Search Results (23)

This study explores the role of artificial intelligence (AI) in human resource management (HRM), with a focus on recruitment, employee retention, and performance optimization. Through a PRISMA-based systematic literature review, the paper examines many machine learning algorithms including XGBoost, SVM, random forest, and linear regression in decision-making related to employee-attrition prediction and talent management. The findings suggest that these technologies can automate HR processes, reduce bias, and personalize employee experiences. However, the implementation of AI in HRM also presents challenges, including data privacy concerns, algorithmic bias, and organizational resistance. To address these obstacles, the study highlights the importance of adopting ethical AI frameworks, ensuring transparency in decision-making, and developing effective integration strategies. Future research should focus on improving explainability, minimizing algorithmic bias, and promoting fairness in AI-driven HR practices. Full article

Background: Employee turnover poses a multi-faceted challenge to organizations by undermining productivity, morale, and financial stability while rendering recruitment, onboarding, and training investments wasteful. Traditional machine learning approaches often struggle with class imbalance and lack transparency, limiting actionable insights. This study introduces an Explainable AI (XAI) framework to achieve both high predictive accuracy and interpretability in turnover forecasting. Methods: Two publicly available HR datasets (IBM HR Analytics, Kaggle HR Analytics) were preprocessed with label encoding and MinMax scaling. Class imbalance was addressed via GAN-based synthetic data generation. A three-layer Transformer encoder performed binary classification, and SHapley Additive exPlanations (SHAP) analysis provided both global and local feature attributions. Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC AUC metrics. Results: On the IBM dataset, the Generative Adversarial Network (GAN) Transformer model achieved 92.00% accuracy, 96.67% precision, 87.00% recall, 91.58% F1, and 96.32% ROC AUC. On the Kaggle dataset, it reached 96.95% accuracy, 97.28% precision, 96.60% recall, 96.94% F1, and 99.15% ROC AUC, substantially outperforming classical resampling methods (ROS, SMOTE, ADASYN) and recent literature benchmarks. SHAP explanations highlighted JobSatisfaction, Age, and YearsWithCurrManager as top predictors in IBM and number project, satisfaction level, and time spend company in Kaggle. Conclusion: The proposed GAN Transformer SHAP pipeline delivers state-of-the-art turnover prediction while furnishing transparent, actionable insights for HR decision-makers. Future work should validate generalizability across diverse industries and develop lightweight, real-time implementations. Full article

Marked by a highly diverse workforce, the Indian construction industry faces ongoing challenges in fostering employee engagement and minimizing organizational withdrawal. This study examines the role of diversity climate in influencing psychological and physical withdrawal behaviors among construction professionals, assessing job satisfaction as a mediating variable. Grounded in Social Exchange Theory, the research employed a quantitative survey approach, gathering responses from 318 professionals across the sector. Partial least squares structural equation modeling (PLS-SEM) was used to test the hypothesized relationships. Results indicate that reduced psychological (β = –0.462, f² = 0.465, p < 0.01) and physical withdrawal (β = –0.311, f² = 0.194, p < 0.05) are associated with more positive perceptions of the diversity climate. Furthermore, this relationship is partially mediated by job satisfaction, with diversity climate positively influencing job satisfaction (β = 0.618, p < 0.001), which in turn reduces withdrawal tendencies (indirect effect on psychological withdrawal β = −0.094, p < 0.01 and physical withdrawal β = −0.068, p < 0.01). These results show that encouraging a supportive diversity climate not only helps but is also absolutely necessary for enhancing job satisfaction, lowering withdrawal behavior, and retaining trained talent. The findings offer concrete evidence that construction firms and policymakers should prioritize inclusive human resource strategies that directly improve project outcomes, reduce attrition, and enhance workforce engagement in the Indian construction sector. Full article

The exponential growth of the global population and rising life expectancy have placed increasing pressure on healthcare systems to deliver efficient, high-quality, and cost-effective services. In India, private hospitals play a crucial role in meeting these demands. However, they are increasingly challenged by high employee attrition rates, often linked to dissatisfaction with the physical work environment. Improving staff satisfaction has therefore become essential for enhancing organizational performance and retaining skilled personnel. This study aims to assess the impact of the physical environment on employee satisfaction in private Indian hospitals. A mixed-methods research approach was adopted. The qualitative phase involved a review of secondary data to conceptualize the research framework and identify key variables related to architectural, interior, and ambient design features. The quantitative phase involved survey-based data collection from employees across various private hospitals. For analysis, both descriptive and inferential statistics were used to explore relationships between variables. The results reveal statistically significant relationships between physical-environment features—specifically architectural layout, interior design elements, and ambient conditions—and employees’ attitudes. These attitudes were found to significantly influence overall workplace satisfaction. Furthermore, this study confirmed a strong link between the physical environment and employee satisfaction. These findings offer actionable insights for hospital administrators to improve the design of workspaces. Enhancing physical environments can elevate employee satisfaction, reduce attrition, and ultimately contribute to improved hospital performance. By empirically establishing the link between physical-environment features and staff satisfaction, this study provides a foundation for evidence-based design strategies in healthcare settings. Full article

Talent attrition significantly undermines the stable functioning and long-term development of firms in the Architecture, Engineering, and Construction (AEC) industry. Turnover intention is an effective predictor of turnover behavior. Understanding the formation mechanism of turnover intention can help companies maintain the stability of their workforce. However, most of the existing research focuses on the impact of individual factors on turnover intention, lacking an in-depth exploration of the combined effects of multiple factors. This study aims to investigate the underlying mechanisms of employee turnover intention by considering the interplay of various factors. Through an extensive literature review, thirteen hypotheses related to turnover intention are proposed, and a comprehensive theoretical model is developed. Using questionnaire data collected from the AEC industry, the turnover intention model is validated through Structural Equation Modeling (SEM). The validated model shows that turnover intention is directly influenced by working hours (β = 0.127), family-supportive leadership behavior (β = −0.211), and work values (β = 0.356). Meanwhile, turnover intention is indirectly affected by job autonomy (β = −0.089), job demands (β = 0.055) and working hours (β = 0.023), with work interference with family as the mediator, and indirectly affected by family stress (β = 0.037), with work–family interference with work as the mediator. It is worth noting that the impact of family-supportive leadership behavior and job autonomy on turnover intention is negative. This study not only enriches the body knowledge of turnover intention, particularly within the AEC industry, but also provides practical implications for organizations to keep the stability of human resource. Full article

Employee attrition, which causes a significant loss for an organization, is the term used to describe the natural decline in the number of employees in an organization as a result of numerous unavoidable events. If a company can predict the likelihood of an employee leaving, it can take proactive steps to address the issue. In this study, we introduce a deep learning framework based on a Bidirectional Temporal Convolutional Network (Bi-TCN) to predict employee attrition. We conduct extensive experiments on two publicly available datasets, including IBM and Kaggle, comparing our model’s performance against classical machine learning, deep learning models, and state-of-the-art approaches across multiple evaluation metrics. The proposed model yields promising results in predicting employee attrition, achieving accuracy rates of 89.65% on the IBM dataset and 97.83% on the Kaggle dataset. We also apply a fully connected GAN-based data augmentation technique and three oversampling methods to augment and balance the IBM dataset. The results show that our proposed model, combined with the GAN-based approach, improves accuracy to 92.17%. We also applied the SHAP method to identify the key features that most significantly influence employee attrition. These findings demonstrate the efficacy of our model, showcasing its potential for use in various industries and organizations. Full article

Contrary to the widely accepted adage ‘do not engage in business activities with relatives’, individuals from Chinese, Indian, and Pakistani backgrounds are achieving significant progress, while individual groups experience stagnation. While prior research offers substantial insights into the relationship between kinship and entrepreneurial ventures, there exists a paucity of information regarding the mechanisms through which certain kin individuals attain success while others do not. The principal inquiries revolve around the question, ‘In what ways do kinship networks contribute to entrepreneurial success?’ Furthermore, within a multicultural and heterogeneous framework, how might kinship networks serve as essential resources that promote entrepreneurial development, or do they instead pose additional challenges to such advancement? To address these inquiries, this study conducts an integrative review of entrepreneurship through the conceptual framework of kinship (kin-entrepreneurship), a notion distinctly from emerging disciplines. The contextual backdrop of this study is firmly anchored in the rising incidence of business failures and their resultant ramifications for sustainable economic development on a global scale. By employing an integrative review methodology that encompasses both bibliometric and content analyses of extant literature, adhering to the PRISMA guidelines, this research elucidates the fundamental components relevant to kin-entrepreneurship. In tackling this issue, the present investigation explores the trends, trajectories, and potential futures concerning the nexus of kinship and entrepreneurship. A bibliometric analysis was conducted on a dataset comprising 292 scholarly articles focused on kin-entrepreneurship, published between 1980 and 2023, sourced from the Scopus and Web of Science databases. Significant findings highlight “kinship-based business influencers” and “entrepreneurial environment and consequences”, as crucial themes. Subsequent to the discovery of the themes, this paper advocates for a paradigm shift from a narrow familial perspective on business to a more expansive kinship viewpoint, which can enhance comprehension of the complex dynamics within business environments where kinship-based business influencers are multifaceted, affecting economic performance (where entrepreneurs capitalize on these affiliations for economic advantage), decision-making (which improves business sustainability through resource allocation among kin), and employee relations (as kin connections foster both formal and informal employment opportunities) for entrepreneurs. Consequently, this study posits that kinship-oriented business dynamics play a crucial role in influencing entrepreneurial decision-making by offering social capital, resources, and strategic guidance, which are essential for mitigating existing entrepreneurial attrition rates and, in turn, are fundamental for fostering economic development. Full article

The ambivalent experience of superior–subordinate relationships is widespread in organisations and has gradually become an important factor influencing employees to actively engage in extra-role behaviours. However, employees’ constructive deviance is extremely important for organisational development as they are important extra-role behaviours for organisational innovation and change. Owing that academic research on the antecedents of employees’ constructive extra-role behaviours has lacked attention to individual emotional variables such as the leader–member exchange ambivalence, by drawing on self-control resource theory and social cognitive theory, this study examined the effects of leader–member exchange ambivalence on employees’ constructive deviance, as well as the role of ego depletion and role-breadth self-efficacy. Based on a two-point questionnaire survey of 332 employees from different industries in China, the study tested hypotheses with SPSS 27 and AMOS 27 and found that the more leader–member exchange ambivalence, the less likely they were to engage in employees’ constructive deviance, leader–member exchange ambivalence affected employees’ constructive deviance through ego depletion, and when role-breadth self-efficacy is high, the lower the ego depletion of employees with leader–member exchange ambivalence, the more likely they are to engage in employees’ constructive deviance. This study is intended to guide organisations to pay attention to the problem of individual internal conflict arising from superior–subordinate relationships, to remove the barriers to constructive transgression by individuals, and to truly exploit the innovative capacity of individual organisations. The study suggests that managers should pay attention to the negative effects of employees’ perceived ambivalent experiences of supervisor-subordinate relationships, maintain consistency, and build positive social exchange relationships with their employees. Organisations should strengthen the training of leaders and employees to eliminate the serious internal attrition that organisations face from social network relationships. And employees should face the limitations of resources and reduce dependence on the leader–member exchange relationship as the dependence for their work attitudes and behaviours. Full article

A company’s most valuable resource is its workforce, which includes each worker. Because of the crucial role that employees play in the success of an organization, measuring employee turnover rate has become one of the most important metrics that businesses are concentrating on in the modern era. Attrition may occasionally arise owing to unavoidable circumstances such as moving to a distant place, retirement, etc. But when attrition begins creating holes in the pockets of an organization, it is necessary to monitor the situation closely. When hiring new staff, a company must use a significant quantity of its available resources. The process of rehiring employees needs to be eliminated, and a strong workforce needs to be maintained, so it is necessary to adapt the analysis of systematic machine learning models. From these models, a suitable model that gauges the risk of attrition may then be selected. This not only helps an organization save money by preserving its resources but also assists in preserving the status quo of its staff. Full article

Employee attrition and high turnover have become critical challenges faced by various sectors in today’s competitive job market. In response to these pressing issues, organizations are increasingly turning to artificial intelligence (AI) to predict employee attrition and implement effective retention strategies. This paper delves into the application of explainable AI (XAI) in identifying potential employee turnover and devising data-driven solutions to address this complex problem. The first part of the paper examines the escalating problem of employee attrition in specific industries, analyzing the detrimental impact on organizational productivity, morale, and financial stability. The second section focuses on the utilization of AI techniques to predict employee attrition. AI can analyze historical data, employee behavior, and various external factors to forecast the likelihood of an employee leaving an organization. By identifying early warning signs, businesses can intervene proactively and implement personalized retention efforts. The third part introduces explainable AI techniques which enhance the transparency and interpretability of AI models. By incorporating these methods into AI-based predictive systems, organizations gain deeper insights into the factors driving employee turnover. This interpretability enables human resources (HR) professionals and decision-makers to understand the model’s predictions and facilitates the development of targeted retention and recruitment strategies that align with individual employee needs. Full article

Employee attrition, defined as the voluntary resignation of a subset of a company’s workforce, represents a direct threat to the financial health and overall prosperity of a firm. From lost reputation and sales to the undermining of the company’s long-term strategy and corporate secrets, the effects of employee attrition are multidimensional and, in the absence of thorough planning, may endanger the very existence of the firm. It is thus impeccable in today’s competitive environment that a company acquires tools that enable timely prediction of employee attrition and thus leave room either for retention campaigns or for the formulation of strategical maneuvers that will allow the firm to undergo their replacement process with its economic activity left unscathed. To this end, a weights and structure determination (WASD) neural network utilizing Fresnel cosine integrals in the determination of its activation functions, termed FCI-WASD, is developed through a process of three discrete stages. Those consist of populating the hidden layer with a sufficient number of neurons, fine-tuning the obtained structure through a neuron trimming process, and finally, storing the necessary portions of the network that will allow for its successful future recreation and application. Upon testing the FCI-WASD on two publicly available employee attrition datasets and comparing its performance to that of five popular and well-established classifiers, the vast majority of them coming from MATLAB’s classification learner app, the FCI-WASD demonstrated superior performance with the overall results suggesting that it is a competitive as well as reliable model that may be used with confidence in the task of employee attrition classification. Full article

The COVID-19 pandemic has induced a sudden shift from work in an office setting to work from home. The flexibility and job autonomy achieved through telecommuting ought to facilitate positive outcomes among employees. Apart from a few contradicting studies, telecommuting literature predominantly revolves around the positive aspects of working from home. However, the number of employees voluntarily leaving their jobs has increased since “the great resignation” in March 2021. Therefore, building upon the conservation of resource theory and the job demands and resources framework, the current study tests the influence of specific job resources, job autonomy (JA), and perceived organizational support (POS) on employees’ intention to stay (IS) directly and indirectly through a unique serial mediation pathway of psychological capital (PsyCap) and work–life balance (WLB). The results affirmed that JA and POS have a positive association with employees’ IS. Moreover, PsyCap and WLB were also found serially mediating the direct association between JA, POS, and employees’ IS. The current study’s findings offer valuable insights for HR managers on the relevance of specific job resources and the role of psychological capital in controlling attrition rates. The findings of this study could be helpful for HR managers to design measures to reduce attrition rates and foster work–life balance and positive outcomes among employees. This study is among the first to instrument the indirect role (serial mediation) of PsyCap between job resources, WLB, and employees’ IS, thus significantly contributing to the literature. Full article

Employee attrition is a major problem that causes many companies to incur in significant costs to find and hire new personnel. The use of machine learning and artificial intelligence methods to predict the likelihood of resignation of an employee, and the quitting causes, can provide HR departments with a valuable decision support system and, as a result, prevent a large waste of time and resources. In this paper, we propose a preliminary exploratory analysis of the application of machine learning methodologies for employee attrition prediction. We compared several classification models with the goal of finding the one that not only performs best, but is also well interpretable, in order to provide companies with the possibility of improving those aspects that have been shown to produce the quitting of their employees. Among the proposed methods, Logistic Regression performs the best, with an accuracy of 88% and an AUC-ROC of 85%. Full article

Background: Digital psychological interventions can target deficit-oriented and asset-oriented psychological outcomes in the workplace. This review examined: (a) the effectiveness of digital interventions for psychological well-being at work, (b) associations with workplace outcomes, and (c) associations between interventions’ effectiveness and their theory-base. Methods: six electronic databases were searched for randomised controlled trials (RCT) and quasi-experimental studies. The methodological quality of studies that used randomisation was conducted with the “Cochrane Collaboration’s Risk of Bias” tool, while the “JBI Critical Appraisal Checklist” was used for non-randomised studies. Studies’ theory-base was evaluated using an adaptation of the “theory coding scheme” (TSC). Due to heterogeneity, narrative synthesis was performed. Results: 51 studies were included in a synthesis describing four clusters of digital interventions: (a) cognitive behavioural therapy, (b) stress-management interventions and workplace well-being promotion, (c) meditation training and mindfulness-based interventions, and (d) self-help interventions. Studies demonstrated a high risk of contamination effects and high attrition bias. Theory-informed interventions demonstrated greater effectiveness. Cognitive behavioural therapy demonstrated the most robust evidence for reducing depression symptoms among healthy employees. With the exception of the Headspace application, there was weak evidence for meditation training apps, while relaxation training was a key component of effective stress-management interventions. Full article

Employee attrition refers to the natural reduction in the employees in an organization due to many unavoidable factors. Employee attrition results in a massive loss for an organization. The Society for Human Resource Management (SHRM) determines that USD 4129 is the average cost-per-hire for a new employee. According to recent stats, 57.3% is the attrition rate in the year 2021. A research study needs to be implemented to find the causes of employee attrition and a learning framework to predict employee attrition. This research study aimed to analyze the organizational factors that caused employee attrition and the prediction of employee attrition using machine learning techniques. The four machine learning techniques were applied in comparison. The proposed optimized Extra Trees Classifier (ETC) approach achieved an accuracy score of 93% for employee attrition prediction. The proposed approach outperformed recent state-of-the-art studies. The Employee Exploratory Data Analysis (EEDA) was applied to determine the factors that caused employee attrition. Our study revealed that the monthly income, hourly rate, job level, and age are the key factors that cause employee attrition. Our proposed approach and research findings help organizations overcome employee attrition by improving the factors that cause attrition. Full article