A Study on the Determinants of Employee Turnover in the Academic Institutions of Kathmandu Valley

Khadayat, Asmita; Basyal, Devid Kumar; Lawaju, Purnima; Paudel, Udaya Raj

doi:10.3126/spectrum.v2i1.64771

Cited by 1 publication

(1 citation statement)

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“…Factors such as supervisor relationships, work conditions, and recognition significantly impact job satisfaction, which in turn influences turnover intentions. A study in Kathmandu Valley's academic institutions revealed that addressing challenges in salary, recognition, work-life balance, and career growth can enhance job satisfaction and reduce turnover (Khadayat et al, 2024). This emphasizes the need for comprehensive retention strategies that consider both intrinsic and extrinsic motivators.…”

Section: Introduction Importance Of Predicting Employee Turnovermentioning

confidence: 99%

Utilizing machine learning to predict employee turnover in high-stress sectors

Kudirat Bukola Adeusi,

Prisca Amajuoyi,

Lucky Bamidele Benjami

2024

Int. j. manag. entrep. res

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This study investigates the application of machine learning techniques to predict employee turnover in high-stress sectors. The primary objective is to enhance retention strategies by accurately identifying potential turnover risks. The research utilizes a comprehensive dataset comprising various factors, including employee demographics, job satisfaction, performance metrics, and stress levels. Multiple machine learning algorithms, such as logistic regression, decision trees, random forests, and neural networks, are employed to build predictive models. The methodology involves data preprocessing, feature selection, model training, and evaluation. Cross-validation and hyper parameter tuning are performed to ensure the robustness and accuracy of the models. The performance of each algorithm is assessed using metrics such as accuracy, precision, recall, and the area under the receiver operating characteristic curve (AUC-ROC). Key findings reveal that machine learning models can effectively predict employee turnover, with random forests and neural networks demonstrating superior performance. Significant predictors of turnover include job satisfaction, stress levels, and performance ratings. The study concludes that integrating machine learning models into human resource practices can provide valuable insights for preemptive interventions, ultimately reducing turnover rates in high-stress environments. Future research should explore the integration of real-time data and the potential of deep learning techniques to further enhance predictive accuracy. Additionally, the ethical implications of using predictive models in HR decisions warrant careful consideration to ensure fairness and transparency. Keywords: Machine Learning (ML), Employee Turnover, Predictive Analytics, Human Resources (HR), High-Stress Sectors, Decision Trees, Random Forests, Extreme Gradient Boosting (XGBoost), Personalized Retention Strategies, Business Intelligence (BI) Tools, Data Quality, Ethical Considerations, Data Privacy, Natural Language Processing (NLP), Deep Learning, Real-time Data Analysis, Employee Engagement, Work-Life Balance, Organizational Performance, Data-Driven Insights.

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Section: Introduction Importance Of Predicting Employee Turnovermentioning

confidence: 99%