This research identifies factors that explain the liquidity of commercial banks in the Vietnam banking system from 2010 to 2015. Using the OLS regression method for analysis, it was found that: the interbank market helps commercial banks improve their liquidity; the larger the loan size, the higher the liquidity risk; good credit risk management has a positive impact on liquidity risk management; and long-term interest rate is negatively related to the liquidity of commercial banks. The research also makes recommendations on liquidity risk management policies to banks and policy-makers from the Government and the State Bank of Vietnam.

Non-financial factors including quality of services, the flexibility of a company, utilization of resources, and market orientation are regarded as significant determinants that enhance the profitability-based performance of a service company or a hotel. The study investigated the interaction between these factors and hotel operating performance measured by the hospitality performance and results indicators. Data on 346 executives of Vietnam’s hospitality companies were collected. A structural equation modeling (SEM) method was utilized to examine the positive-direct and moderating effects of non-financial factors on hotel performance in terms of occupancy rate (OCR), average daily rate (ADR), and the revenue per available room (RevPAR). The findings showed that service quality ( = 0.118, p < 0.05), flexibility ( = 0.173, p < 0.05) and resource utilization (= 0.172, p < 0.05) positively affected the performance of Vietnam’s hospitality companies. Meanwhile, innovation showed no direct influence (p = 0.068) but an indirect impact on the performance through service quality ( = 0.311, p < 0.05). Market orientation did not impact the performance (p = 0.076) but it positively affected both innovation ( = 0.322, p < 0.05) and service quality ( = 0.146, p < 0.05). The study contributed to a theoretical enhancement of the current level of knowledge on the factors that affect the performance and developed a reliable scale for measuring the performance of hotels in Vietnam.

In emerging markets like Vietnam, where student borrowers often lack traditional credit histories, accurately predicting loan eligibility remains a critical yet underexplored challenge. While machine learning and deep learning techniques have shown promise in credit scoring, their comparative performance in the context of student loans has not been thoroughly investigated. This study aims to evaluate and compare the predictive effectiveness of four supervised learning models—such as Random Forest, Gradient Boosting, Support Vector Machine, and Deep Neural Network (implemented with PyTorch version 2.6.0)—in forecasting student credit eligibility. Primary data were collected from 1024 university students through structured surveys covering academic, financial, and personal variables. The models were trained and tested on the same dataset and evaluated using a comprehensive set of classification and regression metrics. The findings reveal that each model exhibits distinct strengths. Deep Learning achieved the highest classification accuracy (85.55%), while random forest demonstrated robust performance, particularly in providing balanced results across classification metrics. Gradient Boosting was effective in recall-oriented tasks, and support vector machine demonstrated strong precision for the positive class, although its recall was lower compared to other models. The study highlights the importance of aligning model selection with specific application goals, such as prioritizing accuracy, recall, or interpretability. It offers practical implications for financial institutions and universities in developing machine learning and deep learning tools for student loan eligibility prediction. Future research should consider longitudinal data, behavioral factors, and hybrid modeling approaches to further optimize predictive performance in educational finance.

Objective: Environmental education fosters awareness, behaviors, and actions towards environmental sustainability. This research used the Theory of Planned Behavior to investigate the determinants of intention for waste separation at Vietnamese universities toward enhancing awareness related to environmental activities.Methods: The study collected data from 490 responses from students from higher education institutions in Vietnam and employed quantitative methods to analyze factors affecting the intention to separate waste at higher education campusesResults: The finding indicates that higher education institutions have formal activities in their training programs and extracurricular activities to raise awareness of environmental issues. The findings also reveal that attitude toward environmental issues, subjective norms, perceived behavior control, and environmental education positively affect the intention to separate the campus waste.Conclusions: This study contributes to the growing body of evidence reinforcing environmental education's role in improving the intention to address environmental issues. The results imply that promoting environmental education activities will help increase environmental intentions, helping higher education institutions green their training programs