Explore the vast range of titles available.

Over the recent years, blockchain, a digitalization phenomenon, has leveraged its superior features to remodel the relationships of logistics partners. This cutting-edge technology has brought a faster, more transparent, and cost-effective logistics industry. This study, therefore, aims to investigate the behavioral intention to use the blockchain of individuals who work in logistics companies in Ho Chi Minh City (HCMC), Viet Nam, through the Unified Theory of Acceptance and Use of Technology (UTAUT) and its extended factors. Accordingly, non-probability sampling with convenience sampling has been chosen. A questionnaire was used to collect data from logistics workers before exploring and clarifying factors affecting the users' intention, namely performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). In addition, experience (EXP) was also expected to influence the relationships. Therefore, a multi-analytical hybrid structural equation modeling-artificial neural network (SEM-ANN) approach was used to evaluate the gathered data empirically. The expert panel examined the established questionnaire through face validity and content validity to ensure the validity and reliability of the survey instrument. The findings revealed the different positive impacts of factors on the intention to use blockchain. While the result of the PLS-SEM technique is a descending order impact of PE, FC, EE, SI, and EXP was found that have no meaningful effect on the relationships, the ANN approach produces a surprising conclusion when SI ranks first in the magnitude of influence.

In the future, when the process of economic integration in the banking sector is more powerful, and competitive, diversifying revenue is an inevitable and objective trend to help the banks increase profits, minimize risks and improve their competitive position in the system. The research is on the relationship between revenue diversification, risk and bank performance using data from audited financial statements and annual reports of 26 commercial banks listed and unlisted in Vietnam during the period 2010–2018. The research method uses Generalized Method of Moment (GMM) modeling techniques to solve endogenous problems, variance and autocorrelation in the research model. Research results show that diversification negatively impacts profitability and the higher the diversification, the higher the risk of commercial banks. However, the more diversified listed banks, the more increased the bank’s stability. The banks show the weakness and lack of experience of the banking system in developing a reasonable profit transformation model. The revenue diversification of banks is currently passive and moves slowly. Interest income is still the motivation of bank development, boosting profit growth. Growth, as well as the contribution from service activities, is not commensurate with potentials; although there are many positive points, they are not enough to cover risks from net interest income activities.

Earnings management (EM) is the practice of adjusting profits or earnings on financial statements at the discretion of the management. This study undertook a regression analysis of the earnings management model, with data from 300 non-financial listed companies on the Vietnamese stock exchange during the period 2016 to 2021 to determine whether the COVID-19 pandemic has had an impact on earnings management. To estimate earnings management, compared with the original model, we add the leverage variable (LV) was used as earnings management proxy in this research. The results show that the COVID-19 pandemic positively and significantly impact the earnings management of non-financial listed companies. Moreover, it was found that managers are more involved in real earnings management than accrual earnings management, implying that financially distressed firms need to reassure investors to raise more capital during the pandemic. However, there is no evidence of a trade-off between these two techniques during the pandemic compared to before the pandemic. Additionally, this study provides evidence that the model by Roychowdhury, following Cohen et al., and the performance-matched model by Jones, following Kothari et al. are more suitable for detecting EM behavior when factoring in the financial leverage (LV) variable for firms with a high debt ratio in developing countries. JEL Classification: H8, M42 Plain language summary Purpose. This study determines whether the COVID-19 pandemic has impacted earnings management (levels, trends, and techniques) using emerging markets datasets. Methods. The regression analysis of the earnings management (EM) model, with data from 300 non-financial listed companies on the Vietnamese stock exchange from 2016–2021. We use the model by performance-matched Jones, following Kothari et al. and the model by Roychowdhury, following Cohen et al. to measure the dependent variable is EM through the choice of method—optimal estimation between Pool OLS, FEM, and REM. The results of measuring EM with the above models are not statistically significant with our research data. Therefore, we have added leverage (LV) as an EM proxy. After adding the LV, the models estimate the EM dependent variable with statistical significance. Conclusions. The results show that the COVID-19 pandemic positively and significantly impact the EM. Moreover, it was found that managers are more involved in REM than AEM, implying that financially distressed firms need to reassure investors to raise more capital during the pandemic. However, there is no evidence of a trade-off between these two techniques during the pandemic compared to before the pandemic. Additionally, this study provides evidence that he original EM estimator model are more suitable for detecting EM behavior when factoring in the financial leverage (LV) variable for firms. Implications. In developing countries with large debt-bearing firms, leverage impacts earnings management. Limitations. The analysis of the income management behavior of enterprises by industry has yet to be conducted.

In this work, we introduce a new node attack strategy removing nodes with the highest conditional weighted betweenness centrality (CondWBet), which combines the weighted structure of the network and the node’s conditional betweenness. We compare its efficacy with well-known attack strategies from literature over five real-world complex weighted networks. We use the network weighted efficiency (WEFF) like a measure encompassing the weighted structure of the network, in addition to the commonly used binary-topological measure, i.e., the largest connected cluster (LCC). We find that if the measure is WEFF, the CondWBet strategy is the best to decrease WEFF in 3 out of 5 cases. Further, CondWBet is the most effective strategy to reduce WEFF at the beginning of the removal process, whereas the Strength that removes nodes with the highest sum of the link weights first shows the highest efficacy in the final phase of the removal process when the network is broken into many small clusters. These last outcomes would suggest that a better attacking in weighted networks strategy could be a combination of the CondWBet and Strength strategies.

Purpose: This study aims to explore how state ownership moderates the impact of voluntary IFRS application on financial reporting quality. Design/methodology/approach: This study analyzes data from 552 public companies listed on the Ho Chi Minh Stock Exchange and Ha Noi Stock Exchange during 2019-2022, employing a fixed-effects model with clustered standard errors. Findings: The findings of this study indicate that voluntary IFRS application is positively associated with financial reporting quality. Moreover, this study identifies that state ownership moderates the negative association between voluntary IFRS application and financial reporting quality. Research limitations/implications: The results imply that the relationship between voluntary IFRS application and financial reporting quality is moderated by state ownership. Policymakers should take these findings into consideration when developing or revising policies concerning financial reporting standards and practices, particularly for state-owned companies. Originality/value: This study provides insights into how state ownership influences the impact of voluntary IFRS adoption on financial reporting quality in the Vietnamese market. KCI Citation Count: 0

To investigate the relationship between neutralization escape and persistent high-level BK polyomavirus replication after kidney transplant (KTx), VP1 sequences were determined by Sanger and next-generation sequencing in longitudinal samples from KTx recipients with persistent high-level viruria (non-controllers) compared to patients who suppressed viruria (controllers). The infectivity and neutralization resistance of representative VP1 mutants were investigated using pseudotype viruses. In all patients, the virus population was initially dominated by wild-type VP1 sequences, then non-synonymous VP1 mutations accumulated over time in non-controllers. BC-loop mutations resulted in reduced infectivity in 293TT cells and conferred neutralization escape from cognate serum in five out of six non-controller patients studied. When taken as a group, non-controller sera were not more susceptible to neutralization escape than controller sera, so serological profiling cannot predict subsequent control of virus replication. However, at an individual level, in three non-controller patients the VP1 variants that emerged exploited specific “holes” in the patient’s humoral response. Persistent high-level BK polyomavirus replication in KTx recipients is therefore associated with the accumulation of VP1 mutations that can confer resistance to neutralization, implying that future BKPyV therapies involving IVIG or monoclonal antibodies may be more effective when used as preventive or pre-emptive, rather than curative, strategies.

Mutations in the BK polyomavirus (BKPyV) capsid accumulate in kidney transplant (KTx) recipients with persistent virus replication. They are associated with neutralization escape and appear to arise as a result of cytosine deamination by host cell APOBEC3A/B enzymes. To study the mutagenic processes occurring in patients, we amplified the typing region of the VP1 gene, sequenced the amplicons to a depth of 5000–10,000×, and identified rare mutations, which were fitted to COSMIC mutational signatures. Background mutations were identified in amplicons from plasmids carrying the BKPyV genome and compared to mutations observed in 148 samples from 23 KTx recipients in France and in Vietnam. Three mutational signatures were consistently observed in urine, serum, and kidney biopsy samples, two of which, SBS2 and SBS13, corresponded to APOBEC3A/B activity. In addition, a third signature with no known etiology, SBS89, was detected both in patient samples, and in cells infected in vitro with BKPyV. Quantitatively, APOBEC3A/B mutation rates in urine samples were strongly correlated with urine viral load, and also appeared to vary between individuals. These results confirm that APOBEC3A/B is a major, but not the only, source of BKPyV genome mutations in patients.

The surge in digital platforms has revolutionized how consumers purchase, favoring online shopping. Despite its popularity, customer loyalty in this sphere requires enhancement. Companies are striving to augment loyalty and repurchase intention among consumers. However, the factors driving repurchase intention through shopping well-being in the online context, particularly in Vietnam, remain incompletely understood. This study examines shopping value components, including utilitarian and hedonic values, while exploring their relationships with customer trust and impulsive buying, influencing repurchase intention through customer’s shopping well-being on digital platforms. Employing a mixed-method approach, the study conducts qualitative interviews with online shoppers, marketers, and researchers to refine assessment scales for the Vietnamese context. A quantitative survey will gather data and use Covariance-based Structural Equation Modeling (CB-SEM) to test proposed hypotheses. In addition, the PRISMA model is applied in the systematic evaluation of literature reviews. This research offers a theoretical model for understanding consumer behavior in the Vietnamese online shopping landscape. Additionally, it furnishes valuable insights for digital platform sellers aiming to improve customers’ repurchase intention by refining the shopping experience and well-being.

Investigating the network response to node removal and the efficacy of the node removal strategies is fundamental to network science. Different research studies have proposed many node centralities based on the network structure for ranking nodes to remove. The random walk (RW) on networks describes a stochastic process in which a walker travels among nodes. RW can be a model of transport, diffusion, and search on networks and is an essential tool for studying the importance of network nodes. In this manuscript, we propose four new measures of node centrality based on RW. Then, we compare the efficacy of the new RW node centralities for network dismantling with effective node removal strategies from the literature, namely betweenness, closeness, degree, and k-shell node removal, for synthetic and real-world networks. We evaluate the dismantling of the network by using the size of the largest connected component (LCC). We find that the degree nodes attack is the best strategy overall, and the new node removal strategies based on RW show the highest efficacy in regard to peculiar network topology. Specifically, RW strategy based on covering time emerges as the most effective strategy for a synthetic lattice network and a real-world road network. Our results may help researchers select the best node attack strategies in a specific network class and build more robust network structures.

Computing the robustness of a network, i.e., the capacity of a network holding its main functionality when a proportion of its nodes/edges are damaged, is useful in many real applications. The Monte Carlo numerical simulation is the commonly used method to compute network robustness. However, it has a very high computational cost, especially for large networks. Here, we propose a methodology such that the robustness of large real-world social networks can be predicted using machine learning models, which are pretrained using existing datasets. We demonstrate this approach by simulating two effective node attack strategies, i.e., the recalculated degree (RD) and initial betweenness (IB) node attack strategies, and predicting network robustness by using two machine learning models, multiple linear regression (MLR) and the random forest (RF) algorithm. We use the classic network robustness metric R as a model response and 8 network structural indicators (NSI) as predictor variables and trained over a large dataset of 48 real-world social networks, whose maximum number of nodes is 265,000. We found that the RF model can predict network robustness with a mean squared error (RMSE) of 0.03 and is 30% better than the MLR model. Among the results, we found that the RD strategy has more efficacy than IB for attacking real-world social networks. Furthermore, MLR indicates that the most important factors to predict network robustness are the scale-free exponent α and the average node degree . On the contrary, the RF indicates that degree assortativity a, the global closeness, and the average node degree are the most important factors. This study shows that machine learning models can be a promising way to infer social network robustness.