INTERNAL GOVERNANCE MECHANISMS AND FIRM PERFORMANCE: THE CASE OF VIETNAM

Good corporate governance would contribute to the sustainable development of the economy. Better corporate governance is supposed to lead to better corporate performance and expropriation of controlling shareholders is supposed to be prevented. Studies of impacts of corporate governance on organizational performance had started since 1990s. Vietnam is a developing country with an underdeveloped financial market and week regulatory principles. Therefore, an approach of internal mechanism is supposed to be a better way to improve the quality of corporate governance than external mechanisms. Two internal governance mechanisms (IGMs) are examined in the relationship with corporate performance in this study include (1) Ownership structure and (2) Board of Directors. The results shows that largest shareholder, controlled directors and duality have negative impacts on firm performance while family ownership, board of director ownership, institutional ownership and foreign ownership have positive impacts on firm performance. The study makes theoretical and empirical contribution to the understanding for the development of an effective corporate governance framework in Vietnamese market

A Trade-Based Analysis of the Economic Impact of Non-Compliance with Illegal, Unreported and Unregulated Fishing: The Case of Vietnam

Illegal, unreported and unregulated (IUU) fishing is a threat to the sustainable use of fishing resources. To eliminate the destructive fishing practices, the whole value chain of fish trade needs to be well regulated. Trade-related policy measures show potential for contributing towards the elimination of unsustainable fishing practices. The EU’s launch of the IUU-combating fishing program and the introduction of measures to deal with countries that exploit, produce and export fishery products with illegal fishing origin, is indispensable in addressing harmful trends and a concern of the whole world, especially the fishing community. The program includes the flagship use of a warning card system. The EU is a very important trading partner for Vietnam and major importer of Vietnam’s fish products, of which seafood plays an important role. The EU market helps pave the way for Vietnamese seafood to enter the world market. Vietnam’s seafood export to the EU has increased sharply over the past 20 years, from USD 90 million in 1999 to nearly USD 1.5 billion in 2017 (and since decreased to closer to USD 1.3 billion in 2019). The year of 2017 marked a critical turning point for Vietnam’s fisheries when the EU issued a yellow card warning to Vietnam for not cooperating and making enough efforts to combat IUU fishing. The EU made nine recommendations to improve the Vietnamese fisheries management system following the warning. Over the past two years, the Government of Vietnam, ministries and the entire Vietnamese fishing community have actively improved to meet the recommendations of the EU to remove the IUU yellow card. The EU has appreciated Vietnam’s efforts to combat IUU exploitation, however, so far, the IUU yellow card has not yet been removed. In the past two years, the quantity of seafood exports to the EU have decreased significantly, showing the immediate impact of the yellow card warning on Vietnam’s seafood industry. However, that is only part of the negative impact as visible in export figures. There will be many other consequences from the IUU yellow card warning and the impact will be more serious if Vietnam does not remove the yellow card soon or receives a red card warning

Human brain dynamics during multitasking physical navigation

University of Technology Sydney. Faculty of Engineering and Information Technology.Spatial navigation is an essential skill that helps one to keep track of their location and orientation and navigate efficiently through the environment. Investigating spatial cognitive processing can be beneficial by rendering a mechanism underlying diseases such as Alzheimer’s disease, which might be diagnosed based on impairments in spatial tests long before established diagnostic criteria. Furthermore, navigation in real-life often involves multiple cognitive processes, such as landmark encoding, cognitive map anchoring, and goal-oriented planning, even in the simplest situation. Thus, investigating spatial navigation under multitasking situation might provide more insight of brain dynamics underlying navigation in our daily activities. However, most studies on active physical navigation in 3D space are based on animal research, or the studies are confined to a specific patient population with limited movement ranges. These limitations hinder the generalization of findings in stationary laboratory set-ups to active navigation in healthy human participants. In this work, we investigated human brain dynamics while multitasking in active navigation tasks in a more natural set-up that could be used with healthy populations. We performed simulated driving and physical spatial navigation task experiments, which mimic typical navigation tasks in our daily lives. Participants performed the tasks in a virtual environment, while their brain signal was measured simultaneously. We investigated brain dynamics of concurrent multitasking in the simulated driving experiment, where participants performed the driving task, and dynamic attention shifting task concurrently. We then further investigated brain dynamics in a physical spatial navigation experiment, where participants actively ambulated from a location to several others. We found an increase in the information flow of brain connectivity in the period of concurrent task response in the simulated driving experiment. Furthermore, in the same experiment, we observed an increase in frontal beta during the secondary task response. We then obtained a significant modulation of theta oscillations in the retrosplenial complex (RSC) during heading changes in the physical spatial navigation experiment; this is an essential mechanism for heading computation and generating the grid cell signal. Finally, we reported that local information processing in the RSC increases linearly with the navigation load level. The findings unpack the insight of brain dynamics and offer unprecedented benefits for estimating cognitive load in active navigation

Consumer’s perception and acceptance of lumpfish used in salmon cages

publishedVersio

Motivation to consume fish (seafood) in Vietnam

This study extends Theory of Planned Behavior (TPB) to investigate the motivation to consume fish in a representative survey of Vietnamese individuals. The emperical study is based on using the structural equation approach to test construct valitidy of measures and the emperical fit of theoretical model. The results show that the TPB is signficantly applicable to the Vietnam situation. The 23% of the variation of eating fish frequency is signficantly explained by motivation and perceived behavioral control. The motivation to consume fish as measured by intention is signficantly determined by subjective norms and attitude toward fish as meal; the two constructs explained for 35% of the motivation variation. At the specific level, the study found that negative affect, perceived quality and price are significant indicators that explains for 60% of the variation of the attitude construct; perceived price, time using to cook and prepare fish as meal and availability of fresh fish are important factors explaining for 63% of the variation of the perceived control over fish consumption. As one of the first attempts, the study provide some managerial suggestions for seafood sector to expand the domestic markets and also raise some recomendations for future research

Numerical investigation of force transmission in granular media using discrete element method

In this paper, a numerical Discrete Element Method (DEM) model was calibrated to investigate the transmission of force in granular media. To this aim, DEM simulation was performed for reproducing the behavior of a given granular material under uniform compression. The DEM model was validated by comparing the obtained shear stress/normal stress ratio with results published in the available literature. The network of contact forces was then computed, showing the arrangement of the material microstructure under applied loading. The number and distribution of the contacts force were also examined statistically, showing that the macroscopic behavior of the granular medium highly depended on the force chain network. The DEM model could be useful in exploring the mechanical response of granular materials under different loadings and boundary conditions

Business group, corporate governance and business performance: A study in Vietnam

Corporate governance is essential for businesses, especially those with complex structures. This research examines the relationship between business group structures, corporate governance, and business performance in Vietnam, providing theoretical insights and practical management recommendations. Analyzing data from over 100 of the largest listed companies on the Vietnam Exchange AllShare, the study used the Fixed Effects Model, Random Effects Model, and Feasible Generalized Least Square estimations to assess the impact of business groups on governance and performance. The study reveals a positive but relatively weak impact of expanding the scale of business groups on the quality of corporate governance and business efficiency. Audit Committees, Independent Directors, and company size positively influence governance quality, while liquidity has a negative impact. Similarly, these factors positively affect business efficiency, whereas financial leverage has an inverse relationship. This research enhances the understanding of how business group structures influence corporate governance and business efficiency in Vietnam. It underscores the importance of good governance principles to improve accountability, transparency, and protect stakeholder interests, highlighting the positive role of certain governance structures in enhancing business efficiency

Investigation of anti-inflammatory lignans from the leaves of Symplocos sumuntia Buch-Ham ex D Don (Symplocaceae)

Purpose: To investigate the anti-inflammatory activity of Symplocos sumuntia Buch.-Ham. ex D. Don and identify the main secondary metabolites responsible for this effect.Methods: The in vitro anti-inflammatory activity of the plant extract and isolated compounds was determined in terms of the ability to inhibit the production of nitric oxide (NO), and expressions of iNOS and COX-2 proteins in RAW264.7 cells stimulated by lipopolysaccharide (LPS). Compounds were isolated and identified by spectroscopic methods.Results: The methanol extract of S. sumuntia leaves showed strong inhibitory effects on nitric oxide (NO) production and expression of iNOS and COX-2 in LPS-induced RAW264.7 cells. A phytochemical assay-guided fractionation of the methanol extract of S. sumuntia leaves led to the isolation of four lignans which are arctigenin (1), matairesinol (2), monomethylpinoresinol (3) and pinoresinol (4). These compounds were identified for the first time from S. sumuntia. All four compounds inhibited the production of nitric oxide (NO), with arctigenin showing the most potent activity with half-maximal inhibitory concentration (IC50) value of 4.08 μM.Conclusion: S. sumuntia is a promising source of anti-inflammatory agents, which may clarify to the therapeutic use of this plant in Vietamese traditional medicine.Keywords: Symplocos sumuntia, Symplocos caudata, Lignan, Arctigenin, Anti-inflammator

A novel semi-supervised consensus fuzzy clustering method for multi-view relational data

Multi-view data is widely employed in various domains, highlighting the need for advanced clustering methodologies to efficiently extract knowledge from these datasets. Consequently, multi-view clustering has emerged as a prominent research topic in recent years. In this paper, we propose a novel approach: the semi-supervised consensus fuzzy clustering method for multi-view relational data (SSCFMC). This method combines the advantages of fuzzy clustering and consensus clustering to address the challenges posed by multi-view data. By leveraging available labeled information and the relational structure among views, our method aims to enhance clustering performance. Extensive experiments on benchmark datasets demonstrate that our method surpasses existing single-view and multi-view relational clustering algorithms in terms of accuracy and stability. Specifically, the SSCFMC algorithm exhibits superior clustering performance across various datasets, achieving an adjusted rand index (ARI) of 0.68 on the multiple features dataset and an F-measure of 0.91 on the internet dataset, highlighting its robustness and efficiency. Overall, this study advances multi-view clustering techniques for relational data and provides valuable insights for researchers in this field