The outbreak of the COVID-19 pandemic has led to significant alterations in people’s social and economic behaviour. This paper aims to study the pandemic’s influence on online shopping and travel behaviour and discover how these phenomena are related. To this end, eight variables were identified that describe socio-demographic status, COVID-19 variables, online shopping variables, and travel behaviour. The structural equation modelling (SEM) approach was adopted to analyse the relationships between these variables. A conceptual model was formed by devising hypothetical relationships, and then the validity and reliability of the model were evaluated using SEM tools. Among the 19 theoretical relationships, 17 were verified. It was found that socio-demographic status directly affects the COVID-19 variables, influencing online shopping variables. As a result, it was inferred that during the pandemic, people’s daily travel habits had been affected by their inclinations toward online shopping, and the more people are aware of COVID-19 and feel responsible about the pandemic, the more they are persuaded to shop online rather than in-person shopping. Policymakers can use the findings of this study to change the public’s travel and shopping behaviour to tackle the pandemic.

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.