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Network Structure and Governance Performance

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Bibliographic Data

ID6364827
AuthorsHongtao Yi (0000-0002-6753-4627, The Ohio State University, corresponding author)
Year2017
Volume78
Issue2
Pages195-205
Publication date2017-11-16
Open AccessYes
TypeARTICLE
VenuePublic Administration Review (JOURNAL)
Journal identifiersISSN: 0033-3352 • E-ISSN: 1540-6210
PublisherWiley (PUBLISHER • GB)
DOI10.1111/puar.12886
OpenAlexW2769940772
LanguageEN
Citations received54
References cited53

Comparing and evaluating the performance of governance networks are important tasks for researchers and practitioners of network governance and public administration. Limited by the lack of network data across space and time, the study of network performance and effectiveness at the network level is not on pace with advances in theories and methodologies in network analysis. With a novel methodology to measure clean energy governance networks using hyperlink network analysis across the contiguous United States, this article collects a large sample of self‐organizing policy networks in the same policy domain across geographic locations. This article proposes that governance networks with high overall bridging and bonding social capital perform better. Regression analyses show that network structures have statistically significant effects on governance outcomes. States with high average closeness and average clustering in their governance networks are more likely to have faster clean energy development

Bridging (networking · Business · Closeness · Cluster analysis · Computer security · Corporate governance · Data mining · Geography · Network analysis · Network governance · Pace · Political science · Social capital · Social network (sociolinguistics · Social network analysis · Artificial Intelligence · Computer Science · Engineering · Local Government Finance and Decentralization · Policy Transfer and Learning · Public Policy and Administration Research

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Citations per year6.75
Citation span2018 - 2026 (9)
Highly citedNo

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