Studying the electronic word-of-mouth (eWOM) in the foodservice industry can not only provide guidance for merchants, but also spatially optimize the urban foodservice industry, restaurants’ location selection, and customers’ purchasing decisions. In this study, taking Sanya city as the research object, using big data crawling technology to collect the directory and their attribute information of 2107 restaurants with more than 100 reviews. Kernel density analysis, grid analysis and the geographically weighted regression (GWR) model were applied to reveal the distribution characteristics and influencing factors of eWOM in the foodservice industry in Sanya, China. The main results are as follows. The foodservice industry in Sanya extends along the southern coastline and is characterized by little dispersion and agglomeration at the macro level. The overall eWOM score of the foodservice industry is low. Market popularity, restaurant rating, transportation conditions, and commercial development all have a positive impact on the eWOM of the foodservice industry. Population and price have both positive and negative effects and the public services has a nonsignificant impact on the eWOM. This study not only improves the theoretical understanding of the foodservice industry, but also provides a general reference for its development in other industries and cities.

Studying the electronic word-of-mouth (eWOM) in the foodservice industry can not only provide guidance for merchants, but also spatially optimize the urban foodservice industry, restaurants’ location selection, and customers’ purchasing decisions. In this study, taking Sanya city as the research object, using big data crawling technology to collect the directory and their attribute information of 2107 restaurants with more than 100 reviews. Kernel density analysis, grid analysis and the geographically weighted regression (GWR) model were applied to reveal the distribution characteristics and influencing factors of eWOM in the foodservice industry in Sanya, China. The main results are as follows. The foodservice industry in Sanya extends along the southern coastline and is characterized by little dispersion and agglomeration at the macro level. The overall eWOM score of the foodservice industry is low. Market popularity, restaurant rating, transportation conditions, and commercial development all have a positive impact on the eWOM of the foodservice industry. Population and price have both positive and negative effects and the public services has a nonsignificant impact on the eWOM. This study not only improves the theoretical understanding of the foodservice industry, but also provides a general reference for its development in other industries and cities.

Studying the electronic word-of-mouth (eWOM) in the foodservice industry can not only provide guidance for merchants, but also spatially optimize the urban foodservice industry, restaurants’ location selection, and customers’ purchasing decisions. In this study, taking Sanya city as the research object, using big data crawling technology to collect the directory and their attribute information of 2107 restaurants with more than 100 reviews. Kernel density analysis, grid analysis and the geographically weighted regression (GWR) model were applied to reveal the distribution characteristics and influencing factors of eWOM in the foodservice industry in Sanya, China. The main results are as follows. The foodservice industry in Sanya extends along the southern coastline and is characterized by little dispersion and agglomeration at the macro level. The overall eWOM score of the foodservice industry is low. Market popularity, restaurant rating, transportation conditions, and commercial development all have a positive impact on the eWOM of the foodservice industry. Population and price have both positive and negative effects and the public services has a nonsignificant impact on the eWOM. This study not only improves the theoretical understanding of the foodservice industry, but also provides a general reference for its development in other industries and cities.

Studying the electronic word-of-mouth (eWOM) in the foodservice industry can not only provide guidance for merchants, but also spatially optimize the urban foodservice industry, restaurants’ location selection, and customers’ purchasing decisions. In this study, taking Sanya city as the research object, using big data crawling technology to collect the directory and their attribute information of 2107 restaurants with more than 100 reviews. Kernel density analysis, grid analysis and the geographically weighted regression (GWR) model were applied to reveal the distribution characteristics and influencing factors of eWOM in the foodservice industry in Sanya, China. The main results are as follows. The foodservice industry in Sanya extends along the southern coastline and is characterized by little dispersion and agglomeration at the macro level. The overall eWOM score of the foodservice industry is low. Market popularity, restaurant rating, transportation conditions, and commercial development all have a positive impact on the eWOM of the foodservice industry. Population and price have both positive and negative effects and the public services has a nonsignificant impact on the eWOM. This study not only improves the theoretical understanding of the foodservice industry, but also provides a general reference for its development in other industries and cities.