Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Autonomous vehicles (AVs) are receiving attention in many countries, including Thailand. However, implementing an intelligent transport system has many challenges, such as safety and reliability and the lack of policy supporting such technology use, leading to hazards for passengers and pedestrians. Hence, factors affecting the adoption of autonomous vehicles require better understanding. This research proposes and employs an extended Technology Acceptance Model (TAM) by integrating ethical standards, legal concerns, and trust to predict the intended use of autonomous vehicles by Thai citizens. A total of 318 questionnaires were collected from online panel respondents. Research hypotheses were tested using a structural equation modelling approach. The study results suggest that ethical standards have a significant positive effect on the intention to use the technology. Meanwhile, the intention was negatively affected by perceived usefulness, perceived ease of use and legal concerns. On the other hand, the results indicate that perceived ease of use directly affected trust, leading to AV adoption. However, other factors influenced trust insignificantly. This study demonstrates the vital role of trust in AV adoption. The study also suggests ideas for further study and discusses the implications for the government and autonomous vehicle companies. The article aims to forecast a success factor that the Thai government should use to consider the policy for autonomous vehicle adoption in Thailand. This paper relies on the technology acceptance model to assess and forecast autonomous vehicle adoption. The theoretical model also includes ethical issues, legal concerns and trust in technology. The model was analysed using the structure equation modelling technique to confirm the factor affecting Thailand’s successful autonomous vehicle adoption. This research confirmed that ethical standards, legal concerns, and trust in technology are the factors significantly affecting the intention to use an autonomous vehicle in Thailand. On the other hand, the perceived ease of use significantly affects the trust in autonomous vehicle technology. This research found that such social factors as ethical standards, legal concerns, and trust in technology affect technology adoption significantly, especially technology related to AI operation. Therefore, the technology acceptance model could be modified to confirm technology adoption in terms of social factors. The government could use the research results to develop a public policy for the regulation and standard supporting autonomous vehicle adoption in Thailand.
Rocznik
Tom
Strony
49--62
Opis fizyczny
Bibliogr. 80 poz., tab., wykr.
Twórcy
autor
- Thammasat University, Thailand
autor
- Thammasat University, Thailand
Bibliografia
- Akbari, M., Rezvani, A., Shahriari, E., Zúñiga, M. Á., & Pouladian, H. (2020). Acceptance of 5 G technol¬ogy: Mediation role of Trust and Concentration. Journal of Engineering and Technology Management, 57, 101585. doi: 10.1016/j.jengtecman.2020.101585
- Alhashmi, S. F., Salloum, S. A., & Abdallah, S. (2019). Critical success factors for implementing artificial intelligence (AI) projects in Dubai Government United Ara Emirates (UAE) health sector: applying the extended technology acceptance model (TAM). International Conference on Advanced Intelligent Systems and Informatics.
- Alhashmi, S. F., Salloum, S. A., & Mhamdi, C. (2019). Implementing artificial intelligence in the United Arab Emirates healthcare sector: an extended technology acceptance model. International Journal of Information Technology and Language Studies, 3(3), 27-42.
- Alraja, M. N. (2016). Government acceptance from the individual employees’ perspective. Polish Journal of Management Studies, 14(2), 18-27. doi: 10.17512/pjms.2016.14.2.02
- Al-Sharafi, M. A., Arshah, R. A., Herzallah, F. A., & Alajmi, Q. (2017). The effect of perceived ease of use and usefulness on customers intention to use online banking services: the mediating role of perceived trust. International Journal of Innovative Computing, 7(1), 9-14.
- Alzamel, S. (2021). The Moderating Role of Resource Accessibility to the Theory of Planned Behaviour Components: A Study of E-Entrepreneurship Intention among Saudi Women. Polish Journal of Management Studies, 24(1), 30-44. doi: 10.17512/pjms.2021.24.1.02
- Amin, M., Rezaei, S., & Abolghasemi, M. (2014). User sat¬isfaction with mobile websites: the impact of perceived usefulness (PU), perceived ease of use (PEOU) and trust. Nankai Business Review International, 5(3), 258-274.
- Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
- Bezai, N. E., Medjdoub, B., Al-Habaibeh, A., Chalal, M. L., & Fadli, F. (2021). Future cities and autonomous vehicles: analysis of the barriers to full adoption. Energy and Built Environment, 2(1), 65-81. doi: 10.1016/j.enbenv.2020.05.002
- Bharadwaj, S., & Deka, S. (2021). Behavioural intention towards investment in cryptocurrency: an integration of Rogers’ diffusion of innovation theory and the technology acceptance model. Forum Scientiae Oeconomia, 9(4), 137-159.
- Carr, N. K. (2019). As the Role of the Driver Changes with Autonomous Vehicle Technology, so, Too, Must the Law Change. Mary’s Law Journal, 51(4), 817-843.
- Chailungka, P., Preittigun, A., & Ramjan, S. (2021). Public Policy Design for Artificial Intelligence Adoption: A Case Study of Autonomous Vehicle in Thailand. 11th National Conference of Southern College of Technology Research., Southern College of Technology, Nakorn Sri Thammarat.
- Chang, H. S., Lee, S. C., & Ji, Y. G. (2016). Wearable device adoption model with TAM and TTF. International Journal of Mobile Communications, 14(5), 518-537. doi: 10.1504/IJMC.2016.078726
- Chao, C.-M. (2019). Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Frontiers in Psychology, 10, 1652. doi: 10.3389/fpsyg.2019.01652
- Chong, B., Yang, Z., & Wong, M. (2003). Asymmetrical impact of trustworthiness attributes on trust, perceived value and purchase intention: a conceptual framework for cross-cultural study on consumer perception of online auction. 5th International Conference on Electronic Commerce.
- Chung, J. E., Park, N., Wang, H., Fulk, J., & McLaughlin, M. (2010). Age differences in perceptions of online community participation among non-users: An exten¬sion of the Technology Acceptance Model. Computers in Human Behavior, 26(6), 1674-1684. doi: 10.1016/j.chb.2010.06.016
- Coeckelbergh, M., Pop, C., Simut, R., Peca, A., Pintea, S., David, D., & Vanderborght, B. (2016). A survey of expectations about the role of robots in robot-assisted therapy for children with ASD: ethical acceptability, trust, sociability, appearance, and attachment. Science and Engineering Ethics, 22(1), 47-65.
- Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. doi: 10.2307/249008
- Diop, E. B., Zhao, S., Song, S., & Van Duy, T. (2020). Modelling travellers’ route switching behaviour in response to variable message signs using the technology acceptance model. Transport, 35(5), 533-547.
- Ejdys, J. (2018). Building technology trust in ICT application at a University. International Journal of Emerging Market, 13(5), 980-997. doi: 10.1108/IJoEM-07-2017-0234
- Ejdys, J. (2020). Trust-Based Determinants of Future Intention to Use Technology. Foresight and STI Governance, 14(1), 60-68. doi: 10.17323/2500-2597.2020.1.60.68
- Ejdys, J., & Halicka, K. (2018). Sustainable adaptation of new technology – the case of humanoids used for the care of older adults. Sustainability, 10(10), 3770. doi: 10.3390/su10103770
- Felzmann, H., Villaronga, E. F., Lutz, C., & Tamò-Larrieux, A. (2019). Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns. Big Data & Society, 6(1). doi: 10.1177/2053951719860542
- Gempton, N., Skalistis, S., Furness, J., Shaikh, S., & Petrovic, D. (2013). Autonomous control in military logistics vehicles: Trust and safety analysis. International Conference on Engineering Psychology and Cognitive Ergonomics.
- Gerbing, D. W., & Anderson, J. C. (1992). Monte Carlo evaluations of goodness of fit indices for structural equation models. Sociological Methods & Research, 21(2), 132-160.
- Gill, T. (2020). Blame it on the self-driving car: how autonomous vehicles can alter consumer morality. Journal of Consumer Research, 47(2), 272-291. doi: 10.1093/ jcr/ucaa018
- Hadi, S. H., Permanasari, A. E., Hartanto, R., Sakkinah, I. S., Sholihin, M., Sari, R. C., & Haniffa, R. (2021). Developing augmented reality-based learning media and users’ intention to use it for teaching accounting ethics. Education and Information Technologies. doi: 10.1007/s10639-021-10531-1
- Hair, J. F. (2010). Multivariate data analysis: a global per¬spective (7th ed. ed.). Pearson/Prentice-Hall. Hernandez-Ortega, B. (2011). The role of post-use trust in the acceptance of a technology: Drivers and consequences. Technovation, 31(10-11), 523-538.
- Hinkin, T. R. (2005). Scale Development Principles and Practices. In R. A. Swanson, & E. F. Holton III (Eds.), Research in Organizations: Foundations and Methods of Inquiry (pp. 161-179). San Francisco, CA: Berrett-Koehler Publishers.
- 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.
- Hutchins, N., Kirkendoll, Z., & Hook, L. (2017). Social impacts of ethical artifical intelligence and autonomous system design. 2017 IEEE International Systems Engineering Symposium (ISSE).
- Jamšek, S., & Culiberg, B. (2020). Introducing a three‐tier sustainability framework to examine bike‐sharing system use: An extension of the technology acceptance model. International Journal of Consumer Studies, 44(2), 140-150.
- Kangwansil, K., & Leelasantitham, A. (2020). Factors Affecting the Acceptance of Technology Adoption Model in Digital Painting on Tablet of Media Arts Students. 59th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE).
- Kaushik, A. K., Agrawal, A. K., & Rahman, Z. (2015). Tourist behaviour towards self-service hotel technology adoption: Trust and subjective norm as key antecedents. Tourism Management Perspectives, 16, 278-289. doi: 10.1016/j.tmp.2015.09.002
- Kim, S. (2018). Chapter Two - Blockchain for a Trust Network Among Intelligent Vehicles. In P. Raj & G. C. Deka (Eds.), Advances in Computers (pp. 43-68). Elsevier. doi: 10.1016/bs.adcom.2018.03.010
- Księżak, P., & Wojtczak, S. (2020). AI versus robot: in search of a domain for the new European civil law. Law, Innovation and Technology, 12(2), 297-317.
- Lee, C., & Wan, G. (2010). Including subjective norm and technology trust in the technology acceptance model: a case of e-ticketing in China. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 41(4), 40-51.
- Lee, L., & Charles, V. (2021). The impact of consumers’ perceptions regarding the ethics of online retailers and promotional strategy on their repurchase intention. International Journal of Information Management, 57, 102264. doi: 10.1016/j. ijinfomgt.2020.102264
- Lee, W., Xiong, L., & Hu, C. (2012). The effect of Facebook users’ arousal and valence on intention to go to the festival: Applying an extension of the technology acceptance model. International Journal of Hospitality Management, 31(3), 819-827.
- Liao, C., Liu, C.-C., & Chen, K. (2011). Examining the impact of privacy, trust and risk perceptions beyond monetary transactions: An integrated model. Electronic Commerce Research and Applications, 10(6), 702-715. doi: 10.1016/j.elerap.2011.07.003
- Liu, A.-C., & Chou, T.-Y. (2020). An integrated technology acceptance model to approach the behavioral intention of smart home appliance. International Journal of Organizational Innovation, 13(2), 95-118.
- Ljungholm, D. P. (2020). Regulating Autonomous Vehicles in a Smart Urban Transport System: Safety, Security, and Privacy Issues. Contemporary Readings in Law and Social Justice, 12(2), 9-15.
- Lu, J., Yu, C. S., Liu, C., & Yao, J. E. (2003). Technology acceptance model for wireless Internet. Internet Research, 3(13), 206-222.
- Luarn, P., & Juo, W.-J. (2010). The role of trust in technology within the TAM in the context of NFC mobile payment. Journal of Information and Optimization Sciences, 31(4), 875-896.
- Lui, H. K., & Jamieson, R. (2003). TriTAM: a model for integrating trust and risk perceptions in business-to-consumer electronic commerce. 16th Bled Electronic Commerce Conference, Slovenia.
- Man, S. S., Xiong, W., Chang, F., & Chan, A. H. S. (2020). Critical factors influencing acceptance of automated vehicles by hong kong drivers. IEEE Access, 8, 109845-109856.
- Manfreda, A., Ljubi, K., & Groznik, A. (2021). Autonomous vehicles in the smart city era: An empirical study of adoption factors important for millennials. International Journal of Information Management, 58, 102050. doi: 10.1016/j.ijinfomgt.2019.102050
- McKnight, D. H., Liu, P., & Pentland, B. T. (2020). Trust Change in Information Technology Products. Journal of Management Information Systems, 37(4), 1015- 1046.
- Mousa, A. H., Mousa, S. H., Aljshamee, M., & Nasir, I. S. (2021). Determinants of customer acceptance of e-banking in Iraq using technology acceptance model. Telkomnika, 19(2), 421-431.
- Nadeem, W., & Al-Imamy, S. (2020). Do ethics drive value co-creation on digital sharing economy platforms? Journal of Retailing and Consumer Services, 55, 102095.
- Nasri, W., & Charfeddine, L. (2012). Factors affecting the adoption of Internet banking in Tunisia: An integration theory of acceptance model and theory of planned behavior. The Journal of High Technology Management Research, 23(1), 1-14.
- Nelson, A. (2020). Smart transportation systems: Sustainable mobilities, autonomous vehicle decision-making algorithms, and networked driverless technologies. Contemporary Readings in Law and Social Justice, 12(2), 25-33.
- Noor, N. L. M., Hashim, M., Haron, H., & Aiffin, S. (2005). Community acceptance of knowledge sharing system in the travel and tourism websites: an application of an extension of TAM. 13th European Conference on Information Systems, Information Systems in a Rapidly Changing Economy, ECIS, Regensburg, Germany.
- Park, E., Cho, Y., Han, J., & Kwon, S. J. (2017). Comprehensive approaches to user acceptance of Internet of Things in a smart home environment. IEEE Internet of Things Journal, 4(6), 2342-2350. doi: 10.1109/JIOT.2017.2750765
- Patil, K. (2016). Retail adoption of Internet of Things: Applying TAM model. 2016 International conference on computing, analytics and security trends (CAST).
- Poisson, C., Safin, S., Langlois, S., Forzy, J.-F., & Decortis, F. (2016). Determinants and experience of the takeover of an autonomous vehicle. 15th Ergo’IA “Ergonomie Et Informatique Avancée” Conference.
- Prakken, H. (2017). On making autonomous vehicles respect traffic law: a case study for dutch law. 16th edition of the International Conference on Articial Intelligence and Law.
- Rathnaweera, L., & Karunasena, A. (2020). Influencial Factors of Adopting Digital Banking by Users in Western Province of Sri Lanka. 2nd International Conference on Advancements in Computing (ICAC).
- Raut, R., Priyadarshinee, P., & Jha, M. (2018). Understand¬ing the mediation effect of cloud computing adoption in Indian organization: integrating TAM-TOE-Risk model. In Technology Adoption and Social Issues: Concepts, Methodologies, Tools, and Applications (pp. 675-697). IGI Global.
- Revels, J., Tojib, D., & Tsarenko, Y. (2010). Understanding consumer intention to use mobile services. Australasian Marketing Journal, 18(2), 74-80.
- Roth, M. L. (2019). Regulating the Future: Autonomous Vehicles and the Role of Government. Iowa Law Review, 105, 1411-1446.
- Sangkaew, P., Jago, L., & Gkritzali, A. (2019). Adapting the Technology Acceptance Model (TAM) For Business Events: The Event Organizer Perspectives. Event Management, 23(6), 773-788. doi: 10.3727/152599519X15506259855832
- Schwab, K. (2017). The Fourth Industrial Revolution. Penguin Books Limited.
- Shao, S. (2020). Iterative Autonomous Vehicle Regulation and Governance: How Distributed Regulatory Experiments and Inter-Regional Coopetition within Federal Boundaries Can Nurture the Future of Mobility. Journal of Law, Technology & Policy, 2020(2), 325-359.
- Showalter, S. (2005). The law governing autonomous undersea vehicles: what an operator needs to know. OCEANS 2005 MTS/IEEE.
- Sıcakyüz, C., & Hacire, Y. O. (2020). Exploring resistance factors on the usage of hospital information systems from the perspective of the Markus’s Model and the Technology Acceptance Model. Journal of Entrepreneurship, Management and Innovation, 16(2), 93-131. doi: 10.7341/20201624
- Straub, E. R., & Schaefer, K. E. (2019). It takes two to Tango: Automated vehicles and human beings do the dance of driving – Four social considerations for policy. Transportation Research Part A: Policy and Practice, 122, 173-183. doi: 10.1016/j.tra.2018.03.005
- Tho, Q. H., Phap, H. C., & Phuong, P. A. (2019). A solution to ethical and legal problem with the decision-making model of autonomous vehicles. 11th International Conference on Knowledge and Systems Engineering (KSE).
- Thongkoo, K., Daungcharone, K., & Thanyaphongphat, J. (2020). Students’ Acceptance of Digital Learning Tools in Programming Education Course using Technology Acceptance Model. Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON).
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425- 478.
- Wang, S.-M., Huang, Y.-K., & Wang, C.-C. (2020). A model of consumer perception and behavioral intention for AI service. 2nd International Conference on Management Science and Industrial Engineering.
- Wang, T.-L. (2011). An effect of trust and attitude in the initial adoption of online shopping: An empirical study. International Conference on Information Society (i-Society 2011).
- Williams, B. (2021). Automated Vehicles and MaaS: Removing the Barriers. John Wiley & Sons.
- Wright, S. A. (2020). AI in the Law: Towards Assessing Ethical Risks. 2020 IEEE International Conference on Big Data (Big Data).
- Yijia, Z., Jiaqi, H., Guiqin, L., Feng, C., & Zhiyuan, G. (2019). Autonomous Driving Ethics Case Study for Engineering Ethics Education. International Conference on Modern Educational Technology.
- Yin, H., To, K. H., Keung, C. P. C., & Tam, W. W. Y. (2019). Professional learning communities count: Examining the relationship between faculty trust and teacher professional learning in Hong Kong kindergartens. Teaching and Teacher Education, 82, 153-163.
- Zhao, J., Fang, S., & Jin, P. (2018). Modeling and quantifying user acceptance of personalized business modes based on TAM, trust and attitude. Sustainability, 10(2), 356.
- Zhou, J., Chen, F., Berry, A., Reed, M., Zhang, S., & Savage, S. (2020). A Survey on Ethical Principles of AI and Implementations. IEEE Symposium Series on Computational Intelligence (SSCI).
- Zolotov, M. N., Oliveira, T., & Casteleyn, S. (2018). E-participation adoption models research in the last 17 years: A weight and meta-analytical review. Computers in Human Behavior, 81, 350-365. doi: 10.1016/j.chb.2017.12.0
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0cfeb0c9-723d-4431-b015-030ca3da06a7