Strategic Human Resource Analytics As A Catalyst For Workforce Agility: An Empirical Assessment Of Data-Driven Decision-Making

Abstract

In an era of rapid market shifts and technological disruption, workforce agility has become a defining trait of resilient organisations. This study investigates the role of strategic human resource analytics (SHRA) as a catalyst for enhancing workforce agility through data-driven decision-making. Drawing on a primary dataset collected from 320 employees across technology, healthcare, and financial services sectors, the research integrates statistical techniques and machine learning models—including logistic regression, Random Forest, and XGBoost—to forecast agility readiness and identify its key drivers. Ten complementary analytical approaches, including correlation analysis, chi-square testing, and ROC-AUC evaluation, were employed to provide both predictive accuracy and interpretive clarity. Results indicate that factors such as adaptive skills development, cross-functional collaboration, workload flexibility, and digital literacy significantly influence agility scores. Among the tested models, Random Forest achieved the highest classification accuracy (AUC = 0.87), with SHAP and permutation importance analyses confirming the centrality of capability-building and flexible work policies in predicting agility outcomes.

The findings hold significant implications for HR leaders, suggesting that agility is not merely a cultural attribute but a measurable, improvable competency. By embedding analytics into strategic HR processes, organisations can anticipate capability gaps, tailor interventions, and foster a workforce capable of rapid adaptation. While the models demonstrated strong predictive performance, contextual variations and the absence of longitudinal measures remain limitations. Future research should explore cross-industry and global datasets to enhance generalisability and deepen understanding of agility as a competitive differentiator.