Contrasting Multiple Social Network Autocorrelations for Binary Outcomes, With Applications To Technology Adoption

TL;DR

This paper introduces a hierarchical auto-probit model for analyzing multiple social network autocorrelations in binary outcomes, addressing challenges in modeling network influence and multiple measures of social closeness.

Contribution

It extends the auto-probit framework to handle multiple network regimes and binary data, providing a flexible model for social influence analysis in networks.

Findings

The model performs well under various sensitivity conditions.

Application to Caller Ring-Back Tone adoption demonstrates practical utility.

Model captures complex network effects on binary decisions.

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 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, and demonstrate on several examples of correlated binary data 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.