Abstract:

Presently, the e-commerce industry is one of the most globally competitive industries and China has a huge e-commerce market. It is essential to understand e-commerce to develop the service industry. As a comprehensive online shopping platform mainly based on a customer-to-customer (C2C) model, Taobao is a market leader in China’s C2C market, with garment products being the most traded category. Located in the eastern part of mainland China, the Jiangsu, Zhejiang, and Shanghai region consists of 25 cities, covering the main part of the Yangtze River Delta Urban Agglomerations. The comprehensive strength and the extent of e-commerce in the region has a highly demonstrative effect, and the overall scope of its garment and service industry is relatively high. The scale, grade, and transaction status of Taobao stores are expressed through credit rating, which largely reflects the overall development of Taobao stores. In the literature on e-commerce, to improve the new location theory and optimize the pattern of e-commerce, it is vital to further consider the overall development of e-commerce stores and study the spatial distribution rules with store credit rating as the objective. The present study investigates the city-level size distribution of Taobao C2C clothing stores’ credit rating and its determinants in Jiangsu, Zhejiang, and Shanghai based on the data gathered and measured from online individual Taobao stores. Quantitative methods such as concentration analysis, rank-size rule, and regression analysis were adopted to obtain the following findings. 1) According to the ln-ln graph, due to the production, market characteristics, and consumption patterns of children’s clothing, the credit rating’s rank-size distribution of stores focusing on men’s and women’s clothing obeys the Zipf law, but that of stores focusing on children’s clothing does not. The credit rating’s rank-size distribution of all the surveyed stores also obeys the Zipf law. The government should boost children’s clothing industry and provide the necessary funds and technical support to enhance the balance of the stores’ credit rating. 2) Since the Zipf parameter is less than 1, the rank-size distribution pattern of the credit rating of Taobao clothing stores in this area follows a log normal distribution, which implies that the credit rating of high-order cities is not prominent enough, the number of middle-order cities is large, and the overall scale distribution is relatively balanced. However, the overall credit rating of Taobao clothing stores in this area is distributed in a weak spatial concentration. Therefore, the government should encourage communication and learning of improving credit rating of e-commerce stores. 3) The nonscaling ranges of the credit rating of Taobao clothing stores include most of the cities in Jiangsu, Zhejiang, and Shanghai, which reveals a relatively optimized pattern in terms of the size distribution structure in this area. 4) The credit rating of Taobao clothing stores is affected by factors such as the degree of informatization, logistics, comprehensive strength, and education, as well as economic level and locational advantage. However, the industrial concentration of the local clothing industry has not affected the credit rating of Taobao clothing stores.

Key words: C2C e-commerce, clothing store, credit rating, size distribution, Jiangsu, Zhejiang, and Shanghai