Model Of Consumer Behavior Decision In An E-commerce Environment Based On Prospect Theory

With the rapid development of e-commerce, the network consumer shows more and more than before the different behavior characteristics. The purpose of this study is to clarify the factors of online consumer behavior: the decision making process and influence, can through the modeling of the decision making process of the online shopping consumer behavior, to provide practical suggestions for the development of operation management and service marketing management of enterprises. The main contents of this research include:(1) Consumer behavior research and modeling of network shopping context. First of all, the service process stage were determined through the service blueprint method, this paper will B2 C e-commerce service process is divided into decision-making, access, transaction, payment, logistics and customer service of 6 sub process; then, for each process to establish the function and Prospect of the value function, weight function; then, the concepts of intuitionistic fuzzy number perfect mathematical model; finally, the definition of multiple attribute decision making problems, determine the structure and the corresponding decision index attribute Jingdong mall, 1 shop and dangdang.com set for alternatives.(2) A quantitative decision model based on Internet word of mouth. Based on the behavior decision model has been established on the Internet word of mouth, through mining quantitative representation of decision model. First of all, the use of Java programming language, the classification of word-of-mouth network according to the service process, the realization of the classification model based on SVM algorithm for network reputation service process; when the sorting is completed, call the Weka tool for data analysis, cluster analysis was performed on the within class. The main use of EM algorithm, to determine the within class clustering number of clusters(i.e. index number), and make sure each kind of neutron decision index assisted QFD research methods. After clustering, the sub process by a total of 13 decision index; then, analysis tools for emotional score for word-of-mouth network using ROST emotion, according to the rating results, in accordance with the emotional polarity is divided into two categories of evaluation results, and in accordance with the different transformation emotional tendency for direct perception of triangular fuzzy number, so as to realize the quantification model the.(3) Cases analysis and computation based on B2 C consumer behavior decision model of prospect theory. The decision model was calculated, the main application o f the relevant operational intuition of triangular fuzzy number properties, calculated each attribute value and draw the prospect of comprehensive prospect value. Through carries on the decision-making behavior and model of expected utility theory to construct comparison, found this article constructed behavior decision model based on prospect theory can be closer to the actual situation. In addition, sorting through analysis of the results, to discuss the merits of degree of each alternative, get inspiration to management various alternatives.The results showed that:(1) overall, comprehensive prospect Jingdong mall store No. 1, the highest score, the second lowest, dangdang.com. But the analysis found that cumulative comprehensive score, 1 store in the pre service level behind the dangdang.com case, provided by a high level of service in the late stage, in the total score more than dangdang.com, for the enterprise how to implement service recovery has the reference value;(2) cluster analysis of Internet word of mouth got 13 the evaluation index, in these indicators, Jingdong mall in which 7 index approached King optimal, shop 1, 4 best prospect value. Jingdong mall, although the highest scores, but the after sale service stage was lower than the rest of the two scores, the total score is also narrow the gap. The No. 1 shop was originally a low score, but through excellent service recovery, the final score ranked second. This shows that the service recovery strategy, good, plays an important role in the enterprise to enhance the quality of service.