Redai dili (May 2021)
Location Selection and Correlation Characteristics of Chain Stores against the Background of New Retail
Abstract
Research on the spatial relationship of retail activities is a hot topic in urban geography. With the continuous upgrade of Internet technology, the development mode of business has evolved from the traditional retail model into the e-commerce model, and now the new retail model. While the new retail model influences the locational decision-making behavior of enterprises, it also affects the intrinsic mechanism of attraction and avoidance among retail stores, which in turn affects the spatial association relationship of commercial retail. In such a context, we take the Starbucks, COSTA, and Luckin Coffee stores in Shanghai's inner ring as our research objects, and use a variety of spatial statistical methods and field research to analyze the spatial correlation characteristics among the three. The results show that, first, the spatial distribution of coffee stores under both traditional and new retail models generally exhibit the spatially oriented characteristics of being close to the consumer market. Therefore, it can be indirectly inferred that although Luckin Coffee, which is characterized by new retail, can create "infinite space" to meet consumers' consumption needs in different spaces by virtue of its own Internet development advantage, it remains difficult to completely break away from the spatial orientation of the offline consumption market. Second, in terms of spatial agglomeration, Starbucks' high sensitivity to specific consumer groups and its sales strategy of providing a comfortable environment have led the business to open stores in dense proximity in locations with high consumption potential, thus contributing to its strongest spatial agglomeration. Luckin Coffee, by contrast, has a certain degree of flexibility in choosing store locations due to its independent instant delivery service, and in order to occupy a wider market as soon as possible, it chooses store locations in favor of uniform coverage, resulting in the weakest degree of spatial agglomeration. Third, based on multivariate spatial statistics, it can be seen that Starbucks, COSTA, and Luckin coffee stores all exhibit positive spatial relationship characteristics in the two corresponding spatial relationships. Among them, traditional coffee retailers Starbucks and COSTA show a more obvious spatial relationship of mutual attraction, indicating that both can increase their market shares by converting the fierce price competition between them into an attraction drive to increase their total profits. At the same time, the stores of traditional retailer Starbucks and new retailer Luckin Coffee also show a significant spatial relationship of mutual attraction in space, indicating that the market share effect dominates. Finally, micro-location analysis reveals that Starbucks and COSTA stores have a stronger mutual attraction relationship and often appear in pairs in the center of shopping districts or business areas, while Luckin Coffee stores are often located in "non-central" areas. As a representative of new retail, Luckin Coffee can make up for its location disadvantage to a certain extent by virtue of its mobile application online service and instant delivery service. The store can also utilize its Internet platform, big data analysis, and other technical advantages, so that it can combine its own product positioning characteristics when choosing store locations, and accurately find potential store locations and opening models. Therefore, when carrying out urban planning, especially the planning of commercial areas, attention should be paid to the impact of the new retail model on location selection, the role of the Internet, and big data in location decision making. The development possibilities of traditionally weaker locations should be explored, and the efficiency of urban land use should be improved.
Keywords
