Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models

  • Gan C
  • Limsombunc V
  • Clemes M
  • et al.
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Abstract

Conventional econometric models, such as discriminant analysis and logistic regression have been used to predict consumer choice. However, in recent years, there has been a growing interest in applying artificial neural networks (ANN) to analyse consumer behaviour and to model the consumer decision-making process. The purpose of this paper is to empirically compare the predictive power of the probability neural network (PNN), a special class of neural networks and a MLFN with a logistic model on consumers' choices between electronic banking and non-electronic banking. Data for this analysis was obtained through a mail survey sent to 1,960 New Zealand households. The questionnaire gathered information on the factors consumers' use to decide between electronic banking versus non-electronic banking. The factors include service quality dimensions, perceived risk factors, user input factors, price factors, service product characteristics and individual factors. In addition, demographic var

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Gan, C., Limsombunc, V., Clemes, M., & Weng, A. (2005). Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models. Journal of Social Sciences, 1(4), 211–219. https://doi.org/10.3844/jssp.2005.211.219

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