This article measures the performance of listed commercial banks in Vietnam and identifies factors influencing their efficiency. The study follows a two-stage approach: (i) In the first stage, scale efficiency scores from 2016 to 2022 are assessed using the Data Envelopment Analysis (DEA) method; (ii) In the second stage, Tobit regression analyzes internal factors, macroeconomic conditions, and the impact of Covid-19. Key findings show that internal factors such as return on assets positively affect efficiency, while the ratio of equity to total capital has a negative and statistically significant impact. Bank size positively influences efficiency scores. Macroeconomic factors, including economic growth and inflation, were statistically insignificant. However, the Covid-19 pandemic had a significant negative effect on bank efficiency.

This study aims to explore the urban resilience strategies and public service innovations approaches adopted by the Shanghai Government in response to COVID-19 pandemic. The study utilized a combination of primary and secondary data sources, such as government reports, policy documents, and interviews with important individuals involved in the matter. The current research focused on qualitative data and examined the different aspects resilience, including infrastructure, economy, society, ecology, and organizations. The findings indicate that infrastructure resilience plays a crucial role in maintaining the stability and dependability of essential public facilities, achieved through online education and intelligent transportation systems. Implementing rigorous waste management and pollution control measures with a focus on ecological resilience has significantly promoted environmentally sustainable development. Shanghai city has achieved economic resilience by stabilizing its finances and providing support to businesses through investments in research, technology and education. Shanghai city has enhanced its organizational resilience by fostering collaboration across several sectors, bolstering emergency management tactics and enhancing policy execution.

This research aims to examine the influence of IHRMP, recruitment and selection, training, compensation, and performance appraisal on the productivity of Faculty Members (FM) productivity working in private universities in the UAE. The study also examines the mediating role of Organizational Commitment (OC) and the moderating role of the Entrepreneurial Mind-set (EM). The research adopted the social exchange theory. A survey was conducted comprising 160 FM. The data was analyzed using Structural Equation Modelling, Smart-PLS. The findings indicate a positive relationship between IHRMP and the productivity of the FM. The findings also show that OC mediates the relationship between IHRMP and the productivity of FM. Finally, an EM was found to moderate the relationship between IHRMP and the productivity of FM.

Our study investigates the relationship between firm profitability, board characteristics, and the quality of sustainability disclosures, while examining the moderating effects of financial leverage and external audit assurance. A key focus is the distinction between Big 4 and non-Big 4 audit firms. Using data from Malaysia’s top 100 publicly listed organizations from 2018 to 2020, we analyze sustainability reports based on the Global Reporting Initiative (GRI) standards. Unexpectedly, our results indicate a negative association between firm profitability and board characteristics, challenging traditional assumptions. We find that non-Big 4 audit firms significantly enhance sustainability disclosure quality, contradicting the widely held belief in the superiority of Big 4 firms. Our finding introduces the “Big 4 dilemma” in the Malaysian context and calls for a reassessment of audit firm selection practices. Our study offers new perspectives on the strategic role of board composition and audit firm selection in advancing sustainability disclosures, urging Malaysian organizations to evaluate audit firms on criteria beyond the global prestige of Big 4 firms to improve sustainability reporting.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

In the context of a globalized economic environment, businesses are facing an increasing number of environmental challenges, prompting them not only to pursue economic benefits but also to focus on environmental protection and social responsibility. Green supply chain management (GSCM) and green innovation have become key strategies for enterprises aiming for sustainable development. This study explores the impact of green supply chain practices on green innovation performance, with a focus on how knowledge management and organizational integration serve as mediating variables in this relationship. Grounded in the resource-based view (RBV) and knowledge-based view (KBV) theories, this research employs surveys and in-depth interviews with companies across various industries, combined with the analysis of structural equation modeling, to reveal the complex relationship between GSCM practices, knowledge management capabilities, levels of organizational integration, and green innovation performance. The results show that GSCM practices significantly enhance corporate green innovation performance through effective knowledge management and organizational integration. These findings enrich the theories of GSCM and green innovation, providing practical guidance for enterprises on how to enhance green innovation performance through strengthening knowledge management and organizational integration. Finally, this study discusses its limitations and suggests possible directions for future research, such as exploring the differences in findings across different industry backgrounds and examining other potential mediating or moderating variables.