Modeling adoption of genetically modified foods: Application of Rough Set Theory and Flow Network Graph

Introduction The main purpose of this study is to extract the rules and patterns governing behavioral intention consumers towards adoption genetically modified foods (GMFs). Method proposed method a combination Rough Set Theory (RST) Flow Network Graph (FNG). Data was collected from 386 rough rules. 13 have been chosen 289 original that were divided into three groups: low, medium, high use GMFs. They because support values other indexes used in RST. Eventually, interpret performance generated rules, FNG illustrated for each decision-making class, seven extracted. Results findings confirm corporate social responsibilities, consumer concerns, occupational status, autonomy are more important than observed dimensions consumers' decision-making. Moreover, illustrate combining could predict customers' intentions provide valuable information policy-makers related active industries. Discussion Based on analysis outcomes, most significant factors affect GMFs are: “consumer perception CSR”; concerns”; “occupational status”; autonomy”. Thus, managers policymakers must pay attention these concepts when they survey behavior.