Turnover Risk Research Articles

Predicting Employee Turnover in the Financial Company: A Comparative Study of CatBoost and XGBoost Models

Abstract: Employee turnover is a significant issue for financial institutions, impacting productivity, increasing recruitment costs, and disrupting critical operations. In this project, this study aimed to predict employee turnover using a dataset containing attributes such as employee satisfaction, performance, and tenure. By framing the task as a binary classification problem, this study employed CatBoost and XGBoost, two advanced regression-based algorithms, to develop predictive models. This paper's analysis demonstrated that CatBoost outperformed XGBoost across all evaluation metrics, including MAE, MSE, RMSE, and R, making it the more effective model for predicting turnover in the financial sector. The study highlights key factors contributing to employee attrition, such as job satisfaction, tenure, and promotion opportunities, offering actionable insights for retention strategies. Additionally, by predicting probabilities rather than binary outcomes, this study aims to make more detailed decisions about employee retention. This research provides valuable tools for financial institutions to mitigate the risk of turnover, retain critical talent, and ensure operational continuity.

Grounding Your "Flight Risk" Newly Licensed Nurses.

Transition into practice is plagued by personal, work, and patient-related stress leading to burnout and turnover of newly licensed nurses. Leaders must recognize transition shock and moral distress and act immediately. Nursing professional development practitioners at a healthcare organization in Illinois developed the "Flight Risk" Program to recognize, address, and escalate turnover risks as a leadership team. This resulted in retaining 47% of identified "flight risks" and contributed over $3.9 million in cost avoidance.

A Neural Network Approach to Understanding Employee Retention Dynamics: Insights from Feature Importance Analysis

In today’s business environment, employee retention has become a significant challenge, as employee turnover can lead to decreased productivity, increased costs, and reduced morale. This study aims to leverage neural network technology to predict employees' retention intentions and conduct in-depth analyses based on employee data. A classification model was established considering various factors, including gender, marital status, number of children, education level, years of service, weekly working hours, career development opportunities, salary, and bonuses, to identify the potential risk of employee turnover. The results indicated that the model achieved an accurate classification rate of 95.12% on the test set, demonstrating high effectiveness in identifying employee retention intentions. Feature importance analysis revealed that education level (29.63%), bonuses (27.50%), and the number of children (21.81%) were the primary factors influencing retention decisions. Additionally, working hours, marital status, and career development opportunities also impacted employees’ retention intentions. This research not only provides insights into employee mobility but also offers data support for enterprises to develop effective retention strategies, suggesting that businesses prioritize enhancing education and training opportunities, designing competitive bonus systems, and providing flexible work arrangements and benefits tailored to employees with children.

Burnout and turnover risks for healthcare workers in the United States: downstream effects from moral injury exposure

Moral injury has emerged as a construct of interest in healthcare workers’ (HCW) occupational stress and health. We conducted one of the first multidisciplinary, longitudinal studies evaluating the relationship between exposure to potentially morally injurious events (PMIEs), burnout, and turnover intentions. HCWs (N = 473) completed surveys in May of 2020 (T1) and again in May of 2021 (T2). Generalized Linear Models (robust Poisson regression) were used to test relative risk of turnover intentions, and burnout at T2 associated with PMIE exposure, controlling for T1 covariates. At T1, 17.67% reported they had participated in a PMIE, 41.44% reported they witnessed a PMIE and 76.61% reported feeling betrayed by healthcare or a public health organization. In models including all T1 PMIE exposures and covariates, T2 turnover intentions were increased for those who witnessed a PMIE at T1 (Relative Risk [RR] = 1.66, 95% Confidence Interval [CI] 1.17–2.34) but not those that participated or felt betrayed. T2 burnout was increased for those who participated in PMIE at T1 (RR = 1.38, 95%CI 1.03–1.85) but not those that witnessed or felt betrayed. PMIE exposure is highly prevalent among HCWs, with specific PMIEs associated with turnover intentions and burnout. Organizational interventions to reduce and facilitate recovery from moral injury should account for differences in the type of PMIE exposures that occur in healthcare work environments.

Assessing the Impact of Human Resource Competency and Employee Commitment on Performance at Urban Republic, a Member of the Erajaya Group, Palembang

Companies must innovate and improve employee performance in today’s competitive business climate to maintain their market share. Urban Republic, a company headquartered in Palembang and part of the Erajaya Group, is the subject of this study, which examines the relationship between employee participation and HR competency and performance. Improving employee performance requires HR competency, including technical and non-technical skills, and employee commitment, characterized by loyalty and dedication. For Urban Republic to remain competitive, they must understand how these elements affect performance. This study aimed to assess the impact of employee commitment and HR competency on performance. Three sections of labor area heads, store leaders, and staff were given surveys as part of the quantitative approach. Multiple regression analysis was used to analyze the data, supported by validity, reliability, and classical assumption tests. The results showed that employee dedication showed almost significant significance, indicating a possible influence, while employee competence had a statistically significant impact on employee performance. Dedication and HR competency significantly impact performance when the two work together. This analysis emphasizes the importance of continuous HR development investment and the potential benefits of employee commitment. To reduce the risk of employee turnover, it is recommended that HR training programs be improved, employee recognition programs be strengthened, commitment issues be addressed, and staff well-being be monitored to avoid burnout. It is recommended that more research be conducted to examine the intricacies of employee dedication and how it impacts work outcomes. The study concluded that a balanced focus on HR competency and employee commitment is essential to optimize employee performance at Urban Republic

Impression management in the boardroom: How CEO facial trustworthiness influences turnover risk

This paper examines the effect of perceived trustworthiness on the turnover of Chief Executive Officers (CEOs) and its moderating role in the relationship between firm performance and CEO turnover. We employ a unique dataset comprising headshots of male CEOs in US-listed firms from 2010 to 2019 and a machine learning-based facial landmark detector to construct a composite facial trustworthiness index. Our results show that CEOs exhibiting high facial trustworthiness experience a lower turnover risk. We also find that facial trustworthiness strongly influences the link between firm performance and CEO turnover. Well-performing executives have a lower risk of being dismissed from their positions, and the dismissal risk is even lower among CEOs perceived as having a high facial trustworthiness. Conversely, underperforming CEOs are more likely to be dismissed, and the dismissal probability is greater among CEOs who have been perceived as being highly trustworthy. Our findings are consistent with the predictions of the expectancy violation theory, which posits that trustworthy-looking executives are more likely to be punished if they do not live up to the expectations held by the board of directors.

Turnover Intention among Staff Who Support Older Adults Living Alone in Japan: A Cross-Sectional Study

Older adults in Japan traditionally rely on family members for care. However, the growing number of older people living alone has increased staff burden in community general support centers (CGSCs), which provide care for older people in Japan. This study aimed to identify factors linked to turnover intention among CGSC staff. Of 1002 staff invited to participate, 283 completed a survey (response rate: 28.2%). Logistic regression analysis of 183 valid questionnaires examined demographic, job-related, and attitudinal factors associated with turnover intention. The results showed that the perceived difficulty of supporting older adults living alone was the main predictor of turnover intention. Staff reporting high perceived difficulty levels were more likely to consider leaving. Negative attitudes toward supporting this population also increased turnover risk. These findings highlight a pressing need for targeted interventions to strengthen the ability of CGSC staff to manage the challenges of caring for isolated older individuals. Addressing staff perceptions and providing essential training and resources could reduce turnover rates and increase the quality of care for this vulnerable group.

Impact of the COVID-19 Pandemic on Turnover Rates Among Staff in Community Mental Health and Substance Use Organizations.

This article explores the historic impact of the COVID-19 pandemic on turnover rates among staff in community mental health and substance use organizations in the United States. While existing literature extensively covers turnover in physical healthcare settings, the mental health and substance use sector's unique challenges have received less attention. The study utilizes a six-year dataset from a large multi-service organization in the northeast to analyze turnover patterns before, during, and after the pandemic. Key findings include a spike in turnover during the pandemic, particularly in Hospital Diversion Programs, and significant variations by age and gender. Despite the pandemic not significantly affecting overall turnover rates, the results offer insights into specific groups at risk for turnover, such as staff under 29 years old. The study concludes by discussing the implications for targeted retention strategies, especially as mental health organizations nationwide adopt the Certified Community Behavioral Health Center model.

Enhancing the hiring process: A predictive system for soft skills assessment

Human Resource Management faces the ongoing challenge of identifying top-performing candidates to enhance organizational success. Traditional recruitment methods heavily rely on assessing hard skills alone, overlooking the importance of soft skills in identifying individuals who excel in their roles. To address this, our paper introduces a novel predictive model that leverages Artificial Intelligence in the hiring process. By analyzing soft skills extracted from CVs, cover letters, websites, professional social media, and psychometric tests, the model accurately predicts potential candidates suitable for specific job roles. This system effectively eliminates poor hiring decisions, reduces time and effort, minimizes recruitment costs, and mitigates turnover risks. The implementation of our proposed model employs various predictive machine learning classifiers, with key input soft skills including creativity, collaboration, empathy, curiosity, and critical thinking. Notably, the Support Vector Machine classifier emerges as the top-performing model in terms of predictive accuracy

Factors associated with the non-retention of health sector employees recruited in Iran: a survival analysis

BackgroundHealth staff are an essential component of the health system and a significant factor in improving health outcomes. As a result, without a suitable number of trained and supported workforces, health-related goals will not be achieved. As a result, one of the most significant tasks of the government and healthcare human resource management in healthcare organizations is focusing on development and maintenance of personnel.MethodThis study was a longitudinal and historical cohort study. Data was collected on the 40 medical universities and healthcare organizations under the jurisdiction of Iran Ministry of Health and Medical Education from 2008 to 2018 from the human resources database. In this study the COX regression and survival analysis was used to assess the factors of staff maintenance, retention rate, and the risk of turnover.Result46,939 health workforces were included in the study, of which 13,328 (28.4%) were men and 33,611 (71.6%) were women. Based on the finding of cox regression model, the retention of staff were statistically different between male and female. Employees whose current work place and birthplace were similar had a significantly higher probability of retention. Accordingly, the cox regression result showed, the risk of employee turnover for single personnel was higher than the married ones.Conclusionan applicable policy for increasing maintenance among workforce recruitment, could be considering the native born professionals instead of non-natives born which reduce the costs of employee turnover, including re-hiring, initial and on-the-job training, housing, and other extra living expenses away from home and family.

Management and prediction of employee turnover in enterprises based on big data analytics and machine learning

In the context of aging population and social transformation, employees also put forward higher demands on corporate culture and values, which also increases the risk of employee turnover. This paper explores the correlation between various indicators and leavers through statistical analysis and visualization of various data indicators and the distribution of leavers and non-leavers on various indicators by using whether they left the company as a classification criterion. In addition, this paper also carries out Pearson correlation analysis for each indicator and draws a correlation heat map to quantitatively explore the correlation between indicators. In order to predict whether employees will leave the company, this paper uses random forest model, support vector machine model, KNN model, plain Bayesian model and logistic regression model for training and testing. The results show that the best prediction in terms of employee turnover is the Random Forest model with a prediction accuracy of 98.8%. This was followed by the Support Vector Machine model with an accuracy of 95.1%. In addition, the KNN model also achieved an accuracy of 94.8%. Ordinary Bayesian model and logistic regression model have lower accuracy rates of 80.4% and 77.2% only. This is of great significance for enterprises to realize sustainable development, and is worthy of in-depth study and practice by enterprise managers.

Nurses’ Generational Differences of Burnout and Turnover Risk

This study aimed to determine the impact of nurse generation on (a) turnover risk factors (low meaning/joy in work, low resilience, high occupational fatigue, high burnout) and (b) turnover intentions before and during the COVID-19 pandemic. Survey methods were used to compare two samples of hospital nurses who completed online surveys in 2017 or 2021. The results were compared on burnout and turnover risk by generation. Results indicate that in 2017, high acute fatigue and low inter-shift recovery differed by nurse generation, with Baby Boomers (Boomers) being the least affected. Additionally, Boomers were the least likely to report a likelihood of leaving the hospital within 6-12 months. However, in 2021, Millennial nurses experienced significantly higher levels of low meaning and joy in work, low resilience, high acute fatigue, low inter-shift recovery, and high burnout than Generation X (GenX) and Boomers. Millennials were the most likely generation to report turnover intention. This leads to the conclusion that the pandemic may have worsened the work experience of millennial nurses and increased their risk of leaving hospital jobs and the profession.

Overqualified Employees’ Actual Turnover: The Role of Growth Dissatisfaction and the Contextual Effects of Age and Pay

AbstractOverqualified employees are those with more knowledge, skills, and abilities than their job requires. While these competent workers can bring benefits to firms, they also pose a high risk of turnover. However, the evidence is insufficient on whether, why, and when they actually leave and how organizations can manage their turnover effectively. Based on self-determination theory, we propose that unfulfilled internal aspirations for personal growth can be a major driving force of turnover among overqualified employees. Moreover, we explore how such a mechanism varies by employee’s age and whether pay can exert a mitigating influence. Using longitudinal data over three years (from 2017 to 2019) with a large sample of workers (N = 2473), we found an indirect effect where perceived overqualification reduces growth satisfaction, which in turn leads to actual turnover. Further, we found a three-way moderating effect of pay and age on the relationship between growth satisfaction and turnover, indicating that pay’s effect of deterring turnover was stronger for older employees. This study has theoretical implications for a better understanding of overqualified employees’ turnover driven by their internal impetus, as well as implications for pay policies that effectively manage overqualified workers in age-diverse modern organizations.

An Empirical Study on the Turnover Intention among the SME Service Sectors Employees in Beijing, China: A Theory of Planned Behaviour Approach

This empirical study investigates the turnover intention among employees in the SME service sector in Beijing, China, using the Theory of Planned Behavior (TPB) as a theoretical framework. The research aims to understand the key psychological and social factors influencing employees' intentions to leave their current positions. Data were collected through a structured questionnaire administered to 400 employees across various SMEs in Beijing's service industry. The questionnaire measured attitudes towards turnover, subjective norms, perceived behavioral control, and turnover intention. The findings indicate that all three components of TPB significantly predict turnover intention. Attitudes towards turnover, shaped by job satisfaction and perceived job alternatives, emerged as the strongest predictor. Subjective norms, influenced by peer and family expectations, also play a critical role in shaping turnover intentions. Perceived behavioral control, which includes employees' confidence in securing new employment and perceived ease of leaving their current job, further contributes to the prediction model. The study highlights the importance of addressing both individual and contextual factors in managing turnover intentions. For SME managers, fostering a supportive work environment and enhancing job satisfaction can mitigate turnover risks. Moreover, understanding the influence of external social pressures and enhancing employees' perceived control over their career decisions can provide additional strategies to retain talent. This research contributes to the limited literature on employee turnover in China's SME service sector and offers practical insights for improving employee retention strategies. Future studies could explore longitudinal data to understand the dynamics of turnover intention over time and examine the role of additional variables such as organizational commitment and job stress.

Employee turnover in multinational corporations: a supervised machine learning approach

AbstractThis research explores the potential of supervised machine learning techniques in transforming raw data into strategic knowledge in the context of human resource management. By analyzing a database with over 205 variables and 2,932 observations related to a telco multinational corporation, this study tests the predictive and analytical power of classification decision trees in detecting the determinants of voluntary employee turnover. The results show the determinants of groups of employees who may voluntarily leave the company, highlighting the level of analytical depth of the classification tree. This study contributes to the field of human resource management by highlighting the strategic value of the classification decision tree in identifying the characteristics of groups of employees with a high propensity to voluntarily leave the firm. As practical implication, our study provides an approach that any organization can use to self-assess its own turnover risk and develop tailored retention practices.

The Effect of the New Working Days System on Employees Productivity in Sharjah

Unique Contribution to Theory, Practice and Policy: Lastly, future research might consider longer-term data collection methods and more intricate ways to gauge how employees react to work-life balance based on four working days in a week. The HRM needs to reconsider the current economic climate because the employees have access to a variety of offers in the market. Therefore, it is beneficial for the organization to start providing perks to their potential employees. There is a high risk of employee turnover if they are not satisfied with the job, in this case, the implication of the four working days policy can benefit the workplace. By using relevant keywords like Work system design, job satisfaction, employee performance, productivity, and JEL codes (J24, J28, and M54) the study becomes more noticeable and accessible. Our study implications emphasized the importance of performance metrics, including productivity and job satisfaction.

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

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.

Fostering Resilience and Well-Being Among New Graduate Nurses.

Nearly one third of the U.S. nursing workforce is considering leaving their current job, with millennials new to the role most likely to leave. Newly licensed registered nurses (NLRNs) often feel unprepared and overwhelmed, contributing to the risk of turnover in this workforce segment and amplifying unstable healthcare environments. This evidence-based quality improvement project integrated a resilience and well-being program into a nurse residency program, improving resilience and well-being in NLRNs transitioning to professional practice.

Work-Life Balance Practices: Drivers of Job Engagement and Mitigators of Turnover Intention

The research aims to investigate how Work-Life Balance Practices act as catalysts for Job Engagement and as effective measures in reducing Turnover Intention. By examining these dynamics, the study seeks to provide insights into the mechanisms through which organizations can enhance employee commitment and well-being while mitigating turnover risks. Methodology – A quantitative study was conducted. The data was collected by distributing questionnaires to 233 executive employees in Infopark, Kochi. Research Findings - Interestingly, the study found a link between work-life issues and intentions to leave one's job and to engage in it. Employee inclination to leave is also connected with job engagement. Originality – WLB, work engagement, and turnover intention are the three study variables that are all holistically related. Organisational inferences in the Kochi environment - Work-life balance (WLB) initiatives must be put into place inside companies in order to promote employees' well-being, which will eventually increase engagement and retention. These results, which were obtained from Malaysian executives, provide practitioners with practical advice on how to create a supportive work environment. Research Limitations and Implications - The majority of the sample population consisted of individuals from the millennial generation, potentially introducing variances in their perceptions of work-life balance compared to those of more mature adults.

Turnover Rates and Factors Associated With Turnover: A Longitudinal Analysis of the Retention Period of Clinical Nurses in Korea Using National Data.

Many countries, including Korea, are struggling with a nursing workforce shortage. This study aimed to identify the actual turnover rate of Korean clinical nurses and the factors affecting the turnover rate, considering the time required for nurses to gain experience at their current medical institution. This longitudinal study followed up on a cohort consisting of all 107,682 nurses from January 1, 2017 to July 30, 2020. Differences in the distribution of retention and turnover according to the medical institutions' and nurses' characteristics were analyzed using the chi-square test. The hazard ratios (HRs) for turnover in each analysis interval were analyzed using multilevel Cox proportional-hazards analysis. The mean turnover rate was 10.0% within 1 year and 33.4% within 3.5 years. Several organizational characteristics (the type and ownership of the hospital, its location, and the bed-to-nurse ratio) and individual characteristics (gender, age, and clinical experience) were found to be associated with turnover risk. Among these factors, compared to hospitals with a bed-to-nurse ratio in general wards of 6.0 or more, those with a ratio of 3.5-3.9 had an HR for 1-year turnover of 0.81 (95% confidence interval [CI] = 0.67-0.98), and those with a ratio of 2.5-2.9 had an HR for 3.5-year turnover of 0.77 (95% CI = 0.66-0.90). The bed-to-nurse ratio is a condition that can be modified through collaboration between government policy-makers and medical institutions. To reduce nurse turnover and retain experienced nurses, appropriate staffing should be implemented.