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• W3137356546 abstract "Much has changed in the wake of the COVID-19 pandemic, consumer behavior notwithstanding. In this study, we explore the drivers of retail mobility in several metropolitan areas in the United States under the perceived risks of COVID-19. We measure risk using both local and national case and death counts of COVID-19 and assess the response to these risks using real-time Google Trends searches for personal protective equipment (PPE) including masks, hand sanitizer, and disinfectant. We further investigate the effects of perceived risk, Google Trends searches for PPE, and COVID-19 tweets on retail mobility, as measured via real-time Google Mobility data. We focus our analysis on the period immediately preceding and following the formal declaration of a pandemic when the first nation-wide social distancing policies were implemented, and uncertainty regarding the severity and transmissibility of the virus was the highest. To conduct our investigation, we develop a novel optimization scheme and procedure to induce linear models containing strictly significant covariates and no multicollinearity, which we then analyze. Our findings suggest that there are a variety of unique factors that contribute to and drive retail mobility in different geographic locations, as well as several factors that are common to all locations." @default.
• W3137356546 created "2021-03-29" @default.
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• W3137356546 date "2021-01-01" @default.
• W3137356546 modified "2023-09-25" @default.
• W3137356546 title "Predicting Retail Mobility during the COVID-19 Pandemic" @default.
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• W3137356546 doi "https://doi.org/10.2139/ssrn.3787078" @default.
• W3137356546 hasPublicationYear "2021" @default.
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