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Network Structure and Governance Performance: What Makes a Difference?
Author(s) -
Yi Hongtao
Publication year - 2017
Publication title -
public administration review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.721
H-Index - 139
eISSN - 1540-6210
pISSN - 0033-3352
DOI - 10.1111/puar.12886
Subject(s) - bridging (networking) , closeness , corporate governance , pace , network analysis , network governance , social network analysis , cluster analysis , social network (sociolinguistics) , computer science , social capital , data mining , business , political science , geography , computer security , artificial intelligence , engineering , finance , mathematical analysis , mathematics , geodesy , world wide web , law , electrical engineering , social media
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 .