Research ArticleJournal of Scientometric ResearchVol. 15 | Issue 2 | 2026 | pp. 667–684Open access
From Self-Directed Digital Learning to Human Resource Development: Tracing the Knowledge Attainment Journey through a Bibliometric Lens
- 1*,
- 1,
- 1
- 1 Department of Management, PSGR Krishnammal College for Women, Coimbatore, Tamil Nadu, INDIA.
Published in Journal of Scientometric Research
Correspondence: Reshma Mohammed
Department of Management, PSGR Krishnammal College for Women, Coimbatore, Tamil Nadu, INDIA.
Email: reshmam@grgsms.ac.in
Copyright: © 2026 Manuscript Technomedia. This is an open access article.
- Published:
- Aug 13, 2026
- Received:
- Feb 13, 2026
- Accepted:
- Jun 22, 2026
How to cite
Mohammed, R., Velumani, D. S., & Ganesan, B. (2026). From Self-Directed Digital Learning to Human Resource Development: Tracing the Knowledge Attainment Journey through a Bibliometric Lens. Journal of Scientometric Research, 15(2), 667–684. https://doi.org/10.5530/jscires.20260216
Abstract
Self-directed learning has gained considerable importance in digital learning environments especially in the post pandemic era. As learning becomes more spontaneous, learner centered and technology mediated, this ability to independently plan, manage and evaluate their own learning process has become critical for academic success, professional development and organizational competitiveness. Despite this, research on SDL in digital learning contexts remains fragmented and under researched. Also, its implications for employee development and organizational learning require scholarly attention. The present study has adopted a bibliometric approach to map the thematic evolution, intellectual structure and research landscape of SDL in digital learning environments. 2268 articles retrieved from the Scopus database through the PRISMA framework, were analyzed through Biblioshiny (Bibliometrix R) and VOSviewer software. Productivity analysis, co-authorship analysis, co-citation analysis, thematic evolution, keyword co-occurrence analysis, and thematic mapping were conducted to identify major research trends, influential contributors, and emerging themes. Additionally, a subset of 109 studies was examined to explore the integration of SDL with employee development, workplace learning, and human resource development. The findings reveal an exponential increase in SDL-related research particularly after the COVID-19 pandemic. Thematic analysis indicates the emergence of dominant subthemes like self-regulated learning, learner autonomy, motivation, metacognition, online learning, mobile learning, and e-learning. Co-citation analysis demonstrates that the field is theoretically grounded in social-cognitive, motivational, and technology-enhanced learning perspectives. Further analysis also revealed that more studies integrate SDL with workplace learning, continuous professional development and lifelong learning. The study finds that SDL is being positioned more as a strategic capability that can encourage continuous learning, workforce agility, and professional competence in technology-driven workspaces. These are quite relevant to the lifelong learning demands associated with emerging Industry 5.0 contexts.
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Article metadata
| Title | From Self-Directed Digital Learning to Human Resource Development: Tracing the Knowledge Attainment Journey through a Bibliometric Lens |
|---|---|
| Authors | Reshma Mohammed; Dharchana Sivasamy Velumani; Brindha Ganesan |
| Affiliations | Department of Management, PSGR Krishnammal College for Women, Coimbatore, Tamil Nadu, INDIA. |
| Corresponding author | reshmam@grgsms.ac.in |
| Journal | Journal of Scientometric Research |
| Volume / Issue | Vol. 15, Issue 2 (2026) |
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- How Scientific and Technological Interaction Accelerates Co-Evolution of Artificial Intelligence and Quantum Systemspp. 317–330
- A Bibliometric Analysis of Explainable Artificial Intelligence (XAI): Trends, Themes, and Global Research Dynamicspp. 331–346
- From Equations to Algorithms: A Bibliometric Analysis and Visualization of Physics-Informed Machine Learning Researchpp. 347–355
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