Advertisement
Published in:
2012 | OriginalPaper | Chapter
Using Social Network Classifiers for Predicting E-Commerce Adoption
Authors : Thomas Verbraken, Frank Goethals, Wouter Verbeke, Bart Baesens
Published in: E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.