Prediction Of Turnover Research Articles

Employee Turnover Prediction Based on Ensemble Learning DGNK Model

Employee turnover is a problem that can have significant negative impacts on an enterprise. It not only results in the loss of valuable talent and knowledge but also incurs substantial costs in terms of hiring, onboarding, and training new employees. Therefore, predicting employee intent to quit can be crucial for organizations to take proactive measures to prevent it from happening. Early detection of employee turnover intention will help enterprise develop and enhance core competitiveness. This study aims to predict the employee intension to quit. In the present study, more than 1,400 samples containing 31 features of a company’s employees were collected from Kaggle website as data sets. A two-layer DGNK model was designed with decision tree, gradient boosting, naive bayes and k-nearest neighbor model as the primary classifier and gradient boosting as the secondary classifier to build the predictive model of employee turnover intention. The experimental outcomes show that DGNK model based on two-layer ensemble learning has the best outcome, while naive bayes model has the worst outcome. In conclusion, this study highlights the importance of predicting employee turnover intention as an effective strategy to enhance organizational performance and competitive advantage. Furthermore, the success achieved in the study suggests that machine learning models like DGNK can play a crucial role in achieving this goal.

The role of employee personality in employee satisfaction and turnover: insights from online employee reviews

PurposeOnline employee reviews have emerged as a crucial information source for business managers to evaluate employee behavior and firm performance. The purpose of this paper is to test the relationship between employee personality traits, derived from online employee reviews and job satisfaction and turnover behavior at the individual level.Design/methodology/approachThe authors apply text-mining techniques to extract personality traits from online employee reviews on Indeed.com based on the Big Five theory. They also apply a machine learning classification algorithm to demonstrate that incorporating personality traits can significantly enhance employee turnover prediction accuracy.FindingsPersonality traits such as agreeableness, conscientiousness and openness are positively associated with job satisfaction, while extraversion and neuroticism are negatively related to job satisfaction. Moreover, the impact of personality traits on overall job satisfaction is stronger for former employees than for current employees. Personality traits are significantly linked to employee turnover behavior, with a one-unit increase in the neuroticism score raising the probability of an employee becoming a former employee by 0.6%.Practical implicationsThese findings have implications for firm managers looking to gain insights into employee online review behavior and improve firm performance. Online employee review websites are recommended to include the identified personality traits.Originality/valueThis study identifies employee personality traits from automated analysis of employee-generated data and verifies their relationship with employee satisfaction and employee turnover, providing new insights into the development of human resources in the era of big data.

Study on the application of data visualization technology in human resource management and employee turnover prediction

Abstract This paper presents a method to process the dataset of departing employees. The clustering categories of separated employees are accurately determined by fuzzy c-mean clustering and improved clustering FCM algorithm, and new samples of separated employees are generated using SMOTE algorithm to reduce noisy data. The kernel function trick of SVM is used to achieve clustering oversampling, improving classification accuracy. For imbalanced data, this paper uses a new integrated learning algorithm for constructing evaluations, PIBoost, combined with a full-sample cost-aware weight algorithm to improve the generalization ability of the SVM classifier, which has better classification results for various data sets. In the model performance comparison, K-AFCM-SMOTE-SVM has the highest accuracy with a value of 0.89. In the ten-fold cross-validation accuracy comparison, the K-AFCM-SMOTE-SVM model has a better overall performance index than the other two, with an average cross-validation accuracy of 0.932.

Hybrid GA–DeepAutoencoder–KNN Model for Employee Turnover Prediction

Organizations strive to retain their top talent and maintain workforce stability by predicting employee turnover and implementing preventive measures. Employee turnover prediction is a critical task, and accurate prediction models can help organizations take proactive measures to retain employees and reduce turnover rates. Therefore, in this study, we propose a hybrid genetic algorithm–autoencoder–k-nearest neighbor (GA–DeepAutoencoder–KNN) model to predict employee turnover. The proposed model combines a genetic algorithm, an autoencoder, and the KNN model to enhance prediction accuracy. The proposed model was evaluated and compared experimentally with the conventional DeepAutoencoder–KNN and k-nearest neighbor models. The results demonstrate that the GA–DeepAutoencoder–KNN model achieved a significantly higher accuracy score (90.95\%) compared to the conventional models (86.48% and 88.37% accuracy, respectively). Our findings are expected to assist HR teams identify at-risk employees and implement targeted retention strategies to improve the retention rate of valuable employees. The proposed model can be applied to various industries and organizations, making it a valuable tool for HR professionals to improve workforce stability and productivity.

A decade of research on machine learning techniques for predicting employee turnover: A systematic literature review

This study presents a comprehensive and systematic review of the machine learning (ML) techniques used to predict employee turnover in the past decade. A total of 52 relevant peer-reviewed studies published between 2012 and April 2023 were selected. The results indicate that over 20 ML techniques have been used to predict employee turnover in various institutions. In addition, this comprehensive review demonstrates that most machine learning approaches used to predict employee turnover were based on supervised learning, with 96% of the articles (50 out of 52) in this category, random forest technique. Furthermore, the review reveals that the most critical factors for predicting employee turnover have been salary and overtime. This study makes a valuable contribution to the field by offering a systematic analysis of the ML algorithms used for predicting employee turnover, in addition to providing an overview of the most significant works in this field produced in the past decade. This study offers important reference regarding the essential ML approaches used in employee turnover prediction and provides future directions for researchers and industries.

Comparison of Machine Learning Models for Employee Turnover Prediction

In the era of big data, companies are able to more easily record, analyze, and utilize data, making it feasible to predict employee turnover. In the current economic situation, how to predict employee turnover has become particularly important. The core of this article is the establishment of a forecasting model based on real data, and then through the application to reflect the use and importance of the forecasting model. This paper uses XGBoost, LGBM, random forest and decision tree models, uses IBM's real data to construct prediction data, and evaluates the models to select the model that is most suitable for the lost data. Additionally, the role of Synthetic Minority Over-sampling Technique (SMOTE) in improving classification accuracy was explored. The results of this work indicate that: 1) balancing the data set using SMOTE oversampling technique is more effective than using the original data set; and 2) The XGBoost algorithm performs better, with higher precision and greater flexibility.

MANE: Organizational Network Embedding With Multiplex Attentive Neural Networks

Every organization has organizational networks for exchange of ideas and information. It is believed that organizational network analysis (ONA) can help the business be more effective. While considerable research efforts have been made for visualizing and analyzing relationships in organizational networks, it lacks a holistic way to model the complex social structures and rich semantic information of these networks. Indeed, employee behaviors can occur across different communication platforms, such as email and instant messaging systems, which naturally lead to the multiplex structure of organizational social networks. Meanwhile, it is also a challenge to model the impact of semantic information, such as employee attributes and organization charts, and the collaboration relationships of employees. To this end, in this paper, we propose a Multiplex Attentive Network Embedding (MANE) approach for modeling organizational social networks in a holistic way. Specifically, we first develop a multiple attributed random walk approach to jointly model multiple networks, with the integration of external work information. Then, we preserve the network structure by maximizing the probability of predicting the central node based on the surrounding context nodes. In particular, we introduce an attention mechanism to assign a weight to each context node in the training process, according to its attributed relation and structural relation with the central node by utilizing the k-core algorithm and the shortest path algorithm. In this way, the embedding results can be kept consistent with their structural relationships. Furthermore, to solve some department-level tasks, we introduce an attentive relational transition method to learn the representation of departments in the organizational networks. Finally, we evaluate the performance of MANE with extensive experiments on real-world data for three important talent management tasks, namely employee performance prediction, employee turnover prediction and department performance prediction. We also conduct a link prediction task to validate the effectiveness of employee embedding. Experimental results clearly show the effectiveness and interpretability of MANE for organizational network analysis.

Human Resource Management of Internet Enterprises Based on Big Data Mobile Information System

With the progress of science and technology, the advent of VR panorama, the era of Big Data has arrived, which has set off a revolution in the development and future destiny of Internet companies. Human resource management (HRM) is an important department of Internet companies. This paper introduces the principle and value of Big Data and related theories of HRM. This paper discusses three aspects of HRM in our company using Big Data theory: employee turnover prediction, salary comparison, and talent evaluation method, and puts forward suggestions for optimization and improvement of the company’s HRM. The results show that, through the company’s project manager’s ability to quantify data and analyze a set of data quantitative research, comparative analysis of the ability of three candidates Q, W, and E showed that W is the most suitable for the company’s project manager staff.

A machine learning-based analytical framework for employee turnover prediction

Employee turnover (ET) can cause severe consequences to a company, which are hard to be replaced or rebuilt. It is thus crucial to develop an intelligent system that can accurately predict the likelihood of ET, allowing the human resource management team to take pro-active action for retention or plan for succession. However, building such a system faces challenges due to the variety of influential human factors, the lack of training data, and the large pool of candidate models to choose from. Solutions offered by existing studies only adopt essential learning strategies. To fill this methodological gap, we propose a machine learning-based analytical framework that adopts a streamlined approach to feature engineering, model training and validation, and ensemble learning towards building an accurate and robust predictive model. The proposed framework is evaluated on two representative datasets with different sizes and feature settings. Results demonstrate the superior performance of the final model produced by our framework.

DBGE: Employee Turnover Prediction Based on Dynamic Bipartite Graph Embedding

The issue of employee turnover is always critical for companies, and accurate predictions can help them prepare in time. Most past studies on employee turnover have focused on analyzing impact factors or using simple network centrality measures. In this paper, we study the problem from a completely new perspective by modeling users’ historical job records as a dynamic bipartite graph. Specifically, we propose a bipartite graph embedding method with temporal information called dynamic bipartite graph embedding (DBGE) to learn the vector representation of employees and companies. Our approach not only considers the relations between employees and companies but also incorporates temporal information embedded in consecutive work records. We first define the Horary Random Walk on a bipartite graph to generate a sequence for each vertex in chronological order. Then, we employ the skip-gram model to obtain a temporal low-dimensional vector representation for each vertex and apply machine learning methods to predict employee turnover behavior by combining embedded features with employees’ basic information. Experiments on a real-world dataset collected from one of China’s largest online professional social networks show that the features learned through DBGE can significantly improve turnover prediction performance. Moreover, experiments on public Amazon and Taobao datasets show that our approach achieves better performance in the link prediction and visualization task than other graph embedding methods that do not consider temporal information.

The Application of The Decision Tree Algorithm Based on K-means in Employee Turnover Prediction

The employee’s voluntary turnover is one of the most difficult problems for the company. The research uses a human resource data as an example. K-means is used to classify employees, and then each type of the decision tree algorithm is used to conduct prediction and analysis of turnover. After research and analysis according to the experimental results, the two types of employees with high turnover rate are analyzed, and the management is provided with measures to prevent the occurrence of some acts of turnover.

Exploiting the Contagious Effect for Employee Turnover Prediction

Talent turnover often costs a large amount of business time, money and performance. Therefore, employee turnover prediction is critical for proactive talent management. Existing approaches on turnover prediction are mainly based on profiling of employees and their working environments, while the important contagious effect of employee turnovers has been largely ignored. To this end, in this paper, we propose a contagious effect heterogeneous neural network (CEHNN) for turnover prediction by integrating the employee profiles, the environmental factors, and more importantly, the influence of turnover behaviors of co-workers. Moreover, a global attention mechanism is designed to evaluate the heterogeneous impact on potential turnover behaviors. This attention mechanism can improve the interpretability of turnover prediction and provide actionable insights for talent retention. Finally, we conduct extensive experiments and case studies on a realworld dataset from a large company to validate the effectiveness of the contagious effect for turnover prediction.

An introductory study to machine learning and its application to employee turnover prediction

The objective of this paper is to study the problem of employee turnover prediction and to develop a classifier that uses employee's data to identify those who have a greater tendency to leave the company voluntarily. For such purpose, the data of 8724 employees from a real Brazilian beverage company was used to train an Extreme Learning Machine (ELM) classifier, assigning to each sample a weight inversely proportional to the size of the respective class. After the training, the classifier displayed an overall accuracy of 79% of the test data.

Employee Turnover Prediction Based on State-transition and Semi-Markov- A Case Study of Chinese State-owned Enterprise

As a main direction of Human Resource Management, employee turnover can provide decision support for managers. In this paper, we aim at predicting the turnover amount of employee on condition of different variable values. The properties of employee and job position are formulated as two variables, where the value of variable varies according to the the state of properties. Additionally, state-transition model is applied to describing employee’s job-state as well as the turnover type. Subsequently, we proposed a semi-Markov model to calculate the conditional turnover amount of employee. Then, we provide a dataset of employee records to illustrate how these models work in reality. Finally, it is proven that the proposed method in this paper is with great significance for managers to develop recruitment plans, promote rules, and retire regulations

Employee turnover forecasting for human resource management based on time series analysis

ABSTRACTIn some organizations, the hiring lead time is often long due to responding to human resource requirements associated with technical and security constrains. Thus, the human resource departments in these organizations are pretty interested in forecasting employee turnover since a good prediction of employee turnover could help the organizations to minimize the costs and impacts from the turnover on the operational capabilities and the budget. This study aims to enhance the ability to forecast employee turnover with or without considering the impact of economic indicators. Various time series modelling techniques were used to identify optimal models for effective employee turnover prediction. More than 11-years of monthly turnover data were used to build and validate the proposed models. Compared with other models, a dynamic regression model with additive trend, seasonality, interventions, and a very important economic indicator effectively predicted the turnover with training R2 = 0.77 and holdout R2 = 0.59. The forecasting performance of optimal models confirms that time series modelling approach has the ability to predict employee turnover for the specific scenario observed in our analysis.