Title: Predicting online channel acceptance using social network data
Authors: Verbraken, Thomas # ×
Goethals, Frank
Verbeke, Wim #
Baesens, Bart #
Issue Date: 2014
Series Title: Decision Support Systems vol:63 pages:104-114
Abstract: The goal of this paper is to identify a new way to predict whether a specific person believes buying online is appropriate for a specific product. By analyzing data that was gathered through a survey, we show that knowledge of a person's social network can be helpful to predict that person's e-commerce acceptance for different products. Our experimental setup is interesting for companies because (1) knowledge about only a small number of connections of potential customers is needed, (2) knowing the intensity of the relation is not necessary, and (3) data concerning variables such as age, gender and whether one likes working with the PC is not needed. Hence, companies can rely on publicly available data on their customers' social ties. Network-based classifiers tend to perform especially well for highly durable goods and for services for which few customers think it is appropriate to reserve them online.
ISSN: 0167-9236
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Management Informatics (LIRIS), Leuven
Faculty of Business and Economics, Campus Kulak Kortrijk – miscellaneous
Faculty of Bioscience Engineering
× corresponding author
# (joint) last author

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