国内C2C电子商务网上交易量影响因素分析

发布时间：2018-05-06 11:28

本文选题：电子商务 + 网上购物交易量　；参考：《哈尔滨工业大学》2011年硕士论文

【摘要】：随着我国互联网普及率的节节攀升以及人们消费意识的转变,网络购物的用户规模及市场规模均达到了一个新的高度,其中更是以C2C电子商务的发展最为迅猛。在网上交易中,买家可以通过浏览商品的网页获取商品的价格、商家信用等级、卖家好评率等信息,研究其中各个因素对于不同类别商品的网上购物交易量究竟如何影响很有现实意义。本文针对这一问题,对这些影响交易量的因素进行了实证性研究。本文首先从电子商务网店经营者的视角出发,结合我国C2C网上交易平台的现行客观情况,建立了网上交易量影响因素的模型,并提出了九个研究假设。之后利用MetaSeeker数据抓取工具,根据本文研究需要在淘宝网抓取了搜索型、体验1型、体验2型、信任型、数字型五类商品的代表商品所需研究数据,并编写了数据入库程序以实现数据的处理。本文还根据所需研究内容对回归方程的变量做了定义和取值,并对各交易量影响因素进行了描述性分析,根据其均值及变异系数分析了各影响因素数据之间所存在的差异性的原因。之后根据本文研究建立了多元回归模型,对所抓取的数据进行多元回归分析并对结果进行了探讨。本文的研究结果表明卖家信用等级对于五类商品的网上购物交易量都有着显著影响;卖家好评率、服务态度评分高低、卖家发货速度评分高低及付款方式的多少对于五类商品的网上购物交易量均没有显著的影响;对于除数字型商品外的其他四类商品,价格的高低对于网上购物交易量的影响都很显著;商品描述相符评分对于体验1型商品及信任型商品的网上交易量有显著影响,对于其他三种类型商品影响不显著。最后本文根据所得结果对销售不同类型商品的C2C电子商务网店主提出了一些改进建议。
[Abstract]:With the increasing popularity of Internet in China and the change of people's consumption consciousness, the scale of users and the market scale of online shopping have reached a new height, and the development of C2C e-commerce is the most rapid. In online transactions, buyers can obtain information about the price of goods, the credit rating of merchants, the favorable rating of sellers, and so on by browsing the web page of the goods. It is of practical significance to study the influence of various factors on the online shopping volume of different types of commodities. In order to solve this problem, this paper makes an empirical study on the factors that affect the trading volume. This paper first from the perspective of e-commerce shop operators, combined with the current situation of China's C2C online trading platform, established the online trading volume impact factors model, and put forward nine research hypotheses. Then using the MetaSeeker data capture tool, according to the research needs of Taobao to grab the search type, experience 1 type, experience 2 type, trust type, digital type of five types of goods on behalf of the research data, In order to realize the data processing, the program of data storage is written. In this paper, the variables of regression equation are defined and evaluated according to the research contents, and the influencing factors of each trading volume are analyzed in a descriptive way. According to the mean value and coefficient of variation, the reason of the difference between the factors is analyzed. Then, the multivariate regression model is established according to the research in this paper, and the multivariate regression analysis of the captured data is carried out and the results are discussed. The results of this study show that the credit rating of sellers has a significant impact on the online shopping volume of five categories of goods, the seller's praise rate, service attitude score, The seller's delivery speed score and payment method had no significant effect on the online shopping volume of the five categories of goods, while for the other four categories of goods except digital goods, The effect of price on online shopping volume is significant, while the score of commodity description matching has a significant impact on the online trading volume of type 1 and trust type commodities, but not on the other three types of commodities. Finally, according to the results, this paper puts forward some suggestions for the improvement of C2C e-commerce network shopkeepers selling different types of goods.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：F724.6;F224

【引证文献】