ISSN: 2222-6990
Open access
This study explores the critical role of artificial intelligence (AI) enabled transformation among employees in higher education institutions, a shift essential for enhancing operational efficiency and educational outcomes. The primary aim was to investigate the impact of self-efficacy and technology innovativeness on the acceptance of AI, with attitude as a mediating factor, thus extending the Technology Acceptance Model to understand the dynamics of AI adoption better. Data were collected using a structured survey distributed via email, targeting a purposive sample of employees across various institutions, resulting in 422 valid responses for analysis. The Partial Least Squares Structural Equation Modeling (PLS-SEM) technique was employed to test the hypotheses, providing robust insights into the relationships among variables. Results revealed that both self-efficacy and technology innovativeness significantly influence acceptance, with attitude playing a crucial mediating role. Specifically, self-efficacy contributes positively to forming favourable attitudes, while perceptions of innovativeness substantially enhance the acceptance of AI technologies. These findings highlight the need for higher education institutions to invest in training programs that boost employees' confidence and demonstrate the tangible benefits of AI innovations. For future studies, it is suggested that research could expand on demographic variables and explore the longitudinal impacts of AI exposure. Furthermore, the role of organizational culture and leadership in shaping technology perceptions warrants further investigation. The implications of this study are twofold: theoretically, it extends the TAM framework by integrating new dimensions pertinent to AI adoption, and practically, it provides actionable insights for institutions to facilitate successful AI integration by addressing employee readiness and cultural adaptation. Overall, the study underscores the transformative potential of AI in educational settings and the strategic measures needed to harness it effectively.
Alam, A. (2021, November). Possibilities and apprehensions in the landscape of artificial intelligence in education. In 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) (pp. 1-8). IEEE.
AlGerafi, M. A., Zhou, Y., Alfadda, H., & Wijaya, T. T. (2023). Understanding the factors influencing higher education students’ intention to adopt artificial intelligence-based robots. IEEE Access.
Chang, P. C., Zhang, W., Cai, Q., & Guo, H. (2024). Does AI-Driven technostress promote or hinder employees’ artificial intelligence adoption intention? A moderated mediation model of affective reactions and technical self-efficacy. Psychology Research and Behavior Management, 413-427.
Chou, C. M., Shen, T. C., Shen, T. C., & Shen, C. H. (2022). Influencing factors on students’ learning effectiveness of AI-based technology application: Mediation variable of the human-computer interaction experience. Education and Information Technologies, 27(6), 8723-8750.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. doi:10.1037/0033- 2909.112.1.155
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 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.
George, B., & Wooden, O. (2023). Managing the strategic transformation of higher education through artificial intelligence. Administrative Sciences, 13(9), 196.
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., 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.
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 partial least squares structural equation modeling. Thousand Oakes, CA: Sage Publications.
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.
Ibrahim Hassan, A. H., Baquero, A., Salama, W. M., & Ahmed Khairy, H. (2024). Engaging Hotel Employees in the Era of Artificial Intelligence: The Interplay of Artificial Intelligence Awareness, Job Insecurity, and Technical Self-Efficacy.
Jia, X. H., & Tu, J. C. (2024). Towards a New Conceptual Model of AI-Enhanced Learning for College Students: The Roles of Artificial Intelligence Capabilities, General Self-Efficacy, Learning Motivation, and Critical Thinking Awareness. Systems, 12(3), 74.
Jorzik, P., Yigit, A., Kanbach, D. K., Kraus, S., & Dabi?, M. (2023). Artificial intelligence-enabled business model innovation: Competencies and roles of top management. IEEE transactions on engineering management, 71, 7044-7056.
Kandoth, S., & Shekhar, S. K. (2022, September). Social influence and intention to use AI: the role of personal innovativeness and perceived trust using the parallel mediation model. In Forum Scientiae Oeconomia (Vol. 10, No. 3, pp. 131-150).
Kebah, M., Raju, V., & Osman, Z. (2019). Growth of online purchase in Saudi Arabia retail industry. International Journal of Recent Technology and Engineering, 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
Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925.
Labrague, L. J., Aguilar-Rosales, R., Yboa, B. C., Sabio, J. B., & de Los Santos, J. A. (2023). Student nurses' attitudes, perceived utilization, and intention to adopt artificial intelligence (AI) technology in nursing practice: A cross-sectional study. Nurse Education in Practice, 73, 103815.
Li, X. T., Rahman, A., Connie, G., & Osman, Z. (2020). Examining customers' perception of electronic shopping mall's e-service quality. International Journal of Services, Economics and Management, 11(4), 329-346.
Liao, S., Lin, L., & Chen, Q. (2023). Research on the acceptance of collaborative robots for the industry 5.0 era--the mediating effect of perceived competence and the moderating effect of robot use self-efficacy. International Journal of Industrial Ergonomics, 95, 103455.
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.
Montag, C., Kraus, J., Baumann, M., & Rozgonjuk, D. (2023). The propensity to trust in (automated) technology mediates the links between technology self-efficacy and fear and acceptance of artificial intelligence. Computers in Human Behavior Reports, 11, 100315.
Obenza, B., Baguio, J. S. I., Bardago, K. M., Granado, L., Loreco, K. C., Matugas, L., ... & Caangay, R. B. (2024). The Mediating Effect of AI Trust on AI Self-Efficacy and Attitude Toward AI of College Students. International Journal of Metaverse (IJM), 2(1).
Osman, Z., Mohamad, W., Mohamad, R. K., Mohamad, L., & Sulaiman, T. F. T. (2018). Enhancing students’ academic performance in Malaysian online distance learning institutions. Asia Pacific Journal of Educators and Education, 33, 19-28.
Rahiman, H. U., & Kodikal, R. (2024). Revolutionizing education: Artificial intelligence empowered learning in higher education. Cogent Education, 11(1), 2293431.
Ringle, C. M., and Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems 116: 1865–1886.
Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2023). Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education. arXiv preprint arXiv:2309.10892.
Sikder, A. S. (2023). Artificial Intelligence-Enabled Transformation in Bangladesh: Overcoming Challenges for Socio-Economic Empowerment.: AI-Driven Transformation in Bangladesh. International Journal of Imminent Science & Technology, 1(1), 77-96.
Smith, T. G., Norasi, H., Herbst, K. M., Kendrick, M. L., Curry, T. B., Grantcharov, T. P., ... & Cleary, S. P. (2022). Creating a Practical Transformational Change Management Model for Novel Artificial Intelligence–Enabled Technology Implementation in the Operating Room. Mayo Clinic Proceedings: Innovations, Quality & Outcomes, 6(6), 584-596.
Udvaros, J., & Forman, N. (2023). Artificial intelligence and Education 4.0. In INTED2023 proceedings (pp. 6309-6317). IATED.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
Wagan, A. A., Khan, A. A., Chen, Y. L., Yee, P. L., Yang, J., & Laghari, A. A. (2023). Artificial intelligence-enabled game-based learning and quality of experience: A novel and secure framework (B-AIQoE). Sustainability, 15(6), 5362.
Wang, S., Sun, Z., & Chen, Y. (2023). Effects of higher education institutes’ artificial intelligence capability on students' self-efficacy, creativity and learning performance. Education and Information Technologies, 28(5), 4919-4939.
Wang, Y. Y., & Chuang, Y. W. (2024). Artificial intelligence self-efficacy: Scale development and validation. Education and Information Technologies, 29(4), 4785-4808.
Osman, Z., Mohamad, R. K., & Kasbun, N. (2024). Unveiling Artificial Intelligence-Enabled Transformation Acceptance among Employees in Higher Education Institutions: The Role of Attitude as a Mediator. International Journal of Academic Research in Business and Social Sciences, 14(9), 2323–2337.
Copyright: © 2024 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