using social network analysis to enhance npo research

8/13/2019 Using Social Network Analysis to Enhance NPO Research

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Journal of Community Practice, 18:493512, 2010Copyright Taylor & Francis Group, LLCISSN: 1070-5422 print/1543-3706 onlineDOI: 10.1080/10705422.2010.519683

Using Social Network Analysis to EnhanceNonprofit Organizational Research Capacity:

A Case Study

JENNIFER A. JOHNSON, JULIE A. HONNOLD, and F. PAUL STEVENSL. Douglas Wilder School of Government and Public Affairs, Virginia Commonwealth

University, Richmond, Virginia, USA

As donor agencies become more specific in funding requirements,research that can demonstrate the collaborative efforts of a non-

profit agency with its organizational neighbors and how thoseefforts pay off in terms of capacity and provision of services ishighly useful. Recognizing these benefits, a local funding agency inVirginia commissioned a study to look at the ways in which socialnetwork analysis (SNA) can enhance the data resources availableto nonprofits for funding and grant requests. In this article, we

present a case study of a network of 52 nonprofit organizations

to illustrate the viability of SNA in terms of funding and researchneeds specific to nonprofit organizations. We discuss the outcomesof the case study in terms of how the visual and metric outputsof SNA can be used by nonprofits to enhance the accomplish-ment of their organizational missions and strengthen their grantrequests.

KEYWORDS social network analysis, capacity, grants, research,outcomes

INTRODUCTION

As donor agencies become more specific in their funding requirements(Barman, 2008), particularly with regard to resource sharing and collabora-tion, research that can demonstrate a nonprofit organizations collaborative

Address correspondence to Jennifer A. Johnson, L. Douglas Wilder School ofGovernment and Public Affairs, 919 W. Franklin Street, Richmond, VA 23284. E-mail:

[email protected]


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494 J. A. Johnson et al.

efforts with its organizational neighbors, as well as how well those effortspay off in terms of capacity and provision of services, is highly useful.

Yet, Stoecker (2007) found that, although most nonprofits collect data ona wide variety of topics, most do not use the data, nor do they collect data

useful to their larger interorganizational network. Furthermore, most non-profits have little to no staff trained in research methods, but all claim areal need for such training and skills. This lack of research data and skillshas real consequences for the nonprofit community. Research shows non-profits with stronger research abilities are better able to influence socialpolicy (Appleton, 2003; Fox, 2001) and are more likely to accomplish theirorganizational missions (Bryson, 1995; Letts, Ryan, & Grossman, 1999). It isfor these reasons, among others, that Stoecker (2007) stated that the mostpressing need for nonprofit capacity building efforts is building a strongerresearch tradition inside the organizations.

Recognizing this need to improve research within the nonprofit commu-nity, a prominent local funding agency in Virginia commissioned a study tolook at the ways in which social network analysis (SNA) can enhance non-profit data resources available for funding and grant requests. The researchobjectives for the project were twofold: (a) to explore the viability of SNAin terms of research questions specific to nonprofit organizations and (b) toconduct a pretest of a networking initiative just launched by the fundingagency intended to facilitate interorganizational connections among localnonprofits in a specified geographical region. The start of the project, which

we call the social network analysis of the networking initiative (SNA-NI),

coincided with the October 2007 launch of the online networking initiativedesigned to establish and enhance collaborative relationships among localnonprofits. This article presents the outcome of the first phase of the SNA-NIproject, which is a baseline measurement (pretest) of the interorganizationalnetwork structure of a particular service region to illustrate the usefulness ofthe methodology.

SNA is a descriptive social science methodology that maps, measures,and finds patterns in the connections between people and/or organizations.SNA is interested in how an organization is embedded in a larger system

and how its location influences its actions, power, and resources. SNA yieldstwo forms of dataa visual representation of the network and an orderedlist of the organizations based on the centrality or importance of the organi-zation to the overall network. These forms of data can assist an organizationin demonstrating how extensively they are working with other organiza-tions, both currently and across time, where networking opportunities exist,the networks of neighboring organizations, as well as resource sharing andflow. The methodology can also be used in conjunction with other stan-dard statistical measures to assess whether the organizations collaborationefforts are translating into increased capacity and/or service provisions.


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Social Network Analysis to Enhance Nonprofit Organizational Research Capacity 495

Last, using SNA, a nonprofit organization (NPO) can easily produce com-pelling visual images and ordered data to demonstrate their organizationalcapabilities and needs in support of funding requirements and grantapplications.

To build the network, we used a modified version of Himmelmans(2001) coalition framework to map the relationships between 52 randomlysampled nonprofit organizations along five dimensions, ranging from sim-ple awareness of other organizations up to more intense collaborations.Our aim in this article is to provide an example of how SNA can be usedto strengthen the research agendas of nonprofits that they may better com-pete for scarce resources, better meet funding requirements, and advancetheir interests through policy. We begin with an overview of the theory andmethodology of SNA. We follow with a literature review illustrating whya network approach to research can provide a stronger understanding of

nonprofit organizations. After describing the research design, variables, andmethodological considerations used in this study, we present the SNA resultsincluding visual representations of select relationships. We conclude with adiscussion of the implications our results have for advancing the researchskills and agendas of nonprofit organizations.

SNA: Theory and Method

SNA is both theory and method. Theoretically, the approach takes seriouslythe sociological axiom that all social actors, including both humans andorganizations, are positioned in and influenced by larger social structures.Social structureis a term used to describe persistent patterns of relationshipsamong interacting social actors (Laumann & Knoke, 1986). A social struc-ture is an amalgamation of lasting, patterned social relationshipseitherdirect or indirect linkagesbetween two or more social actors. The ana-lytical focus of SNA is on the relationship between the actors, not on theindividual actor. This relational perspective is built upon several theoreti-cal postulates (Laumann & Knoke, 1986; Wasserman & Faust, 1994). First,the patterns of relational ties comprising a social structure are not purely

random. Instead, the logic of a pattern is governed by the type of socialrelationships, i.e., friendships, business ties, neighborhoods, or organiza-tions comprised within. Second, relational ties operate as exchange conduitsthrough which both material and nonmaterial resources are transferred. Last,an actors position within the structure, and hence access to the resourcesflowing within, constrains and/or enables social action. The theoretical goalof SNA is, therefore, to discover how relationships are patterned insidediverse social structures and how those patterns influence both the flowof resources and the actions, opportunities and power of the social actors

who are operate within.


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Methodologically, SNA provides a precise, quantitative process throughwhich social structures and its constituent relationship patterns can be oper-ationalized, mapped, and measured (Wasserman & Faust, 1994). Because thefundamental element of a social structure is relational, SNA requires three

points of dataactor A, actor B, and the tie or link between them. Thesethree pieces of data comprise the basic SNA unit of analysis. Actors, or whatare called nodes in the SNA lexicon, can be people, organizations, com-puters, or any entity that can process or exchange information or resources.For the purposes of this artilce, all nodes refer to organizations. Relationshipsbetween nodes are called ties, connections, or edges, and can represent anexchange of information, a type of relationship such as collaboration, shar-ing of resources, or any type of contact be it positive or negative. SNAproduces two forms of output; one is visual and the other is mathematical.The visual output is a map or rendering of the network called a social net-

work diagram which displays the nodes and their adjacent links. Figure 1 isan example of the visual output of SNA.

The diagram works well to visually answer questions about the natureof organizational connections. For example, the lack of a connectionbetween F and I in Figure 1 suggests the possibility of new network-ing opportunities. Organization G obviously plays a very important roleconnecting two parts of the network. With three ties each, B and C exhibitthe largest number of connections to other organizations in the network.

FIGURE 1 Example of social network diagram.


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The social network diagram is accompanied by a set of quantitativeSNA metrics, most often comprised of centrality measures. The centralityof a node, such as an organization, is a metric that identifies the promi-nence or importance of a node to the overall functioning of the network.

How important is that organization to the networked community? Does itplay a large or small role? Is it most active? Or is it well-positioned in theflow of information? These questions can be answered using basic central-ity metrics, including degree, betweenness and closeness.1 Degree measuresthe number of ties adjacent to a particular node. For example, in Figure 1,node B has a degree of three. Betweenness measures the extent to which anorganization controls the flow of resources in the network. For example, inFigure 1, nodes E, G, and H have very high betweenness because they con-trol the flow between two otherwise disconnected regions of the network.Closeness represents the distance between a node and all other nodes in the

network. In other words, how many steps or ties between that organizationand all other organizations in the network? For example, in Figure 1, node Eis the closest to all other nodes in the network; its average distance (three)is the lowest of all other nodes. Nodes are rank ordered according to theircentrality with those at the top of the ranking playing the most prominentrole in the network.

These three centrality metrics offer different ways of identifying promi-nent players and their associated roles in the network. Prominent playersare those who are most actively involved in the network both in terms ofactivity and in terms of criticality. Prominent players not only know the

most players, but also know the right people. As such, these measures canbe used independently of one another or used in conjunction to asses theactivity of organizations in a network. The value of each of these metrics isdetermined by the analytical question at hand. For example, if the researchquestion seeks to assess the number of opportunities an organization has tocollaborate (Ahuja, 2000; Powell, Koput, & Smith-Doerr, 1996), then degree

would be appropriate. If the question seeks to differentiate between avenuesfor collaboration to assess which ones have the greatest potential benefit(Brass, Galaskiewicz, Greve, & Tsai, 2004; Gulati & Gargiulo, 1999), then

betweenness would also be beneficial. Or, if the question seeks to assessthe organizations level of embeddness in an organization (Galaskiewicz,Bielefeld, & Dowell, 2006), then closeness would be an appropriate mea-sure. Further inferences can be drawn by layering in organizational attributessuch as size, age and funding levels into the analysis.

As a set of descriptive metrics, these measures cannot tell a researcherwhat the network structure should be, but rather they can effectively informthe researcher as to what the structure actually is. These measures assess

1 See Wasserman and Faust (1994) for a full review of these centrality measures.


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the position and prominence of a node in a particular network. If the num-ber of ties or the presence or absence of particular nodes changes in thedata, the metrics and rankings will possibly change, as well. This presentsa significant challenge for data collection because the researcher must be

able to clearly define the boundary of the population to be sampled; whois in the network and who is not? Network boundaries can expand fromego-networks or networks centered around a single node whereby theego nominates those who should be considered members of the networkstructure (Laumann & Pappi, 1976), to complete networks of an identifiablegroup (Knoke, 1983), to diffuse network that span an entire nation (Levine,1972). Solutions to the boundary problem can include: (a) a position-basedapproach where those actors who occupy a particular position in a socialstructure, such as an organization, would be included and all others wouldbe excluded; (b) an event-based approach identifies boundaries using a par-

ticular event, time period, or region (this is the sampling method used in thisstudy); or (c) a relation-based approach whereby those actors who are ina select type of relationshipscoworkers, family, friendshipinside a par-ticular social arenaschool, business, neighborhoodwould be included(See Laumann, Marsden & Prensky, 1983 or Marin & Wellman, 2009, for acomplete discussion). Sampling procedures can include asking the groupmembers to identify who is in or out, using rosters or membership lists,snowballing where members nominate subsequent members, or randomsampling (Frank, 1977, 1981; Scott, 2000). Decisions on how to solve theboundary specification problem must be made early in the analytical pro-

cess and should be driven by the theoretical and methodological questionsat hand (Scott, 2000).

The Relevancy of a Network Approach to Nonprofit Organizations

Research on the impact of social networks on the capacity-building efforts ofnonprofit organizations is sparse. However, a significant amount of researchhas been done on the networks of for-profit organizations in competitivemarkets such as business or scientific research. Such research finds that

interorganizational networks play a significant role in enhancing a for-profitorganizations competitiveness and strategic outreach. Through interorgani-zational network ties, for-profit organizations can increase their access toa broader array of resources, manage uncertainty in the market, enhancetheir legitimacy and attain collective goals (Galaskiewicz, 1982, 1985; Burt1983). Organizations that are successful in establishing strong or effectiveinterorganizational ties will see enhanced social capital that comes in vari-ous forms: higher experiential learning, which leads to more opportunitiesto collaborate (Ahuja, 2000; Powell et al., 1996); stronger trust bonds amongorganizations, which leads to greater control over external uncertainties, as

organizations will work with those they trust to mitigate instability (Brass


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et al., 2004; Gulati & Gargiulo, 1999; Seabright, Levinthal, & Fichman, 1992);more reliable norms and the ability to monitor the behavior of other organi-zations, thereby limiting an organizations exposure to the reckless behaviorof less regulated organizations (Coleman, 1988; Gulati, 1995; Ostrom, 1998);

more formalized, stable and longer lasting collaborative relationships (Guo& Acar, 2005); and lastly, a stronger sense of organizational equity in themarket (those that are similar in status and power), which leads to the abil-ity to select more profitable and effective partnerships (Brass et al., 2004;Gulati & Gargiulo, 1999).

Similar nonprofit organization research on the positive value of networkposition and enhanced social capital is more complex, primarily because thenonprofit sector is more diverse in its goals and includes a broader arrayof organizations, which creates particular challenges for organizational andnetwork analysis (Blau & Rabrenovic, 1991). Nevertheless, research sug-

gests that interorganizational networks are particularly critical to the goalsand outcomes of NPOs (Galaskiewicz et al., 2006). Interorganizational tiesamong NPOs are more important than bureaucratic hierarchies for control-ling and coordinating, because they are used to integrate programs withina community, coordinate client services, obtain resources, and deal withgovernmental agencies (Blau & Rabrenovic, 1991). Organizations in the non-profit sector have more complex links than in the for-profit sector, becauseinterorganizational integration is imperative as an increasingly large numberof actors and issues must be organized to implement a program (Milner,1980, p. 160). Therefore, understanding the nature of interorganizationalnetworks takes on added importance in the nonprofit environment.

For the most part, research on nonprofit interorganizational networksfocuses on public charities and private foundations (Galaskiewicz et al.,2006). This research suggests that NPOs can effectively use networks toenhance innovation in services and acquisition of resources (Ahuja, 2000;Coleman, 1988); increase chances for survival, particularly for younger,newer nonprofit organizations (Hager, Galaskiewicz, & Larson, 2004); and, insome cases, improve organizational performance (Galaskiewicz et al., 2006).

Nonprofit organizations with more extensive networks have signifi-

cantly higher survival rates, whether their networks result either from largesize and a higher dependence on private donations or from listings in com-munity directories, charitable registration numbers, or having large boards ofdirectors (Hager et al., 2004; Singh, Tucker, & Meinhard, 1991). Furthermore,Uzzi (1997) found that NPOs that are moderately embedded in an interor-ganizational network have higher survival rates than those that are either

weakly embedded or too deeply embedded. In other words, the organiza-tions with the highest survival rates are those that benefit from a mixtureof embedded ties where trust was high and arms-length ties that pro-

vide valuable information from outside the network core without too much

dependence or encumbrance.


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Regarding network conditions that lead to higher organizational perfor-mance, although some similar findings to those for higher survival rates havebeen found, the conclusions are more mixed. Brass et al. (2004) found thata blend of strong (high interdependence) and weak (low interdependence)

ties can help create status and a reputation that can be used to furthernetwork ties and procure resources. However, the benefits generated bynetwork ties are sometimes not worth the costs of establishing, maintaining,and managing them (Smith-Doerr & Powell, 2005), and such ties can weakenand compromise organizational boundaries (Galaskiewicz et al., 2006).

In sum, this research shows that a strong understanding of interorgani-zational networks is critical for nonprofits because networks are at the centerof their activities and they have environmental uncertainties that are uniqueto their sector. Some interorganizational consequences of networks appearto be less important for nonprofits, such as innovation; others, such as sur-

vival and performance, are in some ways more sensitive to and dependenton networking. What is clear is the success of an NPO is, in part, con-tingent upon its interorganizational networks and as such future researchon nonprofit interorganizational networks should focus on fleshing out thisrelationship between network structure and the capacity of nonprofit orga-nizations to fulfill their missions and contribute to the social capital of thecommunities in which they operate.

METHODOLOGY

The research project from which this article is drawn was commissionedby a prominent funding agency that operates in a large service region incentral Virginia. The service region consists of eight highly diverse com-munities, including three small manufacturing cities with 2004 populationsranging from approximately 17,500 to 36,000 and four rural counties with2004 populations ranging from 7,000 to 35,000, as well as a large subur-ban county with a 2004 population of approximately 250,000. The area ishome to several universities and community colleges, as well as a large

military base.The sampling frame was created by first selecting all the organiza-tions listed in Guidestar that were located in the service region (N = 637).The study sample was then limited to those organizations that were listedunder the IRS Subsection 501(c)3 Public Charity label, excluding religiousorganizations, because they use different criteria for assessing capacity than

was included in the survey for this project (N = 283). Sixty organizationswere randomly selected from our sampling frame. Of the 52 directors whoagreed to participate (87% of the 60 sampled), 39 completed the survey infull (75% of the 52 directors). This sampling frame, although appropriate for

the assessment of the impact of a Web site designed to stimulate connections


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in a specified geographical area, does not produce either a complete net-work or a series of ego-networks, either of which are needed to make claimsregarding the functionality of this network. As such, our focus here is not ondescribing how nonprofit interorganizational networks function but rather

on the benefits of a network approach to nonprofit organizations. For exam-ple, we explore how both the social network diagram and the centralitymetric of degree can be used in a grant application, a funding report, or a

year-end status report to illustrate current inter-organizational work as wellas opportunities for inter-organizational growth.

The survey consisted of two sectionsan organizational relationshipassessment and an organizational capacity assessment. For this article, wefocus on the organizational relationship assessment, which included fivequestions asking the respondent to select from the list of 52 participatingorganizations all those to which their organization was connected accord-

ing to a specific set of criteria for determining a relationship as specifiedusing Himmelmans (2001) coalition framework. Connection types rangedfrom simple awareness up to collaboration. The SNA analysis was con-ducted using Blue Spider, an SNA software tool. SNA is based in matrixalgebra and, therefore, can include some intensive calculations and outputlarge amounts of data, both of which require sophisticated and specializedsoftware.

Using a modified version of Himmelmans (2001) coalition frame-work, we measure degree across five relationship dimensionsawareness,networking, coordinating, cooperating, and collaborating. Himmelmans

coalition framework theoretically operationalizes four strategies nonprofitorganizations use to interact and develop their relationships: (a) network-ing is the exchanging of information for mutual benefit; (b) coordinating isthe exchanging of information and altering activities for mutual benefit andto achieve a common purpose; (c) cooperating is the exchanging of infor-mation, altering activities, and sharing resources for mutual benefit and toachieve a common purpose; and (d) collaborating is the exchanging of infor-mation, altering activities, sharing resources, and enhancing the capacity ofanother for mutual benefit and to achieve a common purpose. His original

continuum does not include awareness. We added awareness as a baselinemeasure to assess the name recognition of the NPOs. We define awarenessas having a general knowledge or consciousness of another organizationsexistence. The list of 52 participating organizations was presented for eachstrategy type, and the respondents were asked to select from the list allthose with which they had the type of relationship in question. From theseresponses, five relationship networks were constructed.

For this analysis, we focus on the SNA metric of degree. Because theanalytical question driving the SNA-NI research project is the number ofconnections before and after the launch of the Web site, degree is the most

informative SNA metric. As a reminder, degree, as measured by the number


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of ties a particular node possesses, is a measure of power or influence in anetwork. Those with high degree are organizations that are more central tothe overall functioning of a network and have the most influence in the net-

work. These organizations know the greatest number of other organizations,

have the greatest access to resources and information, and have the great-est reach out into the network. However, as research indicates, high degreealso means that these organizations are the most exposed and have a higherlevel of interorganizational dependency, making them more vulnerable toexternal uncertainties (Smith-Doerr & Powell, 2005; Uzzi, 1997).

Degree centrality comes in two forms: in-degree and out-degree.In-degree is the number of organizations that indicate they are connectedto a particular organization, and out-degree is the number of organizationsto which a particular organization indicates it has a connection. In other

words, in-degree is the number of people who say they know a person,

but out-degree is the number of people that person says he or she knows.Degree is a description of an organizations position in the network, ratherthan an assessment of good or bad positioning. SNA can only measure whatan organizations degree is; it cannot evaluate what it should be. Whetherit is better to have a high or low degree, relative to others in the network,depends on the research question and/or goals of the organization.

We also use the SNA measure of density is to compare the networkscreated from the five relationship matrices. Density measures the level ofinterconnectivity of a network and is measured by the total number ofties divided by the total number of possible ties. In other words, density

measures how many connections exist in a network out of all the possibleconnections that could exist. The measure ranges from zero to one, with

values closer to one reflecting higher density. A network with high densitymeans that there are many ties among the members of the network, but anetwork with low density means that there are few ties among members.Density is a structural measure in that it describes the network as a wholeas opposed to the centrality measures which describe the position of a nodein the network.

RESULTS AND DISCUSSION

As a descriptive analysis, presentations of SNA findings are generally accom-panied by a discussion of the implications of the results. Therefore, wepresent the results of our analyses, as well as discuss the specific waysin which nonprofit organizations can benefit from this type of analysis.

We begin with a discussion of how the metric outputs can be used by orga-nizations in developing strategic goals and in support of funding requests.

We will follow with a discussion of the visual outputs and how these can be

used for setting organizational goals.


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Table 1 presents a ranking of participating organizations across the fiverelationship dimensions. To describe organizations both relative to othersand across the five dimensions, we use ranks, rather than raw scores. We useone standard deviation from the raw score mean on the dimension as a

descriptive method of distinguishing organizations that are relatively high(more than 1 SD above the mean) or relatively low (more than 1 SD below

TABLE 1 Ranks and Standard Deviation Data (1 SD) on All Variables Arranged from High toLow Collaborating Degree

Awareness AwarenessOrganization in-degree out-degree Networking Coordinating Cooperating Collaborating

H 2 2 2 1 3 1

N 17 16 24 14 9 2

I 10 11 5 9 7 3Q 7 7 9 3 3 3

XX 7 9 10 11 1 3

C 18 20 8 3 9 6W 5 4 6 5 14 6AA 3 3 4 1 2 8EE 18 18 13 9 12 8P 4 4 7 8 18 10LL 28 26 17 11 14 11TT 22 23 14 18 22 11QQ 32 32 27 24 29 13

X 7 8 1 6 6 13Y 10 11 16 20 5 13ZZ 30 26 30 27 30 13GG 14 11 27 18 20 17HH 37 35 24 24 26 17II 10 10 21 24 14 17OO 29 26 17 21 20 17

VV 18 18 11 6 7 17Z 6 6 14 16 9 17F 25 26 23 27 26 23

WW 15 11 11 16 18 23YY 37 38 37 36 34 23D 37 38 33 34 22 26DD 18 21 30 38 31 26

G 30 31 35

27 31

26M 1 1 2 11 13 26NN 22 21 17 14 22 26U 10 11 20 21 22 26

J 25 26 32 32 37 32L 24 24 24 32 34 32T 25 25 27 21 34 32

A 35 35 36 36 37 35

BB 36 35 33 27 26 35

K 15 16 21 27 14 35

KK 34 33 39 38 37 35

S 32 33 37 34 31 35

Note.

More than 1 SD below the mean.

More than 1 SD above the mean.


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the mean) compared to others on that dimension.2 Those organizations thatstand out at or near the top of the rankings are highlighted with two stars.Those that stand out at or near the bottom are highlighted with one star.Organization H, the sponsor of this research project, is the most active node

in the network, ranking in the top three organizations across all relationshipdimensions. This is not surprising, given that organization H is the primaryfunding organization for the service region under study. From this study,organization H can find validation in its efforts. It is well known in thecommunity (in-degree), and its executive director is well aware of the orga-nizations in the service region (out-degree). Organization H can also use thisresearch to illustrate its success in developing advanced relationships withits organization neighbors. It is in the top spot in terms of coordinating andcollaborating relationships and is in the top two and three in networkingand cooperating.

Not only can organization H use SNA to substantiate its own capacitydeveloping efforts, but, as a funding organization, it can see where thereare gaps in the network and which organizations could best use its support.For example, organization M ranks very high in in-degree and out-degree,as well as networking, illustrating that the executive director is aware ofmany organizational neighbors and the neighbors know of organizationM. Organization M has also been able to develop an exceptional num-ber of networking relationships but has not been able to move into morecomplex and productive cooperative, coordinating, or collaborative relation-ships. Organization M is one of the oldest, most established nonprofits in thearea providing foster care services, but has seen some instability on its board,and its community activities have waned. What this analysis illustrates is thatorganization M has a strong networking foundation that can be built upon

with some concerted organizational development.Funding organizations such as organization H can use this information

to make strategic decision on where to invest. For example, in comparisonto organization M, organizations S and KK have very little social capital interms of interorganizational connectivity. Depending on the service goals ofthe expenditures, a funding organization may decide that money is better

spent on organizations with a baseline source of social capital on whichthe organization can build (M), or it could decide that funds would be bet-ter spent cultivating relationships with established, yet weakly connectedorganizations (S or KK). Instead, funding effort may be concentrated onorganization P, which has strong name recognition, but has not been ableto translate that into strong reciprocal relationships even at the networkinglevel. Although SNA can inform these funding decisions with quantitativereasoning, it cannot stand alone; SNA is best used in conjunction with an

2 In a normal distribution, a majority of cases (about 68%) fall within one standard deviation of the

mean, and a minority (about 32%) are higher or lower.


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agencys qualitative knowledge of the service region and the specific servicegoals of the organizational mission statements.

Participating organizations can also use SNA metrics to help them setorganizational goals and compete for external funding. Awareness can be

useful to an organization in understanding where they stand relative toothers in terms of how many organizations they are aware of, as well asrelative to how many are aware of them. Not only can an organization sim-ply draw comparisons between itself and others in terms of rankings, but itcan also better understand where to concentrate its efforts. Research showsthat nodes that are more aware of their surroundings are in a better posi-tion to advance their interests than nodes that have limited understandingof their network neighborhood (Burt, 1992). For example, organization X isdoing a good job at forming productive, complex relationships (networkingand coordinating), but very few organizations beyond those with which it is

working are aware of its existence, nor does it have a strong understandingof its service region. As such, organization X is not well positioned to viewthe network and understand how to expand its network reach (out-degree)nor is it likely to be seen (in-degree). By comparison, organizations Z orQ are better positioned to see a broad scope of the network (out-degree),but are not well positioned to be seen (in-degree). These two organizations

would benefit from some name recognition efforts to enhance their abilityto fully benefit from their relationships.

By providing an agency a broad understanding of who is most activein the network, SNA can help an organization strategize on where to con-

centrate its connection efforts. For example, organization W stands out inits awareness, networking, and coordination efforts, but is in the middleof the pack in terms of cooperation and collaboration. Organization Wcan look at the activities of others to find an organization with the samedevelopment needs, e.g., organization X or M, where mutual need woulddrive the development of more complex relationships. Also, organization Wcould identify those organizations that have an established record of suc-cessful cooperative and/or collaborative relationships in order to benefitfrom their expertise. Finding these critical gaps in connections, or what Burt

(1992) called structural holes, can tremendously enhance the prestige of anorganization inside the network. Burts work on structural holes illustrateshow a node can gain power and prestige in a network by being able tobridge open spaces in a network. If an organization can step in and filla critical gap in the network, the organization can enhance its position inacquiring the information, resources, and influence that flow through thenetwork.

The visual output or social network diagram can assist in searching forthese structural holes by providing a map of the organizational network.For example, Figure 2 presents a visual representation of the in-degrees and

out-degrees of the awareness network. The arrows indicate the direction of


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FIGURE 2 Awareness network.

the relationship. The visual display reveals a relatively well-connected ordense network. The density measure of this network is .373, meaning that37% of all possible ties are present in the network. In a well-connectedcommunity such as this one, news and information will flow between orga-

nizations more effectively and efficiently (Haythornthwaite, 1996); however,highly dense networks can stifle innovation and creativity (Uzzi & Spiro,2005). With regard to structural holes, high density networks present feweropportunities for an organization to close critical gaps, whereas lower den-sity networks, such as coordinating or collaborating networks, present moreopportunities for closure.

Figure 3 displays the coordinating network. The two organizations thatplay the most dominant roles in the network, represented by squares inFigure 3, have coordinating degree scores more than two standard devi-ations above the mean. As the time and resource requirements grow for

establishing these types of connections, the number of ties decreases.


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FIGURE 3 Coordinating network.

The coordinating network has a density of .085, or in other words, 8% ofall possible ties are present. There are five isolated organizations and sev-eral organizations lying on the periphery of the network, loosely connected

with only one tie. The coordinating network is centered on a handful of keyplayers. Organizations H and AA are crucial to the success of the overall net-work, because many organizations work through them to coordinate eventsand activities. They are key nodes or hubs in the larger nonprofit structuralnetwork. As the networks grow sparser, they begin to rely more heavily onthe actions of a few key players. There are many open spaces or structuralholes that an organization can strategically work to fill, yet the network is

vulnerable to fracturing if one of the key nodes is removed.This network vulnerability can be seen most clearly in the collaborating

network (Figure 4), where the density level is .056, meaning that 6% of all

possible ties are present. The network is heavily reliant on one organization,


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FIGURE 4 Collaborating network.

H, whose collaborating degree is more than two standard deviations higherthan the mean for the entire network. This illustrates its importance in thesuccess of the overall network structure. Many of the nonprofit organiza-tions in the service region depend on the organization H for their survival.Organization H ties or links many of the organizations together, and its

removal would devastate the collaboration network. This heavy reliance onone organization to facilitate collaboration means that there are many struc-tural holes for other agencies to bridge and in the process, enhance theirpositions in the network (Burt, 1992).

This type of information can be very helpful in making strategic deci-sions about which kinds of ties with which organizations would produce thegreatest benefit. An organization that wants to partner with another organi-zation that has a high number of connections would look to those at thecenter of the network for allies. An organization that would like to reachout to others who would most benefit from a connection would look to

the periphery of the network for partners. An organization can also use this


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visual output to strategically identify where connections are most needed.By using the visual output of SNA, an organization can get a lay of the landto acquire a better sense of where it stands and how to take strategic actionto better position itself to achieve its organizational goals.

SUMMARY AND CONCLUSIONS

Our goal in this article is to introduce the methodology of SNA to thenonprofit research community. Evidenced-based programs are increasinglypreferred by funders (Snyder, 1997), yet the lack of research skills andresources available to most nonprofits limits their ability to produce suchevidence (Stoecker, 2007). Furthermore, research shows that participationin networks enhances an NPOs innovation in services and acquisition of

resources (Ahuja, 2000; Coleman, 1988), can improve organizational per-formance (Galaskiewicz et al., 2006), can sustain and strength collaborativerelationships (Guo & Acar, 2005), and increases the organizations chancesof survival (Hager et al., 2004). It is, therefore, in the best interests of non-profit organizations to develop a strong research foundation that focuseson understanding the network of the local service region. We suggest thatSNA is a robust methodology that can help fill this research gap. ThroughSNA, an organization can get a sense of where it stands relative to otherorganizations, which organizations would be ideal partners for achievingparticular goals, which organizations need outreach, and which organiza-tions would be best suited for gathering information about the network.Centrality measures provide the researcher with quantitative data on howorganizations are related to one another, which can enhance the funding,resource management, and service provision strategies of an organization.

The growing demand for evidenced-based programs requires thatorganizations be able to demonstrate growth in their capacity to servetheir clients. SNA metrics are effective tools in grant applications, fundingrequests, or year-end reports to quantitatively illustrate an organizationsposition relative to others in the service region as well as how that position

changes over time. First, these metrics can be used to assess a baseline levelof connectivity for the service region and the current status of the NPOsnetwork connections, as well as providing insight into other organizationsnetworks. This information can be used to develop strategies to enhancecapacity. Nodes that are more aware of their network position relative toothers are more empowered to negotiate the network to their advantage(Burt, 1992). An organization can set goals of increasing its connectivityquantitatively, to specific organizations or to new regions in the network tonegotiate a stronger position in the network (Uzzi, 1997). The rank orderedlistings can also be used to chronicle change over time; an organization can

use its rankings to illustrate how well it is establishing new connections or


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maintaining ties over time, both of which are critical to managing capacitybuilding efforts (Galaskiewicz et al., 2006).

The visual output of a social network diagram provides a valuable andunique way of conceptualizing how the service region functions. This visual

map can be used to identify which organizations would be better part-ners for achieving a desired end or to demonstrate community needs toa funding organization. An organization can see how embedded it is in thenetwork; does it reside at the core or at the periphery? Research shows thatbeing too deeply embedded in a network can stifle growth (Uzze & Spiro,2005) and leave an organization more vulnerable to external uncertainties(Smith-Doerr & Powell, 2005; Uzzi, 1997). Instead, a blend of stronger and

weaker connections provides the benefits of social capital without the costsof inflexibility (Brass et al., 2004). A visual rendering of the network canhelp an organization strike the right balance by providing an understand-

ing of which organizations would be better connection partners, to eitherestablish stronger ties to the core or facilitate ties to the periphery.

SNA is a unique social science methodology that uses special com-putational software. As is the case with popular statistical programs, suchas SPSS, SAS, or STATA, there are several SNA software programs avail-able. The one that is used most frequently by academics is UCINet(www.analytictechnologies.com). For this analysis, we used Blue Spider, anew SNA software program on the market (www.bluespiders.net). The mainadvantage of Blue Spider over UCINet, relative to nonprofit research needs,is the ability to create and save a repeatable set of methodological steps

that can easily and reliably be opened and executed. An organization neednot have a deep pool of SNA expertise on staff. Rather, the organizationcan work with a consultant to design a methodology to meet its researchneeds and then, using the software, can quickly and effectively run themethodology and interpret the results. The organizations research needs areappropriately focused on interpretation and implementation. As nonprofitorganizations vie for an increasingly smaller pool of resources, those withstronger research skills and a deeper understanding of their network positionare better able to compete. This current analysis reveals the potential of SNA

to provide unique insights into the ways in which NPOs can enhance theircapacity building efforts and better meet increasingly demanding fundingrequirements.

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