Principles of multivariate analysis: a user's perspective
Principles of multivariate analysis: a user's perspective
A practical Bayesian framework for backpropagation networks
Neural Computation
How Much Do You Trust Big Brother?
IEEE Internet Computing
Consumer reactions to electronic shopping on the world wide web
International Journal of Electronic Commerce
Electronic Commerce Customer Relationship Management: A Research Agenda
Information Technology and Management
Categorizing commercial products for customer oriented online retailing
Computers and Industrial Engineering - Special issue: Computational intelligence and information technology applications to industrial engineering selected papers from the 33 rd ICC&IE
Factors affecting the implementation success of Internet-based information systems
Computers in Human Behavior
Categorizing commercial products for customer oriented online retailing
Computers and Industrial Engineering
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Realizing the full potential of the on-line consumer market requires careful identification of customer needs and expectations. As research on Internet consumer behavior is still in its infancy, a quantitative framework to characterize user profiles for e-commerce has not yet been established. This study proposes a quantitative framework that uses factor analysis to identify latent factor descriptors of Internet users' opinions on Web vendors and on-line shopping. Predictive models based on logistic discrimination and neural networks then select the factors most predictive of the propensity to buy on-line and classify Internet users accordingly. The application of this framework shows that the obtained latent factors agree in general with the major indicators identified in previous qualitative research. A small subset of the obtained factors is shown to retain the predictive power of the whole set. Neural networks are found to perform only marginally better than logistic discrimination in the task of classification.