Separating core and noncore knowledge: an application of neural network rule extraction to a cross-national study of brand image perception

Recent advances in algorithms that extract rules from artificial neural networks make it feasible to use neural networks as a tool for acquiring knowledge hidden in the data. Findings are reported from the use of such algorithms to separate core and noncore knowledge in a cross-national study of automobile brand image perception. Respondents from five Western European countries have been asked to associate individual and corporate brand associations for a number of well-known automobile brands. Knowledge, expressed as concise and accurate rules that distinguish between the respondents' perceptions of German and Japanese brands, is extracted from trained neural networks. This paper explains how both core knowledge, which captures the perceptions shared by the respondents in all countries, and country-specific noncore knowledge can be acquired and differentiated by a proposed two-step approach to train and extract rules from a multi-neural network system. The experimental results show that, in addition to providing a better understanding of the differences and similarities in the brand image perceptions of consumers in various countries, the proposed approach also yields better predictive accuracy than a decision tree method.