Knowledge acquisition and revision using neural networks: an application to a cross-national study of brand image perception

• Published in: The Journal of the Operational Research Society 2006-03, Vol.57 (3), p.231-240
• Main Authors: Setiono, R, Pan, S-L, Hsieh, M-H, Azcarraga, A
• Format: Article
• Language: English
• Subjects: Accuracy; Applied sciences; Artificial intelligence; Artificial neural networks; Automobile industry; Automobiles; Brand image; Brands; Case-Oriented Paper; Classification; Computer science; control theory; systems; Corporate image; Datasets; Exact sciences and technology; Experiments; global brand image perceptions; Knowledge acquisition; Knowledge management; knowledge revision; knowledge transfer; Neural networks; Operations research; Perceptions; Polls & surveys; Pruning; Retraining; Studies; Training
• ISSN: 0160-5682; 1476-9360
• DOI: 10.1057/palgrave.jors.2602006

A three-tier knowledge management approach is proposed in the context of a cross-national study of car brand and corporate image perceptions. The approach consists of knowledge acquisition, transfer and revision using neural networks. We investigate how knowledge acquired by a neural network from one car market can be exploited and applied in another market. This transferred knowledge is subsequently revised for application in the new market. Knowledge revision is achieved by re-training the neural network. Core knowledge common to both markets is retained while some localized knowledge components are introduced during network re-training. Since the knowledge acquired by a neural network can be expressed as an accurate set of simple rules, we are able to compare the knowledge extracted from one network with the knowledge extracted from another. Comparison of the originally acquired knowledge with the revised knowledge provides us with insights into the commonalities and differences in car brand and corporate perceptions across national markets.