The role of digital transformation in creating value for commercial banks has been interesting to researchers for a long time. While many commercial banks have significantly investigated digital transformation, researchers and managers have still met many difficulties examining the distribution of digital transformation to business performance. This paper aims to evaluate the impact of digital transformation on Vietnamese commercial banks’ performance by different sizes, from there proposing policy implications of digital transformation to improve the banking performance. To achieve this goal, we used a quantitative research method. Specifically, we applied the GMM system (SGMM) of Blundell and Bond for the data of 13 joint-stock commercial banks in Vietnam in the period from 2011 to 2019. Then Bayesian analysis is performed to test the robustness of the models estimated by the SGMM method. The result shows that the digital transformation has a positive impact on the performance of Vietnamese commercial banks. Besides, we also find that the larger the banks, the greater the positive impact of digital transformation on bank performance. Therefore, the efficiency of digital transformation depends on a bank scale.

The COVID-19 pandemic, a public health crisis of worldwide importance, announced by the World Health Organization (WHO) in January 2020 as an outbreak, has made distance education through the E-learning system an urgent and irreplaceable requirement. The study assessed factors affecting students’ online learning outcomes during the COVID-19 pandemic through interviews with 404 students who were subjects of the survey using the convenience sampling method via questionnaires. The study utilized the reliability analysis through Cronbach’s Alpha and the Bayesian Exploratory Factor Analysis (BEFA). The evaluation results of the research scales showed that 28 observed variables were used to measure 7 research concepts. Test results of the hypotheses showed that students’ online learning outcomes are affected by 6 factors in the descending order, respectively, learner characteristics, perceived usefulness, course content, course design, ease of use, and faculty capacity.

This study leverages machine learning and advanced variable selection techniques to enhance the prediction of the Bank Financial Stability Index (Z-score) in emerging ASEAN markets. Utilizing a comprehensive secondary dataset comprising macroeconomic and bank-specific indicators from 61 commercial banks across Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam (2010–2023), we systematically evaluate the predictive power of multiple machine learning models. A rigorous cross-validation framework is employed to optimize forecasting accuracy, integrating Linear Regression, Random Forest, K-Neighbors, Decision Tree, Gradient Boosting, AdaBoost, Support Vector Regression, and XGBoost with Lasso, Ridge, and Elastic Net regularization. Empirical results reveal that key drivers of financial stability include equity capital, financial leverage, return on equity, GDP growth, inflation, technological advancements, and systemic shocks like the COVID-19 pandemic. Notably, the Ridge-optimized XGBRegressor model achieves the highest predictive accuracy (~89%), demonstrating the efficacy of hybrid machine learning approaches in financial stability forecasting. These findings offer crucial insights for policymakers and regulators, facilitating data-driven strategies to strengthen banking resilience and mitigate systemic risks in volatile economic environments. Jel Classifier: C45, C52, C55, G21, G32.

The purpose of this paper is to investigate the impact of crude oil market structure on stock market volatility in Asian countries in the period 2008–2017. We integrate network analysis with the SGMM estimation technique to achieve the research objective. Network analysis was conducted with 43 Asian countries, while analysis of the impact of crude oil market structure on stock markets was performed with a sample of 19 countries. The results show that the stock market has a positive growth in countries with stronger export capacity while it is negatively affected in larger importing countries. In addition, the research results show that the stock market’s growth is greater in countries with a central position in the crude oil market. The study results will be useful for countries in reducing the undesirable impact of crude oil market on the stock market.

Objective: The objective of this study is to evaluate the key factors influencing digital transformation in supply chain management among enterprises in the Southeastern region of Vietnam. The goal is to provide insights into how these factors impact the successful adoption of digital transformation practices. Theoretical Framework: The research is grounded in theories of digital transformation, technology acceptance, and organizational performance. Concepts such as Digital Transformation Performance, Technology Acceptance, Policies and Regulations, and Digital Transformation Drivers form the foundation for understanding the dynamics within supply chain management. Method: The methodology of this study includes a survey of 250 enterprises, with data analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The approach provides a robust framework for exploring the relationships between digital transformation factors and supply chain management outcomes. Results and Discussion: The results indicate that Digital Transformation Performance and Effectiveness have a positive impact on supply chain management. Moreover, Technology Acceptance and Policies and Regulations significantly promote the adoption of digital transformation. These findings are discussed in relation to the theoretical framework, highlighting the importance of these factors in fostering digital innovation. Research Implications: The implications of this research extend to both theoretical and practical domains. For enterprises, the results emphasize the need to focus on enhancing digital performance and leveraging supportive policies to facilitate transformation. In practice, these findings can inform strategies to accelerate digital transformation across industries in Vietnam. Originality/Value: This study contributes to the literature by exploring the under-researched area of digital transformation in Vietnam's Southeastern region. The findings provide practical insights and theoretical contributions that may influence future research and industry practices in supply chain management and digital transformation.

The purpose of this study was to assess the impact of the COVID-19 epidemic on the daily stock returns of 177 businesses listed on the Vietnam Stock Exchange during the period spanning from the second quarter of 2020 to the first quarter of 2023. Next, we proceed to evaluate the influence of COVID-19 exposure on the cash holding ratio of these enterprises. We used research methods for panel data, such as the fixed effects method and the system GMM method. Our findings demonstrate that the exposure of enterprises to the COVID-19 pandemic has a discernible and statistically significant adverse effect on their financial reserves. When conducting research on the transmission channel of COVID-19 exposure to cash holding policies, we found that this impact persists in firms through financial frictions. Based on the findings, we have put forth several policy implications for businesses to formulate cash management policies in anticipation of future pandemics of a similar nature.