving car. Transp. Res. f Traffic Psychol. Behav. 44, 42–52 (2017). https://doi.org/10.1016/j.trf.2016.10.013
Kyriakidis, M., Happee, R., Winter, J.D.: Public opinion on automated driving: results of an international questionnaire among 5000 respondents. Transp. Res. f Traffic Psychol. Behav. 32, 127–140 (2015). https://doi.org/10.1016/j.trf.2015.04.014
Levin, S., Wong, J.C.: Self-driving Uber kills Arizona woman in first fatal crash involving pedestrian. The Guardian. (2018). Retrieved from https://www.theguardian.com/technology/2018/mar/19/uber-self-driving-car-kills-woman-arizona-tempe
Liu, P., Xu, Z., Zhao, X.: Road tests of self-driving vehicles: Affective and cognitive pathways in acceptance formation. Transp. Res. Part a Policy Pract. 124, 354–369 (2019a). https://doi.org/10.1016/j.tra.2019.04.004
Liu, P., Yang, R., Xu, Z.: Public acceptance of fully automated driving: Effects of social trust and risk/benefit perceptions. Risk Anal. 39(2), 326–341 (2019b). https://doi.org/10.1111/risa.13143
Madrigal, A.: Waymo’s robots drove more miles than everyone else combined. The Atlantic. (2019) Retrieved from https://www.theatlantic.com/technology/archive/2019/02/the-latest-self-driving-car-statistics-from-california/582763/
Mays, K.: Which cars have self-driving features for 2020? Cars.com. (2020) Retrieved from https://www.cars.com/articles/which-cars-have-self-driving-features-for-2020-418934/
National Highway Traffic Safety Administration: Critical reasons for crashes investigated in the national motor vehicle crash causation survey. (2015) Retrieved from https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812115
National Highway Traffic Safety Administration: Automated vehicles for safety. (2020) Retrieved May 2, 2020, from https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety
Naujoks, F., Befelein, D., Wiedemann, K., Neukum, A.: A review of non-driving-related tasks used in studies on automated driving. In: Stanton, N. (ed.) Advances in human aspects of transportation, pp. 525–537. Springer, Cham (2018)
Nielsen, T.A.S., Haustein, S.: On sceptics and enthusiasts: what are the expectations towards self-driving cars? Transp. Policy 66, 49–55 (2018). https://doi.org/10.1016/j.tranpol.2018.03.004
Nunes, A., Hernandez, K.: The cost of self-driving cars will be the biggest barrier to their adoption. Harvard Business Review. (2019) Retrieved from https://hbr.org/2019/01/the-cost-of-self-driving-cars-will-be-the-biggest-barrier-to-their-adoption
Nyholm, S., Smids, J.: Automated cars meet human drivers: responsible human-robot coordination and the ethics of mixed traffic. Ethics Inf. Technol. (2018). https://doi.org/10.1007/s10676-018-9445-9
Panagiotopoulos, I., Dimitrakopoulos, G.: An empirical investigation on consumers’ intentions towards autonomous driving. Transp. Res. Part c Emerg. Technol. 95, 773–784 (2018). https://doi.org/10.1016/j.trc.2018.08.013
R Core Team: R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2020) Retrieved from http://www.r-project.org/index.html
Raue, M., Dambrosio, L.A., Ward, C., Lee, C., Jacquillat, C., Coughlin, J.F.: The influence of feelings while driving regular cars on the perception and acceptance of self-driving cars. Risk Anal. 39(2), 358–374 (2019). https://doi.org/10.1111/risa.13267
SAE International: SAE International releases updated visual chart for its “Levels of Driving Automation” standard for self-driving vehicles. SAE Mobilus. (2018) Retrieved from https://www.sae.org/news/press-room/2018/12/sae-international-releases-updated-visual-chart-for-its-%E2%80%9Clevels-of-driving-automation%E2%80%9D-standard-for-self-driving-vehicles
Schoettle, B., Sivak, M.: A survey of public opinion about autonomous and self-driving vehicles in the US, the UK, and Australia (Technical Report No. UMTRI-2014–21). Ann Arbor, US: University of Michigan (2014)
Shariff, A., Bonnefon, J.F., Rahwan, I.: Psychological roadblocks to the adoption of self-driving vehicles. Nat. Hum. Behav. 1(10), 694–696 (2017). https://doi.org/10.1038/s41562-017-0202-6
Statistics Bureau of Japan: 2015 Population census.(2015) Retrieved from https://www.stat.go.jp/english/data/kokusei/index.html
Suda, Y., Oguchi, T.: Automated driving and mobility society. Bull. Jpn. Soc. Mech. Eng. 121, 8–11 (2018)
Tucker, L.R., MacCallum, R.C.: Exploratory factor analysis. (1997) Retrieved from http://inis.jinr.ru/sl/M_Mathematics/MV_Probability/MVas_Applied%20statistics/Tucker%20L.R.,%20MacCallum%20R.C.%20Exploratory%20factor%20analysis%20(1997)(459s).pdf
Turner, A.: Toyota to invest $100 million in self-driving and robotic technology start-ups. CNBC. (2020) Retrieved from https://www.cnbc.com/2019/05/02/toyota-to-invest-100-million-in-autonomous-driving-and-robotic-startups.html
U.K.’s Department for Transport: The pathway to driverless cars: a code of practice for testing. (2015) Retrieved from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/446316/pathway-driverless-cars.pdf
U.S. National Science and Technology Council & Department of Transportation: Ensuring American leadership in automated vehicle technologies: Automated vehicles 4.0. (2020) Retrieved from https://www.transportation.gov/sites/dot.gov/files/docs/policy-initiatives/automated-vehicles/360956/ensuringamericanleadershipav4.pdf
Vandenberg, R.J., Lance, C.E.: A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organ. Res. Methods 3, 4–70 (2000). https://doi.org/10.1177/109442810031002
Vivek, S., Yanni, D., Yunker, P.J., Silverberg, J.L.: Cyberphysical risks of hacked internet-connected vehicles. Phys. Rev. E 100(1), 012316 (2019). https://doi.org/10.1103/physreve.100.012316
Wakabayashi, D: Waymo includes outsiders in $2.25 billion investment round. The New York Times. (2020) Retrieved from https://www.nytimes.com/2020/03/02/technology/waymo-outside-investors.html
Watkins, M.W.: Exploratory factor analysis: a guide to best practice. J. Black Psychol. 44(3), 219–246 (2018). https://doi.org/10.1177/0095798418771807
World Health Organization: Road traffic injuries. (2020) Retrieved from https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
Xu, Z., Zhang, K., Min, H., Wang, Z., Zhao, X., Liu, P.: What drives people to accept automated vehicles? Findings from a field experiment. Transp. Res. Part c Emerg. Technol. 95, 320–334 (2018). https://doi.org/10.1016/j.trc.2018.07.024
Yves, R.: lavaan: an R package for structural equation modelling. J. Stat. Softw. 48(2), 1–36 (2012). https://doi.org/10.18637/jss.v048.i02
Zhang, T., Tao, D., Qu, X., Zhang, X., Lin, R., Zhang, W.: The roles of initial trust and perceived risk in public’s acceptance of automated vehicles. Transp. Res. Part c Emerg. Technol. 98, 207–220 (2019). https://doi.org/10.1016/j.trc.2018.11.018
Funding
This work was supported by JSPS KAKENHI Grant-in-Aid for Scientific Research (B) (16H03726) and JSPS KAKENHI Grant-in-Aid for Early-Career Scientists (18K13266). The funder had no role in study design, data collection, analysis, and preparation of the manuscript.
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All authors contributed to the study conception, design, and material preparation. YJT conducted the analysis and wrote the first draft of the manuscript. All authors read and approved the final manuscript.
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Tham, Y.J., Hashimoto, T. & Karasawa, K. Underlying dimensions of benefit and risk perception and their effects on people’s acceptance of conditionally/fully automated vehicles. Transportation 49, 1715–1736 (2022). https://doi.org/10.1007/s11116-021-10225-0
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DOI: https://doi.org/10.1007/s11116-021-10225-0