HR Predictive Analytics and Its Effect on Job Sustainability through Data-Driven Decision Making: An Applied Study at Baghdad Al-Rusafa Health Directorate
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Abstract
This study aimed to analyze the role of HR Predictive Analytics in achieving Job Sustainability and to examine the mediating role of Data-Driven Decision Making in this relationship, at the Baghdad Al-Rusafa Health Directorate, a vital health institution facing operational pressures and challenges in workforce management and retention. The study addressed the problem of the limited use of predictive analytics in HR functions and the weak linkage between HR decisions and data, which affects the stability, development, and sustainability of health-sector personnel. The research gap this study addresses is the scarcity of integrative models that empirically test the mediating mechanism between predictive analytics and job sustainability within a governmental health setting, since most prior work treats these variables separately or examines them in private-sector and Western contexts; the added scientific value lies in providing an empirically tested Arabic-region model that clarifies this transmission mechanism.
A descriptive-analytical method was adopted. A questionnaire was administered to a sample of 234 respondents from managerial levels (the directorate director and deputies, hospital directors and deputies, department heads, and division managers) drawn from a population of 600, with the sample size determined by the statistical sampling formula at a 95% confidence level and 5% margin of error. Data were analyzed using means, standard deviations, Cronbach's alpha, Pearson correlation, regression, and mediation testing.
The findings revealed a significant positive correlation and effect of HR Predictive Analytics on Job Sustainability, and that Data-Driven Decision Making partially and significantly mediates this relationship (proportion mediated ≈ 52%), with the full model explaining 50.3% of the variance in Job Sustainability. The study recommended institutionalizing predictive analytics within HR management, building a data-driven decision culture, and adopting the proposed measurement-and-improvement model.
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