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“What-are-you-looking-at?”: Implicit Behavioural Measurement Indicating Technology Acceptance in the Field of Automated Driving

  • Marc KuhnEmail author
  • Viola Marquardt
Conference paper
  • 5 Downloads
Part of the Developments in Marketing Science: Proceedings of the Academy of Marketing Science book series (DMSPAMS)

Abstract

Automated driving functions are gradually entering individual mobility markets. First studies on consumer acceptance show that parts of the classical innovation acceptance models can be applied to autonomous driving, but others do not work in this context. As it is expected that perception and evaluation of automated driving functions are correlated with the behaviour of the driver, we investigated if eye-tracking data as an implicit behavioural measurement could indicate the acceptance of automated driving. We developed and conducted a user experience study with a pre- and post-questionnaire, a standardized test track, and 98 test drivers with eye-tracking glasses using level 2 driver assistant systems either with a Mercedes-Benz E-Class or S-Class. The study refers to the Consumer Acceptance of Technology model and adds eye distraction from forward road scenes as an antecedent indicator, while activating the automated “Lane Keeping” function in separated 1-minute slot. Results of structural equation modelling show that despite a lack of significance, our general line of argument is largely confirmed according to which a longer eyes-off-road-time indicates a higher acceptance of automated driving technology. It is assumed that the effects could become more apparent when participants use the automated driving function within a longer period.

Keywords

Automated driving Eye-tracking Implicit measurement Technology acceptance 

References

  1. Bansal, P., & Kockelman, K. M. (2018). Are we ready to embrace connected and self-driving vehicles? A case study of Texans. Transportation, 45(2), 641–675.CrossRefGoogle Scholar
  2. Bertrandias, L., Sadik-Rozsnyai, O., & Kuhn, M. (2018). Nouveaux modes de mobilité, nouveaux mecanismes d´adoption? Une étude sur les facteurs d´acceptation de la voiture autonome. Strasbourg, EM Strasbourg Business School, 34th AFM (Congrès international de l´association française du marketing) Conference.Google Scholar
  3. Boyd, T. C. & Mason, C. H. (1999). The link between attractiveness of “extrabrand” attributes and the adoption of innovations. Journal of the Academy of Marketing Science, 27(3), 306–319.CrossRefGoogle Scholar
  4. Buckley, L., Kayec S.-A., & Pradhanb A. (2018). Psychosocial factors associated with intended use of automated vehicles: A simulated driving study. Accident Analysis and Prevention, 115(6), 202–208.CrossRefGoogle Scholar
  5. Davis, F. D., Bagozzi, Richard, & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of two theoretical models. Management Science, 35(8), 982–1001.CrossRefGoogle Scholar
  6. Dijkstra, T. K., & Henseler, J. (2015). Consistent Partial Least Squares Path Modeling. MIS Quarterly, 39(2), 297–316.CrossRefGoogle Scholar
  7. Haboucha, C. J., Ishaq, R., & Shiftan, Y. (2017). User preferences regarding autonomous vehicles. Transportation Research Part C, 78(5), 37–49.CrossRefGoogle Scholar
  8. Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Richter, N. F., & Hauff, S. (2017). Partial Least Squares Strukturgleichungsmodellierung: eine anwendungsorientierte Einführung. München: Verlag Franz Vahlen.CrossRefGoogle Scholar
  9. Henseler, J., Hubona, G., & Ray, P. A. (2015). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2–20.CrossRefGoogle Scholar
  10. Hong, J.-C., Hwang, M.-Y., Ting, T.-Y., Tai, K.-H., & Lee, C.-C. (2013). The innovativeness and self-efficacy predict the acceptance of using iPad2 as a green behavior by the government’s top administrators. The Turkish Online Journal of Educational Technology, 12(2), 313–320.Google Scholar
  11. Jones, G. R. (1986). Socialization tactics, self-efficacy, and newcomers’ adjustments to organizations. Academy of Management Journal, 29(2), 262–279.Google Scholar
  12. König, M., & Neumayr, L. (2017). Users’ resistance towards radical innovations: The case of the self-driving car. Transportation Research Part F, 44(1), 42–52.CrossRefGoogle Scholar
  13. Köpsel, A., Selinka, S., & Kuhn, M. (2018). Beaten Tracks? Application of existing innovation acceptance models on automated driving. Porto, Universidade Lusiada-Norte, 21st AMS (Academy of Marketing Science) World Marketing Congress.Google Scholar
  14. Kraft, A. K., Naujoks, F., Wörle, J. & Neukum, A. (2018). The impact of an in-vehicle display on glance distribution in partially automated driving in an on-road experiment. Transportation Research Part F, 52(1), 40–50.CrossRefGoogle Scholar
  15. Kulviwat, S., Bruner, G., Kumar, A., Nasco, S., & Clark, T. (2007). Toward a Unified Theory of Consumer Acceptance Technology. Psychology & Marketing, 24(12), 1059–1084.CrossRefGoogle Scholar
  16. Meuter, M., Bitner, M.J., Ostrom, A.L., & Brown, S.W. (2005). Choosing Among Alternative Service Delivery Modes: An Investigation of Customer Trial of Self-Service Technologies. Journal of Marketing, 69(2), 61–83.CrossRefGoogle Scholar
  17. Molina, A. I., Redondo, M. A., Lacave, C., & Ortega, M. (2013). Assessing the effectiveness of new devices for accessing learning materials: An empirical analysis based on eye tracking and learner subjective perception. Computers in Human Behavior, 31(2), 475–490.Google Scholar
  18. Nasco, S. A., Kulviwat, S., Kumar, A., Bruner, I. I., & Gordon, C. (2008). The CAT model: Extensions and moderators of dominance in technology acceptance. Psychology & marketing, 25(10), 987–1005.CrossRefGoogle Scholar
  19. NHTSA (2010). Visual-Manual NHTSA Driver Distraction Guidelines for In-Vehicle Electronic Devices.Google Scholar
  20. Nielsen, T. A., & Haustein, S. (2018). On sceptics and enthusiasts: What are the expectations towards self-driving cars? Transport Policy, 66(8), 49–55.CrossRefGoogle Scholar
  21. Planing, P. (2014). Innovation Acceptance - The Case of Advanced Driver-Assistance Systems. Stuttgart.Google Scholar
  22. Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3.Google Scholar
  23. Rödel, C., Stadler, S., Meschtscherjakov, A., & Tscheligi, M. (2014). Towards autonomous cars: the effect of autonomy levels on acceptance and user experience. Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 1–8.Google Scholar
  24. SAE International. (24.07.2018). Automated Driving levels of driving automation are new sae international standard J3016. Retrieved July 24, 2018 from https://www.smmt.co.uk/wp-content/uploads/sites/2/automated_driving.pdf
  25. Selinka, S., & Kuhn, M. (2018). Influences of User Experience on Consumer Perception – A study on “Autonomous Driving”. Porto, Universidade Lusiada-Norte, 21st AMS (Academy of Marketing Science) World Marketing Congress.Google Scholar
  26. Waytz, A., Heafner, J., & Epley, N. (2014). The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle. Journal of Experimental Social Psychology, 52(5), 113– 117.CrossRefGoogle Scholar

Copyright information

© The Academy of Marketing Science 2020

Authors and Affiliations

  1. 1.Baden-Wuerttemberg Cooperative State UniversityStuttgartGermany

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