HR Analytics and Decision-Making: A Data-Driven Approach to Employee Performance Management
DOI:
https://doi.org/10.52783/jns.v14.2425Keywords:
HR Analytics, Data-Driven Decision-Making, Employee Performance, Technology Adoption, Organizational PerformanceAbstract
In the era of digital transformation, HR analytics and data-driven decision-making (DDDM) have emerged as strategic tools for enhancing workforce management and organizational performance. This study examines the impact of HR analytics—spanning talent acquisition, employee performance, training and development, and engagement—on DDDM and its subsequent influence on employee productivity, retention, and overall organizational success. Furthermore, the study explores the moderating role of technology adoption in strengthening the impact of DDDM on key workforce outcomes.
The study employs structural equation modeling (SEM) to analyze data from professionals across diverse industries, confirming that HR analytics significantly enhances DDDM (β ranging from 0.32 to 0.41, p < 0.001). In turn, DDDM positively impacts employee productivity (β = 0.45), retention (β = 0.42), and organizational performance (β = 0.48), all at p < 0.001. Technology adoption further amplifies these relationships, reinforcing the role of AI-driven HR tools in shaping modern workforce strategies. The indirect path analysis also confirms that HR analytics indirectly contributes to workforce outcomes through DDDM (β ranging from 0.13 to 0.22, p < 0.001).
The findings underscore the strategic importance of integrating HR analytics and digital tools to optimize decision-making and enhance business outcomes. Organizations that leverage AI-powered HR analytics experience greater efficiency in talent management, improved employee engagement, and stronger workforce retention. This study contributes to both theoretical and managerial perspectives, emphasizing the need for HR professionals to invest in advanced analytics, predictive modeling, and AI-driven HR strategies to maintain a competitive edge.
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