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Extending of TAM through Perceived Trust and its Application to Autonomous Driving

지각된 신뢰에 기반한 기술수용모델의 확장과 자율주행에의 적용에 관한 실증연구

  • Lee, Kangmun (Dept. of Business Administration, Kyungnam University) ;
  • Roh, Taewoo (Dept. of International Trade and Commerce, Soonchunhyang University)
  • Received : 2018.02.02
  • Accepted : 2018.05.20
  • Published : 2018.05.28

Abstract

The purpose of this study is to investigate the effect of technology acceptance model (TAM) on behavioral intention in order to grasp the degree of technology acceptance on autonomous driving among the various factors that consumers perceive as unmanned vehicle system becomes commercialized. In addition to the mediating effect of perceived usefulness proposed by the existing TAM, this study proposed the perceived trust (PT) and hypothesized its mediating effect on behavioral intention to use the self-driving. Path anlaysis is adopted to investigate our hypothesis using the structural equation model. The sample used for the analysis was 149 valid data among 160 responses. The effects of total effect, direct effect, and indirect effect were confirmed by hypothesis test on mediating effect. Non-parametric bootstrapping analysis was also performed to confirm the robustness. All the hypotheses were significant and we found a partial indirect effect, which implies that mediation effect of PT on behavioral intention.

본 연구는 무인자동차 시스템이 상용화에 가까워짐에 따라 소비자들이 느끼게 되는 다양한 요인들 중에서 자율주행에 대한 기술수용정도를 파악하기 위해 기술수용모델(TAM)을 활용하여 사후행동에 미치는 영향을 파악하고자 하였다. 기존 기술수용모델이 제시한 지각된 사용 이용성의 매개효과와 더불어 본 연구에서는 지각된 신뢰(perceived trust)를 제안하여 사후행동에 대한 매개효과를 가설로 제시하였다. 분석방법은 구조방정식을 활용한 경로분석을 활용하였으며, 분석에 사용된 표본은 160명의 응답 중 149개의 유효한 자료를 이용하였다. 매개효과에 대한 가설검증으로 총효과, 직접효과, 간접효과를 확인하였으며, 비모수 bootstrapping 분석을 추가적으로 실시해 가설검증을 실시하였다. 모든 가설은 유의미하였으며 부분적인 간접효과가 있는 것으로 확인되어 매개효과가 있다는 것을 발견하였다.

Keywords

References

  1. CDC. (2016. 6. 16). Impaired Driving: Get the Facts. Centers for Disease Control and Prevention. https://www.cdc.gov/motorvehiclesafety/impaired_driving/impaired-drv_factsheet.html
  2. Yandron, D. & Tynan, D. (2016. 6. 30). Tesla Driver Dies in First Fatal Crash While Using Autopilot Mode. The Guardian. https://www.theguardian.com/technology/2016/jun/30/tesla-autopilot-death-self-driving-car-elon-musk
  3. Morris, D. Z. (2016. 10. 15). Mercedes-Benz's Self-Driving Cars Would Choose Passenger Lives over Bystanders. Forbes. http://fortune.com/2016/10/15/mercedes-self-driving-car-ethics/
  4. Verberne, F. M., Ham, J. & Midden, C. J. (2012). Trust in Smart Systems: Sharing Driving Goals and Giving Information to Increase Trustworthiness and Acceptability of Smart Systems in Cars. Human Factors, 54(5), 799-810. https://doi.org/10.1177/0018720812443825
  5. Ramsey, M. (2017. 1. 26). On the Road to Driverless Cars. Forbes. https://www.forbes.com/sites/gartnergroup/2017/01/26/on-the-road-to-driverless-cars/#c2988a317ede
  6. SAE. (2014). Automated Driving. Warrendale, Pennsylvania: SAE International.
  7. David, A. (2016. 8. 26). Everyone Wants a Level 5 Self-Driving Car-Here's What That Means. Wired. https://www.wired.com/2016/08/self-driving-car-levels-sae-nhtsa/
  8. Bertoncello, M., &Wee, D. (2015. 6). TenWays Autonomous Driving Could Redefine the Automotive World. Mckinsey. Http://www.mckinsey.com/industries/automotive-and-assembly/our-insights/ten-ways-autonomous-driving-could-redefine-the-automotive-world.
  9. Lutin, J. M., Kornhauser, A. L. & Lerner-Lam, E. (2013). The Revolutionary Development of Self- Driving Vehicles and Implications for the Transportation Engineering Profession. ITE Journal, 83(7), 28.
  10. Neubauer, C., Matthews, G., Langheim, L. & Saxby, D. (2012). Fatigue and Voluntary Utilization of Automation in Simulated Driving. Human Factors, 54(5), 734-746. https://doi.org/10.1177/0018720811423261
  11. Elbanhawi, M., Simic, M. & Jazar, R. (2015). In the Passenger Seat: Investigating Ride Comfort Measures in Autonomous Cars. IEEE Intelligent Transportation Systems Magazine, 7(3), 4-17. https://doi.org/10.1109/MITS.2015.2405571
  12. Diels, C., & Bos, J. E. (2015). Self-Driving Car Sickness. Applied Ergonomics, 53, 374-382.
  13. 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
  14. Davis, F. D., Bagozzi, R. P. & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
  15. Chauh, S. H. W., Rauschnabel, P. A., Kery, N., Ramayah, T. & Lade, S. (2016). Wearable Technologies: The Role of Usefulness and Visibility in Smartwatch Adoption. Computers in Human Behavior, 65, 276-284. https://doi.org/10.1016/j.chb.2016.07.047
  16. Koo, W., Cho, E. & Kim, Y. K. (2014). Actual and Ideal Self-Congruity Affecting Consumer's Emotional and Behavioral Responses toward an Online Store. Computers in Human Behavior, 36, 147-153. https://doi.org/10.1016/j.chb.2014.03.058
  17. Wu, J. H., & Wang, S. C. (2005). What Drives Mobile Commerce?: An Empirical Evaluation of the Revised Technology Acceptance Model. Information &Management, 42(5), 719-729. https://doi.org/10.1016/j.im.2004.07.001
  18. Jeong, J. Y. & Roh, T. W. (2017). The Intention of Using Wearable Devices : Based on Modified Technology Acceptance Model. Journal of Digital Convergence, 15(4), 205-212. https://doi.org/10.14400/JDC.2017.15.4.205
  19. Legris, P., Ingham, J. & Collerette, P. (2003). Why Do People Use Information Technology? A Critical Review of the Technology Acceptance Model. Information & Management, 40(3), 191-204. https://doi.org/10.1016/S0378-7206(01)00143-4
  20. Jang, H. J. & Noh, G. Y. (2017). Extended Technology Acceptance Model of Vr Head-Mounted Display in Early Stage of Diffusion. Journal of Digital Convergence, 15(5), 353-361. https://doi.org/10.14400/JDC.2017.15.3.353
  21. Kim, S. & Park, H. S. (2017). Impacts of Individual and Technical Characteristics on Perceived Risk and User Resistance of Mobile Payment Services. Journal of Digital Convergence, 15(12), 239-253. https://doi.org/10.14400/JDC.2017.15.12.239
  22. Lee, Y. S. (2017). The Effects of Consumer Characteristics on the Acceptance of Mobile Commerce. Journal of Digital Convergence, 15(5), 173-187. https://doi.org/10.14400/JDC.2017.15.2.173
  23. Kramer, R. M. (1999). Trust and Distrust in Organizations: Emerging Perspectives, Enduring Questions. Annual Review of Psychology, 50(1), 569-598. https://doi.org/10.1146/annurev.psych.50.1.569
  24. 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, 113-117. https://doi.org/10.1016/j.jesp.2014.01.005
  25. Alotaibi, M. B. (2016). Exploring Users' Attitudes and Intentions toward the Adoption of Cloud Computing in Saudi Arabia: An Empirical Investigation. Journal of Computer Science, 10(11), 2315-2329. https://doi.org/10.3844/jcssp.2014.2315.2329
  26. Hu, L. T. & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  27. Lee, K. & Roh, T. W. (2017). The Effect of Public Service Motivation on Job Satisfaction and Perceived Job Performance: Focusing on the Mediation Effect of Person-Organization Fit. Journal of Digital Convergence, 15(9), 155-165. https://doi.org/10.14400/JDC.2017.15.2.155
  28. Tak, J. G. & Roh, T. W. (2017). Effects of Supervisor's Authentic Leadership on Ocb and Job Performance for Employees. Journal of Digital Convergence, 15(1), 171-179. https://doi.org/10.14400/JDC.2017.15.1.171
  29. Tak, J. G. & Roh, T. W. (2017). The Effectiveness of Authentic Leadership on Public and Private Organizations. Journal of Digital Convergence, 15(10), 161-171. https://doi.org/10.14400/JDC.2017.15.10.161