Investigating the Importance of Trust on Adopting an Autonomous Vehicle

REFERENCES

• Adell, E. (2010). Acceptance of driver support systems. Proceedings of the European Conference on Human Centred Design for Intelligent Transport Systems, 475–486.
   Google Scholar
• Bacon, L. D. (1999, February). Using LISREL and PLS to measure customer satisfaction. Paper presented at the Seventh Annual Sawtooth Software Conference, La Jolla, CA.
   Google Scholar
• Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 74–94.
   Google Scholar
• Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration. Technology Studies, 2, 285–309.
   Google Scholar
• Beiker, S. A. (2012). Legal aspects of autonomous driving. Santa Clara Law Review, 52 (4), Article 1.
   Google Scholar
• Berry, L. L. (1995). Relationship marketing of services—Growing interest, emerging perspectives. Journal of the Academy of Marketing Science, 23, 236–245.
   Google Scholar
• Burns, P. C., & Wilde, G. J. (1995). Risk taking in male taxi drivers: Relationships among personality, observational data and driver records. Personality and Individual Differences, 18, 267–278.
   Web of Science ®Google Scholar
• Carter, L., & Bélanger, F. (2005). The utilization of e-government services: Citizen trust, innovation and acceptance factors. Information Systems Journal, 15, 5–25.
   Web of Science ®Google Scholar
• Chau, P. Y. K., & Hu, P. J. H. (2002). Investigating healthcare professionals’ decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39, 297–311.
   Web of Science ®Google Scholar
• Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295, 295–336.
   Google Scholar
• Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14, 189–217.
   Web of Science ®Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319–340.
   Web of Science ®Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982–1003.
   Web of Science ®Google Scholar
• 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, 19–45.
   Web of Science ®Google Scholar
• Doney, P. M., & Cannon, J. P. (1997). An examination of the nature of trust in buyer–seller relationships. The Journal of Marketing, 61, 35–51.
   Web of Science ®Google Scholar
• Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. Akron, OH: University of Akron Press.
   Google Scholar
• Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human–Computer Studies, 59, 451–474.
   Web of Science ®Google Scholar
• Feypell, V., & Scheunemann, J. (2012). Road deaths: Latest traffic safety data released. Retrieved from http://internationaltransportforum.org/Press/PDFs/2012-05-02IRTAD.pdf
   Google Scholar
• Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19, 440–452.
   Web of Science ®Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.
   Web of Science ®Google Scholar
• Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27, 51–90.
   Web of Science ®Google Scholar
• Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: a study of e-commerce adoption. Journal of the Association for Information Systems, 1, 8.
   Google Scholar
• Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4, 7.
   Google Scholar
• Ghazizadeh, M., Peng, Y., Lee, J. D., & Boyle, L. N. (2012, September). Augmenting the technology acceptance model with trust: Commercial drivers’ attitudes towards monitoring and feedback. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2286–2290.
   Google Scholar
• Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6). Upper Saddle River, NJ: Pearson Prentice Hall.
   Google Scholar
• Hasan, Z., Krischkowsky, A., & Tscheligi, M. (2012). Modelling user-centered-trust (UCT) in software systems: Interplay of trust, affect and acceptance model (pp. 92–109). Berlin, Germany: Springer.
   Google Scholar
• Jarvenpaa, S. L., Tractinsky, N., & Saarinen, L. (1999). Consumer trust in an Internet store: A cross-cultural validation. Journal of Computer-Mediated Communication, 5(2).
   Google Scholar
• Johnson, T. (2013, January 31). Enhancing safety through automation (SAE gov’t-industry meeting, automation and connected vehicle safety). Washington, DC: National Highway Traffic Safety Administration. Available from http://www.sae.org/events/gim/presentations/2013/johnson_tim.pdf
   Google Scholar
• Jonah, B. A. (1997). Sensation seeking and risky driving: A review and synthesis of the literature. Accident Analysis & Prevention, 29, 651–665.
   PubMed Web of Science ®Google Scholar
• Kieras, D. E., & Bovair, S. (1984). The role of a mental model in learning to operate a device. Cognitive Science, 8, 255–273.
   Web of Science ®Google Scholar
• King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43, 740–755.
   Web of Science ®Google Scholar
• Krause, N., & Stryker, S. (1984). Stress and well-being: The buffering role of locus of control beliefs. Social Science & Medicine, 18, 783–790.
   PubMed Web of Science ®Google Scholar
• Lee, J., & Moray, N. (1992). Trust, control strategies and allocation of function in human–machine systems. Ergonomics, 35, 1243–1270.
   PubMed Web of Science ®Google Scholar
• Lee, J. D., & Moray, N. (1994). Trust, self-confidence, and operators’ adaptation to automation. International Journal of Human–Computer Studies, 40, 153–184.
   Web of Science ®Google Scholar
• Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46, 50–80.
   PubMed Web of Science ®Google Scholar
• Leimeister, J. M., Ebner, W., & Krcmar, H. (2005). Design, implementation, and evaluation of trust-supporting components in virtual communities for patients. Journal of Management Information Systems, 21, 101–131.
   Web of Science ®Google Scholar
• Lippert, S. K. (2001). An exploratory study into the relevance of trust in the context of information systems technology (Unpublished doctoral dissertation). Washington, DC: George Washington University.
   Google Scholar
• Ma, Q., & Liu, L. (2004). The technology acceptance model: A meta-analysis of empirical findings. Journal of Organizational and End User Computing, 16, 59–72.
   Google Scholar
• Madsen, M., & Gregor, S. (2000, December). Measuring human–computer trust. Proceedings of Eleventh Australasian Conference on Information Systems, 6–8.
   Google Scholar
• Maltz, M., Sun, H., Wu, Q., & Mourant, R. (2004). In-vehicle alerting system for older and younger drivers: Does experience count? Transportation Research Record: Journal of the Transportation Research Board, 1899, 64–70.
   Google Scholar
• Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20, 709–734.
   Web of Science ®Google Scholar
• Meschtscherjakov, A., Wilfinger, D., Scherndl, T., & Tscheligi, M. (2009, September). Acceptance of future persuasive in-car interfaces towards a more economic driving behaviour. Proceedings of the 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 81–88.
   Google Scholar
• Mitchell, V. W. (1999). Consumer perceived risk: Conceptualisations and models. European Journal of Marketing, 33, 163–195.
   Google Scholar
• Moray, N., Inagaki, T., & Itoh, M. (2000). Adaptive automation, trust, and self-confidence in fault management of time-critical tasks. Journal of Experimental Psychology: Applied, 6, 44.
   PubMed Web of Science ®Google Scholar
• Muir, B. M. (1987). Trust between humans and machines, and the design of decision aids. International Journal of Man–Machine Studies, 27, 527–539.
   Google Scholar
• Norman, D. A. (2002). The design of everyday things. New York, NY: Basic Books.
   Google Scholar
• Numan, J. H. (1998). Knowledge-based systems as companions: Trust, human computer interaction and complex systems. University of Groningen. Doctoral dissertation.
   Google Scholar
• Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2008). Situation awareness, mental workload, and trust in automation: Viable, empirically supported cognitive engineering constructs. Journal of Cognitive Engineering and Decision Making, 2, 140–160.
   Google Scholar
• Parkes, K. R. (1984). Locus of control, cognitive appraisal, and coping in stressful episodes. Journal of Personality and Social Psychology, 46, 655–668.
   PubMed Web of Science ®Google Scholar
• Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7, 101–134.
   Web of Science ®Google Scholar
• Payre, W., Cestac, J., & Delhomme, P. (2014). Intention to use a fully automated car: Attitudes and a priori acceptability. Transportation Research Part F: Traffic Psychology and Behaviour, 27, 252–263.
   Web of Science ®Google Scholar
• Peter, J. P., & Ryan, M. J. (1976). An investigation of perceived risk at the brand level. Journal of Marketing Research, 13, 184–188.
   Web of Science ®Google Scholar
• Ratnasingham, P., & Kumar, K. (2000, December). Trading partner trust in electronic commerce participation. Proceedings of the Twenty-First International Conference on Information Systems, 544–552.
   Google Scholar
• Riley, V. A. (1994). Human use of automation (Unpublished doctoral dissertation). Minneapolis, MN: University of Minnesota.
   Google Scholar
• Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs: General and Applied, 80(1), Whole No. 609.
   Google Scholar
• Rudin-Brown, C. M., & Ian Noy, Y. (2002). Investigation of behavioral adaptation to lane departure warnings. Transportation Research Record: Journal of the Transportation Research Board, 1803, 30–37.
   Google Scholar
• Rudin-Brown, C. M., & Parker, H. A. (2004). Behavioural adaptation to adaptive cruise control (ACC): Implications for preventive strategies. Transportation Research Part F: Traffic Psychology and Behaviour, 7, 59–76.
   Web of Science ®Google Scholar
• Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44, 90–103.
   Web of Science ®Google Scholar
• Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15, 325–343.
   Web of Science ®Google Scholar
• Sheridan, T. B. (1975). Considerations in modeling the human supervisory controller. International Federation of Automatic Control, Triennial World Congress, 6th, 40.
   Google Scholar
• Sheridan, T. B., & Hennessy, R. T. (1984). Research and modeling of supervisory control behavior. Report of a workshop. Washington DC: National Research Council, Committee on Human Factors.
   Google Scholar
• Söllner, M., Hoffmann, A., Hoffmann, H., & Leimeister, J. M. (2011, December). Towards a theory of explanation and prediction for the formation of trust in IT artifacts. Annual Workshop on HCI Research in MIS (Vol. 4).
   Google Scholar
• Stanton, N. A., & Marsden, P. (1996). From fly-by-wire to drive-by-wire: Safety implications of automation in vehicles. Safety Science, 24, 35–49.
   Web of Science ®Google Scholar
• Stanton, N. A., & Young, M. S. (2005). Driver behaviour with adaptive cruise control. Ergonomics, 48, 1294–1313.
   PubMed Web of Science ®Google Scholar
• Stutts, J. C. (1998). Do older drivers with visual and cognitive impairments drive less? Journal of the American Geriatrics Society, 46, 854–861.
   Google Scholar
• Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6, 144–176.
   Web of Science ®Google Scholar
• Thatcher, J. B., McKnight, D., Baker, E. W., Arsal, R. E., & Roberts, N. H. (2011). The role of trust in postadoption it exploration: An empirical examination of knowledge management systems. IEEE Transactions on Engineering Management, 58, 56–70.
   Web of Science ®Google Scholar
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46, 186–204.
   Web of Science ®Google Scholar
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478.
   Web of Science ®Google Scholar
• Ward, N. J., Fairclough, S., & Humphreys, M. (1995). The effect of task automatisation in the automotive context: A field study of an Autonomous Intelligent Cruise Control system. Proceedings of the International Conference on Experimental analysis and Measurement of Situation Awareness, November, 1–3, Daytona Beach, Florida.
   Google Scholar
• West, C. G., Gildengorin, G., Haegerstrom-Portnoy, G., Lott, L. A., Schneck, M. E., & Brabyn, J. A. (2003). Vision and driving self-restriction in older adults. Journal of the American Geriatrics Society, 51, 1348–1355.
   PubMed Web of Science ®Google Scholar
• Wu, J. H., Shen, W. S., Lin, L. M., Greenes, R. A., & Bates, D. W. (2008). Testing the technology acceptance model for evaluating healthcare profes-sionals’ intention to use an adverse event reporting system. International Journal for Quality in Health Care, 20, 123–129.
   PubMed Web of Science ®Google Scholar
• Zuckerman, M. (1994). Behavioral expressions and biosocial bases of sensation seeking. New York, NY: Cambridge University Press.
   Google Scholar