Companies are impacted by toxic leadership phenomena, resulting in many dissatisfied employees, low morale, and reduced progress. The fundamental mismatch between good leadership and harmful actions of toxic leaders is the primary cause of the problem. Toxic leadership can also be developed from narcissistic behavior of considering personal interests or using humiliation to maintain power. In this context, employees are negatively affected, resulting in higher stress levels, poorer job satisfaction, and a significant decrease in trust. Therefore, this research aims to explore the impact of toxic leadership and other factors on companies. The sample consists of 187 senior employees in the accounting department who worked in manufacturing companies. The results showed that toxic leadership influences role stress, while role stress affects emotional exhaustion and reactive work behavior. Moreover, future research should be conducted using other samples such as hospital employees or pay attention to other aspects related to role stress.

This study investigates the relationship between corporate social responsibility (CSR), capital structure, and financial distress in Jordan’s financial services sector. It tests the mediating effect of capital structure on the CSR-distress linkage. Utilizing a panel data regression approach, the analysis examines a sample of 35 Jordanian banks and insurance firms from 2015–2020. CSR is evaluated through content analysis of sustainability disclosures. Financial distress is measured using Altman’s Z-score model. The findings reveal an insignificant association between aggregated CSR engagement and bankruptcy risk. However, capital structure significantly mediates the impact of CSR on financial distress. Specifically, enhanced CSR enables higher leverage capacity, subsequently escalating distress risk. The results advance academic literature on the nuanced pathways linking CSR to financial vulnerability. For practitioners, optimally balancing CSR and financial sustainability is recommended to strengthen resilience. This study provides novel empirical evidence on the contingent nature of CSR financial impacts within Jordan’s understudied financial services sector. The conclusions offer timely insights to inform policies aimed at achieving sustainable and stable financial sector development.

COVID-19 is among the tremendous negative pandemics that have been recorded in human history. The study was conducted to give a breakdown of the effect of post-COVID-19 mental health among individuals residing in a developing country. The two scales, namely DASS-21 and IES-R, were employed to collect the essential related data. The findings indicated that anxiety was a typical and common mental issue among the population, including up to 56.75% of the participants having extremely severe anxiety, 13.18% reporting severe anxiety. Notably, no one has anxiety and depression under moderate levels. Additionally, there is 51.92% depression and 43.64% stress ranging from severe to extremely severe levels. Furthermore, there were significant statistical differences among the data on stress, anxiety, and depression according to gender (males and females) and subgroups (students, the elderly, and medical healthcare workers). Besides, the prevalence of post-traumatic stress disorder in the study was relatively high, especially when compared to the figures reported by the World Health Organization. Moreover, stress, anxiety, and depression all displayed positive correlations with post-traumatic stress disorder. This is big data on the mental health of the entire population that helps the country’s government propose policy strategies to support, medical care and social security for the population.

This study addresses the present limited understanding of the complex relationship between ethical leadership, job stress, and employee job performance in the hotel business. This study shows that job stress moderates the association between ethical leadership and employee job performance, underlining the necessity for more research in the industry. The present study fills a crucial research void in our understanding of the complex interaction between these factors. The study utilizes a sample of 292 employees in the accommodation and hotel industry. Prior to commencing data collection, the questionnaire underwent thorough validation and reliability testing to ensure that the instrument met all specified criteria and demonstrated robustness. Using hierarchical regression analysis, the study reveals substantial findings. It has been discovered that ethical leadership has a direct and positive effect on employee job performance. Notably, job stress emerges as a significant moderating variable that affects the relationship between ethical leadership and employee job performance. This highlights the crucial role that job stress plays in determining outcomes. The research indicates that reducing workplace stress and fostering ethical leadership can result in improved employee job performance. In addition, the study highlights the importance of social learning theory in enhancing employee job performance, with job stress and ethical leadership serving as significant moderating factors.

This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.