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

Reham Ershaid Nusair

المؤلفون

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

الكلمات المفتاحية:

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

الملخص

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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