Electronic commerce: a manager's guide
Electronic commerce: a manager's guide
Integrating arbitrage pricing theory and artificial neural networks to support portfolio management
Decision Support Systems - Special double issue: unified programming
Decision Support Systems - Special issue on economics of electronic commerce
Predictors of online buying behavior
Communications of the ACM
Marketing on the internet - who can benefit from an online marketing approach
Decision Support Systems
Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics
Information Systems Research
CAIA '95 Proceedings of the 11th Conference on Artificial Intelligence for Applications
Risk profile and consumer shopping behavior in electronic and traditional channels
Decision Support Systems
Performance evaluation of neural network decision models
Journal of Management Information Systems - Special section: Strategic and competitive information systems
Choice of Transaction Channels: The Effects of Product Characteristics on Market Evolution
Journal of Management Information Systems
An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks
Journal of Management Information Systems
A formal selection and pruning algorithm for feedforward artificial neural network optimization
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Application of BPN with feature-based models on cost estimation of plastic injection products
Computers and Industrial Engineering
Investigating potentially affective factors of online sales: a study on Malaysian business online
International Journal of Information Systems and Change Management
Identifying bloggers with marketing influence in the blogosphere
Proceedings of the 11th International Conference on Electronic Commerce
Identifying influential reviewers for word-of-mouth marketing
Electronic Commerce Research and Applications
How to improve e-government use: an empirical examination of multichannel marketing instruments
Information Polity - Special issue on Freedom of Information
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Web stores, where buyers place orders over the Internet, have emerged to become a prevalent sales channel. In this research, we developed neural network models, which are known for their capability of modeling noncompensatory decision processes, to predict and explain consumer choice between web and traditional stores. We conducted an empirical survey for the study. Specifically, in the survey, the purchases of six distinct products from web stores were contrasted with the corresponding purchases from traditional stores. The respondents' perceived attribute performance was then used to predict the customers' channel choice between web and traditional stores. We have provided statistical evidence that neural networks significantly outperform logistic regression models for most of the surveyed products in terms of the predicting power. To gain more insights from the models, we have identified the factors that have significant impact on customers' channel attitude through sensitivity analyses on the neural networks. The results indicate that the influential factors are different across product categories. The findings of the study offer a number of implications for channel management.