Using Social Network Classifiers for Predicting E-Commerce Adoption

Authors: Thomas Verbraken, Frank Goethals, Wouter Verbeke, and Bart Baesens
Presented at: WEB2011: The Tenth Workshop on E-Business, Shanghai (China), 4 December 2011
Keywords: E-commerce adoption, Social network analysis, Classification, Data mining

This paper indicates that knowledge about a personís social network is valuable to predict the intent to purchase books and computers online. Data was gathered about a network of 681 persons and their intent to buy products online. Results of a range of networked classification techniques are compared with the predictive power of logistic regression. This comparison indicates that information about a personís social network is more valuable to predict a personís intent to buy online than the personís characteristics such as age, gender, his intensity of computer use and his enjoyment when working with the computer.

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