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This study aims to investigate how consumer's use of online food delivery (OFD) services is driven by its self-protective nature. Drawing on protection motivation theory, the unified theory of use and acceptance of technology, and diffusion of innovation theory, an integrated model was tested with 1,000 empirical data points to explain consumers' OFD use during the pandemic. Results confirmed the self-protective nature of OFD use by uncovering a significant positive effect of fear of COVID-19 on consumers' OFD ordering frequency. Perceived vulnerability contributed more strongly to an individual's fear of COVID-19 than perceived severity in dining activities. These findings theoretically expand the current understanding of OFD services and provide practical implications for OFD platforms, restaurateurs, and governments.
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Guided by Community of Inquiry (Garrison and Vaughan, Blended learning in higher education: Framework, principles, and guidelines. Jossey-Bass Publishers, 2008), this study investigated faculty's cognitive, social, and teaching presences in teaching Chinese as a foreign language classroom during emergency remote teaching (ERT) necessitated by the COVID-19 pandemic during the spring semester of 2020. The study collected data from five videoconferencing interviews with five faculty participants. The five participants, purposefully sampled, taught Chinese language classes across varying proficiency levels from five different four-year college institutions in the United States. The study analyzed the engagement strategies the participants employed in organizing their social, cognitive, and teaching presences. It further suggests pedagogical implications and future research for language instructors, teacher education programs, and university administrators to consider. © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022.
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This study applies time-series analysis to observe investor sentiment in the tourism stock market. We infer that investor sentiment positively affects the capital flows to illustrate the behavioral finance in the tourism stock market. The vector autoregression and autoregressive-moving-average models of time-series analysis are adopted to analyze individual and overall capital flows of herding behavior. The empirical study collected quarterly data on 45 tourism-related stocks in China from 2018 to 2020. Results reaffirm that investor sentiment causes irrational investment and strong fluctuations of capital flows, including those during the Coronavirus 2019 pandemic. In practice, the overreaction of tourism-related stocks is discovered in the tourism market that requires long-term resilience. Theoretically, the rational capital asset pricing model needs adjustments with the sentiment factor based on behavioral finance theory. © 2022 Elsevier B.V.
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During COVID-19, the explosive growth of demand for fresh agricultural products on E-commerce platform has increased the difficulty of maintaining the greenness and freshness in delivery. The traditional cold chain delivery is effective in keeping greenness, but its information asymmetry makes the freshness-keeping activities unable to be regulated, which may lead to the supply chain members giving up their freshness-keeping efforts. Can the blockchain technology effectively solve these problems? We consider a fresh agricultural product supply chain consisting of a supplier and an E-commerce platform (retailer). The retailer is responsible for the wholesale and sales of fresh agricultural products, and determines the blockchain adoption degree and advertising effort. The supplier is responsible for delivering fresh agricultural products to consumers, and determines the greenness investment and freshness-keeping effort. Based on the traditional and blockchain-based fresh agricultural product supply chain, we discuss the dynamic optimization of freshness-keeping effort, advertising effort, and blockchain adoption degree. Results show that the supplier will give up the freshness-keeping effort after receiving the wholesale funds in the traditional fresh agricultural product supply chain, which will naturally worsen the fresh agricultural products. When adopting blockchain technology, the supplier continues to make the freshness-keeping effort in delivery. And five specific settings are proved that blockchain is effective in maintaining freshness. But two other specific settings are determined that it is not suited for adopting blockchain. In addition, compared with the traditional fresh agricultural product supply chain, blockchain adoption can effectively reduce the freshness-keeping effort, advertising investment and goodwill for achieving the same profit margin level, and will not affect the greenness investment decision of the supplier. Our research can provide some insights into the cold chain logistics management of fresh blockchain. © 2022
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During COVID-19, the explosive growth of demand for fresh agricultural products on E-commerce platform has increased the difficulty of maintaining the greenness and freshness in delivery. The traditional cold chain delivery is effective in keeping greenness, but its information asymmetry makes the freshness-keeping activities unable to be regulated, which may lead to the supply chain members giving up their freshness-keeping efforts. Can the blockchain technology effectively solve these problems? We consider a fresh agricultural product supply chain consisting of a supplier and an E-commerce platform (retailer). The retailer is responsible for the wholesale and sales of fresh agricultural products, and determines the blockchain adoption degree and advertising effort. The supplier is responsible for delivering fresh agricultural products to consumers, and determines the greenness investment and freshness-keeping effort. Based on the traditional and blockchain-based fresh agricultural product supply chain, we discuss the dynamic optimization of freshness-keeping effort, advertising effort, and blockchain adoption degree. Results show that the supplier will give up the freshness-keeping effort after receiving the wholesale funds in the traditional fresh agricultural product supply chain, which will naturally worsen the fresh agricultural products. When adopting blockchain technology, the supplier continues to make the freshness-keeping effort in delivery. And five specific settings are proved that blockchain is effective in maintaining freshness. But two other specific settings are determined that it is not suited for adopting blockchain. In addition, compared with the traditional fresh agricultural product supply chain, blockchain adoption can effectively reduce the freshness-keeping effort, advertising investment and goodwill for achieving the same profit margin level, and will not affect the greenness investment decision of the supplier. Our research can provide some insights into the cold chain logistics management of fresh blockchain.
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This collaborative work brings together international lawyers and political scientists to explore whether and how the retreat of the US, and the simultaneous rise of China, affect the dynamics of multilateralism to which the EU claims to adhere. It focuses on the trilateral interaction between these three actors and the policy impact their interactions have in specific multilateral settings and examines cooperation, competition and confrontation of these three actors in key international organizations such as the WTO, UNESCO, Human Rights Council and UNCLOS, NATO, the ASEAN Regional Forum and the World Health Organization in times of Covid-19. It also addresses their approaches and attitudes toward international humanitarian norms and the peace process in the Middle-East. This book offers an insightful exploration of the future of multilateralism under the impact of the Trump administration and probes the future of the liberal international order. It will provide excellent reading material on current affairs for both graduate and undergraduate students in international law and international relations, in particular for courses relating to international organization, multilateralism, or the US, China and the EU in international affairs. For experienced researchers the book proposes in-depth studies that relate to major debates in the disciplines of international law and international relations. © 2023 selection and editorial matter, Chien-Huei Wu, Frank Gaenssmantel and Francesco Giumelli;individual chapters, the contributors.
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Purpose: To report the degree to which post-graduate trainees in radiation oncology perceive their education has been impacted by COVID-19. Materials and Methods: A cross-sectional online survey was administered in June 2020 to trainee members of Canadian Association of Radiation Oncology (CARO). The 82-item survey was adapted from a similar survey administered during SARS and included the Stanford Acute Stress Reaction and Ways of Coping Questionnaires. The survey was developed using best practices including expert review and cognitive pre-testing. Frequency statistics are reported. Results: Thirty-four trainees (10 fellows, 24 residents) responded. Nearly half of participants indicated that the overall impact of COVID-19 on training was negative/very negative (n=15;46%) or neutral (n=15;46%) with a small number indicating a positive/very positive (n=3;9%). Majority of trainees agreed/ strongly agreed with the following statements: “I had difficulty concentrating on tasks because of concerns about COVID-19” (n=17;52%), “I had fears about contracting COVID-19” (n=17;52%), “I had fears of family/loved ones contracting COVID-19” (n= 29;88%), “I felt socially isolated from friends and family because of COVID-19” (n=23;70%), “I felt safe from COVID-19 in the hospital during my clinical duties“ (n=15;46%), and “I was concerned that my personal safety was at risk if/when I was redeployed from my planned clinical duties” (n=20;61%). The changes that had a negative/very negative impact on learning included “the impact of limited patient contact” (n=19;58%), “the impact of virtual patient contact” (n=11;33%), and “limitations to travel and networking” (n=31;91%). Most reported reduced teaching from staff (n=22;66%). Two-thirds of trainees (n=22, 67%) reported severe (>50%) reduction in ambulatory clinical activities, 16 (49%) reported a moderate (<50%) reduction in new patient consultations, while virtual follow-ups (n=25: 76%) and in-patient clinical care activities (n=12;36%) increased. Nearly half of respondents reported no impact on contouring (n=16;49%), on-treatment management (n=17;52%) and tumour boards (n=14;42%) with the majority of other respondents reporting a decrease in these activities. Electives were cancelled in province (n=10/20;50%), out-of-province (n=16/20;80%) and internationally (n=15/18;83%). Conclusions: Significant changes to radiation oncology training were wrought by the COVID-19 pandemic and roughly half of trainees perceive that these changes had a negative impact on their training. Safety concerns for self and family were significant and strategies to mitigate these concerns should be a priority.
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Epidemic disease outbreaks are among the major threats to the sustenance and health of human societies, as evidenced by the crises caused by the COVID-19 pandemic. Many people have lost their lives because of this pandemic, and the impact of it on the global economy has also been severe. Modeling the infectious disease outbreak in search of the set of optimal strategies to control the epidemics can help the public health policy makers to better decide and design relevant policies. In this study, spatial games under public goods policies are used to model the social response of different interacting populations to a new epidemic, where the decision makers are not individuals but societies. This approach is of great importance for policy evaluation, since there are usually not just individuals who decide to change their behavior in response to an epidemic, but societies who affect the change of individuals behaviors by setting relevant health policies, standards and regulations. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.
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Amid the coronavirus outbreak, many countries are facing a dramatic situation in terms of the global economy and human social activities, including education. The shutdown of schools is affecting many students around the world, with face-to-face classes suspended. Many countries facing the disastrous situation imposed class suspension at an early stage of the coronavirus outbreak, and Asia was one of the earliest regions to implement live online learning. Despite previous research on online teaching and learning, students' readiness to participate in the real-time online learning implemented during the coronavirus outbreak is not yet well understood. This study explored several key factors in the research framework related to learning motivation, learning readiness and student's self-efficacy in participating in live online learning during the coronavirus outbreak, taking into account gender differences and differences among sub-degree (SD), undergraduate (UG) and postgraduate (PG) students. Technology readiness was used instead of conventional online/internet self-efficacy to determine students' live online learning readiness. The hypothetical model was validated using confirmatory factor analysis (CFA). The results revealed no statistically significant differences between males and females. On the other hand, the mean scores for PG students were higher than for UG and SD students based on the post hoc test. We argue that during the coronavirus outbreak, gender differences were reduced because students are forced to learn more initiatively. We also suggest that students studying at a higher education degree level may have higher expectations of their academic achievement and were significantly different in their online learning readiness. This study has important implications for educators in implementing live online learning, particularly for the design of teaching contexts for students from different educational levels. More virtual activities should be considered to enhance the motivation for students undertaking lower-level degrees, and encouragement of student-to-student interactions can be considered.
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New product development to enhance companies’ competitiveness and reputation is one of the leading activities in manufacturing. At present, achieving successful product design has become more difficult, even for companies with extensive capabilities in the market, because of disorganisation in the fuzzy front end (FFE) of the innovation process. Tremendous amounts of information, such as data on customers, manufacturing capability, and market trend, are considered in the FFE phase to avoid common flaws in product design. Because of the high degree of uncertainties in the FFE, multidimensional and high-volume data are added from time to time at the beginning of the formal product development process. To address the above concerns, deploying big data analytics to establish industrial intelligence is an active but still under-researched area. In this paper, an intelligent product design framework is proposed to incorporate fuzzy association rule mining (FARM) and a genetic algorithm (GA) into a recursive association-rule-based fuzzy inference system to bridge the gap between customer attributes and design parameters. Considering the current incidence of epidemics, such as the COVID-19 pandemic, communication of information in the FFE stage may be hindered. Through this study, a recursive learning scheme is established, therefore, to strengthen market performance, design performance, and sustainability on product design. It is found that the industrial big data analytics in the FFE process achieve greater flexibility and self-improvement mechanism on the evolution of product design.
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The whole globe has cranked up for coping with the COVID-19 situation. The hands-on tutorial targets at providing a comprehensive and pragmatic end-to-end walk-through for building an academic research paper recommender for the use case of COVID-19 related study, with the help of knowledge graph technology. The code examples that demonstrate the theories are reproducible and can hopefully provide value for researchers to build tools that support conducting research to find a cure to COVID-19. © 2020 Owner/Author.
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Objective: To investigate the causes of death in patients with severe COVID-19. Methods: A retrospective analysis was performed on 64 patients with severe COVID-19 admitted to Wuhan Pulmonary Hospital from January 12, 2020 to February 28, 2020. There were 36 males and 28 females, aging from 44 to 85 years[median 68 (62, 72)]. Fifty-two patients (81%) had underlying comorbidities. The patients were divided into the death group (n=40) and the survival group (n=24) according to the treatment outcomes. In the death group, 24 were male, and 16 were female, aging from 49 to 85 years [median 69 (62, 72)], with 31 cases (78%) complicated with underlying diseases. In the survival group, there were 12 males and 12 females, aging from 44 to 82 years[median 66 (61,73)], with 21 cases (88%) with comorbidities. Clinical data of the two groups were collected and compared, including general information, laboratory examinations, imaging features and treatments. For normally distributed data, independent group t test was used; otherwise, Mann Whitney test was used to compare the variables. χ(2) test and Fisher exact test was used when analyzing categorical variables. Results: The median of creatine kinase isozyme (CK-MB) in the death group was 19.0 (17.0,23.0) U/L, which was higher than that in the survival group 16.5 (13.5,19.6) U/L. The median level of cTnI in the death group was 0.03 (0.03, 0.07) µg/L, which was significantly higher than that in the survival group (0.02, 0.03) µg/L, with a statistically significant difference between the two groups (P=0.007). The concentration of myoglobin in the death group was 79.5 (28.7, 189.0) µg/L, which was higher than 33.1 (25.7, 54.5) µg/L in the survival group. The level of D-dimer in the death group was 2.0 (0.6, 5.2) mg/L, which was higher than 0.7 (0.4, 2.0) mg/L in the survival group. The LDH level of the death group was 465.0 (337.5,606.5) U/L, which was higher than that of the survibal group, 341.0 (284.0,430.0) U/L, the difference being statistically significant (P=0.006). The concentration of alanine aminotransferase in the death group was 40.0 (30.0, 48.0) U/L, which was higher than 32.5 (24.0, 40.8) U/L in the survival group, and the difference was statistically significant (P=0.047).Abnormal ECG was found in 16 cases (62%) in the death group, which was significantly higher than that in the survival group (29%), the difference being statistically significant (P=0.024) .The main causes of death were severe pneumonia with acute respiratory distress syndrome (ARDS, n=20), acute heart failure(n=9), atrial fibrillation(n=3) and multiple organ dysfunction syndrome (MODS, n=3). Conclusions: ARDS caused by severe pneumonia and acute heart failure and atrial fibrillation caused by acute viral myocarditis were the main causes of death in severe COVID-19 patients. Early prevention of myocardial injury and treatment of acute viral myocarditis complicated with disease progression may provide insights into treatment and reduction of mortality in patients with severe COVID-19.