Network Structure and Governance Performance: What Makes a Difference?

<italic>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...

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Publicado en:Public Administration Review Vol. 78; no. 2; pp. 195 - 206
Autor principal: Yi, Hongtao
Formato: Artículo
Publicado: Wiley-Blackwell Mar/Apr2018
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Mar/Apr2018
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      pub: Wiley-Blackwell
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        atl: Network Structure and Governance Performance: What Makes a Difference?
      aug:
        au: Yi, Hongtao
        affil: The Ohio State University
      su:
        Network governance
        Public administration
        Policy networks
        Social capital
        Clean energy
      sug:
        subj:
          Network governance
          Public administration
          Policy networks
          Social capital
          Other General Government Support
          Clean energy
      ab: <italic>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</italic>.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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