Geography of online network ties: A predictive modelling approach
| Autor: | Mani R. Subramani, Akbar Zaheer, Swanand J. Deodhar |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Inverse Association
Information Systems and Management business.industry 05 social sciences Disease cluster Social trading computer.software_genre Data science Management Information Systems Geography Arts and Humanities (miscellaneous) Geographical distance 0502 economics and business Developmental and Educational Psychology 050211 marketing Psychic distance The Internet Data mining business computer 050203 business & management Predictive modelling Information Systems |
| Zdroj: | Decision Support Systems. 99:9-17 |
| ISSN: | 0167-9236 |
| DOI: | 10.1016/j.dss.2017.05.010 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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