Essay: Building Social Capital with Social Entrepreneurs, “Me” and “We” approaches

This lit review (of sorts) was written as the final paper for a course on Urban Social Networks taught by Zak Neal in the Soc department at MSU (after this class, I asked Zak to join my dissertation committee and he agreed. Hooray!).

It teases out some of the ideas I’ve been trying to think through to relate this strange ambiguous concept of “social entrepreneurship” with social networks and social movements. Specifically, I ask how scholars of entrepreneurship versus scholars of social movements think about network capital, or  patterns of relationships. In the entrepreneurship context, social capital accrues to and benefits the individual. In the movement context, social capital accrues to and benefits the whole (even if sometimes the research methods focus on an individual organization as the unit of analysis).

What does this have to tell us about how networks of entrepreneurs who are interested in broad-scale social change (aka social entrepreneurs?) might function?

This is particularly interesting in the context of some of the practical work I’ve been doing recently on worker-owned cooperatives and other cooperative ownership models, and all of the deep thinking happening in Detroit around the commons… what are forms of network capital or “social architecture” as Marjorie Kelly calls it, that will replace current modes of organizing and organizations?


“As social entrepreneurs, we seek a balance between nurturing our individual enterprises, and understanding and acting according to our place within broader social and environmental systems.”

– FoodLab Detroit Charter, August 2011

FoodLab Detroit is a young organization that supports food-based social entrepreneurs[1]. FoodLab calls itself a network in the vein of other business networks that connect entrepreneurs to one another and to external stakeholders including small business development agencies, policy makers, and customers. In this sense, the network functions as a business incubator, connecting individual businesses with information, opportunities, and resources. At the same time, FoodLab also participates in Detroit’s “good food movement,” creating space for members to deepen awareness of problems within Detroit’s food system and encouraging and enabling collective action to move towards a more just, sustainable, and resilient alternative.

Social entrepreneurship is a relatively young field of inquiry which is still in the process of developing consistent definitions and terminology and lacks a history of empirical studies (Mair & Marti, 2006; Short, Moss & Lumpkin, 2009). As social enterprise grows in popularity as a strategy for social change (Deresiewicz 2011; Dees & Anderson 2006), national groups and local organizations like FoodLab are learning how to support this new breed of change agent[2]. Studies of social entrepreneurs suggest that the sum of their relationships, or social capital, is critical to emergence and persistence (Van Ryzin et. al. 2009; Sharir 2006). Others suggest that social capital is an outcome of social enterprise and an enterprise should be defined and measured by its capacity to build social capital within its sphere of influence (Thompson 2002; Smallbone 2001). Whether we consider it an input or outcome of social enterprise, what social capital actually looks like, and the process by which it is created and has an effect, is something of a black box. What is the role of relationships or social capital in social entrepreneurship? How does it (or does it) differ from the role and structure of relationships in conventional entrepreneurship or social activism? What does this tell us about the nature of social entrepreneurship?

Here, network analysis approach can help specify exactly what kind of ties that we mean when we talk about social capital (more friends? more advice? more funders? more customers?). Perhaps more significantly, a network approach re-imagines social capital as existing not only through the content, but also through the configuration or structure of relationships. This essay will synthesize network-based literature in the fields of entrepreneurship and social movements, focusing on structural definitions of social capital[3], which I will also refer to as network capital. The goals of the paper are (first) to help organizations like FoodLab better design and evaluate their network-building efforts, and (second) to inform the field of social entrepreneurship studies.

The first section sets up broad distinctions between bridging and bonding social capital and social capital that accrues to individuals versus groups, organizations, or movements. Next I review how the entrepreneurship literature operationalizes social capital as a network measure and look at studies that describe the particular benefits resulting from brokerage and closure in a network. I repeat the exercise with literature on social movements, and then summarize similarities and differences between the two strands. Finally, I close with directions for future research.

Types of Social Capital: Brokerage versus Closure and “Me” versus “We”

Social capital can be separated into two broad categories. Bridging capital (also known as brokerage or leverage capital) refers to relationships that connect sets of otherwise disconnected actors. People with bridging capital find themselves in positions as translators, agents, brokers, and gatekeepers between actors and groups of actors who do not interact directly. Bridging capital can provide access to a diversity of ideas and the potential for competitive advantage and innovation (Granovetter 1973, 1983; Burt 1992). On the other hand, bonding capital (also known as closure or support capital) refers to dense in-group relations. It can generate the internal cohesion, shared norms, and the trust necessary for collective action (Coleman 1988; Putnam 1995).

While early writing on social capital tended to emphasize one type of network capital or the other, current thinking generally acknowledges that a mix is essential to productive groups and organizations (Taube 2004; Burt 2001; Borgatti et. al. 1998). Whereas sparse networks offer the opportunity to connect to diverse ideas and can spur “out-of-the-box” thinking (or, more accurately, “out-of-the-network” thinking), denser networks make it easier to coordinate action by facilitating trust and shared language that lower the barriers to transactions and coordinated action (Obstfeld, 2004: 101). Figure 1, borrowed from Burt (2001), describes how bridging and bonding capital interact to affect a team’s performance. In networks where brokerage opportunities are similarly high for all actors, actors with more bonding capital are more successful. In networks where all actors are part of cohesive groups, those with more opportunities to bridge to dissimilar people or groups are more successful. A combination of  internal closure and external bridges leads to optimal performance.

Figure 1: The interaction of bridging and bonding capital, from Burt (2001)

Burt (2001) considers how patterns of brokerage and closure put a team and its members at an advantage relative to other external groups and teams. Entrepreneurship studies tend to take the same approach, asking how the benefits of social capital accrue to an individual entrepreneur or business. The focus in this case is on the returns to an entity via its web of immediate relationships, or ego-network. On the other hand, network studies of social movements are more interested in benefits that result from bridging and bonding in the whole movement. Generally the field is less interested in how a particular organization or activist gains individual advantage from its position within the network and more interested in how the movement is enabled or constrained by its overall pattern of relationships. This is particularly important when considering social entrepreneurship and the case of FoodLab since we are interested in how social capital affects the capacity and behavior of the individual entrepreneur and the group as a whole.

Figure 2 above represents four broad ways of thinking about social capital according to the network mechanism (bridging versus bonding) and the entity that benefits from the capital (the individual versus the group as a whole). In the upper left quadrant is “me-bridging” capital where an individual benefits from its position between two disconnected actors; below it is “me-bonding” capital where an individual benefits from being part of a cohesive group. A FoodLab entrepreneur who has a loosely connected set of advisors who do not know one other (a local leader, a peer in another town, a couple of fellow business owners, and a family member) has opportunity for new ideas and fresh perspectives. On the other-hand, an entrepreneur who has been involved in the food industry in Detroit for years, and has a dense set of relationships with farmers, suppliers, and activists (who all also know one another), has an advantage because she has access to specialized insider language and trust built up over time which makes for more potential for collaboration. In the upper right quadrant is “we-bridging” capital. An organization like FoodLab may benefit from internal bridging ties between a cluster group of gourmet foodies and a group of food justice advocates because this increases its legitimacy and power. In the lower right quadrant is “we-bonding” capital. Here, FoodLab benefits from a tight-knit steering committee or a dense subset (or subsets) of committed members. Again, a mix of “me-bridging” and “me-bonding” capital are necessary for entrepreneurial success, while a mix of “we-bridging” and “we-bonding” capital makes for a more effective movement.

(Click here or on chart above for larger view)

Entrepreneurship: Social Capital for “Me”

The previous section set up some broad distinctions between conceptions of network capital in entrepreneurship and social movement studies. The next two sections provide more detail on how social capital tends to be specified and measured in each field.  Figure two offers a general orientation to five aspects that define networks in each field: defining social capital, nodes, network boundaries, ties, structural measures. I start in this section elaborating on these five aspects in the entrepreneurship literature.

“Me” capital enables and constrains the individual entrepreneur

In network studies of entrepreneurs and social capital, individual entrepreneurs are generally the unit of interest. Social capital, even when it is a property of relationships, still belongs to the individual entrepreneur. For example, in Davidsson & Honig’s (2003) large sample of Swedish entrepreneurs, social capital is treated in the same way as other oft-studied entrepreneurial characteristics like innovativeness and human capital. Even when studies of entrepreneurs move beyond endogenous characteristics to consider the entrepreneur’s network position, the focus is still on how this position offers advantage to that individual business.

A counter example may clarify this point. Scholars who study entrepreneurship and regional development might be curious how the structure of connections between businesses in a region enhances or limits opportunities for the region as a whole, however, even this body of literature tends to focus on how network capital affects individual business success rather than the overall health of a sector or region (McQuaid 1996) . Assuming a pure market system, these two approaches might be one and the same, but empirical evidence makes it clear that even in contexts that we perceive to be “pure markets,” social dynamics often concentrate resources or advantage in ways that fail to maximize overall benefit (Baker 1990).

Social capital as independent variable

Network studies in entrepreneurship ask how an entrepreneur’s network affects her success by mitigating access to tangible and intangible resources (Hoang & Antoncic, 2003). Some examples of resources include advice, information, emotional support, and opportunity. Some studies examine the effect of social capital on the intervening resource and make assumptions about how access to resources relate to success; for example, Singh (2000) considers how network capital enhances or impedes entrepreneurial opportunity recognition, assuming that opportunity recognition is a key component of success. Hansen (1995) on the other hand looks at the effect of network capital on a direct measures of success: number of employees.


In the case of network studies in entrepreneurship, nodes can be entrepreneurs or enterprises. Studies that focus on earlier stage and emerging entrepreneurs appear to use individuals (Davidsson & Honig 2003; Singh 2000) whereas studies that focus on later-stage businesses use businesses (Baum et. al. 2000; Uzzi 1996; Hansen 1995). This may be because in earlier stages, organizational boundaries are not as clearly defined.

Network boundaries

In all the studies that I reviewed, the individual entrepreneur or enterprise was the unit of analysis and data was limited to the entrepreneur’s personal ties (also known as alters) rather than a complete network. This relates to my first observation that network studies are primarily interested in how network position offers an individual advantage rather than characteristics of the network on the whole. Some data collection methods limit boundaries further by using name generators that only allow the respondent to name five or ten significant alters (Singh 2000). This compromises the power of the results since it offers an incomplete picture of an entrepreneur’s range of network capital.


Studies measure many different types of ties including friendship, advice giving and seeking, idea and opportunity sharing, reputation signaling, and business exchange ties. Some studies delineate between “strong” or “weak” ties based on the frequency or type of interaction (Hansen 1995; Davidsson & Honig 2003).

Structural measures

Most studies start with degree centrality or simply counting up the subject’s ties. Since most studies focus on an incomplete egocentric view of the network (Hoang & Antoncic 2003), they tend to use proxies like network density or network heterogeneity to understand the degree to which networks exhibit closure or possess structural holes.

Brokerage and Entrepreneurship

Students of entrepreneurship generally start with a proposition that network size and her centrality within it is central to her success. The more people she knows, the more likely she is to be able to find the resources and information she needs. Quickly, though, scholars shift to address not just the number of ties, but the degree to which these ties are connected to one another:

Whereas network size and centrality delimit the amount of resources an actor can access, the presence of structural holes in the network changes the ability of actors to gain access to a diversity of resources” (Hoang & Antoncic 2003: 166).

There are two ways to think about this type of bridging capital:  as a conduit of diverse, non-duplicative information that entrepreneurs can access, but don’t have to spend much time to maintain (what Granovetter (1973; 1983) calls weak ties), or as an opportunity for an entrepreneur to exploit structural holes between disconnected parties for competitive advantage (Burt 1992).

In their review of 70 articles that employed network analysis to study entrepreneurship, Hoang & Antoncic (2003) found “consistent support for Burt’s structural hole argument – advantage to occupying a bridging position” (p. 173). They cite McEvily and Zaheer’s (1999) study of competitiveness of manufacturing firms and Baum et al’s. (2000) analysis of Canadian biotechnology firms that both show a positive relationship between structural holes and firm performance and innovation.

Singh (2000), who was also included in this review, did not find a significant relationship between existence of structural holes (as measured by number of unrealized relationships between alters) and whether entrepreneurs saw and seized opportunities. However he did find a positive relationship between number of weak ties and entrepreneurial opportunity. This discrepancy highlights the danger of conflating “weak ties” and bridging capital. Singh only allowed respondents to name five alters on the survey’s name generator. Since it is likely that entrepreneurs learned of opportunities from many more than five sources, and were likely to name resources who they knew especially well (and who therefore likely also knew one another), this may have skewed the results.

Granovetter (1983) demonstrates that weak ties (defined as low frequency, low intensity, or low reciprocity relationships like casual acquaintances, old co-workers, etc.) tend to be bridges whereas strong ties (friends, family, and other close relationships) tend to create closure because of the law of transitivity: If Joe is friends with Bob and Greg, it’s likely that Bob and Greg will also know each other since Joe spends time with both of them and they probably share similar interests. However, the mapping of weak to bridging and strong to bonding ties is not one to one. Zaheer & McEvily (1999) for instance, found that weak ties (as measured by infrequency of contact) did not correspond to bridging ties and did not predict the firm’s adoption of  new strategies.

The brokerage advantage is not only in the mere existence of a weak or bridging tie, but in the non-redundant nature of the tie. Diverse ties are only useful insomuch as an entrepreneur is able and willing to act on this diverse information. In his recent book, Neighbor Networks Burt (2010) argues that the benefit of brokerage is not only access to opportunities, but the development of internal capabilities to understand, combine, and act on these opportunities in meaningful ways. Brokerage, he explains is a “forcing function” for the “cognitive skills of analogy and synthesis, and emotional skills for reading, engaging, and motivating” (Burt 2010: 10).

Closure and Entrepreneurship

Social capital associated with closure tends to get less emphasis in entrepreneurship literature. However two studies offer some insight into the benefit of density. Hansen (1995) studied the ego-networks[4] of 44 service-based start-ups in Tennessee. He compared the size, density, and frequency of interactions within an entrepreneur’s network of advisors to the organization’s performance, measured by payroll. He found that size and density of an entrepreneur’s network did correlate positively with payroll, but the analysis of the regression coefficients suggested a more complex interaction between the number of ties in an entrepreneur’s set, their degree of connectedness, and the frequency with which they interacted. Specifically, adding another member to the network did predict an increase in performance, but this increase would be offset by the decrease in closure unless the new member knew other members. The negative effect increased if the entrepreneur only interacted with the advisor infrequently.

Hansen’s (1995) findings suggests that at certain thresholds, new contacts can compromise performance unless there is some degree of closure. The theory he uses to explain this comes largely from organizational performance literature which suggests that a more integrated network structure leads to lower barriers to exchanging tacit information and making other transactions (consider, as an example, a team who has been working together for many years and has developed insider language, trust, and familiarity that make it easier to work together). He concludes by advising entrepreneurs to consider new relationships instrumentally: first consider what resources are required,  next seek out new network members who have these mission resources, and finally incorporate these new members into the group (Hansen 1995: 15).

Uzzi (1996) takes a slightly different approach, examining relationships between contractors and manufacturers in New York’s garment industry, and the relationship between social capital and the firm’s likelihood of failure. In this case, ties are based on “special relationships” or  “embedded exchanges” between a firm and its “preferred” trading partners (Uzzi 1996: 686). He calculates first-order coupling (the degree to which ego is sending work to a few preferred partners versus many random transactions) and second-order coupling (the degree to which a firms’ network partners are also transacting with a few preferred partners versus many random transactions). The second measure serves as a proxy for density or embeddedness, though it does not measure it directly. The results suggest that firms that operate in a more embedded context are less likely to fail up to a certain threshold point, after which the social capital associated with embeddedness or closure switches from asset to liability. This corresponds with Levitte’s (2004) qualitative study of how bonding capital can be an asset to small business development in Aboriginal communities, but can also be a barrier to business success.

Specifically, Uzzi (1996) points out, density in business relationships can support entrepreneurial success by “concentrating on cultivating long-term cooperative relationships that have both individual and collective level benefits for learning, risk-sharing, investment, and speeding products to market” (p. 693)[5]. However, it is possible to have too much of a good thing and it appears that dense structure of bonding capital can have negative implications at the extreme, in effect, “sealing-off firms in the network from new and novel information or opportunities that exist outside the network” (p. 675). Too much closure leads to a loss of “adaptive capacity,” and more susceptibility to shocks. In the same vein as Burt (1992; 2010), Uzzi (1996) concludes that, “optimal networks are not composed of either all embedded ties or all arm’s-length ties, but integrate the two” (p .694).

Social Movements: Social Capital for “We”

Snow, Soule and Kriesi (2003) define a social movements as:

Collectivities acting with some degree of organization and continuity outside of institutional or organizational channels for the purpose of challenging or defending extant authority, whether it is institutionally or culturally based, in the group, organization, society, culture, or world order of which they are a part. (P. 7)

Because they are not defined by institutional or organizational boundaries, but often include a mix of formal social movement organizations, informal organizing, and individuals activists, movements can be difficult to pin-down. A network analysis approach that looks at the relationships between movement entities can be useful to describe and understand the nature and social processes that make up a movement.

“We” capital enables and constrains the movement as a whole

Movement scholars use network analysis to examine the structure of movement networks to consider how bridging and bonding capital relates to capacity for collective action, identity formation, or political engagement. Granovetter (1973) for instance offers early insight into the role of “we” capital in movements when he describes how community social structure in Boston’s West End may have contributed to its inability to halt processes of “urban renewal” that ultimately destroyed the neighborhood. Though West-enders were organized in tight cliques, characterized by a high degree of bonding capital, they lacked bridging capital between groups that could have broadened their collective power, and lacked external ties to decision-making entities that ultimately influenced their fate.

One branch of network studies on social movements pays attention to how network capital affects how individuals are recruited and participate in a movement. Who do they know? Who are their friends and who do their friends know? What opportunities for participation exist in their social sphere? This line of inquiry asks how personal networks influence the choices of individual activists and rarely examines how overall network structure in a movement enables or inhibits outcomes (Diani 2003a: 8). The approach may look the same as studies on entrepreneurial “me-capital,” but is different. Though the unit of analysis is the individual and his network, studies like Kriesi et. al.’s (1995) study of gay movement subcultures are not as interested in how individual networks benefit the individual, but how they ultimately shape and constrain broader movement community.

Social capital as dependent variable

Movement scholars have had limited success linking network capital directly to movement outcomes. The connection between a movement’s structure and its effect on macro-level social or political change is complex and difficult to specify; on the other hand, micro-level analysis of a particular protest or organization is more precise, but offers less generalizable meaning (Diani 1997: 129). Rather than attempt to link network structures to specific outcomes, Diani (1997) suggests that researchers reverse the equation and consider specific network configurations as the outcome of social movement processes. Analysts can assess a movement’s efficacy indirectly in terms of the scope, density, heterogeneity, and overall pattern of its relationships. These relationships formed through various movement processes are assumed to constrain or enable future action,  and so on in a cycle over time:

According to this perspective, the central problem is no longer whether and how mobilization campaigns, and cycles of protest determine specific changes at different levels of the political and the social system. It becomes instead whether they facilitate the emergence of new networks, which in turn allow advocacy groups, citizens’ organizations, action committees, and alternative intellectuals and artists to be more influential in processes of political and cultural change.” (Pp. 130-135).

This view of social movements fits nicely with definitions of social movements as fluid “multi-organizational fields” (Evans 1997, Klandermans 1992; Curtis & Zurcher 1973) and with the new social movements perspective championed first by Melucci (1989) that conceives of social movements as ever-shifting processes of meaning and identity-construction that challenge existing cultural norms. The studies reviewed in this paper tend to take this approach to heart. Some, for example, explicitly examine correlations between a movement’s bridging and bonding capital and the nature and strength of collective identity frames (Ackland & O’Neill 2011, Carroll & Ratner 1996).


When studies focus on measuring network capital within the movement as a whole, we see meso-level networks with nodes defined as social movement organizations (SMOs) (Ackland & O’Neill 2011, Ansell 2003, Diani 2003b, Phillips 1991, Rosenthal 1985). When studies focus on the role of network capital in activist recruitment, we see micro-level nodes: individual activists or would-be activists (Ohlemacher 1996, Della Porta 1988). Some movement studies also look at the relationship between sub-movements or entire movements. For example, Carroll & Ratner (1996) are interested in the relationship between progressive movements in the Toronto area; in this case, sub-movement and movement nodes are not structurally defined agglomerations of organizations (for example, a group of organizations defined by a modularity-Q score), but are defined according to the researcher’s qualitative analysis.

Network Boundaries

Scholars of activist recruitment generally use ego-networks, similar to scholars of entrepreneurship. this gets at questions of how a person’s position in her immediate web of contacts may affect her likelihood to engage, but doesn’t comment directly on the way bridging and bonding capital within a movement may provide differing opportunities for engagement. This decision may mostly have to do with expediency since it is more difficult and resource-intensive  to map a complete network of activists in a movement (Diani 2003a: 8).

Other social movement analysts set network boundaries based on topical (e.g. environmental), geographic (e.g. Bay Area), and chronological (e.g. current day) criteria. For example, Ansell (2003) studies environmental organizations in the San Francisco Bay Area  and Rosenthal (1985) looks at 19th century women’s movements in New York. Defining a particular movement case makes data collection more manageable while setting up the possibility to draw inferences about the nature of movements generally.

Carroll & Ratner’s (1996) study spans multiple movement topics  in order to understand the development of more general “master” frames about social justice. No studies that I reviewed examined movements over time, but Diani (2003a) suggests that this could be a useful way to understand how network capital changes as a result of movement tactics.  Practically speaking, researchers use historical documents, on and off-line archives, and snowball surveys and interviews to identify activists and organizations to include in a study.


When it comes to specifying what ties constitute “social capital” within a movement network, some scholars often use two-mode analysis to identify where organizations or activists are connected. Generally this is done either through overlapping membership (e.g. if organization A and B share a member, they are “connected” and if member A and B belong to the same organization, they are “connected) (Rosenthal 1985; Carroll & Ratner 1996) or mutual participation in some action or coalition (Phillips 1991; Diani 2003b).

Again, it appears that this approach is mostly a matter of expediency. It is simpler and less resource intensive to collect member lists rather than survey organizations directly. It also may be a more viable option for scholars studying historical movements where member lists are available, but other good organizational documentation is sparse. The only drawback here is that these types of ties generally measure a likelihood of relationship rather than an actual relationship, depending on how the author interprets the tie: for example, two organizations with four members in common may not work together on any common campaigns or have any sort of “real” connection. The overlap may be happenstance.

Ansell (2003) is an exception to the two-mode trend; he surveyed organizations to ask directly if they had worked together in some capacity. This approach made sense in this research case because Ansell already needed to perform a survey to collect other information.

Structural measures

Network analysis in social movements employs similar structural measures of social capital as in entrepreneurship studies, but some studies are also able to get at brokerage measures like cliques and betweenness centrality scores more directly because they are mapping a complete field of relationships.

Brokerage and Social Movements

Social movement scholars borrow from the same seminal literature on social capital as scholars of entrepreneurship (Granovetter 1973; 1983; Coleman 1988; Burt 1992; 2010; Putnam 1995), and studies come to similar general conclusions about the necessity of a healthy mix between bridging and bonding capital[6].

The key difference between the concept of bridging capital in the social movements versus in entrepreneurship is an emphasis on the value of bridging to the overall movement, rather than to the individual actor. This relates to Obstfeld’s (2005) distinction between tertius gaudens and tertius iungens-style orientations to bridging positions. Whereas a tertius gaudens (“third who enjoys”) approach describes brokers who controls the relationship between partners for their individual benefit, a tertius iungens strategy (“third who joins”) relates to brokers that seek to be a bridge between otherwise disconnected alters for the purpose of “longer-term generativity and coordination” (p. 125). Both the tertius iungens and tertius gaudens orientations start from the same premise: a network full of structural holes or potential bridging capital. Whereas brokers with gaudens orientations attempt to maintain their unique position by acting as a gatekeeper, iungens-oriented brokers seek to introduce alters to one another and close structural holes, either maintaining an active coordinating role over time or disappearing from the triad once the connection is made. This strategy does not necessarily maximize direct and immediate returns to iungens; in fact, it often constitutes a loss of “me-bridging” capital, but it is key to understanding the nature of “we-bridging” capital in social movements. The mechanism behind bridging capital in the social movement literature is similar to cooperative game theory: organizations engage in relationships not necessarily because of their immediate transactional benefit, but because of how those relationships change the overall game (Obstfeld 2005: 125).

As Diani (2003) finds in his study on brokerage and centrality within Italian environmental organizations, whereas central SMOs tend to attract more resources and media, SMOs with high brokerage scores allow “the establishing of communication across different subgroups of the movement and in this sense facilitate[] the integration of otherwise heterogeneous organizations” (p. 113). While it may be the case that SMOs that operate as brokers enjoy some return on their advantage (whether in terms of exposure to more diverse ideas which in turn affects their capacity to innovate or in resulting increased capacity to occupy a liminal position as Burt (2010) suggests), the more significant point is that this form of bridging is crucial to building a broad base of support. For example, Diani (2003b) and Phillips (1991) studies both show that  removing key broker organizations would result in a fragmented and parochialized landscape of disparate sub-movements, each alone lacking the capacity to enact social, cultural, or political change. Carroll and Ratner’s (1996) analysis of 272 SMOs across 13 movements in greater Toronto comes to a similar conclusion and comments at length on a case in which one movement is not connected to the broader field by any bridges.

The Toronto example also suggests a relationship between we-bridging capital and movement framing, showing that SMOs with more cross-movement or bridging ties (as measured by overlapping memberships of activists) tend to employ a more inclusive master frame that focuses on the interconnectedness of multiple issues. Despite some methodological shortcomings[7] the authors’ main points seem to be corroborated by qualitative evidence. There is potential for more work to examine the relationship between the role of “me-bridging” capital in strategic framing processes including frame bridging, frame amplification, frame extension, and frame transformation, and in the resolution of frame disputes (Benford & Snow 2000).

Finally, Ansell (2003) comes to the same conclusion from the opposite end. In his study on environmental SMOs in the San Francisco Bay Area, relational embeddedness or closure (or relative lack of bridging capital) negatively affected SMOs attitudes towards collaboration with governmental organizations and groups with opposing interests.

What kinds of organizations make up bridging capital in movements? Diani (2003b) notes that bridges within movements are not necessarily characterized by a higher degree of ties to other SMOs, they are not necessarily more central. However, brokers tend to be more bureaucratic and neutral, opening up “opportunities for communication (and possibly collaboration) within the movement network which would not otherwise be available” (p. 119). Carroll & Ratner (1996) and Phillips (1991) come to similar conclusions when they note the high-degree of bridging capital tends to belong to organizations that are well resourced and more neutral in framing or explicitly designed to bridge movement boundaries. This, interestingly, is reminiscent of Burt’s (2010) description of how the individual broker exercises the advantage of her structural position: not necessarily by way of simply being connected to many diverse alters, but rather because of the characteristics and capacity to synthesize seemingly opposing views and more objectively weigh the risks and rewards of action and thus take advantage of these opportunities.

Closure and Social Movements

Broad conversations of bonding social capital within movements suggests that dense clustering and closure relates to trust and collective action, yet relatively few studies get at a causal relationship between bonding capital and collective action. Diani (1997) suggests that we flip the equation and consider how movement action contributes to differing degrees of density rather than assess the existence of social capital a priori and then use this to predict a cycle of action.

Rosenthal et. al.’s study (1985) shows high density within a network of organizations that comprised a 19th century women’s movement in New York, as compared to a network study of corporate interlocks. It also finds distinct relational clusters within the movement that corresponds to particular nodes of activity. As a result, the authors suggest that closure is a necessary component of action and that social movements on the whole tend to exhibit high density (closure) because of their stance in opposition to or apart from a dominant structure. They conclude that closure is both the result of a social movement’s position as a status quo alternative and a driver of the movement’s success in altering that status quo. If altering an extant social or cultural system requires connection with that system, then closure may indeed facilitate action, but at the same time, successful action will expand the movement and reduce closure. Thus as movements move from marginal to mainstream, we expect to witness less closure and more bridging (Diani 1997; Oliver & Marwell 1988). Taken to the extreme, a movement that is completely successful would eventually melt into broader society and nullify itself.

Deepening this line of thinking, Oliver & Marwell (1988, 1993) use mathematical modeling to examine the effect of movement size, heterogeneity, and density on the capacity for collective action. They find that larger, less dense groups are more effective than smaller, denser groups if the cost of action remains constant as the group increases in size. Larger groups become even more effective to the extent that ties are non-random and heterogeneous, which indicates selective linking.

Diani (2003), in the vein of new social movement scholars, points out that it is not just collective action, but also shared identity that distinguishes a movement network from a loose coalition network or random network of actors. Persistent clusters of dense ties within a movement are likely to indicate shared identities, values, and theories of change. In movement terms, these collective identities are called frames. Both Carroll & Ratner (1996) and Rosenthal et. al. (1985) find correspondence between dense clusters and shared identity or movement framing.

Social Capital, or the Goldilocks Story on Repeat

In the last few pages, I have described some of the ways that scholars think about network capital in entrepreneurship and social movements. For both the entrepreneur and the movement collective, a mix of bridging and bonding capital is associated with the most positive outcomes.. For both brokerage and closure, entrepreneurs and movement leaders can adopt a “Goldilocks” approach. Bridging capital is generally good, but  may be detrimental after a certain threshold especially when there is an associated decline in closure. Consider for example an entrepreneur with an overload of contacts, all of whom are pulling her in opposite directions or a movement full of randomly connected individuals each of whom has her own theory of change. On the other hand, closure can be useful to facilitate action, but too much closure can lead to stagnation and parochialism.  Too loose, too tight, or just right? The ideal mix depends on a variety of factors including the existence of other types of capital (human capital, political capital, financial resources, etc.), the goals of the entrepreneur or collective, and the stage of development.

In both cases, more work is needed to examine the process by which network capital changes over time. Both for the entrepreneur and for the movement, social capital should be conceived as a cycle rather than a static entity: brokerage and closure leads to outcomes which lead to new patterns of network capital. In reality, the Goldilocks story doesn’t end, but replays as her appetite and preferences change. Understanding these cycles through longitudinal approaches and action is key to accurately understanding the fluid, changing nature of entrepreneurs and movements.

Social Entrepreneurs: Collapsing “Me” and “We” Social Capital

Returning at last to our FoodLab example, from its inception, FoodLab members described the group as a network and used words like “connecting,” “networking,” “sharing,” “bringing together,” “building community,” and “building relationships” to describe our activities. FoodLab members expressed multiple reasons for engaging in networking exercises:

  • Information, ideas, and opportunities: Documenting and exchanging information on best-practices; sharing information about events, opportunities for accessing education, funding or technical assistance;
  • Reputation and legitimacy: Associating with a recognizable institution and connecting lesser-known businesses with well-connected or established institutions and individuals;
  • Emotional support: Addressing day-to-day feelings of isolation (many entrepreneurs were sole-proprietors) and uncertainty (many entrepreneurs had never started a business before and had little-to-know experience);
  • Developing shared resources: Shared kitchen or office space, cooperative purchasing, and sharing other tangible resources like equipment or even labor;
  • Accountability to shared values: Engaging in ongoing dialogue and some sort of self-regulation of social, and environmental bottom lines;
  • Advocacy and collective action: Working together to change broader political, economic, cultural and social structures.

These six goals for the network represent six different possible benefits of network capital. The first three relate to the individual entrepreneur’s “me-capital”. The next three relate to the “we-capital” of the group both internally and relative to the larger social sphere. In the case of FoodLab, we are interested not only in supporting the individual success of businesses in creating social, environmental and financial value, but also in fostering shared norms (e.g. a commitment to social and environmental values) and the capacity for collective action to effect broad-scale change. In both cases, we recognize the advantage of fostering closure within the group to facilitate trust, but also in bridging between otherwise disparate clusters of entrepreneurs (and entrepreneurial allies) to facilitate innovation and broaden our base of power. Students of food systems change in the US have commented on this bridging capacity as one of the major strengths of the movement (Hassanein, 2003; Starr, 2010). The US food movement is made up of a diverse array of loosely connected clusters of public health, environmental, sustainable agriculture, social justice, and other groups. Scholars have yet to undertake a comprehensive network mapping project; I hope to start with one chunk: How do social entrepreneurs fit within this multi-organizational field?

Scholars worry that market-based approaches within the food movement  are individualistic, unaccountable and depolicitized (Starr 2010; Donald 2008). They suggest that “entrepreneurship” as a tactic usually fails to address and sometimes exacerbate issues like food security for the most vulnerable and racial and cultural injustice (Allen et. al., 2003). A social entrepreneur’s tension between “me-capital” that facilitates individual action and “we-capital” that represents accountability to the collective may be a response to contemporary conceptions and criticisms of entrepreneurship in the food movement.

The literature seems to suggest that both the “me-capital” approach in entrepreneurship and “we-capital” in movements look similar structurally, but the “we-capital” approach optimizes bridging and bonding ties for the good of the whole whereas the “me-capital” approach attempts to optimize for the individual. Given what we know about scale-free networks and preferential attachment where actors rich in connections tend to continue to get richer over time (Neal 2012), the entrepreneur scholar’s approach to network capital means those who start with an advantage continue to accrue returns over time. Even in cases where the entrepreneur is benevolent, the unequal distribution of social capital has consequences because it often manifests along the lines of race and gender. Certain social, cultural and environmental values can come to dominate, marginalizing other interests. For example in Detroit, both bridging and bonding appears to be concentrating in a circle of (mostly) white entrepreneurs who tend to live in certain neighborhoods, who focus on “quality,” local,” and “artisanal” production and food as a means to attract more young educated people to the city. Building up the “me-capital” of these entrepreneurs may forward these goals (and the financial viability of these businesses) but at the expense of a focus on other neighborhoods, on increasing minority-owned businesses, on delivering culturally-appropriate and affordable food to neighborhoods.

The “we-capital” approach introduces an aspect of agency that may spread connections more equitably depending on which entrepreneurs are considered to be “within” the boundaries of the network. Social entrepreneurship may be the place where “me-bridging” meets “we-bridging”: entrepreneurs make decisions that weigh their own needs, but also the good of the collective.

Future research should combine these entrepreneurial and social movement perspectives on network capital to systematically study networks of social entrepreneurs. From this we can understand how and why network capital changes over time, how this affects the success of individual entrepreneurs, and how the structure of network capital enables or constrains directions for broader cultural, political and social change. The following are some specific propositions to test in the FoodLab case:


Proposition 1: Entrepreneurs whose idea and opportunity networks (e.g. where have learned of new ideas or opportunities for your business?) contain more structural holes and are more heterogeneous will be more likely to have positive entrepreneurial outcomes (e.g. entrepreneurial emergence, entrepreneurial persistence, achieving financial sustainability, and an entrepreneur’s progress towards social, environmental, and financial goals) up to a threshold.

Proposition 2: The degree of closure in support (e.g. who encouraged or supported you in starting your business?) and advice networks (e.g. who did you go to for information or advice while starting your business?) will correlate with positive entrepreneurial outcomes up to a threshold point (after the threshold, more closure will lead to worse outcomes).

Proposition 3: The ratio of bridging and bonding capital among “successful” businesses will vary according to the stage of business development and personal characteristics (human capital, race, and gender).


Proposition 4: Entrepreneurs with high bridging capital who are strongly affiliated with FoodLab (and are assumed to be more enrolled in the food movement) will be more likely to adopt a tertius iungens orientation than new members and non-members.

Proposition 5: Network closure within FoodLab subgroups strengthens specific “good food frames”; bridges connect otherwise disparate groups with universal frames that synthesize multiple viewpoints.

Proposition 6: The mix of bridging and bonding capital changes over time within FoodLab and correlates to differing potential for collective social, political, and cultural action.

Works Cited

Ackland, R., and M. O’Neil. 2011. “Online collective identity: The case of the environmental movement.” Social Networks.

Ansell, Christopher. 2003. “Community Embeddedness and Collaborative Governance in the San Francisco Bay Area Environmental Movement.” Pp. 123-144 in Social Movements and Networks: Relational Approaches to Collective Action., edited by Mario Diani and Doug McAdam. Oxford University Press.

Baker, W. E. 1990. “Market networks and corporate behavior.” American Journal of Sociology. Pp. 589–625.

Baum, J.A.C., T. Calabrese, and B.S. Silverman. 2000. “Don’t go it alone: Alliance network composition and startups’ performance in Canadian biotechnology.” Strategic management journal 21(3):267–294.

Benford, R. D., and D. A. Snow. 2000. “Framing Processes and Social Movements: An Overview and Assessment.” Annual Review of Sociology, 639.

Borgatti, S.P., C. Jones, and M.G. Everett. 1998. “Network measures of social capital.” Connections 21(2):27–36.

Burt, R.S. 2010. Neighbor networks: Competitive advantage local and personal. Oxford University Press, USA.

Burt, Ronald. 2005. Brokerage and Closure: An Introduction to Social Capital. Oxford: Oxford University Press.

Carroll, W.K., and R.S. Ratner. 1996. “Master Framing and Cross-Movement Networking in Contemporary Social Movements.” The Sociological Quarterly 37(4):601–625.

Coleman, J.S. 1988. “Social capital in the creation of human capital.” American journal of sociology 95–120.

Davidsson, Per, and Benson Honig. 2003. “The role of social and human capital among nascent entrepreneurs.” Journal of Business Venturing 18(3):301-331. Retrieved February 12, 2012.

Dees, J. G., and B.B. Anderson. 2006. Framing a Theory of Social Entrepreneurship: Building on Two Schools of Practice and Thought.

Della Porta, D. 1988. “Recruitment processes in clandestine political organizations: Italian left-wing terrorism.” International Social Movement Research 1:155–169.

Deresiewicz, William. 2011. “Generation Sell.” New York Times, November 12, Online Retrieved November 12, 2011 (

Diani, Mario. 1997. “Social movements and social capital: a network perspective on movement outcomes.” Mobilization: An International Quarterly 2(2):129–147.

Diani, Mario. 2003a. “Introduction: Social Movements, Contentious Actions, and Social Networks: ‘From Metaphor to Substance’?.”Pp. 1-18 in Social Movements and Networks: Relational Approaches to Collective Action., edited by Mario Diani and Doug McAdam. Oxford University Press.

Diani, Mario. 2003b. “‘Leaders’ or Brokers? Positions and Influence in Social Movement Networks.”Pp. 105-123 in Social Movements and Networks: Relational Approaches to Collective Action., edited by Mario Diani and Doug McAdam. Oxford University Press.

Doh, J.P., and N.M. Dahan. 2010. “Social Movements and Social Networks: An Evolutionary Perspecctive on Contemporary US Student Advocacy Campaigns.”

Granovetter, M. 1983. “The strength of weak ties: A network theory revisited.” Sociological theory 1(1):201–233.

Granovetter, M.S. 1973. “The strength of weak ties.” American journal of sociology 1360–1380.

Hansen, E.L. 1995. “Entrepreneurial networks and new organization growth.” Entrepreneurship theory and practice 19(4).

Hoang, Ha, and Bostjan Antoncic. 2003. “Network-based research in entrepreneurship: A critical review.” Journal of Business Venturing 18(2):165-187. Retrieved March 6, 2012.

Kriesi, Hanspeter. 1995. “Chapter 7: Gay Subcultures between Movement and Market.” in New Social Movements in Western Europe: A Comparative Analysis. University of Minnesota Press.

Levitte, Y. 2004. “Bonding Social Capital in Entrepreneurial Developing Communities-Survival Networks or Barriers?” Community Development 35(1):44–64.

Marwell, G., P. E Oliver, and R. Prahl. 1988. “Social networks and collective action: A theory of the critical mass. III.” American Journal of Sociology 502–534.

McQuaid, R. W. 1996. “Social networks, entrepreneurship and regional development.” Small firm formation and regional economic development 118–131.

Obstfeld, D. 2005. “Social networks, the tertius iungens orientation, and involvement in innovation.” Administrative science quarterly 50(1):100.

Ohlemacher, Thomas. 1996. “Bridging People and Protest: Social Relays of Protest Groups against Low-Flying Military Jets in West Germany.” Social Problems 43:197.

Oliver, P., and G. Marwell. 1988. “The Paradox of Group Size in Collective Action: A Theory of the Critical Mass. II.” American Sociological Review 1–8.

Oliver, P., and G. Marwell. 1993. The critical mass in collective action: a micro-social theory. New York: Cambridge University Press.

Phillips, S. 1991. “Meaning and structure in social movements: mapping the network of national Canadian women’s organizations.” Canadian Journal of Political Science 24(4).

Putnam, Robert. 1995. “Bowling Alone: America’s Declining Social Capital.” Journal of Democracy, 78.

Rosenthal, N., M. Fingrutd, M. Ethier, R. Karant, and D. McDonald. 1985. “Social movements and network analysis: A case study of nineteenth-century women’s reform in New York State.” American Journal of Sociology 1022–1054.

Van Ryzin, G.G., S. Grossman, L. DiPadova-Stocks, and E. Bergrud. 2009. “Portrait of the social entrepreneur: Statistical evidence from a US Panel.” Voluntas: International Journal of Voluntary and Nonprofit Organizations 20(2):129–140.

Sharir, M., and M. Lerner. 2006. “Gauging the success of social ventures initiated by individual social entrepreneurs.” Journal of world business 41(1):6–20.

Singh, R.P. 2000. “Entrepreneurial opportunity recognition through social networks.” New York, London.

Smallbone, D. 2001. Researching social enterprise: Final report to the Small Business Service. SBS Research Directorate.

Starr, Amory. 2010. “Local Food: A Social Movement?” Cultural Studies, Critical Methodologies, 490.

Täube, V.G. 2004. “Measuring the social capital of brokerage roles.” Connections 26(1):29–52.

Thekaekara, M.M., and S. Thekaekara. 2007. Social Justice and Social Entrepreneurship: Contradictory Or Complementary? Skoll Centre for Entrepreneurship, Saïd Business School.

Thompson, J.L. 2002. “The world of the social entrepreneur.” International Journal of Public Sector Management 15(5):412–431.

Uzzi, B. 1996. “The sources and consequences of embeddedness for the economic performance of organizations: The network effect.” American sociological review 674–698.

Zaheer, A., and B. McEvily. 1999. “Bridging ties: A source of firm heterogeneity in competitive capabilities.” Strategic management journal 20(12):1133.

[1] Academics and practitioners have yet to achieve consensus on a definition of social enterprise (Mair & Marti, 2006; Peredo & McLean, 2006; Short, Moss & Lumpkin, 2009; Weerawardena & Mort, 2006), but for the purposes of this paper, we conceive of it as a business (for-profit, non-profit, or hybrid) for whom the creation of social or environmental value is prominent to their mission – at least as important as profits.

[2] See, for example, the Green Garage in Detroit (; Social Edge (,) run by the Skoll Foundation; Ashoka Changemakers (; Business Alliance for Local Living Economies sustainable business networks (; Echoing Green network (; The Hub Bay Area (; and more.

[3] I  limit this review to the structural elements of social capital as opposed to the content or governance of relationships, though I know these three concepts are often related (Hoang & Antoncic 2003).

[4] To get at the ego-network, Hansen (1995) asked entrepreneurs to list “the people with whom you were interacting most during that six-month period that preceded the hiring of your first FTE employee] to secure the business information and the resources that were important to your business” (p. 11). He then asked entrepreneurs to specify the relationship between every pair of alters that the respondent listed.

[5] This is similar to Coleman’s (1988) famous example of diamond traders in New York City that demonstrate that “close ties, through family, community, an religious affiliation, provide the insurance that is necessary to facilitate the transactions in the market. […] The strength of these ties makes possible transactions in which trustworthiness is taken for granted and trade can occur with ease” (p. 97).

[6] As a quote from Rosenthal et. al.’s study on a 19th century women’s rights movement (1985) illustrates, there is also a similar conflation of strong ties with bonding ties and weak ties with bridging ties: Weak ties are channels for communication with diverse publics. Strong ties, however, show more enduring bonds. They demonstrate the existence of alliances. It is through these channels that resources (both material and ideological) flow from one organization to another, exchange of leadership may occur, and influence and control can be exercised. (Rosenthal et. al. 1985: 1051).

[7] By choosing a purposive sample for their two-mode analysis rather than including the entire population of activists, the authors may have mischaracterized the structure of relationships between SMOs and between movements. This is especially true given their finding that a large degree of cross-movement ties were initiated by what they call “organic intellectuals,” or individuals who appear to specialize in making cross-movement connections. If the sampling missed just a handful of these connectors, then the structure presented in the paper  would be inaccurate. A more robust analysis might use a cluster sample: purposive sampling to identify organizations, and then collection of complete member lists for these organizations.

July 3, 2012   No Comments

Essay: Unraveling Agro-food Network(s)

This was written as a response paper for a course on social networks. We were asked to write three essays critiquing network research in our area of interest at the micro (people), meso (organization/community/infrastructure), and macro (nation scale) levels.

Generally, we chose essays that used structural network analysis themselves; in this case, I chose a paper that adopted a network (or relational) way of looking at the world, but didn’t use these formal methods. Structural network methods are a set of (mostly) quantitative approaches that (as their name implies)  describe the structure of relationships (ties) between different actors (nodes) or the position of a particular actor within this structure.

For more on network analysis, here’s a pretty good simple overview of some basic concepts.


Unraveling Agro-food Network(s)

Drawing inspiration from Granovetter’s (1985) seminal work on embeddedness, food systems researchers in the late 1990s began to integrate economic, social, and political approaches to food systems into a network-based ontology.  Rather than look at global food systems as structurally ossified “regimes,” linear commodity chains, or markets made up of rational, disconnected actors, researchers re-imagined food systems as complex webs of actors linked by social, political, economic, and physical ties. Despite the popularity of the network metaphor, there are still few examples of researchers employing formal network methods to describe the structure of agro-food networks.

Raynolds’ (2004) is no exception. Her study of organic agro-food networks falls within a family of research that has blossomed in the last decade, which focuses on “alternative” agrifood networks (e.g. local and regional, fair trade, artisanal, etc).  She employs commodity network analysis to examine consolidation in global organic networks focusing on network governance, or the mechanisms that underlie the development of network ties. She demonstrates that certification standards play a major role in determining and maintaining an inequitable structure of relations between organic food actors in periphery and core (South-North) nations, but stops short of explicitly specifying and measuring this structure. Finally she observes that there is a “bifurcation” in organic agro-food networks between this “globalized system of formally regulated trade” and networks based in “alternative movement conventions,” and suggests that these alternative networks may offer opportunity to upend the reproduction of traditional South-North inequities, as well as inequities between large and smaller scale firms (Raynolds, 2004:725).

By design, the commodity network approach looks at multiple dimensions of global organic networks simultaneously. It describes social, political, cultural, and economic ties. Nodes aren’t limited to one type, but at times are hemispheres, at times, nations, firms, and individual consumers. The boundaries of analysis shift at times from a North-centered organic processing and distribution network to a movement network of consumers directly connected to local farmers to a global exchange between North-South nations.  What might we might learn by focusing in and using formal network methods to measure the observable interactions between a specified set of actors? In the following paragraphs, I unravel three of the many networks that Raynolds (2004) invokes, specify the nodes, ties, and boundaries, and use her analysis to make guesses at network measures like degree, density, and centrality. Then I describe how network analysis might be used specifically to add depth to Raynolds’ final conclusion about the “bifurcation” between mainstream and alternative organic agro-food networks.

The main thrust of the argument takes place at the macro-level, looking at the relationships between periphery-core nations, specifically between Southern countries (especially in Latin America) and Northern markets (especially in the United States, Canada, Western Europe, and Japan). In this network, the nodes are countries, the ties are imports and exports, and the boundaries are (mostly) limited to Latin America and the major markets described above. From this, we can infer that Northern countries will tend to have higher in-degree centrality than Southern countries (hence their “core” status).  Raynolds also describes a robust “inter-core” trade “dominated by US exports to Europe and Japan, trade between European nations, and exports from Australia, New Zealand, and South Africa to the top markets” (p. 725). Considering this, and that products might flow more than one step (e.g. organic tomatoes produced in Chile, processed and canned in the US, and sold in Japan; peanuts grown and shelled in Canada, included in mueslix in Germany, shipped to the UK), we might meaningfully measure betweenness and closeness centrality. This might help to identify particular Latin American countries as “bridges” that are serving as a gateway between Southern producers and Northern markets; certain Northern countries (the US, for example) with high betweenness scores might also be brokers with more power to set the global organic agenda. These measures would require data measuring the flow of some subset of organic products (all edible organic products, organic fruits and vegetables, all processed products etc.) between each country dyad. With this data, we could also compare a network of actual trade with a network of trade that we might estimate based on a gravity model based on “distance” as measured by cost of transport between countries, and “size” as measured by number of organic hectares, length of growing season, and total population. The differences between the actual and estimated networks would shed light on political, social, and cultural structures that intervene in the network. Though the data required for this analysis would not be easy to compile, it might be possible to get at by combining a variety of sources and using estimates, and the result could a more nuanced view of North-South organic agro-food trade dynamics.

Raynolds (2004) also considers a meso-level network of organic agro-food firms. In this case, the nodes are all organic firms (including farmers, aggregators, distribution companies, processors, and retailers) and the ties could be any type of business relationship (e.g. sales between firms). Raynolds describes a change from a “loosely coordinated local network of producers and consumers to a globalized system of formally regulated trade which links socially and spatially distant sites of production and consumption” (p. 725). The trend is towards greater spatial distance between nodes (which would not necessarily be captured in the network I specified above), and also towards consolidation: in network terms, a decrease in the overall size of the network and increased density. Howard (2009) documents this trend in his visualization of consolidation in the North American organic industry, but his study also does not employ formal network measures. Again, data is difficult to obtain on relationships between organic firms, especially given such a broad boundary; however, it is possible to limit the boundary to a particular commodity or limit the type of firm (e.g. only farmers and distributors) to get at a particular aspect of this broader network. This would make it possible to identify more “powerful” firms, not just in terms of endogenous characteristics like size, but also in terms of their position in the network.

Finally, operating beside the macro and meso-level networks is a micro/meso-level network of movement actors and industry groups that shape and challenge norms within the organic movement as well as certification standards. This network could be operationalized as a two-mode network of organic food movement organizations, industry groups and policy-making bodies like the USDA tied by common individuals (e.g. Deputy Secretary Kathleen Merrigan of the USDA formerly staff at the National Sustainable Agriculture Coalition, or former president of the Organic Farming Research Foundation currently head of an organic department at USDA); it could be a two-mode network of movement organizations tied by association with broader trade associations or participation in specific political campaigns; or it could be a network of social movement organizations tied by some other indicator of collaboration. Specifying and mapping these relationships would allow us to see more clearly which agencies, industry groups, and movement organizations occupy more influential positions in the network. If we notice particular clusters of groups, we might look to see if shared norms exist within these clusters and if they specific capacity for collective action. We might also be able to characterize more clearly the “conflict” that Raynolds (2004) describes between movement actors and industry groups in determining certification standards. If we were able to measure this over time, we might also see whether movement advocates like Fred Kirschenmann (2007), who have advocated for a more harmonious marriage between the organic industry and the organic movement into a more integrated organic community, have had any effect.

The article ends by reasserting this conflict between two parts of the organic agro-food network: the one that is governed by organic certification standards driven by commercial and industrial conventions that privilege economies of scale and efficiency, versus the one that is governed by domestic and civic conventions of trust, tradition, and overall good to society. Raynolds (2004) bases this on her observations of “alternatives” to “mainstream” organic networks that represent the “theoretically important […] contested terrain negotiated within and between commodity networks” (p. 738). This dichotomization of movement-based “alternative” networks versus “mainstream” or “industrial” networks is typical of contemporary food systems studies, yet little research has been done to examine these supposedly different networks systematically to compare their structures and ask whether they are really as “bifurcated” as theory assumes.

To systematically analyze this assumed separation, we might choose a particular organic product within a given geography that we believe has strong “mainstream” and “alternative” networks of production and consumption; say, for example, organic berries in the Pacific Northwest which might be produced by small local farms and sold at Farmers Markets and through Community Supported Agriculture schemes or produced in Latin American countries, imported, and sold at larger retailers. We could set the nodes as all firms that participate in production, aggregation, processing, and sale of the particular product, and stipulate ties as total volume of transactions between firms. The data could be collected through a mix of interviews, publicly available data, and estimates based on observations. With this data, we could do a better job answering questions like: Are “mainstream” and “alternative” networks really so bifurcated, or do firms actually overlap (we might expect, for example, some overlap in mid-sized producers who sell both at farmers markets and to larger supermarkets)? If two separate cliques of firms do emerge, are they different structurally: More or less dense? More or less centralized? Which firms have power in each clique? Are norms really different in each clique? How so?

To date, food systems researchers have not yet embraced structural network analysis despite a network-based ontology that recognizes the relational aspect of both industrial and alternative food chains. For one thing, as in the case of the above examples in the organic agro-food sector, data can be difficult to collect. In network analysis, missing data has particularly strong negative consequences on the statistical validity of the data. Even where it is possible to collect data, social network methods can seem inaccessible and overly technical. Yet these methods have the potential to bring more clarity to specific questions about how global, organizational, and individual actors connect to one another to both uphold and upend our current systems of producing, processing, distributing, selling, and consuming food.


Works Cited

Granovetter, M. 1985. “Economic action and social structure: the problem of embeddedness.” American journal of sociology 481–510.

Howard, Philip. 2009. “Consolidation in the North American Organic Food Processing Sector, 1997 to 2007.” International Journal of Sociology of Agriculture and Food, 30.

Kirschenmann, Fred. 2007. “Guest Feature: Beyond Organic, What’s Really At Stake?”

Raynolds, Laura T. 2004. “The Globalization of Organic Agro-Food Networks.” World Development 32(5):725-743. Retrieved April 17, 2012.

April 18, 2012   No Comments

Essay: Social Capital, Networks, and Entrepreneurial Development

I sometimes wonder whether academic writing has any purpose other than to 1) exclude and create a class of “experts” that have legitimacy and power (check out this great TED talk on when experts are warranted and when they’re dangerous) and 2) to obscure fuzzy thinking in jargon so that it can’t be exposed as such.

I’ve been reading William Zinsser’s classic On Writing Well and trying to apply it to my own writing (it’s a process…. :/)  I find that I’m sometimes able to translate ideas I come across in academia and bring them to everyday conversations, but more often than I’d like (and especially when I’m still working out an idea, or still unclear) I slip into using big words to say not much of anything.

Why do I (why do we as scholars) write the way I (we) do? How might changing the way I (we) write change the way I (we) think?


FoodLab Detroit is a network of entrepreneurs supporting one another in developing businesses with a “triple-bottom-line” (social environmental and financial). FoodLab was founded at a small informal gathering of peers in January 2011; by August, the group had grown to nearly 30, adopted a charter, and created a steering committee. As of February 2011, we have 73 entrepreneurs on our online listserve and engage in a broadening portfolio of activities including regular meetings and networking events, business planning workshops, coordination of shared use kitchen space and other resources, and advocacy through speaking events and engagement with community partners. The organization participates in Detroit’s good food movement, a meta-movement comprised of diverse sub-movements united by the recognition that “today’s food and farming economy is ‘unsustainable’ – that it can’t go on in its current form much longer without courting a breakdown of some kind.” (Pollan, 2010). The organization is also a part of a movement to “reimagine” the city of Detroit. FoodLab members grapple with questions about GHGs and energy use, how to support local growers and connect people to their food, but also how to create more living-wage jobs, use vacant space, rebuild neighborhoods, connect an otherwise largely segregated city, and build a more participatory, responsive, and democratic community.

Our vision is to build a “network of thriving, diverse locally-owned food production, processing, and retail businesses that contribute to the well-being of our communities and are collectively committed to increasing healthy, green, fair, and accessible food options in Detroit area.” In service of this vision, we support nascent entrepreneurs in order to help them develop socially and environmentally conscious businesses that can be sustained over time. One of our primary strategies is helping to build relationships between entrepreneurs themselves and entrepreneurs and relevant external stakeholders and service providers. As FoodLab grows, how can research help us design more effective activities within this broad strategy?

This essay describes how Davidsson and Honig’s (2003) study of nascent entrepreneurs legitimizes FoodLab’s focus on relationship-building and offers suggestions on how network analysis (to which the authors allude) could provide even more nuanced and useful guidance. I begin with a summary of the authors’ findings on the effects of social capital on early-stage entrepreneurs; I go on to examine how their approach, despite its focus on the importance of relationships and its informal invocation of networks, differs from network analysis; I conclude with examples of how FoodLab could use network analysis to inform what structures of relationships are most beneficial to the development of early-stage social entrepreneurs, and how different activities might foster particular types of structure.

Social capital as a predictor of entrepreneurial “success”

How do different sorts of non-financial capital affect entrepreneurial success? Specifically, Davidsson and Honig (2003) measure the extent to which an individual’s stock of human and social capital can predict three stages in the entrepreneurial process: 1) whether or not she engages in nascent activities; 2) the frequency of her “gestation” activities (e.g. writing a business plan); and 3) first sale or profitability of the business. We will focus primarily on their findings related to social capital.

Researchers collected data from 380 nascent entrepreneurs and 608 non-entrepreneur control participants via an initial phone interview, then followed up with entrepreneur-participants after 6, 12, and 18 months to gauge the life of the venture over time. The data supported the claim that individual social capital is strongly associated with reaching all three stages in the entrepreneurial process (see Figure 1). While the authors characterized certain measures as “strong” versus “weak” ties (e.g. family bonds versus business network bridges) and acknowledged the theoretical difference between the two, data did not support claims about the effect of one type of tie versus another on the entrepreneurial process. Overall though, social capital did appear to explain a greater percentage of entrepreneurial success human capital (including formal education and attendance at business classes), especially when it came to achieving a first sale or profitability. Specifically, the authors found that

  • Having parents in business, being encouraged by friends or family, or having close friends or neighbors in business increased the likelihood that someone would become a nascent entrepreneur.
  • Being a member of a business network, contact with an assistance agency, being a member of a startup team, being encouraged by family or friends, having close friends or neighbors in business, and being married increased the rate at which entrepreneurs engaged in gestation activities.
  • Finally, only one variable reliably predicted whether or not an entrepreneur would achieve sales or profitability within the 18-month study: whether or not the entrepreneur participated in a business network.

The authors highlight that connection with an entrepreneurial assistance agency did not necessarily correlate with whether an entrepreneur made an initial sale or achieved profitability within 18 months. Based on this, they argue,

[Social] relations are more important than maintaining contact with assistance agencies, or even in taking general business classes. […] The facilitation and support of business networks and associations may provide the most consistent and effective support for emerging businesses. […] Furthering our understanding of these specific nascent networks and learning how best to facilitate them represents an important activity for future entrepreneurship research. (P. 324-325)

As a nascent network, FoodLab implicitly recognizes the value of social capital and relationship-building. If Daviddson and Honig’s (2003) findings help to justify our existence and general approach, can they also lend more specific insight into how we should design and structure our activities?

Social capital as an generalized individual attribute versus specific relationship

While this particular study suggests that various relationships, and business networks in particular, can play an important role in entrepreneurial emergence, it does not explain the process by which these networks have an effect. The business network, and the social capital it represents, is a black box. In order to understand how a business network affects an entrepreneur (and not just that it does somehow) we would need first to understand the specific kinds of social capital or ties that are created in the context of a business network, and also to re-imagine social capital as a structure of relationships rather than an endogenous characteristic of an entrepreneur (e.g. membership versus non-membership).

Neal (forthcoming) points out that describing a network as such without engaging in network analysis tells us “very little about what networks are or how they work, frequently because they do not identify exactly who or what is connected or in what ways” (p. 5). In the case of Daviddson and Honig (2003) we see that entrepreneurs who belong to a business network are more likely to achieve a sale or profitability. The authors assume that this is because participation in a business network affords entrepreneurs with more bridging (rather than bonding) capital which Granovetter (1973) and others have suggested is important for the diffusion of new ideas and innovation. This assumption may be true, but there also may be instances in which networks (or dense clusters within networks) provide entrepreneurs with the bonding or “strong” ties that might foster exchange of resources or reinforce norms of behavior (e.g. calculated risk-taking or opportunism) that increase the likelihood of success. Rather than make a priori assumptions, we could define and measure specific ties within a network in order to understand more clearly how the nature and structure of relationships surrounding an entrepreneur contribute to success. Some specific examples of the sorts of ties that might develop within a business network include advice-giving, information or opportunity sharing, business partnership or collaboration, emotional support, inspiration, motivation, or emulation.

This approach would also shift focus from the individual to relationships as the unit of interest, and imagine social capital as a structural pattern of relationships (many or few, dense or thinly spread, reciprocal or not?) rather than an individual characteristic (does someone have it, or not?) Traditional approaches that emphasize personal attributes have a number of drawbacks. For one, they “treat each social system member as an astructural independent unit” which “assume(s) random linkages,” whereas in reality, relationships are not random (Wellman & Berkowitz, 1988, p. 31). Entrepreneurs may exhibit homophilic tendencies along characteristics like race, gender, and  age, as well as industry and level of experience. An emphasis on categorical attributes also creates false groups (for example, people who belong to a business network versus those that don’t) and ties these categories to certain outcomes.

These groupings do not get at the root of the matter. FoodLab entrepreneurs will not succeed because they belong to FoodLab, but because of the specific patterns of relationships they might build as a result of membership. The categorical approach may be expedient, but has less explanatory power and may lead to false conclusions. By actually measuring an entrepreneurial network, we can understand how certain types of social capital as evidenced in particular structures of relationship might facilitate diffusion of information versus actual adoption of new practices (Neal et al., 2011). By comparing more than one network, comparing the structure of an informal social network with an intentional business network, or comparing the effect of different activities in the same network over time, we might understand what types of programs and activities foster what type of social capital to what ends.

Future research: social capital and the individual, social capital and the group

In order to inform FoodLab’s strategic direction, we need to know more about:

  1. How different network structures (aka types of social capital) lead to different outcomes. [IND VAR: network structure, DEP VAR: entrepreneurial outcomes]
  2. How different activities lead to different network structures (aka types of social capital) [IND VAR: activities, DEP VAR: network structure)

In part one, we might ask questions like, do we prefer a more densely clustered or more loose network? Do reciprocal relationships matter? What effect do bridging versus bonding ties have on outcomes? In part two, we might ask things like: how does operation of a listserve versus in-person meetings affect the structure of relationships that form within FoodLab? How does intentional recruiting of diverse participants affect our network structure?

Borgatti (1998) gives some suggestions on using network measures to describe an individual’s social capital. He suggests looking at network size, density, heterogeneity, compositional quality, effective size, constraint, closeness, betweenness, and eigenvector values. In this case, we would measure the ego-networks of various entrepreneurs and see how they correlate to various entrepreneurial outcomes. However, Borgatti (1998), in line with Coleman (1988) recognizes that social capital doesn’t only belong to an individual, but can be construed as a public good external to the individual and contained within the broader group.

[My thinking on the exact measures I might look at is still in the very very baby stages... as my prof Zach Neal pointed out, there's no point throwing all these measures around if they aren't getting at something we care about (in his words, they need theoretical grounding.. in my words, they need a grounding in measuring some value we care about)]

Entrepreneurism is seen as a uniquely individual pursuit. Why might we be interested in measuring the overall social capital within a group of entrepreneurs? For one, Coleman (1988) argues that even when social capital doesn’t accrue immediate or apparent benefits to the individual, it can benefit a community as a whole by increasing the stock of overall obligation, expectation, and trust, thus facilitating future interactions. Measuring the effect of relationships on an individual entrepreneur’s success would not account for this.

Also, in the case of FoodLab, we are interested not only in supporting the individual success of businesses in creating social, environmental and financial value, but also in fostering a set of shared norms (e.g. a commitment to social and environmental values). We recognize the advantage of fostering closure within the group to facilitate trust, but also in bridging between otherwise disparate clusters of entrepreneurs (and entrepreneurial allies) to encourage innovation and facilitate more effective collective action. Students of the food movement in the US have commented on this bridging capacity as one of the major strengths of the movement (Hassanein, 2003; Starr, 2010). This shift from considering individual outcomes to outcomes for communities or groups marks a significant difference between traditional entrepreneurship and social enterprise, or enterprise in the service of social change (Thekaekara & Thekaekara, 2006). Systematically measuring FoodLab’s network and linking structural methods to outcomes for individual entrepreneur and for the broader community will be useful in articulating and demonstrating our value to members and supporters, as well as in guiding the mix and design of programs and activities.

Works Cited

Borgatti, S.P., C. Jones, and M.G. Everett. 1998. “Network measures of social capital.” Connections 21(2):27–36.

Coleman, J.S. 1988. “Social capital in the creation of human capital.” American journal of sociology 95–120.

Davidsson, Per, and Benson Honig. 2003. “The role of social and human capital among nascent entrepreneurs.” Journal of Business Venturing 18(3):301-331. Retrieved February 12, 2012.

Granovetter, M.S. 1973. “The strength of weak ties.” American journal of sociology 1360–1380.

Hassanein, N. 2003. “Practicing food democracy: a pragmatic politics of transformation.” Journal of Rural Studies 19(1):77–86.

Neal, J.W., Z.P. Neal, M.S. Atkins, D.B. Henry, and S.L. Frazier. 2011. “Channels of Change: Contrasting Network Mechanisms in the Use of Interventions.” American Journal of Community Psychology 1–10.

Starr, Amory. 2010. “Local Food: A Social Movement?” Cultural Studies, Critical Methodologies, 490.

Thekaekara, M.M., and S. Thekaekara. 2007. Social Justice and Social Entrepreneurship: Contradictory Or Complementary? Skoll Centre for Entrepreneurship, Saïd Business School.

Wellman, B., and S.D. Berkowitz. 1988. Social structures: A network approach. Cambridge Univ Pr.

[1] Including a control sample allowed researchers to test how human and social capital affected whether or not an individual engaged in entrepreneurship at all. The longitudinal design sidestepped the problem of “success bias.” Rather than only measure sustained or successful activity, the data also captured “efforts that fail or are abandoned at early stages,” and shed light on the effect of education and relationships at various stages of the start-up process (p. 311). These two features of the research design set this analysis apart from other research on the emergence of new enterprise, which generally employ cross-sectional data on early-stage businesses.

February 17, 2012   No Comments

Dealing with complexity in the Third Revolution

In response to a post by an inspiring friend:

I’ve been thinking about this a whole lot lately in the context of my own work and life here in Detroit. I moved here in part for a sense of *community* and connectedness and I find that many of the people close to me are drawn & remain in the city for that reason — and yet that interdependence, that rich social web, that “deep participation” is so complicated, and often a source of discomfort.

I wonder how to motivate and manage participation, collaboration, decision-making in “flatter” systems and networks…. how greater interdependence & “richness and diversity of one’s experiences and the strength of one’s social bonds,” while magical on the surface, can be exhausting in practice… the constant give/take/brokering of our values/needs/actions within our networks is a lot in itself. Given our technology as a species, we are no longer operating at the scale of tribes, so we’re negotiating an ever increasing number of connections at varying scales… not to mention the fact that different people are able/willing to “enroll” to different degrees and those who have stronger ties end up being asked to give more than they can sustain as individuals or businesses or organizations (e.g. studies on entrepreneurs with stronger family ties being alternately a blessing and a burden on the business)…

So I guess I just wonder how we deal with this complexity?

When we move out of more bureaucratic, hierarchical command-based approaches to leadership to more participatory, emancipatory, democratic, distributed/chaordic models … and when we move from linear, cumulative models of progress or development to a systems approach focusing on sustainability and resiliency, what are the new kinds of tools (technological, cognitive, emotional, social, political) that we need to manage these changes?

Network modeling? Systems analysis? Ethnography? Facilitative leadership skills?

    Spirituality and religion!?

October 18, 2011   No Comments