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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ali, S., Smith, K.A.: On learning algorithm selection for classification. Applied Soft Computing 6(2), 119–138 (2006)
Anderson, P.F., Chambers, T.M.: A Reward/Measurement model of organizational buying behavior. Journal of Marketing 49, 7–23 (1985)
Blau, P.M.: Inequality and Heterogeneity: A Primitive Theory of Social Structure. Free Press, New York (1977)
Chakrabarti, S., Dom, B., Indyk, P.: Enhanced hypertext categorization using hyperlinks. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 307–319 (1998)
Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S., Nanavati, A.A., Joshi, A.: Social ties and their relevance to churn in mobile telecom networks. In: Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology, EDBT 2008, pp. 697–711 (2008)
Davis, F.: Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information. MIS Quarterly 3(13), 319–339 (1989)
Dillion, A., Morris, M.G.: User acceptance of information technology: Theories and Models. Annual Review of Information Science and Technology, 3–32 (1996)
Engel, J.F., Blackwell, R.D., Miniard, P.W.: Consumer Behavior, 8th edn. The Dryden Press, Chicago (1995)
Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–741 (1984)
Hand, D.J.: Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine Learning 77(1), 103–123 (2009)
Hansen, T.: Perspectives on consumer decision making: an integrated approach. Journal of Consumer Behaviour 4(6), 420–437 (2005)
Jackson, C.M., Chow, S., Leitch, R.A.: Towards an understanding of the behavioral intention to use an information system. Decision Sciences 28(2), 357–389 (1997)
Jensen, D., Neville, J.: Linkage and autocorrelation cause feature selection bias in relational learning. In: Proceedings of the 19th International Conference on Machine Learning, pp. 259–266 (2002)
Karahanna, E., Straub, D.W.: The psychological origins of perceived usefulness and ease of use. Information and Management 35(4), 237–250 (1999)
Lu, Q., Getoor, L.: Link-based classification. In: Proceedings of the 20th International Conference on Machine Learning (ICML), pp. 496–503 (2003)
Macskassy, S.A., Provost, F.: Classification in networked data. Journal of Machine Learning Research 8, 935–983 (2007)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual Review of Sociology 27, 415–444 (2001)
Newman, M.E.J.: Networks: An Introduction. Oxford University Press, Oxford (2010)
Taylor, S., Todd, P.A.: Understanding information technology usage: a test of competing models. Information Systems Research 6(4), 144–176 (1995)
Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly 27(3), 425–478 (2003)
Warshaw, P., Davis, F.: Disentangling Behavioral Intention and Behavioral Expectation. Journal of Experimental Social Psychology 21(3), 213–228 (1985)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-29873-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29872-1
Online ISBN: 978-3-642-29873-8
eBook Packages: Computer ScienceComputer Science (R0)