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Problem statement: Nowadays, the Internet is increasingly becoming the fastest growing shopping channel in these days. Moreover, it has been predicted that the city of Isfahan in Iran will experience a sharp increase in the Internet and the Web usage in the next decade. However, the factors affect the shopping of different products via the Internet have received a little direct research attention so far. Thus, there is an inherent need to investigate the nature and perceptions of consumers and the suitability of different types of products and services and also the role of each factor which impacts consumers’ behavior in choosing between buying from the Internet or traditional stores. Our case study is Isfahan Iran. Approach: The present study aimed to consider the influencing factors on consumer e-shopping behavior for different types of products. The data were obtained from 412 volunteers who had the Internet shopping experience and were analyzed using MLP neural networks and logistic regression for each types of product. Then, after comparing the accuracy of these methods, the most important factors which motivate the consumers to buy online were determined by the trained neural network. Results: Compared to the logistic regression, the neural network method showed a better performance in predicting the factors which affect on consumer online shopping behavior with the accuracy of at around 93% for all types of products included in this study. Conclusion: The results showed that companies should invest on different factors for different types of products to motivate consumers to shop online from them. Again, for each sort of products some factors are more important than the others. This study also suggested the merits of ANNs as non-linear predictors in commercial studies which can be used in reverse engineering as well.
