Thuongmai University

This study develops a conceptual framework to theorise how digitally augmented Communities of Practice (CoPs), such as the Slow Food Movement, can support sustainable food systems transformation through advanced knowledge management. Although digital innovation is increasingly applied in agri-food systems, much of the literature remains technocentric, focusing on infrastructure and automation, while overlooking how digital tools mediate community-based knowledge flows and adaptive capabilities. Addressing this gap, we integrate Nonaka and Takeuchi's SECI model with Teece's dynamic capabilities framework to examine how Artificial Intelligence (AI) and Metaverse technologies enable CoPs to create, share, and transform knowledge. The main contribution is the DEKA-CoPs model (Digitally Enabled Knowledge Architecture in Communities of Practice), which explains how digital mediation can enhance epistemic agility, collaborative innovation, and system adaptability. Methodologically, the paper uses a theory-building approach to develop four propositions that can guide future empirical work. This framework advances knowledge management and sustainability literature by shifting the focus from firm-based innovation to digitally enabled, community-led knowledge infrastructures. It offers practical implications for policymakers, technologists, and sustainability practitioners interested in designing inclusive, adaptive platforms that embed local knowledge in agri-food transitions. • Theorisation of digitally-enabled communities of practice by merging SECI and dynamic capabilities frameworks. • Unified model for innovation knowledge architecture for digital communities of practice in sustainable food systems. • AI and Metaverse as participatory epistemologies for sustainable food system innovation. • AI and Metaverse's role in inclusive, community-led agrifood system innovation. • Conceptualisation of digitally-enabled communities of practice as knowledge architectures.

This study examines the influence of the investment ratio in current assets and the investment ratio in fixed assets on the financial capacity of pharmaceutical firms listed on the Vietnamese stock market. Using panel data from 22 firms over the period 2021–2024, the study employs descriptive statistics, correlation analysis, Ordinary Least Squares (OLS), and Generalized Least Squares (GLS) regression models to analyze the relationship between asset allocation and financial capacity indicators. Financial capacity is measured through capital growth rate and the possibility of financing through debt. The GLS regression results indicate that the investment ratio in current assets negatively affects capital growth rate, while the investment ratio in fixed assets negatively influences both capital growth and the ability to finance through debt. However, the investment ratio in current assets does not show a statistically significant relationship with debt financing capacity. These findings suggest that excessive investment in both current assets and fixed assets may reduce financial flexibility and weaken firms’ financial capacity. The study provides practical implications for financial managers in pharmaceutical firms by highlighting the importance of optimizing asset allocation strategies to improve financial stability and long-term financial performance.

Artificial intelligence (AI) and machine learning (ML) have been increasingly adopted in the banking sector due to their ability to analyze large-scale datasets, process complex variables, and uncover hidden patterns, especially in the context of liquidity risk, posing a significant challenge for commercial banks. This study contributes to the field by conducting a comprehensive evaluation of several widely used early warning models, such as least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and extreme gradient boosting (XGBoost), to identify the most suitable approach for forecasting liquidity risk in Vietnamese commercial banks (VCBs) based on VCBs data over the period of 2014–2023. By pinpointing key indicators associated with liquidity crises, these models can assist banks and regulatory authorities in implementing timely preventive measures and enhancing risk management strategies. As a result, the RF model outperforms other methods in identifying possible liquidity crises, according to the empirical results, with an accuracy rate of 99.8 percent. These findings provide bank managers and policymakers with a powerful tool for timely preventive measures, thereby enhancing the resilience and stability of the financial system.

Traditional forecasting methods that depend on official surveys and macroeconomic data often lag behind real-world developments, missing early indicators of change. To address this gap, this study utilizes unstructured Vietnamese news articles as an alternative information source. By applying natural language processing and sentiment analysis, it extracts employment-related signals of optimism or concern and converts them into predictive features. The proposed occupation sentiment ranking algorithm integrates occupation extraction and sentiment analysis to detect real-time shifts in labor market dynamics. Empirical findings reveal that news-based sentiment significantly enhances prediction accuracy and responsiveness, particularly during times of disruption. This research provides a timely and effective early-warning framework for Vietnam's labor market, offering actionable insights for policymakers, universities, and job seekers navigating the challenges of the digital economy.

Environmental pollution, energy scarcity, and advancements in electric vehicle (EV) technology are critical global issues. Combining these factors offers a promising solution for sustainable transportation development, particularly for emerging economies like Vietnam, through the implementation of green mobility solutions via EV passenger transport services (EVPTS). This study investigates the motivations behind the willingness to switch from internal combustion engine vehicles to EVPTS. It focuses on factors related to the perceived benefits of EVs and individual pro-environmental characteristics. Data were collected from a survey of 352 individuals in Hanoi, Vietnam, and analyzed using SmartPLS 4.0 software. The findings revealed a significant positive correlation among environmental concerns, environmental personal norms, perceived inconvenience of private vehicles, and the perceived usefulness of EVPTS. Moreover, excluding the factor of environmental concerns, willingness to switch to EVPTS was positively correlated with perceived usefulness of EV (β=0.380), perceived inconvenience (β=0.318), and environmental personal norms (β=0.251). The major contribution of this study is twofold: First, by selectively combining elements of the Technology Acceptance Model (TAM) and the Norm Activation Model (NAM) and adding the concept of "Perceived inconvenience," our study develops a more comprehensive framework for understanding this behavioral shift. Second, this study demonstrates how perceived benefit attributes (encompassing the benefits of EVPTS and the inconvenience of personal transport), as well as individuals' pro-environmental characteristics, influence their willingness to switch to EVPTS within the context of a developing country. These findings further provide a foundation for suggesting managerial implications to foster the shift to EVPTS in Vietnam.

Phát thải khí nhà kính (KNK) từ giao thông vận tải chiếm tỷ trọng lớn trong tổng phát thải từ lĩnh vực năng lượng tại Việt Nam và dự báo tiếp tục tăng. Hệ số phát thải KNK đặc trưng quốc gia (CSEF) cần được xây dựng để nâng cao độ chính xác kết quả kiểm kê KNK và hỗ trợ chính sách giảm phát thải. Nghiên cứu này thiết lập CSEF cho xe buýt đô thị, gồm CSEF trực tiếp cho xe buýt điêzen và gián tiếp cho xe buýt điện, thông qua mô phỏng trên phần mềm MOVES có hiệu chỉnh theo đặc trưng lái thực tại Hà Nội. Theo đó, đặc trưng lái thực của buýt đô thị tại Hà Nội có tốc độ trung bình 16,7 km/h và tập trung chủ yếu ở vùng tải thấp. Kết quả cho thấy xe buýt điêzen phát thải khoảng 1.552,9 gCO2eq/km, trong khi xe buýt điện phát thải gián tiếp 1.368,2 gCO2eq/km khi hệ số phát thải lưới điện là 676,6 g/kWh. Sai số giữa mô phỏng và số liệu đo nhỏ hơn 10%, khẳng định độ tin cậy của phương pháp. Kết quả nghiên cứu cho thấy giao thông điện sẽ đóng góp giảm phát thải KNK đáng kể khi Quy hoạch điện VIII được thực thi. Nghiên cứu đóng góp cơ sở khoa học cho kiểm kê KNK Mức 3 theo hướng dẫn IPCC và hỗ trợ hoạch định chính sách giao thông bền vững.AbstractGreenhouse gas (GHG) emissions from the transport sector account for a significant share of total energy-related emissions in Vietnam and are projected to continue rising. Developing country-specific emission factors (CSEF) is essential to improve inventory accuracy and support mitigation policies. This study establishes CSEF for urban buses, including direct emissions for diesel buses and indirect emissions for electric buses, through simulations using the MOVES model calibrated with real-world driving characteristics in Hanoi. The Hanoi urban bus driving pattern features an average speed of 16.7 km/h and is concentrated in low-load conditions. Results show that diesel buses emit approximately 1,552.9 gCO₂eq/km, while electric buses cause 1,368.2 gCO₂eq/km of indirect emissions based on a national grid emission factor of 676.6 g/kWh. The difference between simulated and measured data is less than 10%, confirming the reliability of the approach. The study results indicate that electric mobility will contribute substantially to GHG emission reductions as Electricity Plan VIII is implemented. This study provides a scientific basis for Tier 3 GHG inventories under IPCC guidelines and supports policy development for sustainable urban transport.Keywords: Emission factor, greenhouse gas, urban bus, MOVES model, electric bus, Vietnam.

Bài báo này trình bày phân tích và đánh giá hoạt tải do các xe tải điện hạng nặng gây ra lên cầu đường bộ trong điều kiện khai thác thực tế, đồng thời so sánh với các hoạt tải thiết kế quy ước như HL93 (TCVN 11823-2017) và H30 (22TCN 18-79). Nghiên cứu xem xét phân bố tải trọng trục, cấu hình đoàn xe, khoảng cách giữa các xe và chiều dài đường ảnh hưởng, qua đó đánh giá chi tiết tác động của chúng đến nội lực trong kết cấu cầu. Thông qua việc khảo sát các mẫu xe tải điện điển hình, bao gồm những mẫu mới xuất hiện trên thị trường trong nước và quốc tế, nghiên cứu chỉ ra các sai khác đáng kể về hiệu ứng tải so với các giả định thiết kế tiêu chuẩn, đặc biệt đối với cầu nhịp lớn. Trên cơ sở các kết quả này, bài báo đề xuất các hướng dẫn xây dựng mô hình hoạt tải mới phản ánh điều kiện giao thông thực tế và xu hướng gia tăng phổ biến của xe tải điện hạng nặng. Kết quả nghiên cứu góp phần cung cấp cách hiểu toàn diện về hoạt tải thiết kế cầu, hỗ trợ cả quá trình thiết kế và đánh giá khả năng chịu lực của công trình

Purpose This study investigates the impact of Environmental, Social, Governance (ESG) overall score and its pillars on firm risk and the mediating role of earnings management. Design/methodology/approach The research applies Generalised Method of Moments (GMM) regression to address endogeneity in a panel of Australian-listed firms from 2014 to 2023. Findings The findings reveal that higher ESG scores are associated with lower firm risk, with governance and social pillars exerting the most substantial immediate effects. In contrast, the environmental pillar demonstrates a delayed risk-reducing impact, reflecting long-term benefits rather than short-term volatility reduction. Moreover, the study identifies earnings management as a significant mediator that partially offsets ESG's stabilising effects, highlighting that firms with strong ESG practices are less likely to engage in accrual-based earnings management, thus reducing risk. Practical implications These findings have critical implications for investors, regulators, and policymakers. They underscore the importance of pillar-level ESG evaluation, long-term orientation in environmental assessments, and integrating financial transparency into ESG frameworks. Originality/value This study contributes to the extant knowledge of ESG overall and the individual pillar effect on firm risk in Australian companies, highlighting the mediating role of earnings management (EM). By identifying earnings management as a partial mediating mechanism, the study extends agency and stakeholder theories beyond direct ESG–firm risk association through the lens of financial reporting behaviour. This integrated framework bridges sustainability and earnings management literatures, offering a more comprehensive theoretical understanding of how ESG performance is related to firm risk.