Research Article Open Access

Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models

Christopher Gan, Visit Limsombunchai, Mike Clemes and Amy Weng


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 variables including age, gender, marital status, ethnic background, educational qualification, employment, income and area of residence are considered in the analysis. Empirical results showed that both ANN models (MLFN and PNN) exhibit a higher overall percentage correct on consumer choice predictions than the logistic model. Furthermore, the PNN demonstrates to be the best predictive model since it has the highest overall percentage correct and a very low percentage error on both Type I and Type II errors.

Journal of Social Sciences
Volume 1 No. 4, 2005, 211-219


Submitted On: 18 January 2006 Published On: 31 December 2005

How to Cite: Gan, C., Limsombunchai, V., Clemes, M. & Weng, A. (2005). Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models. Journal of Social Sciences, 1(4), 211-219.

  • 26 Citations



  • Electronic banking
  • artificial neural network
  • logistic regression