Understanding the behavioural factors that influence employees’ decisions to leave an organisation is an essential component of human resource management in service-based industries such as banking. Job stress has emerged as one of the most critical contributors to employees’ turnover intentions, particularly in urban banking environments where performance pressures, customer expectations, and regulatory demands remain consistently high. The present study examines whether job stress meaningfully predicts job-hopping intentions among bank employees working in Jaipur. A descriptive research design was adopted, and data were collected from bank employees using a structured questionnaire. Simple linear regression revealed a statistically significant relationship between job stress and job-hopping intentions, F(1, 208) = 91.71, p < .001, with job stress accounting for 30.6 per cent of the variance in job-hopping intention. The findings suggest that as stress levels rise, employees are more likely to consider changing organisations in search of relief or improved working conditions. The study highlights the importance of stress management programmes, supportive leadership practices, and balanced work expectations within banking institutions. The results offer insights for organisational policymakers seeking to minimise voluntary turnover and promote employee wellbeing.

Abstract Research Summary Prosocial CEOs, characterized by greater concern for their employees, enhance employee motivation but incur higher costs when implementing layoffs. We develop a psychological model of CEO‐firm matching wherein negative industry shocks requiring downsizing asymmetrically erode the match quality for prosocial CEOs. Leveraging increases in Chinese import competition, we show that layoff pressures lead to higher rates of both forced and voluntary turnover among prosocial CEOs. They are succeeded by less prosocial CEOs who are externally recruited, use less employee‐friendly language, lean Republican in political orientation, or are less likely to volunteer at charities. Our study highlights psychological characteristics as a key consideration in the executive labor market and draws attention to the “first‐stage” selection dynamics that shape the types of CEOs who lead firms. Managerial Summary Which firms CEOs choose to join and which CEOs are selected or retained by boards depend not only on their skills but also on the fit between their ‘personality’ and the firm's needs. We show that during industry downturns that require aggressive downsizing, prosocial CEOs are more likely to depart voluntarily, and boards actively replace them with low‐prosocial “wartime” CEOs. This dynamic nature of the CEO‐firm fit provides insights into why and when previously effective leadership may become ineffective and empirical grounds for the common distinction between peacetime and wartime CEOs. A key implication is that increasing Chinese import competition has shaped not only firm economic activities but also the psychological profiles of business leaders.

The rapid digital transformation in Human Resource Management (HRM) has accelerated the adoption of Artificial Intelligence (AI) to optimize workforce engagement, enhance retention strategies, and drive talent management innovations. This study examines the impact of AI-driven HR systems on key performance indicators such as employee satisfaction, voluntary turnover, and HR operational efficiency. Empirical data collected from over 15 multinational organizations, encompassing 12,000 employee records across diverse industries, indicate that AI-powered HR solutions improve workforce engagement by 42%, reduce voluntary turnover rates by 27%, and increase HR process efficiency by 35%. AI-driven sentiment analysis enhances dissatisfaction detection by 33%, enabling proactive interventions. Additionally, AI-based predictive hiring models demonstrate 85% accuracy in forecasting employee attrition, compared to 72% for traditional HR models. The study employs a hybrid AI framework integrating logistic regression and Long Short-Term Memory (LSTM) networks to analyze employee engagement trends, job satisfaction fluctuations, and retention patterns over time. By leveraging AI-powered chatbots, predictive analytics, and intelligent automation tools, HR processes are optimized, reducing administrative workloads by 38% and improving response times from 20 hours to 1.5 hours. Comparative analysis with conventional HR approaches highlights AI’s superior ability to personalize career development plans and detect high-risk attrition cases, ensuring timely interventions. Despite these advantages, AI adoption in HRM presents challenges such as bias in decision-making, lack of transparency in AI-driven recommendations, and employee concerns regarding data privacy. This study addresses these challenges by proposing a scalable, explainable AI (XAI) framework that ensures fairness, transparency, and ethical compliance in AI-driven HRM systems. The research findings provide valuable insights for organizations aiming to integrate AI into HR strategies while fostering trust, employee engagement, and long-term workforce sustainability. By bridging the gap between academic research and real-world HR applications, this study offers actionable recommendations for optimizing AI-driven talent management in the modern digital era.

Abstract This paper examines how flexible working models (FWMs) — including hybrid work, remote work, flexible hours, compressed workweeks, and job sharing — influence employee retention. Drawing on recent large-scale studies and meta-analytic evidence, the paper develops a theoretical framework linking FWMs to retention through job satisfaction, work–life balance, organizational commitment, and perceived career prospects. A mixed-methods empirical design is proposed (survey + interviews + organizational HR metrics) to test hypotheses about which FWM types and implementation features most effectively reduce voluntary turnover. Practical recommendations for HR practitioners and avenues for future research are presented. Key findings from existing literature indicate that hybrid models in particular reduce quit rates substantially without harming performance, while the effect of full-time remote work is more nuanced. Keywords Flexible working models, hybrid work, remote work, employee retention, turnover, work–life balance, organizational commitment.

ABSTRACT Working remotely during the pandemic reshaped workplace culture. Many employers face pushback when implementing return‐to‐office (RTO) policies, primarily because the beliefs and expectations of employers and employees differ. This paper reviews the effects of RTO policies on various outcomes, including employee productivity, voluntary turnover, corporate culture, psychological safety, workplace civility, mental health and well‐being, and burnout incidence. Drawing on theory and research in these areas, it then proposes 10 best practices to support RTO transitions, such as framing the conversation, repairing the psychological contract, and developing RTO and communication strategies. To facilitate further improvements, the paper concludes with a framework comprising six focal areas related to RTO transitions, along with specific questions for future research.

BackgroundAssessing sources of job stress in intensive care units is a critical issue for preventing many occupational health and care-related issues, such as burnout, voluntary turnover and decrease in quality and safety of care. Accordingly, this French nation-wide multicentre study aims to provide supplementary evidence regarding the validity of a recent tool: the Perceived Stressors in Intensive Care Units (PS-ICU) scale. More precisely, this study has three main objectives: to 1) confirm the metrological properties of the PS-ICU scale on a large sample of professionals; 2) test its measurement invariance between nurses, physicians and residents (initial population targeted by the scale); 3) examine whether the scale would also be suited for use with nursing auxiliaries. In addition, depending on the results (which may suggest the removal of several items), this study offers the possibility to shorten the scale to facilitate its use.Method and results2241 ICU professionals (1135 nurses, 308 physicians, 179 residents, and 619 nursing auxiliaries; overall participation rate of 58.10%) from 42 ICUs in France, voluntarily completed an online questionnaire collecting socio-demographic data and perceived job stressors (PS-ICU). Exploratory structural equation modelling (ESEM), unidimensional reliability (McDonald’s Omega) and item response theory (IRT) analyses overall confirmed the metrological properties of the scale, while several items were removed and the sixth factor (“lack of support and resources from the organisation”) measured by the scale was revised. Results regarding measurement invariance show that the PS-ICU scale can be used to compare occupational groups, including nursing auxiliaries. Finally, all analyses resulted in a reduction of the scale to a 26-item version.ConclusionsThe PS-ICU scale, which measures generic and ICU-specific job stress factors, is a valid and reliable scale that can be used to collect data from nurses, physicians and residents, as well as from nursing auxiliaries. With 26 items, it can be used by researchers and managers in ICUs to assess the extent and type of stress factors perceived by healthcare professionals.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13613-025-01572-7.

In the public accounting industry, the increasing trend of auditors leaving their jobs has become a serious concern, as it can negatively affect audit firms’ performance and client retention. A study by Knechel et al. (2021) found that audit firms are likely to lose clients when auditors resign. Consequently, understanding the reasons behind auditors’ voluntary turnover has attracted growing attention from researchers, industry practitioners, and policymakers. This paper presents a narrative literature review examining how job satisfaction influences voluntary turnover among auditors. Fifty (50) peer-reviewed studies were systematically identified from databases such as Web of Science, Emerald Insight, ScienceDirect, ResearchGate and Google Scholars. Three major themes emerged from the synthesis: perceptions of the profession, work conditions, and work environment. The findings highlight that perceptions of the profession, work conditions, and work environment affect job satisfaction, hence auditor turnover. A conceptual framework is proposed to illustrate the relationship between these factors, job satisfaction, and voluntary turnover. The review concludes by suggesting practical recommendations for audit firms and future research directions to address turnover in the auditing profession.

Purpose This study seeks to increase our understanding of how employees make the decision to voluntarily leave an organization (the military) and a profession viewed by many as a “calling” (DiRenzo et al., 2022). Specifically, (1) do employees make this decision on their own or do they involve their partner in the voluntary turnover decision-making process, (2) the factors that influence the decision to “walk away from” a calling and (3) the role of work-identity in this process? Design/methodology/approach We employ a constructivist grounded theory approach to explore how 10 mixed gender dual-earner couples who both are or were members of the Canadian Armed Forces voluntarily made the decision for one partner to leave the organization and a career that they considered a calling. Findings Our research demonstrates the following about voluntary leaving decisions for dual-earner couples: the voluntary turnover decision in our respondents was made by the couple, not the individual; voluntary turnover is triggered by “shock events” and/or prolonged periods of job dissatisfaction (as theorized by Lee et al., 1999). Individuals will voluntarily walk away from a job that they consider core to their personal identity (a calling) when it conflicts with family needs, even though this decision was shown by our research to be emotive on the part of the leaver. Research limitations/implications Analysis of our data supports four main conclusions. Firstly, voluntary turnover decisions are made by the couple, not the individual. Our research also contributes to the couple decision-making literature, which has not studied the decision to leave. Findings support that voluntary turnover is triggered by either shock events and/or prolonged periods of job dissatisfaction (what we refer to as a slow simmer), as theorized by Lee et al. (1999). Practical implications Our couple narratives show many of our leavers were dissatisfied with how their organization was treating them and the organizational norms in place long before making the decision to leave but only made the decision to leave after a shock event occurred. Those organizations that work hard to develop a strong culture (such as militaries, police forces and other organizations where work identity enmeshes with personal identity) may be missing key factors regarding retention when they fail to understand the impacts of employer-related shock events and long-term employee dissatisfaction. Social implications Our analysis showed that individuals will voluntarily walk away from a job that they consider core to their personal identity (i.e. a calling) for family reasons and when confronted with a clear case of value incongruence, and that this decision is made by the couple, not the individual leaver. The social implications of this are that organizations need to proactively deal with dissatisfaction with the organizational culture in place if they are going to effectively address employee turnover. Originality/value To the best of the authors’ knowledge, our study is one of the only ones we could find that approach the leaving decision from the perspective of the couple and apply a multi-identity approach to studying the voluntary leaving decision.

Orientation: The hospitality industry is known for its high employee turnover rates, which pose challenges to organisational stability and service quality. Human resource management practices play a crucial role in shaping employees’ job satisfaction, engagement and retention. Research purpose: Using empirical data from formally employed hospitality employees, this study examined how selected human resource management practices, notably training and development, teamwork, work-life balance and compensation, impact turnover intentions with psychological capital as a mediating variable. Motivation for the study: As a result of the labour-intensive and customer-oriented focus of the hospitality industry, high employee turnover rates are a continuous concern. Research approach/design and method: A structured questionnaire was administered using QuestionPro. Participants were identified using snowball sampling. One hundred and fifty respondents who are formally employed in the hospitality industry completed the questionnaire. Main findings: The findings of this study suggest that human resource management practices significantly enhance psychological capital, leading to lower turnover intentions. Organisations that invest in employee well-being and development should foster resilience, hope, optimism and self-efficacy, reducing the likelihood of voluntary turnover. Practical/managerial implications: The value of sound human resource management practices for hospitality establishments is a main implication, and management needs to develop strategies to retain talent in an increasingly competitive labour market. Contribution/value-add: The findings support the importance of strengthening compensation and benefit structures, fostering work-life balance, and leveraging teamwork as a retention strategy for hospitality establishments.

Retaining critical knowledge is essential for organizational success, but remote work, the “Great Resignation,” and quiet quitting have disrupted knowledge sharing. The goal of this research is to develop the Enhanced Knowledge Loss Risk Model (EKLRM) that extends Jennex’s (2014) original model by explicitly incorporating insights from Psychological Contract Theory. This theoretical integration enables the model to more effectively account for how unmet expectations regarding flexibility, support, and recognition can lead to both voluntary turnover and engagement-based knowledge loss. Building on a review of literature on the Great Resignation, quiet quitting, Psychological Contract Theory, and knowledge management, the study proposes and empirically tests hypotheses for new contributing factors. A survey of knowledge workers was conducted, revealing four significant influences on employee departure and engagement: the absence of remote work or flexible hours, provision of equipment and technical support for remote work, and the need for high-speed internet connectivity.

Voluntary employee turnover presents a dichotomous nature. While it can foster innovation and structural renewal, it also leads to dysfunctions such as the loss of social capital and elevated recruitment costs. Through a qualitative literature review, presented as a theoretical essay, this study explores this phenomenon. The primary reflections encourage managers to consider turnover as a strategic indicator with multiple dimensions and dual impacts. The study proposes future research to simultaneously examine the positive and negative effects of this phenomenon and, subsequently, to investigate the moderating role of turnover rates and variations in the hierarchical level of employees who have resigned.

The role of CFO has become increasingly important since the enactment of SOX 2002. The existing literature suggests that CFOs tend to leave office within 6 to 12 months of a CEO’s departure, but studies addressing whether forced and voluntary turnover behave differently are unexplored. There is also no attempt to explore whether financial performance or institutional shareholding play moderating roles in mitigating successive CFO and CEO turnovers. We find that forced (voluntary) CEO turnover has a higher significant marginal effect on forced (voluntary) CFO turnover. We also find that, in general, higher financial performance and institutional ownership play significant moderating roles in the relationship between CEO turnover and CFO turnover. Our study indicates a strong disciplinary mechanism by the board due to its intention to signal that it is concerned about the firm's reputation. The voluntary turnover of a CFO as a result of a voluntary turnover of a CEO also indicates that a CFO considers their position to survive reputational damage.

In increasingly complex public-sector environments, employee dissatisfaction and labor–management conflict pose significant challenges to organizational effectiveness. This study analyzes 823 administrative separation records and 822 survey responses from Queensland, Australia to examine predictors of voluntary turnover and interpersonal conflict. Because several outcomes are rare and the data contain potential separation, Firth penalized logistic regression is employed as the primary estimator to reduce small-sample bias, with standard logistic models reported as sensitivity checks. The Firth models yielded no statistically significant predictors, a finding interpreted as informative evidence of measurement constraints, low variance in outcome variables, and limited explanatory power of dichotomous indicators in administrative datasets. Sensitivity analyses using standard logistic regression revealed associations between lack of recognition, work–life balance, and voluntary exit; however, these results are presented cautiously due to potential bias under rare-event conditions. Interpersonal conflict exhibited only directional (non-significant) relationships with job insecurity and weak supervisory support. Rather than presenting a definitive predictive model, the study contributes methodologically by demonstrating how penalized regression and sensitivity checks can clarify interpretation when null results arise. The paper recommends refined measurement (using multi-item scales), richer longitudinal or mixed-methods designs, and the integration of unstructured data to better capture the drivers of employee dissatisfaction and conflict in future research.

Abstract Background Clinical laboratories continue to struggle with increased vacancies due to ongoing workforce shortages. The most recent American Society for Clinical Pathology Vacancy Survey in 2022 cited increased retirements and overall increased vacancy rates over 2020 (Garcia et al., 2023). Laboratory teams can reference CLIA regulation 42CFR493.1423 to identify options for non-traditional lab techs to not only fill immediate vacancies but to be a part of a larger staffing plan including educational development and promotion pathways. Methods Each laboratory can customize a solution meeting their needs focusing on immediate needs and long-term staffing stability. The core labs at Fred Hutchinson Cancer Center and Jefferson Healthcare combined examples into an option that works for their individual facilities, patients, and available workforce.1. Waived and moderate complexity testing by non-traditional lab techs:Consider a specific role with pay reflecting the increased responsibility assigned in your department;Increased duration of training;Strengthen operating procedures;Assess quality assurance monitoring infrastructure;Ensure IT solutions including auto-verification and remote differential reviews are fully utilized.2. Educational support for medical laboratory technician training:a. Ensure organizational tuition assistance or MLT specific program is available to promote internal candidates.3. Education and training support for categorical certification:On-the-job training;Didactic educational development enhancing theory and disease knowledge.4. Promotional program:Promotion within a designated role (MLT I/II/III, MLS I/II/III, etc.) through a structured program promotes growth and development, participation in department projects, collaboration, and shared ownership in department operations and improvements;Additional levels for each role improves merit recognition for team members when annual wage increases may be insufficient;Pathway supports long-term retention and identification of future department leaders for further growth and development. Results In 2024, Fred Hutchinson Cancer Center supported expanded services on multiple campuses with testing personnel voluntary turnover (less retirements) of 4%, down from 26% in 2023. Jefferson Healthcare saw 0% turnover in 2024, down from 20% in 2023. While a myriad of factors contribute to team stability and turnover, the implementation of these programs at both organizations are facilitating growth, development, and stability. Conclusion Clinical laboratories can hire a more diverse workforce, provide education, growth, and development opportunities, and fill their vacancies while maintaining safe and compliant lab services. Reference: Garcia, E., Kundu, I., Kelly, M., & Soles, R. (2023). The American Society for Clinical Pathology 2022 Vacancy Survey of medical laboratories in the United States. American Journal of Clinical Pathology, 161(3), pages 289-304. https://doi.org/10.1093/ajcp/aqad149

Employee attrition poses a significant challenge for organizations, adversely affecting productivity, operational efficiency, and workforce stability. Traditional methods of addressing attrition are often reactive and lack predictive capabilities. This study examines how AI can reduce employee attrition in organizational settings through advanced mechanisms. By analyzing extensive datasets, including employee performance metrics, engagement surveys, and organizational culture indicators, AI models can identify early signs of potential attrition. These insights empower HR professionals to design targeted retention strategies, improve employee experience, and minimize voluntary turnover. This research offers a comprehensive exploration of AI applications in mitigating employee attrition, supported by case studies from multinational corporations that have successfully implemented AI-driven workforce retention strategies. The findings highlight AI’s transformative potential in Human Resource Management (HRM), enabling organizations to transition from reactive to proactive attrition management approaches. The paper concludes with recommendations for future research, focusing on the evolving role of AI in predictive HR analytics and its integration with emerging technologies such as blockchain and the metaverse for advanced workforce planning.

Promoting internal employees to managerial positions (internal sourcing) is a popular employee retention tactic. Although some research indicates that internal sourcing reduces voluntary turnover, conflicting evidence suggests that internal sourcing strategies make employees more difficult to retain in strong job markets (i.e., when job opportunities are plentiful relative to job seekers) because promotions increase an employee's external marketability. The onset of the COVID-19 pandemic-a global exogenous shock that triggered an event chain characterized by a weak job market followed by a historically strong one-provided a unique opportunity to test these competing perspectives. Drawing upon event system theory and the unfolding model of turnover, we argue that internal sourcing creates positive perceptions among employees about their employer, making them less inclined to seek external opportunities during periods of heightened employee mobility. Specifically, we predict that internally sourced employees perceive lower levels of employment threat and higher levels of organizational support than those hired externally, which mitigates their turnover risk in strong job markets. We tested these predictions in two studies: a longitudinal field study involving 11,072 restaurant managers who were newly promoted or hired into their roles in the years surrounding the onset of the COVID-19 pandemic and the strong job market that followed and an experiment designed to mirror the field study conditions, in which we examined the psychological mechanisms underlying this phenomenon. Collectively, the results of our studies support our predictions, offering valuable insights into the effects of internal versus external sourcing on employee retention. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Existing research presents mixed perspectives on the impact of abusive supervision climate on team processes and effectiveness. This discrepancy prompts an important question: when, why, and how does abusive supervision climate become more or less detrimental to teams? By integrating the social functional perspective of gossip with recent theoretical advancements on abusive supervision climate, we develop a novel theoretical model to explain how leader-targeted negative team gossip-defined as the extent to which team members share negative evaluations of the leader's behaviors with each other when the leader is absent-can mitigate the adverse effects of abusive supervision climate on teams. Our model posits that leader-targeted negative team gossip serves its social function in two key ways: (a) It diminishes team members' perception of the leader as a role model, thereby reducing the influence of abusive supervision climate on team aggressive behavior, and (b) it fosters perceived similarity among team members regarding their negative attitudes toward the leader, which lessens the impact of abusive supervision climate on team affective trust. We further argue that these buffering effects of leader-targeted negative team gossip have significant downstream implications for team effectiveness, specifically in terms of team performance and team voluntary turnover. Our model was tested using two multiwave, multisource field studies employing a round-robin design, with samples of 111 and 237 work teams, respectively. The results largely supported our model. We conclude by discussing the theoretical and practical implications of our findings. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Purpose The aim of this study is to develop a method for predicting voluntary employee turnover using grey machine learning and to demonstrate its effectiveness as a decision-support tool in HR management. Design/methodology/approach This study presents an innovative approach that integrates machine learning with grey system theory to predict voluntary employee turnover. The developed method follows a six-step procedure, encompassing variable selection, data preparation, and model training. A grey cluster-based decision model is applied as an activation function within a novel machine learning architecture, enabling the incorporation of imprecise, vague, and error-prone data. A quantitative study was conducted using survey data from 1,030 employees, collected through a CAWI method. The dataset was analyzed using machine learning techniques, and model validation was performed through cross-validation. This methodology enhances HR decision-making by addressing uncertainty in turnover prediction. Findings The study demonstrates that integrating grey system theory with machine learning enhances the prediction of voluntary employee turnover by effectively handling imprecise and uncertain data. The developed grey cluster-based decision model improves the representation of social science complexities, capturing key turnover factors such as salary, job satisfaction, and organizational culture. The model’s performance was validated through a quantitative study, achieving an average accuracy of 80.94%. These findings confirm its applicability in HR decision-making, suggesting that grey machine learning provides a robust framework for predicting employee departures and enabling organizations to implement proactive retention strategies while minimizing turnover-related costs. Originality/value The originality of this study lies in the unique integration of grey system theory with machine learning architecture through the application of a grey cluster-based decision model as an activation function. This approach has not been widely applied to predicting voluntary employee turnover, and its novelty stems from the ability to operate directly on imprecise and incomplete data without the need for full “whitening”. The developed model captures complex, multidimensional, and often subjective factors influencing employee decisions, making it more resilient to informational noise than traditional algorithms. An additional value is the method’s potential transferability to other areas of social science research where uncertainty and incomplete data pose significant challenges, opening opportunities for further development in both theoretical and practical analytical contexts.

There is an increasing demand for robust methodologies to rigorously evaluate the psychometric properties of measurement scales used in quantitative research across various scientific disciplines. This article proposes an integrative method that combines structural equation modelling (SEM) with machine learning (ML) to jointly assess model fit and predictive accuracy, limitations often addressed separately in traditional approaches. Using a measurement scale for voluntary employee turnover intention, the method demonstrates clear improvements: RMSEA decreased from 0.073 to 0.065, and classifier accuracy slightly increased from 0.862 to 0.863 after removing three redundant items. Compared to standalone SEM or ML, the integrated framework yields a shorter, better-fitting scale without compromising predictive power. For practitioners, this method enables the creation of more efficient, theoretically grounded, and predictive tools, facilitating faster and more accurate assessments in organisational settings. To this end, this study employs Covariance-Based SEM (CB-SEM) in conjunction with classifiers such as naive Bayes, linear and nonlinear support vector machines, decision trees, k-nearest neighbours, and logistic regression.

This paper presents a critical analysis of factors that influence company profitability in the retail industry. The goal was to present four factors that contribute to financial success in three retail leaders in the United States. Those factors include voluntary employee turnover, employee wages, capital spending, and inventory turnover. The four factors were chosen to have a representative range of employee and financial factors. The methodology used for this research was a regression analysis using R Studio and Excel with extracted data from financial reports of three leading United States retailers. The results of this study were mixed among the four factors. Increased wages did not support lower turnover rates among employees. From a financial perspective, capital expenditure and profitability indicate that spending on long-term assets does not always lead to improved financial performance. This study further supports the need for more research on the factors that support profitability in an ever-changing retail industry.