Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
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All Journal International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET)
Wan Rita Azmiyati
Universitas Riau Kepulauan
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education

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HUMAN RESOURCE ANALYTICS AND DATA-DRIVEN DECISION MAKING: A SYSTEMATIC REVIEW OF CONTEMPORARY PRACTICES Wan Rita Azmiyati; Ramon Zamora; Lukmanul Hakim
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 5 No. 10 (2026): SEPTEMBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.23205271

Abstract

Human resource analytics (HRA) has rapidly evolved from a descriptive reporting function into a strategic imperative that drives organizational agility, evidence-based decision-making, and workforce Optimization. This systematic review critically synthesizes 16 benchmark empirical and conceptual studies published between 2021 and 2026 to evaluate contemporary practices, architectural frameworks, and performance outcomes associated with data-driven HR practices. Following PRISMA 2020 guidelines, literature was extracted across leading management and analytics databases. Findings reveal that while predictive talent modeling and machine learning algorithms significantly enhance decision quality (β = 0.51, p < 0.001) and strategic talent ROI (β = 0.34, p < 0.001), full organizational adoption remains constrained by data silos, privacy governance gaps, and a prevalent HR data literacy deficit. The review synthesizes a multi-level maturity framework illustrating the transition from descriptive dashboards to prescriptive machine intelligence. Ultimately, successful digital HR transformation requires balancing technical analytics capability with robust ethical data governance, continuous skill re-tooling, and executive alignment to ensure sustainable competitive advantage.
Co-Authors Lukmanul Hakim Ramon Zamora