ABSTRACT
PurposeThe purpose of this study is to investigate whether the lockdown and the increased spread of food delivery applications (FD Apps) during COVID-19 pandemic have augmented the consumption of fast foods. Design/methodology/approachA cross-sectional study was conducted on 673 adults from different regions of Saudi Arabia using an online questionnaire. FindingsData showed that 61% (N = 410) of participants used FD Apps during the pandemic. Among those users, 54.9% (225) were females and 70.5% were in the 18-44 years old group. Most FD Apps' users were university graduates (74.4%). The increased use of FD Apps during the pandemic significantly affected the eating behavior and the nutritional pattern. It has further significantly augmented the consumption of fast foods (p-value < 0.05). Originality/valueThis study reports on the use of FD Apps during COVID-19 pandemic in Saudi Arabia and its impact on consumer eating pattern. This study shows the need for prudent use of these applications to limit ordering fast foods and consider healthier choices. It further calls for education programs, awareness campaigns, legislative measures and formal policies to rationalize the use of such applications for better nutrition, health and well-being.
ABSTRACT
COVID-19 cripples the restaurant industry as a crucial socioeconomic sector that contributes immensely to the global economy. However, what the current literature less explored is to quantify the effect of COVID-19 on restaurant visitation and revenue at different spatial scales, as well as its relationship with the neighborhood characteristics of customers' origins. Based on the Point of Interest (POI) measures derived from SafeGraph data providing mobility records of 45 million cell phone users in the US, our study takes Lower Manhattan, New York City, as the pilot study, and aims to examine 1) the change of restaurant visitations and revenue in the period prior to and after the COVID-19 outbreak, 2) the areas where restaurant customers live, and 3) the association between the neighborhood characteristics of these areas and lost customers. By doing so, we provide a geographic information system-based analytical framework integrating the big data mining, web crawling techniques, and spatial-economic modelling. Our analytical framework can be implemented to estimate the broader effect of COVID-19 on other industries and can be augmented in a financially monitoring manner in response to future pandemics or public emergencies.