Contrasting multiple social network autocorrelations for binary outcomes, with applications to technology adoption

Bin Zhang, Andrew C. Thomas, Patrick Doreian, David Krackhardt, Ramayya Krishnan

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

The rise of socially targeted marketing suggests that decisions made by consumers can be predicted not only from their personal tastes and characteristics, but also from the decisions of people who are close to them in their networks. One obstacle to consider is that there may be several different measures for closeness that are appropriate, either through different types of friendships, or different functions of distance on one kind of friendship, where only a subset of these networks may actually be relevant. Another is that these decisions are often binary and more difficult to model with conventional approaches, both conceptually and computationally. To address these issues, we present a hierarchical auto-probit model for individual binary outcomes that uses and extends the machinery of the auto-probit method for binary data. We demonstrate the behavior of the parameters estimated by the multiple network-regime auto-probit model (m-NAP) under various sensitivity conditions, such as the impact of the prior distribution and the nature of the structure of the network. We also demonstrate several examples of correlated binary data outcomes in networks of interest to information systems, including the adoption of caller ring-back tones, whose use is governed by direct connection but explained by additional network topologies.

Original languageEnglish (US)
Article number18
JournalACM Transactions on Management Information Systems
Volume3
Issue number4
DOIs
StatePublished - Jan 1 2013
Externally publishedYes

Keywords

  • Autocorrelation model
  • Bayesian method
  • Diffusion
  • Social network

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Contrasting multiple social network autocorrelations for binary outcomes, with applications to technology adoption'. Together they form a unique fingerprint.

  • Cite this