How service operations, perceived benefit, and psychological ownership enhance customer retention in retail - evidence in Vietnam supermarkets

In a world with enormous opportunities and challenges from the 4.0 revolution and the lingering COVID-19 pandemic, customer retention is more important than ever for retailers. While marketing and advertising can be more or less limited during the pandemic, retailers pay more attention to the supply and service operations of products as salvage to satisfy the essential demands of customers. However, few scholars discuss the effects of service operations on customer retention in retail because it is lower consumer awareness and challenging to measure accurately and adequately. Therefore, with the foundation of commitment-trust theory, this study examines service operations' direct and indirect effects on customer retention through perceived benefit in omnichannel retailers. Simultaneously, it assesses how psychological ownership affects customer retention and moderates the effect of perceived benefit on customer retention in the Vietnamese supermarket as empirical evidence. The combination of a qualitative method (with 32 in-depth interviews) and a quantitative method (through a survey conducted with 374 shoppers) is implemented. Partial least-squares structural equation modelling with SmartPLS software is utilized for data analysis and hypothesis testing. From the findings, the study offers an operations perspective and a customer view of how to store service operations contribute to customer perception of benefits and customer retention. Interestingly, the study discovered that psychological ownership is not only a critical antecedent of customer retention but also enhances the effect of perceived benefit on customer retention as its moderating role. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Bank funding diversity, risk and profitability: Evidence from Vietnam in the context of the Covid-19 pandemic

This study empirically explores the effects of bank funding diversity on Vietnamese commercial banks' profitability and risk in the context of the COVID-19 pandemic. The panel regression method was used to analyze quarterly data from 27 Vietnamese commercial banks from Q1-2016 to Q1-2021. The study findings demonstrate that commercial banks with diverse financing sources are more profitable and riskier. In the meanwhile, the COVID-19 outbreak did not diminish the short-term profitability of Vietnamese commercial banks, but it did increase their exposure to risk. On the basis of the empirical findings, this paper also proposes a number of strategies to assist Vietnamese commercial banks in operating more effectively and securely in the context of the COVID-19 pandemic. © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

The impacts of capital inflows on bank lending in the ASEAN-6 countries

Purpose: The authors investigate the impacts of international capital inflows on bank lending in the Association of Southeast Asian Nations-6 (ASEAN-6) countries on the dynamics of both bank loan volumes and credit risk-taking. The authors further explore the heterogenous impacts of different components of the foreign capital. As a robustness check, the authors also examine the role of crisis periods and agency problem on the relationship between international capital inflows and bank lending. Design/methodology/approach: The authors explore the impacts of international capital inflows on bank lending in the ASEAN-6 countries, including Malaysia, Indonesia, Thailand, Philippines, Singapore and Vietnam. The authors employ quarterly data from 2005Q1 to 2021Q2 from 45 commercial banks in the ASEAN-6 countries. The article uses bank-fixed and time-fixed effects in the panel dataset to account for any unobserved heterogeneity. Findings: The authors find that capital inflows to the ASEAN-6 countries are associated with higher bank loan growth and lower loan loss provisions to net interest income ratios. Moreover, the positive relationships between capital inflows to the bank loan growth and credit risk-taking are mainly driven by the dynamics in foreign direct investments (FDIs) and other inflow (OI) components. Contrary to the global financial crisis (GFC), the authors note that the mediating role of capital inflows on bank lending is of particular importance in the COVID-19 pandemic. Research limitations/implications: This study has some limitations that provide vendors for future research. First, while the authors focus on the impact of capital inflows on bank-level lending activities, future research can also explore the role of foreign capital on bank efficiency and financial stability. Second, although foreign capital fluctuates the most during crisis periods, the movement of capital inflows is also sensitive to other periods of heightened global uncertainty. Thus, rather than focus on the behavior of foreign capital during crisis periods, future research can examine and explore the impacts of capital inflows in different periods of "stop” and "surge” for sudden contraction and boom in capital inflows to the ASEAN-6 countries. Originality/value: First, the authors provide a comprehensive analysis of international capital inflows' impact on bank lending in the ASEAN region on both bank loan volumes and credit risk-taking. Second, the authors provide evidence of the impact of different forms of foreign capital on the bank lending. Third, the authors investigate the heterogeneous impact of foreign capital on crisis periods and bank sizes, which the authors emphasize the unusual characteristics of the COVID-19 crisis compared with the GFC. © 2023, Emerald Publishing Limited.

A Correct Face Mask Usage Detection Framework by AIoT

The COVID-19 pandemic, which affected over 400 million people worldwide and caused nearly 6 million deaths, has become a nightmare. Along with vaccination, self-testing, and physical distancing, wearing a well-fitted mask can help protect people by reducing the chance of spreading the virus. Unfortunately, researchers indicate that most people do not wear masks correctly, with their nose, mouth, or chin uncovered. This issue makes masks a useless tool against the virus. Recent studies have attempted to use deep learning technology to recognize wrong mask usage behavior. However, current solutions either tackle the mask/non-mask classification problem or require heavy computational resources that are infeasible for a computational-limited system. We focus on constructing a deep learning model that achieves high-performance results with low processing time to fill the gap in recent research. As a result, we propose a framework to identify mask behaviors in real-time benchmarked on a low-cost, credit-card-sized embedded system, Raspberry Pi 4. By leveraging transfer learning, with only 4-6 h of the training session on approximately 5,000 images, we achieve a model with accuracy ranging from 98 to 99% accuracy with the minimum of 0.1 s needed to process an image frame. Our proposed framework enables organizations and schools to implement cost-effective correct face mask usage detection on constrained devices.

Proposing Algorithm for Warning Minimum Distance Violation and Tracking Covid-19 Object Using YOLOV4 Model

Currently, the prevention of the spread of the Covid-19 epidemic is still a matter of concern with many new variants that are more infectious and making it more difficult to prevent it. In addition, several respiratory viral diseases such as influenza A, monkeypox, etc. help promote the management and prevention of epidemics. The paper presents the system using the YOLOV4 object recognition model to identify human objects from videos extracted. To increase accuracy with the desired context, we build a dataset of people and perform training on them. We use the Euclidean algorithm to calculate the distance between bounding box pairs. We then use a physical distance that approximates the pixel and set a threshold. It is possible to determine who has violated the minimum social distance threshold. In addition, we apply a tracking algorithm to be able to detect and trace those who have been in close contact with the cases to be monitored. The system has been performed on video and the accuracy of the model is up to 95.6%. © 2022 IEEE.

A Correct Face Mask Usage Detection Framework by AIoT

The COVID-19 pandemic, which affected over 400 million people worldwide and caused nearly 6 million deaths, has become a nightmare. Along with vaccination, self-testing, and physical distancing, wearing a well-fitted mask can help protect people by reducing the chance of spreading the virus. Unfortunately, researchers indicate that most people do not wear masks correctly, with their nose, mouth, or chin uncovered. This issue makes masks a useless tool against the virus. Recent studies have attempted to use deep learning technology to recognize wrong mask usage behavior. However, current solutions either tackle the mask/non-mask classification problem or require heavy computational resources that are infeasible for a computational-limited system. We focus on constructing a deep learning model that achieves high-performance results with low processing time to fill the gap in recent research. As a result, we propose a framework to identify mask behaviors in real-time benchmarked on a low-cost, credit-card-sized embedded system, Raspberry Pi 4. By leveraging transfer learning, with only 4–6 h of the training session on approximately 5,000 images, we achieve a model with accuracy ranging from 98 to 99% accuracy with the minimum of 0.1 s needed to process an image frame. Our proposed framework enables organizations and schools to implement cost-effective correct face mask usage detection on constrained devices. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The COVID-19 global pandemic: a review of the Vietnamese government response

The profound effect of COVID-19 pandemic has not eluded Vietnam, a lower-middle-income country that borders China, the country where the outbreak originated. Currently facing a second wave, Vietnam experienced several months of insignificant community-transmission, when the epidemic was effectively under control. This paper provides an account of the policies developed by the national COVID-19 response team during the first wave, from January to July 2020. Three key components were identified, including (i) the timely and decisive responses from the national and local authorities in the early phase of the pandemic, (ii) a society-wide approach, supported by an effective risk communication strategy which managed to gain the public trust, and (iii) an effective preventive medicine and infectious disease control system, residing in early case identification, strict isolation, effective contact tracing and compulsory quarantine of close contacts. While several other important components of the health system, such as financing and human resources remain largely under-explored, the results of this study show that a mixture of measures may lead to an effective epidemic management.

A Tourism Support Framework using Beacons technology

Due to the Covid-19 pandemic, Vietnam's tourism industry has been severely affected. Innovative technology should be applied to overcome the difficulties and challenges in the tourism system with low-quality human resources. In this research, we introduced a tourism support framework that leverages the Internet of Things (IoT) technology to improve the performance of the tourist industry and transform traditional travel into smart travel. As the key technology, Bluetooth Low Energy Beacons are employed at the core of our framework. Furthermore, a mobile application that interacts with beacons to satisfy visitors' demands was developed. By recognizing the user's actual location, our solution allows visitors to access information everywhere rapidly. Thanks to the capabilities of beacons, the system can also monitor the high accuracy indoor traffic at the small area tourist landmarks where the Global Positioning System (GPS) cannot work correctly. IUTour - a case study application was developed to validate the key functionalities of the proposed framework. In addition, the proposed framework further enables the tracking location and indoor navigation feature in real-time for buildings, museums, university campuses, and libraries to be integrated. The functionality comparison between IUTour and other applications indicated that our proposed software offers better performance than previous models. © 2021 IEEE.

Protocol for and Efficacy of Monoclonal Antibody (mAb) Treatment of SARS-CoV-2 at a VA Medical Center

Background. Bamlanivimab and casirivimab/imdevimab were the first monoclonal antibodies (mAb) developed against SARS-CoV-2 and proved beneficial early in the course of infection. However, real-world administration of these therapies presents logistical challenges. We present our experience implementing mAb treatment at a large VA Medical Center and review the efficacy of therapy in preventing hospitalization from COVID-19 in a closed healthcare system. Methods. All positive outpatient COVID tests performed at VA Greater Los Angeles Healthcare System (GLA) were reviewed by the Emergency Medicine (EM) and Infectious Diseases (ID) Sections for mAb eligibility beginning 12/2/2020. Due to limited supply, treatment was prioritized for patients at highest risk of developing severe disease, as determined by EM/ID with input from a machine learning ensemble risk estimation model produced by VA National Artificial Intelligence Institute (Figure 1). If a patient declined or did not reply, treatment was offered to the next patient on a ranked eligibility list. Those who declined or were eligible but not treated were included in the analysis. Patients were excluded if they were hospitalized before treatment was offered. We collected data on age, comorbidities, date of diagnosis, and admission at 30 days after diagnosis. A multivariate log binomial regression was performed to determine the relative risk of admission within 30 days of diagnosis for those who received mAb therapy as compared to those who did not, adjusting for age and comorbidity. All analysis was done in R (version 4.0.5). Results. 139 patients met inclusion criteria. 45 (32%) received mAb therapy, 48 (35%) declined mAb therapy, and the remaining 46 (33%) either did not respond or were not offered mAb therapy. Hospitalizations following diagnosis in each group are illustrated in Figure 2. There was a trend towards reduced absolute and relative risk of hospitalization (Table 1). There were no anaphylactic events in patients who received mAb therapy. Conclusion. At our facility, a system for rapid identification of candidates and a coordinated distribution plan was essential in ensuring timely administration of mAb therapy to eligible patients. Administration of mAb showed a trend towards decreased risk of hospitalization due to SARS-CoV-2.

Analysis and detection of COVID-19 cases on chest X-ray images using a novel architecture self-development deep-learning

The recent ongoing pandemic coronavirus disease 2019 (COVID-19) is growing increasingly out of control globally, posing a severe threat to human health. The use of artificial intelligence (AI) in predicting COVID-19-positive individuals becomes a promising tool that may enhance the existing diagnosis modality. Algorithms in supporting classifications for chest X-ray images face challenges in terms of dependability. With this aim, a convolutional neural network (CNN) model FASNet is proposed to identify chest X-ray images of three distinct conditions: pneumonia, COVID-19, and normal (or healthy) cases. The FASNet model consists of four convolution layers and two fully connected layers. The pre-trained deep learning models were used and included in our self-development FASNet CNN model. In the first convolutional layer, the size of the kernel 1 times 1 is used on each pixel as a fully connected connection with the aim of reducing the channel depth and number of parameters of the model. The early-stopping class and dropout layer are used to limit the number of neural connections and prevent overfitting. The dataset for this study was derived from an open-source collection of 6, 432 images for training and testing. As the result, our approach successfully detected COVID-19 infected individuals, pneumonia, and healthy ones with a 98.48% accuracy. This promising preliminary results lead us to expect that the FASNet model can be used in further development research to assist in diagnosing COVID-19. The result with FASNet model has a high correlation in comparison with other popular models such as ResNet50V2 and MobileNetV2. © 2021 IEEE.

Public's Travel Intention Following COVID-19 Pandemic Constrained: A Case Study in Vietnam

The COVID-19 pandemic has impacted the tourism industry due to the resulting travel restrictions as well as a slump in demand among travelers. The tourism industry has been massively affected by the spread of coronavirus, as many countries have introduced travel restrictions in an attempt to contain its spread. In Vietnam, the government has largely been credited for the country's success in keeping COVID-19 transmission rates under control. Early awareness of the pandemic, appropriate, drastic, and people-centric measures, as well as public support, are the main factors behind the success of Vietnam. In that context, it is observed that people's travel demand has bounced back and this research will examine factors driving the public's travel intention in the post-crisis (pandemic) period. The survey was conducted on the Internet using questionnaires designed in the Google platform. Data was collected from April 16 to May 31, 2020, from 154 Vietnamese participants. Research findings demonstrate 4 direct and indirect determinants of travel intention. The strongest effects come from perceived behavioral control which is influenced by subjective well-being. Perceived risk negatively correlates with Self-efficacy and subjective well-being. Conducted in the context of post-COVID-19, the research implies that once the pandemic has been controlled, perceived risks, although still exist, insignificantly influence the public's travel intention.

A Systematic and Critical Review on the Research Landscape of Finance in Vietnam from 2008 to 2020

This paper endeavors to understand the research landscape of finance research in Vietnam during the period 2008 to 2020 and predict the key defining future research directions. Using the comprehensive database of Vietnam's international publications in social sciences and humanities, we extract a dataset of 314 papers on finance topics in Vietnam from 2008 to 2020. Then, we apply a systematic approach to analyze four important themes: Structural issues, Banking system, Firm issues, and Financial psychology and behavior. Overall, there have been three noticeable trends within finance research in Vietnam: (1) assessment of financial policies or financial regulation, (2) deciphering the correlates of firms' financial performances, and (3) opportunities and challenges in adopting innovations and ideas from foreign financial market systems. Our analysis identifies several fertile areas for future research, including financial market analysis in the post-COVID-19 eras, fintech, and green finance.

Vietnam’s healthcare system decentralization: How well does it respond to global health crises such as COVID-19 pandemic?

This article discussed Vietnam’s ongoing efforts to decentralize the health system and its fitness to respond to global health crises as presented through the Covid-19 pandemic. We used a general review and expert’s perspective to explore the topic. We found that the healthcare system in Vietnam continued to decentralize from a pyramid to a wheel model. This system shifts away from a stratified technical hierarchy of higher- and lower-level health units (pyramid model) to a system in which quality healthcare is equally expected among all health units (wheel model). This decentralization has delivered more quality healthcare facilities, greater freedom for patients to choose services at any level, a more competitive environment among hospitals to improve quality, and reductions in excess capacity burden at higher levels. It has also enabled the transformation from a patient-based traditional healthcare model into a patient-centered care system. However, this decentralization takes time and requires long-term political, financial commitment, and a working partnership among key stakeholders. This perspective provides Vietnam’s experience of the decentralization of the healthcare system that may be consider as a useful example for other countries to strategically think of and to shape their future system within their own socio-political context. Copyright © 2020 Via Medica