Labor market transformation in the hospitality gig economy in a post pandemic era: impacts of institutional governance

PurposeThe COVID-19 pandemic has caused the food delivery sector to boom as people continue to rely on services provided by online catering platforms (OCPs). However, because of the nature of sharing economy employment, gig workers' contributions went largely ignored until intervention from institutional governance. This study aims to explore the impacts of labor market transformation after the Chinese Government issued guidance to promote gig workers' welfare as a focal case.Design/methodology/approachFocus groups and the Delphi technique were used to explore associated impacts on OCPs and gig workers based on governance theory.FindingsResults show that institutional governance negatively affected OCPs' operating cost structure but sustained gig workers' welfare. The dual effects of market mechanism and institutional governance in the sharing economy are needed to be balanced for labor market transformation.Research limitations/implicationsLong-term equilibrium can be fulfilled, given the growing food-related demand for the market mechanism. Social reciprocity is expected to be realized through institutional governance for gig workers' welfare.Originality/valueThis study suggests that moving from market governance to stakeholder governance, as mediated by state governance, could transform gig workers' labor structure in the gig economy. This study presents an integrated governance theory to enhance the epistemology of institutional governance.

Labor market transformation in the hospitality gig economy in a post pandemic era: impacts of institutional governance

Purpose The COVID-19 pandemic has caused the food delivery sector to boom as people continue to rely on services provided by online catering platforms (OCPs). However, because of the nature of sharing economy employment, gig workers' contributions went largely ignored until intervention from institutional governance. This study aims to explore the impacts of labor market transformation after the Chinese Government issued guidance to promote gig workers' welfare as a focal case. Design/methodology/approach Focus groups and the Delphi technique were used to explore associated impacts on OCPs and gig workers based on governance theory. Findings Results show that institutional governance negatively affected OCPs' operating cost structure but sustained gig workers' welfare. The dual effects of market mechanism and institutional governance in the sharing economy are needed to be balanced for labor market transformation. Research limitations/implications Long-term equilibrium can be fulfilled, given the growing food-related demand for the market mechanism. Social reciprocity is expected to be realized through institutional governance for gig workers' welfare. Originality/value This study suggests that moving from market governance to stakeholder governance, as mediated by state governance, could transform gig workers' labor structure in the gig economy. This study presents an integrated governance theory to enhance the epistemology of institutional governance.

Heterologous boost with mRNA vaccines against SARS-CoV-2 Delta/Omicron variants following an inactivated whole-virus vaccine (preprint)

The coronavirus SARS-CoV-2 has mutated quickly and caused significant global damage. This study characterizes two mRNA vaccines ZSVG-02 (Delta) and ZSVG-02-O (Omicron BA.1), and associating heterologous prime-boost strategy following the prime of a most widely administrated inactivated whole-virus vaccine (BBIBP-CorV). The ZSVG-02-O induces neutralizing antibodies that effectively cross-react with Omicron subvariants following an order of BA.1>BA.2>BA.4/5. In native animals, ZSVG-02 or ZSVG-02-O induce humoral responses skewed to the vaccine's targeting strains, but cellular immune responses cross-react to all variants of concern (VOCs) tested. Following heterologous prime-boost regimes, animals present comparable neutralizing antibody levels and superior protection across all VOCs. Single-boost only generated ancestral and omicron dual-responsive antibodies, probably by "recall" and "reshape" the prime immunity. New Omicron-specific antibody populations, however, appeared only following the second boost with ZSVG-02-O. Overall, our results support a heterologous boost with ZSVG-02-O, providing the best protection against current VOCs in inactivated virus vaccine-primed populations.

Investor sentiment in the tourism stock market

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.

Hospitality education evolution observed in online learning dataset.

The research article "Realtime Online Courses Mutated amid the COVID-19 Pandemic: Empirical Study in Hospitality Program" aims to explore the education evolution amid the pandemic [1]. Data were collected by recruiting 956 respondents; 926 responses were adopted after the valid screening through a cooperative survey company. A random sampling of targeted groups was required when outsourcing the data collection to the survey company Wenjuanxing, a platform with a majority population database providing functions equivalent to Amazon Mechanical Turk [2]. We asked the company to deliver the designed questionnaire to teachers and students in hospitality programs. The reliability and validity of all constructs showed that the questionnaire is proper for measurement [3]. Data analysis applied the structural equation model with Mplus to examine the CFA model and research hypothesis. Structural equation modeling was applied to conduct the hypotheses test and model fitness through the statistical tool Mplus. Results imply that the data is suitable for conducting replication studies.

Realtime online courses mutated amid the COVID-19 pandemic: Empirical study in hospitality program.

Real-time online courses (RTOCs), a new online learning mode, have been developed because of a longitudinal suspension of classes amid the COVID-19 pandemic worldwide. We explore an information model to review the learning process and outcomes of RTOCs, which conducted educational activities via social media. Results show that social media can be a potent mediation factor with the moderation of structural differentiation to facilitate online learning outcomes. Conclusions imply that the life-changing impact of COVID-19 has caused an evolutionary online education mode that can be hybridized with face-to-face education and massive open online courses to flourish education approaches and pedagogies.

Simultaneous Detection of Seven Human Coronaviruses by Multiplex PCR and MALDI-TOF MS

Human coronaviruses (HCoVs) are associated with a range of respiratory symptoms. The discovery of severe acute respiratory syndrome (SARS)-CoV, Middle East respiratory syndrome, and SARS-CoV-2 pose a significant threat to human health. In this study, we developed a method (HCoV-MS) that combines multiplex PCR with matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), to detect and differentiate seven HCoVs simultaneously. The HCoV-MS method had high specificity and sensitivity, with a 1–5 copies/reaction detection limit. To validate the HCoV-MS method, we tested 163 clinical samples, and the results showed good concordance with real-time PCR. Additionally, the detection sensitivity of HCoV-MS and real-time PCR was comparable. The HCoV-MS method is a sensitive assay, requiring only 1 μL of a sample. Moreover, it is a high-throughput method, allowing 384 samples to be processed simultaneously in 30 min. We propose that this method be used to complement real-time PCR for large-scale screening studies.

Impact of COVID-19 on the Robustness of the Probability of Default Estimation Model

Probability of default (PD) estimation is essential to the calculation of expected credit loss under the Basel III framework and the International Financial Reporting Standard 9. Gross domestic product (GDP) growth has been adopted as a key determinant in PD estimation models. However, PD models with a GDP covariate may not perform well under aberrant (i.e., outlier) conditions such as the COVID-19 pandemic. This study explored the robustness of a PD model with a GDP determinant (the test model) in comparison with that of a PD model with a credit default swap index (CDX) determinant (the alternative model). The test model had a significantly greater ratio of increase in Akaike information criterion than the alternative model in comparisons of the fit performance of models including 2020 data with that of models excluding 2020 data (i.e., that do not cover the COVID-19 pandemic). Furthermore, the Cook’s distance of the 2020 data of the test model was significantly greater than that of the alternative model. Therefore, the test model exhibited a serious robustness issue in outlier scenarios, such as the COVID-19 pandemic, whereas the alternative model was more robust. This finding opens the prospect for the CDX to potentially serve as an alternative to GDP in PD estimation models.

Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.

Resilience of Tourists’ Repurchase Intention during the COVID-19 Pandemic: The Shared Accommodation Sector

The economy has suffered unprecedentedly during the COVID-19 pandemic, including the shared accommodation sector. This study aims to discover the pandemic consumer behavior model for the recovery of the sector as well as investigate the economic resilience of tourists’ behavior to prevent and control the normalized pandemic. Most of the resilience literature discussed the level of economic and industry revitalization. There are relatively few studies on the individual level of tourists’ resilience. Therefore, we applied the adjusted theory of planned behavior with pandemic-related intrinsic factors to construct the research model, which is analyzed by the SEM approach. The results show that perceived risk affects tourists’ perceived value, trust, and behavioral attitude when repurchasing shared accommodation during the pandemic. The repurchase intention is indirectly affected by the behavioral attitude and perceived value. We concluded that the perceived risk of the pandemic could be resilient with respect to the perceived value, trust, and behavioral attitude for the repurchase intention of the shared accommodation for the sector to recover.

A selective sweep in the Spike gene has driven SARS-CoV-2 human adaptation.

The coronavirus disease 2019 (COVID-19) pandemic underscores the need to better understand animal-to-human transmission of coronaviruses and adaptive evolution within new hosts. We scanned more than 182,000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes for selective sweep signatures and found a distinct footprint of positive selection located around a non-synonymous change (A1114G; T372A) within the spike protein receptor-binding domain (RBD), predicted to remove glycosylation and increase binding to human ACE2 (hACE2), the cellular receptor. This change is present in all human SARS-CoV-2 sequences but not in closely related viruses from bats and pangolins. As predicted, T372A RBD bound hACE2 with higher affinity in experimental binding assays. We engineered the reversion mutant (A372T) and found that A372 (wild-type [WT]-SARS-CoV-2) enhanced replication in human lung cells relative to its putative ancestral variant (T372), an effect that was 20 times greater than the well-known D614G mutation. Our findings suggest that this mutation likely contributed to SARS-CoV-2 emergence from animal reservoirs or enabled sustained human-to-human transmission.

COVID-19 and older people in Asia: Asian Working Group for Sarcopenia calls to actions.

The coronavirus disease 2019 (COVID-19) pandemic has casted a huge impact on global public health and the economy. In this challenging situation, older people are vulnerable to the infection and the secondary effects of the pandemic and need special attention. To evaluate the impacts of COVID-19 on older people, it is important to balance the successful pandemic control and active management of secondary consequences. These considerations are particularly salient in the Asian context, with its diversity among countries in terms of sociocultural heritage, healthcare setup and availability of resources. Thus, the Asian Working Group for Sarcopenia summarized the considerations of Asian countries focusing on responses and difficulties in each country, impacts of health inequity related to the COVID-19 pandemic and proposed recommendations for older people, which are germane to the Asian context. More innovative services should be developed to address the increasing demands for new approaches to deliver healthcare in these difficult times and to establish resilient healthcare systems for older people. Geriatr Gerontol Int 2020; 9999: n/a-n/a.

Drug Repurposing for COVID-19 using Graph Neural Network with Genetic, Mechanistic, and Epidemiological Validation (preprint)

Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug’s representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment. This paper had been uploaded to arXiv : https://arxiv.org/abs/2009.10931

The Progression of SARS Coronavirus 2 (SARS-CoV2): Mutation in the Receptor Binding Domain of Spike Gene.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) is a positive-sense single-stranded RNA (+ssRNA) that causes coronavirus disease 2019 (COVID-19). The viral genome encodes twelve genes for viral replication and infection. The third open reading frame is the spike (S) gene that encodes for the spike glycoprotein interacting with specific cell surface receptor - angiotensin converting enzyme 2 (ACE2) - on the host cell membrane. Most recent studies identified a single point mutation in S gene. A single point mutation in S gene leading to an amino acid substitution at codon 614 from an aspartic acid 614 into glycine (D614G) resulted in greater infectivity compared to the wild type SARS-CoV2. We were interested in investigating the mutation region of S gene of SARS-CoV2 from Korean COVID-19 patients. New mutation sites were found in the critical receptor binding domain (RBD) of S gene, which is adjacent to the aforementioned D614G mutation residue. This specific sequence data demonstrated the active progression of SARS-CoV2 by mutations in the RBD of S gene. The sequence information of new mutations is critical to the development of recombinant SARS-CoV2 spike antigens, which may be required to improve and advance the strategy against a wide range of possible SARS-CoV2 mutations.