Please use this identifier to cite or link to this item: https://doi.org/10.1109/CEC.2008.4630822
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dc.titleMarket research applications of artificial neural networks
dc.contributor.authorAzcarraga, A.P.
dc.contributor.authorHsieh, M.-H.
dc.contributor.authorSetiono, R.
dc.date.accessioned2013-07-11T10:13:32Z
dc.date.available2013-07-11T10:13:32Z
dc.date.issued2008
dc.identifier.citationAzcarraga, A.P., Hsieh, M.-H., Setiono, R. (2008). Market research applications of artificial neural networks. 2008 IEEE Congress on Evolutionary Computation, CEC 2008 : 357-363. ScholarBank@NUS Repository. https://doi.org/10.1109/CEC.2008.4630822
dc.identifier.isbn9781424418237
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/42605
dc.description.abstractEven in an increasingly globalized market, the knowledge about individual local markets could still be invaluable. In this cross-national study of brand image perception of cars, survey data from buyers in the top 20 automobile markets have been collected, where each respondent has been asked to associate a car brand with individual brand images and corporate brand images. These data can be used as tool for decision making at the enterprise level. We describe an algorithm for constructing auto-associative neural networks which can be used as a tool for knowledge discovery from this data. It automatically determines the network topology by adding hidden units as they are needed to improve accuracy and by removing irrelevant input attributes. Two market research applications are presented, the first is for classification, and the second is for measuring similarities in the perceptions of the respondents from the different markets. In the first application, the constructed networks are shown to be more accurate than a decision tree. In the second application, the constructed networks are able to reproduce the training data very accurately and could be used to identify country-level (i.e. local) markets which share similar perceptions about the car brands being studied. © 2008 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/CEC.2008.4630822
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentINFORMATION SYSTEMS
dc.description.doi10.1109/CEC.2008.4630822
dc.description.sourcetitle2008 IEEE Congress on Evolutionary Computation, CEC 2008
dc.description.page357-363
dc.identifier.isiut000263406500055
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