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Research Article

Acceptance of Artificial Intelligence Application in the Post-Covid Era and Its Impact on Faculty Members’ Occupational Well-being and Teaching Self Efficacy: A Path Analysis Using the UTAUT 2 Model

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Article: 2175110 | Received 15 Sep 2022, Accepted 27 Jan 2023, Published online: 22 Feb 2023

References

  • Abu-al-aish, A., and S. Love. 2013. Factors influencing students’ acceptance of m-learning: an investigation in higher education. International Review of Research in Open and Distributed Learning 14 (5):82–604. doi:10.19173/irrodl.v14i5.1631.
  • Abu Gharrah, A., and A. Aljaafreh. 2021. Why students use social networks for education: Extension of UTAUT2. Journal of Technology and Science Education 11 (1):53–66. doi:10.3926/jotse.1081.
  • Acar, S. 2022. The association of career talent self-efficacy, positive future expectations and personal growth initiative. Psycho-Educational Research Reviews 11 (1):246–53. doi:10.52963/PERR_Biruni_V11.N1.15.
  • Alkhwaldi, A. F., and A. A. Abdulmuhsin. 2022. Crisis-centric distance learning model in Jordanian higher education sector: Factors influencing the continuous use of distance learning platforms during COVID-19 pandemic. Journal of International Education in Business 15 (2):250–72. doi:10.1108/JIEB-01-2021-0001.
  • Aly, N. E. 2020. Factors affecting technology acceptance during COVID-19 crisis in Egyptian higher education. Science, Business and Environmental Studies 11 (4):287–346. doi:10.21608/jces.2020.143630.
  • Arslan, A., and E. N. Karameşe. 2018. The effects of prospective teacher-lecturer: rapport on prospective teachers’ attitudes and self-efficacy beliefs towards teaching profession. Psycho-Educational Research Reviews 7 (1):42. https://perrjournal.com/index.php/perrjournal/article/view/247.
  • Bandura, A. 1982. Self-efficacy mechanism in human agency. The American Psychologist 37 (2):122. doi:10.1037/0003-066X.37.2.122.
  • Bandura, A., and E. Locke. 2003. Negative self-efficacy and goals effects revisited. The Journal of Applied Psychology 88 (1):87–99. doi:10.1037/0021-9010.88.1.87.
  • Brown, N., and I. Brown (2019). From digital business strategy to digital transformation – how: A systematic literature review. SAICSIT ‘19: Proceedings of the South African Institute of Computer Scientists and Information Technologists, 2019 (9), Article 13. 10.1145/3351108.3351122
  • Bucea-Manea-Țoniş, R., V. Kuleto, S. C. D. Gudei, C. Lianu, C. Lianu, M. P. Ilić, and D. Păun. 2022. Artificial intelligence potential in higher education institutions enhanced learning environment in Romania and Serbia. Sustainability 14 (10):5842. doi:10.3390/su14105842.
  • Butler, T. 2020. What’s next in the digital transformation of financial industry. IT Professional 22 (1):29–33. doi:10.1109/MITP.2019.2963490.
  • Casey, T., and E. Wilson-Evered. 2012. Predicting uptake of technology innovations in online family dispute resolution services: An application and extension of the UTAUT. Computers in Human Behaviour 28 (6):2034–45. doi:10.1016/j.chb.2012.05.022.
  • Chao, C. M. 2019. Factors determining the behavioural intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology 10:1652. doi:10.3389/fpsyg.2019.01652.
  • Chatterjee, S., N. P. Rana, S. Khorana, P. Mikalef, and A. Sharma. 2021. Assessing organizational users’ intentions and behavior to ai integrated CRM systems: a meta-UTAUT approach. Information Systems Frontiers. doi:https://doi.org/10.1007/s10796-021-10181-1.
  • Çoban, C., and D. Yazıcı. 2022. Examining peer relationships in transition to primary school in the pandemic process with teacher and parent opinions. Psycho-Educational Research Reviews 11 (1):31–45. doi:10.52963/PERR_Biruni_V11.N1.03.
  • Davis, F. D., R. P. Bagozzi, and P. R. Warshaw. 1989. User acceptance of computer technology: a comparison of two theoretical models. Management Science 35 (8):982–1003. doi:10.1287/mnsc.35.8.982.
  • Dobrescu, E. M., and E. M. Dobrescu. 2018. Artificial intelligence (ai)—the technology that shapes the world. Global Economic Observer; Bucharest 6 (2):71–81.
  • Duangekanong, S. 2022. Applications of artificial intelligence for strategic management of organization. ABAC ODI JOURNAL Vision, Action, Outcome 9 (2):202–17. doi:10.14456/abacodijournal.2022.13.
  • Düzyol, E., and G. Yıldırım. 2022. Examination of the opinions of pre-school teachers regarding the COVID-19 pandemic period’s reflection of pre-school education. Psycho-Educational Research Reviews 11 (2):261–80. doi:10.52963/PERR_Biruni_V11.N2.17.
  • Eimler, S. C., N. C. Krämer, and A. M. von der Pütten. 2011. Empirical results on determinants of acceptance and emotion attribution in confrontation with a robot rabbit. Applied Artificial Intelligence 25 (6):503–29. doi:10.1080/08839514.2011.587154.
  • Ekin, C. Ç. 2022. Eğitimde yapay zeka uygulamaları ve zeki öğretim sistemleri [Artificial intelligence applications and smart teaching systems in education]. Eğitimde Dijitalleşme ve Yeni Yaklaşımlar. Istanbul: Efe Akademi Puplication.
  • Fatimah, F., S. Rajiani, and E. Abbas. 2021. Cultural and individual characteristics in adopting computer-supported collaborative learning during COVID-19 outbreak: Willingness or obligatory to accept technology? Management Science Letters 11 (2):373–78. doi:10.5267/j.msl.2020.9.032.
  • Gharrah, A., A. Aljaafreh, and N. Al-Ma’aitah. 2021. Toward a model for actual usage of social networks sites for educational purposes in Jordanian universities. Journal of Technology and Science Education (JOTSE) 11 (1):53–66. doi:10.3926/jotse.1081.
  • Gültekin, M. 2022. Human-social robot interaction, anthropomorphism and ontological boundary problem in education. Psycho-Educational Research Reviews 11 (3):751–73. doi:10.52963/PERR_Biruni_V11.N3.11.
  • Gündoğdu, K., F. Dursun, and A. S. Saracaloğlu. 2020. Investigation of educational philosophies and general self-efficacy perceptions of graduate students in educational sciences programs. Psycho-Educational Research Reviews 9 (1):21–32. https://perrjournal.com/index.php/perrjournal/article/view/138.
  • Hair, J., B. Babin, and N. Krey. 2017. Covariance-based structural equation modeling in the journal of adertising: Review and recommendations. Journal of Adertising 64 (1):163–77.
  • Heaven, C., and D. J. Power. 2018. Challenges for digital transformation: towards a conceptual decision support guide for managers. Journal of Decision Systems 27 (Sup. 1):38–45. doi:10.1080/12460125.2018.1468697.
  • Holmstrom, J. 2022. From AI to digital transformation: the AI readiness framework. Business Horizons 65 (3):329–39. doi:10.1016/j.bushor.2021.03.006.
  • Huang, C. Y., and Y. S. Kao. 2015. UTAUT2 based predictions of factors influencing the technology acceptance of phablets by DNP. Hindawi Publishing Corporation Mathematical Problems in Engineering 2015:1–23. doi:https://doi.org/10.1155/2015/603747.
  • Hwang, T. J., K. Rabheru, C. Peisah, W. Reichman, and M. Ikeda. 2020. Loneliness and social isolation during the COVID-19 pandemic. International Psychogeriatrics 32 (10):1217–20. doi:10.1017/S1041610220000988.
  • İ̇çen, M. 2022. The future of education utilizing artificial intelligence in Turkey. Humanities and Social Sciences Communications 9 (1):268. doi:https://doi.org/10.1057/s41599-022-01284-4.
  • Kuleto, V., M. Ilić, M. Dumangiu, M. Ranković, O. M. D. Martins, D. Păun, and L. Mihoreanu. 2021. Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability 13 (18):10424. doi:https://doi.org/10.3390/su131810424.
  • Kurtdede Fidan, N., and N. Yıldırım. 2022. Teacher education in Turkey in the covid-19 pandemic: experiences of the pre-service teachers about the online teaching practice. Psycho-Educational Research Reviews 11 (1):77–92. doi:10.52963/PERR_Biruni_V11.N1.06.
  • Matt, C., T. Hess, and A. Benlian. 2015. Digital transformation strategies. Business & Information Systems Engineering 57 (5):339–43. http://dx.doi.org/10.1007/s12599-015-0401-5.
  • McKenna, B., T. Tuunanen, and L. Gardner. 2013. Consumers’ adoption of information services. Information & Management 50 (5):248–57. doi:10.1016/j.im.2013.04.004.
  • Moorthy, K., T. T. Yee, L. C. T’ing, and V. V. Kumaran. 2019. Habit and hedonic motivation are the strongest influences in mobile learning behaviours among higher education students in Malaysia. Australasian Journal of Educational Technology 35 (4):174–91. doi:10.14742/ajet.4432.
  • Nguyen, T. D., and T. H. Chao. 2014. Acceptance and use of information system: E-Learning based on cloud computing in Vietnam. Lecture Noted in Computer Science 139–49.
  • Nikolopoulou, K., V. Gialamas, and K. Lavidas. 2020. Acceptance of mobile phone by university students for their studies: An investigation applying UTAUT2 model. Education and Information Technologies 14 (1):1–17. doi:10.1007/s10639-020-10157-9.
  • Ocaña-Fernández, Y., L. A. Valenzuela-Fernández, and L. L. Garro-Aburto. 2019. Artificial intelligence and its implications in higher education. Journal of Educational Psychology-Proposal Representation 7 (2):553–68. doi:10.20511/pyr2019.v7n2.274.
  • Park, S. Y. 2009. An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Journal of Educational Technology & Society 12 (3):150–62. Accessed: 4 December 2020. https://www.jstor.org/stable/pdf/jeductechsoci.12.3.150.pdf
  • Raghavan, R. S., K. R. Jayasimha, and R. V. Nargundkar. 2020. Impact of software as a service (SaaS) on software acquisition process. Journal of Business and Industrial Marketing 35 (4):757–70. doi:10.1108/JBIM-12-2018-0382.
  • Sanal-Erginel, S. 2022. Mothers’ involvement in emergency remote education: a case study in the COVID-19 pandemic era. Psycho-Educational Research Reviews 11 (2):212–31. doi:10.52963/PERR_Biruni_V11.N2.14.
  • Shen, C., and H. Chuang. 2010. Exploring users’ attitudes and intentions toward the interactive whiteboard technology environment. International Review on Computers and Software 5:200–08.
  • Sikdar, S. 2018. Artificial intelligence, its impact on innovation, and the google effect. Clean Technologies and Environmental Policy; Berlin 20 (1):1–2. doi:10.1007/s10098-017-1478-y.
  • Sucu, İ. 2019. Yapay Zekânın toplum üzerindeki etkisi ve yapay zekâ (AI) filmi bağlamında yapay zekâya bakış [The effect of artifiticial intelligence on society and artificial intelligence the view of artificial intelligence in the context of film (I.A.)]. Uluslar Ders Kitapları Eğitim Materyalleri Derg 2 (2):203–15.
  • Suki, N. M., and N. M. Suki. 2017. Determining students’ behavioural intention to use animation and storytelling applying the UTAUT model: The moderating roles of gender and experience level. The International Journal of Management Education 15 (3):528–38. doi:10.1016/j.ijme.2017.10.002.
  • Sulak, M. 2021. Yapay zeka teknikleri ile açik öğretim lisesi öğrencilerinin mezuniyet tahmini [Predicting graduation of open education high school students with artificial intelligence technics] [ Unpublished M.Sc.]. Social Science Institute, Karabük University, Karabük.
  • Sultan, A. 2021. Determining the factors that affect the use of virtual classrooms: a modification of the UTAUT model. Journal of Information Technology Education: Research 20:117–35. doi:10.28945/4709.
  • Sultana, J. 2020. Determining the factors that affect the uses of mobile cloud learning (MCL) platform blackboard—A modification of the UTAUT model. Education and Information Technologies 25 (1):223–38. doi:10.1007/s10639-019-09969-1.
  • Taner, A., S. Akyıldız, A. Gülay, and C. Özdemir. 2021. Investigating education faculty students’ views about asynchronous distance education practices during covid-19 isolation period. Psycho-Educational Research Reviews 10 (1):34–45. https://perrjournal.com/index.php/perrjournal/article/view/90.
  • Tarhini, A., R. Masa’deh, K. A. Al-Busaidi, A. B. Mohammed, and M. Maqableh. 2017. Factors influencing students’ adoption of e-learning: A structural equation modeling approach. Journal of International Education in Business 10 (2):164–82. doi:10.1108/JIEB-09-2016-0032.
  • Ulaş, S. D., H. İ. Kurt, S. Bayındır, Ö. Cihan, Ö. F. Vural, and M. Başaran. 2021. Teachers’ metaphoric perceptions of covid-19 and school in the process of covid-19. Psycho-Educational Research Reviews 10 (3):393–410. doi:10.52963/PERR_Biruni_V10.N3.25.
  • Upadhyay, N., S. Upadhyay, and Y. K. Dwivedi. 2022. Theorizing artificial intelligence acceptance and digital entrepreneurship model. International Journal of Entrepreneurial Behavior & Research 28 (5):1138–66. doi:10.1108/IJEBR-01-2021-0052.
  • Uyar, Ş., and N. Öztürk. 2022. Pre-service teachers’ self-efficacy perceptions and metacognitive skills in predicting the measurement and evaluation course achievement. Psycho-Educational Research Reviews 11 (3):692–705. doi:10.52963/PERR_Biruni_V11.N3.21.
  • Uzir, M. U. H., H. Al Halbusi, R. Lim, I. Jerin, A. B. Abdul Hamid, T. Ramayah, and A. Haque. 2021. Applied artificial intelligence and user satisfaction: smartwatch usage for healthcare in Bangladesh during COVID-19. Technology in Society 67:101780. doi:10.1016/j.techsoc.2021.101780.
  • Varzaru, A. A. 2022. Assessing artificial intelligence technology acceptance in managerial accounting. Electronics 11 (14):2256. doi:https://doi.org/10.3390/electronics11142256.
  • Venkatesh, V., M. Morris, G. Davis, and F. Davis. 2003. User acceptance of information technology: toward a unified view. MIS Quarterly 27 (3):425–78. doi:10.2307/30036540.
  • Venkatesh, V., J. Y. L. Thong, and X. Xin. 2012. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly. 36(1):157–78. doi:10.2307/41410412.
  • Verma, P. 2018. The natural impact of artificial intelligence. International Journal of Critical Infrastructure Protection 22:150–51. doi:10.1016/j.ijcip.2018.08.009.
  • Vial, G. 2019. Understanding digital transformation: A review and research agenda. Journal of Strategic Information Systems 28 (2):118–44. doi:10.1016/j.jsis.2019.01.003.
  • Wang, T., C.-H. Jung, M.-H. Kang, and Y.-S. Chung. 2014. Exploring determinants of adoption intentions towards enterprise 2.0 applications: an empirical study. Behaviour & Information Technology 33 (10):1048–64. doi:10.1080/0144929X.2013.781221.
  • Willcocks, L. P. 2021, February 16. A digital catch-22: after the crisis, who’s betting on digital transformation? LSE Business Review. http://eprints.lse.ac.uk/109039/1/businessreview_2021_02_16_a_digital_catch_22_after_the_crisis_whos.pdf
  • Xuelin, X. 2021. Psychological factors in consumer acceptance of artificial intelligence in leisure economy: a structural equation model. Journal of Internet Technology 22 (3):697–705.
  • Zhou, M., C. Dzingirai, K. Hove, T. Chitata, and R. Mugandani. 2022. Adoption, use and enhancement of virtual learning during COVID-19. Education and Information Technologies 27 (7):8939–59. doi:https://doi.org/10.1007/s10639-022-10985-x.
  • Ziyadin, S., S. Suieubayeva, and A. Utegenova. 2020. Digital transformation in business. In Digital age: chances, challenges, and future: ISCDTE 2019. Lecture notes in networks and systems, ed. S. Ashmarina, M. Vochozka, and V. Mantulenko, vol. 84, 408–15. Springer. doi:10.1007/978-3-030-27015-5_49.