One of the approaches to assessing the organizational culture is to understand its effect on management functions, including the quality of accounting information systems. However, empirical studies about the influences of organizational culture on accounting information system quality, especially in the context of emerging countries like Vietnam, where accounting information is not reliable enough for decision making, have not yet provided enough evidence to confirm this relationship. This paper aims to investigate the effect of innovative organizational culture on the quality of accounting information systems by using the Structural Equation Model. A questionnaire was sent to the CEO, CFO, managers, and accounting staff of non-financial enterprises in Vietnam. With 649 valid respondents, the two-step approach was carried out, including exploratory factor analysis and confirmatory factor analysis and examining the significance of the coefficient through a path analysis. Our findings identified that organizational culture is an essential factor in enhancing the quality of accounting information systems of Vietnamese firms. Implementing an innovative corporate culture will improve system quality and information quality of the accounting information systems that help accounting information more sufficient for making decisions.

Improving the quality of accounting information systems through accountant resources is beneficial to the performance and sustainable development of SMEs. This study investigated the impact of accountant resources on the quality of accounting information systems in Vietnamese SMEs. Accounting information system quality was measured by a multidimensional scale including system quality, information quality, and usefulness. The study tested hypotheses using Path analysis of Structural Equation Model based on 434 respondents. The findings indicated a strong interaction between the components of the accounting information system quality under the effect of accountant resources. The results showed a positive direct effect of accountant resources on system quality and the path analysis results also revealed an influence of accountant resources on information quality and usefulness via mediating variables. The results highlighted the importance of accountant resources for the quality of accounting information systems. This study contributed theoretically to the non-financial indicator for measuring accounting information system quality.

The high-quality of financial reporting provides suitable information for economic decision-making of the country whilst, the low quality of financial reporting causes a serious impact on the economy. This research aims to classify financial reporting quality (FRQ) as well as determines the drivers of FRQ. This study uses a panel dataset from 2014 to 2020 that is collected from the Vietnamese listed companies. The study applies machine learning algorithms to classify and assess FRQ of non-financial companies on the Vietnamese stock exchange. New contribution considers the FRQ, on the auditor's opinion and the variance between pre-audit and post-audit profit. This research classifies FRQ into normal and poor categories, and a rate of 9.35% in the sample is considered poor FRQ. This research shows that the return on assets’ ratio and the ownership concentration have the most important influence on FRQ. Furthermore, the results which are predicting FRQ by using the random forest algorithm have an accuracy rate of 94%. This study is valuable for the forecast of FRQ and for the support of stakeholders in decision-making. With the high accuracy of machine learning techniques and its usage, it can help analysts and investors in generating reliable accounting information for decision-making purposes. Corporate sector needs to pay attention towards financial ratios and reinforcement of corporate governance.

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.

LLC “CPC “Business Perspectives” - publishing platform for academic journals