Abstract

With the maturity of the online food delivery (OFD) industry in China, the growth of the market in recent years is mainly driven by the increase of the usage frequency of existing users rather than the number of new users. The usage frequency of users is affected by various factors, with the delivery charge as one of the most significant ones. The purpose of this study is to examine the impact of delivery charge and other factors on the probability of consumers choosing to use OFD service. In this study, 391 questionnaire records from China were collected, based on which a logistic regression model was established. The results of the model show that age, occupation, monthly income, city tier of residence, location and time period of usage, and delivery charges all play a role on the probability of consumers using the service, and the delivery charge has the greatest impact. For every one yuan increase in the delivery charge, consumers will be less likely to choose “certainly” of using OFD (OR: 0.435; 95% CI: 0.415, 0.455). Sensitivity analysis shows that when the delivery charge changes between 2~5 yuan, it has the greatest impact on the probability of consumers using the service. The analysis further shows that delivery charge has different impacts under different scenarios composed of three key factors, i.e., the city tier of residence, locations of usage, and time period of usage. From a management perspective, these findings help to understand the behavior of OFD consumers and provide insights for the OFD operators to establish best pricing strategies for long-term economic sustainability.

Highlights

  • In recent years, the explosive growth of the Internet has greatly promoted the development and maturity of e-commerce and online retail in general [1,2,3,4]

  • This section constructs an ordered choice model to research the impact of delivery charges and other factors on the possibility of consumers using online food delivery (OFD)

  • All relevant variables are considered in the ordered logistic regression model, and the result is shown in Table 4 under the title of Model 1

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Summary

Introduction

The explosive growth of the Internet has greatly promoted the development and maturity of e-commerce and online retail in general [1,2,3,4]. People’s daily lives have changed dramatically, with the emergence of online food delivery (OFD) allowing people to gradually accept OFD as a convenient way to solve their dietary needs [7,8]. The mature development of technologies such as mobile payment, cloud computing, and same-city delivery provides a good practical application environment for OFD [9,10]. Compared with the traditional catering industry, OFD services can provide consumers with a rich choice and convenient dining experience [12]. People have the flexibility to choose OFD at different times and locations to meet their dietary needs [13]

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