A META DATA-DRIVEN DECISION SUPPORT IN HUMAN CAPITAL MANAGEMENT: REVIEWING HRIS AND PREDICTIVE ANALYTICS INTEGRATION

Abstract

This systematic review explores the transformative role of predictive analytics in Human Resource Information Systems (HRIS), emphasizing its strategic impact on talent forecasting, workforce optimization, and organizational decision-making. Drawing from a comprehensive analysis of 155 peer-reviewed studies, the review reveals that predictive HRIS has shifted HR planning from reactive, spreadsheet-based processes to proactive, algorithm-driven forecasting tools. Findings indicate that organizations using predictive analytics experienced measurable gains in internal mobility, attrition reduction, and leadership succession planning accuracy. Over 70 studies highlighted the critical importance of technological infrastructure—including cloud-based platforms, middleware solutions, and API integration—in enabling scalable predictive functionality. However, the study also brings to light the rising significance of ethical, legal, and compliance considerations, particularly regarding employee surveillance, data privacy, and algorithmic bias. Additionally, global and sectoral disparities in adoption underscore the influence of cultural, regulatory, and infrastructural contexts. The review concludes that while predictive HRIS holds significant strategic value, its effective implementation demands not only technical readiness but also ethical stewardship, legal compliance, and contextual adaptation.