VNUHCM Journal of Economics, Business and Law

Machine Learning Applications for Predicting Brewery By-Products in the Context of Circular Economy

2026-05-08T15:05:08+07:00

It can be seen that the brewing industry generates significant by-products such as spent grains, hops, and yeast, which are often underutilized or disposed of inefficiently despite their potential value. To align with the Circular Economy (CE) principles, which require manufacturers to keep resources in use for as long as possible, breweries must transform by-products into value-added resources, thereby contributing to sustainable development by reducing environmental impacts and resource depletion. While brewing factories recognize the importance of waste management and by-product reuse, there is a lack of comprehensive studies on predicting by-product quantities. This gap hinders efforts to optimize resource usage and reduce waste disposal.

To address this challenge, this paper aims to optimize waste management and enhance the reuse of brewing by-products, contributing to the sustainability goals of the CE. Machine learning (ML) models are applied to predict by-product quantities. The models’ performance is then evaluated using regression metrics, with Gradient Boosting emerging as the most accurate. At the same time, the dashboard is employed to visualize the predicted and actual by-product data. The study uses real-world data, covering multiple production variables over five years, ensuring industrial relevance and empirical validity. The research assesses prediction accuracy and interprets how predictive insights can be integrated into daily brewery operations through dashboard analytics, enabling data-driven decision-making.

The resulting insights enable breweries to forecast waste volumes, adjust production, optimize operations and reduce inefficiencies. Furthermore, this approach provides a scalable framework for other food and beverage manufacturers seeking to apply predictive analytics for sustainability purposes. The study also highlights the potential for data-driven models to improve resource management in the brewing industry, fostering sustainable practices and advancing the goals of the CE.

Building an Information Retrieval System for University Documents Based on Generative AI Technologies

2026-05-08T15:08:33+07:00

This study presents the development of a Generative AI-Based Information Retrieval System tailored for university documentation. The system leverages cutting-edge technologies, including LangChain, Facebook AI Similarity Search (FAISS) vector search, and OpenAI’s API, to facilitate semantic search and natural language processing (NLP), ensuring efficient and accurate document retrieval. The backend is built with Django, while the frontend leverages Angular for a responsive and user-friendly interface. The system's architecture ensures scalability and high retrieval accuracy through a Retrieval-Augmented Generation (RAG) model. Challenges faced include optimizing system performance, managing dependency conflicts across different libraries, and ensuring secure authentication mechanisms. This paper discusses the design, implementation, and evaluation of the system, highlighting its impact on academic document accessibility and future research directions.

THE MIXED PROPERTY LAW SYSTEM OF SCOTLAND AND PROPOSALS FOR VIETNAM

2026-05-08T15:12:11+07:00

This paper examines the mixed property law system of Scotland, a distinctive model that harmonizes Civil Law and Common Law traditions, in order to derive valuable insights for the improvement of Vietnam's property law. By analyzing key components such as the doctrine of real rights,
heritable property, security rights, and the institution of trust within Scots law, the study highlights
the system's defining features, including its structural coherence, conceptual clarity, and strong
adaptability to evolving socio-economic conditions. It further demonstrates how Scottish law successfully integrates classical Roman law principles with the flexibility of Common Law mechanisms,
thereby creating a legal framework that is both stable and responsive to modern developments.
From a comparative legal perspective, the paper proposes several directions for reforming Vietnamese property law. These include redefining the concept and scope of ``property'' by embracing a dual understanding of property as both ``things'' and ``rights''; developing a clearer and more
systematic classification of property; refining the Civil Code to explicitly recognize and structure the
doctrine of real rights; and considering the introduction of the trust as a legal institution to enhance
flexibility in asset management. Such reforms are expected to strengthen the consistency, transparency, and overall effectiveness of Vietnam's property law system, particularly in the context of
digital transformation and increasing global integration. Ultimately, these recommendations aim
to support the modernization and international alignment of Vietnam's legal framework governing
property relations.

THE MEDIATING ROLE OF INTRINSIC MOTIVATION IN THE RELATIONSHIP BETWEEN PERCEIVED PERSONAL ACCOUNTABILITY AND SOCIAL LOAFING: A STUDY OF MEDIA, ADVERTISING, AND EVENT SERVICE COMPANIES IN HO CHI MINH CITY

2026-05-12T14:15:33+07:00

This study aims to examine the mediating role of intrinsic motivation in the relationship between perceived personal accountability and social loafing. The survey was conducted among employees working at 18 service enterprises operating in the fields of media, advertising, and event organization in Ho Chi Minh City. Data were collected through a questionnaire using a 5-point Likert scale, with 343 valid responses analyzed using structural equation modeling (SEM) via SPSS 26.0 and AMOS 24.0. The results indicate that perceived personal accountability has a direct negative effect on social loafing and a positive effect on intrinsic motivation. At the same time, intrinsic motivation also exerts a negative effect on social loafing. Notably, intrinsic motivation partially mediates the relationship between perceived personal accountability and social loafing. These findings suggest that reducing loafing behavior in work groups requires not only enhancing employees’ personal accountability but also stimulating their intrinsic motivation. The study provides additional empirical evidence on the link between accountability psychology, work motivation, and group behavior, while offering managerial implications to improve group performance in service enterprises in Ho Chi Minh City.

Combating Counterfeit Goods in Vietnam: Perspectives of Customers and Businesses toward Blockchain Technology Applications

2026-05-12T14:06:36+07:00

Vietnam has emerged as one of the fastest-growing e-commerce marketplaces in recent years, bringing considerable financial benefits while increasing challenges and concerns, particularly counterfeit goods. Counterfeit goods not only cause massive financial losses for both companies and customers, but they also harm the community and our planet. Thus, eliminating counterfeit goods has become a top priority for protecting customers, brand authenticity, and ensuring long-term economic growth. Blockchain technology (BC) can improve product traceability and authentication, thus preventing counterfeit goods. This study examined the current situation of counterfeit goods in Vietnam and the potential of BC as an anti-counterfeiting solution. This study conducted in-depth interviews with businesses and customers. The findings revealed that buyers frequently encounter counterfeit products, particularly when purchasing through online platforms. Conventional anti-counterfeiting technology like QR codes and holographic labels have been widely employed. However, these methods still have several shortcomings, emphasizing the need for more advanced technology options, such as BC-based solutions. BC has been incrementally adopted and proven effective in preventing counterfeit products. Furthermore, this study suggests practical strategies to encourage the use of BC to prevent counterfeiting, increase customer trust, safeguard legitimate enterprises, and ensure the sustainable development of e-commerce.

Price forecasting using machine learning: A solution driving sustainable Vietnamese coffee supply chains

2026-05-15T09:50:01+07:00

Coffee is a vital commodity in Vietnam, providing economic and social significance. Additionally, Vietnamese coffee significantly contributes to global coffee production and is a crucial component of the worldwide coffee supply chain. Price forecasting provides essential information to supply chain members, enabling them to manage their supply chains better, minimize waste, and promote sustainable development. Thus, improving price forecasting accuracy will be beneficial in stabilizing and boosting coffee production by enabling supply chain actors to make informed production decisions, promoting sustainable coffee supply chains, and enhancing Vietnamese coffee competitiveness. However, coffee prices have shifted considerably since the COVID-19 pandemic, making forecasting challenging. As a result, adopting advanced forecasting techniques is critical to enhancing forecasting accuracy. Machine learning (ML), a subset of artificial intelligence (AI), has proven effective and widely used in various fields, particularly forecasting. This study employed a quantitative approach to evaluate the effectiveness of ML models, specifically the Long Short-Term Memory (LSTM) model, in forecasting coffee prices. This study utilized a comprehensive dataset comprising daily global coffee prices for over 50 years. The LSTM model delivered a remarkable forecast accuracy of 98.31%. The experiment results showed that LSTM outperformed Autoregressive Integrated Moving Average (ARIMA) models, one of the most frequently employed classical forecasting methodologies. Moreover, this study demonstrated the effectiveness of ML techniques in forecasting coffee prices, including LSTM, artificial neural networks (ANN), Gradient Boosting, and Random Forest. All ML models achieved excellent forecast accuracy, with values above 98%. LSTM produced the best performance among ML models. Furthermore, this study offers practical solutions to promote the application of ML methods, enhance forecasting accuracy, and improve coffee production efficiency, ultimately developing sustainable supply chains. These solutions involve major stakeholders, including the government, supply chain agents, technology suppliers, and researchers. Collaboration among these stakeholders is critical to successfully installing and optimizing ML forecasting systems.

Đánh giá ảnh hưởng của Đổi mới sáng tạo đến phát triển kinh tế địa phương ở Việt Nam

2026-05-15T10:26:05+07:00

Trong bối cảnh phát triển mới của Việt Nam, đổi mới sáng tạo được nhận diện và khẳng định là một trong ba trụ cột chính cho phát triển kinh tế. Nhiều định hướng, chủ trương, chính sách đã được ban hành nhằm đẩy mạnh đổi mới sáng tạo sâu rộng, đặc biệt là ở cấp độ địa phương và doanh nghiệp. Bài viết lượng hóa và đánh giá ảnh hưởng của đổi mới sáng tạo tới phát triển kinh tế địa phương qua việc xây dựng và kiểm định các mô hình kinh tế lượng khác nhau, sử dụng dữ liệu cắt ngang (cross-sectional data) về kinh tế - xã hội năm 2023 ở cấp độ địa phương ở Việt Nam. Kết quả nghiên cứu cho thấy cải thiện trình độ đổi mới sáng tạo có tác động đa chiều đến phát triển kinh tế địa phương: góp phần tăng trưởng kinh tế, nâng cao năng suất lao động và thúc đẩy phát triển xanh, giúp giảm bất bình đẳng thu nhập, nhưng cũng làm gia tăng khoảng cách giàu nghèo. Cùng với đó là một số bằng chứng thực nghiệm bổ sung về vai trò của một số nhân tố khác với phát triển kinh tế địa phương. Nghiên cứu tiên phong trong ứng dụng bộ chỉ số đổi mới sáng tạo địa phương (PII) được Bộ Khoa học và Công nghệ công bố năm 2023; góp phần làm rõ thêm hiểu biết, nhận thức về quan hệ giữa đổi mới sáng tạo và phát triển kinh tế, cung cấp thêm thông tin tham khảo thực tiễn cho công tác hoạch định phát triển trong thời gian tới. Hi vọng rằng bài viết sẽ khơi gợi sự tham gia nghiên cứu rộng rãi hơn về đổi mới sáng tạo ở Việt Nam trong thời gian tới