Abstract

Mainland China has become one of the most important markets for international fast-food chains over the past decade. To study the regional spread of KFC and McDonald’s outlets in Chinese cities, the correlation of their distributions and degree of market expansion were explored and compared to analyze both the local and the global spatial autocorrelations. A geographically weighted Poisson regression model was also used to examine the influence of demographic, economic, and geographic factors on their spatial distributions. The findings of this comparative study reveal the site selection criteria at the city level by studying the differences and similarities in outlet distributions for KFC and McDonald’s. The presented results can guide other chains to enhance business location planning and formulate regional development policy.

Highlights

  • Over the past thirty years, retail sales in Mainland China have risen at a steady double-digit growth rate, far surpassing the country’s GDP growth

  • The overseas expansion of the food industry necessitates a comprehensive consideration of many factors such as investment risk, food quality, management strategy, eating habits, and cultural differences [3], which means site selection is vital for success [4,5]

  • Black et al [16] used multivariate regression method to estimate the associations between the spatial distribution of food stores and the urban planning and socio-demographic variables in British Columbia

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Summary

Introduction

Over the past thirty years, retail sales in Mainland China have risen at a steady double-digit growth rate, far surpassing the country’s GDP growth. Powell et al [14] studied the relations among the racial, ethnic, and income characteristics of customers and the accessibility of fast-food restaurants in the United States using multi-factor regression analysis. They concluded that higher proportions of fast-food restaurants in predominantly black neighborhoods may contribute to racial differences in obesity rates. The spatial autocorrelation and spatial non-stationarity of their outlet distributions is worth studying to find out their locational differences and similarities and to understand why they are distributed in this way These results would benefit the commercial planning processes of other retail chains

Study Area
Data Sources
General Distribution
Correlation in Major Cities
Market Expansion
Spatial Autocorrelation Analysis
Global Spatial Autocorrelation Analysis
Local Spatial Autocorrelation Analysis
GWPR Analysis
Findings
Conclusions

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