Bridging Digital Literacy and Academic Readiness: A Mediated Model of Self-Efficacy and Learning Climate among University Students in Eastern Indonesia
Abstract
This study examines the direct and mediated effects of digital literacy on academic readiness through digital self-efficacy and digital learning climate among university students in eastern Indonesia, a context characterised by heterogeneous digital access and infrastructure constraints. A quantitative, cross-sectional survey was conducted with 372 undergraduate students at Universitas Negeri Makassar (UNM) using validated scales. Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0 was employed to test the measurement and structural models, including mediation analyses via bootstrapping (5,000 subsamples). The results indicate that digital literacy has a significant direct effect on academic readiness (β = 0.287, p < 0.001). Digital literacy also positively affects digital self-efficacy (β = 0.528) and digital learning climate (β = 0.467), both of which significantly predict academic readiness (β = 0.305 and β = 0.263, respectively). The model explains 52.4% of the variance in academic readiness. Both digital self-efficacy and digital learning climate partially mediate the relationship between digital literacy and academic readiness, with a total indirect effect of 0.284, accounting for approximately half (49.7%) of the total effect. Exploratory multi-group analysis reveals that the direct effect of digital literacy on academic readiness is stronger for students with intermittent mobile data access (β = 0.352) compared to those with stable broadband (β = 0.241). This study is among the first to empirically test a dual-mediation model in the under-researched context of eastern Indonesia, demonstrating that psychological (self-efficacy) and environmental (learning climate) factors are essential transmission mechanisms linking digital literacy to academic readiness. The findings suggest that universities should not only enhance students' digital skills but also invest in building digital confidence and fostering a supportive digital learning environment, particularly for students with limited connectivity
Downloads
References
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), Article 103168. https://doi.org/10.1016/[j.im](https://j.im).2019.05.003
Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design. Harvard University Press.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In Modern Methods for Business Research (Vol. 295, Number 2, pp. 295–336). Lawrence Erlbaum Associates.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
Conley, D. T. (2007). Redefining college readiness (Vol. 3). Educational Policy Improvement Center.
DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). SAGE Publications.
Fornell, C., & Larcker, D. F. (1981a). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Fornell, C., & Larcker, D. F. (1981b). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017a). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd, Ed.). Sage Publications.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017b). A primer on partial least squares structural equation modeling (PLS-SEM) (2, Ed.). Sage Publications.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017c). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage Publications.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019a). 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
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019b). 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
Hatlevik, O. E., Throndsen, I., Loi, M., & Gudmundsdottir, G. B. (2018). Students’ ICT self-efficacy and computer and information literacy: Determinants and relationships. Computers & Education, 118, 107–119. https://doi.org/10.1016/j.compedu.2017.11.011
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
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431. https://doi.org/10.1108/IMR-09-2014-0304
Kock, N. (2015). Common method bias in PLS-SEM: a full collinearity assessment approach. International Journal of E-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
Lai, C. L., & Hwang, G. J. (2014). Effects of mobile learning time on students’ conception of collaboration, communication, complex problem-solving, meta-cognitive awareness and creativity. International Journal of Mobile Learning and Organisation, 8(3/4), 276–291. https://doi.org/10.1504/IJMLO.2014.067029
Li, Y., & Ranieri, M. (2013). Educational and social correlates of the digital divide for rural and urban children: A study on primary school students in a provincial city of China. Computers & Education, 60(1), 197–209. https://doi.org/10.1016/j.compedu.2012.08.011
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
OECD. (2021). OECD Digital Education Outlook 2021: Pushing the frontiers with AI, blockchain, and robots. OECD Publishing. https://doi.org/10.1787/589b283f-en
Pagani, L., Argentin, G., Gui, M., & Stanca, L. (2016a). The impact of digital skills on educational outcomes: Evidence from performance tests. Educational Studies, 42(2), 137–162. https://doi.org/10.1080/03055698.2016.1148588
Pagani, L., Argentin, G., Gui, M., & Stanca, L. (2016b). The impact of digital skills on educational outcomes: Evidence from performance tests. Educational Studies, 42(2), 137–162. https://doi.org/10.1080/03055698.2016.1148588
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Redecker, C. (2017a). European framework for the digital competence of educators: DigCompEdu. Publications Office of the European Union. https://doi.org/10.2760/159770
Redecker, C. (2017b). European framework for the digital competence of educators: DigCompEdu. Publications Office of the European Union. https://doi.org/10.2760/159770
Ringle, C. M., Wende, S., & Becker, J. M. (2022). SmartPLS 4. SmartPLS GmbH. https://www.smartpls.com
Rohatgi, A., Scherer, R., & Hatlevik, O. E. (2016). The role of ICT self-efficacy for students’ ICT use and their achievement in a computer and information literacy test. Computers & Education, 102, 103–116. https://doi.org/10.1016/j.compedu.2016.07.002
Setyaningsih, R., Abdullah, Y., Prihantoro, E., & Hustinawaty, H. (2019). Model penguatan literasi digital melalui pemanfaatan e-learning. Jurnal ASPIKOM, 3(6), 1200–1214.
Spante, M., Hashemi, S. S., Lundin, M., & Algers, A. (2018). Digital competence and digital literacy in higher education research: A systematic review of concept use. Cogent Education, 5(1), 1519143. https://doi.org/10.1080/2331186X.2018.1519143
Wang, J., Tigelaar, D. E. H., & Admiraal, W. (2021). Rural teachers’ sharing of digital educational resources: From motivation to behavior. Computers & Education, 161, 104055. https://doi.org/10.1016/j.compedu.2020.104055
Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257
Copyright (c) 2026 Hasrul Bakri
This work is licensed under a Creative Commons Attribution 4.0 International License.
By submitting the article/manuscript of the article, the author(s) agree with this policy. No specific document sign-off is required.
License
The use of the article will be governed by the Creative Commons Attribution license as currently displayed on Creative Commons Attribution 4.0 International License.
Author(s)' Warranties
The author warrants that the article is original, written by the stated author(s), has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author(s).
User/Public Rights
Under the Creative Commons license, users are allowed to copy, distribute, display, and perform the work. Users will also need to attribute authors on distributing works in the journal and other media of publications. Unless otherwise stated, the authors are public entities as soon as their articles got published.
Rights of Authors
Authors retain all their rights to the published works, such as (but not limited to) the following rights;
- Copyright and other proprietary rights relating to the article, such as patent rights,
- The right to use the substance of the article in own future works, including lectures and books,
- The right to reproduce the article for own purposes,
- The right to self-archive the article,
- The right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the article's published version (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
Co-Authorship
If the article was jointly prepared by more than one author, any authors submitting the manuscript warrants that he/she has been authorized by all co-authors to be agreed on this copyright and license notice (agreement) on their behalf, and agrees to inform his/her co-authors of the terms of this policy. The journal will not be held liable for anything that may arise due to the author(s) internal dispute. The journal will only communicate with the corresponding author.
Royalties
Being an open accessed journal and disseminating articles for free under the Creative Commons license term mentioned, author(s) aware that the journal entitles the author(s) to no royalties or other fees.
Miscellaneous
The article will be published in the journal if the article’s editorial process is successfully completed. The editors may modify the article to a style of punctuation, spelling, capitalization, referencing, and usage that deems appropriate. The author acknowledges that the article may be published so that it will be publicly accessible and such access will be free of charge for the readers.
