Working with artificial intelligence surveillance during the COVID-19 pandemic: a mixed investigation of the influence mechanism on job engagement in hospitality industry

This paper examines the interaction effects of social support (supervisor support and coworker support) and AI surveillance on employee job engagement drawing on the social exchange theory. Mixed research methods were applied. Researchers collected 358 valid time-lagged designed questionnaires and tested the model using a path analysis approach. The results revealed that there is a moderated mediation mechanism in relationships between supervisor support/coworker support and job engagement, in which both self-efficacy and self-esteem are mediating variables and AI surveillance plays a moderating role. Specifically, when the degree of job control with AI surveillance is at a low level, the effect of supervisor support/coworker support on employees' self-efficacy/self-esteem is stronger. The indirect relationship between supervisor support/coworker support and job engagement through self-esteem is moderated by job control with AI surveillance as well, and the indirect relationship becomes stronger when job control with AI surveillance is lower. Findings from a series of semi-structured post-hoc interviews with 18 hotel employees interpretatively support the survey results. This research fills this gap by analyzing relationships among social support, AI surveillance, and job engagement and provides positive suggestions for hotel operation and employee management with AI surveillance during pandemic.

Health policy response to mobility during the pandemic: Evaluating the effectiveness using location-based services big data.

As an emergent health policy response, the population mobility restriction policy was implemented to cope with the unprecedented pandemic that outbroke in early 2020, but its effectiveness showed vast disparities even within a single country. Using multisource data from Baidu mobility big data and the statistics of novel coronavirus disease in China, mobility restrictions (including restrictions on inflow-mobility, outflow-mobility, and intra-city mobility) were examined. It was found that the mobility restriction had contained the development of pandemic, but such effect would gradually recede over time. Moreover, there existed region-specific policy effectiveness. Specifically, outflow-mobility restrictions were ineffective in reducing death cases in population influx areas, and restrictions on inflow-mobility (or intra-city mobility) were ineffective in reducing confirmed cases (or death cases) in population outflow areas. It was concluded that the mobility restriction policy can be effective in epidemic prevention and control in spatial-temporal pattern. However, there was a remarkable disparity in policy effectiveness between different regions with different population mobility patterns.

Global Population Aging, National Development Level, and Vulnerability to the Pandemic.

PURPOSE: This study examined the roles of population aging and national development level in affecting different phases of novel coronavirus disease development with a view to advancing preparedness and corresponding policy. METHODS: Regression analysis was conducted using multisource data from the World Bank and Johns Hopkins COVID-19 Dashboard. RESULTS: Population aging is positively associated with confirmed cases of day-10, day-20, and day-30. The positive association between population aging and death does not emerge until day-20. Countries with a higher proportion of older males face higher risks of death.. Countries with a higher proportion of males aged 70-74 years are at the highest risk of confirmed cases. National development level is not associated with confirmed cases, but developing countries face significantly higher risks of death of day-10 and day-20. CONCLUSION: Prevention and control policies for older adults and underdeveloped areas and sex differences need to be studied.