“Human–Computer Interaction and Learning Behavior: Analyzing LMS Adoption Among Gen-Z in Higher Education”
Authors
Post-Graduate School, Pakuan University, Bogor, Indonesia (Indonesia)
Post-Graduate School, Ibnu Khaldun University, Bogor, Indonesia (Indonesia)
Faculty of Economics and Business, Pakuan University, Bogor, Indonesia (Indonesia)
Article Information
DOI: 10.47772/IJRISS.2026.1026EDU0481
Subject Category: Education
Volume/Issue: 10/26 | Page No: 6551-6571
Publication Timeline
Submitted: 2026-07-17
Accepted: 2026-07-22
Published: 2026-08-01
Abstract
The rapid digitalization of higher education has increased the importance of Learning Management Systems (LMS), requiring a clearer understanding of Human–Computer Interaction (HCI) factors that influence engagement and continuous use, especially among Gen-Z students. This study investigates LMS adoption by extending the Technology Acceptance Model (TAM) with HCI constructs such as perceived usability, interactivity, system quality, and user satisfaction. Data were collected through an online survey involving 720 university students from various faculties and analyzed using Partial Least Squares Structural Equation Modeling (SEM-PLS). Results show that perceived usability and interactivity significantly enhance perceived ease of use and usefulness, leading to stronger behavioral intention to continue LMS use. System quality indirectly influences actual use through user satisfaction, emphasizing the role of positive user experience. These findings demonstrate that digitally savvy Gen-Z students respond more favorably to user-centered LMS design, offering practical guidance for improving LMS interface quality and engagement strategies.
Keywords
Human–Computer Interaction, Learning Management System, Technology Acceptance Model.
Downloads
References
1. Abdel-Maksoud, N. F. (2018). The Relationship between Students’ Satisfaction in the LMS “Acadox” and Their Perceptions of Its Usefulness, and Ease of Use. Journal of Education and Learning, 7(2), 184–190. https://doi.org/10.5539/jel.v7n2p184 [Google Scholar] [Crossref]
2. Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256. https://doi.org/10.1016/J.CHB.2015.11.036 [Google Scholar] [Crossref]
3. Aditya, A. D. (2023). Analisis Technology Acceptance Model (TAM) Pada Penggunaan e-Wallet Di Kalangan Mahasiswa. Sekolah Tinggi Ilmu Ekonomi Yayasan Keluarga Pahlawan Negara. [Google Scholar] [Crossref]
4. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T [Google Scholar] [Crossref]
5. Al-Emran, M., Al-Maroof, R., Al-Sharafi, M. A., & Arpaci, I. (2022). What impacts learning with wearables? An integrated theoretical model. Interactive Learning Environments, 30(10), 1897–1917. https://doi.org/10.1080/10494820.2020.1753216 [Google Scholar] [Crossref]
6. Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/J.CHB.2019.08.004 [Google Scholar] [Crossref]
7. Al-Samarraie, H. (2019). A Scoping Review of Videoconferencing Systems in Higher Education: Learning Paradigms, Opportunities, and Challenges. International Review of Research in Open and Distributed Learning, 20(3), 121–140. [Google Scholar] [Crossref]
8. Aprilia, A. R., & Santoso, T. (2020). Pengaruh Perceived Ease Of Use, Perceived Usefulness dan Attitude Towards Using terhadap Behavioural Intention to Use pada Aplikasi OVO. AGORA, 8(1). [Google Scholar] [Crossref]
9. Bansah, A. K., & Darko Agyei, D. (2022). Perceived convenience, usefulness, effectiveness and user acceptance of information technology: evaluating students’ experiences of a Learning Management System. Technology, Pedagogy and Education, 31(4), 431–449. https://doi.org/10.1080/1475939X.2022.2027267 [Google Scholar] [Crossref]
10. Bleier, A., & Eisenbeiss, M. (2015). The Importance of Trust for Personalized Online Advertising. Journal of Retailing, 91(3), 390–409. https://doi.org/10.1016/J.JRETAI.2015.04.001 [Google Scholar] [Crossref]
11. Calvo-Porral, C., & Pesqueira-Sanchez, R. (2020). Generational differences in technology behaviour: comparing millennials and Generation X. Kybernetes, 49(11), 2755–2772. https://doi.org/10.1108/K-09-2019-0598 [Google Scholar] [Crossref]
12. Cheng, E. W. L. (2019). Choosing between the theory of planned behavior (TPB) and the technology acceptance model (TAM). Educational Technology Research and Development, 67(1), 21–37. https://doi.org/10.1007/S11423-018-9598-6/METRICS [Google Scholar] [Crossref]
13. Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates Publishers. https://psycnet.apa.org/record/1998-07269-010 [Google Scholar] [Crossref]
14. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems : theory and results [Massachusetts Institute of Technology]. https://dspace.mit.edu/handle/1721.1/15192 [Google Scholar] [Crossref]
15. 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 [Google Scholar] [Crossref]
16. Davis, F. D. ., & Granić, Andrina. (2024). The Technology Acceptance Model: 30 years of TAM. Springer International Publishing. [Google Scholar] [Crossref]
17. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/MNSC.35.8.982 [Google Scholar] [Crossref]
18. Dhingra, V., & Dhingra, M. (2021). Who doesn’t want to be happy? Measuring the impact of factors influencing work–life balance on subjective happiness of doctors. Ethics, Medicine and Public Health, 16, 100630. https://doi.org/10.1016/J.JEMEP.2021.100630 [Google Scholar] [Crossref]
19. Fearnley, M. R., & Amora, J. T. (2020). Learning management system adoption in higher education using the extended technology acceptance model. IAFOR Journal of Education, 8(2), 89–106. https://doi.org/10.22492/IJE.8.2.05 [Google Scholar] [Crossref]
20. Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research - Martin Fishbein, Icek Ajzen. Addison-Wesley Publishing Company. [Google Scholar] [Crossref]
21. Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly: Management Information Systems, 21(4), 389–400. https://doi.org/10.2307/249720 [Google Scholar] [Crossref]
22. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–233. https://doi.org/10.2307/249689 [Google Scholar] [Crossref]
23. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). In SAGE Publication (3rd ed.). SAGE Publication, Inc. [Google Scholar] [Crossref]
24. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203/FULL/XML [Google Scholar] [Crossref]
25. Helsper, E. J., & Eynon, R. (2010). Digital natives: where is the evidence? British Educational Research Journal, 36(3), 203–520. https://www.jstor.org/stable/27823621 [Google Scholar] [Crossref]
26. Henseler, J., Ringle, C. M., & 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. https://doi.org/10.1007/S11747-014-0403-8/FIGURES/8 [Google Scholar] [Crossref]
27. Lai, K. W., & Hong, K. S. (2015). Technology use and learning characteristics of students in higher education: Do generational differences exist? British Journal of Educational Technology, 46(4), 725–738. https://doi.org/10.1111/BJET.12161 [Google Scholar] [Crossref]
28. Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & Management, 42(8), 1095–1104. https://doi.org/10.1016/J.IM.2003.10.007 [Google Scholar] [Crossref]
29. Lee, Y. C. (2006). An empirical investigation into factors influencing the adoption of an e‐learning system. Online Information Review, 30(5), 517–541. https://doi.org/10.1108/14684520610706406 [Google Scholar] [Crossref]
30. Lewis, J. R., & Sauro, J. (2024). Effect of Perceived Ease of Use and Usefulness on UX and Behavioral Outcomes. International Journal of Human–Computer Interaction, 40(20), 6676–6683. https://doi.org/10.1080/10447318.2023.2260164 [Google Scholar] [Crossref]
31. Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078. https://doi.org/10.1016/J.COMPEDU.2012.04.016 [Google Scholar] [Crossref]
32. Obaid, F., Babadi, A., & Yoosofan, A. (2020). Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks. Applied Computer Systems, 25(1), 57–61. https://doi.org/10.2478/ACSS-2020-0007 [Google Scholar] [Crossref]
33. Piaget, J. (1950). The Psychology of Intelligence The Nature of Intelligence. London Routledge Classics. https://www.scirp.org/reference/referencespapers?referenceid=2382978 [Google Scholar] [Crossref]
34. Prensky, M. (2001). Digital Natives, Digital Immigrants Part 1. On the Horizon: The International Journal of Learning Futures, 9(5), 1–6. https://doi.org/10.1108/10748120110424816 [Google Scholar] [Crossref]
35. Prensky, M. (2010). Teaching Digital Natives Partnering for Real Learning. Corwin. https://www.scirp.org/reference/referencespapers?referenceid=1207643 [Google Scholar] [Crossref]
36. Putra, I. S., Triatmanto, B., & Zuhro, D. (2021). The Effect of Perceived Ease of Use on User’s Intention to Use E- learning with Moodle Application in Higher Education Mediated by Perceived Usefulness. MEC-J (Management and Economics Journal), 5(3), 211–220. https://doi.org/10.18860/MEC-J.V5I3.13146 [Google Scholar] [Crossref]
37. Quinn, A. J., & Bederson, B. B. (2011). Human computation: A survey and taxonomy of a growing field. Conference on Human Factors in Computing Systems - Proceedings, 1403–1412. https://doi.org/10.1145/1978942.1979148 [Google Scholar] [Crossref]
38. Salloum, S. A., Qasim Mohammad Alhamad, A., Al-Emran, M., Abdel Monem, A., & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access, 7, 128445–128462. https://doi.org/10.1109/ACCESS.2019.2939467 [Google Scholar] [Crossref]
39. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022). Partial Least Squares Structural Equation Modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of Market Research (pp. 587–632). Springer, Cham. https://doi.org/10.1007/978-3-319-57413-4_15 [Google Scholar] [Crossref]
40. Sheppard, M., & Vibert, C. (2019). Re-examining the relationship between ease of use and usefulness for the net generation. Education and Information Technologies, 24(5), 3205–3218. https://doi.org/10.1007/S10639-019-09916-0/METRICS [Google Scholar] [Crossref]
41. Spector, J. M. (2014). Conceptualizing the emerging field of smart learning environments. Smart Learning Environments, 1(1), 1–10. https://doi.org/10.1186/S40561-014-0002-7/FIGURES/1 [Google Scholar] [Crossref]
42. Stern, P. J. (2002). Generational differences. Journal of Hand Surgery, 27(2), 187–194. https://doi.org/10.1053/jhsu.2002.32329 [Google Scholar] [Crossref]
43. Šumak, B., Heričko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067–2077. https://doi.org/10.1016/J.CHB.2011.08.005 [Google Scholar] [Crossref]
44. Tytyk, E. (2004). Evolutional background for humanizing technology. Human Factors and Ergonomics in Manufacturing & Service Industries, 14(3), 307–319. https://doi.org/10.1002/HFM.10063 [Google Scholar] [Crossref]
45. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/J.1540-5915.2008.00192.X [Google Scholar] [Crossref]
46. Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://www.jstor.org/stable/2634758 [Google Scholar] [Crossref]
47. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. [Google Scholar] [Crossref]
48. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly: Management Information Systems, 36(1), 157–178. https://doi.org/10.2307/41410412 [Google Scholar] [Crossref]
49. Vygotsky, L. S. (1978). Mind in Society The Development of Higher Psychological Processes. Harvard University Press. https://www.scirp.org/reference/referencespapers?referenceid=2107373 [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
- Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
- “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
- Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
- Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study