Geography of online network ties: A predictive modelling approach

Swanand J. Deodhar, Mani Subramani, Akbar Zaheer

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Internet platforms are increasingly enabling individuals to access and interact with a wider, globally dispersed group of peers. The promise of these platforms is that the geographic distance is no longer a barrier to forming network ties. However, whether these platforms truly alleviate the influence of geographic distance remains unexplored. In this study, we examine the role of geographic distance with machine learning approach using a unique dataset of the network ties between traders in an online social trading platform. Specifically, we determine the extent to which, compared to other types of distances, geographic distance predicts the occurrences of the network ties in country dyads. Using cluster analysis and predictive modelling, we show that not only the geographic distance and network ties exhibit an inverse association but also that geographic distance is the strongest predictor of such ties.

Original languageEnglish (US)
Pages (from-to)9-17
Number of pages9
JournalDecision Support Systems
Volume99
DOIs
StatePublished - Jul 2017

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Keywords

  • Cluster analysis
  • Geography
  • Online network ties
  • Predictive modelling
  • Psychic distance

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