Combating High Employee Turnover in Knowledge-Based Organizations: A Predictive HR Analytics Framework for Talent Retention and Strategic Workforce Planning

Authors

  • Reham Ershaid Nusair Human Resources Department, Faculty of Leadership and Management, University Science Islam (USIM), Nilai, Malaysia Author

Keywords:

employee turnover, knowledge-based organizations, predictive HR analytics, talent retention, workforce planning, machine learning

Abstract

High employee turnover in knowledge-based organizations leads to loss of intellectual capital and increased costs. This paper presents a predictive human resources (HR) analytics framework for talent retention and strategic workforce planning in knowledge-driven firms. We use a publicly available HR dataset of 14,999 employees to identify patterns and risk factors for voluntary turnover. Key features such as job satisfaction, years at company, work-life balance, and compensation are analyzed using machine learning models (logistic regression, support vector machines, and random forests). Predictive models achieve up to ~88% accuracy in identifying employees at risk of leaving. Important predictors include low employee satisfaction, lack of recent promotion, long working hours, and mismatch in projects studio-pubs-static.s3.amazonaws.com. We propose an integrated framework where predictive insights trigger targeted retention interventions, such as career development or compensation adjustments, aligning HR strategy with business needs.

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Published

2025-10-12

Issue

المجلد الأول - العدد الثاني - 2025

Section

البحوث المنشورة في العدد

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Reham Ershaid Nusair. (2025). Combating High Employee Turnover in Knowledge-Based Organizations: A Predictive HR Analytics Framework for Talent Retention and Strategic Workforce Planning. Sada Al-Jamia Journal for Financial and Administrative Sciences (SAJFAS), 1(2), 54-58. https://oujournals.ly/index.php/sajfas/article/view/67