IT automation versus conventional systems in private universities: TAM and JD-R predictors of academic outcomes in Kupang city
DOI:
https://doi.org/10.46963/asatiza.v7i2.3690Keywords:
IT Automation, Conventional Systems, Student Engagement, TAM, JD-RAbstract
A significant gap exists in quantitative comparative research on IT-based automation versus conventional systems in resource-constrained regional universities, particularly in eastern Indonesia. This study addresses this gap by examining comparative associations between IT-based and conventional systems at private universities in Kupang City, East Nusa Tenggara. Guided by the Technology Acceptance Model (TAM) and Job Demands-Resources (JD-R) theory, a quantitative cross-sectional survey was conducted. A two-stage sampling procedure yielded 215 valid respondents (130 students, 55 lecturers, 30 administrative staff). A 36-item Likert-scale questionnaire demonstrated satisfactory internal consistency (Cronbach's α = .81 to .87). Independent samples t-tests and multiple regression (SPSS v.25) revealed that IT-adopting institutions reported significantly higher student engagement (M = 3.82 vs. 3.31, p < .001), academic productivity (M = 3.91 vs. 3.28, p < .001), and lecturer effectiveness (M = 3.75 vs. 3.29, p = .003). Regression showed perceived usefulness (β = .41, p < .001) and perceived ease of use (β = .29, p = .002) as the strongest productivity predictors (R² = .56). However, IT-adopting institutions also reported higher technostress and reduced face-to-face peer interaction. These findings suggest that a hybrid model may optimize outcomes. This study contributes comparative evidence from an under-researched regional context.
Downloads
References
Aluman, A. (2023). Trust-perilaku imitasi dalam membangun ekonomi keluarga miskin di kecamatan Biboki Tanpah Kabupaten Timor Tengah Utara Provinsi Nusa Tenggara Timur. JIMPS: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah, 8(4), 3840-3849. https://doi.org/10.24815/jimps.v8i4.26720
Aquino, J., Alarcón, R., Guevara, L., Bravo-Jaico, J., Germán, N., Valdivia-Salazar, C., Serquén, O., Maquen-Niño, G. L. E., & Tesén-Arroyo, A. (2025). Impact of digital transformation: assessing the knowledge and adoption of disruptive technologies in a higher education institution. Frontiers in Computer Science, 7, 1611952, 01-15. https://doi.org/10.3389/fcomp.2025.1611952
Bakker, A. B., & de Vries, J. D. (2021). Job Demands–Resources theory and self-regulation: new explanations and remedies for job burnout. Anxiety, Stress, & Coping, 34(1), 1–21. https://doi.org/10.1080/10615806.2020.1797695
Bakker, A. B., & Demerouti, E. (2017). Job demands – resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273-285. https://doi.org/10.1037/ocp0000056
Bandura, A. (1978). Self-efficacy: Toward a unifying theory of behavioral change. Advances in Behaviour Research and Therapy, 1(4), 139-161. https://doi.org/10.1016/0146-6402(78)90002-4
Barata, R. G. (2024). Back to school: Students’willingness to attend face-to-face classes in the new normal education system. Journal of Education and Culture (JEaC), 4(2), 147-161. https://doi.org/10.47918/jeac.v4i2.1841
Butson, R., & Spronken-Smith, R. (2024). AI and its implications for research in higher education: A critical dialogue critical dialogue. Higher Education Research & Development, 43(3), 563-577. https://doi.org/10.1080/07294360.2023.2280200
Cornelius-White, J. (2007). Learner-centered teacher-student relationships are effective: A meta-analysis. Review of educational research, 77(1), 113-143. https://doi.org/10.3102/003465430298563
Creswell, J. W; Creswell, D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th edition). SAGE Publications
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
Deacon, B., Laufer, M., Mende, M. A., & Tschache, T. (2025). Resisting digital change at the university: An exploration into triggers and organisational countermeasures. European Journal of Higher Education, 15(S1), 119-142. https://doi.org/10.1080/21568235.2025.2512735
Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior research methods, 39(2), 175-191. https://doi.org/10.3758/BF03193146
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109. https://doi.org/10.3102/00346543074001059
Monageng, T., Rafifing, N., & Mabina, A. (2025). Factors influencing gen z ’ s adoption and usability on internet of things (Iots) in higher education: Case of Botswana. Competitive: Journal of Education, 4(4), 370-382. https://doi.org/10.58355/competitive.v4i4.176
Poma, S. (2024). Impact of economic growth, unemployment, education level on poverty in Indonesia’s East Nusa Tenggara Province. Aksioma: Jurnal Manajemen, 3(1), 1-14. https://doi.org/10.30822/aksioma.v3i1.3162
Selwyn, N. (2016). Is technology good for education?. John Wiley & Sons.
Song, Y., & Mukundan, J. (2025). The influence of humanistic education on tertiary English teachers' writing assessment practices: A systematic review. Frontiers in Education, 10, 1-12. https://doi.org/10.3389/feduc.2025.1605368
Vahdat, A., Alizadeh, A., Quach, S., & Hamelin, N. (2021). Would you like to shop via mobile app technology? The technology acceptance model, social factors and purchase intention. Australasian Marketing Journal, 29(2), 187-197. https://doi.org/10.1016/j.ausmj.2020.01.002
Xiong, Y., & Zhou, Y. (2025). The impact of online social interaction on college students’ socio-emotional competence mediated by bonding social capital. Scientific Reports, 15(1), 1-13. https://doi.org/10.1038/s41598-025-18957-0
Xue, L., Mahat, J., & Ghazali, N. (2026). Technology Acceptance Model in Artificial Intelligence in Education: A Meta-Analysis. Sage Open, 16(1), 1-13. https://doi.org/10.1177/21582440251409441
Younas, M., El-dakhs, D. A. S., & Noor, U. (2025). The impact of artificial intelligence-based learning tools in academic innovation: A review of Deep Seek, GPT, and Gemini (2020-2025). Frontiers in Education, 1-17. https://doi.org/10.3389/feduc.2025.1689205
Zawacki-Richter, O., Marín, V. I., & Bond, M. (2019). Systematic review of research on artificial intelligence applications in higher education-where are the educators? International Journal of Educational Technology in Higher Education, 16(39), 1-27. https://doi.org/10.1186/s41239-019-0171-0
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Adrianus Aluman, Sarlianus Poma

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright on any article is retained by the author(s).
2. The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution shareAlike 4.0 International License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
3. 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 acknowledgment of its initial publication in this journal.
4. 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.
5. The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License





2.png)


