Financial Inclusion for Women in the Informal Economy: An SDG Agenda Post Pandemic

Prior to the Covid-19 pandemic, a gender divide and inequality already existed within the world's economies;the global pandemic exacerbated this further. Many developing countries have been impacted enormously by the pandemic. Also, a sizeable portion of the workforce in these developing countries works within the informal economy, and they are predominantly women who are financially excluded. This study provided a conceptual framework illustrating the place of women in the informal economy and how the pandemic has affected their prospects in contributing to the achievement of the United Nations Sustainable Development Goals (SDGs). Based on the literature on financial inclusion and participation of women in the informal economy, this study provides several policy recommendations within the context of Africa. The study also offers theoretical contributions for academics, students, researchers, gender specialists, activists, financial service providers, and policymakers on the interconnections between gender, financial inclusion, and sustainable development. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Systemic risk and financial system network using financial risk meter: the case of Vietnam

In the financial market, systemic risk is defined as the possibility that an event at the company level could trigger severe instability or collapse of an entire industry or the whole economy. Thus, understanding systemic risk is crucial for the financial institutions, large corporations, investors and regulators. This article investigates systemic risk and spillover effect using the new Financial Risk Meter ((Formula presented.)) index, which is obtained from running quantile linear regression and Least Absolute Shrinkage and Selection Operator ((Formula presented.)) method. The (Formula presented.) index is obtained to identify the highly risky periods, the contributors to systemic risk and the potential activators of spillover effect. Moreover, interconnection between firms can be visualized as a network. We use a data set consisting of daily stock returns from 35 financial institutions and real estate firms in Vietnam, combined with 4 macroeconomic variables over the period from November 2011 to December 2020. The findings indicate that over the considered period, some detected highly risky periods are 2012, 2018 and 2020, probably due to the non-performing loan crisis in Vietnam, US-China trade war and global COVID-19 outbreak. Some active activators of risk spillover effect are also identified. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

AI-based traffic counting: A Case Study in Vietnam

Ho Chi Minh City, particularly Vietnamese cities in general, is so busy and crowded since tremendous numbers of motorbikes move on roads. Ho Chi Minh City leaders have encountered several challenges in fully understanding and effectively dealing with problems of urban traffic for the past few decades. Software-based solutions are proper and dramatically necessary, currently. This paper presents the deployment of an AI-based application at the Ho Chi Minh City Department of Transportation. The paper mainly concentrates on traffic counting problems during the outbreak of the Covid-19 pandemic from June 2021. The performance of the AI-based application was compared with medical declaration data and achieved an accuracy of 93.80%. © 2022 IEEE.

Tracking Student Attendance in Virtual Classes Based on MTCNN and FaceNet

All classes are held online in order to ensure safety during the COVID pandemic. Unlike onsite classes, it is difficult for us to determine the full participation of students in the class, as well as to detect strangers entering the classroom. Therefore, We propose a student monitoring system based on facial recognition approaches. Classical models in face recognition are reviewed and tested to select the appropriate model. Specifically, we design the system with models such as MTCNN, FaceNet, and propose measures to identify people in the database. The results show that the system takes an average of 30 s for learning and 2 s for identifying a new face, respectively. Experiments also indicate that the ability to recognize faces achieves high results in normal lighting conditions. Unrecognized cases mostly fall into too dark light conditions. The important point is that the system was less likely to misrecognize objects in most of our tests. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Factors Affecting Students in Vietnam's Intention on Using Smartphones for Learning on the Mobile Learning Platforms

In the context of the COVID-19 pandemic breaking out in countries, online learning is the most effective measure that can be implemented. This study was conducted to find out the factors affecting the acceptance intention of Vietnamese learners on learning on the Mobile learning platform. Researches on the application of the model of acceptance and use of extended technology (UTAUT2) has been revised. The data is processed by using SPSS software v.26 and Smart PLS v.3.3.3 on 831 valid questionnaires surveyed from students in some Vietnam's universities. This study use PLS-SEM to test hypotheses on 831 samples. The results showed that there are six factors affect the behavioral intention on using smartphones in learning on M-learning, including: the efficiency expectation, the effort expectation, the enjoyment motivation, the service quality and the habit. At the same time, the result also shows that the factors affecting the behavior of using smartphones are habits, favorable conditions, and the strongest influencing factor of behavioral intention. The study also provides suggestions for governance to help educational managers and business units in the education sector improve the service quality to attract, satisfy customers to enhance competitive advantage. © 2022 Tri Minh Cao and Nam Phuong Nguyen

The role of perceived workplace safety practices and mindfulness in maintaining calm in employees during times of crisis

While prior studies in human resource management have investigated how employee outcomes have been affected in high-risk workplaces, this study stands out by examining this issue through the role played by COVID-19 as a specific stressor. We explained how employees' perceived health risks due to COVID-19 (CV19PHR) and perceived workplace safety practices (PWSPs) affected job performance via burnout and how PWSPs moderated the CV19PHR-burnout and CV19PHR-JP relationships. We also examined how mindfulness moderated the direct effects of CV19PHR and PWSPs on burnout and JP and the indirect effects of CV19PHR and PWSPs on JP via burnout. We performed three studies using an explanatory sequential mixed-method design. In study 1, a three-phase survey with 987 respondents was conducted to test the hypotheses. In study 2, by analyzing verbatim from 22 informants, the findings of study 1 and some main points concerning mindfulness and PWSPs were explained. In study 3, using data from 12 informants, we investigated how COVID-19 affected individuals differently compared with other high-risk workplaces and whether the impact of COVID-19 on individuals was curvilinear. We demonstrated that employees' CV19PHR was positively correlated with burnout, negatively influencing JP. Moreover, PWSPs reduced burnout, which adversely affected JP. Interestingly, PWSPs positively moderated the CV19PHR-burnout relationship but not the CV19PHR-JP relationship. We also found that mindfulness moderated the CV19PHR-burnout and PWSPs-burnout relationships. Furthermore, mindfulness significantly moderated the mediating effects of burnout on the CV19PHR-JP and PWSPs-JP relationships.

Relationship between coronavirus (Covid-19) and social factors: Evidence from 24 countries over the world during 88-day period from jan 31 to april 27, 2020

The paper investigates the relationship among social and demographic factors and a new case of spreading coronavirus in 24 countries over 88-day period from Jan 31, 2020 to April 27, 2020. This study collected second data from World Health Organisation (WHO)’s and World Bank’s database, applied Co-integration test and Causality test for a panel data set. The research provides crucial finding: there is causality between new cases of spreading coronavirus and time to close school too late. Moreover, Asian region, where the studied countries are being increases, new case of spreading coronavirus decrease. Furthermore, classification of income of the research countries is statistically insignificant to new case equivalent to absent association between the changes in the new case of spreading coronavirus and classification of income of the research countries. The spreading COVID – 19 can happen everywhere from high income, middle income to low-income countries. Another finding is that late closing school will enhance spreading COVID – 19 goes more than one thousand cases.