How COVID-19 Pandemic Affected Urban Trips? Structural Interpretive Model of Online Shopping and Passengers Trips during the Pandemic

Mostafa Ghodsi,Arash Rasaizadi,Ali Ardestani,Hao Yang,Seyednaser Ghadamgahi

doi:10.3390/su132111995

Mostafa Ghodsi, Arash Rasaizadi + Show 3 more

Open Access

https://doi.org/10.3390/su132111995

Copy DOI

Abstract

Changing people’s shopping behavior from face-to-face to online shopping during the COVID-19 pandemic led to reduced shopping trips, and this decrease directly affects traffic congestion and air pollution. Identifying the factors influencing the increase of online shopping behavior during the pandemic can be helpful for policymakers in the post-COVID-19 era. This study aims to discover the effect of factors related to the COVID-19 pandemic and demographic characteristics on shopping attitude and, consequently, on shopping trips. Based on the interviews of ten experts, factors associated with COVID-19 and demographic characteristics are selected as influential factors on shopping attitude and shopping trips. For pairwise comparisons between these factors, a web-based questionnaire was designed and given to thirty experts. The relationship between all factors is examined using interpretive structural modeling (ISM) and Microscopic–Macroscopic (MICMAC) analysis. In addition, to prioritize factors, the IAHP model is employed. Based on the results, five levels of influential factors affect shopping attitude, which affects shopping trips: level 1, age and gender; level 2, income and education; level 3, the household size and the COVID-19 awareness; level 4, COVID-19 attitude and COVID-19 practice; and level 5, norm subject and shopping personal control.

Highlights

The amount of online shopping increased to an unprecedented level during the COVID-19 epidemic
A review of the pandemic impact on retail e-commerce shows unusual global traffic growth, and the number of visits to retail websites increased from 16.07 billion worldwide in January 2020 [3]. These changes were influenced by various factors, and understanding the position of each of these variables in relation to online shopping and city travel during the pandemic could influence the decisions of industry owners as well as governments in similar crises
The final step consisted of creating and mapping a network of relationships based on the criteria of interpretive structural modeling (ISM) and their relationships between them

Summary

Introduction

The amount of online shopping increased to an unprecedented level during the COVID-19 epidemic. A review of the pandemic impact on retail e-commerce shows unusual global traffic growth, and the number of visits to retail websites increased from 16.07 billion worldwide in January 2020 [3]. These changes were influenced by various factors, and understanding the position of each of these variables in relation to online shopping and city travel during the pandemic could influence the decisions of industry owners as well as governments in similar crises.

Objectives

Methods

Results

Conclusion