Muna I. Alyousef

Employee turnover presents a significant challenge to modern organizations, often resulting in operational disruptions, substantial hiring costs, and a loss of institutional knowledge. While traditional human resource practices have historically been reactive, the emergence of machine learning has introduced a proactive capability to anticipate and mitigate attrition before it occurs. This research utilizes the IBM HR Analytics dataset, which contains 1470 employee records and 35 distinct features, to develop a hybrid machine learning model designed to enhance the accuracy of turnover predictions. To ensure the model’s effectiveness, the researchers employed a comprehensive preprocessing phase that included eliminating non-informative features, applying label encoding to categorical data, and using StandardScaler to normalize quantitative values. A critical component of the study addressed the common issue of class imbalance within HR data. To resolve this, a hybrid sampling strategy was implemented, combining Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) to create a more balanced learning environment for the algorithms. The core of the predictive engine is a soft voting ensemble that integrates three powerful algorithms: Random Forest, XGBoost, and logistic regression. Evaluated on an 80/20 train–test split, the tuned XGBoost model achieved an impressive 84% accuracy and an Area Under the Curve (AUC) of 0.80. Meanwhile, the logistic regression component contributed the highest F1-score, reinforcing the overall strength and balance of the ensemble approach. These metrics confirm that the hybrid model is both robust and reliable for identifying at-risk employees. Beyond simple prediction, the study prioritized interpretability by using SHapley Additive exPlanations (SHAP) to identify the primary drivers of attrition. The analysis revealed that the most significant variables influencing an employee’s decision to leave include the interaction between job level and experience, frequent overtime, monthly income, current job level, and total years spent at the company. By providing these data-driven insights, the model empowers HR teams to transition from reactive troubleshooting to proactive retention planning, ultimately securing the organization’s talent and stability.

Employee attrition represents a significant burden, yet predictive models often fail to provide actionable retention strategies, creating a critical prediction–prescription gap. This paper introduces the Counterfactual–Dialectical Optimisation (CDO) framework, a novel methodology that bridges this gap by integrating predictive modeling, robust causal inference, and budget-constrained optimization. We first illustrate the framework’s mechanics on the synthetic Human Resources (HR) dataset, then conduct a rigorous proof-of-concept on the empirical ‘Saudi Employee Attrition Dataset’ to test its real-world applicability. In our empirical validation, we employ Propensity Score Matching (PSM) to estimate the causal effects of interventions while controlling for confounding variables. The results on the real-world dataset show that while predicting attrition is challenging Area Under the Curve (AUC ≈ 0.60), the framework successfully identified a deserved promotion as a powerful retention lever, causally reducing attrition probability by an estimated 23.9 percentage points. Acting on this insight, the optimization layer efficiently allocated the entire budget to this single, high-impact strategy for high-priority employees. This work provides a validated blueprint for shifting from passive prediction to active, Return on Investment (ROI)-driven prescription in strategic workforce management, demonstrating how to derive clear, actionable guidance even from complex, real-world data.

Purpose: This study examines the constructive role of benign professional envy in predicting nurses’ work engagement, highlighting the sequential mediating effects of help-seeking and learning behaviors, as well as the moderating influence of inclusive leadership. Design/methodology/approach: Data were collected from nurses working in Turkish hospitals using a questionnaire. Using structural equation modelling (SEM), this study tested a moderated sequential mediation model grounded in the Conservation of Resources (COR) theory. Findings: The results revealed that benign professional envy positively and significantly predicted nurse work engagement. Furthermore, help-seeking and learning behaviors sequentially mediate this relationship, indicating that envy motivates nurses to seek advice and engage in active learning, which, in turn, enhances engagement. Importantly, inclusive leadership significantly moderates the indirect effects, such that the positive impact of benign envy on engagement through help-seeking and learning is stronger under conditions of high-inclusiveness leadership. Practical implications: The findings suggest that healthcare managers and nurse leaders should reframe benign envy as a potential resource, rather than a liability. By fostering inclusive leadership practices and encouraging help-seeking and learning behaviors, hospitals can transform envy into a driver of engagement, ultimately improving nurses’ well-being, reducing turnover, and enhancing patient care outcomes. Theoretical implications: This study extends COR theory by demonstrating how benign envy initiates a gain spiral of resources when supported by adaptive behavior and inclusive leadership. It also contributes to the literature on emotions in nursing by clarifying the mechanisms through which envy fosters engagement in high-demand healthcare settings. Originality/value: This study is among the first to investigate the constructive dynamics of benign envy in the nursing profession, offering a novel model that integrates emotional, behavioral, and leadership perspectives to explain work engagement. Clinical trial number: Not applicable.

In many developing regions, recruitment still relies heavily on traditional methods that often ignore the importance of aligning a candidate’s personality with the job role. This mismatch can lead to poor performance, dissatisfaction, and high turnover. To address this, the study presents a personality-aware recommendation system that combines the Myers–Briggs Type Indicator (MBTI) with machine learning to support smarter hiring decisions. The system is tailored for the South Asian job market and includes two main components: a web-based MBTI assessment for applicants and a dashboard for HR professionals powered by a XGBoost classifier. This model was trained on a dataset correlating applicant profiles and the flagged preferences of MBTI with the job. Experience and the number of skills, education level, and encoded MBTI types were the key features, and the SMOTE method was employed to balance the dataset. The model attained an accuracy of 74.30%, having balanced precision and recall measures. It was also discriminative, the ROC AUC was 0.84, and the precision–recall AUC was 0.85. One example of utilizing the Software Developer position in real life demonstrated the success of the system to filter and rank candidates at the same time according to both technical and personality-specific criteria. Overall, this study emphasizes the worth of combining insights from psychological profiling with machine learning in order to develop a more holistically, fair, and efficient hiring process.