RESUMO
[This corrects the article DOI: 10.1016/j.ijhm.2022.103241.].
RESUMO
This paper leverages natural language processing, spatial analysis, and statistical analysis to examine the relationship between restaurants' safety violations and COVID-19 cases. We used location-based consumers' complaints data during the early stage of business reopening in Florida, USA. First, statistical analysis was conducted to examine the correlation between restaurants' safety violations and COVID-19 transmission. Second, a neural network-based deep learning model was developed to perform topic modeling based on consumers' complaints. Third, spatial modeling of the complaints' geographic distributions was performed to identify the hotspots of consumers' complaints and COVID-19 cases. The results reveal a positive relationship between consumers' complaints about restaurants' safety violations and COVID-19 cases. In particular, consumers' complaints about personal protection measures had the highest correlation with COVID-19 cases, followed by environmental safety measures. Our analytical methods and findings shed light on customers' behavioral shifts and hospitality businesses' adaptive practices during a pandemic.
RESUMO
The COVID-19 pandemic has significantly disrupted the hospitality industry. This research combines different data to examine the US labor market trends during COVID-19. It is found that low-preparation jobs in leisure and hospitality are the hardest hit and slow to recover. The pandemic has highlighted growing issues in workplace safety, skill gaps, technology adoption, and work reorganization in the hospitality industry. This paper develops two propositions about preparing hospitality workers for the future of work and providing flexible work arrangements.
