Effects of High Staff Turnover Rate on Organizations

This review comprehensively examines the causes, types, and effects of high employee turnover in organizations and strategies to reduce it. High employee turnover is not just a number; it is a multidimensional indicator that affects many critical factors such as organizational commitment, customer satisfaction, knowledge loss and business image. In the light of theoretical approaches in literature and recent empirical findings, turnover types such as voluntary and involuntary, functional, and dysfunctional, avoidable, and unavoidable are explained and how individual, organizational, and environmental factors shape this phenomenon is detailed. In addition, concrete recommendations for reducing employee turnover are presented through strategies developed with contemporary technology-based methods such as machine learning, neural networks, and counterfactual analysis. In this context, the aim of the study is to contribute to the strategic and data-driven decisions of organizations in human resources management and to support a sustainable workforce structure.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-20
DOI
https://doi.org/10.5281/zenodo.15477195
Primary Topic
AI and HR Technologies
Type
article
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article

Effects of High Staff Turnover Rate on Organizations

Zenodo (CERN European Organization for Nuclear Research)
AI and HR Technologies
article

Effects of High Staff Turnover Rate on Organizations

article en

Abstract

This review comprehensively examines the causes, types, and effects of high employee turnover in organizations and strategies to reduce it. High employee turnover is not just a number; it is a multidimensional indicator that affects many critical factors such as organizational commitment, customer satisfaction, knowledge loss and business image. In the light of theoretical approaches in literature and recent empirical findings, turnover types such as voluntary and involuntary, functional, and dysfunctional, avoidable, and unavoidable are explained and how individual, organizational, and environmental factors shape this phenomenon is detailed. In addition, concrete recommendations for reducing employee turnover are presented through strategies developed with contemporary technology-based methods such as machine learning, neural networks, and counterfactual analysis. In this context, the aim of the study is to contribute to the strategic and data-driven decisions of organizations in human resources management and to support a sustainable workforce structure.

Zenodo (CERN European Organization for Nuclear Research)
St Clements University Higher Education School (NU)
Openalex Percentile: Top 50%
AI and HR Technologies
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