C2C e-commerce transaction volume analysis of factors affecting

[Abstract] In this paper, typical of China's C2C website - Taobao, as the object of study, according to the stratified random sampling method to select three types of products for data collection, the factors affecting the C2C e-commerce conducted an empirical study.

[Keywords] C2C e-commerce web store transaction volume factor

C2C flexibility and freedom of shopping patterns have been recognized more and more consumers, C2C transaction volume since 2005 showed rapid growth, however, the rise in the C2C is behind the decline of numerous and close shop, in the end what is factors have contributed to a shop to better survival and development? In the end is what factors affect the C2C trading volume size? This article from the perspective of the seller to explore the impact of C2C e-commerce transaction volume environment factors.

First, the hypothesis put forward
Looking at the success of domestic and foreign scholars to study the development of e-commerce process, the main success factors for e-commerce systems from the perspective of the consumer behavior perspective of online shopping and online stores to study the operator's perspective, including Delone & Mclean (2003), Dong Yali , Yang Pei (2007), Xiaomei (2009), Rose and Staub (2001), Rose, Lees and Meuter (2001). In this paper, and e-commerce transactions related research hypotheses.

Assumption 1: the seller's credit, the more trustworthy, more trading volume.

Assumption 2: Consumers feel the higher price competitiveness, the more likely to conduct electronic transactions, more volume.

Hypothesis 3: the better the attitude, the more trading volume.

Hypothesis 4: contact the seller to communicate the more specific, the higher the consumer perception of service quality, more volume.

Hypothesis 5: The more the seller to ensure the consumer, the more trading volume.

Hypothesis 6: The more clearly describe the product Information, trading volume more.

Second, the study design
1 Select the survey
Taobao in this paper is divided into two major categories of goods, namely, physical goods and digital goods standardization, each type of the selection of 10 kinds of products, the random network of 100 stores.

2 variable definitions and values
In this paper, four major factors that affect commodity trading network for variable definitions and values ​​(as shown in Table 1.

Third, data analysis and results
1 descriptive statistics
In this paper, the goods on Taobao station search functions, respectively, commodity groups, level of credibility, as the value of the goods condition, come 2010, 2 to 3 months of data, and these two types of shops 300 samples descriptive statistics analysis (Table 2, where A, B, respectively, on behalf of the entity table standardization sample of goods and digital goods sample.

(2) correlation analysis
By SPSS software, draw a standardized commodity product samples and samples of the digital variable correlation matrix (Table 3
Can be seen from Table 3, standardized commodity correlation matrix between the variables significant at the 0.01 level of moderate positive correlation, consistent description of goods and consumers to ensure that the 0.01 degree of significance level is related to the weakest, its independent variable The correlation coefficient between less than 0.57, no significant correlation, indicating that there is no relevant collinearity between variables. digital goods between the correlation matrix of variables significant at the 0.01 level of moderate positive correlation, the seller communication and consumers to ensure that the 0.01 level of significance level is related to the weakest, its on the correlation coefficient between independent variables were less than 0.56, no significant correlation, indicating that there is no relevant collinearity between variables.

3 regression modeling, analysis and results
(1 dependent variable and variable according to the relationship that exists, this paper establishes the following regression model:
TV = β0 + β1E + β2V + β3D + β4S + β5C + β6G + μ
Links to free download http://www.hi138.com which, TV (Trading Volume) that trading volume, E (Evaluation) that the seller's credit, V (Value), said commodity prices, D (Description) that description of goods in line with the degree, S (Service) that the attitude of the seller, C (Contact) that the communication process with the seller, G (Guarantee) that the consumer protection service, μ is the test equation residuals, β5 are parameters to be estimated for the intercept term, other measures are not taken into account variable factors.

(2 analysis and results
Table 3-2,3-3,3-4 available standardized commodity (TV1 and digital goods (TV2 regression model is as follows:
TV1 = 488.2405 + 2.056E - 0.752V - 48.437D - 56.906S + 13.744G + 10.1101C + μ
TV2 = 289.0527 +5.3722 E + 0.2041V - 41.637D - 24.2441S + 8.0338G - 0.3111C + μ
Standardized commodity value of goods regression coefficients in the P <0.005 significance level significantly, the seller's communication of the regression coefficients at P <0.05 significance level significantly, consumers ensure that the regression coefficient at P <0.1 significance level significantly, The remaining variables are not significant regression coefficients of these variables significantly affect the level of trading volume of descending to the value of commodities, sellers and consumers to ensure the degree of communication, therefore, the standardization of commodities, assuming 2, assuming 4 and assumptions 5 set up, assuming a hypothesis 3 and hypothesis 6 is not established. digital goods sellers credit regression coefficient P <0.1 significance level significantly, and the remaining variables in the regression coefficients are not significant, so the number of commodities, assumption 1 holds, assuming 2, Hypothesis 3, assuming that 4, 5 and Hypothesis 6 assumed not established.

Table 5 trading volume and return the result of various factors
CONCLUSIONS
According to our findings for the C2C website the seller recommends the following: (1) show a good reputation, improve their professional quality, (2) non-discriminatory treatment, (3) the use of a variety of communication tools and buyer exchange ( 4) active participation "by the consumer protection service" program.

References:
[1] Delone WH and Mclean ER The Delone and Mclean model of Information systems success: a ten-year update [J]. Journal of Management Information Systems, 2003, 19 (4) :121-144.

[2] Dongya Li, Yang Pei. C2C e-commerce platform factors affecting consumer purchase behavior analysis [J]. Consumer Economics, 2007 (3) :32-35.

[3] Xiaomei. The technology acceptance model in China C2C e-commerce sites in the study [J]. Information Science, 2009 (2:297-300. Links to free download http://www.hi138.com