A Model for Detecting Customer Level Intentions to Purchase in B2C Websites Using TOPSIS and Fuzzy Logic Rule-Based System

The perception of people and attitude towards e-commerce and B2C websites significantly affect their intention to make purchase online. The technique for order preference by similarity to ideal solution (TOPSIS), one of the multi-criteria decision making methods, was developed to solve real-world decision problems that has continued to work satisfactorily across different application areas. This paper presents a model using TOPSIS and fuzzy logic for detecting the level of customer intentions to purchase against factors affecting the intention to purchase in business-to-customer (B2C) websites. The paper extends the work of Schaupp and Belanger (J Electron Commer Res 6(2), 95–111, 2005) which suggested technology, shopping, and product characteristics as important factors influencing customer satisfaction and purchase intention. The three main factors and a comprehensive list of attributes and features of B2C websites are investigated by synthesizing prior literatures. Furthermore, TOPSIS method is used for ranking the most important features of attributes and then fuzzy logic for building model to show real levels of the influencing factors, attributes and features on customer intentions to purchase in B2C websites based on customer’s perception of B2C websites. Two questionnaires were used in this paper, the first questionnaire used the TOPSIS method and the second questionnaire used data gathered from B2C websites. Respondents in this study are 450 online customers with experience in online shopping from B2C websites. Our model assists as an instrument to vendors and customers in determining the real-level effects of these factors on purchase intention in B2C websites.