DOI : https://doi.org/10.18517/ijaseit.15.2.12250

Evaluating Digital Learning Security Model in Higher Education: UTAUT-Based Empirical Study

Azliza Yacob (1), Noraida Haji Ali (2), Aimi Dalila Roslim (3)
(1) Faculty of Computers, Media & Technology Management, University College TATI, Terengganu, Malaysia
(2) Faculty of Computer Science and Mathematics, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
(3) Faculty of Computers, Media & Technology Management, University College TATI, Terengganu, Malaysia
Fulltext View | Download
How to cite (IJASEIT) :
[1]
A. Yacob, N. H. Ali, and A. D. Roslim, “Evaluating Digital Learning Security Model in Higher Education: UTAUT-Based Empirical Study”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 2, pp. 491–497, Apr. 2025.
Citation Format :
This research seeks to enhance privacy and data protection while preserving an effective learning environment by utilizing the UTAUT framework to analyze the adoption of a novel digital security model through protected e-learning. The research model preserves the original UTAUT constructs and items of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), and Behavioral Intention (BI), along with the supplementary constructs of Technological Knowledge (TK), Pedagogical Knowledge (PK), and Technological Pedagogical Knowledge (TPK). Data was gathered from educators, technicians, and administrators of the e-learning management system within Higher Education Institutions. The data is evaluated with SmartPLS 4 employing structural equation modeling. The results indicate that only facilitating environments significantly impact behavioral intention, corroborating the H2 hypothesis. The hypothesis testing results demonstrate that Facilitating Conditions (FC) significantly positively influence Behavioral Intention (BI). This indicates that individuals are more inclined to behave when they perceive enough external resources and support. Furthermore, Performance Expectancy (PE) significantly impacts Behavioral Intention (BI), suggesting a potential influence that requires additional examination.

O. Zawacki-Richter, “The current state and impact of Covid-19 on digital higher education in Germany,” Hum. Behav. Emerg. Technol., 2021, doi: 10.1002/hbe2.238.

Olukunle Oladipupo Amoo, Akoh Atadoga, Femi Osasona, Temitayo Oluwaseun Abrahams, Benjamin Samson Ayinla, and Oluwatoyin Ajoke Farayola, “GDPR’s impact on cybersecurity: A review focusing on USA and European practices,” Int. J. Sci. Res. Arch., 2024, doi:10.30574/ijsra.2024.11.1.0220.

K. Demertzis, K. Rantos, and G. Drosatos, “A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem,” Big Data Cogn. Comput., vol. 4, no. 2, p. 9, Apr. 2020, doi: 10.3390/bdcc4020009.

N. Muscanell, “The Cybersecurity and Privacy Workforce in Higher Education, 2023,” EDUCAUSE, 2023, [Online]. Available: https://www.educause.edu/ecar/research-publications/2023/the-cybersecurity-and-privacy-workforce-in-higher-education/introduction-and-key-findings.

V. Venkatesh, R. H. Smith, M. G. Morris, G. B. Davis, F. D. Davis, and S. M. Walton, “User Acceptance of Information Technology: Toward a Unified View,” MIS Q., vol. 27, no. 3, pp. 425–578, 2003, doi:10.47191/ijmra/v6-i8-52.

T. Lehmann, P. Blumschein, and N. M. Seel, “Accept it or forget it: mandatory digital learning and technology acceptance in higher education,” J. Comput. Educ., 2023, doi: 10.1007/s40692-022-00244-w.

I. Ajzen and M. Fishbein, “Understanding Attitudes and Predicting Social Behavior,” 1980.

I. Ajzen, “The theory of planned behavior,” Organ. Behav. Hum. Decis. Process., 1991, doi: 10.1016/0749-5978(91)90020-T.

F. D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Q., vol. 13, no. 3, p. 319, Sep. 1989, doi: 10.2307/249008.

J. Wang, X. Li, P. Wang, Q. Liu, Z. Deng, and J. Wang, “Research Trend of the Unified Theory of Acceptance and Use of Technology Theory: A Bibliometric Analysis,” Sustainability, vol. 14, no. 1, p. 10, Dec. 2021, doi: 10.3390/su14010010.

M. Jamalova, “Modelling User Behavior Towards Smartphones and Wearable Technologies,” Int. J. Interact. Mob. Technol., vol. 18, no. 12, pp. 143–160, Jun. 2024, doi: 10.3991/ijim.v18i12.48035.

S. S. Chand, B. aklesh Kumar, M. S. Goundar, and A. Narayan, “Extended UTAUT Model for Mobile Learning Adoption Studies,” Int. J. Mob. Blended Learn., vol. 14, no. 1, pp. 1–20, Oct. 2022, doi:10.4018/IJMBL.312570.

V. A. Nguyen, “An Application of Model Unified Theory of Acceptance and Use of Technology (UTAUT): A Use Case for a System of Personalized Learning Based on Learning Styles,” Int. J. Inf. Educ. Technol., vol. 14, no. 11, pp. 1574–1582, 2024, doi:10.18178/ijiet.2024.14.11.2188.

M. J. Koehler, P. Mishra, K. Kereluik, T. S. Shin, and C. R. Graham, “The technological pedagogical content knowledge framework,” in Handbook of Research on Educational Communications and Technology: Fourth Edition, Springer New York, 2014, pp. 101–111. doi: 10.1007/978-1-4614-3185-5_9.

P. Mishra and M. J. Koehler, “Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge,” Teach. Coll. Rec. Voice Scholarsh. Educ., vol. 108, no. 6, pp. 1017–1054, Jun. 2006, doi:10.1111/j.1467-9620.2006.00684.x.

A. Hasan, S. Habib, M. A. Khan, and N. N. Hamadneh, “Student Adoption of E-Learning in Higher Education Institutions in Saudi Arabia: Opportunities and Challenges,” Int. J. Inf. Commun. Technol. Educ., vol. 19, no. 1, 2023, doi: 10.4018/IJICTE.322792.

S. S. M. Ajibade and A. Zaidi, “Technological Acceptance Model for Social Media Networking in e-Learning in Higher Educational Institutes,” Int. J. Inf. Educ. Technol., vol. 13, no. 2, pp. 239–246, Feb. 2023, doi: 10.18178/ijiet.2023.13.2.1801.

M. A. Alqahtani, M. M. Alamri, A. M. Sayaf, and W. M. Al-Rahmi, “Exploring student satisfaction and acceptance of e-learning technologies in Saudi higher education,” Front. Psychol., vol. 13, Oct. 2022, doi: 10.3389/fpsyg.2022.939336.

P. S. Lim, W. A. Din, N. Z. Nik Mohamed, and S. Swanto, “Development And Validation Of A Survey Questionnaire Assessing Technological Pedagogical Content Knowledge And E-Learning Acceptance For Malaysian English Teachers,” Int. J. Educ. Psychol. Couns., vol. 7, no. 48, pp. 206–220, Dec. 2022, doi:10.35631/ijepc.748015.

D. A. Schmidt, E. Baran, A. D. Thompson, P. Mishra, M. J. Koehler, and T. S. Shin, “Technological pedagogical content knowledge (Track): The development and validation of an assessment instrument for preservice teachers,” J. Res. Technol. Educ., vol. 42, no. 2, pp. 123–149, 2009, doi: 10.1080/15391523.2009.10782544.

R. F. Guy and M. Norvell, “The Neutral Point on a Likert Scale,” J. Psychol., vol. 95, no. 2, pp. 199–204, Mar. 1977, doi:10.1080/00223980.1977.9915880.

S. M. Nowlis, B. E. Kahn, and R. Dhar, “Coping with Ambivalence: The Effect of Removing a Neutral Option on Consumer Attitude and Preference Judgments,” J. Consum. Res., vol. 29, no. 3, pp. 319–334, Dec. 2002, doi: 10.1086/344431.

P. R. Regmi, E. Waithaka, A. Paudyal, P. Simkhada, and E. Van Teijlingen, “Guide to the design and application of online questionnaire surveys,” Nepal J. Epidemiol., vol. 6, no. 4, pp. 640–644, May 2017, doi: 10.3126/nje.v6i4.17258.

P. M. Podsakoff, S. B. MacKenzie, and N. P. Podsakoff, “Sources of method bias in social science research and recommendations on how to control it,” 2012. doi: 10.1146/annurev-psych-120710-100452.

N. Kock, “Common Method Bias in PLS-SEM,” Int. J. e-Collaboration, vol. 11, no. 4, pp. 1–10, Oct. 2015, doi:10.4018/ijec.2015100101.

G. Franke and M. Sarstedt, “Heuristics versus statistics in discriminant validity testing: a comparison of four procedures,” Internet Res., 2019, doi: 10.1108/IntR-12-2017-0515.

C. M. Ringle, S. Wende, and A. Will, “SmartPLS 4,” 2024, Bönningstedt: SmartPLS. [Online]. Available: https://www.smartpls.com.

J. F. Hair, L. M. Matthews, R. L. Matthews, and M. Sarstedt, “PLS-SEM or CB-SEM: updated guidelines on which method to use,” Int. J. Multivar. Data Anal., vol. 1, no. 2, p. 107, 2017, doi:10.1504/ijmda.2017.087624.

J. F. Hair, Jr., M. Sarstedt, C. M. Ringle, and S. P. Gudergan, Advanced Issues in Partial Least Squares Structural Equation Modeling. Thousand Oaks, CA, USA: SAGE Publications, 2017.

J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” J. Acad. Mark. Sci., 2015, doi: 10.1007/s11747-014-0403-8.

J. F. Hair, H. G. Tomas, C. M. Ringle, and S. Marko, “A primer on partial least squares structural equation modeling (PLS-SEM),” Int. J. Res. Method Educ., 2017.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).