This research delves into the intricate dynamics of ethical leadership within the context of Vietnamese Small and Medium Enterprises (SMEs). By scrutinizing its impact on organizational effectiveness, the study unveils a comprehensive understanding of the interconnectedness between ethical leadership, knowledge sharing, and organizational learning. Employing a mixed-methods approach, the research investigates the mediating roles played by knowledge sharing and organizational learning in the relationship between ethical leadership and organizational effectiveness. Through empirical analysis and case studies, this study contributes valuable insights to the literature, offering practical implications for fostering ethical leadership practices in Vietnamese SMEs to enhance overall organizational effectiveness. The findings shed light on the nuanced mechanisms through which ethical leadership contributes to sustainable success, emphasizing the pivotal roles of knowledge sharing and organizational learning in this intricate relationship.

This study uses the annual financial data of Chinese A-share listed firms from 2010 to 2020 to investigate the relationship between multiple large shareholders (MLS) and earnings management (EM). After analyzing the samples using the Ordinary Least Squares (OLS) model and endogenous switching regression (ESR) model, the empirical results show that the presence of MLS can increase corporate EM activities and the MLS have a significantly positive effect on EM in both the treatment and control groups. In addition, this conclusion still holds after conducting multiple robustness tests. The cross-section analysis shows that the external audit supervision quality, institutional shareholders, and the uncertainty of the external economic environment have significant impacts on the baseline model results. Lastly, mediation effect analysis shows that the presence of MLS increases the corporate operating risk through EM activities. The conclusions of this paper are critical for policymakers to supervise China’s capital market, improve the level of corporate governance of China’s listed firms, and further promote reform of ownership structure.

The world has never been more developed, yet humanity is on the brink of irreversible environmental loss. Despite the urgency of the situation, there is a limited body of studies addressing environmental concerns in higher education institution, particularly in developing countries, i.e., Saudi Arabia. Sustainable development is the only viable solution, albeit it requires the courage to initiate and sustain efforts dedicated to preserving the environment for the well-being of future generation. The article delves into this issue and examines the impact of environmental education program (EEP) on environmental performance (EP) via waste minimization behaviour (WMB). The research involved meticulous data collection from a sample of 597 students, representing diverse genders and academic specialties at the esteemed public university—King Faisal University (KFU) in Saudi Arabia. The study used statistical software (including SPSS and AMOS, v 25) for rigorous analysis and revealed significant findings. Firstly, the study showed a significant and positive relationship between EEP and EP. Secondly, it revealed a significant and positive association between EEP and WMB. Thirdly, the study ascertained a significant and positive association between WMB and EP. Finally, the study found that the relationship between EEP and EP remains significant even after presenting WMB as a mediator, proposing that WMB has a partial mediation role between EEP and EP. The results highlighted the significance role of EEP in stimulating WMB and achieving EP in the Saudi universities, which contributes to national initiative of green Saudia.

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 investigates the impact of supply chain agility on customer value and customer trust while investigating the role of price sensitivity as a mediating variable in the healthcare industry. A quantitative methodological approach was used. This was cross-sectional descriptive research based on a survey method, and data were collected using a structured questionnaire. The sample consisted of 384 respondents who had already used healthcare facilities. The sampling technique was convenience sampling and collected data were analyzed using structural equation modeling. The study indicated that supply chain agility positively impacts customer value and customer trust, while there is no moderation role of price sensitivity in the healthcare industry. Previous scholars revealed that there is a strongly available association between supply chain agility and customer value. But no attempt was undertaken to investigate the impact of supply chain agility on customer trust while moderating the role of price sensitivity.

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.