Please use this identifier to cite or link to this item: https://doi.org/10.1057/palgrave.jors.2602646
Title: Predicting consumer preference for fast-food franchises: A data mining approach
Authors: Hayashi, Y.
Hsieh, M.-H.
Setiono, R. 
Keywords: Consumer brand preference
Data mining
Decision tree
Neural network
Issue Date: 2009
Source: Hayashi, Y.,Hsieh, M.-H.,Setiono, R. (2009). Predicting consumer preference for fast-food franchises: A data mining approach. Journal of the Operational Research Society 60 (9) : 1221-1229. ScholarBank@NUS Repository. https://doi.org/10.1057/palgrave.jors.2602646
Abstract: The objectives of the study reported in this paper are: (1) to evaluate the adequacy of two data mining techniques, decision tree and neural network in analysing consumer preference for a fast-food franchise and (2) to examine the sufficiency of the criteria selected in understanding this preference. We build decision tree and neural network models to fit data samples collected from 800 respondents in Taiwan to understand the factors that determine their brand preference. Classification rules are generated from these models to differentiate between consumers who prefer the brand and those who do not. The generated rules show that while both decision tree and neural network models can achieve predictive accuracy of more than 80% on the training data samples and more that 70% on the cross-validation data samples, the neural network models compare very favourably to a decision tree model in rule complexity and the numbers of relevant input attributes. © 2009 Operational Research Society Ltd.
Source Title: Journal of the Operational Research Society
URI: http://scholarbank.nus.edu.sg/handle/10635/42575
ISSN: 01605682
DOI: 10.1057/palgrave.jors.2602646
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