ISSN: 2225-8329
Open access
Artificial intelligence (AI) has risen as a transformative catalyst in higher education, heralding substantial potential for the academic domain. By leveraging AI's capabilities, scholars access advanced tools for intricate tasks like data analysis, predictive modeling, and revolutionary insights. The present study delves deeply into the intricate nexus of relationships encompassing attitude, perceived behavioral control, trust, intention, and AI application within academia. The research framework integrates three distinct variables: attitude, perceived behavioral control, and trust, with intention serving as a mediating factor and AI usage as the outcome variable. A meticulous survey, thoughtfully adapted from prior research, was employed to collect primary data. Employing structural equation modeling, which is adept at dissecting intricate variable interactions, the analysis was performed on 362 comprehensive datasets to confirm convergent and discriminant validity. The assessment of the structural model decisively validated the hypotheses, unveiling seven direct relationships and two intermediary connections. These findings underline the profound import of these factors in sculpting users' intentions and facilitating the efficacious incorporation of AI within the academic realm. Theoretical implications extend beyond these empirical discoveries, underscoring the pivotal roles played by attitude, perceived behavioral control, and trust in shaping intentions and consequent behaviors regarding AI integration. As educators worldwide grapple with the integration of AI, the significance of this study extends to actionable insights that can inform policy-making, training curricula, and the evolution of educational paradigms. By furthering our understanding of these underlying dynamics, this research contributes to optimizing AI's integration into higher education. It empowers educators to harness AI's potential, thus engendering innovative and enriching educational experiences that equip students for the demands of the modern world.
Ahmed, S., Khalil, M. I., Chowdhury, B., Haque, R., bin S Senathirajah, A. R., & bin Omar Din,
F. M. (2022). Motivators and barriers of artificial intelligent (AI) based teaching. Eurasian Journal of Educational Research, 100(100), 74-89.
Ahmad, B., Irfan, M., Salem, S., & Asif, M. H. (2022). Energy efficiency in the post-COVID-19
era: exploring the determinants of energy-saving intentions and behaviors. Frontiers in Energy Research, 9.
Andrews, J. E., Ward, H., & Yoon, J. (2021). UTAUT as a model for understanding intention to
adopt AI and related technologies among librarians. The Journal of Academic Librarianship, 47(6), 102437.
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhi & J.
Beckmann (Eds.), Action-control: From cognition to behavior (pp. 11ó39). Heidelberg: Springer
Ashaari, M. A., Singh, K. S. D., Abbasi, G. A., Amran, A., & Liebana-Cabanillas, F. J. (2021). Big
data analytics capability for improved performance of higher education institutions in the Era of IR 4.0: A multi-analytical SEM & ANN perspective. Technological Forecasting and Social Change, 173, 121119.
Bilquise, G., Ibrahim, S., & Salhieh, S. E. M. (2023). Investigating student acceptance of an
academic advising chatbot in higher education institutions. Education and Information Technologies, 1-26.
Chanda, R. C., Vafaei-Zadeh, A., Hanifah, H., & Ramayah, T. (2023). Investigating factors
influencing individual user's intention to adopt cloud computing: a hybrid approach using PLS-SEM and fsQCA. Kybernetes.
Chatterjee, S., & Bhattacharjee, K. K. (2020). Usage of artificial intelligence in higher
education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443-3463.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. doi:10.1037/0033-
2909.112.1.155
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.
Gupta, P., & Yadav, S. (2022, July). A TAM-based Study on the ICT Usage by the Academicians
in Higher Educational Institutions of Delhi NCR. In Congress on Intelligent Systems: Proceedings of CIS 2021, Volume 2 (pp. 329-353). Singapore: Springer Nature Singapore.
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., M. Sarstedt, C.M. Ringle, and S.P. Gudergan. (2018). Advanced issues in partial
least squares structural equation modeling. Thousand Oakes, CA: Sage Publications.
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., 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.
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation
of artificial intelligence technologies in medicine. Nature medicine, 25(1), 30-36.
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.
Jasielska, D., Rogoza, R., Zajenkowska, A. et al. (2021). General trust scale: Validation in
cross-cultural settings. Curr Psychol 40, 5019–5029. https://doi.org/10.1007/s12144-019-00435-2
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the
interpretations, illustrations, and implications of artificial intelligence. Business horizons, 62(1), 15-25.
Khalid, N. (2020). Artificial intelligence learning and entrepreneurial performance among
university students: evidence from malaysian higher educational institutions. Journal of Intelligent & Fuzzy Systems, 39(4), 5417-5435.
Khan, W., Sohail, S., Roomi, M. A., Nisar, Q. A., & Rafiq, M. (2023). Opening a new horizon in
digitalization for e-learning in Malaysia: Empirical evidence of Covid-19. Education and Information Technologies, 1-30.
Lee, C. S., & Tajudeen, F. P. (2020). Usage and impact of artificial intelligence on accounting:
Evidence from Malaysian organisations. Asian Journal of Business and Accounting, 13(1).
Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., &
Ringle, C. M. (2021). Prediction: Coveted, Yet Forsaken? Introducing a Cross-
validated Predictive Ability Test in Partial Least Squares Path Modeling. Decision Sciences, 52(2), 362-392.
Li X, Du J, Long H (2020) Mechanism for green development behavior and performance of
industrial 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
Malhotra, G., & Ramalingam, M. (2023). Perceived anthropomorphism and purchase
intention using artificial intelligence technology: examining the moderated effect of trust. Journal of Enterprise Information Management.
Masa’deh, R. E., AlHadid, I., Abu-Taieh, E., Khwaldeh, S., Alrowwad, A. A., & Alkhawaldeh, R.
S. (2022). Factors influencing students’ intention to use E-textbooks and their impact on academic achievement in Bilingual environment: An empirical study Jordan. Information, 13(5), 233.
Mohd Rahim, N. I., A. Iahad, N., Yusof, A. F., & A. Al-Sharafi, M. (2022). AI-based chatbots
usage model for higher-education institutions: a hybrid PLS-SEM-neural network modelling approach. Sustainability, 14(19), 12726.
Ofosu-Ampong, K., Acheampong, B., & Kevor, M. O. (2023). Acceptance of artificial
intelligence (ChatGPT) in education: Trust, innovativeness and psychological need of students. Ofosu-Ampong, K., Acheampong, B., Kevor, MO, & Amankwah-Sarfo, F.(2023). Acceptance of Artificial Intelligence (ChatGPT) in Education: Trust, Innovativeness and Psychological Need of Students. Information and Knowledge Management, 13(4), 37-47.
Okunlaya, R. O., Syed Abdullah, N., & Alias, R. A. (2022). Artificial intelligence (AI) library
services innovative conceptual framework for the digital transformation of university education. Library Hi Tech, 40(6), 1869-1892.
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
Ragheb, M. A., Tantawi, P., Farouk, N., & Hatata, A. (2022). Investigating the acceptance of
applying chat-bot (Artificial intelligence) technology among higher education students in Egypt. International Journal of Higher Education Management, 8(2).
Rahman, M., Ming, T. H., Baigh, T. A., & Sarker, M. (2021). Usage of artificial intelligence in
banking services: an empirical analysis. International Journal of Emerging Markets.
Ringle, C.M., and M. Sarstedt. (2016). Gain more insight from your PLS-SEM results: The
importance-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
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.
Srivastava, A., Dasgupta, S. A., Ray, A., Bala, P. K., & Chakraborty, S. (2021). Relationships
between the “Big Five” personality types and consumer attitudes in Indian students toward augmented reality advertising. Aslib Journal of Information Management, 73(6), 967-991.
Wang, C., Ahmad, S. F., Ayassrah, A. Y. B. A., Awwad, E. M., Irshad, M., Ali, Y. A., ... & Han, H.
(2023). An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce. Heliyon, 9(8).
(Osman et al., 2023)
Osman, Z., Khan, B. N. A., Ismail, M. N., Yusoff, Y., Alwi, N. H., & Jodi, K. H. M. (2023). Artificial Intelligence Usage in Higher Education: Academicians’ Perspective. International Journal of Academic Research in Accounting, Finance and Management Sciences, 13(4), 22–35.
Copyright: © 2023 The Author(s)
Published by HRMARS (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode