Research On The Measure Model For E-commerce Business Credit Based On Intuitionistic Fuzzy Sets

E-commerce develops rapidly in our country, and by2012, the scale of the network consumers has reached242million. However in recent years, the development of E-commerce has been slowing down, for lack of credit. Because of the multiple factors such as virtual nature of Internet and information asymmetry in commodity trading process, business credit has become one of the essential measures for online consumers. But measure indicators and models algorithms used in the existing business credit measure systems are relatively simple, which result in artificially high level of business credit. It’s easily to occur that some businesses intentionally use the means of unfair competition through false transactions to enhance their own credibility or pull down other businesses credibility by malicious poorly comment. Above all, it is difficult for network consumers to trust the businesses directly based on the existing business credit measure system. Therefore, the paper setting the E-commerce credit measure model as its research direction has a certain theoretical and actual value.The paper set the E-commerce business credit as a starting point, then collated and summarized the E-commerce business credit study published by scholars from home and abroad. The existing theoretical studies studied several aspects such as reasons for E-commerce credit problems, factors affecting E-commerce credit, models measuring E-commerce credit and management mechanism of E-commerce credit. These studies conducted more on qualitative research while less on quantitative research. Therefore, the paper combined the quantitative and qualitative point of views to form the measure index, selected measure methods and ultimately constructed the E-commerce business credit measure model based on intuitionistic fuzzy sets.Firstly, the paper initially identified E-commerce business credit factors by means of studying literature extensively, investigating the major E-commerce sites and personal experience. Secondly, the paper interviewed the E-commerce professionals in form of questionnaire. Finally, through statistical analysis of the questionnaire, the paper removed the low and high interconnection related indicators and identified two measure indicators from business static and dynamic credit evaluation. Business static credit includes business basic information assessment and business service information assessment. Business dynamic credit includes commodity evaluation, service evaluation, and logistics evaluation, each part of which has a more detailed three level measure indicator as the support and the weight of each indicator is set by means of sequential relation analysis. The paper respectively offered the detailed measure procedure on E-commerce business static credit and dynamic credit. The measurement of business static credit is similar with traditional business credit measure. The business static credibility can be getting from weighted score based on actual operations. Business dynamic credit is obtained by the evaluation from consumers to business. The paper introduced intuitionistic fuzzy sets algorithm to build indicators, and particularly put the influence of evaluation time and transaction amount into consideration. All things above considered, the paper calculated the business credit. Combined with static credit and dynamic credit, general credit came out. Finally, the feasibility of the model had been verified by the case data. And compared with the existing model, the model in this paper was proved to be effective, and can reflect the changes of the business credit realistically and comprehensively.The paper considered the uncertainty and vagueness in the process consumers evaluate the business credit. As a result, the paper introduced intuitionistic fuzzy sets as credit measure methods to provide a more human cognitive model. In addition, the paper considered the influence of evaluation time and transaction amount weight, and reflected the business credit positively and dynamically. Meanwhile, paper introduced influence of business basic information and operating performance as measurement of business static credit, which expanded the scope of E-commerce business credit measure and reflected business credit comprehensively. However, a mere sound E-commerce business credit measure system cannot yet fully solve the problem of credit loss. Government still needs to improve E-commerce legislation and industry standards, and third-party credit rating agencies have to be established. Besides, consumer credit feedback mechanism and punishment for malicious competitors need to be strengthened. Each participant has to make effort to build the E-commerce credit system construction to promote E-commerce development.