A New Method for Means-End Chain Analysis: Genetic Algorithm-Based Fuzzy Association Mining (GAFAM) Rules
Nai-Hua Chen, Stephen C.T. Huang, Chi-Hsun Lee
Chienkuo Technology University; National Kaohsiung First University of Science and Technology; National United University

In Marketing and Information Management fields, means-end chain (MEC) is widely employed to depict the hierarchical relationships among product/service attributes, consequences, and values with which consumers intend to obtain and identify, through purchasing and consuming the products/services. Practically, such hierarchical relationships are important for marketers to develop their marketing communication strategies to consumers. Methodologically, MEC analysis is often qualitatively deployed with depth interviews, or employed with network analysis along with surveys in a quantitative way. The current study provides an alternative method to MEC analysis by introducing genetic algorithm-based fuzzy association mining (GAFAM) rules to this arena. We used organic foods as the research context, in which consumers may concern environmental issues, altruism, egoism, health and premium prices they will pay for, and the attributes related to these consequences and values when they consider purchasing organic foods. We showed the superiority of this method as well.

Keywords: Fuzzy association rule; Means-End Chain; Genetic algorithm

Biography: Nai-Hua Chen is currently an Assistant Professor at the Department of Information management, Chienkuo Technology University (CTU), TAIWAN. She received her MS degree from Maryland University and Ph.D. degree in Computational Science and Informatics from George Mason University, USA. Her current research and teaching interests are in Computational Statistics and Soft Computing.