The state of network research in Public Administration

Administration & Society 1 –34

© The Author(s) 2014DOI: 10.1177/0095399714555752

aas.sagepub.com

Article

The State of Network Research in Public Administration

Naim Kapucu1, Qian Hu1, and Sana Khosa1

AbstractThe wide use of networks warrants a closer examination of network research in public administration. This article focuses on the methodological issues of network research and examines how social network analysis has been used and can be used to advance network research in public administration. Through a content analysis of 81 network articles, we found that the topics examined through network analysis have become more diverse in recent years. Yet relatively few articles have examined the intersection of policy networks, governance networks, and collaborative networks. The field needs more mixed-methods research designs and more research on the substructures of networks and multilevel networks.

Keywordssocial network analysis, collaborative networks, governance networks, policy networks

Over the past few decades, the study of networks has become an important field of social science research. In recent years, we have seen a rapidly grow-ing interest in networks in public administration (Isett, Mergel, LeRoux, Mischen, & Rethemeyer, 2011; Lecy, Mergel, & Schmitz, 2013; Provan

1University of Central Florida, Orlando, FL, USA

Corresponding Author:Naim Kapucu, School of Public Administration, University of Central Florida, HPA II Suite 238M, Orlando, FL 32816, USA. Email: [email protected]

555752 AASXXX10.1177/0095399714555752Administration & SocietyKapucu et al.research-article2014

doi:10.1177/0095399714555752Administration & Society OnlineFirst, published on November 6, 2014 as

at University of Central Florida Libraries on May 18, 2015aas.sagepub.comDownloaded from

http://aas.sagepub.com/

2 Administration & Society

& Lemaire, 2012). It is, “for many, the age of networks and collaboration” (McGuire, 2006, p. 34). This phenomenon has been driven by the practical need to address concerns that grew out of the “hollowing out of the state,” or collaborative governance (Frederickson, 1999; Rhodes, 1996), as well as the methodological advancements of network research in neighboring disciplines (Berry et al., 2004).

Collaborative governance, different from traditional bureaucratic approaches to government, is characterized by the engagement of nonstate stakeholders in public service and more horizontal interactions between the public, private, and nonprofit sectors (Ansell & Gash, 2008; McGuire, 2006). The wide application of networks to public management warrants a closer examination of network research in public administration (Popp, MacKean, Casebeer, Milward, & Lindstrom, 2013). Networks are used both as a meta-phor to describe and explain intergovernmental, cross-sector, and interorga-nizational relationships (Koliba, Meek, & Zia, 2010) and as methodological tools to examine relational data (Choi & Kim, 2007; Kapucu, 2006a ; Kapucu & Demiroz, 2011; J. Lee & Kim, 2011; Wachhaus, 2009).

Laurence O’Toole (1997) recommended that researchers “treat networks seriously,” conduct a systematic assessment of various types of networks, and study the structural aspects of networks in public administration (p. 45). Over the past few years, scholars have published comprehensive reviews of net-work research in public administration (e.g., Berry et al., 2004; Isett et al., 2011; Lecy et al., 2013; Provan, Fish, & Sydow, 2007; Provan & Lemaire, 2012; Robinson, 2006). These review articles highlighted challenges facing network scholarship and offered recommendations about future research. Common recommendations in these studies indicate that researchers need to clearly define the key concepts, integrate qualitative with quantitative stud-ies, conduct large-N network analysis and comparative network analysis, dis-tinguish formal or contractual network relationships from informal social networks, and utilize appropriate network analysis methods and tools (Berry et al., 2004; Isett et al., 2011; Kapucu & Demiroz, 2011; Lecy et al., 2013; Provan & Lemaire, 2012). In this article, we review the methodological advancement of network research in public administration to examine the implementation of these recommendations and to consider what additional research work can be done to further advance the field.

Rather than broadly examining research on collaboration and networks in public administration, this article addresses how social network analysis (SNA) as a method has been used for analyzing the structural and relational aspects of networks in public administration. SNA refers to analysis methods for studying social processes, social structures, and interaction patterns within social structures (Scott, 2013). Unlike conventional statistical



Kapucu et al. 3

analysis, SNA allows researchers to examine the dynamic interactions between actors, the evolving nature of social process, and the complexity of social systems (Kapucu & Demiroz, 2011). SNA presents a set of qualitative and quantitative as well as descriptive and inferential approaches to analyz-ing relational data. It has been widely used in sociology, psychology, and anthropology to analyze social structures in various contexts (Knoke & Yang, 2008; Scott, 2013; Wasserman & Faust, 1994).

In the public organization context, researchers apply SNA to study social structures within an organization, interorganizational relations, and organiza-tional relationships with their external environments. SNA has been used to examine a wide range of management and policy issues, including but not limited to, emergency management (Kapucu, 2006b), regional economic development (I. W. Lee, Feiock, & Lee, 2012), education performance (Schalk, Torenvlied, & Allen, 2010), transportation policy (Henry, Lubell, & McCoy, 2011; Weir, Rongerude, & Ansell, 2009), environmental manage-ment (Jasny, 2012; Robins, Bates, & Pattison, 2011; Weible, 2011), network performance (Kapucu & Demiroz, 2011), health and social service delivery (Milward, Provan, Fish, Isett, & Huang, 2009; Provan & Huang, 2012; Provan, Isett, & Milward, 2004; Valente, 2010), and nonprofit development and growth (Galaskiewicz, Bielefeld, & Dowell, 2006; Isett & Provan, 2005). The use of SNA has become more common and has been used to make sig-nificant contributions to public administration research.

Through extensive content analysis, this article reviews the state of net-work research in the field of public administration. This article first reviews the definitions of networks, research streams, and challenges in the field. Then, it examines the current status of social network research in public administration by addressing the following questions: What are the subject areas/key topics in which SNA has been used? What is the unit and level of analysis in these studies? What key network analysis measures and methods are used? This systematic analysis of existing literature on SNA includes a broad spectrum of journals in various subfields of public administration and policy. A total of 39 public administration journals were included for a con-tent analysis on the use of SNA in public administration. This article focuses on the methodological issues of network research and explores how SNA as a method has been used to advance network research in public administra-tion. This research addresses some of the methodological concerns of net-work research and further identifies areas where more research efforts are needed to advance network research in public administration.

We found that, although the topics examined through SNA have become more diverse over the past few decades, relatively few articles have exam-ined the intersection of policy networks, governance networks, and




collaborative networks. The field needs more mixed-methods research designs. Research on the substructures of networks and multilevel networks remains limited. The following sections begin with a discussion of the key definitions of networks and research streams, the applications of SNA, and a short review of challenges facing the development of network research. Then, the “Method” section details the selection of the 81 articles and the content analysis, followed by the “Results” section. The article concludes with the key highlights and proposes future research directions.

Networks and SNA in Public Administration

During the last two decades, there has been a clear surge of interest in net-works and network science in the field of public administration (Agranoff & McGuire, 2001; Isett et al., 2011; Lecy et al., 2013; Provan & Lemaire, 2012). This section reviews the definitions of networks and SNA, network research streams in public administration, and challenges in network research.

Networks and Research Streams

There remains a wide disagreement on the definition of networks. A few scholars use networks as a metaphor to describe interorganizational relation-ships (Isett et al., 2011) or as organizational forms that are different from markets or hierarchies (Powell, 1990). Some scholars maintain that it is not even necessary to provide a universal definition of networks (e.g., Borgatti & Foster, 2003). Networks can be generally defined as a set of nodes or actors and relationships between these nodes in sociology, network sciences, and many other disciplines (Borgatti, Everett, & Johnson, 2013). In the field of public administration, networks are defined either as interorganizational collaboration arrangements or as new governance structures designed to achieve a common goal that cannot be achieved (or that cannot be achieved effectively) by one single organization (Agranoff & McGuire, 2001; Koliba et al., 2010; O’Toole, 1997). Despite the differences, most definitions of networks in public administration highlight the importance of collective action, common goals, and relationships between organizations (Provan et al., 2007). The majority of network studies in public administration are focused either on the interorganizational collaborations to achieve manage-ment or policy goals that are beyond the scope of a single organization, or on the network governance structure and process that differs from traditional bureaucratic structure and involves nonstate stakeholders in policy-making and implementation. Although some scholars use networks and SNA inter-changeably, the latter refers to analysis methods for studying structures and



Kapucu et al. 5

interactions within social structures (Scott, 2013). The variations in defining networks connote the wide applicability and scope of the concept of net-works in the field.

To further disentangle the complexity of networks in public administra-tion, researchers categorize network research into different research streams based on the research foci. Rethemeyer and Hatmaker (2008) maintained that existing policy network research and collaborative network research are related but different. Policy network research focuses on networks involving traditional policy makers such as public agencies and legislative officials, along with nontraditional players such as private entities, interest groups, and nonprofits that have an interest in specific policy domains. Collaborative net-work research, however, concerns the provision and delivery of public goods and services or the implementation of public programs (Rethemeyer & Hatmaker, 2008). Isett et al. (2011) reflected on the current status of scholar-ship on networks in public administration and added governance networks to the two network research streams discussed by Rethemeyer and Hatmaker (2008). Governance networks focus on the coordination and governance pro-cesses to achieve common goals. Similarly, Lecy et al. (2013) identified three domains of network research in public administration: policy formation net-works, governance networks, and policy implementation networks. Policy formation networks received relatively little attention compared with the other two domains and also noted that advanced network analysis remains limited in the field. Whole service delivery networks (Provan & Lemaire, 2012), or policy implementation networks (Lecy et al., 2013), are most prev-alent in public administration.

Challenges in Network Research

Although network research has gained momentum in the past few years, some fundamental challenges remain to be addressed (Isett et al., 2011). More research is needed to reach a consensus on definitions of key terms, improve conceptual clarity, and clearly define theoretical frameworks of net-work research (Lecy et al., 2013). Isett et al. (2011) summarized the concep-tual concerns facing public administration network scholarship. They noted a lack of agreement on the definition of networks and units of analysis, incon-sistency in terminology, insufficient attention given to informal networks, and difficulty in defining network boundaries and collecting network data. Lecy et al. (2013) found that several articles failed to describe and define the networks clearly and that very few articles clarified their units of analysis and network boundaries. These issues may pose hurdles to advancing network scholarship within the discipline.




To address these challenges, most of these review articles highlight the need to advance network research methods and apply proper analytical tools and methods. Significant improvement has been made in understanding net-works in public administration from theoretical and conceptual perspectives; however, more work is needed to enhance the methodological rigor of social network research (Robinson, 2006). The ensuing section reviews SNA in public administration.

SNA in Public Administration

SNA is an analysis method and tool used to analyze theoretical constructs and concepts that are defined as relational processes and outcomes (Wasserman & Faust, 1994). A social network consists of both nodes and ties. Nodes, or actors, within a network can represent individuals, groups, organizations, communities, and nations that make up the networks. The relationships between nodes or actors are linked through ties. These ties can indicate com-munication between nodes, information exchange, formal contractual rela-tions, or friendship ties between nodes. The relationships between nodes or actors can be either formal (legal/contractual) or informal (based on trust and understanding, or interpersonal relationships; Binz-Scharf, Lazer, & Mergel, 2012; Borgatti et al., 2013; Provan et al., 2007; Scott, 2013). These various types of relationships can form different types of networks, including but not limited to affiliation networks, resource exchange networks, mentorship net-works, advice networks, information exchange networks, friendship net-works, shared belief networks, and formal coordination networks.

SNA presents ways to analyze dynamic relationships between various actors and to examine complex social processes and various types of interac-tions within social systems. Both intraorganizational and interorganizational relationships can be studied using SNA. In public administration, the unit of analysis can be government employees within a department or an agency, or organizations within a city or a county, or counties within a state, or even states. Networks are being studied using SNA to illustrate the collaborative, cooperative, and conflicting relations between actors involved in the policy process and in policy output (Ingold, 2011).

SNA has been applied to a limited extent in public administration when compared with other fields such as sociology. Yet, with a movement toward collaborative governance and management through networks, there is ample room for the application of SNA in theory and praxis. To understand collab-orative processes, challenges of collective action, and evaluate network out-comes and performance, public administration researchers need to adopt both an egocentric micro approach (common in organizational science) and a



Kapucu et al. 7

macro approach to studying whole networks (Provan & Lemaire, 2012). The past few years have seen an increasing amount of network research and SNA use, in particular. Yet simpler measures of SNA are mostly utilized in the field, and advanced network measures and analytic methods remain under-used when compared with neighboring disciplines such as sociology and political science (Lecy et al., 2013).

Berry et al. (2004) suggested that intellectual cross-fertilization between disciplines such as sociology, political science, and public administration is required to build strong theoretical and methodological frameworks for the understanding and application of networks. Recently, Provan and Lemaire (2012) have drawn lessons and ideas from social and business network litera-ture to advance the network literature in public administration. A recent con-tent analysis of network publications conducted by Lecy and his colleagues suggested that few articles that focus on networks actually utilize SNA meth-ods and measures; those that did apply SNA usually used simple measures, such as centrality. Their sample of 82 articles was chosen from among fre-quently cited journal articles (Lecy et al., 2013). To further understand the status of SNA in public administration, a closer examination of SNA research is needed. This article focuses on methodological issues of network research and reviews a more comprehensive list of articles that apply SNA in public administration. This article describes the types of networks studied in public administration research, discusses methodological advancements and chal-lenges, and identifies research gaps for future network research.

Method

There are a myriad of prominent journals that discuss topics related to public administration and policy issues. We used the list of 39 public administration journals developed by Bernick and Krueger (2010) and Forrester and Watson (1994) to identify relevant network research articles. In their comprehensive review of public administration journals, these authors selected the list of public administration journals based on the mission statements of the jour-nals. They defined these journals as public administration journals in a broader sense. The selected journals publish research on broad topics in pub-lic administration or public policy as well as research covering the subfields of public administration, including public budgeting and finance, public per-sonnel administration, and public organization studies (Forrester & Watson, 1994). Forrester and Watson (1994) ranked public administration and policy journals based on perceptions of journal editors and editorial board members. Bernick and Krueger (2010) provided a more comprehensive ranking of pub-lic administration journals by including citation-based rankings (by including




ISI Thompson Impact Factor scores) along with the perception approach. Bernick and Krueger (2010) combined both objective and subjective mea-sures of gauging quality and rankings of public administration journals. We adopted Forrester and Watson’s (1994) and Bernick and Krueger’s (2010) list of public administration journals, as their list is relatively comprehensive and their research was focused on the quality of public administration journals.

Articles for the study were identified in four steps. First, all 39 journals were searched by relevant keywords, including networks, network analysis, collaboration, and collaborative (to include articles discussing both collabor-ative governance and collaborative public management). Articles were selected based on what was found using the search terms either in article titles, abstracts, or keywords. Our first round of searching fetched 1,279 articles in relevant journals. Second, once these articles were identified, we read the abstracts to short list articles that focused only on networks. We excluded articles that used networks as metaphors and discussed the broader themes of collaborative governance and collaboration. Of these 1,279 articles, 677 focused on networks. The third step of screening was carried out by identify-ing articles that used SNA as part of their methods sections. A total of 141 such articles were identified. To make sure that no SNA article was missed, a full-text search for “network analysis” was conducted for each identified journal. Some SNA articles in journals such as Organization Studies, Journal of Management Studies, and Human Relations discussed topics and issues that fall outside the realm of public administration and public policy, such as inter-organizational trust in private companies. In the last step, we excluded these articles from our final analysis, which left us with a total of 81 articles for further analysis. We reviewed each article carefully to ensure that each SNA article in these journals covered a topic relevant to public administration.

Table 1 includes the list of journals and the number of network-related articles in each of the four steps. The Journal of Public Administration Research and Theory has the highest number of SNA articles (15) short-listed for our further content analysis, followed by Policy Studies Journal (10) and Public Administration Review (8). Although we used a sophisticated method to search for network studies and SNA research in 39 journals, we might not have included all the relevant network articles in our discussion. To join recent conversations that review the status of network research in public administration (e.g., Lecy et al., 2013; Robinson, 2006), we reviewed articles that were published after 1997, when O’Toole (1997) published his influen-tial article on “treating networks seriously” (p. 45).

Once the list of 81 articles was finalized, we read each of the articles care-fully and conducted content analysis. There are several methods for qualita-tive data coding, such as open coding, axial coding, and selective coding



Kapucu et al. 9

Table 1. Number of Relevant Articles Found in PA Journals.

No. of articles

No. of articles

SNA articles

SNA PA articles

Journal First step Second step Third step Fourth step

Administration & Society (A&S)

61 28 4 3

The American Review of Public Administration (ARPA)

45 34 6 6

Australian Journal of Public Administration (AJPA)

46 25 1 1

Canadian Public Administration (CPA)

39 9 0 0

Evaluation Review (ER) 6 1 1 1Financial Accountability and

Management (FAM)6 2 0 0

Human Relations (HR) 17 16 12 3International Journal of Public

Administration (IJPA)36 23 2 2

International Review of Administrative Sciences (IRAS)

40 19 1 1

Journal of Accounting and Public Policy (JAPP)

3 0 0 0

Journal of Health Politics, Policy, and Law (JHPPL)

9 7 2 1

Journal of Management Studies (JMS)

33 33 5 2

Journal of Policy Analysis & Management (JPAM)

29 19 18 3

Journal of Public Administration Research &Theory (JPART)

76 52 15 15

Journal of Public Budgeting, Accounting and Financial Management (JPBAFM)/Public Budgeting and Financial Management (PBFM)

2 0 0 0

Journal of Public Policy (JPP) 17 13 1 1Journal of Urban Affairs (UA) 32 21 6 5Municipal Finance Journal

(MFJ)0 0 0 0

National Tax Journal (NTJ) 0 0 0 0

(continued)




No. of articles

No. of articles

SNA articles

SNA PA articles

Journal First step Second step Third step Fourth step

Nonprofit Management & Leadership (NML)

36 12 4 4

Nonprofit & Voluntary Sector Quarterly (NVSQ)

80 40 5 4

Organization Studies (OS) 102 43 14 2Policy Sciences (PS) 21 9 3 0Policy Studies Journal (PSJ) 55 29 13 10Policy Studies Review

(PSR)/Review of Policy Research

44 23 2 2

Political Psychology (PP) 13 8 1 0Political Science Quarterly

(PSQ)0 0 0 0

Public Administration & Development (PAD)

42 10 0 0

Public Administration: An International Quarterly (PAIQ)

146 56 4 1

Public Administration Quarterly (PAQ)

14 3 1 0

Public Administration Review (PAR)

133 96 8 8

Public Budgeting and Finance (PBF)

4 0 0 0

Public Finance Quarterly (PFQ)/Public Finance Review (PFR)

0 0 0 0

Public Performance and Management Review (PPMR)

28 9 4 1

Publius (Pub) 12 5 1 0Review of Public Personnel

Administration (RPPA)2 0 0 0

Social Science Quarterly (SSQ) 9 5 3 1State & Local Government

Review (SLGR)8 7 0 0

Urban Affairs Review (UAR) 33 20 4 4Total Number 1,279 677 141 81

Note. PA = public administration; SNA = social network analysis.

Table 1. (continued)



Kapucu et al. 11

(Saldaña, 2012; Strauss & Corbin, 2007). As the focus of this research is to review existing network research, rather than to develop a theory of net-works, we used the open-coding process to explore and further categorize the key concepts of the network articles (Strauss & Corbin, 2007). Following previous research that reviewed the topical themes in the field of public administration (Bingham & Bowen, 1994; Lan & Anders, 2000), we inte-grated the open-coding process with the use of preestablished coding themes (Bowen & Bowen, 2008). We conducted open coding to identify the topics by examining research questions, hypotheses, and key findings. We also used preestablished categories and concepts from Borgatti et al. (2013) and Babbie (2012) to code the units and levels of analysis, data collection methods, net-work measures, and analysis. We applied the three network research streams developed by Rethemeyer and Hatmaker (2008) and Isett et al. (2011) to code the research streams. The coded data were organized and saved in spread-sheets for further comparison, categorizing, and analysis.

Results

This section reports how SNA has been used in the field of public administra-tion. In particular, it identifies the public management problems and policy domains in which SNA is used and examines whether specific topics are clustered around a certain network research stream. Then, it discusses the units and levels of analysis, key measures, and analysis tools applied to study these particular problems and policy issues.

To provide an overall picture of the evolution of SNA research, the num-ber of SNA-related publications was plotted against years in Figure 1. Over the past decade, public administration has experienced a rapid increase in network research (Berry et al., 2004; Kapucu & Demiroz, 2011; Provan, Veazie, Staten, & Teufel-Shone, 2005). More than 600 articles emphasize the role and significance of networks in public administration and related disci-plines. Yet applying SNA in network research is a fairly recent practice in public administration. As shown in Figure 1, the application of SNA gained momentum in network research in public administration after 2005.

Topics in Public Administration Network Research

Previous reviews of network research in public administration have grouped various types of networks into three categories: policy networks, governance networks, and collaborative networks (Isett et al., 2011). As Figure 2 sug-gests, our review of the 81 SNA articles shows that half of the articles fall under the governance networks research stream. Articles in the policy




networks and collaborative networks streams each account for nearly one third of the total number, respectively. In addition, 7.5% of the articles fall under both policy and governance networks research, and approximately 9% fall under both governance and collaborative networks research.

0

10

20

30

40

50

60

70

80

90

100

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

# of Articles on

Networks

Figure 1. Number of articles on networks and SNA articles published since 1997.Note. SNA = social network analysis.

Figure 2. SNA utilization in network research streams in public administration.Note. SNA = social network analysis.



Kapucu et al. 13

In addition to examining the broad research streams, we took one step further to look closely at what specific management and policy issues are studied through the lens of SNA within each research stream. Nearly 85% of the 81 SNA articles (68) center on the following management issues or policy domains: health and human services, regional or community alliances, eco-nomic development, emergency management, environmental policy, educa-tion policy, urban planning and development (Figure 3).

It is interesting to note that some of these management or policy issues are closely clustered around a specific network research stream identified in the literature. The network visualization of the 81 articles in Figure 4 shows that governance networks clearly stand out as the network research stream that is utilizing SNA. Moreover, the topic of regional and community alliances is the most popular topic addressed through governance network research. Within the policy networks research stream, economic development is the most popular policy area of study. Another important finding from this net-work is that SNA research on emergency management is restricted to collab-orative and governance networks and does not currently fall under the policy networks research stream.

A closer look at the specific topics shows that the topic in public admin-istration that is most intensively studied through SNA has been health and human services. Among these 15 articles (18.5%), 6 are on community mental health service provider networks (Huang & Provan, 2007; Milward et al., 2009; Provan & Huang, 2012; Provan, Huang, & Milward, 2009;

Figure 3. Management issues and policy domains of network research in public administration.Note. R&D = research and development; SNA = social network analysis.




Provan et al., 2004; Rethemeyer, 2007). Provan and Milward’s research on collaboration networks of health service organizations has been widely rec-ognized in the field. Besides community mental health networks, Provan, Beagles, Mercken, and Leischow (2012) have recently examined the net-work of a public smoking cessation program. Other topics covered under health and human services are relocation of the disabled (Poole, 2008), behavioral health of children (Bunger, 2012), federal health policy for Medicare prescription (Heaney, 2006), and public health cooperation on the U.S.–Mexico border (Collins-Dogrul, 2012). This research focuses on how health and human services are administered and managed through net-works. Relevant policies and programs are studied, and their effectiveness is evaluated by the performance of service delivery networks.

The second most commonly researched topic utilizing SNA is regional and community alliances and collaboratives, with 13 articles (16%). This stream of research includes various subtopics such as interlocal agreements (ILAs; LeRoux & Carr, 2010), elite interaction and interlocal networks (Moore, Sobieraj, Whitt, Mayorova, & Beaulieu, 2002; Ruigrok, Peck, & Keller, 2006; Vidovich & Currie, 2012), community development and com-munity collaboratives (Nowell, 2009; Pope & Lewis, 2008; Shea, 2011;

Figure 4. Network research streams and topics in network research.Note. Each SNA article node has a number attached to it. These numbers connote the number attached to each SNA article reviewed that is provided in the “References” list. For instance, Node 1 is the article in the reference list followed by [1] after its reference; Node 2 is the article in the reference list followed by [2]. SNA = social network analysis; R&D = research and development.



Kapucu et al. 15

Varda, 2011). Other subjects covered under this research stream include Danish mayor networks (Villadsen, 2011), NGO networks with government (Neal, 2008), European cohesion policy (EU partnership principle; Jordana, Mota, & Noferini, 2012), and local government contractual networks (Andrew, 2009).

Of the 81 articles that we reviewed, 10 focused on economic development issues. Five articles studied local economic development collaboration and networks (Feiock, Lee, & Park, 2012; Feiock, Lee, Park, & Lee, 2010; Hawkins, 2010; I. W. Lee, Feiock, & Lee, 2012; Y. Lee, Lee, & Feiock, 2012), while the rest explored subtopics of economic development, such as creation of employment avenues (Jokisaari & Vuori, 2010), regulation of markets (Fischer, Ingold, Sciarini, & Varone, 2012), innovation policy (Caloffi & Mariani, 2011; Cao & Prakash, 2011), and international sustain-ability networks (Zeemering, 2012).

Emergency management—in particular, disaster response networks—was discussed in 10 (12%) of the 81 articles identified as using SNA. Of the 10 articles, 6 were written by Kapucu and his colleagues. Besides discussing emergency response networks pertaining to certain events, such as 9/11 and Hurricane Katrina, some articles discussed the role of the public sector in emergency management (Kapucu & Van Wart, 2006), interstate partnerships in disasters (Kapucu, Augustin, & Garayev, 2009), emergency management at the local level (Choi & Brower, 2006), and communication networks (Kapucu, 2006a).

More recently, topics studied through SNA have become more diverse. Topics on environmental management and government climate policy are being addressed and described using a network approach. All nine articles on environmental management and climate policy using SNA were published in 2010 or later. Recent articles published in public administration journals on environmental management and climate policy are on the Swiss climate pol-icy (Ingold, 2011; Ingold & Varone, 2012), watershed policy and partnerships (Jasny, 2012; Weible, 2011), and natural resource management (Robins et al., 2011). Other interesting and diverse topics using SNA include advocacy coalitions in carnivore management systems (Matti & Sandström, 2011), natural heritage areas (NHAs; Laven, Krymkowski, Ventriss, Manning, & Mitchell, 2010), and rural water supply and sanitation (Shrestha, 2012, 2013). Other topics identified through this research are education policy and reform (seven articles), urban planning and development (six articles), human resource management (three articles), nonprofit management (three articles), and research and development (two articles). Compared with network research in the 1990s or early 2000s, SNA research has been recently used in a much broader management and policy context.




Units of Analysis and Levels of Analysis

The unit of analysis in most of the SNA publications in public administration has predominantly been organizations and agencies. Approximately 72% of the articles that we reviewed use organizations as the unit of analysis. Some examples are disaster response agencies (Kapucu, Arslan, & Collins, 2010; Kapucu & Demiroz, 2011; Kapucu & Garayev, 2012; Kapucu & Van Wart, 2006; Marcum, Bevc, & Butts, 2012), agencies providing health and human services and serving SMI patients (Huang & Provan, 2006; Milward et al., 2009; Provan & Huang, 2012; Provan et al., 2004), and agencies and organi-zations working for economic development in metropolitan regions (Hawkins, 2010; I. W. Lee, Feiock, & Lee, 2012; Y. Lee, Lee, & Feiock, 2012). Among these studies, individuals or organizational representatives were often sur-veyed to collect organizational network data. For instance, Henry et al. (2011) surveyed key stakeholders to specify their partnerships involved in land use and transportation planning in four regions of California. Feiock et al. (2012) surveyed administrators and elected officials in four counties in Florida to understand the collaboration between city governments in economic development.

Relatively few network studies in public administration (14% of the 81 articles reviewed) have focused on individuals as the unit of analysis. Vardaman, Amis, Dyson, Wright, and Randolph (2012) surveyed public school teachers to understand how teachers’ position in a network influenced their perception of changes. Chen and Krauskopf (2012) examined how orga-nizational merge affected the formal and informal networks within a non-profit organization. As Isett et al. (2011) suggested, research on informal networks remains limited in existing literature. Even fewer network studies in public administration have communities or countries as the unit of analy-sis. There are, however, three exceptions (4%). Shrestha (2012, 2013) studied how internal and external social capital can influence the success of commu-nity water projects in Nepal. Cao and Prakash (2011) examined the policy diffusion of ISO 9000 Quality Standards across nations. The units of analysis in the remaining 10% of the 81 articles do not fall under single categories of individuals, organizations, communities, or countries. Some of the articles have mixed units of analysis, while other articles do not provide sufficient information to clearly specify their units of analysis. Overall, the number of studies with foci on individuals, communities, or nations is much smaller than the number of studies focusing on interorganizational networks.

Although the units of analysis are largely organizations, the levels of anal-ysis can vary from individual nodes (ego), to ties between two nodes (dyadic) or three nodes (triadic), to substructures, and even to complete systems at the



Kapucu et al. 17

whole-network level of analysis (Borgatti et al., 2013). The SNA approach allows the researcher to examine the same management problem or policy issue across different levels of analysis (Provan & Milward, 2001). However, the node-level analysis (54%) and whole-network-level analysis (56%) are common in public administration to describe the power or position of an organization, to analyze interorganizational interactions, and to understand the performance, governance, and structural characteristics of a network (Kapucu & Demiroz, 2011; Provan et al., 2007; Provan & Lemaire, 2012). By contrast, triadic-level analysis and substructure-level analysis remain lim-ited. Of the 81 articles reviewed, 17 included triadic measures in their net-work analysis and 23 had measures on substructures of networks. Future research can delve further into the substructures of networks to understand the subgroups within a network, the grouping of powers, or other clustering effects. Researchers can conduct more multilevel analysis of networks to fully take advantage of SNA.

Data Collection Methods

Data for SNA have been collected using both primary data collection meth-ods (e.g., field surveys, online survey questionnaires, face-to-face structured and semistructured interviews) and secondary data (e.g., archival data from newspapers, news reports, situation reports, online company profiles, data-bases). The methods categories discussed below are not mutually exclusive. For instance, surveys, interviews, and documents may be used in the same article, which denotes a mixed-methods design.

Administering surveys is the most frequently used method of collecting network data in public administration research. Of 81 articles, 50 (62%) used surveys to collect network data. Twenty-nine articles (38%) used secondary data collection methods that involved archival data. Content and document analyses were conducted to analyze newspapers and situation reports, annual company reports, and government reports. Of the 29 articles that used archi-val data, 16 also used primary methods of data collection, such as surveys, questionnaires, and interviews, to obtain additional information on the net-works being studied. In many cases, secondary data are used only as a means to develop a list of network actors to be surveyed or interviewed as part of the methodology. Of the 81 articles, 43 (53%) collected network data from inter-views, 9 of which were specified as semistructured interviews. Interviews are commonly applied as a supportive method to achieve triangulation in net-work research or to get qualitative data and rich information about networks. Other data collection methods are rarely used. Four articles used observa-tional data, and only one article used focus groups for data collection.




Overall, there is an increasing use of mixed methods of data collection. Of the 81 articles, 28 (35%) used both quantitative and qualitative methods to collect network data. However, the majority of articles relied mostly on sur-vey data or qualitative interviews. Studies relying heavily on survey data require participants to recall information pertaining to networks and interac-tions before a policy change, an emergency, or the implementation of a pro-gram. As a consequence, researchers may face the challenges of selection bias and the internal validity of the results. On the other side, collecting sec-ondary data and archival data may help produce less biased data; however, the challenge of getting information on the complete network datasets is dif-ficult. Hence, more future research should consider integrating quantitative approaches with qualitative approaches. Another challenge in the field is to study the sustainability, maturation, and evolution of networks. Systematic longitudinal designs and analysis remain largely missing. These findings lend support to previous research that identified the need to integrate quantitative and qualitative designs in network research and to conduct more longitudinal analysis of network change and evolution (Berry et al., 2004; Provan et al., 2007; Provan & Lemaire, 2012). As the field continues to grow, more diverse approaches to collect network data are needed to further advance network research in public administration.

Key Measures and Analytical Methods

The content analysis further identified the key network measures used for network analysis. Most articles used both whole-network measures, such as density and centralization, along with node-level measures, such as degree centrality and betweenness centrality (see Borgatti et al., 2013, or Scott, 2013, for more information about these measures and advanced analytic tech-niques). Although most articles used measures that applied to both the node-level analysis and whole-level analysis, many authors did not clearly spell out the level at which analysis was carried out or the rationale behind using measures that belong to different levels of analysis.

The use of centrality measures is common in social network research in public administration. Of the 81 articles, 52 (65%) used some measures of centrality, the most commonly used being degree centrality, betweenness centrality, and eigenvector centrality. Degree centrality and eigenvector cen-trality measures are used to measure the position, power, or resource acces-sibility of individual nodes within networks (e.g., Choi & Brower, 2006; Huang & Provan, 2007). Betweenness centrality is used to understand how a node influences the flow of resources or information within networks (e.g., Ansell, Reckhow, & Kelly, 2009) or the brokering role of individual nodes



Kapucu et al. 19

(e.g., Ingold, 2011; Ingold & Varone, 2012; Jokisaari & Vuori, 2010). The second most commonly applied measure was network density (a measure of whole networks), which is found in 32 (40%) of the articles. A total of 15 (19%) articles also used network centralization. Network density and central-ization are used to measure the cohesion or connectedness of networks (Borgatti et al., 2013) and describe the structural characteristics of networks (e.g., Milward et al., 2009).

Group-level network measures were applied to a limited extent. A few articles applied triad network measures (e.g., Andrew, 2009; Feiock et al., 2012; Feiock et al., 2010; Fischer et al., 2012; Henry, Lubell, & McCoy, 2012; Y. Lee, Lee, & Feiock, 2012; Oliver & Montgomery, 2008; Varda, 2011). The key measure used for analyzing triads is transitivity effect (for directed data, transitivity measures whether the connection between Nodes A and B and the connection between Nodes B and C can lead to a connection between Nodes A and C). Analysis of the subgroups in existing network stud-ies has been limited. Three articles included clique analysis, and three included cluster analysis. Kapucu et al. (2010) and Kapucu et al. (2009) applied clique analysis to identify the subset of organizations that work closely within disaster response networks. Ansell et al. (2009) conducted clique analysis to identify the key stakeholder groups in the context of urban school reform. Cluster analysis is also used to classify communities into dif-ferent groups according to the “similarity of network connections” (Shrestha, 2012, p. 15) and to cluster the stakeholders of environmental management into different groups based on their beliefs (Weible, 2011).

Another important measure for analyzing the network position and net-work substructures is structural equivalence. Structural equivalence exam-ines “the direct connections of an actor to others in the network” (Borgatti et al., 2013, p. 207). For undirected data, two nodes are structurally equivalent when they are connected with the same other actors. For directed data, two nodes are structurally equivalent when they send ties to the same actors and receive ties from the same actors in the network (Borgatti et al., 2013). Eight articles applied structural equivalence/blockmodeling using CONCOR to understand the roles played by different stakeholder groups, to measure trade competition, to identify advocacy coalitions, and to evaluate the impact of similar network positions on the adoption of a new program, cooperative behavior, and individual perception about victimization at the workplace (e.g., Cao & Prakash, 2011; Ingold, 2011; Ingold & Varone, 2012; Jokisaari & Vuori, 2010; Lamertz & Aquino, 2004; Lambright, Mischen, & Laramee, 2010; Rethemeyer, 2007; Robins et al., 2011).

Besides descriptive network measures, researchers have begun to use more advanced statistical analysis techniques such as Quadratic Assignment




Procedure (QAP; for example, Bunger, 2012; Chen & Krauskopf, 2012), Exponential Random Graph Modeling (ERGM; for example, Feiock et al., 2010; Robins et al., 2011), and Stochastic Actor-Oriented Models (SAOMs; for example, Andrew, 2009; Fischer et al., 2012). Table 2 provides a detailed list of research topics studied using these inferential network analysis models. Advanced SNA techniques such as QAP were used in nine articles that com-pare and analyze the relationships between different network datasets (e.g., Bunger, 2012; Chen & Krauskopf, 2012; Jasny, 2012; Lamertz & Aquino, 2004; Y. Lee, Lee, & Feiock, 2012; LeRoux & Carr, 2010; Matti & Sandström, 2011; Provan & Huang, 2012; Stephens, Fulk, & Monge, 2009). Based on the product–moment correlations of random permutations on the rows and col-umns of the network matrices, QAP can correct for autocorrelation (Borgatti, Everett, & Freeman, 2002). Multiple regression with QAP (MRQAP) enables researchers to test the level of association between interdependent network variables (Krackhardt, 1988; van Duijn & Huisman, 2011).

ERGM was used in seven articles to study the emergence of network ties or the stochastic process of network formation by examining the microcon-figurations or structural characteristics of a network, such as reciprocal ties, in two-stars, out two-stars, two paths, three-cycles, and transitive triplets (e.g., Feiock et al., 2010; Henry et al., 2011; Jasny, 2012; Laven et al., 2010; I. W. Lee, Feiock, & Lee, 2012; Park & Rethemeyer, 2014; Robins et al., 2011). SAOMs are developed for analyzing longitudinal network data and explaining the evolution of network structures over time (Lubell, Scholz, Berardo, & Robins, 2012; Snijders, van de Bunt, & Steglich, 2010). Our anal-ysis suggests that advanced SNA methods are mostly utilized in the study of policy and governance networks and are underutilized in the study of imple-mentation or collaborative networks.

Recent years have seen a gradual movement from simple descriptive net-work analysis and visual mapping to more inferential analysis and theory testing. The increasing use of advanced analysis techniques implies that social network research in public administration is moving beyond the early stage that uses simple measures and analysis techniques to describe charac-teristics of nodes, subgroups, or networks. In fact, with recent advancement in statistical network analysis, multivariate techniques such as QAP, ERGM, and SAOMs have been built into the SNA package (Borgatti et al., 2013). With these tools, researchers can study the relationships between multiple network matrices, examine the influence of endogenous network structures and microconfigurations on the formation of networks, and explore the evo-lution of network structures. Researchers can conduct more theory-driven explanatory network research as well as comparative and longitudinal net-work research.



Kapucu et al. 21

Table 2. Statistical Network Analysis Models.

SNA models Topics

QAP: Examine the correlation between two network matrices through a random permutation approach (Borgatti, Everett, & Johnson, 2013).

The relationships between perceived trustworthiness, competition, and administrative cooperation among nonprofit organizations (Bunger, 2012)

Relationship between the workflow network, problem-solving network, mentoring friendship network, and socio-emotional support network (Chen & Krauskopf, 2012)

The relationships between donation behavior and congressional voting, between organizational type and participation in collaboration forums (Jasny, 2012)

The influence of informal and formal status on victimization perception (Lamertz & Aquino, 2004)

The impact of perceived competitive or cooperative relationships on the formation of informal policy networks for economic development (Y. Lee, Lee, & Feiock, 2012)

The relationships between interpersonal networks of elected officials and public administrators, and the formation of interlocal agreements (LeRoux & Carr, 2010)

The influence of perceived influence and perceived belief on the formation advocacy coalitions in the context of environment management (Matti & Sandström, 2011)

Whether the ties between health and human service organizations for information sharing, contracts, influence, service referrals, and reputation change over time (Provan & Huang, 2012)

The relationship between interpersonal communication and interpersonal trust, and the interorganizational partnership building (Stephens, Fulk, & Monge, 2009)

ERGM: Predict or explain the existence of a network tie by modeling dependence among network ties and examining the microconfigurations of a network (Lusher, Koskinen, & Robins, 2012; Robins, Bates, & Pattison, 2011).

The impact of actor attributes and microconfigurations on collaboration in governmental innovation (Robins et al., 2011)

Predict the emergence of collaboration ties in the context of economic development based on the substructural characteristics of the network (Feiock, Lee, Park, & Lee, 2010)

The influence of belief clusters and social capital on collaboration in regional land use and transportation planning (Henry et al., 2010)

(continued)




SNA Software Programs

UCINET (Borgatti, Everett, & Freeman, 2002) is the most widely used soft-ware for SNA in public administration. This is consistent with the finding of Knoke and Yang’s (2008) research that suggested UCINET, with its fre-quently updated and improved versions, is the most popular software for net-work analysis. Our results show that, of the 81 SNA articles short-listed, 50 (62%) used UCINET. Seventeen articles (21%) did not mention the software used in carrying out SNA. Two articles used the software VISONE (Brandes & Wagner, 2004). This software is used for interactive visualization of net-works and sociograms and for analyzing whole networks. For visualization, the more common approach in the majority of the articles was to use the NetDraw subroutine in UCINET.

Three articles used the StOCNET software package for advanced statisti-cal analysis of networks. This software is a comprehensive tool and includes

SNA models Topics

How the reciprocity and social clustering structures influence the formation of collaboration ties among local governments to address economic development issues (I. W. Lee, Feiock, & Lee, 2012)

The influence of the microconfigurations on the formation of collaboration ties in managing national heritage (Laven, Krymkowski, Ventriss, Manning, & Mitchell, 2010)

The influence of network density on the participation in collaboration forums (Jasny, 2012)

The influence of the structural configurations of the network on collaboration in the context of adult basic education policy networks (Park & Rethemeyer, 2012)

SAOMs: Explain the change of network ties and evolution of network structures over time (Snijders, van de Bunt, & Steglich, 2010).

The change of contractual ties between local governments for service integration over time (Andrew, 2009)

The evolution and structural changes of policy networks in the Swiss telecommunication industry over two decades (Fischer, Ingold, Sciarini, & Varone, 2012)

Note. QAP = quadratic assignment procedure; ERGM = exponential random graph modeling; SAOMs = stochastic actor-oriented models.

Table 2. (continued)



Kapucu et al. 23

higher level analysis for structural equivalence/blockmodeling using the BLOCKS and SIENA routine. SIENA (merged into R) is known as the analy-sis of repeated measures on networks and uses the same process as ERGMs. This package was developed by Stokman, Snijders, and van Duijn at the University of Groningen in the Netherlands (Ingold & Varone, 2012; Ripley & Snijders, 2011). SIENA is sophisticated software that allows multiple waves of longitudinal data in analyzing networks (Ripley & Snijders, 2011; Snijders, 2005). The development of these various software programs has allowed researchers to further explore social network research in public administration.

Discussion and Conclusion

As O’Toole’s (1997) call for more research on networks in public administra-tion, the number of network research articles has greatly increased (Isett et al., 2011; Provan et al., 2007; Provan & Lemaire, 2012). However, more methodological advancements and rigor are needed. Previous reviews have suggested that researchers need to integrate quantitative methods with quali-tative methods, explore large-N studies, apply multiple types of analysis approaches, and conduct advanced network analyses (Berry et al., 2004; Isett et al., 2011; Lecy et al., 2013; Provan et al., 2007; Provan & Lemaire, 2012; Robinson, 2006). Taking a methodological focus, this study reviewed 81 SNA articles in public administration journals and examined how SNA has been used. Built on the recommendations provided in previous review arti-cles, findings reiterated below aim to identify the research gaps that need to be addressed. These findings can be helpful for future network research in public administration.

First, network research in public administration has covered an increasing array of management issues, governance challenges, and policy domains. The diverse subtopics examined range from human and health services to emergency management and economic development, and so on. However, relatively few articles have examined the intersection and linkages between policy networks, governance networks, and collaborative networks. The pol-itics–administration divide in network research noted by Rethemeyer and Hatmaker (2008) still remains unaddressed. Future research can pay more attention to the impact of policies and/or governance structures on manage-ment networks or the influence of collaborative networks on the policy-mak-ing, policy implementation, or governance processes.

Second, the field needs more network research on individuals or com-munities as the units of analysis, more research on the substructures of net-works, and multilevel network research. It is not surprising that in public




administration, most of the network studies focus on organizations or agen-cies as units of analysis. Yet, to examine the untapped or understudied net-works such as informal networks (Isett et al., 2011) and to understand the network content and context (Borgatti, Brass, & Halgin, 2014), we need to be more open to exploring interpersonal relationships or interactions between individuals and the dynamic relationships between communities. Furthermore, the majority of existing network research focuses on describ-ing the relational characteristics at the node or network level. Compared with node-level or network-level analysis, substructure analysis of networks remains limited. The analysis of the substructures of a network can allow researchers to identify important stakeholder groups and examine clustering effects. More multilevel network studies are needed to fully take advantage of the nature of network research.

Third, more mixed-methods research designs are needed to further enhance the reliability and validity of network research in public administra-tion. Currently, administering surveys is the most widely used method to col-lect network data. Yet, as noted by Henry et al. (2012), the hybrid method that combines the roster approach and the free-recall name generator is a more effective survey approach in identifying actors and capturing interactions between actors. In addition, qualitative network research can provide in-depth information that quantitative network research cannot capture, such as the barriers to or the rationale for collaboration. Each type of data collection technique has its own strengths and limitations. Our analysis of the 81 articles suggests that there has been an increasing use of mixed methods of data col-lection in network research. Future research designs should continue using multiple types of data collection methods to overcome the constraints of one method.

Finally, use of SNA, especially advanced SNA techniques, remains lim-ited in network research within the field. Besides descriptive research and visual mapping of relational data, recent statistical advancements in SNA have allowed researchers to address complex explanatory questions. SNA is often combined with other statistical methods for advanced analysis of public policies (e.g., Ingold, 2011). Yet SNA can be used as a stand-alone statistical analysis method to examine a wide range of topics in the field. This research shows that scholars in the field have begun to use more advanced SNA techniques, such as QAP, ERGM, and SAOMs, to examine relationships between network matrices, network structures, and network development.

Future research may apply advanced network analysis to explore new research territory. A major body of network research in public administra-tion concerns the performance of networks or the effectiveness of



Kapucu et al. 25

managing networks. Measures such as centrality and density remain useful for analyzing how public management networks change over time or due to an implementation of a service delivery program. Meanwhile, researchers may need to continue to explore other innovative approaches to measure network performance, as the nature of networks in different contexts may require different ways of measuring performance and effectiveness. Inferential network analysis can help address questions about the relation-ships between networks and the dependence of network structure, network formation, and the development of network relationships. For instance, QAP can be used to compare planned emergency networks and response networks of catastrophic disasters. Researchers can go beyond describing the similarity between and the structural characteristics of the two types of emergency networks. Researchers can explain whether the planned emer-gency network correlates with the actual response network and how the structural characteristics of the planned emergency network influence the performance of the actual disaster response network. A remaining challenge in the field is to study the design, development, maturation, evolution, and sustainability of networks. Most scholars have identified that cross-sec-tional research and temporary snapshots of networks are not enough for making generalizations and creating more dynamic models for their theo-retical constructs (Choi & Brower, 2006; Lambright et al., 2010; J. Lee & Kim, 2011). The application of longitudinal analysis is largely missing in the field (Provan et al., 2007; Robinson, 2006). Using advanced network analysis tools such as QAP, ERGM, and SAOMs, researchers can conduct more comparative network studies and longitudinal network analysis to develop stronger theoretical frameworks and to bring conceptual clarity to the study of networks in the field.

This research has some limitations. The list of 39 journals may not have exhausted journals that published public administration–related network research. Future works may further expand the research by including non-public administration journals that publish network research related to public organizational contexts. Compared with the rapidly growing interest in col-laborative governance and network research, empirical studies using SNA remain limited. This makes a quantitative meta-analysis less likely for now. Our future research will conduct co-citation analysis and multidimensional scaling to identify clusters of SNA research and to further analyze theoretical frameworks that are applied in the specific research clusters.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.




Funding

The author(s) received no financial support for the research, authorship, and/or publi-cation of this article.

References

Agranoff, R., & McGuire, M. (2001). Big questions in public network manage-ment research. Journal of Public Administration Research and Theory, 11, 295-396.

Akkerman, A., Torenvlied, R., & Schalk, J. (2012). Two-level effects of interorga-nizational network collaboration on graduate satisfaction: A comparison of five intercollege networks in Dutch higher education. The American Review of Public Administration, 42, 654-677. [2]

Alpert, L., Gainsborough, J. F., & Wallis, A. (2006). Building the capacity to act regionally: Formation of the regional transportation authority in South Florida. Urban Affairs Review, 42, 143-168. [3]

Andrew, S. A. (2009). Regional integration through contracting networks: An empiri-cal analysis of institutional collection action framework. Urban Affairs Review, 44, 378-402. [4]

Ansell, C., & Gash, A. (2008). Collaborative governance in theory and practice. Journal of Public Administration Research and Theory, 18, 543-571.

Ansell, C., Reckhow, S., & Kelly, A. (2009). How to reform a reform coalition: Outreach, agenda expansion, and brokerage in urban school reform. Policy Studies Journal, 37, 717-743. [1]

Babbie, E. (2012). The practice of social research (13th ed.). Belmont, CA: Wadsworth Cengage Learning.

Bernick, E., & Krueger, S. (2010). An assessment of journal quality in Public Administration. International Journal of Public Administration, 33, 98-106.

Berry, F. S., Brower, R. S., Choi, S. O., Goa, W. X., Jang, H. S., Kwon, M., & Word, J. (2004). Three traditions of network research: What the public man-agement research agenda can learn from other research communities. Public Administration Review, 64, 539-552.

Bingham, R. D., & Bowen, W. M. (1994). “Mainstream” public administration over time: A topical content analysis of public administration. Public Administration Review, 54, 204-208.

Binz-Scharf, M., Lazer, D., & Mergel, I. (2012). Searching for answers: Networks of practices among public administrators. The American Review of Public Administration, 42, 202-225.

Borgatti, S. P., Brass, D. J., & Halgin, D. S. (2014). Social network research: Confusions, criticisms, and controversies. In D. J. Brass, G. Labianca, A. Mehra, D. S. Halgin, & S. P. Borgatti (Eds.), Research in the sociology of organizations (Vol. 40, pp. 1-32). Bradford, UK: Emerald.

Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). UCINET for Windows. Harvard, MA: Analytic Technologies.



Kapucu et al. 27

Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. Los Angeles, CA: Sage.

Borgatti, S. P., & Foster, P. C. (2003). The network paradigm in organizational research: A review and typology. Journal of Management, 29, 991-1013.

Bowen, C. C., & Bowen, W. M. (2008). Content analysis. In G. J. Miller & K. Yang (Eds.), Handbook of research methods in public administration (2nd ed., pp. 689-704). Boca Raton, FL: CRC Press.

Brandes, U., & Wagner, D. (2004). Visone—Analysis and visualization of social net-works. In M. Junger & P. Mutzel (Eds.), Graph drawing software (pp. 321-340). New York, NY: Springer.

Bunger, A. C. (2012). Administrative coordination in nonprofit human service deliv-ery networks: The role of competition and trust. Nonprofit and Voluntary Sector Quarterly, 42, 1155-1175. [5]

Caloffi, A., & Mariani, M. (2011). Shaping regional policy responses: The design of innovation poles. Policy Studies, 32, 413-428. [6]

Cao, X., & Prakash, A. (2011). Growing exports by signaling product quality: Trade competition and the cross-national diffusion of ISO 9000 quality standards. Journal of Policy Analysis and Management, 30, 111-135. [7]

Chen, B., & Krauskopf, J. (2012). Integrated or disconnected? Examining formal and informal networks in a merged nonprofit organization. Nonprofit Management & Leadership, 23, 325-345. [8]

Choi, S. O., & Brower, R. S. (2006). When practice matters more than government plans: A network analysis of local emergency management. Administration & Society, 37, 651-678. [9]

Choi, S. O., & Kim, B. T. (2007). Power and cognitive accuracy in local emergency management networks. Public Administration Review, 67(Suppl. s1), 198-209. [10]

Collins-Dogrul, J. (2012). Tertius Iungens brokerage and transnational intersectoral cooperation. Organization Studies, 33, 989-1014. [11]

Currie, G., & White, L. (2012). Inter-professional barriers and knowledge brokering in an organizational context: The case of healthcare. Organization Studies, 33, 1333-1361. [12]

Dekker, K., Volker, B., Lelieveldt, H., & Torenvlied, R. (2010). Civic engagement in urban neighborhoods: Does the network of civic organizations influence par-ticipation in neighborhood projects? Journal of Urban Affairs, 32, 609-632. [13]

Feiock, R. C., Lee, I. W., & Park, H. J. (2012). Administrators’ and elected officials’ collaboration networks: Selecting partners to reduce risk in economic develop-ment. Public Administration Review, 72(Suppl. s1), 58-68. [15]

Feiock, R. C., Lee, I. W., Park, H. J., & Lee, K. H. (2010). Collaboration networks among local elected officials: Information, commitment, and risk aversion. Urban Affairs Review, 46, 241-262. [14]

Fischer, M., Ingold, K., Sciarini, P., & Varone, F. (2012). Impacts of market liberal-ization on regulatory network: A longitudinal analysis of the Swiss telecommu-nications sector. Policy Studies Journal, 40, 435-457. [16]




Forrester, J. P., & Watson, S. S. (1994). An assessment of Public Administration journals. Public Administration Review, 54, 474-482.

Frederickson, H. G. (1999). The repositioning of American Public Administration. PS: Political Science & Politics, 32, 701-711.

Galaskiewicz, J., Bielefeld, W., & Dowell, M. (2006). Networks and organiza-tional growth: A study of community based nonprofits. Administrative Science Quarterly, 51, 337-380. [17]

Gelles, E., Merrick, M., Derrickson, S., Otis, F., Sweeten-Lopez, O., & Folsom, J. T. (2009). Building stronger weak ties among a diverse pool of emergent nonprofit leaders of color. Nonprofit Management & Leadership, 19, 523-548. [18]

Hawkins, C. V. (2010). Competition and cooperation: Local government joint ven-tures for economic development. Journal of Urban Affairs, 32, 253-275. [19]

Heaney, M. T. (2006). Brokering health policy: Coalitions, parties, and interest group influence. Journal of Health Politics, Policy and Law, 31, 887-944. [20]

Henry, A. D., Lubell, M., & McCoy, M. (2011). Belief systems and social capital as drivers of policy network structure: The case of California regional planning. Journal of Public Administration Research and Theory, 21, 419-444.

Henry, A. D., Lubell, M., & McCoy, M. (2012). Survey-based measurement of public management and policy networks. Journal of Policy Analysis and Management, 31, 432-452. [21]

Holman, N. (2007). Following the signs: Applying urban regime analysis to a UK case study. Journal of Urban Affairs, 29, 435-453. [22]

Huang, K., & Provan, K. G. (2007). Resource tangibility and patterns of interac-tion in a publicly funded health and human services network. Journal of Public Administration Research and Theory, 17, 435-454. [23]

Ingold, K. (2011). Network structures within policy processes: Coalitions, power, and brokerage in Swiss climate policy. Policy Studies Journal, 39, 435-459. [24]

Ingold, K., & Varone, F. (2012). Treating policy brokers seriously: Evidence from the climate policy. Journal of Public Administration Research and Theory, 22, 319-346. [25]

Jokisaari, M., & Vuori, J. (2010). The role of reference groups and network position in the timing of employment service adoption. Journal of Public Administration Resesearch and Theory, 20, 137-156.

Isett, K. R., Mergel, I. A., LeRoux, K., Mischen, P. A., & Rethemeyer, R. K. (2011). Networks in public administration scholarship: Understanding where we are and where we need to go. Journal of Public Administration Research and Theory, 21(Suppl. s1), i157-i173.

Isett, K. R., & Provan, K. G. (2005). The evolution of dyadic interorganizational rela-tionships in a network of publicly funded nonprofit agencies. Journal of Public Administration Research and Theory, 15, 149-165. [26]

Jasny, L. (2012). Baseline models for two-mode social network data. Policy Studies Journal, 40, 458-491. [27]

Jokisaari, M., & Vuori, J. (2010). The role of reference groups and network position in the timing of employment service adoption. Journal of Public Administration Research and Theory, 20, 137-156. [28]



Kapucu et al. 29

Jordana, J., Mota, F., & Noferini, A. (2012). The role of social capital within policy networks: Evidence from EU cohesion policy in Spain. International Review of Administrative Sciences, 78, 642-664. [29]

Kapucu, N. (2006a). Interagency communication networks during emergencies boundary spanners in multiagency coordination. The American Review of Public Administration, 36, 207-225. [31]

Kapucu, N. (2006b). Public-nonprofit partnerships for collective action in dynamic contexts of emergencies. Public Administration, 84, 205-220. [30]

Kapucu, N., Arslan, T., & Collins, M. L. (2010). Examining intergovernmental and interorganizational response to catastrophic disasters toward a network-centered approach. Administration & Society, 42, 222-247. [33]

Kapucu, N., Augustin, M. A., & Garayev, V. (2009). Interstate partnerships in emer-gency management: Emergency management assistance compact in response to catastrophic disasters. Public Administration Review, 69, 297-313. [32]

Kapucu, N., & Demiroz, F. (2011). Measuring performance for collaborative public management using network analysis methods and tools. Public Performance & Management Review, 34, 551-581. [34]

Kapucu, N., & Garayev, V. (2012). Designing, managing, and sustaining functionally collaborative emergency management networks. The American Review of Public Administration, 43, 312-330. [35]

Kapucu, N., & Van Wart, M. (2006). The evolving role of the public sector in manag-ing catastrophic disasters: Lessons learned. Administration & Society, 38, 279-308. [36]

Knoke, D., & Yang, S. (2008). Social network analysis. Thousand Oaks, CA: Sage.Koliba, C., Meek, J. C., & Zia, A. (2010). Governance networks in public administra-

tion and public policy. New York, NY: CRC Press.Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression

analysis of dyadic data. Social Networks, 10, 359-381.Lambright, K. T., Mischen, P. A., & Laramee, C. B. (2010). Building trust in pub-

lic and nonprofit networks: Personal, dyadic, and third-party influences. The American Review of Public Administration, 40, 64-82. [37]

Lamertz, K., & Aquino, K. (2004). Social power, social status and perceptual sim-ilarity of workplace victimization: A social network analysis of stratification. Human Relations, 57, 795-822. [38]

Lan, Z., & Anders, K. K. (2000). A paradigmatic view of contemporary public admin-istration research: An empirical test. Administration & Society, 32, 138-165.

Laven, D. N., Krymkowski, D. H., Ventriss, C. L., Manning, R. E., & Mitchell, N. J. (2010). From partnerships to networks: New approaches for measuring US National Heritage Area effectiveness. Evaluation Review, 34, 271-298. [39]

Lecy, J. D., Mergel, I. A., & Schmitz, H. P. (2013). Networks in public administra-tion: Current scholarship in review. Public Management Review, 16, 643-665.

Lee, I. W., Feiock, R. C., & Lee, Y. (2012). Competitors and cooperators: A micro-level analysis of regional economic development collaboration networks. Public Administration Review, 72, 253-262. [40]




Lee, J. (2013). Exploring the role of knowledge networks in perceived e-government: A comparative case study of two local governments in Korea. The American Review of Public Administration, 43, 89-108. [43]

Lee, J., & Kim, S. (2011). Exploring the role of social networks in affective organiza-tional commitment: Network centrality, strength of ties, and structural holes. The American Review of Public Administration, 41, 205-223. [42]

Lee, Y., Lee, I. W., & Feiock, R. C. (2012). Interorganizational collaboration net-works in economic development policy: An exponential random graph model analysis*. Policy Studies Journal, 40, 547-573. [41]

LeRoux, K., & Carr, J. B. (2010). Prospects for centralizing services in an urban county: Evidence from eight self-organized networks of local public services. Journal of Urban Affairs, 32, 449-470. [44]

Lubell, M., Scholz, J., Berardo, R., & Robins, G. (2012). Testing policy theory with statistical models of networks. The Policy Studies Journal, 40, 351-374.

Lusher, D., Koskinen, J., & Robins, G. (Eds.). (2012). Exponential random graph models for social networks: Theories, methods, and applications. London, England: Cambridge University Press.

Marcum, C. S., Bevc, C. A., & Butts, C. T. (2012). Mechanisms of control in emer-gent interorganizational networks. Policy Studies Journal, 40, 516-546. [45]

Matti, S., & Sandström, A. (2011). The rationale determining advocacy coalitions: Examining coordination networks and corresponding beliefs. Policy Studies Journal, 39, 385-410. [46]

McGuire, M. (2006). Collaborative public management: Assess what know and how we know it. Public Administration Review, 66(Suppl. s1), 33-43.

Melkers, J., & Wu, Y. (2009). Evaluating the improved research capacity of EPSCoR states: R&D funding and collaborative networks in the NSF EPSCoR program. Review of Policy Research, 26, 761-782. [47]

Milward, H. B., Provan, K. G., Fish, A., Isett, K. R., & Huang, K. (2009). Governance and collaboration: An evolutionary study of two mental health networks. Journal of Public Administration Research and Theory, 20(Suppl. 1), i125-i141. [48]

Moore, G., Sobieraj, S., Whitt, J. A., Mayorova, O., & Beaulieu, D. (2002). Elite interlocks in three US sectors: Nonprofit, corporate, and government. Social Science Quarterly, 83, 726-744. [49]

Neal, R. (2008). The importance of the state: Political dimensions of a nonprofit net-work in Oaxaca, Mexico. Nonprofit and Voluntary Sector Quarterly, 37, 492-511. [50]

Ney, S. (2012). Making sense of the global health crisis: Policy narratives, conflict, and global health governance. Journal of Health Politics, Policy and Law, 37, 253-295. [51]

Nowell, B. (2009). Out of sync and unaware? Exploring the effects of problem frame alignment and discordance in community collaboratives. Journal of Public Administration Research and Theory, 20, 91-116. [52]

Oliver, A. L., & Montgomery, K. (2008). Using field-configuring events for sense-making: A cognitive network approach. Journal of Management Studies, 45, 1147-1167. [53]



Kapucu et al. 31

O’Toole, L. J. (1997). Treating networks seriously: Practical and research-based agendas in public administration. Public Administration Review, 57, 45-52.

Pappas, J. M., & Wooldridge, B. (2007). Middle managers’ divergent strategic activ-ity: An investigation of multiple measures of network centrality. Journal of Management Studies, 44, 323-341. [54]

Park, H. H., & Rethemeyer, R. K. (2014). The politics of connections: Assessing the determinants of social structure in policy networks. Journal of Public Administration Research and Theory, 24, 349-379. [55]

Ponomariov, B., Kingsley, G., & Boardman, C. (2011). Searching for contracting patterns over time: Do prime contractor and subcontractor relations follow simi-lar patterns for professional services provision? Journal of Policy Analysis and Management, 30, 581-597. [56]

Poole, D. L. (2008). Organizational networks of collaboration for community-based living. Nonprofit Management & Leadership, 18, 275-293. [57]

Pope, J., & Lewis, J. M. (2008). Improving partnership governance: Using a net-work approach to evaluate partnerships in Victoria. Australian Journal of Public Administration, 67, 443-456. [58]

Popp, J. K., MacKean, G., Casebeer, A., Milward, H. B., & Lindstrom, R. (2013). Inter-organizational networks: A critical review of the literature to inform practice. Retrieved from: http://health-leadership-research.royalroads.ca/sites/default/files/Interorganizational%20networks%20a%20critical%20review%20of%20the%20literature%20to%20inform%20practice.pdf.

Powell, W. W. (1990). Neither market nor hierarchy: Network forms of organizations. Research in Organizational Behavior, 12, 295-336.

Provan, K. G., Beagles, J. E., Mercken, L., & Leischow, S. J. (2012). Awareness of evidence-based practices by organizations in a publicly funded smoking cessa-tion network. Journal of Public Administration Research and Theory, 23, 133-153. [62]

Provan, K. G., Fish, A., & Sydow, J. (2007). Interorganizational networks at the net-work level: A review of the empirical literature on whole networks. Journal of Management, 33, 479-516.

Provan, K. G., & Huang, K. (2012). Resource tangibility and the evolution of a pub-licly funded health and human services network. Public Administration Review, 72, 366-375. [59]

Provan, K. G., Huang, K., & Milward, H. B. (2009). The evolution of structural embeddedness and organizational social outcomes in a centrally governed health and human services network. Journal of Public Administration Research and Theory, 19, 873-893. [60]

Provan, K. G., Isett, K. R., & Milward, H. B. (2004). Cooperation and compromise: A network response to conflicting institutional pressures in community mental health. Nonprofit and Voluntary Sector Quarterly, 33, 489-514. [61]

Provan, K. G., & Lemaire, R. H. (2012). Core concepts and key ideas for understand-ing public sector organizational networks: Using research to inform scholarship and practice. Public Administration Review, 72, 638-648.




Provan, K. G., & Milward, B. H. (2001). Do networks really work? A framework for evaluating publis-sector organizational networks. Public Administration Review, 61, 414-423.

Provan, K. G., Veazie, M. A., Staten, L. K., & Teufel-Shone, N. I. (2005). The use of network analysis to strengthen community partnerships. Public Administration Review, 65, 603-613.

Raab, J. (2002). Where do policy networks come from? Journal of Public Administration Research and Theory, 12, 581-622. [63]

Rethemeyer, R. K. (2007). Policymaking in the age of Internet: Is the Internet tending to make policy networks more or less inclusive? Journal of Public Administration Research and Theory, 17, 259-284. [64]

Rethemeyer, R. K., & Hatmaker, D. M. (2008). Network management reconsidered: An inquiry into management of network structures in public sector service provi-sion. Journal of Public Administration Research and Theory, 18, 617-647.

Rhodes, R. A. W. (1996). The new governance: Governing without government. Political Studies, 44, 652-667.

Ripley, R. M., & Snijders, T. A. B. (2011). Manual for SIENA Version 4.0. Oxford, UK: Nuffield College, University of Oxford.

Robins, G., Bates, L., & Pattison, P. (2011). Network governance and environmental management: Conflict and cooperation. Public Administration, 89, 1293-1313. [65]

Robinson, S. E. (2006). A decade of treating networks seriously. Policy Studies Journal, 34, 589-598.

Ruigrok, W., Peck, S. I., & Keller, H. (2006). Board characteristics and involve-ment in strategic decision making: Evidence from Swiss companies. Journal of Management Studies, 43, 1201-1226. [66]

Saldaña, J. (2012). The coding manual for qualitative researchers. Thousand Oaks, CA: Sage.

Sandström, A., & Carlsson, L. (2008). The performance of policy networks: The relation between network structure and network performance. Policy Studies Journal, 36, 497-524. [67]

Schalk, J., Torenvlied, R., & Allen, J. (2010). Network embeddedness and public agency performance: The strength of strong ties in Dutch higher education. Journal of Public Administration Research and Theory, 20, 629-653. [68]

Scott, J. (2013). Social network analysis (3rd ed.). Los Angeles, CA: Sage.Shea, J. (2011). Taking nonprofit intermediaries seriously: A middle-range theory for

implementation research. Public Administration Review, 71, 57-66. [69]Shrestha, M. K. (2012). Self-organizing network capital and the success of collabora-

tive public programs. Journal of Public Administration Research and Theory, 23, 307-329. [71]

Shrestha, M. K. (2013). Internal versus external social capital and the success of com-munity initiatives: A case of self-organizing collaborative governance in Nepal. Public Administration Review, 73, 154-164. [70]

Snijders, T. A. B. (2005). Power and sample size in multilevel linear models. In B. S. Everitt & D. Howell (Eds), Encyclopedia of statistics in behavioral science (Volume 3, pp. 1570–1573). Malden, MA: Wiley.



Kapucu et al. 33

Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. E. G. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32, 44-60.

Stephens, K. J., Fulk, J., & Monge, P. R. (2009). Constrained choices in alliance for-mations: Cupids and organizational marriages. Human Relations, 62, 501-536. [72]

Strauss, A., & Corbin, J. (2007). Basics of quantitative research: Techniques and procedures for developing grounded theory. Thousand Oaks, CA: Sage.

Thurmaier, K., & Wood, C. (2002). Interlocal agreements as overlapping social networks: Picket–fence regionalism in metropolitan Kansas City. Public Administration Review, 62, 585-598. [73]

Valente, T. W. (2010). Social networks and health: Models, methods, and applica-tions. New York, NY: Oxford University Press.

van Duijn, M. A. J. V., & Huisman, M. (2011). Statistical models for ties and actors. In J. Scott & P. J. Carrington (Eds.), The SAGE handbook of social network analysis (pp. 459-483). Thousand Oaks, CA: Sage.

Varda, D. M. (2011). A network perspective on state-society synergy to increase community-level social capital. Nonprofit and Voluntary Sector Quarterly, 40, 896-923. [74]

Vardaman, J. M., Amis, J. M., Dyson, B. P., Wright, P. M., & Randolph, R. V. G. (2012). Interpreting change as controllable: The role of network centrality and self-efficacy. Human Relations, 65, 835-859. [75]

Vidovich, L., & Currie, J. (2012). Governance networks: Interlocking directorships of corporate and nonprofit boards. Nonprofit Management & Leadership, 22, 507-523. [76]

Villadsen, A. R. (2011). Structural embeddedness of political top executives as expla-nation of policy isomorphism. Journal of Public Administration Research and Theory, 21, 573-599. [77]

Wachhaus, A. (2009). Networks in contemporary public administration: A discourse analysis. Administrative Theory & Praxis, 31, 59-77.

Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applica-tions. New York, NY: Cambridge University Press.

Waterhouse, J., & Keast, R. (2012). Strategizing public sector human resource man-agement: The implications of working in networks. International Journal of Public Administration, 35, 562-576. [78]

Weible, C. M. (2011). Political-administrative relations in collaborative environmental management. International Journal of Public Administration, 34, 424-435. [79]

Weir, M., Rongerude, J., & Ansell, C. K. (2009). Collaboration is not enough virtuous cycles of reform in transportation policy. Urban Affairs Review, 44, 455-489. [80]

Zeemering, E. S. (2012). International connections for local government sustainabil-ity initiatives: Networks linking Detroit and Windsor. Journal of Urban Affairs, 36, 119-140. doi:10.1111/j.1467-9906.2012.00648.x [81]

Author Biographies

Naim Kapucu, PhD, is professor of public policy and administration and founding director of the Center for Public and Nonprofit Management (CPNM) in the School of




Public Administration at the University of Central Florida (UCF). He is chair of the Section on Public Administration Research (SPAR) of American Society for Public Administration (ASPA). His main research interests are network governance, emer-gency and crisis management, decision-making in complex environments, and orga-nizational learning and design. His work has been published in Public Administration Review, Administration & Society, Journal of Public Administration Research and Theory, the American Review of Public Administration, and Disasters, among many others. His book Network governance in response to acts of terrorism: Comparative analyses was published in 2012 by Routledge. He teaches collaborative public man-agement, public and nonprofit management, emergency and crisis management, research methodology, and analytic techniques for public administration courses.

Qian Hu, PhD, is an assistant professor in the School of Public Administration at the University of Central Florida. Her research interests include collaborative governance, network studies, policy informatics, and strategic and performance management. Her work has been published or is forthcoming in academic journals such as Public Administration Review, American Review of Public Administration, Public Managemet Review, American Behavioral Scientist, Journal of Computer Informatics, Research Policy, and Journal of Public Affairs Education. She teaches public organization man-agement, research methods, and strategic planning and management courses.

Sana Khosa, PhD, is a visiting instructor at the School of Public Administration at the University of Central Florida. She recently completed her PhD in the Public Affairs doctoral program at the University of Central Florida. Her research interests include collaboration and networking in complex environments and international disaster management.



Documents

The state of network research in Public Administration