Utilizing Technology Acceptance Model (TAM) for driverless car technology
DOI:
https://doi.org/10.4067/S0718-27242018000400037Keywords:
driverless car technology adoption, technology acceptance model, innovation adoption, society, autonomous vehicles.Abstract
This paper examines the relationship between perceived usefulness of driverless car technology, perceived ease of use of driverless car technology, years of driving experience, age and the intention to use driverless cars. This research is a cross-sectional descriptive correlational study with the Technology Acceptance Model as its theoretical framework. The primary method of data collection was an online survey. Pearson’s correlation and multiple linear regression were used for data analysis. This study found significant, positive relationships between perceived usefulness of driverless car technology, perceived ease of use of driverless car technology and intention to use driverless cars. Also, there were significant, negative relationships between years of driving experience, age and intention to use driverless cars.Downloads
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Arts, J. W. C., Frambach, R. T., & Bijmolt, T. H. A. (2011). Generalizations on consumer innovation adoption: A meta-analysis on drivers of intention and behavior. International Journal of Research in Marketing, 28(2), 134–144. https://doi.org/10.1016/j.ijresmar.2010.11.002
Bansal, P. Kockelman, K. M. and Singh, A. (2016). Assessing public opinions of and interest in new vehicle technologies: An Austin perspective. Transportation Research Part C: Emerging Technologies, 67, 1–14. https://doi.org/10.1016/j.trc.2016.01.019
Brett, J. A. (2016). Thinking local about self-driving cars: A local framework for autonomous vehicle development in the United States (Master’s thesis). Retrieved from ProQuest Dissertations and Theses Global. (UMI No. 10138698). http://search.proquest.com/pqdtglobal/docview/1804414282/abstract/D74E736B56B840B8PQ/8
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions, and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487. https://doi.org/10.1006/imms.1993.1022
Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the Technology Acceptance Model: Three experiments. International Journal of Human-Computer Studies, 45(1), 19–45. https://doi.org/10.1006/ijhc.1996.0040
Dillon, A., & Morris, M. G. (1996). User acceptance of new information technology: Theories and models. Annual Review of Information Science and Technology, 14(4), 3–32. Retrieved from
http://hdl.handle.net/10150/105584
Drennan, J., Kennedy, J., & Pisarski, A. (2005). Factors affecting student attitudes toward flexible online learning in management education. The Journal of Educational Research, 98(6), 331–338. https://doi.org/10.3200/JOER.98.6.331-338
Gadepally, V. N. (2013). Estimation of driver behavior for autonomous vehicle applications (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses Global. (UMI No. 3671348). http://search.proquest.com/pqdtglobal/docview/1647127404/abstract/D74E736B56B840B8PQ/11
Ghazizadeh, M., Lee, J. D., & Boyle, L. N. (2012). Extending the Technology Acceptance Model to assess automation. Cognition, Technology & Work, 14(1), 39–49. https://doi.org/10.1007/s10111-011-0194-3
Guerra, E. (2016). Planning for cars that drive themselves: Metropolitan planning organizations, regional transportation plans, and autonomous vehicles. Journal of Planning Education and Research, 36(2), 210–224. https://doi.org/10.1177/0739456X15613591
Heide, A. and Henning, K. (2006). The “cognitive car”: A roadmap for research issues in the automotive sector. Annual Reviews in Control, 30(2), 197–203. https://doi.org/10.1016/j.arcontrol.2006.09.005
Holden, R. J., & Karsh, B. T. (2010). The Technology Acceptance Model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159–172. https://doi.org/10.1016/j.jbi.2009.07.002
Horton, R. P., Buck, T., Waterson, P. E., & Clegg, C. W. (2001). Explaining intranet use with the Technology Acceptance Model. Journal of Information Technology, 16(4), 237–249. https://doi.org/10.1080/02683960110102407
Howard, D. and Dai D. (2014). Public perceptions of self-driving cars: The case of Berkeley, California. In Transportation Research Board 93rd Annual Meeting, No. 14-4502. Retrieved from http://cet.berkeley.edu/wp-content/uploads/Self-Driving-Cars.pdf
Jansson, J., Marell, A., & Nordlund, A. (2010). Green consumer behavior: Determinants of curtailment and eco-innovation adoption. Journal of Consumer Marketing, 27(4), 358–370. https://doi.org/10.1108/07363761011052396
Jiang, T., Petrovic, S., Ayyer, U., Tolani, A., & Husain, S. (2015). Self-driving cars: Disruptive or incremental (No. 2013.05.29) (pp. 3–22). Berkeley: University of California Fung Institute for Engineering Leadership. Retrieved from http://www.funginstitute.berkeley.edu/sites/default/!les/Self_Driving_Cars.pdf
Knight, W. (2013). Driverless cars. Technology Review; Cambridge, 116(6), 44–49. Retrieved from http://search.proquest.com/docview/1459693183/abstract/5F0127A756E64882PQ/1
Lane, M., & Coleman, P. (2012). Technology ease of use through social networking media. Journal of Technology Research, 3(1), 1-12. Retrieved from
Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The Technology Acceptance Model: Past, present, and future. Communications of the Association for Information Systems, 12(50), 750-782. Retrieved from http://aisel.aisnet.org/cgi/viewcontent.cgi?article=3217&context=cais
Li, L. (2010). A critical review of technology acceptance literature. Grambling State University, 19(1), 1-20. Retrieved from http://www.swdsi.org/swdsi2010/sw2010_preceedings/papers/pa104.pdf
López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & Management, 45(6), 359–364. https://doi.org/10.1016/j.im.2008.05.001
Maarafi, A. (2015). The impact of autonomous vehicles on freeway throughput (Master’s thesis). Retrieved from ProQuest Dissertations and Theses Global. (UMI No. 1596625). Retrieved from http://search.proquest.com/pqdtglobal/docview/1708380668/abstract/D74E736B56B840B8PQ/4
Matthews, M. C. (2016). Intent communication between autonomous vehicles and humans (Master’s thesis). Retrieved from ProQuest Dissertations and Theses Global. (UMI No. 10189053). Retrieved from http://search.proquest.com/pqdtglobal/docview/1854866974/abstract/D74E736B56B840B8PQ/35
Menon, N. (2015). Consumer perception and anticipated adoption of autonomous vehicle technology: Results from multi-population surveys (Master’s thesis). Retrieved from ProQuest Dissertations and Theses Global. (UMI No. 1603803). Retrieved from http://search.proquest.com/pqdtglobal/docview/1734473356/abstract/D74E736B56B840B8PQ/44
Mohd, F., Ahmad, F., Samsudin, N., & Sudin, S. (2011). Extending the Technology Acceptance Model to account for social influence, trust, and integration for pervasive computing environment: A case study in the university industry. American Journal of Economics and Business Administration, 3(3), 552-559. Retrieved from http://search.proquest.com/openview/bcb1c53cdac2c8892bbe001a94052176/1?pq-origsite=gscholar&cbl=1216358
Nees, M. A. (2016). Acceptance of self-driving cars: An examination of idealized versus realistic portrayals with a self-driving car acceptance scale. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 60(1), 1449–1453. https://doi.org/10.1177/1541931213601332
Owczarzak, Ł. and Żak, J. (2015). Design of passenger public transportation solutions based on autonomous vehicles and their multiple criteria comparison with traditional forms of passenger transportation. Transportation Research Procedia, 10(2015), 472–482. https://doi.org/10.1016/j.trpro.2015.09.001
Park, N., Kim, Y. C., Shon, H. Y., & Shim, H. (2013). Factors influencing smartphone use and dependency in South Korea. Computers in Human Behavior, 29(4), 1763–1770. https://doi.org/10.1016/j.chb.2013.02.008
Payre, W. Cestac, J. and Delhomme, P. (2014). Intention to use a fully automated car: Attitudes and a priori acceptability. Transportation Research Part F: Traffic Psychology and Behaviour, 27(2014), 252–263. https://doi.org/10.1016/j.trf.2014.04.009
Schoettle, B., & Sivak, M. (2014). A survey of public opinion about autonomous and self-driving vehicles in the US, the UK, and Australia (No. UMTRI-2014-21). Ann Arbor: University of Michigan Transportation Research Institute. Retrieved from https://deepblue.lib.umich.edu/handle/2027.42/108384
Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., … Mahoney, P. (2006). Stanley: The robot that won the DARPA grand challenge. Journal of Field Robotics, 23(9), 661–692. https://doi.org/10.1002/rob.20147
Venkatesh, V., Davis, F. D., & Morris, M. G. (2007). Dead or alive? The development, trajectory, and future of technology adoption research. Journal of the Association for Information Systems, 8(4), 267-286.
Retreievd from
Yang, K. C. C. (2005). Exploring factors affecting the adoption of mobile commerce in Singapore. Telematics and Informatics, 22(3), 257–277. https://doi.org/10.1016/j.tele.2004.11.003
Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: A meta-analysis of the TAM: Part 1. Journal of Modelling in Management, 2(3), 251–280. https://doi.org/10.1108/17465660710834453
Zindler, K. and Geiss, N. (2016). Vehicle ego-localization in autonomous lane-keeping evasive maneuvers. IFAC-PapersOnLine, 49(11), 160–167. https://doi.org/10.1016/j.ifacol.2016.08.025
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