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.

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 investigates the impact of artificial intelligence (AI) integration on preventing employee burnout through a human-centered, multimodal approach. Given the increasing prevalence of AI in workplace settings, this research seeks to understand how various dimensions of AI integration—such as the intensity of integration, employee training, personalization of AI tools, and the frequency of AI feedback—affect employee burnout. A quantitative approach was employed, involving a survey of 320 participants from high-stress sectors such as healthcare and IT. The findings reveal that the benefits of AI in reducing burnout are substantial yet highly dependent on the implementation strategy. Effective AI integration that includes comprehensive training, high personalization, and regular, constructive feedback correlates with lower levels of burnout. These results suggest that the mere introduction of AI technologies is insufficient for reducing burnout; instead, a holistic strategy that includes thorough employee training, tailored personalization, and continuous feedback is crucial for leveraging AI’s potential to alleviate workplace stress. This study provides valuable insights for organizational leaders and policymakers aiming to develop informed AI deployment strategies that prioritize employee well-being.

In recent years, e-sports, as an emerging form of competition, has been rapidly integrated into the daily life of college students, and with its rich interactivity, instant feedback and teamwork, e-sports provides them with an effective channel for emotional catharsis and psychological regulation. This study takes students from four universities as the survey object and adopts quantitative research method to analyze the relationship between different types of e-sports activities and psychological stress resistance through questionnaire survey method combined with spss. The samples were randomly sampled, and a total of 500 valid questionnaires were collected. The results of the study show that: 1. In terms of participation, the ability of students to withstand academic stress and life stress is significantly improved, and e-sports is an effective way to regulate emotions and relieve stress; 2. the three types of games (First-person Shooter, Multiplayer Online Battle Arena, Real-Time Strategy Game) have different impacts on stress tolerance, of which FPS has the greatest impact on stress tolerance; 3. the frequency of playing e-sports affects your stress tolerance; 4. teamwork and strategy play an important role in e-sports resilience.

This study was conducted to comprehensively explore personal assistants for people with disabilities experiences and the current status of client behavioral issues during vocational activities, aiming to seek strategies for advancing worker health protection. The study included 8 participants (Personal assistants for people with disabilities) selected through voluntary convenience sampling method. Qualitative research methods, specifically in-depth interviews, were conducted from August 31 to September 1, 2023. The study categorized client behavioral issues into ‘unreasonable demands,’ ‘verbal and physical abuse,’ and ‘sexual harassment,’ causing stress among workers. Fear of unemployment and job change hindered emotional expression, leading to significant emotional exhaustion and job stress. Furthermore, it was revealed that there are no management policies, management departments, or management systems within the institution to address client problem behavior. To address these issues, the study suggests the establishment of emotional labor management systems and support structures. Furthermore, it emphasizes the need for systematic internal systems and the development of health protection manuals for client interaction.