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U. of Salford

The interaction effects of sparse and interlocked connections in SMEs clusters

Abstract

dc:description.abstract

This study aims to exarnine the relationship between the structures of inter- organisation connections and innovation results in the context of small and medium enterprises (SMEs).Existing literature shows that SMEs can benefit from inter- organisation connections in SNEs development. However: there is a theoretical gap in how a combination of various structures of inter-organisation connections affects SMEs development results. In other words: the theoretical gap in this area is what structures of inter-firm connections can be more beneficial than the others. Thus, this study adopts the network theory and network analysis to explore the effects of network structures on SMEs performances in their development. To close this gap, network theory is employed to support this study's hypotheses. Then, this study uses network analysis to generate network snapshots and test proposed hypotheses. Complementary to prior research, this study suggests that SMEs development results can benefit from having sparse connections, interlocked connections, centrality, and brokerage in their networks. Also, in contrast to prior research, this study emphasizes the influences of these four inter-firm connection structures, sparse connections, interlocked connections, centrality, and brokerage.

Degree

thesis:*
Level dc:type.qualificationlevel
Doctoral (Level 8)
Year dc:date.issued
2020

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Liang, L

Rights

Language dc:language
en

Identifiers

dc:identifier.*
Identifier
oai:salford-repository.worktribe.com:1358998
OAI identifier oai:identifier
oai:salford-repository.worktribe.com:1358998

Chain of custody

source
Harvested from
U. of Salford
Base URL
salford-repository.worktribe.com/oaiprovider
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Liang, L. The interaction effects of sparse and interlocked connections in SMEs clusters. Doctoral (Level 8) thesis, 2020.