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International Conference on Applied Human Factors and Ergonomics

AHFE 2019: Advances in Human Factors of Transportation pp 44–56Cite as

Using Technology Acceptance Model to Explain Driver Acceptance of Advanced Driver Assistance Systems

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 964))

Abstract

Thousands of people die each year due to motor vehicle crashes in the US. Research has found that an overwhelming majority of severe motor vehicle crashes occur due to human error. Advanced Driver Assistance Systems (ADAS) are designed to support drivers with information and added vehicle control in critical situations. However, successful implementation of these technologies requires drivers to accept them, spend money to include them in vehicles and use them while driving. This study investigated the utility of the Technology Acceptance Model (TAM) to explain driver acceptance of ADAS using a driving simulator study. Thirty-seven participants were given a 10-min. driving experience with a simulated driver assistance system. After the drive, they responded to an acceptance survey that measured different constructs of TAM. The results confirm that TAM constructs can significantly predict drivers’ willingness to use an ADAS, explaining more than 68% (Adj. R2) of the variability.

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References

  1. National Center for Statistics and Analysis: 2017 fatal motor vehicle crashes: overview (Traffic Safety Facts Research Note. Report no. DOT HS 812 603) (2018)

    Google Scholar 

  2. NHTSA: U.S. DOT Announces 2017 Roadway Fatalities Down. https://www.nhtsa.gov/press-releases/us-dot-announces-2017-roadway-fatalities-down

  3. Deb, S., Strawderman, L., Carruth, D.W., DuBien, J., Smith, B., Garrison, T.M.: Development and validation of a questionnaire to assess pedestrian receptivity toward fully autonomous vehicles. Transp. Res. Part C Emerg. Technol. 84, 178–195 (2017)

    Article  Google Scholar 

  4. Legris, P., Ingham, J., Collerette, P.: Why do people use information technology: a critical review of the literature. Inf. Manag. 40(3), 191–204 (2003)

    Article  Google Scholar 

  5. Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46, 186–204 (2000)

    Article  Google Scholar 

  6. Rahman, M.M., Lesch, M.F., Horrey, W.J., Strawderman, L.: Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems. Accid. Anal. Prev. 108, 361–373 (2017)

    Article  Google Scholar 

  7. Davis, F.D.: A technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral dissertation, Massachusetts Institute of Technology (1986)

    Google Scholar 

  8. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319–340 (1989)

    Article  Google Scholar 

  9. Osswald, S., Wurhofer, D., Trösterer, S., Beck, E., Tscheligi, M.: Predicting information technology usage in the car: towards a car technology acceptance model. In: Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 51–58 (2012)

    Google Scholar 

  10. Ghazizadeh, M., Peng, Y., Lee, J.D., Boyle, L.N.: Augmenting the technology acceptance model with trust: commercial drivers’ attitudes towards monitoring and feedback. In: Proceedings of the Human Factors and Ergonomics Society, vol. 56, no. 1, pp. 2286–2290 (2012)

    Article  Google Scholar 

  11. Fishbein, M., Ajzen, I.: Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research (1975)

    Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. Meschtscherjakov, A., Wilfinger, D., Scherndl, T., Tscheligi, M.: Acceptance of future persuasive in-car interfaces towards a more economic driving behaviour. In: Proceedings of the 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications (2009)

    Google Scholar 

  14. Xu, C., Wang, W., Chen, J., Wang, W., Yang, C., Li, Z.: Analyzing travelers’ intention to accept travel information. Transp. Res. Rec. J. Transp. Res. Board 2156, 93–110 (2010)

    Article  Google Scholar 

  15. Chen, C.F., Chen, P.C.: Applying the TAM to travelers’ usage intentions of GPS devices. Expert Syst. Appl. 38(5), 6217–6221 (2011)

    Article  Google Scholar 

  16. Park, E., Kim, K.J.: Driver acceptance of car navigation systems: integration of locational accuracy, processing speed, and service and display quality with technology acceptance model. Pers. Ubiquitous Comput. 18(3), 503–513 (2014)

    Article  Google Scholar 

  17. Roberts, S.C., Ghazizadeh, M., Lee, J.D.: Warn me now or inform me later: drivers’ acceptance of real-time and post-drive distraction mitigation systems. Int. J. Hum. Comput. Stud. 70(12), 967–979 (2012)

    Article  Google Scholar 

  18. Rahman, M.M.: Driver acceptance of advanced driver assistance systems and semi-autonomous driving systems. Doctoral dissertation, Mississippi State University (2016)

    Google Scholar 

  19. Kennedy, R.S., Lane, N.E., Berbaum, K.S., Lilienthal, M.G.: Simulator sickness questionnaire: an enhanced method for quantifying simulator sickness. Int. J. Aviat. Psychol. 3(3), 203–220 (1993)

    Article  Google Scholar 

  20. Tavakol, M., Dennick, R.: Making sense of Cronbach’s alpha. Int. J. Med. Educ. 2, 53–55 (2011)

    Article  Google Scholar 

  21. Baron, R.M., Kenny, D.A.: The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51(6), 1173–1182 (1986)

    Article  Google Scholar 

  22. Kenny, D.A., Kashy, D., Bolger, N.: Data analysis in social psychology. In: Gilbert, D., Fiske, S., Lindzey, G. (eds.) The Handbook of Social Psychology, vol. 1, pp. 233–265 (1998)

    Google Scholar 

  23. Rahman, M.M., Strawderman, L., Lesch, M.F., Horrey, W.J., Babski-Reeves, K., Garrison, T.: Modelling driver acceptance of driver support systems. Accid. Anal. Prev. 121, 134–147 (2018)

    Article  Google Scholar 

  24. Rahman, M.M., Strawderman, L., Carruth, D.W.: Effect of driving contexts on driver acceptance of advanced driver assistance systems. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 61, pp. 1944–1948 (2017)

    Google Scholar 

  25. Van Der Laan, J.D., Heino, A., De Waard, D.: A simple procedure for the assessment of acceptance of advanced transport telematics. Transp. Res. Part C Emerg. Technol. 5(1), 1–10 (1997)

    Article  Google Scholar 

  26. Adell, E.: Acceptance of driver support systems. In: Proceedings of the European Conference on Human Centred Design for Intelligent Transport Systems, pp. 475–486 (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. National Institute for Occupational Safety and Health, Morgantown, WV, USA

    Md Mahmudur Rahman

  2. Mississippi State University, Starkville, MS, USA

    Shuchisnigdha Deb, Daniel Carruth & Lesley Strawderman

Authors
  1. Md Mahmudur Rahman
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  2. Shuchisnigdha Deb
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  3. Daniel Carruth
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  4. Lesley Strawderman
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Corresponding author

Correspondence to Md Mahmudur Rahman .

Editor information

Editors and Affiliations

  1. Boldrewood Innovation Campus, University of Southampton, TRG, Southampton, UK

    Neville Stanton

Appendix: Scales Used to Measure the Factors of TAM

Appendix: Scales Used to Measure the Factors of TAM

Attitude (adapted from [25])

  1. 1.

    The use of the system when I am driving would be:

figure a
  1. 2.

    The use of the system when I am driving would be:

figure b
  1. 3.

    The use of the system when I am driving would be (reverse-scaled item):

figure c
  1. 4.

    The use of the system when I am driving would be:

figure d
  1. 5.

    The use of the system when I am driving would be:

figure e
  1. 6.

    The use of the system when I am driving would be:

figure f
  1. 7.

    The use of the system when I am driving would be:

figure g
  1. 8.

    The use of the system when I am driving would be:

figure h

9. The use of the system when I am driving would be (reverse-scaled item):

figure i

Perceived Usefulness (adapted from [5])

  1. 1.

    Using the system would improve my driving performance.

  2. 2.

    Using the system in driving increases my safety.

  3. 3.

    Using the system enhances effectiveness in my driving.

  4. 4.

    I would find the system useful in my driving.

Perceived Ease of Use (adapted from [5])

  1. 1.

    My interaction with the system would be clear and understandable.

  2. 2.

    I would find the system difficult to use (reverse-scaled item).

  3. 3.

    Interacting with the system would not require a lot of mental effort.

  4. 4.

    I would find it easy to get the system to do what I want it to do.

Behavioral Intention (adapted from [26])

  1. 1.

    If the system is available in the market at an affordable price, I intend to purchase the system.

  2. 2.

    If my car is equipped with a similar system, I predict that I would use the system when driving.

  3. 3.

    Assuming that the system is available, I intend to use the system regularly when I am driving.

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© 2020 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply

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Cite this paper

Rahman, M.M., Deb, S., Carruth, D., Strawderman, L. (2020). Using Technology Acceptance Model to Explain Driver Acceptance of Advanced Driver Assistance Systems. In: Stanton, N. (eds) Advances in Human Factors of Transportation. AHFE 2019. Advances in Intelligent Systems and Computing, vol 964. Springer, Cham. https://doi.org/10.1007/978-3-030-20503-4_5

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  • DOI: https://doi.org/10.1007/978-3-030-20503-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20502-7

  • Online ISBN: 978-3-030-20503-4

  • eBook Packages: EngineeringEngineering (R0)

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