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Predictive HR Systems: Architecting Data-Driven Models for Workforce Stability and Strategic Talent Retention
Subject area: Science,Engineering and Technology · Area of research: Process-Oriented Model
DOI: https://doi.org/10.64388/IREV9I6-1716632
Abstract
Workforce instability has emerged as one of the most critical challenges facing modern organizations, driven by increasing employee mobility, evolving expectations, and complex organizational environments. Traditional HR approaches, which rely on retrospective analysis and periodic interventions, often fail to anticipate behavioral shifts that lead to disengagement and turnover. This study introduces a predictive systems perspective to HR design, emphasizing the transition from reactive management to forward-looking, data-informed decision-making. The paper develops a conceptual framework for predictive HR systems that integrates behavioral data, organizational context, and advanced analytics to identify early signals of workforce risk. Rather than treating turnover as an isolated outcome, the proposed model conceptualizes workforce stability as a systemic condition shaped by interconnected variables across performance, leadership, and employee experience. By examining key use cases such as attrition prediction, performance risk detection, and engagement trajectory analysis, the study highlights how predictive models can inform targeted and timely interventions. It further explores the organizational and ethical implications of deploying such systems, particularly in relation to data governance, bias, and trust. The findings suggest that organizations capable of embedding predictive capabilities into their HR architecture gain a structural advantage in maintaining workforce continuity and enhancing decision quality. The study contributes to the emerging field of predictive HR by offering a design-oriented perspective that connects data analytics with system-level thinking.
Keywords
Predictive HR Systems, Workforce Stability, Talent Retention Analytics, HR Data Architecture, Organizational Behavior Modeling
How to cite this paper
@article{1716632,
author = {ARZU OZER},
title = {Predictive HR Systems: Architecting Data-Driven Models for Workforce Stability and Strategic Talent Retention},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {2594-2606},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716632.pdf},
abstract = {Workforce instability has emerged as one of the most critical challenges facing modern organizations, driven by increasing employee mobility, evolving expectations, and complex organizational environments. Traditional HR approaches, which rely on retrospective analysis and periodic interventions, often fail to anticipate behavioral shifts that lead to disengagement and turnover. This study introduces a predictive systems perspective to HR design, emphasizing the transition from reactive management to forward-looking, data-informed decision-making. The paper develops a conceptual framework for predictive HR systems that integrates behavioral data, organizational context, and advanced analytics to identify early signals of workforce risk. Rather than treating turnover as an isolated outcome, the proposed model conceptualizes workforce stability as a systemic condition shaped by interconnected variables across performance, leadership, and employee experience. By examining key use cases such as attrition prediction, performance risk detection, and engagement trajectory analysis, the study highlights how predictive models can inform targeted and timely interventions. It further explores the organizational and ethical implications of deploying such systems, particularly in relation to data governance, bias, and trust. The findings suggest that organizations capable of embedding predictive capabilities into their HR architecture gain a structural advantage in maintaining workforce continuity and enhancing decision quality. The study contributes to the emerging field of predictive HR by offering a design-oriented perspective that connects data analytics with system-level thinking.},
keywords = {Predictive HR Systems, Workforce Stability, Talent Retention Analytics, HR Data Architecture, Organizational Behavior Modeling},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1716632}
}