C2C服装店铺信用等级的规模分布及其影响因素——以江浙沪地区为例

通过搜集与计算得到江浙沪地区各城市淘宝男装、女装、童装及服装店铺整体的信用等级,同时借助集中程度分析、位序—规模法则及回归分析,探讨了该地区淘宝服装店铺信用等级的规模分布特征及其影响因素.结果 表明:1)除童装外,江浙沪地区淘宝服装店铺整体及男装、女装店铺,其信用等级的位序—规模分布满足齐夫法则;2)江浙沪地区淘宝服装店铺信用等级的空间分布形态满足对数正态分布模式;3)淘宝服装店铺信用等级的无标度区涵盖了江浙沪地区绝大部分城市,其规模分布结构相对优化;4)淘宝服装店铺的信用等级受信息化水平、物流指数、基础规模实力、文化教育水平、经济发展水平和区位优势度等因素的综合影响,但当地服装产业的工业集中度未能影响淘宝服装店铺的信用等级.

Size Distribution of Credit Rating of C2C Clothing Stores and Its Influencing Factors: A Case Study of Jiangsu,Zhejiang, and Shanghai

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.