Social Capital as Network Capital: Looking at the Role of Social Networks Among Not-For-Profits

by Christina Prell
University of Sheffield

Sociological Research Online, Volume 11, Issue 4,

Received: 25 May 2006     Accepted: 25 Sep 2006    Published: 31 Dec 2006


Social capital's rise in popularity is a phenomenon many have noted (Kadushin, 2006; Warde and Tampubolon, 2002; Portes, 1998). Although the concept is a relatively old one, it is the works of Bourdieu (1986), Coleman (1988; 1990), and Putnam (1993, 2000) that often get credited for popularizing the concept. These three, while sharing a view that social networks are important for social groups and society, place differing levels of emphasis on the role of networks in building trust or the exchange of various types of resources. In this paper, I briefly revisit these three theorists, and the criticisms each have received, to provide background for discussing recent research on social capital from a social networks approach. The social network approach is then applied to my own case study looking at the relations among not-for-profits, and special attention is given to the unique context of not-for-profits, and how this context might elaborate or challenge current thoughts on social, aka 'network' capital. A final discussion is also given to some measurement problems with the network approach to social capital.

Keywords: Social Capital, Social Networks, Measuring Social Capital, Network Capital, Not-For-Profit Relations


1.1 The increase of social capital studies in recent years is a phenomenon many have noted (Borgatti, 2005; Kadushin, 2006; Portes, 1998). Although the theory has been applied to a variety of contexts, ranging from organisations (e.g. Tsai, 2000; Burt, 1992; 2001; 2005), communities (e.g. Coleman, 1988; 1990; Wellman and Frank, 2001), societies (e.g. Putnam, 1993; 2000), and the Internet (e.g. Hampton and Wellman, 2003; Prell, 2003), the approaches to, definition and measures of this theory vary widely. Points of disagreement include whether social capital is an emergent quality or an individual asset; what constitutes as 'valid' measures; and can 'having' social capital always be seen as something 'good.'

1.2 Tackling such issues inevitably involves close inspection of theory, conceptualisation and method. In this paper, I look briefly at the theorists most often cited for social capital (i.e. Bourdieu, Coleman, and Putnam), the criticisms each have received, to provide background for a social capital study on not-for-profits, which adopts a social networks approach. Social network analysis (SNA) permits a researcher to look more precisely at the composition and structure of social relations, thus allowing one to dive deeper into the 'social network' component of social capital. Although my study does demonstrate the strengths of SNA, certain findings contradict findings from other social capital studies, and my discussion of these contradictions leads to a consideration of the historical, socio-economic and cultural influences surrounding these not-for-profits. These larger contextual issues remain an area that has been under-researched by social capital researchers using an SNA approach. My paper ends with a discussion on areas in need of further research on both a theoretical and methodological level.

Where Social Capital Begins: The Three 'Giants'

2.1 Entering into a discussion on social capital inevitably brings forth the three 'giants' in the social capital literature, namely, Bourdieu, Coleman, and Putnam. Although each share overlaps in their renditions of social capital, all have received criticisms pointing towards a need for greater clarity and consistency both on a conceptual and analytical level.

2.2 Beginning with Bourdieu (1986), social capital is defined as 'the aggregate of the actual or potential resources which are linked to possession of a durable network of more or less institutionalized relationships of mutual acquaintance and recognition' (248). This sentence is loaded with important concepts, each of which Bourdieu elaborates upon at different points in his paper. By 'potential resources' Bourdieu refers to the benefits one receives through membership into a particular class, family, school, or other institution. These 'resources' act, moreover, as a 'collectivity-owned capital' which is either felt in the form of 'gratitude' or 'trust,' or guaranteed through social actors' membership to a particular class or group (Bourdieu, 1986: 249-250). The 'institutionalised relationships' are therefore a form of 'membership' into a cultural or societal institution, and these relationships are constantly reinforced through a complex set of interactions that shape and maintain social capital.

2.3 Bourdieu's description of social capital, with its emphasis on relationships composed of complex social interactions based within larger institutions, has been criticized by some for being 'elusive' and difficult to work with (Foley and Edwards, 1999; Portes, 1998). In addition, although he speaks of social capital as providing a 'collectivity-owned capital' for members in a network, his analytical guidance for how one might measure social capital rests on a more individualist view: 'the volume of the social capital possessed by a given agent…depends on the size of the network of connections he can effectively mobilize and on the volume of the capital…possessed in his own right by each of those to whom he is connected' (Bourdieu, 1986: 249). This conceptual incongruence thus adds to the difficulty in applying his model of social capital to real-life research settings.

2.4 Coleman's (1990) view of social capital, based upon the more functionalist tradition of Durkheim and Parsons, is seen as being clearer than Bourdieu's depiction (Foley and Edwards, 1999; Portes, 1998). Coleman (1990) defines social capital as a variety of "entities" which have "two characteristics in common: They all consist of some aspect of a social structure, and they facilitate certain actions of individuals who are within the structure" (Coleman, 1990: 302). Although Coleman does specify which structures are conducive to social capital (a point made clearer later on in this paper), and he does offer some clear indicators of these structures, his discussion has nonetheless been criticised for not contextualising relationships and structures in a larger socio-economic history (Foley and Edwards, 1999; Portes, 1998; Morrow, 1999).

2.5 While Bourdieu and Coleman are often cited in the social capital literature, and the criticisms each have received has been summarised here, it is Robert Putnam's (1993, 2000) work that is credited with popularising social capital within and outside academia as a way of understanding community, democracy, and general well-being of individuals and society (Burt, 2005; Portes, 1998). Putnam defines social capital as features of a society that help facilitate and coordinate actions within that society. These features include social networks, norms of reciprocity, and levels of trust. This definition of Putnam's thus draws upon Bourdieu's and Coleman's work, yet Putnam (1993, 2000) goes on to combine these scholars' views with the 'civic culture' tradition emanating from the works of de Tocqueville (Foley and Edwards, 1999; Morrow, 1999). Putnam (1993; 2000) uses items from national surveys to measure the various aspects of social capital; Thus, civic behaviour is measured through newspaper reading, membership in voluntary 'civic' associations and voting, trust is measured through a 'generalised trust' item taken from the General Social Survey in the US, and social networks are measured (as with civic behaviour) through respondents' membership in voluntary associations.

2.6 This approach and these measures have been criticized for reflecting a 'circular logic' whereby social capital, civic culture, and the economy are all linked together in a muddled fashion: membership in voluntary associations is a measure for both civic behaviour and social networks, the terms social capital and civic culture are sometimes used as synonyms for one another; and social capital reads as both cause and effect, independent and dependent variable at different times in Putnam's arguments (Edwards and Foley, 2001; Foley and Edwards, 1999; Portes, 1998). In addition, like Coleman and Bourdieu, Putnam tends to ignore how social capital can be used for asocial ends (Portes, 1998), or that issues like gender and ethnicity can also play a role (Morrow, 1999).

2.7 More important to this paper is the criticism that Putnam divorces concepts of trust and reciprocity from the local context that both Bourdieu and Coleman emphasise (Edwards and Foley, 2001; Foley and Edwards, 1999; Portes, 1998). More specifically, through using national survey items and aggregating these into descriptive means to compare across regions, Putnam (and others that follow in this tradition, e.g. Warde and Tampubolon, 2002) reduces social capital down to attitudes and behaviours of individuals, thus ignoring how the relations in which an individual is embedded influence those norms, behaviours, and beliefs. Putnam's great methodological error, then, is that he assumes aggregations can operate as stand-ins for emergent qualities.

2.8 The above discussion highlights both the problems and strengths of social capital. Bourdieu, Coleman, and Putnam tend to disagree slightly in their conceptualisation of social capital, and each theorist has received criticism on either a theoretical or methodological level. In spite of these problems and inconsistencies, critics tend to agree that the strength of social capital lies in the notion that relations make a difference in the way individuals and groups perform the sorts of resources they acquire, and their general well-being. To put this more succinctly, social capital's strength lies in its emphasis on the relational aspects to social life, and it is now seen as scholars' job to return the focus of social capital back to this relation-based context, i.e. to reformulate and study social capital as a relation-based concept that sees trust and resources embedded in social relations (Berger-Schmitt and Noll, 2000; Edwards and Foley, 2001; Foley and Edwards, 1999; Lin, 2001; Woolcock and Narayan, 2000).

2.9 This emphasis on social capital being a relational concept has led several scholars to adopt social network analysis as a method for studying the social capital of individuals and groups. The term 'network capital' has emerged, in fact, to emphasis this networking approach to social capital, i.e. that through social networks, trust and reciprocity arise and are maintained (Kadushin 2004; Wellman and Frank, 2001; Wong and Salaff, 1998). A social network refers to a set of actors (also called 'nodes') who are tied to one another through social relations (Wellman & Berkowitz, 1988). This type of network, i.e. one which consists of a set of actors and the ties between them, is also referred to as a complete network. The networking literature distinguishes between complete networks and ego networks. An ego network consists of a focal actor (referred to as an 'ego') and the other actors to whom ego is directly connected (these are called 'alters'), and also the ties among the alters.

2.10 With a social network analysis approach, relations are analysed for structural patterns, and these structural patterns are compared to particular outcome variables and/or attributes of the actors (Wellman and Gulia, 1999). An analyst of social networks thus looks beyond actor attributes to also examine a) the nature of the relations among actors, b) the positions of actors within the network, and c) the structure of the network as a whole (Scott, 2000; Wellman and Gulia, 1999). Network data are typically gathered by asking respondents with whom they share a tie. Respondents may either refer to a roster of names in formulating an answer, or they may nominate others based on their own memory recall.

2.11 With respect to social capital, a network approach focuses on types of relations (notably trust, reciprocity, and social ties), the structuring of these relations, and whether individuals or groups benefit from such structures and relations. Although social network analysis does not take into consideration the larger socio-economic context (e.g. historical patterns and institutions), the approach does focus an analyst's attention more precisely on how trust and reciprocity flow through social relations (Foley and Edwards, 1999). To what extent this lack of attention to the larger social context is important when discussing network capital is an issue I shall return to later on in this paper.

2.12 In the next sections, I summarise network capital literature. Such research can be placed within two large categories as those focusing on individual actors' positions in a network, aka the individualist approach to social capital, and those looking at how the network as a whole is structured, aka the 'groupist' approach to social capital (Borgatti, et al. 1998; Kadushin, 2004; Lin, 2001).

'Network' capital: the Individualist approach

3.1 In looking at social capital from an individualist approach, the researcher is exploring how much social capital an individual actor has based on features of that actor's network or the position of an actor within a network. For example, Wellman and Frank's (2001) research shows how the size of a person's network and the strength of ties within their network affects what kinds of social support that person receives. Burt's (1992; 2001; 2005) empirical work on social capital is based on the notion of 'brokerage,' a situation where one actor brokers between two other actors who are themselves not linked by any direct tie. In business contexts, this brokerage role provides an actor with key benefits, such as access to new ideas, and is linked to other benefits such as early promotion and better salaries.

3.2 Examples of other empirical work done from an individualist perspective include Nan Lin's development of a 'position generator', which is used to locate the positions within a hierarchy of various people to whom one is tied (Lin, 2001; Lin and Hsung, 2001). More recently, Van Der Gaag, et al, (2005) have developed a 'resource generator,' which operates in a similar fashion to Lin's (2001) position generator. Here, a respondent is asked which relations (e.g. kin, friend, and acquaintance) they use to access various kinds of resources (e.g. a car, loan money, etc.).

3.3 To summarise, the literature on network capital for individual actors sees individuals benefiting from structural features of their networks. These features include the following: a) an actor's ego network size, where larger ego networks composed of weaker ties gives an actor access to diverse kinds of resources, and smaller ego networks composed of strong ties providing emergency, emotional and every day support (Wellman and Frank, 2001; Van Der Gaag; 2005); b) an actor's position within the entire network, with those holding a broker position gaining access to more diverse types of resources than those who do not (Burt, 2001; 2005).

'Network' capital: Groupist approach

4.1 Research that looks at social capital on the group level focuses on structural features of the entire network, and how that network structure enables the rise and maintenance of trust and reciprocity. In Coleman's (1988; 1990) work on schoolchildren, the author argues that a closed network structure, i.e. one where all the actors in the network are tied to one another, creates feelings of mutual obligation and trust among members of the network. In a similar fashion, Putnam (2000) describes 'bonding' social capital as strong ties within a more or less closed, homogenous community that help community members get by, but not ahead.

4.2 Social network analysts refer to these arguments as the 'closure' argument for social capital (Burt 2006). Empirical research on closure has shown closure a) correlating with persons trusting one another on a long-term basis, and b) increasing the perceived credibility of information flows within a network (Burt, 2006, Krackhardt, 1992, Uzzi, 1996). In addition, Granovetter (2005) notes how closure can act as a persuasive influence for friends to behave honestly with one another.

4.3 Other research has also pointed out the negative sides of closure: Burt (2001, 2005) discusses how norms can emerge from such structures that constrain social behaviour and, in the case of business, inhibit innovation. Others note how cohesive structures might enable less socially-desirable behaviours such as those found within the Mafia and neo-Nazi groups, as well as increase the isolation of traditionally-perceived marginalized groups such as immigrant communities and/or urban ghettoes (Huysman and Wulf, 2004; Narayan, 1999). Thus, because closure is not always seen as helpful, a competing view of group structure has surfaced that builds upon the idea that social capital emerges in networks composed of weak ties (Granovetter, 1973; 1983; 2005).

4.4 This 'strength of weak ties' argument has fuelled discussions within the social capital field: Putnam (2000) discusses 'bridging social capital' as ties that link across different community groups, and Burt's (2001; 2004; 2005) recent work on brokerage social capital shows how bridging ties within and outside the organisation can benefit that organisation through bringing in new, innovative ideas.

4.5 Scholars are now arguing that network structures should optimally hold a mixture of brokerage and closure. Narayan (1999) argues that healthy societies need a combination of dense micro units (his examples are the family or tribal clan) that are then linked together through weak ties. Narayan and Woolcock (2000) make a similar argument for policy development purposes, noting that both bonding and bridging social capital are needed 'to avoid making tautological claims regarding the efficacy of social capital' (231). Burt's (2001) argument is similar: after demonstrating the strengths and limitations of closure and brokerage, he concludes 'that while brokerage ... is the source of added value, closure can be critical to realising [that] value' (52).

4.6 The above discussion on groupist social capital thus suggests that closure is correlated with trust and exchange of certain kinds of resources, notably help in times of emergency and emotional support, and brokerage with the exposure to new ideas and diverse kinds of resources. Further, a network that displays characteristics of both closure and brokerage would profit from the benefits each structure offers.

Issues in Applying the Social Network Approach: a Look at Social Context

5.1 The above discussion on network capital research has shown the two main approaches to study social capital from a network approach. One can focus on the structure of ego networks and the structure of the larger, bounded network (Borgatti et al, 1998). One question, at this stage, is to what extent these findings carry across different contexts?

5.2 The network capital literature reviewed here looks at the social networks of randomly selected individuals (Lin, 2001, Van Der Gaag, et al, 2005; Wellman and Frank, 2001), schools and business/organisational contexts (Burt, 2005; Coleman, 1988; Krackhardt, 1992; Uzzi, 1996), and geographically bound communities (Huysman and Wulf, 2004; Narayan, 1999). Although these studies take place in unique settings, they tend to overlook how these unique settings might play a role in the presence or absence of social capital. The question now becomes, to what extent ought we to be making these generalisations across different contexts? What should we do in instances where findings from one research context contradict findings from another? As noted earlier, social networks tend to overlook the larger, socio-economic context, yet perhaps these larger structural features (e.g. institutions, cultures, socio-economic environments) might be the very influences we need to be focusing our attention on in order to gain a fuller sense of the role of networks within the social capital debate.

5.3 Towards this end I would like to introduce a context that has not received much attention in network capital studies, that of not-for-profits. On first consideration, one might be tempted to lump not-for-profits in with other organisational studies, yet research has shown that not-for-profits need a different consideration. Although 'not-for-profits' is a catchall term for a wide array of organisational structures and enterprises, there are nonetheless some important characteristics that distinguish not-for-profits from other organisations (Newman and Wallender, 1978). For example, for-profit organisations have a 'corporate ethos' that tends to emphasize marketing strategies, information systems, and strategic management techniques. This ethos derives from these organisations' primary goal, which is to maintain a profit that can be distributed to shareholders. Rather than being accountable to shareholders, not-for-profits are accountable to external funders, political and governmental bodies. This is a larger range of external influencing bodies than those dealt with by for-profits, and because of this range, not-for-profits must contend with greater external scrutiny of their activities, a greater degree of public accountability, and must balance more goals and services than those primarily guided by the for-profit motive (Potter, 2001; Schwenk,1990). In addition, from my own research, I have noticed that not-for-profits often face greater financial and resource constraints than for-profits, i.e. not-for-profits, especially smaller ones, are more likely to be understaffed and employees underpaid (Prell, 2003).

5.4 Taken together, not-for-profits can be thought of as facing constraints and pressures different from for-profit contexts, and such constraints and pressures may very well ask for a different understanding of the role of networks and of social capital. For example, do strong and weak ties play a similar role in not-for-profit contexts as they do for the individuals studied by Wellman, Granovetter, and Lin? Do relations based on social interactions, trust, and the exchange of resources interact in similar ways as previous empirical research on network capital suggests? Finally, are notions of 'closure' and 'brokerage,' two concepts found within the organisational and policy literature (Burt, 2005; Woolcock and Narayan, 2000), helpful in understanding how not-for-profits operate? These are some of the questions that fall from reconsidering 'network' capital in the context of not-for-profits.

5.5 To address these questions, I discuss a case study looking at the social capital among a group of not-for-profit organisations. My case study utilizes a social network analytical approach to explore notions of network capital within the context of not-for-profits. In discussing the findings of this particular case study, and comparing these findings with those reviewed earlier, I discuss the limitations of the network capital model and suggest possible areas that could benefit from future research.

The Case of Connected Kids

6.1 I explored individual and group social capital in a case study conducted among 24 not-for-profit organisations in Troy, New York. These organisations were participating in a project lead by City Hall and the local university, Rensselaer Polytechnic Institute (RPI), to build an IT system for the local population. The intended purpose of the IT system was to improve communication and collaboration among local not-for-profits serving city and county youth. This perceived lack of communication and collaboration was articulated by numerous members of the community, in one-to-one interviews with me, and some of their quotes are found below in Box 1:

Box 1. Community Members Discussing Communication Problem in Troy

Building an IT system (referred hereafter as 'Connected Kids') was thus seen by many as a technological fix to real communication and other social problems existing among Troy not-for-profits.

6.2 I began observing this process of these organizations working with the University to design and build Connected Kids back in February, 2000. Unfortunately this IT system was not completed during the time of my research (February 2000-June 2002). Nonetheless, I had ample time to gather data on these participating not-for-profits and their relations with one another. The findings from this research are presented below. (Box 1)

6.3 The project leaders of this IT initiative were two professors from RPI, and they selected representatives from these organisations to participate in the project based on specific criteria; these organisations were all not-for-profits and dealing with youth and children, and these organisations were all located within Troy city. I conducted semi-structured interviews among these not-for-profits in February-March 2000, and I began gathering structured interview data in June 2001. These earlier rounds of data gathering lead to a final questionnaire that I developed for measuring social relations among these not-for-profits.

6.4 In June 2002 I gathered network data from 24 respondents using my questionnaire. Each respondent acted as a representative for his or her organisation. These respondents were all administrators or managers of youth programs, as such individuals were deemed the most knowledgeable of their organisations' services for youth and children, and how their organisation linked with others via youth and children services.

6.5 Respondents were approached in their own work settings and handed a roster containing the names of all 23 other organisations, and questions were posed to respondents on their organisation's relationships with these other organisations. The network data gathered consisted of eight relational measures, which together reflect the three relational concepts of social capital, i.e. social networks, trust, and reciprocity. These eight relations as they were conceptualised and measured are described below under the headings of social networks, trust, and reciprocity:

These data permitted a number of analyses to be run to explore social capital among these not-for-profits in Troy. These analyses and findings are discussed below.

Analyses and Findings

7.1 In this section I discuss analyses and findings derived from my network data. These analyses were performed to consider a) how relations based on social interaction, trust, and resource exchange interact with one another, b) how the positions of organisational actors in the network coincided with resource exchanges, and c) how the structure of the entire network coincided with notions of 'closure' and 'brokerage' as discussed earlier in this paper.


8.1 The literature suggests that relations based on the giving or receiving of different types of resources, and relations of trust, are embedded within the social relations among network members. To explore this, I ran QAP correlations[2] (Krackhardt, 1987) on the eight networks described above, which together compose the three aspects of social capital. These are shown below in Table 1:

Table 1. Correlations across the eight relations

8.2 Overall, Table 1 shows social, trust and resource relations being intercorrelated. The correlations between trust and social relations show significant results, with strong communication showing a slightly stronger correlation with trust than weak communication. This is in keeping with the literature whereby stronger ties tend to be the ones endowed with more trust. In addition, the two trust networks and resource-based networks show an overall pattern whereby trust and resource relations are significantly correlated. The one exception here is the relationship between trustworthy information and receiving funds; although being perceived as giving trustworthy information does go hand-in-hand with receiving funds, this is not a significant relationship (r =0.07, p > 0.05). With regards to social relations, both strong and weak communication ties show a similar pattern: both correlate with relations of trust and resource exchange, although stronger communication correlates slightly more with trust and resource exchange than weaker communication. Thus, stronger communication ties seem to be doing more of the work with this set of organisations. This is a point I will return to in the Discussion section of this paper


9.1 To analyse individual social capital, I looked at the size of not-for-profits' ego networks, and the positions of these organisations in the overall network. To analyse ego network size, I calculated the degree centrality for each organisation. Degree centrality refers to the network size of a particular actor (or ego). It involves counting the number of actors directly tied (via a particular relation) to the ego actor. The literature suggests that larger ego networks (i.e. larger degrees of centrality) composed of weaker ties coincide with more resource exchanges (Putnam 2000, Wellman and Frank 2001). To test this relationship, I calculated degree centrality scores for both social and resource exchange relations. These scores were then correlated with one another using a one-tailed Spearman's correlation coefficient test in SPSS[3]. The below table shows the results:

Table 2. Degree correlations between social and resource relations

9.2 These findings show that organisations with large networks composed of weak communication ties tend to also be organisations involved in a lot of programming with others and also ones that give and receive money from many others. In contrast, the size of an organisation's network composed of strong communication ties does not seem to be playing much of a role. These findings build upon the previous sections findings: strong communication, when combined with the additional consideration of ego network size, does not seem to be playing much of a role. In contrast, when ego network size is considered alongside weak communication, it is weak communication that seems to be playing the more important role within this group of organisations.

9.3 In addition to network size, social capital literature indicates that organisations playing a broker role ought to benefit more so than organisations who do not. To explore this relationship, the betweenness centrality for each organisation was calculated for the social and resource exchange relations. Betweenness centrality counts the number of times an organisation occupies a broker position in the overall network. The betweenness scores for organisations across the social and resource relations were then correlated with one another using a one tailed Spearman's correlation coefficient test in SPSS. Table 3 shows these results:

Table 3. Betweenness correlations between social and resource relations

9.4 These findings show that organisations who tend to broker between others do not appear to be getting any strong benefits from such a broker position. The one exception pertains to strong communication ties, where organisations who tend to broker between others with regards to strong communication ties also tend to broker between others with regards to programming. Although this goes against the thinking of Burt (1992; 2005) and Granovetter (1973), who said that weak ties enable actors to become brokers more so than strong ones, the theme of strong ties performing an important role among this group of organisations, as found in QAP correlation findings, still holds. Again, this will be discussed in more detail later on in the article in Table 1.

9.5 Taken together, the size of an ego network (degree centrality) appears to be playing more of a role then the position of an organisation within the overall network (i.e. betweenness centrality). In practical terms, an organisation that has a large network composed of weaker ties seems to participate more in collaborations. However, organizations that do not necessarily have a large network, but do have strong strong ties with others, seem to gain access to a variety of resources via those strong ties. Therefore, at this point in the analysis, there seems to be an interesting tension in this group of not-for-profits, whereby two competing 'strategies' or 'approaches' seem to be at work; either an organisation spreads its tentacles wide, and maintains weak ties with those it comes into contact with, or it appears to put more energy into the ties it already has, gaining resources through its strong ties. I will return to these points later on in the paper.


10.1 Group social capital looks at the extent to which a network of social relations is characterised by closure or brokerage, and whether these structural features are linked to trust and/or resource exchange. In this study, data were gathered only on one set of organisations, and this limits the extent to which one can draw conclusions about social capital on the group level, as it is not possible to compare relative levels of brokerage and closure across different sets of organisations. Nonetheless, one can look at certain descriptive statistics of this set of organisations, and draw some heuristic conclusions about the way closure and brokerage seemed to be operating on the group level.

10.2 To explore closure and brokerage on the group level, a number of descriptive statistics were calculated across all eight relations. These descriptives include the number of isolates, density, and the mean and standard deviation of efficiency. Density is the proportion of possible ties in a given relation that are actually present, and calculating a relation's density is a common measure used for closure (Wasserman and Faust, 1994). Centralization indicates the extent to which ties are concentrated on just a few nodes. If the centralization score for a particular relation is that of 1, this indicates that all ties are clustered around one actor in that relation. A score of 0 indicates the ties are evenly spread out among actors in that relation. Centralization and density are commonly used together as measures of central tendency and spread, much like a mean and standard deviation, and together (along with the presence or absence of isolates) they help an analyst assess the closure of a network across different relations (Wasserman and Faust, 1994: 181-182). Efficiency measures the proportion of possible times an organisation occupies a broker position in a given relation (Burt, 1992). An efficiency score of 1 means that an organisation holds the maximum number of possible broker positions, and a value of 0 means an organisation is never acting as a broker[4]. Although efficiency is not a group level measure for brokerage per se, one can calculate the mean and standard deviation of actors' efficiency scores to get a sense of how brokerage is spread throughout the network.

10.3 These descriptives were calculated for all eight relations, and the results are presented below in Table 4:

Table 4. Descriptives across eight relations

Looking at measures of closure, the density levels of the different relations vary, with joint programming and willingness to collaborate without a contract holding the highest densities and the two networks based on funding flows holding the lowest densities. In addition, the centralisation scores across the networks tend to be higher than the density scores, indicating that these networks tend less towards closure, i.e. an even distribution of ties linking actors together, and more towards a highly centralised structure, i.e. where a few nodes accrue the majority of ties. The exception, however, is with joint programming and willingness to collaborate without a contract. Both these relations are relatively dense, and together, they indicate ongoing, reciprocal interactions involving feelings of trust among a large portion of the network. In reflecting on closure social capital, these not-for-profit organisations do not seem reliant on dense social networks in order to have trust and engage in collaborative ties.

10.4 Looking at brokerage, the mean and standard deviation scores for efficiency are highly variable. This implies that certain organisations occupy broker roles more frequently than others. The relation based on giving funds holds the highest average, meaning that if an organisation gives money to two others, it is very unlikely that these two others give funds to one another. To a lesser degree this pattern also holds for the relations based on receiving funds, strong communication, and sharing clients. Thus, efficiency relates to individual organisations who in some relations vary considerably in their efficiency as indicated by the standard deviation scores. In these relations, some organisations have much more brokerage social capital than others, but one can say nothing about whether the network as a whole is characterised by brokerage social capital.

10.5 In practical terms, it appears that these organizations tend to all communicate with just a few others, i.e. that a few organizations seem to be quite popular. Nonetheless, even though some organizations are more popular when it comes to communication, this does not appear to be the case when it comes to engaging in joint programming. How does this relate to earlier findings, where strong communication ties appear to being doing a lot of 'work' among these not-for-profits (Tables 1 and 2)? Bringing these different results together, it appears that a few organisations are monopolising the majority of ties, and in doing so, they are benefiting from the sorts of exchanges and resources that come with the accruement of larger networks (Wellman and Frank, 2001). At the same time, the remaining organisations use their strong ties to gain access to the resources they most need, e.g. funds.

10.6 Qualitative data seem to support these findings. In my interviews with several not-for-profit employees, I came across a division between the 'large' and 'small' not-for-profits, as illustrated in Box 2. Indeed, these larger organisations tended to be the ones with higher efficiency scores. As such, not only did these organisations tend to have larger ego networks, but their networks were composed of others who were themselves disconnected, thus placing these ‘big fish’ not-for-profits in a stronger position to control the flow of resources around them. (Burt, 2001; 2003; 2005)

Box 2. Unequal Divisions between 'Large' and 'Small' Not-For-Profits

Larger organisations had a structural advantage over smaller ones; they could pick and choose whom they collaborated with and form larger collaborations while still maintaining control over the funds. Smaller organisations had to work with those who would take them in as collaborators. The exception to this rule would be monies received from local municipalities, who would make judgements about funds on a one-to-one basis. Thus, all the not-for-profits I spoke with (both large and small) would make sure to maintain strong ties to both City Hall and the County.

Discussion: Linking Findings to Context

11.1 The findings presented here support, in part, findings from other network studies. For example, the data show how social relations, trust and resources are strongly interrelated (Table 1), and that larger networks composed of weaker ties (Table 2) tend to place one in a better position for both giving and receiving various types of resources. This is in keeping with the literature, and as such, suggests that these data seem to be, at least in certain respects, 'behaving' in ways similar to those found in other network capital studies.

11.2 Yet certain findings found here go against previous research and theory, and these findings deserve some discussion. For example, stronger ties, in general, seem to be doing more of the work in giving organisations access to resources (Table 1). Putnam's (2005) advice that strong ties help one get by, while weak ties help one get ahead, thus seems at odds with the findings found in Table 1. In addition, Granovetter (1973) and Burt (2001; 2005) argue that strong ties tend to be ones that hold redundant information, as the actors involved in such a relation would typically spend a great deal of time with one another, and thus be more likely to share the same pool of information. Yet inter-organisational ties are ones inherently imbued with diverse information and resources; the actors involved in such a relation are each embedded in a different social world. In other words, each is embedded in their own cohesive sub-group, and the link to one another can be construed as a bridge across these cohesive sub-groups. Thus, one might argue that this bridging across different not-for-profits reflects the mixed 'closure' and 'brokerage' structure advocated by certain scholars (e.g. Burt, 2001; Narayan, 1999); yet even here, it is surprising that the strong ties are forming the bridges, and not the weak ties.

11.3 As earlier comments have shown (see Box 2), these not-for-profits had historical tensions and larger structural inequalities than my network analysis showed. In other words, their larger historical, socio-economic context played a role in shaping who communicated and collaborated with whom. Some not-for-profits found it easier to form ties with others, and maintain them, because they had the staff to perform such networking. In general, however, not-for-profits struggled with this activity for a variety of reasons: they lacked the staff numbers to network properly, and they were accountable to an arguably larger number of external bodies, e.g. government and funding agencies, and in addition, larger not-for-profits who operated as 'lead agencies'. These pressures inevitably took their toll; as one respondent said to me in an interview 'we don't have time to network. None of us do. We're too understaffed (not-for-profit employee, June 2001).' In the world of not-for-profit organisations, an organisational actor might very well make highly strategic choices on whom to form ties with, as time and energy for 'networking' needs to have a clear payback. Thus, forming a strong tie might not only be a reaction to larger structural forces, it might also be an efficient response to these same pressures; pressures, moreover, that do not exist (at least to this degree) in other organisational contexts.

11.4 Aside from these issues pertaining to the larger social context, this case study also shows problems in measuring brokerage on the group level. Although group level brokerage is theorised and discussed (Burt 2005, Narayan 1999), there is presently no such satisfactory network measure. Although this article made steps in this direction through aggregating the efficiency scores for individual actors, future research will focus on developing a better measure (Prell, 2003).


12.1 Research on network capital has, according to some, already 'peaked' (Borgatti, 2005). This paper shows at least two areas still in need of further investigation, namely, a) how do network capital findings from studies conducted in certain contexts translate into others, namely the world of not-for-profits, and b) how can one measure brokerage on the group level? Further research is needed in both these areas to build, in the words of Kadushin (2004) a more 'complete' empirically-based model on network capital.


1The author notes that 'communication' could have been broken down into more specific modes of communication, e.g. face to face, email, telephone, meetings, etc. This would certainly have provided more precise insight into how stakeholders were communicating with one another. In fact, research focusing on social capital and the Internet (e.g. Quan Hasse et al., 2002) breaks down communication more precisely to see what types of communication coincide with feelings of trust, community, etc. As this study was not interested in breaking down communication into its various forms, and was not looking at the role of media-type in social capital, but was rather interested in how trust and exchange of resources were embedded within a social relation, and as there was a constraint given time for interviews, etc., a decision was made to use a broad measure for social relations that would capture the variety of ways in which stakeholders might interact.

2The correlation procedure used here is the QAP procedure, which is used to test the association between relations. As relations data have interdependencies that traditional case by variable data do not, calculating statistics for such data needs to account for these interdependencies. Thus the QAP procedure involves first computing Pearson's correlation coefficient between the corresponding cells of the two data matrices (relational data is stored and structured as matrix data). In the second step, it randomly permutes rows and columns of one matrix and recomputes the correlation. This is done hundreds of times in order to compute the proportion of times that a random coefficient is larger than or equal to the observed coefficient calculated in the first step. A low proportion (where p < 0.05) suggests a statistically strong relationship between the two matrices.

3As the measures were not normally distributed, I opted for a nonparametric test.

4The efficiency scores presented in Table 4 are based on an adjustment that averages only overall those nodes having degree 2 or greater. My thanks go to John Skvoretz who helped me think through and articulate the logic behind Burt's efficiency measure.


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