Factors Affect the Intentions to Use Artificial Intelligence Tools and the Actual Use of Students in Higher Educational Institutions

Thai Thi Thuy Oanh *

* Correspondence: Thai Thi Thuy Oanh (email: oanh.thai@eiu.edu.vn)

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Received: 17 Apr 2024
Revised: 25 Apr 2024
Accepted: 31 Jul 2024
Published: 27 Feb 2026
DOI: 10.58810/vhujs.11.1.2026.1118
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How to Cite
Thai, O. T. T. (2026). Factors Affect the Intentions to Use Artificial Intelligence Tools and the Actual Use of Students in Higher Educational Institutions. Van Hien University Journal of Science 11(1), 1-18.
Issue
Volume 11 - Issue 1 (2026)
Section
Education

Main Article Content

Abstract

This study investigated the roles of technology readiness in shaping university students’ perceptions of usefulness and ease of use, and how these perceptions influence their intentions and actual use of AI tools. A total of 343 valid responses were analyzed using the SPSS (version 27) and Smart-PLS (version 4.1.1.0). The findings reveal that among the four elements of technology readiness, optimism and innovativeness positively affect perceived usefulness and ease of use, while discomfort and insecurity had a negative effect. Furthermore, perceived usefulness, perceived ease of use, subject norms, and perceived behavioral control positively impacted the intentions to use AI tools and led to positive actual use. The results suggest that students view AI as a supportive tool in their study. These insights could help universities enhance the effectiveness of teaching and studying, and guide technology companies in developing strategies that align with user readiness and behavioral intentions.
Keywords: artificial intelligence, intention, technology readiness

Article Details

References

Al-Swidi, A., Huque, S. M. R., Hafeez, M. H., and Shariff, M. N. M. (2014). The role of subjective norms in theory of planned behavior in the context of organic food consumption. British Food Journal, 116(10): 1561-1580. https://doi.org/10.1108/BFJ-05-2013-0105

Ali, H., Ali, T., Matar, Z., and Jawad, F. (2015). Citizens’ acceptance and readiness towards adopting e-participation tools in kingdom of Bahrain. International Journal for Infonomics, 8(2): 1029-1036. https://doi.org/10.20533/iji.1742.4712.2015.0121

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. http://www.doi.org/10.35940/ijrte.F7475.0311623

Amin, M., and Nasharuddin, S. Z. (2013). Hospital service quality and its effects on patient satisfaction and behavioural intention. Clinical Governance: An International Journal, 18(3): 238-254. https://doi.org/10.1108/CGIJ-05-2012-0016

Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. In IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 30(4): 411-420. DOI: 10.1109/3468.852435.

Bowen, N. K., and Guo, S. (2011). Structural equation modeling. Oxford University Press.

Burns, A. C., and Bush, R.F. (2000). Marketing Research (3rd ed.). Prentice Hall.

Cambre, M. A., and Cook, D. L. (1985). Computer anxiety: Definition, measurement, and correlates. Journal of Educational Computing Research, 1(1): 37-54. https://doi.org/10.2190/fk5l-092h-t6yb-pyba  

Chai, C. S., Chiu, T. K., Wang, X., Jiang, F., and Lin, X. F. (2022). Modeling Chinese secondary school students’ behavioral intentions to learn artificial intelligence with the theory of planned behavior and self-determination theory. Sustainability, 15(1): 605.

Chen, L., Chen, P., and Lin, Z. (2020). Artificial intelligence in education: A review. IEEE access, 8: 75264-75278.

Chen, Y. M., Hsu, T. H., and Lu, Y. J. (2018). Impact of flow on mobile shopping intention. Journal of Retailing and Consumer Services, 41: 281-287.

Chu, H. C., Hwang, G.-H., Tu, Y.-F., and Yang, K.-H. (2022). Roles and research trends of artificial intelligence in higher education: A systematic review of the top 50 most-cited articles. Australasian Journal of Educational Technology, 38(3): 22-42. https://doi.org/10.14742/ajet.7526

Colton, D., and Covert, R. W. (2007). Designing and constructing instruments for social research and evaluation. John Wiley & Sons.

Comrey, A. L., and Lee, H. B. (2013). A first course in factor analysis. Psychology Press. https://doi.org/10.4324/9781315827506

Cope, B., Kalantzis, M., and Searsmith, D. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(12): 1229-1245.

Cortez, P. M., Ong, A. K. S., Diaz, J. F. T., German, J. D., and Jagdeep, S. J. S. S. (2024). Analyzing Preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool. Heliyon, 10(3): e25896. https://doi.org/10.1016/j.heliyon.2024.e25896

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly: 13(3), 319-340. https://doi.org/10.2307/249008

Dignum, V. (2023). Responsible Artificial Intelligence: Recommendations and Lessons Learned. In Eke, D.O., Wakunuma, K., Akintoye, S. (eds) Responsible AI in Africa: Challenges and Opportunities. Social and Cultural Studies of Robots and AI. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-08215-3_9

Drolet, A. L., and Morrison, D. G. (2001). Do we really need multiple-item measures in service research? Journal of Service Research, 3(3): 196–204. https://doi.org/10.1177/109467050133001

Duan, Y., Edwards, J. S., and Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48: 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021

Godoe, P., and Johansen, T. S. (2012). Understanding adoption of new technologies: Technology readiness and technology acceptance as an integrated concept. Journal of European Psychology Students, 3(1): 38-52. https://doi.org/10.5334/jeps.aq  

Gupta, K., and Arora, N. (2020). Investigating consumer intention to accept mobile payment systems through unified theory of acceptance model: An Indian perspective. South Asian Journal of Business Studies, 9(1): 88-114. https://doi.org/10.1108/SAJBS-03-2019-0037

Hair, J. F., Anderson, R. E., Tatham, R. L., and Black, W. C. (2006). Multivariate data analysis with readings (Vol. 6). New York, NY: Pearson Prentice Hall.

Hair, J. F., Ringle, C. M., and Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2): 139-152. https://doi.org/10.2753/MTP1069-6679190202

Hayani, A., Sari, E. A., and Sukiman, S. (2021). Artificial Intelligence Librarian as Promotion of IAIN Lhokseumawe Library in the Revolutionary Era 4.0. Journal of Robotics and Control (JRC), 2(2): 88-93. https://doi.org/10.18196/jrc.2258

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: 115-135. https://doi.org/10.1007/s11747-014-0403-8

Hu, L. T., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1): 1-55. https://doi.org/10.1080/10705519909540118

Huang, M. H., and Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2): 155-172. https://doi.org/10.1177/1094670517752459

Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic management journal, 20(2): 195-204.

Jöreskog, K. G. (1971). Statistical analysis of sets of congeneric tests. Psychometrika, 36(2): 109-133. https://doi.org/10.1007/BF02291393

Kalinic, Z., and Marinkovic, V. (2016). Determinants of users’ intention to adopt m-commerce: an empirical analysis. Information Systems and e-Business Management, 14: 367-387. https://doi.org/10.1007/s10257-015-0287-2

Kallweit, K., Spreer, P., and Toporowski, W. (2014). Why do customers use self-service information technologies in retail? The mediating effect of perceived service quality. Journal of Retailing and Consumer Services, 21(3): 268-276. https://doi.org/10.1016/j.jretconser.2014.02.002

Kamble, S., Gunasekaran, A., and Arha, H. (2019). Understanding the Blockchain technology adoption in supply chains-Indian context. International Journal of Production Research, 57(7): 2009-2033. https://doi.org/10.1080/00207543.2018.1518610

Kim, T. and Chiu, W. (2019). Consumer acceptance of sports wearable technology: the role of technology readiness. International Journal of Sports Marketing and Sponsorship, 20(1): 109-126. https://doi.org/10.1108/IJSMS-06-2017-0050

Kotler, P., and Armstrong, G. (2014). Principles of marketing. Pearson education.

Kuleto, V., Bucea-Manea-Țoniş, R., Bucea-Manea-Țoniş, R., Ilić, M. P., Martins, O. M., Ranković, M., and Coelho, A. S. (2022). The potential of blockchain technology in higher education as perceived by students in Serbia, Romania, and Portugal. Sustainability, 14(2): 749.

Kumar, V., Rajan, B., Venkatesan, R., and Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4): 135-155. https://doi.org/10.1177/0008125619859317

Liljander, V., Gillberg, F., Gummerus, J., and Van Riel, A. (2006). Technology readiness and the evaluation and adoption of self-service technologies. Journal of Retailing and Consumer Services, 13(3): 177-191. https://doi.org/10.1016/j.jretconser.2005.08.004

Lu, J., Wang, L., and Hayes, L. A. (2012). How do technology readiness, platform functionality and trust influence C2C user satisfaction? Journal of Electronic Commerce Research, 13(1): 50-69.

Momani, A. M., and Jamous, M. (2017). The evolution of technology acceptance theories. International Journal of Contemporary Computer Research (IJCCR), 1(1): 51-58. https://www.researchgate.net/publication/316644779

Netemeyer, R. G., Bearden, W. O., and Sharma, S. (2003). Scaling procedures: Issues and applications. Sage Publications.

Nguyen, P. V., and Tran, H. X. (2019). Behavioural intention to accept mobile wallet: An empirical study of a developing country perspective. International Journal of Information Systems and Supply Chain Management, 12(5): 93-107.

Ouyang, F., and Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2: 100020. https://doi.org/10.1016/j.caeai.2021.100020

Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4): 307-320. https://doi.org/10.1177/109467050024001

Parasuraman, A., and Colby, C. L. (2001). Techno-ready marketing: How and why your customers adopt technology. The Free Press.

Parasuraman, A., and Colby, C. L. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research, 18(1): 59-74. https://doi.org/10.1177/1094670514539730

Pillai, R., Sivathanu, B., and Dwivedi, Y. K. (2020). Shopping intention at AI-powered automated retail stores (AIPARS). Journal of Retailing and Consumer Services, 57: 102207. https://doi.org/10.1016/j.jretconser.2020.102207

Pourmand, G., Doshmangir, L., Ahmadi, A., Noori, M., Rezaeifar, A., Mashhadi, R., Aziminia, R., Pourmand, A. and Gordeev, V. S. (2020). An application of the theory of planned behavior to self-care in patients with hypertension. BMC Public Health, 20: 1290. https://doi.org/10.1186/s12889-020-09385-y

Prasetyo, Y. T., Castillo, A. M., Salonga, L. J., Sia, J. A., and Seneta, J. A. (2020). Factors affecting perceived effectiveness of COVID-19 prevention measures among Filipinos during enhanced community quarantine in Luzon, Philippines: Integrating Protection Motivation Theory and extended Theory of Planned Behavior. International journal of infectious diseases, 99: 312-323.

Roldán, J. L., and Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling: Guidelines for using partial least squares in information systems research. In M. Mora, O. Gelman, A. Steenkamp, and M. Raisinghani (Eds.), Research methodologies, innovations and philosophies in software systems engineering and information systems (pp. 193-221). IGI global.

Roy, R., Babakerkhell, M. D., Mukherjee, S., Pal, D., and 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.

Siqueira, M. S. S., Nascimento, P. O., and Freire, A. P. (2022). Reporting Behaviour of People with Disabilities in Relation to the Lack of Accessibility on Government Websites: Analysis in the light of the Theory of Planned Behaviour. Disability, CBR & Inclusive Development, 33(1): 52-68. https://doi.org/10.47985/dcidj.475

Sudaryanto, M. R., Hendrawan, M. A., and Andrian, T. (2023). The effect of technology readiness, digital competence, perceived usefulness, and ease of use on accounting students’ artificial intelligence technology adoption. In E3S Web of Conferences (Vol. 388, p. 04055). EDP Sciences.

Suhr, D. D. (2006). Exploratory or confirmatory factor analysis?. SAS Institute.

Tsikriktsis, N. (2004). A technology readiness-based taxonomy of customers: A replication and extension. Journal of service research, 7(1): 42-52.

Ullman, J. B., and Bentler, P. M. (2013). Structural equation modeling. In J. A. Schinka, W. F. Velicer, and I. B. Weiner (Eds.), Handbook of psychology: Research methods in psychology (2nd ed., pp. 661-690). John Wiley & Sons.

Verma, P., and Sinha, N. (2018). Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. Technological forecasting and social change, 126: 207-216. https://doi.org/10.1016/j.techfore.2017.08.013

Wheatley, A., and Hervieux, S. (2019). Artificial intelligence in academic libraries: An environmental scan. Information Services & Use, 39(4), 347-356. https://doi.org/10.3233/isu-190065

Xie, Q., Song, W., Peng, X., and Shabbir, M. (2017). Predictors for e-government adoption: integrating TAM, TPB, trust and perceived risk. The Electronic Library, 35(1): 2-20. https://doi.org/10.1108/EL-08-2015-0141

Yakubu, A. S., Yagana, A. A., and Umar, S. I. Y. (2023). Investigating librarians’ intention to use artificial intelligence for effective library service delivery: A partial least square-structural equation modeling-based approach. Dutse Journal of Pure and Applied Sciences, 9(1b): 1-14.

Zeithaml, V. A., Berry, L. L., and Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2): 31-46.