AUTHOR=Akbari Morteza , Mahavarpour Nasrin , Moshkdanian Fatemeh , Maroufkhani Parisa 

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

JOURNAL=Frontiers in Sustainable Food Systems

VOLUME=Volume 6 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2022.992054

DOI=10.3389/fsufs.2022.992054

ISSN=2571-581X

ABSTRACT=The main purpose of this study is to extract the rules and patterns governing the behavioral intention of consumers towards the adoption of GMFs. The proposed method is a combination of Rough Set Theory (RST) and Flow Network Graph (FNG). Data was collected from 386 consumers to extract rough rules. 13 rules have been chosen from 289 original rules that were divided into three groups: low, medium, and high intention to use GMFs. They were chosen because of the support values and other indexes that were used in the RST. Eventually, to interpret the performance of the generated rules, FNG were illustrated for each decision-making class, and seven patterns were extracted. The findings confirm that corporate social responsibilities, consumer concerns, occupational status, and consumer autonomy are more important than other observed dimensions in consumers' decision-making. Moreover, the findings illustrate that combining Rough Set Theory and Flow Network Graph could predict customers' intentions and provide valuable information for policy-makers in related active industries.