01832369

Component

https://annualmeeting.myt...nlineProgram/Details/17401

This paper presents a study of various factors that influenced online and in-store shopping behaviors in New York City during the COVID-19 pandemic. It uses panel data collected by the New York City Department of Transportation (NYC DOT) from the months of May, July, and October of 2020. In the survey, 696 respondents consistently responded in all three months, which were used for analysis. This study adopts random order logit models with marginal effects and dynamic discrete choice models to understand the factors that influenced online and in-store shopping during the pandemic. The models reveal that increased subway usage was correlated with in-store shopping during the pandemic, which was also related to income. Higher-income individuals were less likely to shop in-store, while lower-income individuals (more likely to ride the subway or bus) were more likely to shop in-store. Contrary to previous research, age did not appear to have an impact on online and offline shopping behaviors. Finally, this study discovered that online and in-store shopping are not as indirectly proportional as expected. This means an increase in online shopping does not necessarily directly lead to a decrease in offline shopping. By understanding how individuals reacted to the pandemic, proper policies- especially from a transportation aspect, can be made in advance to prepare for future pandemic threats.

This paper was sponsored by TRB committee AEP35 Standing Committee on Effects of Information and Communication Technologies (ICT) on Travel Choices.

TRBAM-22-02462

English

21p

2022

Transportation Research Board 101st Annual Meeting

Digital/other

Figures; References; Tables

Choice models; COVID-19; Econometric models; Electronic commerce; Shopping; Travel behavior

New York (New York)

Economics; Safety and Human Factors; Transportation (General)

Transportation Research Board Annual Meeting 2022 Paper #TRBAM-22-02462

TRIS, TRB, ATRI

Dec 29 2021 9:17AM

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