Abstract
This paper indicates that knowledge about a person’s 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 person’s social network is more valuable to predict a person’s intent to buy online than the person’s characteristics such as age, gender, his intensity of computer use and his enjoyment when working with the computer.
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Verbraken, T., Goethals, F., Verbeke, W., Baesens, B. (2012). Using Social Network Classifiers for Predicting E-Commerce Adoption. In: Shaw, M.J., Zhang, D., Yue, W.T. (eds) E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life. WEB 2011. Lecture Notes in Business Information Processing, vol 108. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29873-8_2
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DOI: https://doi.org/10.1007/978-3-642-29873-8_2
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