The Dynamic Group Buying And Research On Buyer Coalition In EC Envionment

Since the90s, the mode of Group buying gets steady development in theEuropean and other developed countries for it’ s many advantages. With thedevelopment of network technology and the growth of network users in our country,the group buying as a new type of e-commerce model gets more and more attentionsfor its perfecting advantages. And how to make the buyers’ benefit maximized hasbecome a focus recently. In this paper, we analysis the existing Group buying in theangle of traditional e-business mode and we find that we can’t find the trulyadvantage in existing group buying. In view of the defects of buyers’ satisfaction andthe purchase price in the existing Group buying, we put forward a new dynamicGroup buying model and a new algorithm of buyer coalition.The existing Group buying is still the forms of traditional electronic commerce.Firstly, there is no direct relation between the number of buyers and the price of goodsin the Group buying which can not reflect the real advantage of Group buying.Secondly, the only way those buyers find the goods through inputting keywords andcomparing manually which cannot ensure the satisfaction of buyers. Based on theabove two aspects, a dynamic Group buying is putted forward in this paper. In thisplatform, the way to find goods for buyers no longer by inputting keywords and theseller are not only provides fixed price and the information of goods. The model ofbuyers’demand is gutted by Data mining. We use the analytic hierarchy process (AHP)and multi-attribute utility theory (MAUT) to quantify the demand of buyers andanalyze the goods that can reach the highest buyers’ satisfaction. The seller shouldsubmit a dynamic price list when the seller registering in the dynamic Group buying.The buyers with the same presence can be gathered in the algorithm of buyer coalition,so buyers can get the advantage to bargain with sellers. In the dynamic Group buyingwe proposed, buyers can get the goods which make buyers’ utility maximizing andthe amount of sales for sellers can be guaranteed to some extent. In this paper, the first chapter is the introduction which including thedevelopment background and research significance about this thesis, the maininnovation point of this paper and arrangements of thesis chapters. The secondchapter is the theoretical preparation for this paper which including the developmentof existing mode of Group buying, the introduction of Data mining and application ofData mining in Group buying.The chapter3and chapter4illustrate the core content of this paper. The thirdchapter focuses on the process of building about dynamic Group buying platformwhich including the Data mining on buyers’ demand, the dynamic pricing mechanismfor sellers and algorithm of buyer coalition. On the base of chapter3, the fourthchapter is the introduction of algorithm of buyer coalition which is based on theimprovement of K-means. In this chapter, we get random data by random algorithm.We compare the algorithm of buyer coalition to the random one through theexperiments and we find that the algorithm is operational and effective.The fifth chapter is the summary and prospect of content of this paper. We pointout the deficiency of this paper and needed to be improved in the future.