Using Social Network Classifiers for Predicting E-Commerce Adoption

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.

[1]  Foster J. Provost,et al.  Classification in Networked Data: a Toolkit and a Univariate Case Study , 2007, J. Mach. Learn. Res..

[2]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[3]  Jennifer Neville,et al.  Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning , 2002, ICML.

[4]  Detmar W. Straub,et al.  The psychological origins of perceived usefulness and ease-of-use , 1999, Inf. Manag..

[5]  Michael G. Morris,et al.  User Acceptance of Information Technology: Theories and Models , 1996 .

[6]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[7]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[8]  Torben Hansen.  Perspectives on consumer decision making: an integrated approach , 2005 .

[9]  Peter A. Todd,et al.  Understanding Information Technology Usage: A Test of Competing Models , 1995, Inf. Syst. Res..

[10]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[11]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[12]  P. Blau.  Inequality and Heterogeneity: A Primitive Theory of Social Structure , 1978 .

[13]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Lise Getoor,et al.  Link-Based Classification , 2003, Encyclopedia of Machine Learning and Data Mining.

[15]  Cynthia M. Jackson,et al.  Toward an Understanding of the Behavioral Intention to Use an Information System , 1997 .

[16]  P. F. Anderson,et al.  A Reward/Measurement Model of Organizational Buying Behavior , 1985 .

[17]  Fred D. Davis,et al.  Disentangling behavioral intention and behavioral expectation , 1985 .

[18]  Sougata Mukherjea,et al.  Social ties and their relevance to churn in mobile telecom networks , 2008, EDBT '08.

[19]  Piotr Indyk,et al.  Enhanced hypertext categorization using hyperlinks , 1998, SIGMOD '98.

[20]  David J. Hand,et al.  Measuring classifier performance: a coherent alternative to the area under the ROC curve , 2009, Machine Learning.

[21]  Kate Smith-Miles,et al.  On learning algorithm selection for classification , 2006, Appl. Soft Comput..