Predicting Adoption Probabilities in Social Networks

Information Systems Research, 24(1), 2013

56 Pages Posted: 16 Oct 2012 Last revised: 12 Jul 2013

See all articles by Xiao Fang

Xiao Fang

Lerner College of Business and Economics, University of Delaware

Paul J. Hu

University of Utah - School of Accounting and Information Systems

Zhepeng Li

Schulich School of Business, York University

Weiyu Tsai

University of Utah - David Eccles School of Business

Date Written: October 1, 2012

Abstract

In a social network, adoption probability refers to the probability that a social entity will adopt a product, service, or opinion in the foreseeable future. Such probabilities are central to fundamental issues in social network analysis, including the influence maximization problem. In practice, adoption probabilities have significant implications for applications ranging from social network-based target marketing to political campaigns; yet, predicting adoption probabilities has not received sufficient research attention. Building on relevant social network theories, we identify and operationalize key factors that affect adoption decisions: social influence, structural equivalence, entity similarity, and confounding factors. We then develop the locally-weighted expectation-maximization method for Naïve Bayesian learning to predict adoption probabilities on the basis of these factors. The principal challenge addressed in this study is how to predict adoption probabilities in the presence of confounding factors that are generally unobserved. Using data from two large-scale social networks, we demonstrate the effectiveness of the proposed method. The empirical results also suggest that cascade methods primarily using social influence to predict adoption probabilities offer limited predictive power, and that confounding factors are critical to adoption probability predictions.

Keywords: adoption probability, social network, Bayesian learning, social influence

Suggested Citation

Fang, Xiao and Hu, Paul J. and Li, Zhepeng and Tsai, Weiyu, Predicting Adoption Probabilities in Social Networks (October 1, 2012). Information Systems Research, 24(1), 2013. Available at SSRN: https://ssrn.com/abstract=2162209

Xiao Fang (Contact Author)

Lerner College of Business and Economics, University of Delaware ( email )

Newark, DE 19716
United States

Paul J. Hu

University of Utah - School of Accounting and Information Systems ( email )

1645 Campus Center Drive
Salt Lake City, UT 84112
United States

Zhepeng Li

Schulich School of Business, York University ( email )

Toronto, Ontario
Canada

Weiyu Tsai

University of Utah - David Eccles School of Business ( email )

1645 E Campus Center Dr
Salt Lake City, UT 84112-9303
United States

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