A Study on Predicting Customer Willingness to Order Food Online During Covid-19 Pandemic Using Machine Learning Algorithms

K. Aditya Sobika, S. Vivek Raj
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引用次数: 2

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.
利用机器学习算法预测Covid-19大流行期间客户在线订购食品意愿的研究
在线送餐已成为新冠疫情期间的突出服务之一。在经历了新冠肺炎初期的减速后,由于政府的放松和消费者的支持,印度的在线外卖正在慢慢获得动力。在线送餐服务需要更好地了解客户行为的复杂性,这种复杂性在2019冠状病毒病大流行的卫生危机期间发生了变化。本研究旨在利用机器学习算法预测2019冠状病毒病大流行后客户使用在线服务订餐的意愿。主要数据收集是通过向公众分发的在线调查完成的。收到了415份回复,其中369人更喜欢通过在线外卖服务订餐。使用不同的机器学习模型,我们推断情感信念和工具信念,感知利益(健康信念模型的变量)是客户在线订餐意愿的显著预测因子。调查还发现,手机使用时间、新冠肺炎期间的订餐频率、使用外卖应用程序的便利性、家庭成员人数、年龄、教育程度和职业等人口统计变量对订餐意见也有重要影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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