Predicting E-commerce Adoption Using Data about Product Search and Supplier Search Behavior

In this paper we use a semi-supervised learning model to predict whether a person thinks buying a specific product online is appropriate. As input, information is used about the channels one deems appropriate to find product information or to find suppliers. Both online and offline channel preferences are found to be valuable to predict e-commerce adoption. The practical consequence of the work is that (binary) data about a user’s preferred channel for information retrieval can be helpful to estimate the probability the person is interested to buy a specific product online so that publicity for an online shop is only shown to people who actually believe buying that product online is appropriate. The predictive performance of our approach is considerably better than that reported in earlier research. Our results also show that semi-supervised learning has advantages in terms of predictive performance compared to supervised learning.