Network Structure and Governance Performance: What Makes a Difference?

Published date01 March 2018
Date01 March 2018
Network Structure and Governance Performance: What Makes a Difference? 195
Public Administration Review,
Vol. 78, Iss. 2, pp. 195–205. © 2017 by
The American Society for Public Administration.
DOI: 10.1111/puar.12886.
Hongtao Yi is assistant professor in the
John Glenn College of Public Affairs at The
Ohio State University. His research interests
focus on network governance, policy
process, and energy and environmental
Abstract : 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.
Evidence for Practice
To facilitate the performance of the governance network, policy actors can take the initiative to increase
average linkages and form clustered relationships among close allies.
In the absence of network administrators, entrepreneurial policy actors can enhance the effectiveness of
the policy network by serving as policy brokers who facilitate communication between separated policy
Entrepreneurial policy actors can act as policy entrepreneurs who actively bond organizations together to
achieve better collective outcomes.
Hongtao Yi
The Ohio State University
Network Structure and Governance Performance:
What Makes a Difference?
T he trend toward decentralizing government
and involving diverse actors in public
management has attracted much attention to
the dynamics of network governance (Dowding 1995 ;
Heclo 1978 ; Klijn and Koppenjan 2000 ; Provan and
Milward 1995). Within this research tradition, the
study of network performance or network outcomes
offers a performance-driven perspective on what
factors lead to more effective network governance
(Klijn, Steijn, and Edelenbos 2010 ; Meier and
O Toole 2007; Provan and Milward 1995). Since
the early call for the study of network performance
(Provan and Milward 1995), several studies have
been conducted to compare network performance
across geographic areas (Klijn et al. 2016 ; Lee and
Rethemeyer 2013; Milward and Provan 1998 ; Provan
and Milward 2001; Provan and Kens 2008; Saz-
Carranza and Ospina 2011 ; Ysa, Sierra, and Esteve
2014 ).
Three areas of study, however, need further
research. First, overwhelming emphasis has been
placed on the performance of managed networks
compared with self-organizing networks. Little is
known about the factors that drive the outcomes
of self-organizing policy networks. Second, except
for a limited number of large- N studies of the
effectiveness of whole networks (e.g., Klijn, Steijn,
and Edelenbos 2010 ; Meier and O ’ Toole 2007),
most studies have employed case study methods
and qualitative comparative analysis. In addition,
while many quantitative studies have evaluated the
effectiveness of ego networks, which emphasize
organizational performance, few have examined the
effects of whole networks, which focus on network-
level performance. Thus, a large- N study of whole
network performance is still needed to advance the
understanding of network performance (Provan and
Lemaire 2012 ).
Third, many studies have been conducted to examine
the determinants of network outcomes (Klijn et al.
2016 ; Provan and Milward 1995), but much is still
unknown regarding the structural determinants of
network effectiveness. Do networks that feature
more central coordinators have better performance?
Do networks that feature more clustered groups
have better performance? This article fills these

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