Customers response to online food delivery services during COVID‐19 outbreak using binary logistic regression
By Sangeeta Mehrolia, S. Alagarsamy, Vijay Mallikraj Solaikutty. International Journal of Consumer Studies, 2020
256 citations recorded by the source corpus.
Abstract 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.
Subfields: Economics and Econometrics, Food Science, Modeling and Simulation
View Paper: https://www.semanticscholar.org/paper/8365eeab7f1b5c3eab0f050c3b38be51c0cf2210
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Citations in the corpus
- Covid‐19 pandemic and consumer‐employee‐organization wellbeing: A dynamic capability theory approach (2021, background): “Online channels for sales Covid-19 disruption is likely to provide a huge boost to the usage of online sales channels (Mehrolia et al., 2021).”
- Reviving tourism industry post-COVID-19: A resilience-based framework (2020, unknown): “Future travel will depend on combined self-care, such as the suggestion to open the TransTasman bubble between Australia and New Zealand (Carr, 2020), or the potential fast-tracking of immigration clearance between the Republic of Korea and China (Mostafanezhad et al., 2020).”