From Traditional HR To Digital HR: A Transformation Framework For The Future Workforce
DOI:
https://doi.org/10.51601/ijse.v5i2.159Abstract
The digital revolution has radically reshaped the landscape of human resource management, demanding a fundamental shift from traditional HR practices to more agile, data-driven, and technology-integrated approaches. This study proposes a comprehensive transformation framework for Digital HR, designed to equip organizations with strategic direction in preparing for the future workforce. By synthesizing literature from strategic HRM, digital transformation, and workforce analytics, the framework outlines key dimensions including digital leadership, cultural agility, HR tech adoption, and data-informed decision-making. Using a qualitative multi-case methodology, data were collected from organizations undergoing digital HR transformations across diverse sectors. Findings reveal that successful transitions depend not only on technological readiness but also on leadership alignment, continuous learning ecosystems, and change management capacity. This framework offers both theoretical contribution and practical guidance for HR leaders navigating the evolving digital landscape, ensuring sustainable workforce development in the era of rapid technological advancement.
Downloads
References
Sheikh Muhamad Hizam, Habiba Akter, Ilham Sentosa, Waqas Ahmed, Mohamad Noorman Masrek, & Jawad Ali. (2023). Predicting Workforce Engagement towards Digital Transformation through a Multi-Analytical Approach. Sustainability, 15(8), 6835. https://doi.org/10.3390/su15086835MDPI
Fenwick, A., Molnar, G., & Frangos, P. (2024). The critical role of HRM in AI- driven digital transformation: a paradigm shift to enable firms to move from AI implementation to human-centric adoption. Discover Artificial Intelligence, 4, 34. https://doi.org/10.1007/s44163-024-00125-4SpringerLink
Saram, M., Aburumman, O. J., & Hasan, A. (2023). The impact of HRM practices and employee behavior on career success. Problems and Perspectives in Management, 21(1), 326–335.
Wang, L., Zhou, Y., & Zheng, G. (2022). Linking digital HRM practices with HRM effectiveness: the moderating role of HRM capability maturity from the adaptive structuration perspective.Sustainability,14(2), 1003.
Yulianty, P. D., Senen, S. H., Puspa, N., & Yulianty, D. (2023). Analysis systematic literature review: the study of HRM transformation into digital-based GHRM future research agenda. West Science Business and Management, 1(5). http://dx.doi.org/10.58812/wsbm.v1i05.517
Silva, S., Azevedo, A., & Rodrigues, R. I. (2022). HRM 4.0: Digital transformation in human resource management. Journal of Business Research, 137, 259–267. https://doi.org/10.1016/j.jbusres.2021.08.046
Sivathanu, B., & Pillai, R. (2019). Smart HR 4.0 – how industry 4.0 is disrupting HR. Human Resource Management International Digest, 27(4), 1–5. https://doi.org/10.1108/HRMID-04-2019-0090
Strohmeier, S., & Parry, E. (2018). HR analytics in the digital age: Implementation, implications, and future directions. Human Resource Management Review, 28(3), 304–315. https://doi.org/10.1016/j.hrmr.2017.09.010
Stone, D. L., & Dulebohn, J. H. (2018). Emerging issues in theory and research on electronic human resource management (eHRM). Research in Personnel and Human Resources Management,36,1–30.
Hamilton, R. H., & Sodeman, W. A. (2020). The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources.Business Horizons,63(1),85–95.
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1). https://doi.org/10.1016/j.hrmr.2022.100899
Ciampi, F., Faraoni, M., Ballerini, J., & Meli, F. (2021). The co-evolutionary relationship between digitalization and organizational agility: Ongoing debates, theoretical developments and future research perspectives. Technological Forecasting and Social Change,166,120638.
Qin, C., Zhang, L., Cheng, Y., Zha, R., Shen, D., Zhang, Q., Chen, X., Sun, Y., Zhu, C., & Zhu, H. (2023). A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics. ACM Computing Surveys, 55(7), 1–38. https://doi.org/10.1145/3501297
Aydin, O., Karaarslan, E., & Narin, N. G. (2024). Artificial Intelligence, VR, AR and Metaverse Technologies for Human Resources Management. Journal of Business Research, 158, 113612.
Bohlouli, M., Mittas, N., Kakarontzas, G., Theodosiou, T., Angelis, L., & Fathi,
M. (2020). Competence Assessment as an Expert System for Human Resource Management: A Mathematical Approach. Expert Systems with Applications, 139, 112846. https://doi.org/10.1016/j.eswa.2019.112846.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Muhammad Syafri, Mhd. Andi Rasyid
This work is licensed under a Creative Commons Attribution 4.0 International License.
