Modelling consumer e-commerce adoption behaviour using neural networks

a comparison with logistic regression

Mohammad B. Naseri, Greg Elliott

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

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Prior studies have generally suggested that Artificial Neural Networks (ANNs) are superior to conventional statistical models in predicting consumer buying behavior. There are, however, contradicting findings which raise question over usefulness of ANNs. This paper discusses development of three neural networks for modeling consumer e-commerce behavior and compares the findings to equivalent logistic regression models. The results showed that ANNs predict e-commerce adoption slightly more accurately than logistic models but this is hardly justifiable given the added complexity. Further, ANNs seem to be highly adaptive, particularly when a small sample is coupled with a large number of nodes in hidden layers which, in turn, limits the neural networks’ generalisability.
Original languageEnglish
Title of host publicationANZMAC 2007
Subtitle of host publicationproceedings : 3Rs - reputation, responsibility and relevance
EditorsMaree Thyne, Kenneth R. Deans, Juergen Gnoth
Place of PublicationDunedin, N.Z.
PublisherUniversity of Otago
Number of pages5
ISBN (Print)9781877156299
Publication statusPublished - 2007
EventAustralian and New Zealand Marketing Academy Conference (2007) - Dunedin, New Zealand
Duration: 3 Dec 20075 Dec 2007


ConferenceAustralian and New Zealand Marketing Academy Conference (2007)
CityDunedin, New Zealand

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