Hotel guest satisfaction during COVID-19 outbreak: The moderating role of crisis response strategy.

Taking appropriate strategies in response to the COVID-19 crisis has presented significant challenges to the hospitality industry. Based on situational crisis communication theory (SCCT), this study aims to examine how the hotel industry has adopted strategies in shaping customers' experience and satisfaction. A mixed-method approach was employed by analysing 6556 COVID-19 related online reviews. The qualitative findings suggest that 'rebuild strategies' dominated most hotels' response to the COVID-19 crisis while the quantitative findings confirm the direct impact of affective evaluation and cognitive effort on customer satisfaction. The results further reveal that hotels' crisis response strategies moderate the effects of affective evaluation and cognitive effort on customer satisfaction. The study contributes to new knowledge on health-related crisis management and expands the application of SCCT in tourism research.

Dining with distance during the pandemic: an enquiry from the theory of proxemics and social exchange. (Special Issue: COVID-19.)

Building on proxemics theory and social exchange theory, this study investigated how different levels of psychological social distancing, protective wears, and social interactions influence customers' perceived risk, social exchange with service employees and their intention to avoid dining in restaurants under the 'new normal' of COVID-19. Using an experimental design with a total of 404 participants in US, this study shows that regardless of social distancing measures, both protective wear and social interaction levels can significantly influence customers' risk perception and social exchange quality. The study contributes to the tourism and hospitality literature by providing a timely understanding of customers' psychological perceptions, and responses of dining in restaurants during this difficult transition time. More importantly, this study adds hard empirical evidence to the current debate of restaurant re-open measures beyond widely circulating opinion pieces.

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation.

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivity of the RT-PCR test is not high enough to effectively prevent the pandemic. The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity. However, the chest CT scan test is usually time-consuming, requiring about 21.5 minutes per case. This paper develops a novel Joint Classification and Segmentation (JCS) system to perform real-time and explainable COVID- 19 chest CT diagnosis. To train our JCS system, we construct a large scale COVID- 19 Classification and Segmentation (COVID-CS) dataset, with 144,167 chest CT images of 400 COVID- 19 patients and 350 uninfected cases. 3,855 chest CT images of 200 patients are annotated with fine-grained pixel-level labels of opacifications, which are increased attenuation of the lung parenchyma. We also have annotated lesion counts, opacification areas, and locations and thus benefit various diagnosis aspects. Extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0% and a specificity of 93.0% on the classification test set, and 78.5% Dice score on the segmentation test set of our COVID-CS dataset. The COVID-CS dataset and code are available at https://github.com/yuhuan-wu/JCS.