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A Study on Predicting Customer Willingness to Order Food Online during Covid-19 Pandemic Using Machine Learning Algorithms
4th International Conference on Computing and Communications Technologies, ICCCT 2021 ; : 236-241, 2021.
Article in English | Scopus | ID: covidwho-1769598
ABSTRACT
Online food delivery has become the one of the prominent services during COVID-19 pandemic. After facing deceleration in early COVID-19 phase, online food delivery is slowly gaining momentum in India due to relaxations given by the government and support of the consumers. Online food delivery services need an improved understanding of the complexities of customer behavior which have shifted during this health crisis period of COVID-19 pandemic. The Study is undertaken to predict the customer willingness to order food using online services aftermath of COVID-19 pandemic using Machine Learning algorithms. Primary data collection is done through online survey distributed among public. 415 responses were received out of which 369 people prefer to order through online food delivery services. Using different machine learning models, it is inferred that the Affective and instrumental belief, Perceived benefits (variables of health belief model) are the significant predictors of the customers willingness to order food online. Demographic variables like hours utilized in mobile, frequency of ordering during COVID, Convenience of using food delivery application, number of members in family, age, education qualification and occupation are also found to be significant in determining order opinion. © 2021 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: 4th International Conference on Computing and Communications Technologies, ICCCT 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: 4th International Conference on Computing and Communications Technologies, ICCCT 2021 Year: 2021 Document Type: Article