Artificial intelligence (AI) has emerged as a transformative technology in higher education, holding immense significance for academicians. By harnessing the power of AI, researchers are empowered with advanced capabilities for data analysis, trend prediction, and innovative insights. This study aims to explore the direct correlation between attitude, perceived usefulness, perceived ease of use, and perceived behavioral control with intention and the subsequent usage of AI in the academic setting. The research model integrates three independent variables: attitude, perceived usefulness, perceived ease of use, and perceived behavioral control, with intention acting as a mediator and usage as the dependent variable. Primary data were collected through a well-structured survey questionnaire, which was thoughtfully adopted and adapted from previous studies. The study diligently analyzed 362 clean datasets using the structural equation modeling technique, which is well-suited for assessing complex relationships among variables. In the initial stages of analysis, the measurement model's convergent validity was evaluated by assessing construct reliability and validity. Subsequently, the discriminant validity was assessed and confirmed through cross-loading and Hetrotrait-Monotrait (HTMT) ratios, ensuring that each construct is distinct and not redundant with others. Upon evaluating the structural model, the hypotheses testing yielded significant results. It revealed that attitude, perceived usefulness, perceived ease of use, and perceived behavioral control have a positive and significant influence on intention. Moreover, the study established that intention strongly affects AI usage among academicians. These findings reaffirm the importance of these factors in shaping users' intention to embrace AI technology and subsequently utilize it effectively in their academic pursuits. Theoretical implications derived from this study highlight the critical role of attitude, perceived usefulness, perceived ease of use, and perceived behavioral control in shaping users' intentions and their subsequent behavior toward AI usage.
Abdulla Al Darayseh. (2023), Acceptance of artificial intelligence in teaching sicence: Science
perspective. Computers and Education: Artificial Intelligence, 4 (2023) 100132
Abid, S. K., Sulaiman, N., Chan, S. W., Nazir, U., Abid, M., Han, H., ... & Vega-Muñoz, A.
(2021). Toward an integrated disaster management approach: how artificial intelligence can boost disaster management. Sustainability, 13(22), 12560.
Alhumaid, K., Naqbi, S., Elsori, D., & Mansoori, M. (2023). The adoption of artificial
intelligence applications in education. International Journal of Data and Network Science, 7(1), 457-466.
Alghamdi, A., Alkahtani, A., & Alshammari, A. (2020). The impact of perceived usefulness
and perceived ease of use on the intention to use artificial intelligence in teaching: A study of academicians in Saudi Arabia. International Journal of Educational Technology in Higher Education, 17(1), 1-12. doi:10.1186/s41239-020-00196-1
Al-Sartawi, A. M. M., Razzaque, A., & Kamal, M. M. (Eds.). (2021). Artificial intelligence
systems and the internet of things in the digital era: Proceedings of EAMMIS 2021 (Vol. 239). Springer Nature.
Alzahrani, L. (2023), Analyzing Students’ Attitudes and Behavior Toward Artificial
Intelligence Technologies in Higher Education, International Journal of Recent Technology and Engineering (IJRTE), 11(6), 65-73, doi: 10.35940/ijrte.F7475.0311623
Anouze, AL, and Alamro, AS (2020), "Factors affecting intention to use e-banking in Jordan".
emerald Publishing Limited, 38(1), 86-112.
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022).
Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, 100099.
Bali, M.M E, I, Kumalasani, M.P, Yunilasari, D,(2022). Artificial Intelligence in Higher
Education: Perspicacity Relation between Educators and Students, Journal of Innovation in Educational and Cultural Research, 3(2) (2022) 146-152, https://doi.org/10.46843/jiecr.v3i2.88
Barakina, E. Y., Popova, A. V., Gorokhova, S. S., & Voskovskaya, A. S. (2021). Digital
Technologies and Artificial Intelligence Technologies in Education. European Journal of Contemporary Education, 10(2), 285-296.
Brill T. M. (2018). Siri, Alexa, and other digital assistants: A study of customer satisfaction
with Artificial Intelligence applications (Doctoral dissertation). Retrieved from
http://digitalcommons.udallas.edu/edt/1/
Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K., & Huang, B. (2020).
Factors influencing students' behavioral intention to continue artificial intelligence learning. In 2020 international symposium on educational technology (ISET) (pp. 147-150). IEEE.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. doi:10.1037/0033-
2909.112.1.155
Connie, G., bin S Senathirajah, A. R., Subramanian, P., Ranom, R., & Osman, Z. (2022).
Factors Influencing Students’ Choice Of An Institution Of Higher Education. Journal of Positive School Psychology, 10015-10043.
Davis F. D., Bagozzi R., Warshaw P. (1989). User acceptance of computer technology: A
comparison of two theoretical models. Management Science, 35, 982–1003.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008
Davis, F.D. (1989), Usefulness, perceived ease of use, and user acceptance of information
technology, MIS Quarterly, Vol. 3 No. 13, pp. 319-340
De Cannière, M.H.; De Pelsmacker, P.; Geuens, M. (2009) Relationship quality and the theory of planned behaviour models of behavioral intentions and purchase behavior. J. Bus. Res. 62, 82–92.
Gado, S., Kempen, R., Lingelbach, K., & Bipp, T. (2022). Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence?. Psychology Learning & Teaching, 21(1), 37-56.
Gupta, K. P., & Bhaskar, P. (2020). Inhibiting and motivating factors influencing teachers’ adoption of AI-based teaching and learning solutions: Prioritization using analytic hierarchy process. Journal of Information Technology Education. Research, 19, 693.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks, CA: SAGE.
Hair, J.F., L.D.S. Gabriel, M., da Silva, D. and Braga Junior, S. (2019). Development and validation of attitudes measurement scales: fundamental and practical aspects, RAUSP Management Journal, 54 (4), 490-507. https://doi.org/10.1108/RAUSP-05-2019-0098
Hair, J.F., M. Sarstedt, C.M. Ringle, and S.P. Gudergan. (2018). Advanced issues in partialleast squares structural equation modeling. Thousand Oakes, CA: Sage Publications.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3 ed.). Thousand Oaks, CA: Sage.
Henseler, J., Ringle, C. M., and Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling, Journal of the Academy of Marketing Science, 43(1): 115-135.
Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial intelligence in education. Globethics Publications.
Ili?, M. P., P?un, D., Popovi? Ševi?, N., Hadži?, A., & Jianu, A. (2021). Needs and performance analysis for changes in higher education and implementation of artificial intelligence, machine learning, and extended reality. Education Sciences, 11(10), 568.
Kebah, M., Raju, V., & Osman, Z. (2019). Growth of online purchase in Saudi Arabia retail industry. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 869-872.. ISSN: 2277-3878
Kebah, M., Raju, V., & Osman, Z. (2019). Online purchasing trend in the retail industry in Saudi. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 865-868. ISSN: 2277-3878
Khlaif, Z. N. (2018). Factors influencing teachers’ attitudes toward mobile technology integration in K-12. Technology, Knowledge and Learning, 23(1), 161-175.
Kim H.-Y., Lee J. Y., Mun J., Johnson K. (2017). Consumer adoption of smart in-store technology: Assessing the predictive value of attitude versus beliefs in the technology acceptance model. International Journal of Fashion Design, Technology and Education, 10, 26–36.
Lee, M.K., Cheung, C.M. and Chen, Z. (2005). Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation, Information and Management, 42(8), 1095-1104.
Lindner, A., & Berges, M. (2020, October). Can you explain AI to me? Teachers’ pre-concepts about Artificial Intelligence. In 2020 IEEE Frontiers in education conference (FIE) . 1-9. IEEE.
Li X, Du J, Long H (2020) Mechanism for green development behavior and performance ofindustrial enterprises (GDBP-IE) using partial least squares structural equation modeling (PLS-SEM). Int J Environ Res Public Health 17(22):8450. https://doi.org/10.1007/s11356-018-04090-1
Luik, P., & Taimalu, M. (2021). Predicting the intention to use technology in education among student teachers: A path analysis. Education Sciences, 11(9), 564.
Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining teachers’ behavioral intention to use E-learning in teaching of mathematics: An extended TAM model. Contemporary educational technology, 13(2), ep298.
Mathieson K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2, 173–191.
Mohamad, L., Osman, Z., Mohamad, R. K., Ismail, Z., & Din, M. I. M. (2023). The Perceived Attitude of Bank Customers towards the Intention to Use Digital Banking in Malaysia. International Journal of Academic Research in Business and Social Sciences, 13(1), 1308 – 1323.
Mohamad, L., Osman, Z., Mohamad, R. K., Ismail, Z., & Mohd, M. I. (2023). Uncovering the Intention to Use Digital Banking Services among Commercial Banks' Customers: Structural Equation Modelling Approach. International Journal of Business and Management, 7(3), 43-49. DOI: 10.26666/rmp.ijbm.2023.3.7. e-ISSN: 2590-3721
Molnar, A. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. European Journal of Education, 54(3), 398-409.
Nazaretsky, T., Cukurova, M., Ariely, M., & Alexandron, G. (2021, September). Confirmation bias and trust: Human factors that influence teachers' attitudes towards AI-based educational technology. In CEUR Workshop Proceedings,3042.
Osman, Z., Bakar, R. A., Fadil, N. A., Sulaiman, T. F. T., & Aziz, R. C. (2023). Does the Adoption of Sustainable Development Practices among Online Distance Learning Higher Education Institutions in Malaysia Matter? The Role of Intention as a Mediator. International Journal of Academic Research in Progressive Education and Development, 12(2), 1987–2004. http://dx.doi.org/10.6007/IJARPED/v12-i2/17588
Osman, Z., Aziz, R. C., Sulaiman, T. F. T., Bakar, R. A., & Fadil, N. A. (2023). Exploring Employees’ Adoption of Sustainable Development Practices in Online Distance Learning Higher Education Institutions: Theory of Planned Behavior. International Journal of Academic Research in Economics and Management and Sciences, 12(2), 416 – 439. http://dx.doi.org/10.6007/IJAREMS/v12-i2/17521
Osman, Z., Sulaiman, T. F. T., Aziz, R. C., Bakar, R. A., & Fadil, N. A. (2023). Leadership Styles and Organizational Commitment: Driving the Sustainable Development Practice Adoption in Online Higher Education Institutions through an Intention as a Mediator. International Journal of Academic Research in Business and Social Sciences, 13(7), 476 – 492.
Podsakoff, P.M., & Organ, D.W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531–544. https://doi.org/10.1177/014920638601200408
Rahim, N. I. M., Iahad, N. A., Yusof, A. F., & Al-Sharafi, M. A. (2022). AI-Based chatbots adoption model for higher-education institutions: A hybrid PLS-SEM-Neural network modelling approach. Sustainability, 14(19), 12726.
Rangel-de Lázaro, G., & Duart, J. M. (2023). You Can Handle, You Can Teach It: Systematic Review on the Use of Extended Reality and Artificial Intelligence Technologies for Online Higher Education. Sustainability, 15(4), 3507.
Ringle, C.M., and M. Sarstedt. (2016). Gain more insight from your PLS-SEM results: Theimportance-performance map analysis. Industrial Management & Data Systems 116: 1865–1886.
Ringle, Christian M., Wende, Sven, & Becker, Jan-Michael. (2022). SmartPLS 4.Oststeinbek: SmartPLS. Retrieved from https://www.smartpls.com
Roy, R., Babakerkhell, M. D., Mukherjee, S., Pal, D., & Funilkul, S. (2022). Evaluating the intention for the adoption of artificial intelligence-based robots in the university to educate the students. IEEE Access, 10, 125666-125678.
Sjödén, B. (2020). When lying, hiding and deceiving promotes learning-a case for augmented
intelligence with augmented ethics. In Artificial Intelligence in Education: 21st International Conference, AIED 2020, Ifrane, Morocco, July 6–10, 2020, Proceedings, Part II 21 (pp. 291-295). Springer International Publishing.
Shang Gao, John Krogstie, Keng Siau, (2011)Developing an Instrument to Measure the Adoption Of Mobile Services", Mobile Information Systems, vol. 7, Article ID 831018, 23 pages,. https://doi.org/10.1155/2011/831018
Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2021). Effects of COVID-19
in E-learning on higher education institution students: the group comparison between male and female. Quality & quantity, 55, 805-826.
Shmueli, G., S. Ray, J.M. Velasquez Estrada, and S.B. Chatla. (2016). The elephant in the
room: predictive performance of PLS models. Journal of Business Research 69: 4552–4564.
Shmueli, G., M. Sarstedt, J.F. Hair, J.-H. Cheah, H. Ting, S. Vaithilingam, and C.M. Ringle.
(2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict.
European Journal of Marketing 53: 2322–2347.
Tondeur, J., van Braak, J., Sang, G., Voogt, J., Fisser, P., & Ottenbreit-Leftwich, A. (2019).
Preparing pre-service teachers to integrate technology in education: A synthesis of qualitative evidence. Computers & Education, 133, 27-42.
Vazhayil, A., Shetty, R., Bhavani, R. R., & Akshay, N. (2019, December). Focusing on
teacher education to introduce AI in schools: Perspectives and illustrative findings. In 2019 IEEE Tenth International Conference on Technology for Education (T4E) (pp. 71-77). IEEE.
Venkatesh V., Davis F. D. (1996). A model of the antecedents of perceived ease of use:
Development and test. Decision Sciences, 27, 451–481.
Wu, P., Yang, L., Hu, X., Li, B., Liu, Q., Wang, Y., & Huang, J. (2022). How K12 Teachers’
Readiness Influences Their Intention to Implement STEM Education: Exploratory Study Based on Decomposed Theory of Planned Behavior. Applied Sciences, 12(23), 11989.
Yan, Z., & Sin, K. F. (2014). Inclusive education: teachers' intentions and behaviour analysed
from the viewpoint of the theory of planned behaviour. International Journal of Inclusive Education, 18(1), 72-85.
Yu, H., & Nazir, S. (2021). Role of 5G and artificial intelligence for research and
transformation of English situational teaching in higher studies. Mobile Information Systems, 2021, 1-16.
Zulkarnain, N.S, Yunus, M. (2023) International Journal Of Multidisciplinary Research And
Analysis, 6(5), 2101-2109: https://doi.org/10.47191/ijmra/v6-i5-34
