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Activity Number: 336
Type: Topic Contributed
Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Marketing
Abstract - #308169
Title: Modeling Choice Interdependence in a Social Network
Author(s): Anocha Aribarg*+ and Yves Atchade and Jing Wang
Companies: Ross School of Business, University of Michigan and Statistics Department, University of Michigan and McKinsey - Beijing
Keywords: social interactions ; social influence ; endogeneity ; social network ; Markov random field ; opinion leader
Abstract:

This paper examines how individuals' choice decisions are influenced by observation of choices made by their connected others. To completely understand the mechanism of such choice interdependence, researchers should observe both individuals' choices and the choices' sequence (complete data). However, information on choice sequence is sometimes not available (snapshot data). We suggest the use of a discrete-time Markov chain model for complete data and a Markov Random Field(MRF) model for snap-shot data. We use an experimental approach to alleviate the concern with endogeneity in modeling choice interdependence.

Our model comparison suggests that the MRF model with snap-shot data can provide good prediction for a technology-based, but not a taste-based, product. Our findings show that while both experts and popular individuals exert higher influence for a technology-based product, only popular individuals exert higher influence for a taste-based product. We find choices made by early decision makers to be more influential than choices observed later for the technology-based category. This result touts the disproportionate influence of opinion leaders in diffusion of innovations.


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