The impact of COVID-19 on the service business industry: insights from a bibliometric review

This paper aims to help practitioners and researchers understand the impact of COVID-19 on the service business industry through bibliometric analysis. For this purpose, our study collects 671 publications from Web of Science and Scopus. The bibliometric choices in this paper rely on two techniques: performance analysis and science mapping. The performance analysis is organized by the contribution analysis of research constituents. The science mapping uncovers the cooperative network between research constituents, as well as the co-occurrence analysis of keywords. This paper further explores the research topic with content analysis to summarize some findings and discussions. We find that most service business industries have been negatively affected by COVID-19, especially the aviation and tourism industry. Information technology services are a response driver to the negative pandemic impact. Given the current research status of COVID-19 impact on the service business industry, this paper finally concludes the potential directions for future research.

Ambient air pollutants concentration prediction during the COVID-19: A method based on transfer learning.

Research on the correlation analysis between COVID-19 and air pollution has attracted increasing attention since the COVID-19 pandemic. While many relevant issues have been widely studied, research into ambient air pollutant concentration prediction (APCP) during COVID-19 is still in its infancy. Most of the existing study on APCP is based on machine learning methods, which are not suitable for APCP during COVID-19 due to the different distribution of historical observations before and after the pandemic. Therefore, to fulfill the predictive task based on the historical observations with a different distribution, this paper proposes an improved transfer learning model combined with machine learning for APCP during COVID-19. Specifically, this paper employs the Gaussian mixture method and an optimization algorithm to obtain a new source domain similar to the target domain for further transfer learning. Then, several commonly used machine learning models are trained in the new source domain, and these well-trained models are transferred to the target domain to obtain APCP results. Based on the real-world dataset, the experimental results suggest that, by using the improved machine learning methods based on transfer learning, our method can achieve the prediction with significantly high accuracy. In terms of managerial insights, the effects of influential factors are analyzed according to the relationship between these influential factors and prediction results, while their importance is ranked through their average marginal contribution and partial dependence plots.