Revisiting the small-world phenomenon: efficiency variation and classification of small-world networks

File Description SizeFormat 
ORMNetworks.pdfAccepted version1.47 MBUnknownDownload
Title: Revisiting the small-world phenomenon: efficiency variation and classification of small-world networks
Author(s): Opsahl, T
Vernet, A
Alnuaimi, T
George, G
Item Type: Journal Article
Abstract: Research has explored how embeddedness in small-world networks influences individual and firm outcomes. We show that there remains significant heterogeneity among networks classified as small-world networks. We develop measures of the efficiency of a network, which allow us to refine predictions associated with small-world networks. A network is classified as a small-world network if it exhibits a distance between nodes that is comparable to the distance found in random networks of similar sizes—with ties randomly allocated among nodes—in addition to containing dense clusters. To assess how efficient a network is, there are two questions worth asking: (i) ‘what is a compelling random network for baseline levels of distance and clustering?’ and (ii) ‘how proximal should an observed value be to the baseline to be deemed comparable?’. Our framework tests properties of networks, using simulation, to further classify small-world networks according to their efficiency. Our results suggest that small-world networks exhibit significant variation in efficiency. We explore implications for the field of management and organization.
Publication Date: 2-Nov-2016
Date of Acceptance: 27-Sep-2016
URI: http://hdl.handle.net/10044/1/42065
DOI: https://dx.doi.org/10.1177/1094428116675032
ISSN: 1552-7425
Publisher: SAGE Publications (UK and US)
Start Page: 149
End Page: 173
Journal / Book Title: Organizational Research Methods
Volume: 20
Copyright Statement: © The Author(s) 2016. Published by Sage Publications. The final, definitive version of this paper has been published in Organizational Method Research, Vol.20, Issue 1, Nov-2016 . It is available at: http://online.sagepub.com/doi/10.1177/1094428116675032
Keywords: Social Sciences
Psychology, Applied
Management
Psychology
Business & Economics
computational modeling
longitudinal data analysis
quantitative research
sampling
research design
RANDOM GRAPH MODELS
SOCIAL NETWORKS
COLLABORATION NETWORKS
INNOVATION
CREATIVITY
WEB
Business & Management
1503 Business And Management
1701 Psychology
1505 Marketing
Publication Status: Published
Appears in Collections:Imperial College Business School



Items in Spiral are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons