The Effect of Learning Innovation and Digital Transformation on Student Quality: The Mediating Role of Self-Directed Learning

Authors

  • Fernando Belo Universitas Ciputra Surabaya, Indonesia
  • Murpin Joshua Sembiring Gurky Universitas Ciputra Surabaya, Indonesia
  • David Sukardi Kodrat Universitas Ciputra Surabaya, Indonesia

DOI:

https://doi.org/10.61255/jeemba.v4i5.1780

Keywords:

Learning Innovation, Digital Competence, Self-Directed Learning, Perceived Learning Outcomes, PLS-SEM

Abstract

Purpose – Indonesian universities expanded digital learning platforms after 2020, yet many undergraduates still fail to convert that access into consistent, self-managed study behavior. Prior research disagrees on whether digitalization improves student-level outcomes, leaving unresolved whether learning innovation and digital transformation actually improve student quality directly, or only through self-directed learning. This study tests seven hypothesized paths linking learning innovation and digital transformation to student quality, directly and through self-directed learning as a mediator, grounded in Self-Determination Theory.

Design/methodology/approach – A positivist, cross-sectional survey of 312 undergraduate students at a private university in Surabaya, Indonesia, following an 85-respondent pilot test, analyzed with Partial Least Squares Structural Equation Modeling in SmartPLS 4, supported by Confirmatory Tetrad Analysis, a Cross-Validated Predictive Ability Test, and a full collinearity assessment.

Findings – The measurement model showed strong reliability and validity, and the structural model explained a substantial share of variance in student quality. Learning innovation and digital transformation both significantly raised student quality directly, and learning innovation significantly raised self-directed learning, which in turn significantly raised student quality and significantly mediated the learning-innovation-to-quality relationship. Digital transformation, however, showed no significant relationship with self-directed learning, so self-directed learning did not mediate the digital-transformation-to-quality relationship.

Originality/Value – The findings show learning innovation and digital transformation reach student quality through two distinct routes rather than one shared mechanism, extending Self-Determination Theory into a digitally mediated higher education setting and clarifying that pedagogical innovation, not digital access alone, is the stronger lever for building student autonomy.

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References

Bhuttah, T. M., Xusheng, Q., Abid, M. N., & Sharma, S. (2024). Enhancing student critical thinking and learning outcomes through innovative pedagogical approaches in higher education: The mediating role of inclusive leadership. Scientific Reports, 14(1), 24362. https://doi.org/10.1038/s41598-024-75379-0

Bulman, G., & Fairlie, R. W. (2016). Technology and education: Computers, software, and the internet. In E. A. Hanushek, S. Machin, & L. Woessmann (Eds.), Handbook of the Economics of Education (Vol. 5, pp. 239–280). Elsevier. https://doi.org/10.1016/B978-0-444-63459-7.00005-1

Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01

Fisher, M., King, J., & Tague, G. (2001). Development of a self-directed learning readiness scale for nursing education. Nurse Education Today, 21(7), 516–525. https://doi.org/10.1054/nedt.2001.0589

Gudergan, S. P., Ringle, C. M., Wende, S., & Will, A. (2008). Confirmatory tetrad analysis in PLS path modeling. Journal of Business Research, 61(12), 1238–1249. https://doi.org/10.1016/j.jbusres.2008.01.012

Gutu, I., Medeleanu, C. N., & Asiminei, R. (2024). The limits of learning engagement and academic leadership within the higher education digitalization process: Analysis by using PLS SEM. PLOS ONE, 19(11), e0306079. https://doi.org/10.1371/journal.pone.0306079

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

Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131

Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546–580. https://doi.org/10.17705/1jais.00302

Lee, D. C., & Chang, C. Y. (2025). Evaluating self-directed learning competencies in digital learning environments: A meta-analysis. Education and Information Technologies, 30(6), 6847–6868. https://doi.org/10.1007/s10639-024-13083-2

Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least squares path modeling. Decision Sciences, 52(2), 362–392. https://doi.org/10.1111/deci.12445

Rashid, T., & Asghar, H. M. (2016). Technology use, self-directed learning, student engagement and academic performance: Examining the interrelations. Computers in Human Behavior, 63, 604–612. https://doi.org/10.1016/j.chb.2016.05.084

Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems, 116(9), 1865–1886. https://doi.org/10.1108/IMDS-10-2015-0449

Song, Y., Lv, S., Wang, M., Wang, Z., & Dong, W. (2025). The impact of digital learning competence on the academic achievement of undergraduate students. Behavioral Sciences, 15(7), 840. https://doi.org/10.3390/bs15070840

Sun, T., & Yoon, M. (2025). The impact of digital transformation on faculty performance in higher education: The mediating role of digital self-efficacy and the moderating role of task-technology fit. Frontiers in Psychology, 16, 1693375. https://doi.org/10.3389/fpsyg.2025.1693375

Zakir, S., Hoque, M. E., Susanto, P., Nisaa, V., Alam, M. K., Khatimah, H., & Mulyani, E. (2025). Digital literacy and academic performance: The mediating roles of digital informal learning, self-efficacy, and students’ digital competence. Frontiers in Education, 10, 1590274. https://doi.org/10.3389/feduc.2025.1590274

Zhao, X., Lynch, J. G., Jr., & 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

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Published

2026-08-14

How to Cite

Belo, F., Gurky, M. J. S., & Kodrat, D. S. (2026). The Effect of Learning Innovation and Digital Transformation on Student Quality: The Mediating Role of Self-Directed Learning. Journal of Economics, Entrepreneurship, Management Business and Accounting, 4(5), 533–551. https://doi.org/10.61255/jeemba.v4i5.1780