The use of data analytics to analyze various behaviors during the COVID- 19 pandemic

Abstract

This dissertation outlines findings based on three journal manuscripts. The publications have a common theme: the use of data analytics techniques, economic theory, and knowledge of online shopping to analyze and assess change in behavior after the COVID-19 pandemic. I employed a variety of empirical analysis tools, including different regression methods, statistical analysis, and validity testing. I also utilized well-established theory to add another lens to my findings. Such frameworks include the Transaction Cost Economics (TCE) and the Technology Acceptance Model (TAM). Furthermore, I applied my tools to two domains to further widen my knowledge scope: e-commerce and public health. The first manuscript offered insight into consumer behavior after the COVID-19 pandemic. It analyzed the change in online shopping activity in 12 different product categories and offered an empirical association between it and various demographic factors. The second manuscript builds the first with more focus on online grocery shopping. As a result, the findings offered a managerial perspective on how various customer segments changed their shopping behaviors online, providing insight to guide marketing and merchandising efforts. The third paper presented an association between demographic and occupational factors with worsened mental health conditions of healthcare workers (HCWs) after the pandemic. The finding presented an insight into how different HCW groups reacted to the pandemic and hence aid in providing more effective mental health programming targeting specific groups in future events.