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Customers response to online food delivery services during COVID‐19 outbreak using binary logistic regression
Author(s) -
Mehrolia Sangeeta,
Alagarsamy Subburaj,
Solaikutty Vijay Mallikraj
Publication year - 2021
Publication title -
international journal of consumer studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.775
H-Index - 71
eISSN - 1470-6431
pISSN - 1470-6423
DOI - 10.1111/ijcs.12630
Subject(s) - logistic regression , product (mathematics) , covid-19 , outbreak , order (exchange) , psychology , ordered logit , marketing , geography , business , environmental health , medicine , statistics , mathematics , geometry , disease , finance , pathology , virology , infectious disease (medical specialty)
This study aims to empirically measure the distinctive characteristics of customers who did and did not order food through Online Food Delivery services (OFDs) during the COVID‐19 outbreak in India. Data are collected from 462 OFDs customers. Binary logistic regression is used to examine the respondents’ characteristics, such as age, patronage frequency before the lockdown, affective and instrumental beliefs, product involvement and the perceived threat, to examine the significant differences between the two categories of OFDs customers. The binary logistic regression concludes that respondents exhibiting high‐perceived threat, less product involvement, less perceived benefit on OFDs and less frequency of online food orders are less likely to order food through OFDs. This study provides specific guidelines to create crisis management strategies.