Identification Of The Critical Quality Characteristics Of Online B2C Shopping Services Based On Service Blueprints And Rough Set

The rapid development of the Internet provides a good opportunity for online shopping business. With the increasing competition for online shopping business, service quality is a key factor for online shopping to reflect the competitive advantage. However, because the online shopping process is becoming more complex and the intermediate processes of online shopping are various and have longer duration, there are many factors which lead to dissatisfaction with the quality of service. In order to improve customer satisfaction we should seize the control factors among this factors. How to find an effective method to identify the key quality characteristics of the online shopping service has become a common concern to both academic researchers and online sellers.Firstly, service blueprint theory is introduced to conduct a comprehensive analysis of the B2C online shopping process in this thesis based on the above problem. Six dimensions of service quality which include the reputation of the shop, logistics, service recovery, web interface, online customer service and physical commodities is analyzed to determine the possible factors which impact the service quality of B2C online business and then design a questionnaire. Secondly, Rough Set Model is used to identify the key quality characteristics. The first part of the model is attribute reduction which means removing unimportant attributes to get the key quality characteristics of the online shopping services based on the classical rough set theory and the greedy algorithm, and then sort the key quality characteristics according to the importance of characteristics. The second part is the value reduction and rule extraction. On the basic of the core value of Rough Set theory, we find all the core value of the decision table, delete the redundant samples and sample values, obtain a set of the simplest rule set and program with matlab to achieve the algorithm above. Finally, the data is collected through the questionnaire designed and Rough Set Model is applied for data analysis by taking Taobao as an example, we use regression equation to analyze the data collected, and compare the results with the rough set model, then have proved that the reliability and validity of the rough set model established in this thesis are better than regression equation.Six key factors which affect the B2C online shopping service quality are obtained through the empirical analysis, which sort by importance as follows:physical goods, the evaluation of the services by buyers, goods delivery limitation, the response speed of online customer service, the consistency of the description and the real merchandise, and the timeliness and efficiency to deal with the problem. Some recommendations to improve the B2C online shopping service quality are put forward on the basis of the conclusions of this study:Attaching importance to the quality and the cost-effective of the commodity sold, Improving the credibility of the online shop, Accelerating the transfer aging of the goods and so on. The study of this paper is expected to help improve service quality of the B2C online shopping industry.