The Impact Of Predictive Analytics On Human Resource Management A Study Applied To Employees In The Hafar Al-Batin Municipality

Abstract

The present study investigates the impact of predictive analytics on Human Resource Management (HRM) in Hafar Al-Batin Municipality, Saudi Arabia. Predictive analytics, as a data-driven approach, enables organizations to forecast workforce needs, anticipate employee turnover, and enhance HR decision-making. Guided by the Resource-Based View (RBV), Technology Acceptance Model (TAM), and Socio-Technical Systems (STS) Theory, the study examines how predictive analytics adoption affects workforce planning, turnover reduction, and strategic alignment, considering the roles of organizational readiness and cultural/ethical factors.

A quantitative research design was employed, with data collected through a structured questionnaire distributed to HR managers, supervisors, and administrative staff. Descriptive statistics, reliability and validity testing, correlation analysis, and Structural Equation Modeling (SEM) were applied to analyze the relationships among variables. The results reveal that predictive analytics adoption significantly improves workforce planning, reduces turnover risk, and enhances alignment between HR strategies and municipal objectives. Organizational readiness strengthens the effectiveness of predictive analytics, while cultural and ethical considerations partially mediate its impact on employee outcomes.

The findings confirm that predictive analytics is a valuable strategic resource in municipal HRM. Adoption is influenced by perceived usefulness and ease of use, consistent with TAM, and its success depends on technical infrastructure, data quality, and supportive organizational culture, consistent with RBV and STS perspectives. The study provides practical recommendations for municipalities, including investing in user-friendly predictive tools, enhancing analytics skills, ensuring ethical governance, and aligning HR strategies with organizational goals.

Overall, the study contributes to the literature on HR analytics in public-sector contexts and demonstrates that predictive analytics can play a key role in improving HRM effectiveness, employee engagement, and strategic planning in municipal organizations under Saudi Arabia’s Vision 2030.