Abstract
This paper proposes a well-grounded theoretical model to assess the factors influencing the intention to ride in autonomous vehicles (AVs). The model is based on the Theory of Planned Behavior (TPB), which has been decomposed to account for key components of the Diffusion of Innovation (DoI) theory and extended to include other influential attitudinal components (such as driving-related sensation seeking, safety perceptions, environmental concerns, and affinity to innovativeness). The extent to which these factors are expected to affect the diffusion of AVs uniformly across different urban settings is also examined. Data were collected through stated preference surveys targeting adult residents in three metropolitan statistical areas, Chicago (Illinois), Indianapolis (Indiana), and Phoenix (Arizona). Confirmatory factor analysis was conducted to test the validity and reliability of the components included in the theoretical model, followed by the estimation of a multi-group structural equation model. The findings of the measurement model show that the survey questions are measured equally across the three areas, and hence, the theoretical model is transferrable. The results of the structural model suggest that the synergistic effects between TPB and DoI can better explain the behavioral intention to ride in AVs. It was also found that the effect of the TBP components is similar across various areas; however, this is not the case for the DoI components. In general, the findings reinforce the need for wider testing of AV technology in urban areas coupled with public education campaigns to harvest public awareness and acceptance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of data and materials
The data used in this manuscript is not available compelling with IRB Protocols, which restrict access to the principal investigator and the member of the teams listed in the IRB agreement.
Code availability
The code used for the models presented in this manuscript will be available upon request at lllosadar@gmail.com. The model was run using STATA 15 version.
Notes
The indirect effects are calculated by multiplying the different paths associated with each latent variable. For example, the indirect effect of Self-Efficacy on Behavioral Intention, which was found to be similar in all MSAs, can be calculated by multiplying 0.966*0.533 = 0.051, as also shown in Table 6.
References
Aarts, H., Paulussen, T., Schaalma, H.: Physical exercise habit: on the conceptualization and formation of habitual health behaviours. Health Educ. Res. 12(3), 363–374 (1997). https://doi.org/10.1093/her/12.3.363
Abraham, H., Lee, C., Brady, S., Fitzgerald, C., Reimer, B., & Coughlin, J. F. (2016). Autonomous vehicles, trust, and driving alternatives: a survey of consumer preferences. 16.
Acheampong, R.A., Cugurullo, F.: Capturing the behavioural determinants behind the adoption of autonomous vehicles: conceptual frameworks and measurement models to predict public transport, sharing and ownership trends of self-driving cars. Transport. Res. F Traffic Psychol. Behav. 62, 349–375 (2019). https://doi.org/10.1016/j.trf.2019.01.009
Adell, E. (2010). Acceptance of driver support systems. Drivers’ Needs and Acceptance of Assistance Functions, 12.
Adnan, N., Md Nordin, S., bin Bahruddin, M. A., & Ali, M. (2018). How trust can drive forward the user acceptance to the technology? In-vehicle technology for autonomous vehicle. Transp. Res. Part A Policy Pract. 118, 819–836.https://doi.org/10.1016/j.tra.2018.10.019
Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991). https://doi.org/10.1016/0749-5978(91)90020-T
Allen, J., Muñoz, J.C., de Dios Ortúzar, J.: On the effect of operational service attributes on transit satisfaction. Transportation 47(5), 2307–2336 (2020). https://doi.org/10.1007/s11116-019-10016-8
Alumran, A., Hou, X.-Y., Sun, J., Yousef, A.A., Hurst, C.: Assessing the construct validity and reliability of the parental perception on antibiotics (PAPA) scales. BMC Public Health 14, 73 (2014). https://doi.org/10.1186/1471-2458-14-73
Anderson, J. M., Kalra, N., Stanley, K. D., Sorensen, P., Samaras, C., Oluwatola, T. A. (2016). Autonomous vehicle technology: a guide for policymakers. https://www.rand.org/pubs/research_reports/RR443-2.html
Arnett, J.J.: Sensation seeking, aggressiveness, and adolescent reckless behavior. Personality Individ. Differ. 20(6), 693–702 (1996). https://doi.org/10.1016/0191-8869(96)00027-X
Bamberg, S.: How does environmental concern influence specific environmentally related behaviors? A new answer to an old question. J. Environ. Psychol. 23(1), 21–32 (2003). https://doi.org/10.1016/S0272-4944(02)00078-6
Bamberg, S., Möser, G.: Twenty years after Hines, Hungerford, and Tomera: a new meta-analysis of psycho-social determinants of pro-environmental behaviour. J. Environ. Psychol. 27(1), 14–25 (2007). https://doi.org/10.1016/j.jenvp.2006.12.002
Bandura, A. (1997). Self-Efficacy: The Exercise of Control (1st Edition). Worth Publishers.
Bandura, A.: Guide for constructing self-efficacy scales. Self-Efficacy Beliefs Adolesc. 5, 307–337 (2006)
Bansal, P., Kockelman, K.M.: Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies. Transp. Res. Part A Policy Pract. 95, 49–63 (2017). https://doi.org/10.1016/j.tra.2016.10.013
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
Bateman, I. (Ed.). (2002). Economic Valuation with Stated Preference Techniques: A Manual. Edward Elgar.
Beck, L., Ajzen, I.: Predicting dishonest actions using the theory of planned behavior. J. Res. Pers. 25(3), 285–301 (1991). https://doi.org/10.1016/0092-6566(91)90021-H
Becker, F., Axhausen, K.W.: Literature review on surveys investigating the acceptance of automated vehicles. Transportation 44(6), 1293–1306 (2017). https://doi.org/10.1007/s11116-017-9808-9
Bennett, R., Vijaygopal, R., Kottasz, R.: Willingness of people with mental health disabilities to travel in driverless vehicles. J. Transp. Health 12, 1–12 (2019). https://doi.org/10.1016/j.jth.2018.11.005
Brown, B., Drew, M., Erenguc, C., & Hasegawa, M. (2014). Global Automotive Consumer Study: The Changing Nature of Mobility—Exploring Consumer Preferences in Key Markets Around the World. https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Manufacturing/gx-mfg-geny-automotive-consumer.pdf
Buckley, L., Kaye, S.-A., Pradhan, A.K.: Psychosocial factors associated with intended use of automated vehicles: a simulated driving study. Accid. Anal. Prev. 115, 202–208 (2018). https://doi.org/10.1016/j.aap.2018.03.021
Casley, S., Jardim, A., and Quartulli, A. (2013). A Study of Public Acceptance of Autonomous Cars [Bacherlor of Science, tial fulfillment of the requirements of t]. https://web.wpi.edu/Pubs/E-project/Available/E-project-043013-155601/unrestricted/A_Study_of_Public_Acceptance_of_Autonomous_Cars.pdf
Cestac, J., Paran, F., Delhomme, P.: Young drivers’ sensation seeking, subjective norms, and perceived behavioral control and their roles in predicting speeding intention: how risk-taking motivations evolve with gender and driving experience. Saf. Sci. 49(3), 424–432 (2011). https://doi.org/10.1016/j.ssci.2010.10.007
Chiu, H., Fogel, J.: The role of manager influence strategies and innovation attributes in innovation implementation. Asia-Pacific J. Bus. Admin. 9(1), 16–36 (2017). https://doi.org/10.1108/APJBA-02-2016-0026
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
Christensen, C.M.: The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Harvard Business Review Press, Harvard (1997)
Cronbach, L.J.: Coefficient alpha and the internal structure of tests. Psychometrika 16(3), 297–334 (1951). https://doi.org/10.1007/BF02310555
Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS q. 13(3), 319–340 (1989). https://doi.org/10.2307/249008
Daziano, R.A., Bolduc, D.: Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian hybrid choice model. Transportm. A Transp. Sci. 9(1), 74–106 (2013). https://doi.org/10.1080/18128602.2010.524173
Daziano, R.A., Sarrias, M., Leard, B.: Are consumers willing to pay to let cars drive for them? Analyzing response to autonomous vehicles. Transp. Res. Part C Emerg. Technol. 78, 150–164 (2017). https://doi.org/10.1016/j.trc.2017.03.003
Delhomme, P., Chaurand, N., Paran, F.: Personality predictors of speeding in young drivers: anger vs. sensation seeking. Transp. Res. Part F Traffic Psychol. Behav. 15(6), 654–666 (2012). https://doi.org/10.1016/j.trf.2012.06.006
Delhomme, P., Verlhiac, J.-F., Martha, C.: Are drivers’ comparative risk judgments about speeding realistic? J. Saf. Res. 40(5), 333–339 (2009). https://doi.org/10.1016/j.jsr.2009.09.003
Deng, L., Yuan, K.-H.: Multiple-group analysis for structural equation modeling with dependent samples. Struct. Equ. Model. 22(4), 552–567 (2015). https://doi.org/10.1080/10705511.2014.950534
Deng, X., Doll, W.J., Al-Gahtani, S.S., Larsen, T.J., Pearson, J.M., Raghunathan, T.S.: A cross-cultural analysis of the end-user computing satisfaction instrument: a multi-group invariance analysis. Inf. Manag. 45(4), 211–220 (2008). https://doi.org/10.1016/j.im.2008.02.002
Deng, X., Doll, W.J., Hendrickson, A.R., Scazzero, J.A.: A multi-group analysis of structural invariance: an illustration using the technology acceptance model. Inf. Manag. 42(5), 745–759 (2005). https://doi.org/10.1016/j.im.2004.08.001
Donald, I. J. (2014). An extended theory of planned behaviour model of the psychological factors affecting commuters’ transport mode use. J. Environ. Psychol., 10.
Edison, S.W., Geissler, G.L.: Measuring attitudes towards general technology: antecedents, hypotheses and scale development. J. Target. Meas. Anal. Mark. 12(2), 137–156 (2003). https://doi.org/10.1057/palgrave.jt.5740104
Egbue, O., Long, S.: Barriers to widespread adoption of electric vehicles: an analysis of consumer attitudes and perceptions. Energy Policy 48, 717–729 (2012). https://doi.org/10.1016/j.enpol.2012.06.009
Eisinga, R., te Grotenhuis, M., Pelzer, B.: The reliability of a two-item scale: pearson, cronbach, or spearman-brown? Int. J. Public Health 58(4), 637–642 (2013). https://doi.org/10.1007/s00038-012-0416-3
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
Federal Highway Administration.: 2017 National Household Travel Survey. U.S. Department of Transportation, Washington, DC (2018)
Chen, F., Curran, P.J., Bollen, K.A., Kirby, J., Paxton, P.: An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models. Sociol. Methods Res. 36(4), 462–494 (2008). https://doi.org/10.1177/0049124108314720
Fincham, J.E., Draugalis, J.R.: The importance of survey research standards. Am. J. Pharm. Educ. (2013). https://doi.org/10.5688/ajpe7714
Fishbein, M., Ajzen, I.: Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading (1975)
Gardner, B., Abraham, C.: Going green? Modeling the impact of environmental concerns and perceptions of transportation alternatives on decisions to drive. J. Appl. Soc. Psychol. 40(4), 831–849 (2010). https://doi.org/10.1111/j.1559-1816.2010.00600.x
Gefen, D., Straub, D., Boudreau, M.-C.: Structural equation modeling and regression: guidelines for research practice. Commun. Assoc. Inf. Syst. (2000). https://doi.org/10.17705/1CAIS.00407
Gkartzonikas, C. (2020). A Stated Preference Study for Assessing Public Acceptance Towards Autonomous Vehicles. 2520294 Bytes. https://doi.org/10.25394/PGS.12210293.V1
Gkartzonikas, C., Gkritza, K.: What have we learned? A review of stated preference and choice studies on autonomous vehicles. Transp. Res. Part C Emerg. Technol. 98, 323–337 (2019). https://doi.org/10.1016/j.trc.2018.12.003
Golbabaei, F., Yigitcanlar, T., Paz, A., Bunker, J.: Individual predictors of autonomous vehicle public acceptance and intention to use: a systematic review of the literature. J. Open Innov. Technol. Mark. Complex. 6(4), 106 (2020). https://doi.org/10.3390/joitmc6040106
Golob, T.F.: Structural equation modeling for travel behavior research. Transp. Res. Part B Methodol. 37(1), 1–25 (2003). https://doi.org/10.1016/S0191-2615(01)00046-7
Haboucha, C.J., Ishaq, R., Shiftan, Y.: User preferences regarding autonomous vehicles. Transp. Res. Part C Emerg. Technol. 78, 37–49 (2017). https://doi.org/10.1016/j.trc.2017.01.010
Hair, J. F. (Ed.). (2010). Multivariate Data Analysis (7th ed). Prentice Hall.
Hartwich, F., Witzlack, C., Beggiato, M., Krems, J.F.: The first impression counts—a combined driving simulator and test track study on the development of trust and acceptance of highly automated driving. Transport. Res. F Traffic Psychol. Behav. 65, 522–535 (2019). https://doi.org/10.1016/j.trf.2018.05.012
Heath, Y., Gifford, R.: Extending the theory of planned behavior: predicting the use of public transportation1. J. Appl. Soc. Psychol. 32(10), 2154–2189 (2002). https://doi.org/10.1111/j.1559-1816.2002.tb02068.x
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
Howard, D., & Dai, D. (2014). Public perceptions of self-driving cars: the case of Berkeley, California. In: Transportation Research Board 93rd Annual Meeting, Transportation Research Board. https://trid.trb.org/view/1289421
Hu, L., Bentler, P.M.: Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model. 6(1), 1–55 (1999). https://doi.org/10.1080/10705519909540118
Hulse, L.M., Xie, H., Galea, E.R.: Perceptions of autonomous vehicles: relationships with road users, risk, gender and age. Saf. Sci. 102, 1–13 (2018). https://doi.org/10.1016/j.ssci.2017.10.001
Jansson, J.: Consumer eco-innovation adoption: assessing attitudinal factors and perceived product characteristics. Bus. Strateg. Environ. 20(3), 192–210 (2011). https://doi.org/10.1002/bse.690
Jia, N., Li, L., Ling, S., Ma, S., Yao, W.: Influence of attitudinal and low-carbon factors on behavioral intention of commuting mode choice—a cross-city study in China. Transp. Res. Part A Policy Pract. 111, 108–118 (2018). https://doi.org/10.1016/j.tra.2018.03.010
Jonah, B.A., Thiessen, R., Au-Yeung, E.: Sensation seeking, risky driving and behavioral adaptation. Accid. Anal. Prev. 33(5), 679–684 (2001). https://doi.org/10.1016/S0001-4575(00)00085-3
Jöreskog, K. G., & Sörbom, D. (2001). LISREL 8: User’s reference guide (2. ed., updated to LISREL 8). Scientific Software International.
Kaiser, F.G., Scheuthle, H.: Two challenges to a moral extension of the theory of planned behavior: moral norms and just world beliefs in conservationism. Personal. Individ. Differ. 35(5), 1033–1048 (2003). https://doi.org/10.1016/S0191-8869(02)00316-1
Kaplan, D. (2009). Structural Equation Modeling (2nd ed.): Foundations and Extensions. SAGE Publications, Inc. https://doi.org/10.4135/9781452226576
Kaye, S.-A., Lewis, I., Buckley, L., Rakotonirainy, A.: Assessing the feasibility of the theory of planned behaviour in predicting drivers’ intentions to operate conditional and full automated vehicles. Transport. Res. F Traffic Psychol. Behav. 74, 173–183 (2020). https://doi.org/10.1016/j.trf.2020.08.015
Kenny, D. A. (2015). Measuring Model Fit [Blog]. Structural Equation Modeling. http://www.davidakenny.net/cm/fit.htm
Kline, R.B.: Principles and practice of structural equation modeling, 2nd edn. Guilford Press, New York (2005)
König, M., Neumayr, L.: Users’ resistance towards radical innovations: the case of the self-driving car. Transport. Res. F Traffic Psychol. Behav. 44, 42–52 (2017). https://doi.org/10.1016/j.trf.2016.10.013
Krueger, R., Rashidi, T.H., Rose, J.M.: Preferences for shared autonomous vehicles. Transp. Res. Part C Emerg. Technol. 69, 343–355 (2016). https://doi.org/10.1016/j.trc.2016.06.015
Kyriakidis, M., Happee, R., de Winter, J.C.F.: Public opinion on automated driving: results of an international questionnaire among 5000 respondents. Transport. Res. F Traffic Psychol. Behav. 32, 127–140 (2015). https://doi.org/10.1016/j.trf.2015.04.014
Lavasani, M., Jin, X., Du, Y.: Market penetration model for autonomous vehicles on the basis of earlier technology adoption experience. Transp. Res. Record (2016). https://doi.org/10.3141/2597-09
Lavieri, P.S., Garikapati, V.M., Bhat, C.R., Pendyala, R.M., Astroza, S., Dias, F.F.: Modeling individual preferences for ownership and sharing of autonomous vehicle technologies. Transp. Res. Rec. J. Transp. Res. Board 2665(1), 1–10 (2017). https://doi.org/10.3141/2665-01
Lee, C., Ward, C., Raue, M., D’Ambrosio, L., & Coughlin, J. F. (2017). Age differences in acceptance of self-driving cars: a survey of perceptions and attitudes. In J. Zhou & G. Salvendy (Eds.), Human Aspects of IT for the Aged Population. Aging, Design and User Experience (Vol. 10297, pp. 3–13). Springer. https://doi.org/10.1007/978-3-319-58530-7_1
Lee, J., Lee, D., Park, Y., Lee, S., Ha, T.: Autonomous vehicles can be shared, but a feeling of ownership is important: examination of the influential factors for intention to use autonomous vehicles. Transp. Res. Part C Emerg. Technol. 107, 411–422 (2019). https://doi.org/10.1016/j.trc.2019.08.020
Lee, Y.-C., Mirman, J.H.: Parents’ perspectives on using autonomous vehicles to enhance children’s mobility. Transp. Res. Part C Emerg. Technol. 96, 415–431 (2018). https://doi.org/10.1016/j.trc.2018.10.001
Lei, P.-W., Wu, Q.: Introduction to structural equation modeling: issues and practical considerations. Educ. Meas. Issues Pract. 26(3), 33–43 (2007). https://doi.org/10.1111/j.1745-3992.2007.00099.x
Likert, R. (1932). A Technique for the Measurement of Attitudes. The Science Press.
Little, T.D.: Mean and covariance structures (MACS) analyses of cross-cultural data: practical and theoretical issues. Multivar. Behav. Res. 32(1), 53–76 (1997). https://doi.org/10.1207/s15327906mbr3201_3
Losada-Rojas, L.L., Gkartzonikas, C., Pyrialakou, V.D., Gkritza, K.: Exploring intercity passengers’ attitudes and loyalty to intercity passenger rail: evidence from an on-board survey. Transp. Policy 73, 71–83 (2019). https://doi.org/10.1016/j.tranpol.2018.10.011
Luo, Q., Saigal, R., Chen, Z., Yin, Y.: Accelerating the adoption of automated vehicles by subsidies: a dynamic games approach. Transp. Res. Part B Methodol. 129, 226–243 (2019). https://doi.org/10.1016/j.trb.2019.09.011
MacDonald, K. (2016). Group comparisons in structural equation models: Testing measurement invariance. The Stata Blog. http://blog.stata.com/2016/08/23/group-comparisons-in-structural-equation-models-testing-measurement-invariance/
Madigan, R., Louw, T., Dziennus, M., Graindorge, T., Ortega, E., Graindorge, M., Merat, N.: Acceptance of automated road transport systems (ARTS): an adaptation of the UTAUT model. Transp. Res. Procedia 14, 2217–2226 (2016). https://doi.org/10.1016/j.trpro.2016.05.237
Madigan, R., Louw, T., Wilbrink, M., Schieben, A., Merat, N.: What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems. Transport. Res. F Traffic Psychol. Behav. 50, 55–64 (2017). https://doi.org/10.1016/j.trf.2017.07.007
Mat Nawi, F.A., Abdul Malek A.T., Muhammad F.S., Wan Masnieza, W.M.: A review on the internal consistency of a scale: the empirical example of the influence of human capital investment on Malcom Baldridge quality principles in TVET institutions. Asian People J. (APJ), 3(1), 19–29 (2020). https://doi.org/10.37231/apj.2020.3.1.121
May, K.R., Noah, B.E., and Walker, B.N.: Driving acceptance: applying structural equation modeling to in-vehicle automation acceptance. In: Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct, pp. 190–194 (2017). https://doi.org/10.1145/3131726.3131755
Milakis, D., van Arem, B., van Wee, B.: Policy and society related implications of automated driving: a review of literature and directions for future research. J. Intell. Transp. Syst. 21(4), 324–348 (2017). https://doi.org/10.1080/15472450.2017.1291351
Moons, I., Pelsmacker, P.D.: Emotions as determinants of electric car usage intention. J. Mark. Manag. 28(3–4), 195–237 (2012). https://doi.org/10.1080/0267257X.2012.659007
Moons, I., Pelsmacker, P.D.: An extended decomposed theory of planned behaviour to predict the usage intention of the electric car: a multi-group comparison. Sustainability 7(5), 1–34 (2015)
Moták, L., Neuville, E., Chambres, P., Marmoiton, F., Monéger, F., Coutarel, F., Izaute, M.: Antecedent variables of intentions to use an autonomous shuttle: moving beyond TAM and TPB? Eur. Rev. Appl. Psychol. 67(5), 269–278 (2017). https://doi.org/10.1016/j.erap.2017.06.001
Motional. (2020). Motional Consumer Mobility Report. https://motional.com/mobilityreport/
Musselwhite, C.: Attitudes towards vehicle driving behaviour: categorising and contextualising risk. Accid. Anal. Prev. 38(2), 324–334 (2006). https://doi.org/10.1016/j.aap.2005.10.003
Musselwhite, C.B.A.: Driver Attitudes, Behavior and Speed Management Strategies. PhD thesis. University of Southampton (2007).
Mustonen-Ollila, E., Lyytinen, K.: Why organizations adopt information system process innovations: a longitudinal study using diffusion of innovation theory. Inf. Syst. J. 13(3), 275–297 (2003). https://doi.org/10.1046/j.1365-2575.2003.00141.x
Nastjuk, I., Herrenkind, B., Marrone, M., Brendel, A.B., Kolbe, L.M.: What drives the acceptance of autonomous driving? An investigation of acceptance factors from an end-user’s perspective. Technol. Forecast. Soc. Chang. 161, 120319 (2020). https://doi.org/10.1016/j.techfore.2020.120319
Nazari, F., Noruzoliaee, M., Mohammadian, A.: Shared versus private mobility: modeling public interest in autonomous vehicles accounting for latent attitudes. Transp. Res. Part C Emerg. Technol. 97, 456–477 (2018). https://doi.org/10.1016/j.trc.2018.11.005
NHTS. (2017). National Household Travel Survey. https://nhts.ornl.gov/
Nilsson, M., Küller, R.: Travel behaviour and environmental concern. Transp. Res. Part D Transp. Environ. 5(3), 211–234 (2000). https://doi.org/10.1016/S1361-9209(99)00034-6
Nordhoff, S., de Winter, J., Kyriakidis, M., van Arem, B., Happee, R.: Acceptance of driverless vehicles: results from a large cross-national questionnaire study. J. Adv. Transp. 2018, e5382192 (2018). https://doi.org/10.1155/2018/5382192
Nysveen, H., Pedersen, P.E., Thorbjørnsen, H.: Intentions to use mobile services: antecedents and cross-service comparisons. J. Acad. Mark. Sci. 33(3), 330 (2005). https://doi.org/10.1177/0092070305276149
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
Payre, W., Cestac, J., Delhomme, P.: Intention to use a fully automated car: attitudes and a priori acceptability. Transport. Res. F Traffic Psychol. Behav. 27, 252–263 (2014). https://doi.org/10.1016/j.trf.2014.04.009
Penmetsa, P., Adanu, E.K., Wood, D., Wang, T., Jones, S.L.: Perceptions and expectations of autonomous vehicles—a snapshot of vulnerable road user opinion. Technol. Forecast. Soc. Chang. 143, 9–13 (2019). https://doi.org/10.1016/j.techfore.2019.02.010
Petschnig, M., Heidenreich, S., Spieth, P.: Innovative alternatives take action—investigating determinants of alternative fuel vehicle adoption. Transp. Res. Part A Policy Pract. 61, 68–83 (2014). https://doi.org/10.1016/j.tra.2014.01.001
Pyrialakou, V.D., Gkartzonikas, C., Gatlin, J.D., Gkritza, K.: Perceptions of safety on a shared road: driving, cycling, or walking near an autonomous vehicle. J. Saf. Res. 72, 249–258 (2020). https://doi.org/10.1016/j.jsr.2019.12.017
Pyrialakou, V.D., and Gkritza, N.: Exploring the opinions of passenger rail riders: evidence from the hoosier state train. In: 2016 Joint Rail Conference, pp. V001T08A002–V001T08A002 (2016). http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=2528485
Qu, W., Sun, H., Ge, Y.: The effects of trait anxiety and the big five personality traits on self-driving car acceptance. Transportation (2020). https://doi.org/10.1007/s11116-020-10143-7
Qu, W., Xu, J., Ge, Y., Sun, X., Zhang, K.: Development and validation of a questionnaire to assess public receptivity toward autonomous vehicles and its relation with the traffic safety climate in China. Accid. Anal. Prev. 128, 78–86 (2019). https://doi.org/10.1016/j.aap.2019.04.006
Ren, H., Folmer, H.: Determinants of residential satisfaction in urban China: a multi-group structural equation analysis. Urban Stud. 54(6), 1407–1425 (2017). https://doi.org/10.1177/0042098015627112
Rogers, E. M. (1995). Diffusion of Innovations (4th ed). Free Press.
Rogers, E.M.: Diffusion of innovations (Fifth edition, Free Press trade paperback edition). Free Press, Cambridge (2003).
Sanbonmatsu, D.M., Strayer, D.L., Yu, Z., Biondi, F., Cooper, J.M.: Cognitive underpinnings of beliefs and confidence in beliefs about fully automated vehicles. Transport. Res. F Traffic Psychol. Behav. 55, 114–122 (2018). https://doi.org/10.1016/j.trf.2018.02.029
Schermelleh-Engel, K., Moosbrugger, H., and Müller, H.: Evaluating the fit of structural equation models: tests of significance and descriptive goodness-of-fit measures. 8(2), 52 (2003).
Sener, I.N., Zmud, J., Williams, T.: Measures of baseline intent to use automated vehicles: a case study of Texas cities. Transport. Res. F Traffic Psychol. Behav. 62, 66–77 (2019). https://doi.org/10.1016/j.trf.2018.12.014
Shabanpour, R., Golshani, N., Shamshiripour, A., Mohammadian, A.: Eliciting preferences for adoption of fully automated vehicles using best-worst analysis. Transp. Res. Part C Emerg. Technol. 93, 463–478 (2018). https://doi.org/10.1016/j.trc.2018.06.014
Shin, J., Bhat, C.R., You, D., Garikapati, V.M., Pendyala, R.M.: Consumer preferences and willingness to pay for advanced vehicle technology options and fuel types. Transp. Res. Part C Emerg. Technol. 60, 511–524 (2015). https://doi.org/10.1016/j.trc.2015.10.003
Simpson, J.R., Mishra, S., Talebian, A., Golias, M.M.: An estimation of the future adoption rate of autonomous trucks by freight organizations. Res. Transp. Econ. 76, 100737 (2019). https://doi.org/10.1016/j.retrec.2019.100737
Sprei, F.: Disrupting mobility. Energy Res. Soc. Sci. 37, 238–242 (2018). https://doi.org/10.1016/j.erss.2017.10.029
Sweet, M.N., Laidlaw, K.: No longer in the driver’s seat: how do affective motivations impact consumer interest in automated vehicles? Transportation (2019). https://doi.org/10.1007/s11116-019-10035-5
Tabachnick, B.G., & Fidell, L.S.: Using Multivariate statistics (6th ed). Pearson Education (2013).
Talebian, A., Mishra, S.: Predicting the adoption of connected autonomous vehicles: a new approach based on the theory of diffusion of innovations. Transp. Res. Part C Emerg. Technol. 95, 363–380 (2018). https://doi.org/10.1016/j.trc.2018.06.005
Tavakol, M., Dennick, R.: Making sense of Cronbach’s alpha. Int. J. Med. Educ. 2, 53–55 (2011). https://doi.org/10.5116/ijme.4dfb.8dfd
Taylor, S., Todd, P.A.: Understanding information technology usage: a test of competing models. Inf. Syst. Res. 6(2), 144–176 (1995)
Teo, T., Lee, C.B., Chai, C.S., Wong, S.L.: Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: a multigroup invariance analysis of the Technology Acceptance Model (TAM). Comput. Educ. 53(3), 1000–1009 (2009). https://doi.org/10.1016/j.compedu.2009.05.017
Thøgersen, J.: Promoting public transport as a subscription service: effects of a free month travel card. Transp. Policy 16(6), 335–343 (2009). https://doi.org/10.1016/j.tranpol.2009.10.008
Tornatzky, L.G., Klein, K.J.: Innovation characteristics and innovation adoption-implementation: a meta-analysis of findings. IEEE Trans. Eng. Manag. EM 29(1), 28–45 (1982). https://doi.org/10.1109/TEM.1982.6447463
U.S. Census Bureau. (2019). 2018 American Community Survey 5 Year Estimates. https://data.census.gov/cedsci/table?t=Commuting&tid=ACSST5Y2018.S0801&hidePreview=true
U.S. DoE.: Electric Vehicle Registrations by State. Alternative Fuels Data Center (2020). https://afdc.energy.gov/data/10962
Vagnani, G., Volpe, L.: Innovation attributes and managers’ decisions about the adoption of innovations in organizations: a meta-analytical review. Int. J. Innov. Stud. 1(2), 107–133 (2017). https://doi.org/10.1016/j.ijis.2017.10.001
Venkatesh, V., Davis, F.D.: A model of the antecedents of perceived ease of use: development and test*. Decis. Sci. 27(3), 451–481 (1996). https://doi.org/10.1111/j.1540-5915.1996.tb00860.x
Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS q. 27(3), 425–478 (2003). https://doi.org/10.2307/30036540
Verplanken, B., Aarts, H., van Knippenberg, A., van Knippenberg, C.: Attitude versus general habit: antecedents of travel mode choice1. J. Appl. Soc. Psychol. 24(4), 285–300 (1994). https://doi.org/10.1111/j.1559-1816.1994.tb00583.x
Verplanken, B., Aarts, H., van Knippenberg, A., Moonen, A.: Habit versus planned behaviour: a field experiment. Br. J. Soc. Psychol. 37(Pt 1), 111–128 (1998)
Verplanken, B., Walker, I., Davis, A., Jurasek, M.: Context change and travel mode choice: combining the habit discontinuity and self-activation hypotheses. J. Environ. Psychol. 28(2), 121–127 (2008). https://doi.org/10.1016/j.jenvp.2007.10.005
Wang, C., Hsu, H.-C.K., Bonem, E.M., Moss, J.D., Yu, S., Nelson, D.B., Levesque-Bristol, C.: Need satisfaction and need dissatisfaction: a comparative study of online and face-to-face learning contexts. Comput. Hum. Behav. 95, 114–125 (2019). https://doi.org/10.1016/j.chb.2019.01.034
Wang, S., Fan, J., Zhao, D., Yang, S., Fu, Y.: Predicting consumers’ intention to adopt hybrid electric vehicles: using an extended version of the theory of planned behavior model. Transportation 43(1), 123–143 (2016). https://doi.org/10.1007/s11116-014-9567-9
Wang, X., Yuen, K.F., Wong, Y.D., Teo, C.C.: An innovation diffusion perspective of e-consumers’ initial adoption of self-collection service via automated parcel station. Int. J. Logist. Manag. 29(1), 237–260 (2018). https://doi.org/10.1108/IJLM-12-2016-0302
Washington, S., Karlaftis, M.G., Mannering, F.: Statistical and Econometric Methods for Transportation Data Analysis (2nd ed). CRC Press/Chapman & Hall. (2011)
Weigel, F.K., Hazen, B.T., Cegielski, C.G., Hall, D.J.: Diffusion of innovations and the theory of planned behavior in information systems research: a metaanalysis. Commun. Assoc. Inf. Syst. 34 (2014). https://doi.org/10.17705/1CAIS.03431
Wood, W., Neal, D.T.: A new look at habits and the habit-goal interface. Psychol. Rev. 114(4), 843–863 (2007). https://doi.org/10.1037/0033-295X.114.4.843
Wood, W., Tam, L., Witt, M.G.: Changing circumstances, disrupting habits. J. Pers. Soc. Psychol. 88(6), 918–933 (2005). https://doi.org/10.1037/0022-3514.88.6.918
Wu, J., Liao, H., Wang, J.-W., Chen, T.: The role of environmental concern in the public acceptance of autonomous electric vehicles: a survey from China. Transport. Res. F Traffic Psychol. Behav. 60, 37–46 (2019). https://doi.org/10.1016/j.trf.2018.09.029
Yuen, K.F., Chua, G., Wang, X., Ma, F., Li, K.X.: Understanding public acceptance of autonomous vehicles using the theory of planned behaviour. Int. J. Environ. Res. Public Health 17(12), 4419 (2020a). https://doi.org/10.3390/ijerph17124419
Yuen, K.F., Wong, Y.D., Ma, F. and Wang, X.: The determinants of public acceptance of autonomous vehicles: an innovation diffusion perspective. J. Clean. Prod., 13 (2020).
Zhang, T., & Wang, J.: Human reliability analysis of traffic safety. In S. Long & B. S. Dhillon (Eds.), Proceedings of the 13th International Conference on Man-Machine-Environment System Engineering. Springer, Berlin, pp. 491–498 (2014)
Zhu, G., Chen, Y., Zheng, J.: Modelling the acceptance of fully autonomous vehicles: a media-based perception and adoption model. Transport. Res. f: Traffic Psychol. Behav. 73, 80–91 (2020). https://doi.org/10.1016/j.trf.2020.06.004
Zmud, J., Sener, I., & Wagner, J.: Consumer Acceptance and Travel Behavior Impacts of Automated Vehicles (2016).
Zoellick, J.C., Kuhlmey, A., Schenk, L., Schindel, D., Blüher, S.: Amused, accepted, and used? Attitudes and emotions towards automated vehicles, their relationships, and predictive value for usage intention. Transport. Res. F Traffic Psychol. Behav. 65, 68–78 (2019). https://doi.org/10.1016/j.trf.2019.07.009
Zuckerman, M., Neeb, M.: Sensation seeking and psychopathology. Psychiatry Res. 1(3), 255–264 (1979). https://doi.org/10.1016/0165-1781(79)90007-6
Acknowledgements
The authors would like to acknowledge the Center for Connected and Automated Transportation (CCAT) Region V University Transportation Center (UTC) for supporting this research study. The authors would also like to thank the anonymous reviewers for their constructive feedback that helped us improve the paper.
Funding
This work was partially supported as part of the Center for Connected and Automated Transportation (CCAT) Region V University Transportation Center funded by the U.S. Department of Transportation, Award #69A3551747105. Cost-share was provided by the Indiana Department of Transportation in support of the CCAT UTC.
Author information
Authors and Affiliations
Contributions
The authors confirm contribution to the paper as follows: study conception and design: CG; data collection: CG, VDP, and KG; estimation of results: LL, CG and CS; interpretation of results: CG, LL, VDP and KG; draft manuscript preparation: CG, LL, VDP, and KG. All authors reviewed the results and approved the final version of the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors note that there is no conflict of interest regarding this manuscript.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Gkartzonikas, C., Losada-Rojas, L.L., Christ, S. et al. A multi-group analysis of the behavioral intention to ride in autonomous vehicles: evidence from three U.S. metropolitan areas. Transportation 50, 635–675 (2023). https://doi.org/10.1007/s11116-021-10256-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11116-021-10256-7