AI Adoption in Higher Education Institution: An Integrated TAM and TOE Model
DOI:
https://doi.org/10.38035/dijemss.v6i2.3645Keywords:
Artificial Intelligence (AI), Technology Acceptance Model (TAM), technology organization environment (TOE)Abstract
Artificial Intelligence (AI) impacts various daily activities and features, including higher education. Educators and academics now see AI in education to be essential. The benefits of higher education and how universities adjust to shifting student and faculty attitudes on learning are topics of growing discussion. This study aims to explore how policymakers and educators may apply AI and modify it for the learning domain. The integrated technology acceptance model (TAM)-TOE model was implemented in a conceptual model that was released. It was tested with survey data obtained from 200 respondents who participated in an online survey, and a structural equation model (SEM-PLS) was utilized to assess the suggested hypotheses. The results show that organizational readiness, organizational compatibility, and partner support on perceived ease of use had been correlated with any significant relationship evaluated in the setting of higher education. It is anticipated that the approach will help authorities facilitate the use of AI in higher education. Furthermore, as AI is still in its infancy, more academic study is required before it can be used to the sector of education.
References
Al-Abdullatif, A. M. (2023). Modeling students’ perceptions of chatbots in learning: Integrating technology accept- ance with the value-based adoption model. Education Sciences, 13(11), 1151. https://doi.org/10.3390/educsci13111151
Awa, H.O.; Ojiabo, O.U. A model of adoption determinants of ERP within T-O-E framework. Inf. Technol. People 2016, 29, 901–930
Balzer, H., & Askonas, J. (2016). The triple helix after communism: Russia and China compared. Triple Helix, 3(1), 1. https://doi.org/10.1186/s40604-015-0031-4
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: a quantitative ana- lysis using structural equation modelling. Education and Information Technologies, 25(5), 3443–3463. https://doi. org/10.1007/s10639-020-10159-7
Chatterjee, D.; Grewal, R.; Sambamurthy, V. Shaping up for e-commerce: Institutional enablers of the organizational assimilation of web technologies. MIS Q. 2002, 26, 65
Chen, Y., Khan, S. K., Shiwakoti, N., Stasinopoulos, P., & Aghabayk, K. (2024). Integrating perceived safety and socio- demographic factors in UTAUT model to explore Australians’ intention to use fully automated vehicles. Research in Transportation Business & Management, 56, 101147. https://doi.org/10.1016/j.rtbm.2024.101147
Chiu, T. K. F., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher support and student motivation to learn with artificial intelligence (AI) based Chatbot. Interactive Learning Environments, 2023, 1–17. https://doi.org/10. 1080/10494820.2023.2172044
Cox, J. (2012). Information systems user security: A structured model of the knowing–doing gap. Computers in Human Behavior, 28(5), 1849–1858. https://doi.org/10.1016/j.chb.2012.05.003
Das, S.D.; Bala, P.K. What drives MLOps adoption? An analysis using the TOE framework. J. Decis. Syst. 2023, 1–37
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Eiser, J. R. (1994). Attitudes, chaos and the connectionist mind. In Attitudes, chaos and the connectionist mind. Blackwell Publishing.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research (Vol. 27).
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measure- ment error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Gogus, A., Nistor, N., & Lerche, T. (2012). Educational technology acceptance across cultures: A validation of the uni- fied theory of acceptance and use of technology in the context of Turkish national culture. Turkish Online Journal of Educational Technology, 11, 394–408.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation mod- eling (PLS-SEM). SAGE Publications, Inc.
Helm, J. M., Swiergosz, A. M., Haeberle, H. S., Karnuta, J. M., Schaffer, J. L., Krebs, V. E., Spitzer, A. I., & Ramkumar,
P. N. (2020). Machine learning and artificial intelligence: Definitions, applications, and future directions. Current Reviews in Musculoskeletal Medicine, 13(1), 69–76. https://doi.org/10.1007/s12178-020-09600-8
Hao, J.; Shi, H.; Shi, V.; Yang, C. Adoption of automatic warehousing systems in logistics firms: A technology–organization–environment framework. Sustainability 2020, 12, 5185
Horowitz, M. C., & Kahn, L. (2021). What influences attitudes about artificial intelligence adoption: Evidence from U.S. local officials. PLOS One, 16(10), e0257732. https://doi.org/10.1371/journal.pone.0257732
Ingham, J., Cadieux, J., & Mekki Berrada, A. (2015). e-Shopping acceptance: A qualitative and meta-analytic review.
Information & Management, 52(1), 44–60. https://doi.org/10.1016/j.im.2014.10.002
Jeong, H., Han, S.-S., Jung, H.-I., Lee, W., & Jeon, K. J. (2024). Perceptions and attitudes of dental students and den- tists in South Korea toward artificial intelligence: A subgroup analysis based on professional seniority. BMC Medical Education, 24(1), 430. https://doi.org/10.1186/s12909-024-05441-y
Ji, Y., Fu, X., Ding, F., Xu, Y., He, Y., Ao, M., ... & Dong, C. (2024). Artificial intelligence combined with high-throughput calculations to improve the corrosion resistance of AlMgZn alloy. Corrosion Science, 233, 112062.
Jo, H. (2024). From concerns to benefits: A comprehensive study of ChatGPT usage in education. International Journal of Educational Technology in Higher Education, 21(1), 35. https://doi.org/10.1186/s41239-024-00471-4
Kaminka, G. A., Spokoini-Stern, R., Amir, Y., Agmon, N., & Bachelet, I. (2017). Molecular robots obeying Asimov’s three laws of robotics. Artificial Life, 23(3), 343–350. https://doi.org/10.1162/ARTL_a_00235
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustra- tions, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor. 2018.08.004
Kauffman, R.J.; Walden, E.A. Economics and electronic commerce: Survey and directions for research. Int. J. Electron. Commer. 2001, 5, 5–116
Kumari, R., & Chander, S. (2024). Improving healthcare quality by unifying the American electronic medical report system: time for change. The Egyptian Heart Journal, 76(1), 32. https://doi.org/10.1186/s43044-024-00463-9
Long, Y., & Gil-Garcia, J. R. (2023). Understanding the extent of automation and process transparency appropriate for public services. International Journal of Electronic Government Research, 19(1), 1–20. https://doi.org/10.4018/IJEGR. 322550
Marzuki, Widiati, U., Rusdin, D., Darwin, & Indrawati, I. (2023). The impact of AI writing tools on the content and organization of students’ writing: EFL teachers’ perspective. Cogent Education, 10(2). https://doi.org/10.1080/ 2331186X.2023.2236469
McElheran, K., Li, J. F., Brynjolfsson, E., Kroff, Z., Dinlersoz, E., Foster, L., & Zolas, N. (2024). AI adoption in America: Who, what, and where. Journal of Economics & Management Strategy, 33(2), 375–415. https://doi.org/10.1111/jems. 12576
Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance model of artificial intelligence (AI)-based technologies in construction firms: Applying the Technology Acceptance Model (TAM) in combination with the Technology–Organisation–Environment (TOE) framework. Buildings, 12(2), 90.
Nasrallah, R. (2014). Learning outcomes’ role in higher education teaching. Education, Business and Society: Contemporary Middle Eastern Issues, 7(4), 257–276. https://doi.org/10.1108/EBS-03-2014-0016
Neo, M. (2022). The Merlin project: Malaysian students’ acceptance of an ai chatbot in their learning process. Turkish Online Journal of Distance Education, 23(3), 31–48. https://doi.org/10.17718/tojde.1137122
Neumann, O.; Guirguis, K.; Steiner, R. Exploring artificial intelligence adoption in public organizations: A comparative case study. Public Manag. Rev. 2023, 26, 114–141
Nguyen, T.H.; Le, X.C.; Vu, T.H.L. An extended technology-organization-environment (TOE) framework for online retailing utilization in digital transformation: Empirical evidence from vietnam. J. Open Innov. Technol. Mark. Complex. 2022, 8, 200
Nowrouzi, B., Lightfoot, N., Carter, L., Larivi`ere, M., Rukholm, E., Schinke, R., & Belanger-Gardner, D. (2015). The rela- tionship between quality of work life and location of cross-training among obstetric nurses in urban northeastern Ontario, Canada: A population-based cross sectional study. International Journal of Occupational Medicine and Environmental Health, 28(3), 571–586. https://doi.org/10.13075/ijomeh.1896.00443
O’Hara, K. (2020). The contradictions of digital modernity. AI & Society, 35(1), 197–208. https://doi.org/10.1007/ s00146-018-0843-7
Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An ana- lysis of the manufacturing and services sectors. Information & Management, 51(5), 497–510. https://doi.org/10. 1016/j.im.2014.03.006
Onaolapo, S., & Oyewole, O. (2018). Performance Expectancy, Effort Expectancy, and Facilitating Conditions as Factors Influencing Smart Phones Use for Mobile Learning by Postgraduate Students of the University of Ibadan, Nigeria. Interdisciplinary Journal of E-Skills and Lifelong Learning, 14, 095–115. https://doi.org/10.28945/4085
Palos-Sanchez, P., Saura, J. R., & Ayestaran, R. (2021). An exploratory approach to the adoption process of bitcoin by business executives. Mathematics, 9(4), 355. https://doi.org/10.3390/math9040355
Pradana, M., Elisa, H. P., & Syarifuddin, S. (2023). Discussing ChatGPT in education: A literature review and bibliomet- ric analysis. Cogent Education, 10(2). https://doi.org/10.1080/2331186X.2023.2243134
Rahiman, H. U., & Kodikal, R. (2024). Revolutionizing education: Artificial intelligence empowered learning in higher education. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2023.2293431
Recsko´, M., & Aranyossy, M. (2024). User acceptance of social network-backed cryptocurrency: A unified theory of acceptance and use of technology (UTAUT)-based analysis. Financial Innovation, 10(1), 57. https://doi.org/10.1186/ s40854-023-00511-4
Scherer, R., & Siddiq, F. (2019). The relation between students’ socioeconomic status and ICT literacy: Findings from a meta-analysis. Computers & Education, 138, 13–32. https://doi.org/10.1016/j.compedu.2019.04.011
Scupola, A. SMEs’ e-commerce adoption: Perspectives from Denmark and Australia. J. Enterp. Inf. Manag. 2009, 22, 152–166
Shin, J., Moon, S., Cho, B., Hwang, S., & Choi, B. (2022). Extended technology acceptance model to explain the mech- anism of modular construction adoption. Journal of Cleaner Production, 342, 130963. https://doi.org/10.1016/j.jcle- pro.2022.130963
Solihati, K. D., & Indriyani, D. (2021). Managing artificial intelligence on public transportation (case study Jakarta City, Indonesia). IOP Conference Series: Earth and Environmental Science, 717(1), 012021. https://doi.org/10.1088/1755- 1315/717/1/012021
Susanto, T. D., & Goodwin, R. (2011). User acceptance of SMS-based eGovernment services (pp. 75–87). Springer. https://doi.org/10.1007/978-3-642-22878-0_7
Teniwut, M., (2024), 10 Aplikasi AI Terlaris di Tanah Air, Chat GPT Paling Unggul, available at: https://mediaindonesia.com/teknologi/668243/10-aplikasi-ai-terlaris-di-tanah-air-chat-gpt-paling-unggul
Ter Ji-Xi, J., Salamzadeh, Y., & Teoh, A. P. (2021). Behavioral intention to use cryptocurrency in Malaysia: An empirical study. The Bottom Line, 34(2), 170–197. https://doi.org/10.1108/BL-08-2020-0053
Thakur, R., & Sharma, D. (2019). Quality of work life and its relationship with work performance–A study of employees of Himachal Pradesh power corporation limited. Journal of Strategic Human Resource Management, 8(3), 45–52.
Tornatzky, L.G.; Fleischer, M.; Chakrabarti, A.K. The Processes of Technological Innovation; Lexington Books: Lanham, MD, USA, 1990. [Google Scholar]
Ünver, H. A. (2024). Artificial Intelligence (AI) and Human Rights: Using AI as a weapon of repression and its impact on human rights. https://doi.org/10.2861/907329
Venkatesh, Morris, Davis, & Davis. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
Venkatesh, Thong, & Xu. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
Wagner, G., Schramm-Klein, H., & Schu, M. (2016). Determinants and moderators of consumers’ cross-border online shopping intentions. Marketing ZFP, 38(4), 214–227. https://doi.org/10.15358/0344-1369-2016-4-214
Yanyan, Z. (2023). Construction of a smart classroom for image processing courses in colleges and universities based on artificial intelligence: Taking fundamentals of photoshop as an example. In 2023 IEEE 3rd International Conference on Social Sciences and Intelligence Management (SSIM) (pp. 84–88). https://doi.org/10.1109/SSIM59263. 2023.10469561
Yufei, L., Saleh, S., Jiahui, H., & Abdullah, S. M. S. (2020). Review of the application of artificial intelligence in educa- tion. International Journal of Innovation, Creativity and Change, 12(8), 548–562. https://doi.org/10.53333/IJICC2013/ 12850
Yusriadi, Y., Rusnaedi, R., Siregar, N. A., Megawati, S., & Sakkir, G. (2023). Implementation of artificial intelligence in Indonesia. International Journal of Data and Network Science, 7(1), 283–294. https://doi.org/10.5267/j.ijdns.2022.10. 005
Zawacki-Richter, O., Mar´?n, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelli- gence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
Zhang, Y.; Sun, J.; Yang, Z.; Wang, Y. Critical success factors of green innovation: Technology, organization and environment readiness. J. Clean. Prod. 2020, 264, 121701
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