Teams and Networks

Ninety-five percent of individuals trying to start a business either involved others to help in some significant capacity or intend to do so in the future. Networks serve as important conduits through which information flows, giving well-connected entrepreneurs access to crucial information about potential opportunities. These networks provide advice, resources, and other practical forms of support. While a great deal of research has taught us about entrepreneurial teams and networks, much is still unknown about the key mechanisms that bring together individuals during the founding process and what kind of causal effect it has on performance.


Introduction

It is now commonly accepted that many efforts to initiate new startups are heavily reliant on teams or networks, rather than involving true "solo" entrepreneurs. Fully 95 percent of the individuals trying to start a business either involved others to help in some significant capacity or intend to do so in the near future. About 50 percent of ventures are started in teams, with entrepreneurs mainly drawing on their core networks as initial members during the founding stages (Ruef 2010).

We know these networks tend to be homogenous, centered on familial relationships, and densely constructed (Ruef, Aldrich, and Carter 2003). If a venture progresses, extended networks can be helpful in a variety of ways for advice, resources, and other practical forms of support (Kim, Longest, and Aldrich 2013) (Reddi and Gerard 2012). Additionally, networks serve as important conduits through which information flows, giving well-connected entrepreneurs preferential access to information about potential opportunities. Networks also convey other resources, both material (e.g., access to venture financing) and perceptual (e.g., the legitimacy of an affiliation with a prestigious other).

While these insights are beneficial, we still lack knowledge about the key mechanisms bringing together individuals during the founding process and the intermediate steps taken to create viable ventures from initial ideas (Aldrich and Kim 2007) (Wasserman 2012). Put another way, what network structures lead people to collaborate in the founding of a new firm? Do these networks form as a function of entrepreneurial strategy, or social/geographical proximity? What network structures are useful in attracting talent to uncertain new ventures? What internal and external social structures contribute to success in new ventures?

Finally, with regards to venture performance, we know about the importance of experience and prior affiliations, composition, and cohesion as leading indicators (Beckman and Burton 2008) (Ensley, Pearson, and Amason 2002) (Vissa and Chacar 2009); however, we are unsure whether these teams and networks have a causal impact on startup activities or performance, and if they do have a causal impact, what types of teams and networks ought to be given preference by entrepreneurs.

Discussion

Introduction

Research on teams and networks in entrepreneurship has tackled a diverse set of questions:  how do founding teams and networks form (Ruef, Aldrich, and Carter 2003), who takes on positions of leadership within founding teams (Yang and Aldrich 2014), how does learning and collaboration occur in teams and networks (Beckman 2006), and how do networks and founding teams affect effort, innovation, and growth in new enterprises (Eisenhard and Schoonhoven 1990)? A common thread in these studies is the centrality of social structure – the set of interpersonal relationships connecting entrepreneurs – as either an outcome of or explanation for entrepreneurial activity. Network-based perspectives first developed 30 years ago, largely as a counterbalance to prevailing theories of entrepreneurship that were rooted in the psychology, culture, and decision-making of individual entrepreneurs (Aldrich and Zimmer 1986).

Many scholars in this research domain use the insights from Stinchcombe's essay on the social conditions of organizations as their theoretical starting point. Stinchcombe (1965) wrote about the critical role of initial founding members and the network of relationships they bring into the new organizations they create, and while he did not explicitly spell out the specific mechanisms involved in how teams form, researchers on nascent ventures have made significant progress tackling these issues.

Founding Teams

From this body of work, we know that founding teams are homophilous or tend to associate with similar people in both ascribed (e.g., gender, age, race) and achieved (e.g., education, occupation) characteristics (Ruef, Aldrich, and Carter 2003) (Ruef 2010). Entrepreneurs rely heavily on pre-existing, dense, and homophilous networks in which they are already embedded, so the types of teams they form bear a significant resemblance to their existing relationships (Vissa 2011). Furthermore, their selection of partners is affected by geographic proximity; partners tend to be drawn from the local area (or even, the same household) even though an increasing number of startups are "virtual," with no defined physical location (Ruef 2010). Many founding teams also involve family members initially, as they serve as a key resource for nascent efforts (Aldrich and Cliff 2003) (Kim, Longest, and Aldrich 2013) (Yang and Aldrich 2014). Exceptions to these regularities have also attracted study, such as immigrant entrepreneurs who create startups based on transnational networks (Portes, Haller, and Guarnizo 2002). Even in those cases, one dimension of proximity (e.g., ethnic solidarity) tends to substitute for another (spatial closeness) in the creation of teams and networks.

We know far less about the intentions behind these statistical regularities. Entrepreneurs may strategically choose to work with certain partners based on social and geographic proximity, because they believe this will reduce interpersonal conflict and enhance trust and familiarity. Or such social and geographic proximity may be induced by structural constraints on available partners (Ruef 2010). Given the effort and risks associated with a startup, only a small set of individuals who already know an entrepreneur may be available to commit time and resources to the venture. Further empirical progress in this respect hinges on the ability of social scientists to interview both entrepreneurs and their partners and to identify decision-making contexts where choice and structural constraint can be cleanly separated.

Founder Experience

Besides key demographic and relationship characteristics, founder experience is another important facet of entrepreneurial team research. Experience has a bearing on how and when people decide to pursue entrepreneurial ideas, whether to work with others in their exploitation efforts, and their ability to attract resources (Burton, Sorensen, and Beckman 2002) (Gruber, MacMillan, and Thompson 2012) (Kim and Longest 2013) (McMullen and Shepherd 2002). As anticipated by Stinchcombe (1965), new organizations are imprinted by their initial actions; the influence of founding team experience is no exception, as their initial characteristics affect how roles are structured within growing ventures (Burton and Beckman 2007) (Sine, Mitsuhashi, and Kirsch 2006). From studies on established teams, we know founder experience influences a variety of performance outcomes, ranging from early sales (Delmar and Shane 2006) to later-stage successes (Beckman, Burton, and O'Reilly 2006). Much of the existing literature on entrepreneurial teams favors those in established ventures, which are likely larger than those studies in nascent ventures. The outcomes investigated also occur much later in the founding process, such as growth, venture capital investment, and IPOs (Klotz, Hmieleski, Bradley, and Busenitz 2014).

Networks:  Introduction

Network studies have investigated a variety of issues, such as the entrepreneurial orientation of focal entrepreneurs (Stam, Wouter, and Elfring 2008), network turnover (Vissa and Bhagavatula 2012), and the initiation of economic ties (Vissa 2012). Other scholars focused on venture growth in emerging economies have elevated the relevance of political ties (Peng 2003) or the importance of social capital in weak institutional environments for new venture performance (Batjargal 2010) (Batjargal, Hitt, Tsui, Arregle, Web, and Miller 2012). Below, we discuss network-focused research within the framework of opportunity identification and resource mobilization.

Fundamentally, entrepreneurship consists of two processes:  the identification of attractive opportunities and the mobilization of resources in order to exploit those opportunities (Stuart and Sorenson 2005). Social networks play crucial roles in both.

Networks:  Opportunity Identification

Networks are important in the identification of potential entrepreneurial opportunities. A long history of research has demonstrated the role of networks in providing advantageous access to information to the well-connected (Allen 1977) (Burt 1982) (Coleman, Katz, Elihu, and Menzel 1957). Networks serve as important conduits through which information flows, giving well-connected entrepreneurs preferential access to potential opportunities. Although few studies have focused explicitly on the identification of entrepreneurial opportunities (but see some hints at this in Drori, Israel, Ellis, Shmuel, and Shapira 2013) networks have long been viewed as integral to the identification of other opportunities, including those for employment (Granovetter 1973) (Rider 2012) or investment (Sorenson and Stuart 2001).

Networks:  Access to Capital

Once an opportunity has been identified, networks play further a role in helping to realize the opportunity. Sociological research on networks has identified their role as both pipes through which tangible resources flow and prisms that differentiate actors in a market into a status hierarchy (Podolny 2001). Correspondingly, research on networks in entrepreneurship has focused in two areas:  access to venture financing and prestigious affiliations.

The most direct and tangible effect of social networks on the performance of a new venture comes in their ability to facilitate the flow of valuable resources to the venture. Building on a long stream of research on propinquity effects (i.e., tending to form relationships with those whom you encounter often) in networks (Allen 1977) (Bossard 1932) (Zipf 1949), recent research demonstrated the benefits of geographic proximity for resource flow. For example, Stuart and Sorenson (2003) argue that the geographic distribution of start-up firms mirrors the availability of resources on which they depend. Kalnins and Chung (2004) argue that this effect is contingent on their ability to appropriate the benefits of resource spill-overs. Whittington, Owen-Smith, and Powell (2009) further elucidate the network mechanisms for these effects.

Other research explores the role of networks in securing financing. For example, Uzzi (1999) elucidates the structural conditions under which small business are most likely to receive loans at advantageous terms. Shane and Cable (2002) and Shane and Stuart (2002) show that founders are more likely to obtain seed-stage funding and that university entrepreneurs are more likely to obtain venture funding when they have pre-existing ties to potential investors. Using a more general sample of founders, Hsu (2007) reports survey evidence that social ties to venture capitalists (VCs) increase the likelihood of VC funding. It is clear that networks play a significant role in helping young firms to obtain a range of tangible resources, most notably financing.

Networks:  Signaling

Beyond their role in securing tangible resources, networks have also been shown to serve as valuable signals about the underlying quality of a start-up firm. A key problem faced by entrepreneurial firms is the uncertainty that potential exchange partners have about their quality. But an endorsement by a prominent partner – in the form of a strategic alliance or even a social tie – signals to the market that a relatively high-status player who is both well-informed about the focal firm's prospects and has something to lose through affiliation with it has confidence in its prospects. That signal has been argued to induce confidence in other market actors who have less direct visibility into the quality of the entrant. Their consequent willingness to do business with the entrant – as customers, suppliers or investors – should improve its performance. Research on alliances in the biotech industry (Stuart, Hoang, and Hybels 1999) and a more recent study of social ties among owners in the Ontario ice wine industry finds similar results (Roberts, Peter, and Sterling 2012) provide evidence consistent with the role of networks in conveying valuable status to entrepreneurial ventures.

We know less about the evolution of networks in entrepreneurial organizations over time. Some scholars have hypothesized a life-course effect, in which a firm's network shifts from being cohesive, embedded and identity-based to being sparser and more "calculative" as the firm transitions from emergence to early growth phase (Hite and Hesterly 2001); however, there is little evidence available to assess such claims.

Networks:  Network Diversity

With respect to the substantive effects of social networks, a central debate is whether diverse and 'open' networks encourage entrepreneurship, as opposed to homogenous and 'closed' networks. Much of the literature has argued in favor of the advantages of network diversity and weak ties, suggesting that these interpersonal structures enable entrepreneurs to acquire more information or resources, avoid pressures for conformity, and develop new ideas (Burt 2004). Within founding teams, for instance, entrepreneurs with a diverse set of prior workplace affiliations are more likely to seek innovation (Beckman 2006). This intuition must, however, be reconciled with the entrepreneurs' need for social support and interpersonal trust. Empirical studies of average entrepreneurs find that few begin with diverse networks or teams. Moreover, homogeneous and intimate interpersonal ties can have beneficial effects on entrepreneurial equality, effort, and innovation, as well as the short-term survival of new organizations (Ruef 2010). Even among high-tech entrepreneurs, the right mix of diversity and homogeneity appears to produce the best performance outcomes (Beckman 2006).

Methodological Issues

Another important debate involves the relative importance of personal networks and other contexts for entrepreneurial outcomes. Given data limitations, the existence of interpersonal networks is often inferred from the contexts in which entrepreneurs operate. Entrepreneurs who worked for the same organization in the past are assumed to be part of a co-worker network. Entrepreneurs who lived in the same ethnic enclaves are assumed to part of a co-ethnic network. And interorganizational ties (such as venture capital investments) are assumed to yield information on the personal connections between entrepreneurs and their supporters (Hallen 2012). In all of these cases, it is essential to separate the effects of the entrepreneurs' personal networks from the effects of the contexts to which they have been exposed. In a study of social capital, for instance, Kwon, Heflin, and Ruef (2013) found that the level of generalized trust in U.S. communities affected rates of self-employment above and beyond the level of interpersonal trust experienced by individual entrepreneurs. They posit that many of the benefits of social trust are found in relationships among third parties, rather than with the entrepreneurs themselves. The role of such third-party referral and information networks requires further study, apart from the personal contacts that entrepreneurs themselves may utilize.

Identifying the causal relationship between social relationships and their purported effects also remains a difficult task. A classic test of causality would call for the random assignment of entrepreneurs to "treatment" and "control" conditions, representing different types of teams or networks. Using an experiment, for instance, we might assign some entrepreneurs to participate in demographically homogeneous teams and others to participate in heterogeneous teams; however, such a research design presents severe threats to the validity of any conclusions regarding the association between treatments and outcomes. If entrepreneurs knowingly participate in an experiment, they may conduct their "ventures" with little of the seriousness that they would in starting a real business. Even in the absence of this knowledge, the experimental conditions remove one of the assumptions that is essential to many entrepreneurial partnerships:  a belief in voluntary association. Since the issue of causal identification cannot easily be resolved, it is likely to remain an enduring issue for studies of the effects of entrepreneurial networks and groups.

Future Research

  • Academic entrepreneurship:  Scholars have begun to establish some baseline concepts about how academics teams pursue entrepreneurial ventures based on their research (Forbes, Borchert, Zellmer-Bruhn, and Sapienza 2006) (Vanaelst, Iris, Clarysse, Wright, Lockett, Moray, and S'Jegers 2006); however, we can learn more about the potential parallels between running academic research labs and leading start-up teams, the appropriate combinations of business skills and experience in venture teams involving academics, and the conflicts inherent to commercializing science by academics.
  • Intangibles in team collaborations:  Practitioners often mention the "intangibles" that bring together effective venture teams: having a shared vision, mutually enforcing values, and complementary personalities and skills sets. Future research can build on the existing research about organizational behavioral aspects of venture teams and probe more deeply into these facets of teams, both in terms of antecedents and their consequences for performance (Klotz, Hmieleski, Bradley, and Busenitz 2014).
  • Ownership equity:  Some foundational studies on ownership equity issues have been published by Wasserman (2012) and Kotha, Reddi, and George (2012); however, future work may uncover more details about how team member and venture characteristics predict equity allocations, the evolution of equity allocation over a venture's lifetime, and why some equity configurations lead to better venture outcomes than others. We also need more insights into the role of operating agreements and how they can lead to more stable ventures than those functioning without them.
  • We know little about the role of pre-existing networks in the endogenous formation of entrepreneurial teams (Aldrich and Kim 2007) (Ruef, Martin, Aldrich, and Carter 2003) or the recruitment of additional employees to new ventures (Ruef 2010). The data collection requirements to do this research are daunting, but the potential to develop insights about entrepreneurial teams is significant. Perhaps the most notable empirical gap is that we still do not know enough about the social network patterns of "average" entrepreneurs. Owing to data convenience, many studies have focused on entrepreneurial networks and teams in unusual fields such as biotech, venture capital, and information technology. When studies do focus on representative swaths of entrepreneurs, they tend to limit attention to the egocentric networks that are reported by fairly small samples of focal entrepreneurs. As a result, we have a limited (and, potentially, biased) view of the networks that connect entrepreneurs and their supporters across modern economies, and an even more limited view of how those networks have evolved historically.
  • Among theoretical gaps, social scientists who study teams and networks should focus to a greater extent on conflict and the dissolution of relationships, as well as cooperation and the formation of relationships. Entrepreneurs themselves routinely report relationship problems as reasons for the failures of their ventures, yet scholars have been slow to study social breakdowns in networks and teams.

Data Sources

  • Comparative Immigrant Entrepreneurship Project (CIEP):  Offers extensive data on the social and assistance networks of immigrant entrepreneurs from Colombia, El Salvador, and the Dominican Republic. Publicly available. 
  • Comprehensive Australian Study of Entrepreneurial Emergence (CAUSEE):  Offers basic information on founding team composition for startups in Australia. Data collection still in progress. 
  • Kauffman Firm Survey (KFS):  Offers a smaller set of measures on founding teams for startup businesses in the United States. Funded by the Kauffman Foundation. Gated availability through NORC. 
  • Panel Study of Entrepreneurial Dynamics (I and II):  Offers extensive data on the founding teams and assistance networks of startup businesses in the United States. Funded by the Kauffman Foundation and the the National Science Foundation. Publicly available. 
  • Stanford Project on Emerging Companies (SPEC):  Offers extensive interview data on leadership teams and founder background in high-tech startups in Silicon Valley. Confidential data available. 
Discussion of Data Collection Issues

One of the challenges of conducting research on entrepreneurial teams and networks is the difficulty of collecting high-quality data for nascent ventures. Most research questions require information on multiple individuals associated with a focal actor, but gathering information beyond the focal actor is often time consuming, prone to error, and expensive. As a result, most research projects work within the constraints of an ego-network design, in which only respondents provide information about their teammates or core networks.

Alternate data sets

Other studies have relied on primary data collection methods, usually with smaller sample sizes (because of time and data collection resources). These datasets are confidential and can be used only with permission from the studies' principal investigators.

There are no publicly available data sets covering the personal networks of entrepreneurs. Consistent with a long tradition of network scholarship (Marsden 1990), many studies of the networks of entrepreneurs are based on network data collected by the researcher either through broad surveys (Roberts and Sterling 2012) or through strategic research settings, like inventors at the Massachusetts Institute of Technology (Shane and Stuart 2002). These data are proprietary and are often extremely time-consuming and costly to gather. Sometimes they are available to any researcher, as in broad surveys; other times they require privileged access, as in the data set of MIT inventors.

Recent research has begun to explore the transition from employment in established organization to entrepreneurship using the elaborately detailed census data from Scandinavian countries (e.g., Denmark or Norway). See for example, Dahl and Sorenson (2012) or Sørensen and Sharkey (2014). These data sets contain no explicit network data, but some network-related variables may be inferred and the transition from conventional employment to entrepreneurship is observable. These data are typically available to researchers with a local affiliation at a Danish or Norwegian institution through an arrangement with the census bureau; such researchers often co-author with collaborators around the world.

Finally, studies on teams and networks in established ventures often sample from venture-backed companies or those that have gone public. In these situations, information about the ventures' management teams is more readily available. Data sources for U.S.-based firms – such as Dun and Bradstreet, VentureXpert, InfoUSA (formerly known as CorpTech), Lexis/Nexis, and SEC S-1 filings – are used to identify team members, positions, and other pertinent characteristics. VentureXpert is especially heavily used in network studies of VC firms and startups in which they invest. Many of these data sources require subscriptions for accessing their archives.

Future studies on nascent entrepreneurial teams and networks will likely require some combination of primary data collection in appropriate study contexts and/or merging multiple public datasets. For example, efforts to link the Kauffman Firm Survey with LinkedIn data may offer the possibility of creating a suitable dataset for examining team and network-related questions.

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