@article{bc1040d4308f4053bb8cbde85bc7e9ff,
title = "Do neighborhood food environments matter for eating through online-to-offline food delivery services?",
abstract = "Eating through online-to-offline (O2O) food delivery services has in recent years emerged as an important diet behavior especially amid the COVID-19 pandemic. There is not much understanding of this relatively new diet behavior and its influence factors. In an attempt to answer the research question if the neighborhood food environment matters for eating through O2O food delivery services, this research collects primary data on people's experience of using O2O food delivery services through a sample questionnaire survey and secondary data on foodscapes including the spatial distributions of food sources of various types in Shanghai. Personal sociodemographic characteristics, home and workplace food environments are considered as potential explanatory factors of eating through O2O food delivery services. The study reveals that there are significant differences between home and workplace food environments in Shanghai and that home food environments are more influential on eating through O2O food delivery services than workplace food environments; the healthy food availability and accessibility at residential neighborhoods are found to significantly reduce the probability of eating through O2O food delivery services; lack of healthy food choices and being in the suburb explain O2O food delivery service consumptions at workplace. Findings of this research are relevant for promoting healthy eating through urban planning and urban design practices.",
keywords = "Eating behavior, GIS, Neighborhood food environment, Online-to-offline (O2O) food delivery service, Shanghai",
author = "Lingling Li and Donggen Wang",
note = "Funding Information: This work was supported by a General Research Fund (GRF) grant from the Hong Kong Research Grant Council ( HKBU 12606215 ) and a research grant from Hong Kong Baptist University ( RC-FNRA-IG/19-20/SOSC/02 ). Funding Information: The models are estimated using SPSS 24 and the results are presented in Table 4. As explained earlier, four models with different sets of independent variables are developed. To recap, Model 1 includes only socioeconomic variables, Model 2/Model 3 extends Model 1 by adding variables on residential/workplace food environments, and Model 4 extends Model 1 by adding variables on both residential and workplace food environments. The last rows of Table 4 list the models' AIC and BIC, two model fit indicators, which measure a model's goodness of fit to data accounting for the number of independent variables included in the model (BIC imposes heavier penalties than AIC does for the number of model parameters by assigning the logarithm value of sample size as the weight (AIC fixes the weight at 2). By comparing the model fit indicators across the models, it is easy to find out that both Models 2 and 3 outperform Model 1, suggesting that residential and workplace food environment variables contribute to explain eating through O2O food delivery services. Further, Model 2 outperforms Model 3, indicating that the explanatory power of residential food environment is stronger than that of the workplace food environment. In other words, the food environment at home location is more important than that at workplace explaining the usage of O2O food delivery services. This finding is further supported by Model 4, which has an AIC value larger than that of Model 2 and only slightly smaller than that of Model 3, implying that by adding the variables on workplace food environment do not improve, but worsen the model performance. Both AIC and BIC suggest that among the four models, Model 2 is the best performing one. In the following subsections, we shall present the model details.This work was supported by a General Research Fund (GRF) grant from the Hong Kong Research Grant Council (HKBU 12606215) and a research grant from Hong Kong Baptist University (RC-FNRA-IG/19-20/SOSC/02). Publisher Copyright: {\textcopyright} 2021 Elsevier Ltd",
year = "2022",
month = jan,
doi = "10.1016/j.apgeog.2021.102620",
language = "English",
volume = "138",
journal = "Applied Geography",
issn = "0143-6228",
publisher = "Elsevier BV",
}