Academy of Management Journal

The Past Is Prologue? Venture-Capital Syndicates’ Collaborative Experience and Start-Up Exits

Journal: Academy of Management Journal

Manuscript ID AMJ-2019-1312.R3

Manuscript Type: Revision

Financing of new ventures < Entrepreneurship < Topic Areas, Keywords: Interorganizational linkages < Organization and Management Theory < Topic Areas, Network theory < Theoretical Perspectives

Past research has produced contradictory insights into how prior collaboration between organizations—their relational embeddedness— impacts collective collaborative performance. We theorize that the effect of relational embeddedness on collaborative success is contingent on the type of success under consideration, and we develop a typology of two kinds of success. We test our hypotheses using data from Crunchbase on a sample of almost 11,000 U.S. start-ups backed by venture-capital (VC) firms, using the VCs’ previous collaborative experience to predict the Abstract: type of success that the start-ups will experience. Our findings indicate that, as prior collaborative experience within a group of VCs increases, a jointly funded start-up is more likely to exit by acquisition (which we call a focused success); with less prior experience among the group of VCs, a jointly funded start-up is more likely to exit by IPO (a broadcast success). Our results deepen understanding of the connections between organizational performance and collaboration networks, contributing to entrepreneurship research on the role of investors in technology ventures.

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1 2 3 4 The Past Is Prologue? Venture-Capital Syndicates’ Collaborative 5 Experience and Start-Up Exits 6 7 8 9 10 11 Dan Wang 12 Columbia University 13 [email protected] 14 15 16 Emily Cox Pahnke 17 University of Washington 18 [email protected] 19 20 Rory M. McDonald 21 Harvard University 22 23 [email protected] 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Academy of Management Journal Page 2 of 65

1 2 3 THE PAST IS PROLOGUE? VENTURE-CAPITAL SYNDICATES’ COLLABORATIVE 4 EXPERIENCE AND START-UP EXITS 5 6 7 ABSTRACT 8 Past research has produced contradictory insights into how prior collaboration between 9 organizations—their relational embeddedness—impacts collective collaborative performance. 10 We theorize that the effect of relational embeddedness on collaborative success is contingent on 11 the type of success under consideration, and we develop a typology of two kinds of success. We 12 test our hypotheses using data from Crunchbase on a sample of almost 11,000 U.S. start-ups 13 14 backed by venture-capital (VC) firms, using the VCs’ previous collaborative experience to 15 predict the type of success that the start-ups will experience. Our findings indicate that, as prior 16 collaborative experience within a group of VCs increases, a jointly funded start-up is more likely 17 to exit by acquisition (which we call a focused success); with less prior experience among the 18 group of VCs, a jointly funded start-up is more likely to exit by IPO (a broadcast success). Our 19 results deepen understanding of the connections between organizational performance and 20 21 collaboration networks, contributing to entrepreneurship research on the role of investors in 22 technology ventures. 23 24 INTRODUCTION 25 26 From development of life-extending drugs to production of hit Broadway musicals, 27 28 collaborations between organizations often beget achievements that surpass what any single 29 30 organization can accomplish (Powell, White, Koput, & Owen-Smith, 2005; Uzzi & Spiro, 2005). 31 32 33 Succeeding at inter-organizational collaboration, however, means confronting the challenges of 34 35 coordination and exchange among multiple parties (Gulati, Wohlgezogen, & Zhelyazkov, 2012; 36 37 Kapoor & McGrath, 2014; Rosenkopf & Schilling, 2007; Ter Wal, Criscuolo, McEvily, & Salter, 38 39 40 2019). These challenges include miscommunication, disagreement, and conflict, all of which can 41 42 undermine a collaborative effort and make collective success elusive (Gulati, Sytch, & Mehrotra, 43 44 2008; Kale, Dyer, & Singh, 2002; Kellogg, Orlikowski, & Yates, 2006; Zaheer, McEvily, & 45 46 Perrone, 1998). The juxtaposition of these vexing challenges with the promise of greater success 47 48 49 has motivated a long tradition of scholarship in organizational theory and strategy on the evolution 50 51 and outcomes of inter-organizational collaboration. 52 53 Prior work on inter-organizational collaboration models how relationships form between 54 55 56 organizations and why they persist or dissipate. Factors such as complementary capabilities, 57 58 59 60 Page 3 of 65 Academy of Management Journal

1 2 3 proximity, and similarity in domain specialization can incline two organizations to form a strategic 4 5 6 alliance, investment syndicate, joint venture, or other form of collaboration (Ahuja, 2000; Khanna 7 8 & Rivkin, 2006; Sorenson & Stuart, 2008; Mitsuhashi & Greve, 2009; Shipilov & Li, 2012; 9 10 Whittington, Owen-Smith, & Powell, 2009). One prominent research stream looks at how prior 11 12 relationships between organizations—the extent to which organizations are relationally 13 14 15 embedded1—facilitate later partnerships between the same organizations (Gulati & Gargiulo, 16 17 1999; Powell, Koput, & Smith-Doerr, 1996; Zhang & Guler, 2019). This scholarship has enriched 18 19 our understanding of how organizations take cues from the environment when selecting partners 20 21 22 to forge a collaboration. 23 24 Yet, a puzzle emerges when we examine findings about how prior relationships between 25 26 organizations affect their collective collaborative performance. Some research finds prior 27 28 29 experiences of working together create embedded relationships that provide reliable information 30 31 about partners’ capabilities, engender trust, and produce coordination efficiencies and relationship 32 33 stability, all of which can enhance performance (Granovetter, 1985; Polidoro, Ahuja, & Mitchell, 34 35 2011; Reagans & McEvily, 2003; Tortoriello & Krackhardt, 2010). Other research argues that, 36 37 38 when collaborating organizations are “over-embedded” by virtue of long histories of working 39 40 together, performance suffers: they become rigid and insensitive to novel information as well as 41 42 vulnerable to competency traps, and partners may even begin to lose trust in each other (Rogan, 43 44 45 2014; Uzzi, 1997). At the interpersonal level, for example, repeat teams in creative industries and 46 47 academia can fall victim to groupthink and ignore information from outside their network, 48 49 50 51 1Consistent with prior research (Nahapiet & Ghoshal, 1998), we conceptualize relational embeddedness in terms of 52 the existence of prior relationships (Zhelyazkov & Gulati, 2016); it arises from a history of interactions or repeated 53 direct ties (Polidoro, Ahuja, & Mitchell, 2011). Relational embeddedness is a function of how often the various 54 members of a group have previously collaborated. 55 56 57 58 59 60 Academy of Management Journal Page 4 of 65

1 2 3 diminishing performance over time (Janis, 1972; Uzzi, 1997). Further complicating the picture is 4 5 6 evidence that whether or not prior relationships trigger collective success depends on overlap 7 8 between organizations’ capabilities and specializations (Rodan & Galunic, 2004; Ter Wal et al., 9 10 2016). Tensions among these various perspectives motivate our research question: Under what 11 12 conditions do prior collaborations between organizations breed collaborative success? 13 14 15 Disentangling whether prior collaboration contributes to later collaborative success 16 17 requires, we argue, a clear specification of the type of collaborative performance in question. We 18 19 distinguish between two types of success outcomes—focused and broadcast—whose principal 20 21 22 differences lie in their appraisers, complexity, and prominence. Focused successes are determined 23 24 by domain-specific appraisers who possess the expertise to assess the value of a specialized 25 26 enterprise; by contrast, broadcast successes are determined by appraisers across a range of 27 28 29 domains. In addition, focused successes entail more straightforward, less complex processes 30 31 involving fewer stakeholders than broadcast successes do (Gompers, Kovner, & Lerner, 2009). 32 33 Finally, noteworthy broadcast successes tend to reverberate more prominently across society and 34 35 across markets than do comparable focused successes. 36 37 38 We posit that collaborations characterized by higher levels of relational embeddedness 39 40 among their members promote focused success; lower levels of relational embeddedness promote 41 42 broadcast success. Our reasoning builds on prior work in organization theory, sociology, and social 43 44 45 psychology that has pinpointed the tradeoffs associated with greater and lesser familiarity among 46 47 members of a team. More collaborative experience among team members reduces coordination 48 49 costs, builds greater trust, and generates greater knowledge overlaps, enabling them to accomplish 50 51 52 routine, domain-specific tasks more effectively. Greater relational embeddedness derived from 53 54 repeated collaborations facilitates the development of common shared interpretive schema that 55 56 57 58 59 60 Page 5 of 65 Academy of Management Journal

1 2 3 position a team to converge focused achievements (Simon 1966, Gulati 1995a). However, greater 4 5 6 familiarity and attendant social obligations among team members can create “over-embeddedness” 7 8 (Granovetter, 1985; Gulati & Gargiulo, 1999; Uzzi, 1997) that results in suboptimal performance 9 10 outcomes by constraining members’ access to diverse knowledge and pushing them into 11 12 competency traps. By contrast, whereas lesser relational embeddedness might impede efficient 13 14 15 coordination, teams that have not had prior experience tend to also bring more divergent 16 17 viewpoints and introduce a greater breadth of knowledge that can benefit a joint effort toward 18 19 broadcast successes. 20 21 22 We test our theory by examining venture-capital (VC) syndicates and the successful equity 23 24 exit outcomes of the start-ups in which such syndicates invest. When a VC opts to invest in a start- 25 26 up, it often does so as part of a group of VCs known as a syndicate. Syndicates represent 27 28 29 collaborations among VCs, in which they typically share knowledge and resources in an effort to 30 31 guide their collective investee start-up toward a successful exit outcome (Brander, Amit, & 32 33 Antweiler, 2002; Nanda & Rhodes-Kropf, 2019; Zhelyazkov, 2018; Zhelyazkov & Tatarynowicz, 34 35 2020). In this context, relational embeddedness is a function of how often the various members of 36 37 38 the VC syndicate have previously co-invested in start-ups. We distinguish between two types of 39 40 exits that VC-backed start-ups can experience: an acquisition or an initial public offering (IPO). 41 42 An acquisition exit corresponds to a focused success outcome, and an IPO exit to a broadcast 43 44 45 success outcome. 46 47 We find that a start-up funded by a VC syndicate whose members share more prior co- 48 49 investment experience is more likely to exit via acquisition than via IPO; by contrast, a start-up 50 51 52 funded by a VC syndicate with less prior co-investment experience is more likely to exit via IPO. 53 54 Our additional analyses reveal that funding by a VC syndicate with less prior co-investment 55 56 57 58 59 60 Academy of Management Journal Page 6 of 65

1 2 3 experience is also associated with start-up failure. Our evidence comes from an analysis of 4 5 6 longitudinal data on almost 11,000 U.S.-based start-ups that received first-round VC funding from 7 8 multiple VCs between 1982 and 2011. Building on prior work, we address sample selection bias 9 10 with a Heckman approach and rule out selection on observable variables via inverse probability 11 12 treatment weighting to isolate the effects of a VC syndicate’s relational embeddedness on a start- 13 14 15 up’s likelihood of exiting by acquisition or IPO. 16 17 Our study contributes to research on inter-organizational collaboration, networks, and 18 19 entrepreneurship. First, we advance a theoretical understanding of differences between types of 20 21 22 collective performance by developing a typology that distinguishes between broadcast and focused 23 24 successes. Second, we provide some resolution to contradictions in the existing research on prior 25 26 collaboration and subsequent collaborative success. Our results indicate that groups characterized 27 28 29 by higher relational embeddedness are more strongly associated with focused successes; 30 31 conversely, groups with lower relational embeddedness are more associated with broadcast 32 33 successes. Finally, we shed light on the relationship between start-ups and their VC investors by 34 35 emphasizing the nature of investors’ prior collaborative experience as an underappreciated factor 36 37 38 in start-ups’ successes. 39 40 THEORETICAL BACKGROUND AND HYPOTHESES 41 42 Relational Embeddedness and Firm Advantage 43 44 Research on social capital has examined how inter-organizational relationships—alliances, board 45 46 interlocks, and R&D collaboration—relate to firm performance (Beckman & Haunschild, 2002; 47 48 Beckman, Haunschild, & Phillips, 2004; Dushnitsky & Lavie, 2010; Joshi & Nerkar, 2011; Powell, 49 50 1990; Powell et al., 1996; Uzzi, 1996; Shipilov & Li, 2008). One important research stream has 51 52 53 established that the particular resource and information benefits that a firm gains from its network 54 55 partners are often contingent on the structure of existing ties among those partners (Hoehn-Weiss 56 57 58 59 60 Page 7 of 65 Academy of Management Journal

1 2 3 et al., 2017; Powell et al., 1996; Reagans & McEvily, 2003; Zhang & Guler, 2019). This research 4 5 6 builds on the insight that a firm’s relationships embed it within a network, and thus enable it to 7 8 access valuable information about future opportunities and partners (Granovetter, 1985; Gulati, 9 10 1995a; Uzzi, 1997). 11 12 Such relational embeddedness2 (Granovetter, 1992; Gulati, 1995b; Gulati & Gargiulo, 13 14 15 1999)—the prior direct ties a firm has formed with other firms and the information that flows 16 17 through them—can confer advantage in many ways. For example, firms relationally embedded in 18 19 the better-dress sector of the New York fashion industry were privy to valuable fine-grained 20 21 22 information about emerging trends and aesthetics, enabling them to engage in joint problem 23 24 solving with partners that minimized costly errors with manufacturing (Uzzi, 1996; 1997). Another 25 26 study found that small firms with embedded ties to their bankers were less likely to incur late- 27 28 29 payment penalties on trade credit and more likely to receive discounts from their banks (Uzzi & 30 31 Gillespie, 2002). Similarly, a study of hotel managers who were ostensibly competitors 32 33 demonstrated that they benefitted from embedded friendship ties that facilitated sharing 34 35 information and collectively improving yields at their hotels (Ingram & Roberts, 2000). 36 37 38 Although prior research has documented the benefits of relational embeddedness, it has 39 40 also highlighted the so-called paradox of embeddedness whereby a certain degree of 41 42 embeddedness confers informational and relational advantages, but being overly embedded can 43 44 45 impede a firm from achieving its goals (Uzzi, 1997). For example, a study of mergers and 46 47 acquisitions in the advertising industry found that, when competitors target many of the same 48 49 clients, increased relational embeddedness led to the dissolution of relationships (Rogan, 2014). 50 51 52 53 2 Adopting a distinction made by Granovetter (1992) and Gulati (1998), we focus only on firms’ direct ties (relational 54 embeddedness), not on both direct and indirect ties (structural embeddedness). We thank an anonymous reviewer for 55 helping us to clarify this distinction. 56 57 58 59 60 Academy of Management Journal Page 8 of 65

1 2 3 Related research suggests that both higher and lower levels of relational embeddedness 4 5 6 within a network can enhance the likelihood that a firm will achieve its desired outcome, but that 7 8 the two configurations’ trade-offs result in different firm trajectories (Burt, 2005; Uzzi, 1997). 9 10 Firms that are more embedded in a network tend to develop specialized products and technologies 11 12 whereas firms that are less embedded develop assets and capabilities with broader applicability 13 14 15 (Powell et al., 1996; Lazer & Friedman, 2007). 16 17 A key idea from this research is that repeated collaborative experiences result in common 18 19 interpretive schema: shared frameworks, logics, and assumptions two or more firms use to reason 20 21 22 through a problem or a decision (Sewell 1992, Simon 1966). Being highly relationally embedded 23 24 in a network offers firms the advantage of operating with a common interpretive schema, which 25 26 can create shared identities and solidarity (Granovetter, 1985; Uzzi, 1997). Prior experiences 27 28 29 working together allow firms the opportunity to align on routines and perspectives (Gulati 1995a). 30 31 In other words, the more frequently firms collaborate and learn together, the more likely they are 32 33 to have constructed common repositories of knowledge and insights that can inform their future 34 35 decisions. 36 37 38 Possessing a common interpretive schema aids partners in two ways. First, they increase 39 40 the efficiency with which partners transfer and process knowledge (Hansen, 1999; McEvily & 41 42 Reagans, 2003). Because they operate under shared logics, partners can more effectively anticipate 43 44 45 each other’s needs and approaches when it comes to solving a problem. Such prior relationships 46 47 therefore enable a firm to develop specialized technologies or products effectively because its 48 49 network partners have already established a shared understanding of its practices and routines. By 50 51 52 contrast, having divergent interpretive schemas creates cognitive barriers between partners that 53 54 55 56 57 58 59 60 Page 9 of 65 Academy of Management Journal

1 2 3 may complicate or even preclude them from coming to agreement on the value of new information 4 5 6 (Uzzi & Lancaster 2003). 7 8 Second, having a common schema allows for information and advice to be triangulated via 9 10 partners’ common experience (Tortoriello, McEvily, & Krackhardt, 2015; Gavetti & Warglien, 11 12 2015; Uzzi, 1996). Triangulation refers to the process by which one verifies the meaning and value 13 14 15 of information by looking to the evaluation of others (Gavetti & Waglien, 2015; Hallen &Pahnke, 16 17 2016). Having a shared interpretive schema makes the perspectives of a firm’s partners more 18 19 reliable because their prior collaborative experiences create shared expectations (Gruenfeld et al., 20 21 22 1996). These shared expectations in turn compel partners to focus on elements of information that 23 24 they both value because of their common understanding. Research on social cognition and groups 25 26 has shown how repeated collaboration results in more efficient processing of information in group 27 28 29 members’ shared specialized domains (Gruenfeld et al., 1996). Thus, the convergence of 30 31 interpretive schema, shared expectations about process, and enhanced information processing 32 33 underpin the advantages of collaborating in networks with higher relational embeddedness. 34 35 Lower levels of relational embeddedness in a network confer a different set of advantages. 36 37 38 First, the perspectives of partners without prior collaborative ties draw on a broader range of 39 40 experiences (Beckman, Haunschild, & Phillips 2004). These dissimilar experiences in turn 41 42 generate different approaches to problems and decisions, enabling a firm to pursue novel 43 44 45 recombinations of its partners’ ideas (Baum et al., 2000; Ruef et al., 2003). Thus, a firm is more 46 47 likely to find solutions that appeal to diverse audiences if it is less embedded within a network and 48 49 receives more varied guidance from unconnected partners (Burt, 2005; Pollock, Porac, & Wade, 50 51 52 2004; Beckman et al., 2004). In a similar vein, less relationally embedded partners can access a 53 54 broader range of knowledge because their ties to other organizations are more diverse. According 55 56 57 58 59 60 Academy of Management Journal Page 10 of 65

1 2 3 to Burt (2004), partners with prior direct relationships to one another are also more likely to share 4 5 6 ties to other common organizations whereas firms without prior ties to one another are less likely 7 8 to have common ‘neighbors.’ As a result, through their more diverse networks, less relationally 9 10 embedded partners can access a broader array of knowledge. Being less embedded furthermore 11 12 encourages a firm to engage in a broader search for knowledge by making it harder to fall back on 13 14 15 existing models (Lazer & Friedman, 2007). Although partners less embedded in a network are less 16 17 apt to acquire a deep shared understanding of a firm’s specific needs, their attentiveness to “broader 18 19 market issues” can also prompt them to find common ground and to argue for solutions that appeal 20 21 22 to more diverse market segments (Beckman, 2006). At the same time, shared cognitive schema are 23 24 unlikely to develop in less embedded networks, leading to more varied advice and directions for 25 26 the firms they partner with. 27 28 29 Being more versus less relationally embedded within inter-organizational networks is thus 30 31 associated with different sets of advantages aligned with different goals. Next, we distinguish 32 33 between two types of success, and demonstrate how the distinct advantages conferred by more 34 35 versus less relational embeddedness relate to these different collaborative outcomes. 36 37 38 Focused and broadcast successes. We build on research on valuation processes, audience 39 40 recognition and collaborations to theorize that collaborative successes can be characterized as 41 42 either focused or broadcast. Prior research in organization theory and strategy has characterized 43 44 45 some successes in terms of their impact on subsequent organizational efforts, for example when 46 47 discussing the “halo” (e.g. Sine et al., 2003) or “beacon” (e.g. Bermiss et al., 2017) effects. Such 48 49 terms highlight the impact that certain successes have in increasing the salience or prestige of other 50 51 52 actions; the terms also signify a positive spillover either on an organization’s own future efforts, 53 54 or on other organizations that are related to the focal success. Notably, these prior 55 56 57 58 59 60 Page 11 of 65 Academy of Management Journal

1 2 3 conceptualizations of successes do not account for the collaborative structures and processes 4 5 6 required to develop and execute different kinds of collective performance. Therefore, we develop 7 8 theoretical constructs that consider the type of success that an individual organization achieves, 9 10 irrespective of its impact on other organizations in the field. Specifically, focused and broadcast 11 12 successes differ in terms of the appraisers who determine them, the complexity of coordinating 13 14 15 activities outside of the organization to achieve them, and the prominence with which each kind 16 17 of success is recognized. 18 19 Appraisers act as third parties, who by virtue of their own expertise, determine whether or 20 21 22 not a success is achieved by other organizations. Whether appraisers are specialized domain 23 24 experts or are part of a broader and more diverse group can determine if a success is focused or 25 26 broadcast. For example, novels vying for a National Book Award to gain standing within their 27 28 29 genres are judged by a narrow set of appraisers, who have specialized expertise and experience 30 31 with literary fiction (Kovács & Sharkey, 2014). By contrast, restaurants garner acclaim from 32 33 ratings posted by broad swaths of diners who appraise the restaurants they visit on platforms such 34 35 as Yelp.com (Luca, 2016). Complexity refers to the difficulty and variety of steps required to 36 37 38 orchestrate a collaborative success, and the number of stakeholders involved (Van Knippenberg, 39 40 et al., 2004). The types of coordination and teams that benefit a given outcome depend on its 41 42 complexity. Prior work in social psychology and organizational behavior has found that the 43 44 45 cognitive and demographic diversity of a group benefits its performance more when undertaking 46 47 projects high in complexity (Van Knippenberg et al., 2004; Higgs et al., 2005, Wegge et al., 2008; 48 49 Stahl, 2010). Prominence reflects how widely known and recognized an outcome is across 50 51 52 different types of observers. Prior research on organizational status and reputation conceptualizes 53 54 prominence as the “collective knowledge and recognition of a firm” (Rindova et al., 2005: 1035) 55 56 57 58 59 60 Academy of Management Journal Page 12 of 65

1 2 3 or “large scale public attention” (Rindova, Pollock, & Hayward, 2006: 50). In other words, an 4 5 6 organization’s success is more prominent when it is widely acknowledged by a diverse set of 7 8 observers outside of its immediate peers and experts in its field. 9 10 These dimensions, appraisers, prominence, and complexity, each vary between focused 11 12 and broadcast successes. Focused successes tend to rely on information shared by firms embedded 13 14 15 in the same network, and thus are likely to be evaluated and valued by members of the same 16 17 network. Focused successes are also typically conferred or determined by expert specialists who 18 19 belong to the same domain. These appraisers are equipped to assess knowledge, technologies, and 20 21 22 artistry unlikely to be understood or appreciated by broader audiences. Although focused successes 23 24 depend on such expert appraisal, they are less complex to transact—in terms of the number and 25 26 variety of constituents in need of coordination and implementation—and thus more quickly 27 28 29 executed than broadcast successes. This is the case because experts’ knowledge positions them to 30 31 confer approval, provide resources, and make deals on behalf of an organization or product they 32 33 assess favorably, and to execute these transactions quickly. Focused successes are typically widely 34 35 familiar to industry insiders, but less prominent in networks that lack the knowledge needed to 36 37 38 understand and appreciate them. 39 40 In contrast, broadcast successes are typically determined by a broader set of appraisers who 41 42 belong to different networks and thus do not possess the shared expertise and knowledge unique 43 44 45 to a particular domain. As a result, those who confer broadcast successes are likely to value 46 47 commercial applications produced collaboratively. The diversity of such appraisers makes 48 49 orchestrating broadcast successes complex; doing so calls for reaching, appealing to, and 50 51 52 coordinating assorted constituents. Importantly, appealing to such diverse audiences does not 53 54 obligate a firm to be relationally embedded with them. However, the ability to appeal to a broad— 55 56 57 58 59 60 Page 13 of 65 Academy of Management Journal

1 2 3 and typically larger—set of appraisers increases the potential prominence of broadcast successes 4 5 6 once achieved. 7 8 Focused and broadcast successes for start-ups. We argue that, for VC-backed start-ups, 9 10 acquisition exits are focused successes; IPO exits, by contrast, constitute broadcast successes. 11 12 Although both are desirable outcomes for start-up founders and their VC investors (Beckman, 13 14 15 Burton, & O’Reilly, 2007; Hoehn-Weiss & Karim, 2014; Pollock et al., 2015; Sørensen, 2007; 16 17 Zarutskie, 2010), they differ in important ways3. In an acquisition, a start-up is purchased outright 18 19 by another company that assumes a controlling ownership stake (Kapoor & Lim, 2007). The shares 20 21 22 of the founders and investors thus become fully liquid, but they must sacrifice control over the 23 24 start-up (Graebner & Eisenhardt, 2004). In an IPO, a large fraction of ownership is sold on public 25 26 markets, enabling any investor to purchase shares. Founders and their investors can typically 27 28 29 obtain partial liquidity for their shares, and the management team often remains in place. 30 31 Prior research on differences between start-ups that exit by acquisition and those that exit 32 33 by IPO aligns with our distinction between focused and broadcast successes. For example, start- 34 35 ups whose technologies and products have broad appeal are easier for a diverse market audience 36 37 38 to value; thus an exit through an IPO is apt to generate higher returns than an acquisition (Beatty 39 40 & Ritter, 1986; Ellingsen & Rydqvist, 1998; Poulsen & Stegemoller, 2008). Conversely, greater 41 42 information asymmetry about its products between a start-up and potential purchasers of its stock 43 44 45 in public markets increases the odds that it will exit via acquisition rather than IPO. Specialized 46 47 products—technologies valued only by niche markets or a narrow swath of consumers—are more 48 49 difficult for non-specialists to evaluate (Gompers, Kovner, & Lerner 2009); thus, a single buyer 50 51 52 53 54 3 Our intent is not to explore the financial intricacies of each type of transaction; instead, we summarize their 55 conceptual differences. 56 57 58 59 60 Academy of Management Journal Page 14 of 65

1 2 3 operating in a similar space is better equipped to value such a venture, making an acquisition exit 4 5 6 more likely (Haleblian, Kim, & Rajagopalan, 2006; Poulsen & Stegemuller, 2008). Also, 7 8 coordinating the multiple stakeholders in an IPO—investment banks, underwriters, auditors, and 9 10 lawyers—is a complex challenge that not all start-ups are equipped to handle (Brau et al., 2003; 11 12 Cumming & MacIntoch, 2003; Grégoire, de Koning, & Oviatt, 2008). Generally speaking, those 13 14 15 with more diverse partners are better prepared to mobilize the variety of participants in an IPO. 16 17 Translated into our typology of focused and broadcast successes, prior research indicates 18 19 that a start-up is likely to be acquired (a focused success) if it develops assets of a kind best valued 20 21 22 by industry insiders (Lowry & Schwert, 2004). Furthermore, acquisition of a private company is a 23 24 relatively straightforward financial transaction that can be accomplished rapidly. In contrast, a firm 25 26 is more likely to exit via IPO (a broadcast success) if it shows the promise of serving multiple 27 28 29 market segments, if its growth potential is high, and if it can mobilize a diverse set of partners. 30 31 Like other broadcast successes, an IPO is a complex and time-consuming transaction that requires 32 33 coordinating service providers, such as investment banks, with professional investors such as 34 35 endowments; it also requires appealing to a broader set of potential investors. We broaden our 36 37 38 inquiry into factors in a start-up’s success by suggesting that prior collaboration among its VC 39 40 investors influences the firm to develop in such a way as to promote either an IPO exit (a broadcast 41 42 success) or an acquisition exit (a focused success). 43 44 45 Hypotheses: VCs’ Past Collaboration and Start-ups’ Exits 46 When VCs repeatedly collaborate with other VCs (that is, syndicate their investments), they 47 48 49 minimize their exposure to the risk that a given investment will fail while increasing their chances 50 51 of “home runs” that is, investing in firms that achieve high-valuation exits (Bygrave, 1987; Lerner, 52 53 1994). Having numerous collaborative partners also increases access to sources of human, social, 54 55 56 and financial capital as executive-search firms, lawyers, and investment banks (Gorman & 57 58 59 60 Page 15 of 65 Academy of Management Journal

1 2 3 Sahlman, 1989; Hochberg et al., 2007; Kaplan & Stromberg, 2003; Ferrary & Granovetter, 2009). 4 5 6 Access to such resources tends to enhance VC syndicates’ impact on a start-up’s activities, from 7 8 strategic planning to product development (Dencker, Gruber, & Shah, 2009). Our core argument 9 10 is that the array of information advantages that VC partners bring to a start-up is attributable to 11 12 their past experience collaborating with one another on other start-up investments. 13 14 15 Focused successes. Receiving funding from VCs that exhibit high relational 16 17 embeddedness encourages start-ups to pursue specialized activities that build on existing 18 19 capabilities, positioning them for a focused success via an acquisition. Greater shared collaborative 20 21 22 experience among a start-up’s VCs create shared interpretative schema that reinforce prevailing 23 24 advice-giving norms and information processing among syndicate members in shared domains. In 25 26 turn, these shared schemas facilitate the transfer of complex and tacit knowledge to the start-up, 27 28 29 such as information about specific products and technologies (Levin & Cross, 2004; McEvily & 30 31 Reagans, 2003). Shared expectations also reduce coordination costs among syndicate partners by 32 33 enabling them to reach consensus more quickly and act more efficiently (Friedkin, 2004; Uzzi, 34 35 1996). Efficiency benefits start-ups whose unpredictable and time-sensitive challenges make them 36 37 38 dependent on their VCs for timely advice and resources. Thus, VCs that have previously co- 39 40 invested develop strong relationships characterized by shared beliefs and specialized knowledge 41 42 that can generate targeted advice about firm-specific problems (Reagans & McEvily, 2003; 43 44 45 Petkova et al., 2014; Ter Wal et al., 2016). 46 47 These arguments suggest that start-ups backed by VCs with more joint collaborative 48 49 experience are better equipped to build on existing product-development routines, to make timely 50 51 52 technical improvements, and to respond to the demands of a particular market. Because such 53 54 syndicates acquire a deeper shared understanding of a portfolio firm’s specific challenges, they are 55 56 57 58 59 60 Academy of Management Journal Page 16 of 65

1 2 3 likely to encourage the firm to develop firm-specific assets that address those needs; possession of 4 5 6 such assets, which are difficult for public markets to value, make start-ups more attractive 7 8 candidates for an acquisition than for an IPO. 9 10 Hypothesis 1: A start-up is more likely to exit via acquisition if its VC investors have higher 11 levels of relational embeddedness. 12 13 14 Broadcast successes. By contrast, less extensive shared collaborative experience—which 15 16 implies a lower level of relational embeddedness—promotes development of technologies and 17 18 products that appeal to multiple market categories, making such start-ups better suited to IPO exits 19 20 21 than to acquisition. Shared learning among partners with greater collaborative experience could 22 23 result in mere replication of prior practices, limiting experimentation to improve a start-up’s 24 25 outcomes. By contrast, VC syndicates with less collaborative experience tend to constitute a more 26 27 heterogeneous set of organizational mentors—a configuration that can expose portfolio firms to 28 29 30 diverse perspectives, knowledge, and mentorship styles. Such syndicates are less likely to share 31 32 mental templates and more likely to contribute diverse knowledge, which can prompt portfolio 33 34 firms to pursue strategies that create value in a range of market segments (Beckman, 2006; Fleming 35 36 37 et al., 2007; Ter Wal et al., 2016). Thus, receiving guidance from VC partners with less 38 39 collaborative experience sets the stage for a start-up to discover new high-growth opportunities. 40 41 Specifically, diverse perspectives can foster discovery of novel opportunities at the 42 43 44 intersection of market categories (Fleming et al., 2007; Whittington et al., 2009). Such networks 45 46 can promote a start-up’s pursuit of innovations by situating it at the nexus of diverse viewpoints 47 48 (Burt, 2005; Hargadon & Sutton, 1997; Ibarra et al., 2005; Lingo & O’Mahony, 2005). Access to 49 50 an array of information domains creates recombinative potential by encouraging portfolio firms to 51 52 53 generate products and technologies that result from diverse inputs (Burt, 2005; Padgett & McLean, 54 55 2006; Powell et al., 2005; Wang & Soule, 2016). Such start-ups are likely candidates for broadcast 56 57 58 59 60 Page 17 of 65 Academy of Management Journal

1 2 3 success because of the greater growth potential of firms whose products meet the unfulfilled needs 4 5 6 of customers in different segments; such growth in turn requires subsequent financing, which is 7 8 more readily generated by an IPO exit than an acquisition. 9 10 VC partners with less shared collaborative experience are also more likely to enjoy an array 11 12 of affiliations, roles, and organizational connections, equipping them better to mobilize the 13 14 15 resources necessary to handle the complexities of an IPO (Cumming, 2006; Gulati & Higgins, 16 17 2003). Thus the diverse relationships of VC partners without shared collaborative experience can 18 19 more effectively mitigate the uncertainty associated with IPOs; by contrast, VCs with greater co- 20 21 22 investment experience are apt to consult a narrow set of partners whose evaluation of a start-up’s 23 24 IPO prospects is apt to be less comprehensive. VCs with less co-investment experience draw on 25 26 broader market knowledge, which enables them to better assess a portfolio firm’s potential appeal 27 28 29 in public markets (Beckman & Haunschild, 2002). In short, because it takes diverse information 30 31 both to execute an IPO and to understand the market, VCs with less collaborative experience are 32 33 better equipped to help a portfolio firm go public. 34 35 Hypothesis 2: A start-up is more likely to exit via IPO if its VC investors have lower levels 36 37 of relational embeddedness. 38 39 DATA AND METHODS 40 41 We use data from Crunchbase (CB) to construct our sample of start-ups and VC investors. 42 43 Affiliated with the technology-news website Techcrunch and marketed as “the world’s most 44 45 comprehensive dataset of start-up activity,” Crunchbase was launched in 2007 as a publicly 46 47 48 accessible and crowd-curated online database of information on investment in start-ups 49 50 51 52 53 54 55 56 57 58 59 60 Academy of Management Journal Page 18 of 65

1 2 3 worldwide.4 Crunchbase allows open editing, but contributors must verify their identities at 4 5 6 multiple authentication portals before adding to or making changes in the database. We 7 8 supplemented the Crunchbase data with more detailed hand-collected data on acquisitions and 9 10 IPOs, and checked these exit data against other sources to verify their accuracy.5 We identified 11 12 71,624 rounds of funding, involving 42,027 new ventures and 20,142 investors, between 1982 and 13 14 15 July 2014. 16 17 Crunchbase utilizes multiple data-collection strategies to provide accurate and timely data 18 19 on entrepreneurs, start-ups, VC and angel investors, their investments, and entrepreneurial exits 20 21 22 such as IPOs and acquisitions. Unlike other crowd-sourced platforms, CB vets contributions to 23 24 ensure the accuracy and quality of each data point. To triangulate CB’s data, its staff also mines 25 26 press releases, SEC filings, and other databases, such as VentureSource and CB Insights 27 28 29 (unaffiliated with Crunchbase). A variety of external observers, and such recent scholarship as Ter 30 31 Wal et al. (2016), have checked the accuracy of Crunchbase data and validated its use to study co- 32 33 investment relationships among VC investors. Dalle, den Besten, and Menon (2017) and Koning, 34 35 Hasan, and Chatterji (2019) also endorse the use of Crunchbase data for research on firm behavior. 36 37 38 Because the United States is the primary context of CB’s data collection, we limit our 39 40 analysis to U.S.-based start-ups. And because our focus is collaborative relationships among start- 41 42 ups’ first-round VC investors, we also limit our analysis to start-ups that received first-round 43 44 45 funding from at least one VC firm. Finally, as part of a two-step estimation approach, inclusion in 46 47 48 49 4 Crunchbase was established in 2007; thus casual observers might expect its data on the post-2007 period to be more 50 reliable. We analyzed a subsample of firms that received their first round of VC funding prior to 2007 and found no 51 major differences with our results. 52 5 Analysis by others has shown that, for example, Crunchbase and VentureSource overlap by 85–95 percent in their 53 coverage of investment rounds during our period of study. This finding implies that we would obtain similar results 54 if we drew from those databases (last accessed on October 4, 2019: 55 https://inn0vationmatt3rs.wordpress.com/2013/07/02/crunchbase-accuracy/). 56 57 58 59 60 Page 19 of 65 Academy of Management Journal

1 2 3 our sample requires available information on the locations of the firm itself and of its VC investors; 4 5 6 we use that information to account for initial-selection bias. After dropping cases with missing 7 8 data in our explanatory variables, our final analyzable sample consists of 10,879 start-ups. 9 10 Variables 11 12 Dependent variables. We analyze the likelihood that a venture-backed start-up experiences 13 14 either an IPO or an acquisition exit. In keeping with past research, we define a successful exit as 15 16 6 17 either a focused success in the form of an acquisition or a broadcast success in the form of an IPO. 18 19 An exit can occur at any time after a start-up’s first round of investment; thus we use Cox 20 21 proportional hazards regression models, which allow for inclusion of time-varying covariates (Giot 22 23 & Schwienbacher, 2007). Of the 10,879 U.S.-based VC-backed start-ups we analyze, 1,689 (15.5 24 25 26 percent) experienced an acquisition exit; 317 (2.9 percent) experienced an IPO exit. The mean 27 28 time-to-exit for start-ups that went public was 1,658 days, or 4.5 years from the date of their first 29 30 VC investment round (standard deviation = 1,409 days or 2.9 years); for start-ups that were 31 32 33 acquired, mean time-to-exit was 1,237 days or 3.4 years (standard deviation = 827 days or 2.3 34 35 years). This empirical pattern is largely consistent with prior theoretical models of the speed of 36 37 various exit events for venture-backed start-ups (Bayar & Chemmanur, 2011). 38 39 40 Independent variable: VC joint collaboration experience. Our principal explanatory 41 42 variable operationalizes relational embeddedness among the VC partners in a syndicate by 43 44 45 6 Acquisitions are not all considered successful. We scrutinized each of the initial 1,689 acquisition exits in our dataset 46 by searching news reports and press releases to determine whether a given acquisition could be considered successful. 47 We based our criteria for success on prior research. For example, Arora and Nandkumar (2011) evaluated press 48 releases to determine whether an acquisition was called an asset sale (not successful) and whether the transaction value 49 was greater than the amount the company had raised (successful). For Kerr et al. (2014), an acquisition was deemed a 50 success if it had an exit value greater than a threshold amount ($5 million); failures were those described as asset sales 51 in media reports. Using these criteria, we found that 8.5 percent of our events (144 out of 1,689) could be considered 52 “masked failures” (Cochrane, 2005). We therefore estimated alternative versions of our main models that either 53 eliminate these cases from our data or code them as closures rather than acquisitions. The results from these models 54 do not differ substantially from those of our main models. 55 56 57 58 59 60 Academy of Management Journal Page 20 of 65

1 2 3 measuring the shared collaboration experience of a start-up’s first-round VC funders. We focus 4 5 6 exclusively on first-round VC investors because they shoulder the most risk when investing in 7 8 young, unproven firms (Zhelyazkov & Gulati, 2016; Ter Wal et al., 2016); they are also much 9 10 more likely to guide a start-up’s trajectory via active monitoring and mentorship than are later- 11 12 stage investors, who typically invest more money but exercise less control over a company (Huang 13 14 15 & Knight, 2015; Zarutskie, 2010). Also, later-stage investors tend to be viewed as outsiders and to 16 17 participate less in the group dynamics of the syndicate. In unreported analyses, we calculate VC 18 19 collaborative experience including later-round VC investors; doing so does not meaningfully alter 20 21 22 our results because a large proportion of such investors also invest in the first round. 23 24 We measure VC joint collaboration experience by first observing whether any VC partners 25 26 co-invested in at least one other firm within the five years prior to a given date (Dahlander & 27 28 29 McFarland, 2013; Fleming et al., 2007). In unreported analyses, we also used windows ranging 30 31 from two to ten years, which did not substantially change our findings. For two reasons, simply 32 33 counting the number of collaborative ties among a firm’s investors is too imprecise for our 34 35 purposes: first, two VCs might have co-invested in more than one firm; second, some co- 36 37 38 investments entail more intense engagement than others. 39 40 To capture variation in the intensity of past collaborative ties, we develop a measure that 41 42 weights such ties; we adopt Newman’s (2001) method of weighting one-mode projections of two- 43 44 45 mode networks, drawn from his work on scientific collaboration networks (see also Opsahl, 46 47 2013).7 For each co-investment tie between a start-up’s VCs, we first count the number of 48 49 50 51 52 7 Newman (2001) examines the collaboration networks of authors of scientific papers. To weight a collaboration tie 53 between two scientists, Newman first counts the number of papers they have co-authored. The weight of their 54 collaborative tie for a given paper is inversely proportional to the number of its other co-authors. The assumption is 55 that, on a paper with more co-authors, any two co-authors are less likely, on average, to have become well acquainted 56 57 58 59 60 Page 21 of 65 Academy of Management Journal

1 2 3 companies in which VCs A and B invested prior to time t. For each such company, we then observe 4 5 6 the number of investors other than A and B. The more investors, we assume, the weaker the average 7 8 relationship between any two investors, because each must divide its attention among more co- 9 10 investors. We then assign a tie-strength score to each co-investment experience of A and B. We 11 12 designate the sum of these scores as the weight of the co-investment tie between A and B. Finally, 13 14 15 the sum of the weights of all co-investment ties among a given firm’s investors is our measure of 16 17 VC joint collaboration experience. 18 19 [Figure 1 – Drop.io Example] 20 21 22 Figure 1 illustrates how VC joint collaboration experience is calculated for Drop.io, a start- 23 24 up (eventually acquired) whose service enabled users to create temporary file-sharing spaces 25 26 anonymously. Prior to October 2010, Drop.io had three investors: DFJ Gotham Ventures, RRE 27 28 29 Ventures, and Rose Tech Ventures. Two of these investors, DFJ Gotham and RRE, had previous 30 31 collaborative experience. To calculate the strength of their collaborative relationship, we use the 32 33 following formulas, adapted from Newman’s method (2001: 5) for weighting scientific- 34 35 collaboration networks: 36 37 푚 1 38 ∑ 푆퐴,퐵 = 푖 푗푁 ― 1 (1) 39 ≠ 푖 40 퐶 = ∑ 푆 (2) 41 푗 퐴 ≠ 퐵 퐴,퐵 42 43 In formula 1, the strength S of the collaborative relationship between A and B via past 44 A,B 45 46 investee company i is inversely related to company i’s total number of investors, Ni. For example, 47 48 if i had only two co-investors, A and B (if Ni = 2), the strength of their collaborative relationship 49 50 would be equal to 1, given that 1/(2-1) = 1. If i had three investors, A, B, and D, the strength of A 51 52 53 54 because their attention is divided. The weight of the tie between two co-authors is the sum of the weights of each of 55 their collaborations. Our measure substitutes VCs and portfolio firms for authors and scientific papers. 56 57 58 59 60 Academy of Management Journal Page 22 of 65

1 2 3 and B’s collaborative relationship via i would be equal to 1/(3-1) = .50. The weight of A and B’s 4 5 6 overall collaborative relationship in company j, their current investee, is therefore the sum of all 7 8 1/(Ni - 1) values for the m companies in which they co-invested prior to company j. The value of 9 10 VCs’ prior collaboration Cj for company j is the sum of the weights of all of the co-investment 11 12 dyads among j’s investors. 13 14 15 Control variables. For start-up-level control variables, all of our models include dummy 16 17 variables for the year in which a start-up received its first round of funding. We also control for 18 19 location using the state in which the start-up is headquartered, and for the primary Crunchbase- 20 21 8 22 assigned market segment, or sub-industry, to which the start-up belongs. Because many of our 23 24 start-ups are affiliated with multiple market segments, we include a count variable for the number 25 26 of market segments to which a firm belongs. We control for a start-up’s total number of VC 27 28 29 investors to account for variation in the VC joint-collaboration-experience variable attributable to 30 31 simply having more investors. Because we expect the likelihood of an exit to increase as a start- 32 33 up ages, we include the number of years since founding. We also account for the number of years 34 35 between founding and first round of funding because, arguably, start-ups that take longer to secure 36 37 38 funding might exhibit less promise of a successful exit. Our models also include a start-up’s 39 40 number of funding rounds, which approximates its growth potential. Finally, we control for total 41 42 IPOs and total acquisitions during the quarter of a given firm-day observation to account for 43 44 45 whether a given period represents a “hot market” for IPOs or acquisitions (Ritter & Welch, 2002). 46 47 48 49 8 We include dummy variables for California, New York, Massachusetts, Texas, Washington, Illinois, Pennsylvania, 50 Colorado, Florida, and Virginia, which jointly account for 92 percent of the firms in our sample. Our database consists 51 of 444 Crunchbase-created market segments, initially self-assigned by the firms in question and verified by 52 Crunchbase staff. We include dummy variables for the twenty largest segments: software, curated web, advertising, 53 enterprise software, analytics, e-commerce, mobile, clean technology, games, health care, finance, education, 54 hardware & software, health and wellness, semiconductors, cloud computing, security, apps, web hosting, and the 55 Android operating system. 56 57 58 59 60 Page 23 of 65 Academy of Management Journal

1 2 3 At the VC-syndicate level, we control for features that could confound the relationship 4 5 6 between prior collaboration and the likelihood of exit. We account for a VC investor’s average 7 8 number of co-investors (which we call average VC centrality) and for each investor’s average 9 10 number of other portfolio firms: VCs with more co-investors tend to enjoy higher status, and 11 12 having numerous portfolio companies can signify greater experience. A similar approach would 13 14 15 be to control for the time elapsed since the VCs’ earliest investments, a variable that when included 16 17 in our models did not affect our main results. We also control separately for the proportion of other 18 19 portfolio companies funded by a start-up’s first-round VCs that eventually went public, were 20 21 22 acquired, or closed. If, for instance, a start-up’s VCs had invested in many firms that eventually 23 24 went public, their baseline preference might be to take the focal start-up public. We also include 25 26 the proportion of a start-up’s investors who are angel investors. Firms with many angel investors 27 28 29 might be riskier investments: compared to VCs and private-equity firms, angel investors tend to 30 31 lack formal processes for vetting deals, and tend to invest in start-ups based on “rough rules of 32 33 thumb or their gut feeling” (Prowse, 1998: 789). Since we cannot observe the underlying quality 34 35 of a start-up, counting angel investors is an indirect way to gauge its expected performance. Our 36 37 38 models also include the proportion of a start-up’s first-round VCs that had previously invested in 39 40 companies in the same market segment to account for the possibility that VCs with experience in 41 42 the firm’s market segment might be better positioned to offer guidance. 43 44 45 Finally, we control for the market-segment diversity of first-round VCs. To construct this 46 47 variable, we calculate a Herfindahl index based on the market categories represented by other start- 48 49 푞 ups funded by the firm’s VCs with the formula 1 ― ∑ 푠2 . Here, s is the proportion of investors’ 50 푗 = 1 푗 j 51 52 n other portfolio companies that belong to market segment j (out of q market segments 53 54 represented). The scale ranges from 0 to 1; higher values indicate greater market-segment 55 56 57 58 59 60 Academy of Management Journal Page 24 of 65

1 2 3 diversity. Controlling for VCs’ segment diversity accounts for high levels of joint collaboration 4 5 6 experience that might be attributable to similar market-segment expertise. 7 8 Table 1 presents summary statistics for our variables. Our data are longitudinal; for time- 9 10 varying covariates, we present means and standard deviations on the date of the first round of 11 12 funding and the last date of observation (either the date of exit from the risk set via an IPO, 13 14 15 acquisition, or closure or the last date of observation due to right-censoring: July 1, 2014). Table 16 17 2 reports correlations for our variables on start-ups’ dates of first funding.9 18 19 [Table 1 – Summary Statistics] 20 21 22 [Table 2 – Correlation Table] 23 24 Descriptive analysis. Figure 2 reports (1) the mean number of first-round VC investors in 25 26 all start-ups, those that are acquired, those that go public, and those that fail; and (2) mean VC 27 28 29 prior collaboration at first funding. Start-ups that eventually go public or are acquired tend to attract 30 31 more first-round VC investors than average; this pattern suggests that, even at early stages in new 32 33 ventures’ life cycles, those that exhibit greater promise of a successful exit attract more attention 34 35 from investors. The figure shows only slight differences in the average number of first-round 36 37 38 investors between start-ups that are acquired (mean = 2.92 investors), those that go public (mean 39 40 = 2.81 investors), and those that fail (mean = 2.67). However, the figure also reveals that acquired 41 42 firms’ first-round VCs have greater average joint collaborative experience (mean = 2.95) than do 43 44 45 those of firms that go public (mean = 1.20) or fail (mean = 2.36). Consistent with our hypotheses, 46 47 48 49 50 51 52 9 Of note is that VCs’ prior collaborative experience and the Total Number of VCs is correlated at r = 0.61. 53 Although we control for the Total number of VCs in our models, we conducted robustness checks by removing 54 observations that had values of Total Number of VCs above the 90th percentile. Using this subsample did not 55 meaningfully affect our results. 56 57 58 59 60 Page 25 of 65 Academy of Management Journal

1 2 3 this comparison offers preliminary evidence of a correlation between the joint collaborative 4 5 6 experience of a start-up’s first-round VC funders and the start-up’s expected exit outcome. 7 8 [Figure 2 – VC joint collaboration experience by Exit Type] 9 10 Two examples from our data will help to contextualize the values in Figure 2. Gridiron 11 12 Systems and Carbonite are both VC-backed companies whose products provide digital data 13 14 15 storage. In its first funding round, Gridiron attracted investment from Foundation Capital, Mohr 16 17 Davidow Ventures, and Trinity Ventures; Carbonite received funding from 3i Group, Converge 18 19 Venture Partners, and the Kereitsu Forum. The prior collaboration of Gridiron Systems’ VCs was 20 21 22 high (our formula generated a value of 4.3): each pair of VCs had co-invested in at least two 23 24 companies prior to investing in Gridiron. By contrast, the prior collaboration of Carbonite’s VCs 25 26 was equal to 0: none had previously jointly co-invested. Gridiron Systems, which continued to 27 28 29 specialize in storage technology, was acquired in 2013. Carbonite, which extended its cloud- 30 31 services technologies to other markets, went public in 2011. 32 33 Model Estimation 34 35 Competing-risks hazard models. Because we use time-varying data in our sample, we estimate 36 37 proportional-hazards models to examine the instantaneous probability that a start-up will 38 39 40 experience an exit event on a given date. Our start-ups all enter the risk set when they receive their 41 42 first round of VC investment and leave when they go public, are acquired, or fail. Thus, we 43 44 estimate competing-risks models rather than standard Cox proportional-hazards models because 45 46 our start-ups can experience any of three exit events rather than one specific event (Fine & Gray, 47 48 49 1999). According to Fine and Gray (1999), failure to account for all events that can cause 50 51 observations to leave a risk-set in a hazard model can result in biased estimates. Importantly, 52 53 experiencing any of the three events causes a firm to leave the risk set by virtue of the firm either 54 55 56 not existing (through failure) or no longer being defined as a VC-backed start-up (by being 57 58 59 60 Academy of Management Journal Page 26 of 65

1 2 3 acquired, a VC-backed start-up becomes wholly owned by another company, and by going public, 4 5 6 ownership is distributed among public shareholders). As such, even though a firm might 7 8 potentially go public and then fail, going public first causes the firm to leave the risk set. 9 10 To estimate our competing-risks models, we adopt the procedure used by Katila and Shane 11 12 (2005), who study the dynamic process underlying several possible innovation-related events: they 13 14 15 estimate a hazard model for one event while treating observations of “competing” events as right- 16 17 censored. This censoring approach is appropriate when the same underlying process can affect all 18 19 exit events; simultaneously modeling an alternative risk event is only valid when exogenous 20 21 22 factors cause it to occur (Cannella & Shen, 2001). 23 24 Therefore, we estimate one set of competing-risks models of IPO exit, treating acquisition 25 26 exits and failure as right-censored; a second set of models of acquisition exit, treating IPO exits 27 28 29 and failure as right-censored; and a third set of models of failure, treating IPO and acquisition exits 30 31 as right-censored. To test the proportional-hazards assumption associated with these models, we 32 33 plotted the Schoenfeld residuals for each of our independent variables (from each model) against 34 35 their actual values (Allison, 1984). Because none of the best-fit lines for our plots have slopes 36 37 38 significantly different from zero, we consider the proportional-hazards assumption to be 39 40 reasonably satisfied. 41 42 Heckman correction: Initial VC matching. Because we cannot observe all the factors that 43 44 45 prompted a VC syndicate to invest in the start-ups, our sample might suffer from selection bias. 46 47 Some of these factors—such as VCs’ perceptions of the quality of a given start-up—might be 48 49 simultaneously correlated with the probability that the firm will exit successfully. A related 50 51 52 possibility is that a VC syndicate with higher relational embeddedness might be more likely to 53 54 invest in start-ups predisposed to exiting by acquisition; conversely, a VC syndicate with lower 55 56 57 58 59 60 Page 27 of 65 Academy of Management Journal

1 2 3 relational embeddedness might be more likely to fund start-ups predisposed to exiting by IPO. 4 5 6 Therefore, any correlation between VC joint collaboration experience and a given exit outcome 7 8 could be due to an initial selection stage that matches VCs to start-ups. 9 10 Because our sample consists of start-ups across a range of industries, we cannot use 11 12 approaches to matching that rely on uniform sector-specific indicators of quality to rule out 13 14 15 selection in VC funding (Fox et al., 2012). Thus, we adopt Hallen’s (2008) method instead. For 16 17 each start-up in our sample, we match the first-round VC syndicate to those of ten other randomly 18 19 chosen start-ups not funded by the same syndicate. We then generate all of our independent and 20 21 22 control variables for these “simulated” firm–syndicate pairs, and combine our “real” firm– 23 24 syndicate pairs with our simulated pairs to produce a new dataset. Next, we estimate a first-stage 25 26 probit model predicting whether a firm–syndicate pair is “real”—that is, whether it will be included 27 28 29 in our analysis sample. From this probit model, we calculate an inverse Mills ratio for each start- 30 31 up, which we include as a control variable in all of our proportional-hazards models to account for 32 33 sample-selection bias. 34 35 As an instrumental variable in the first-stage probit model, we use the average geographic 36 37 38 distance between a start-up and its VC investors (Sorenson & Stuart, 2001). Others have used 39 40 distance as an instrument in similar analyses, arguing that closer distances make VCs more likely 41 42 to invest in a start-up but does not meaningfully predict the start-up’s performance (Hallen, 2008; 43 44 45 Ter Wal et al., 2016). Table 3 reports the result of our probit model, which shows that a one- 46 47 standard-deviation decrease in the average distance between a syndicate and a start-up increases 48 49 the odds that the syndicate will invest in the start-up by 43 percent (p < .001, Table 3, Model 1). 50 51 52 53 54 55 56 57 58 59 60 Academy of Management Journal Page 28 of 65

1 2 3 Of course, we cannot explicitly test whether the average distance between a start-up and 4 5 10 6 its VCs is related to our ultimate outcome variables of interest. In our sample, however, similar 7 8 proportions of firms being acquired appear above and below the median average distance (15.6% 9 10 and 15.4%). We obtained similar results for the proportion of firms going public (3.1% and 2.7%) 11 12 and failing (6.1% and 5.3%). Nevertheless, we encourage readers to interpret these results 13 14 15 cautiously: some research suggests that less distance between VCs and portfolio firms lowers 16 17 monitoring costs, which can increase the overall value generated by the start-up (Bernstein, 18 19 Giroud, & Townsend, 2016; Cumming & Dai, 2010); other research suggests that improved 20 21 22 virtual-communication technologies have rendered negligible the challenges associated with 23 24 greater distance (Fritsch & Schilder, 2008). This work has yet to explicitly analyze the relationship 25 26 between VC-to-firm distance and differences in start-ups’ exit paths, which is the principal 27 28 29 outcome that we study. Thus, we have no compelling reason to suspect that proximity would make 30 31 a start-up more likely to pursue an IPO than an acquisition exit or vice-versa. As an alternative 32 33 approach, rather than including the inverse Mills ratio as a control variable in our models, we 34 35 include the average distance between a start-up and its VCs as control variable instead. This did 36 37 38 not substantially alter our results. 39 40 [Table 3 – First-Stage Probit Regression Model for Inverse Mills Ratio] 41 42 Inverse probability treatment weights (IPTW). Our principal endogeneity-related concern 43 44 45 is that certain features of a start-up might simultaneously (1) predispose it to aim for a certain exit 46 47 outcome and (2) predict its likelihood of attracting VC investors with more or less extensive prior 48 49 50 51 52 10 Although the exclusion restriction cannot be explicitly tested, we can test the relevance assumption for our 53 instrument. The F-statistic for the first-stage probit model is equal to 16.52, exceeding 10, which as a rule of thumb 54 suggests that the relevance assumption is satisfied. Because we cannot identify another candidate instrumental variable 55 in our dataset, we cannot also use a J-Test of overidentifying restrictions to rule out endogeneity. 56 57 58 59 60 Page 29 of 65 Academy of Management Journal

1 2 3 shared co-investing experience. To address this concern, we adopt inverse-probability treatment 4 5 6 weighted (IPTW) estimation for our models, an approach that has been previously deployed in 7 8 similar empirical settings in which selection and treatment effects might be confounded (Azoulay 9 10 et al., 2009; Rider & Negro, 2015; Wu, 2012). IPTW estimation assumes that we can observe a set 11 12 of confounders that adequately predicts both the outcome of interest and selection into a treatment 13 14 15 (Azoulay et al., 2009; Robins, 1997). The estimation procedure involves calculating a series of 16 17 weights that, when applied to our observations, creates a “pseudo-population” whose set of 18 19 confounders “no longer predicts selection into treatment,” and in which “the causal association 20 21 22 between the treatment and outcome is the same as in the original population” (Azoulay et al., 2009: 23 24 12). In our case, the treatment is VCs’ prior collaboration; the outcome is the firm’s IPO, 25 26 acquisition, or failure. 27 28 29 IPTW estimation resembles a propensity-score matching approach in that both generate a 30 31 quasi-experimental subsample of observations in which the likelihood of receiving a treatment is 32 33 not contingent on other observable variables. We prefer IPTW estimation because propensity- 34 35 score matching is only suitable for binary treatments; IPTW can be used for continuous treatments 36 37 38 (Robins, 1997; van der Wal & Geskus, 2011). IPTW estimation can be applied to longitudinal data 39 40 to create weights for individual spells, whereas matching spells by propensity scores across or 41 42 within firms would create gaps in our longitudinal data structure. To create a weight SW for a 43 it 44 45 start-up i at time t, we use the following equation (van der Wal & Geskus, 2011: 3): 46 47 푡 푓(퐶푖푘|푋푖푘) 푆푊푖푡 = ∏ (3) 48 푘 = 0푓(퐶푖푘|푋푖푘,푍푖,푘 ― 1) 49 50 In equation 3, f(Cik | Xik) gives the predicted value of VC prior collaboration Cik for a start-up i 51 52 given a set of independent variables, Xik, at time k. Zi,k-1 represents a set of lagged confounders. 53 54 55 56 57 58 59 60 Academy of Management Journal Page 30 of 65

1 2 3 To address the assumption that we measured all possible confounders, we categorized 4 5 6 control variables as confounders based on a literature review of the factors that predict co- 7 8 investment ties between VCs, which range from organizational distance (Stuart & Sorenson, 2001; 9 10 Sorenson & Stuart, 2008) to network embeddedness (Uzzi, 1996) and status differences (Podolny, 11 12 2010). Those we designated as confounders are (1) average number of other portfolio firms for 13 14 15 each of a firm’s VCs (lagged one year), (2) average VC centrality (the number of other VCs with 16 17 which a firm’s VC has co-invested, lagged one year), (3) proportion of a firm’s investors who are 18 19 angel investors (lagged one year), (4) proportion of the VCs’ other portfolio companies that 20 21 22 eventually went public/were acquired/closed (lagged one year), (5) density of the VCs’ co- 23 24 investment network (lagged one year), (6) number of years between a firm’s founding and its first 25 26 round of funding, and (7) a complete set of state and market-segment dummy variables. We follow 27 28 29 Azoulay et al. (2009) and Wu (2012), who recommend lagging time-varying confounders to avoid 30 31 the possibility of reverse causality. To calculate our lagged variables, we take the set of firm i’s 32 33 first-round VCs at time t and calculate, for example, its average number of co-investors one year 34 35 before t. Because these confounders were already incorporated in our calculation of treatment 36 37 38 weights, we omit them from the models we estimate with IPTW; including them would incur the 39 40 risk of over-specification (Wu, 2012). The next section presents both weighted and unweighted 41 42 (with and without confounder control variables) model estimates. 43 44 45 RESULTS 46 We use the GLM estimates from our numerator and denominator models to calculate our inverse 47 48 49 probability treatment weights (see equation 1). Using the treatment weights calculated from these 50 51 models (Table 4), we estimate hazard models for each exit type, reported in Table 5: Models 1–3 52 53 use acquisition exit as the dependent variable, Models 4–6 use IPO exit, and Models 7–9 use 54 55 56 closure exit. For each dependent variable’s set of models, we compare specifications for 57 58 59 60 Page 31 of 65 Academy of Management Journal

1 2 3 unweighted models (with and without confounders as control variables) to IPTW estimated 4 5 6 models. In separate analyses, we also test our models’ sensitivity to outliers by omitting firms 7 8 whose VCs’ shared collaborative values were above the 95th percentile; doing so does not 9 10 substantively affect our results. 11 12 [Table 4 – OLS Models Predicting Treatment Weights] 13 14 15 The models reported in Table 5 support Hypothesis 1, which posits that greater VC prior 16 17 collaboration increases the likelihood that a start-up will be acquired. In Model 1, each standard- 18 19 deviation increase in VCs’ prior collaboration boosts the hazard ratio of a firm achieving an 20 21 22 acquisition exit by 6 percent (exp(0.054) = 1.055, β = 0.054, 95% Confidence Interval = [0.028, 23 24 0.080], p = 0.013, Table 5). According to Table 5, when confounders are included, under the IPTW 25 26 estimation, the effect of VCs’ prior collaboration persists with a similar magnitude (β = 0.050, CI 27 28 29 = [0.018, 0.082], p = 0.016, Model 2; β = 0.042, p = 0.015, Model 3). 30 31 [Table 5 – Cox Hazard Models for Acquisition and IPO Exits] 32 33 Table 5, Model 4, shows that as VCs’ joint collaboration experience decreases, the 34 35 probability of a firm exiting by IPO increases, in keeping with Hypothesis 2. A one-standard- 36 37 38 deviation decrease in VCs’ joint collaboration experience leads to a 37 percent increase in the 39 40 hazard ratio of a firm experiencing an IPO exit (exp(-1 × -0.317) = 1.373, β = -0.317, CI = [-0.611, 41 42 -0.023], p = 0.016, Table 5, Model 4). The effect of VCs’ prior collaboration is robust to the 43 44 45 inclusion of confounder variables (β = -0.297, p = 0.012, Model 5); however, the strength of the 46 47 effect diminishes noticeably when subjected to our treatment weights (β = -0.209, CI = [-0.561, - 48 49 0.033], p = 0.018, Model 6). Importantly, these results hold while controlling for the diversity of 50 51 52 the VC syndicate’s market-segment experience, suggesting that the non-redundant knowledge that 53 54 VC investors without prior collaborative experience bring to a portfolio firm outweighs the 55 56 57 58 59 60 Academy of Management Journal Page 32 of 65

1 2 3 knowledge diversity captured in segment-specific expertise.11 Figure 3 illustrates the predicted 4 5 6 hazard for each exit type over the range of values for VCs’ prior collaboration in our data. 7 8 [Figure 3 – Predicted Hazard-Ratio of Exit by VC Joint Collaborative Experience] 9 10 Additional Analysis 11 12 Failure Exits. In addition to modeling the successes in our data, we ran models to understand what 13 14 collaborative structures and relationships might predict failure. To do so, we created a variable for 15 16 17 whether a start-up in our sample fails, such as via bankruptcy, an outcome that 2,388 start-ups 18 19 experienced (21.9 percent). Mean time-to-failure for this group of start-ups was 1,218 days, or 3.3 20 21 years from the date of first investment.12 22 23 Table 6 reports estimates of hazard models specified similarly to those in Table 5, with 24 25 26 firm failure as the outcome variable. Here we find that less shared collaborative experience among 27 28 a firm’s VCs significantly increases the likelihood of failure (Table 5, Models 7–9). In the previous 29 30 section we estimated that a one-standard-deviation reduction in VCs’ prior collaboration increases 31 32 33 the hazard ratio of an IPO exit by 37 percent (Table 5, Model 4); it also more than doubles the 34 35 hazard ratio of a closure (exp(-1 × -0.763) = 2.144, β = -0.763, CI = [-1.201, -0.325], p = 0.000, 36 37 Table 5, Model 7). Thus, although VC syndicates with less shared collaborative experience seem 38 39 40 better poised to take a start-up public (e.g., because they offer access to more diverse resources), 41 42 the greater coordination challenges involved may also create complications for start-ups. This 43 44 unexpected finding reinforces the observation that, though highly prized, VC investment may 45 46 47 48 49 11 We also tested whether VCs’ joint collaboration experience has curvilinear effects on acquisition and IPO exits. 50 Including a quadratic term did not significantly improve model fit for either outcome. 51 12 We conducted further sensitivity analyses by coding a start-up as having experienced a closure if it did not record 52 any form of venture funding activity in the five years or more prior to the end of our study period (July 31, 2014). 53 Here we draw on Ghosh (2011) and Bourgeois and Eisenhardt (1987), who regard a start-up as having essentially 54 ceased operations if it does not report any funding or liquidity event for several years in a row. Coding closure events 55 using this method did not generate substantially different results. 56 57 58 59 60 Page 33 of 65 Academy of Management Journal

1 2 3 entail considerable risk even for generously-backed start-ups. We return to these findings in the 4 5 6 discussion section below and explore their implications for emerging research on exceptions to the 7 8 benefits that VCs bestow on start-ups (Kim & Park, 2017; Pahnke et al., 2015b; Ozmel & Guler, 9 10 2015). 11 12 [Table 6 – Cox Hazard Models for Failure Exits] 13 14 15 The effect of VC joint collaboration on start-up strategy. Hypotheses 1 and 2 assume that 16 17 VCs with less prior collaborative experience contribute more diverse knowledge, encouraging a 18 19 start-up to develop products and assets that will appeal to multiple markets; VCs with more prior 20 21 22 collaborative experience are more likely to guide a start-up to elaborate on its existing capabilities. 23 24 We cannot observe the strategies that our 11,000 start-ups pursue—much less reliably measure 25 26 and compare them—but we can observe whether their actions are indicative of extension into new 27 28 29 market segments or of focus on their original target segments. Specifically, our data allow us to 30 31 test whether start-ups whose VCs have less prior collaborative experience are themselves more 32 33 likely to make acquisitions outside their original target market segments. To do so, we estimate a 34 35 model whose primary independent variable is VC prior collaboration and whose dependent 36 37 38 variable captures the extent of the focal start-up’s market-segment diversification via acquisition. 39 40 [Table 7 – Diversification through Acquisition Regression Analysis Results] 41 42 In Table 7, Model 1 analyzes a sample of 1,692 VC-backed start-ups that made acquisitions 43 44 45 after their first round of VC funding. Here, the dependent variable is the Jaccard similarity between 46 47 a focal start-up’s set of market segments and the market segments associated with the firms it 48 49 acquired. We find that greater VC prior collaboration has a positive association with market- 50 51 52 segment similarity between a focal start-up and its acquisitions (β = 0.023, CI = [0.003, 0.043], p 53 54 = 0.009, Table 6, Model 2). This result suggests that, conditional on making an acquisition, a start- 55 56 57 58 59 60 Academy of Management Journal Page 34 of 65

1 2 3 up tends to target firms that operate in similar spaces if its VCs have more prior collaborative 4 5 6 experience with each other. The results demonstrate that the magnitude of VCs’ prior collaboration 7 8 is associated with a start-up’s acquisition strategy, which is in turn indicative of whether the start- 9 10 up concentrates on its existing market segment or branches out into other market segments. 11 12 VC syndicates’ prior history of exits. Another salient empirical issue is that our main 13 14 15 results do not account for whether VCs and company founders have tendencies toward an IPO or 16 17 acquisition exit prior to establishing an investment relationship—for which, as noted earlier, there 18 19 are few systematically measurable indicators. It is plausible that past exposure to certain types of 20 21 22 start-up success shapes the subsequent preferences of VCs in a syndicate. In particular, the benefits 23 24 of VCs’ collaborative experience may be conditional on the nature of that experience. For example, 25 26 when VCs co-invest in a start-up that is eventually acquired, they develop a set of skills that is apt 27 28 29 to be less applicable to taking a firm public, and vice-versa. Shared experiences in pursuit of a 30 31 specific goal may predispose group members to resort to the same routines when performing 32 33 subsequent collective tasks because they are biased toward “what works” (Woolley et al., 2010). 34 35 To address these questions, we calculate a syndicate’s shared collaborative experience 36 37 38 using only past co-investments in companies that eventually went public or were acquired. To 39 40 measure the acquisition-specific VC joint collaboration experience, or Cj,ACQ, of investors in focal 41 42 firm j, we adapt our method for operationalizing total joint co-investment experience (see formulas 43 44 45 1 and 2) using only the set of companies in which j’s investors previously invested that were 46 47 eventually acquired; we calculate acquisition-specific joint co-investment experience by dividing 48 49 Cj,ACQ by total joint co-investment experience, Cj. We measure IPO-specific VC joint collaboration 50 51 52 experience similarly by calculating Cj,IPO and dividing it by Cj. We divide by Cj for both measures 53 54 to reduce artificial correlation and potential multicollinearity in our models. For example, if all of 55 56 57 58 59 60 Page 35 of 65 Academy of Management Journal

1 2 3 the past co-investments of the VCs in a syndicate were in start-ups that eventually exited via IPO, 4 5 6 its IPO-specific joint experience would be equal to 1. 7 8 [Table 8 – Cox Regression Models with Exit-Specific VC Joint Collaboration Experience] 9 10 The model estimates in Table 7, which include both Cj,ACQ and Cj,IPO, suggest that the nature 11 12 of its VCs’ past collaborative experience strongly influences a start-up’s exit outcome, above and 13 14 15 beyond the VCs’ total collaborative experience. As the proportion of acquisition-specific 16 17 experience increases by a standard deviation, the hazard ratio of a firm being acquired increases 18 19 by 4 percent (exp(0.038) = 1.039, β = 0.038, CI = [0.012, 0.064], p = 0.001, Table 7, Model 1). 20 21 22 The effect is almost identical under the IPTW specification (β = 0.038, CI = [0.010, 0.066], p = 23 24 0.003, Table 7, Model 3), but weakens considerably when confounders are included without 25 26 weighting (β = 0.012, CI = [-0.034, 0.058], p = 0.298, Table 7, Model 2). Similarly, as IPO-specific 27 28 29 co-investment experience increases by a standard deviation, the hazard ratio of a firm exiting via 30 31 IPO increases by 12 percent (exp(0.110) = 1.116, β = 0.110, CI = [0.022, 0.198], p = 0.006, Table 32 33 7, Model 4), an effect that largely holds in terms of magnitude when confounders are included and 34 35 our treatment weights are applied (β = 0.085, CI = [0.011, 0.159], p = 0.010, Model 5; β = 0.012, 36 37 38 CI = [0.021, 0.201], p = 0.007, Model 6). 39 40 Furthermore, Table 7, Models 1, 2, and 3, indicate that IPO-specific collaborative 41 42 experience has a consistently negative relationship with the hazard of an acquisition exit. Likewise, 43 44 45 Table 7, Models 4, 5, and 6, show that acquisition-specific collaborative experience has a 46 47 consistently negative association with the hazard of an IPO exit. These results offer evidence of a 48 49 boundary condition for our main findings: the beneficial effects of VCs’ collaborative experience 50 51 52 on a start-up’s successful exit can be conditional on whether that experience matches the type of 53 54 exit the start-up is seeking. 55 56 57 58 59 60 Academy of Management Journal Page 36 of 65

1 2 3 Ex-ante preferences might confound our findings by creating a selection problem in which 4 5 6 a VC might select into a syndicate with high relational embeddedness precisely because that group 7 8 of VCs have led acquisition exits in the past. Although we cannot measure what the idiosyncratic 9 10 ex-ante exit preference a given VC firm, we can infer preferences by looking at their prior history 11 12 of exits. To explore this possibility, we looked for meaningful variation in the types of exit 13 14 15 outcomes among the portfolio firms for VCs that tended to co-invest in syndicates with low and 16 17 high relational embeddedness. We found that even for VCs in the top quartile of syndicate 18 19 relational embeddedness, 4.0% of their portfolio start-ups went public, and for VCs in the lowest 20 21 22 quartile of syndicate relational embeddedness, 12.6% of their portfolio start-ups were acquired. 23 24 This suggests whether a VC prefers investing in syndicates with high or low relational 25 26 embeddedness does not necessarily reveal the VC’s ex-ante preferences for a certain type of exit 27 28 29 outcome. 30 31 Prior expertise of VC syndicates. Finally, a related question is whether prior expertise 32 33 gained from the separate individual investment experiences of VCs in a syndicate might substitute 34 35 for the benefits of their relational embeddedness. Although we find that greater VC joint 36 37 38 experience reduces a start-up’s likelihood of exiting by IPO, it is conceivable that this effect could 39 40 be reversed if the syndicate’s VCs possessed individual experience investing in a wide variety of 41 42 domains. In other words, in keeping with Ter Wal et al.’s (2016) “best-of-both-worlds” argument, 43 44 45 it is possible that the VC syndicates best poised to take their start-ups public are those with high 46 47 levels of relational embeddedness and more diverse experience investing across market segments 48 49 (see also Fleming et al., 2007).13 50 51 52 53 54 55 13 We appreciate the thoughtful comment of a reviewer that prompted this analysis. 56 57 58 59 60 Page 37 of 65 Academy of Management Journal

1 2 3 To test this possibility, in an unreported set of models we added an interaction between our 4 5 6 VC joint-experience variable and our variable for the market-segment diversity of a start-up’s 7 8 VCs’ other investments. For an IPO exit, we found evidence for the best-of-both-worlds effect 9 10 described above; we found no evidence for an interaction effect when considering acquisition 11 12 exits. To explain this difference, we speculate that, for a complex transaction like an IPO, VCs’ 13 14 15 market-segment diversity is necessary to extend the breadth of its impact, but that coordination is 16 17 also important—and that coordination is lacking when VCs exhibit low levels of relational 18 19 embeddedness. For an IPO exit, therefore, an ideal VC syndicate may have greater joint 20 21 22 experience, allowing for more effective coordination, and can draw on a variety of domains thanks 23 24 to diverse prior investment experience. By contrast, we argue that executing an acquisition exit 25 26 does not require diverse market experience among VCs because it is a focused success, appraised 27 28 29 mainly by industry insiders; thus VC syndicates need not have “the best of both worlds” in the 30 31 form of strong prior relationships and market segment diversity. 32 33 DISCUSSION 34 35 We began by comparing contradictory findings on how prior relationships between 36 37 organizations impact their collective collaborative performance: some studies emphasize the 38 39 40 benefits of prior collaborations (Reagans & McEvily, 2003; Tortoriello & Krackhardt 2010); other 41 42 research finds that shared experience may hinder performance (Rogan, 2014; Uzzi, 1997). Our 43 44 theory and findings help resolve this tension by suggesting that the impact of prior collaboration 45 46 depends on the type of success in question. Specifically, we differentiate between focused and 47 48 49 broadcast successes, defined in terms of their appraisers, complexity, and prominence. 50 51 We find that different levels of relational embeddedness in VC syndicates, traceable to 52 53 their prior collaborations with each other, are associated with different kinds of success for the 54 55 56 start-ups they back. Higher levels of relational embeddedness are likely to support focused 57 58 59 60 Academy of Management Journal Page 38 of 65

1 2 3 successes (acquisitions); lower levels of embeddedness are more frequently associated with 4 5 6 broadcast success (IPOs). Our theory and findings thus refine and extend research on inter- 7 8 organizational collaborations and entrepreneurship, and point to several promising directions for 9 10 future theoretical and empirical explorations. 11 12 Contributions 13 14 We first contribute to research on inter-organizational collaborations and networks. Unlike past 15 16 17 studies that have focused on a single performance outcome at a time, we explore whether two 18 19 different types of success are driven by different patterns of collaboration. Our findings suggest 20 21 that one resolution to “the paradox of embeddedness” is that differing levels of relational 22 23 embeddedness promote different kinds of success. Networks with high embeddedness are 24 25 26 associated with focused successes; repeated collaborations create shared identities, promote 27 28 solidarity, and enhance organizations’ ability to coordinate their activities efficiently. In our 29 30 context, repeated collaboration among VCs creates common interpretative schema, through which 31 32 33 specialized knowledge and expectations are triangulated about how to guide firms toward domain- 34 35 specific outcomes – in particular, equity exits by acquisition. 36 37 In contrast, networks with lower levels of relational embeddedness expose organizations 38 39 40 to more diverse ideas and social ties, resulting in an increased likelihood of a broadcast success. 41 42 VCs with less collaborative experience may lack shared interpretive schema and be apt to provide 43 44 start-ups a wider range of guidance and resources, increasing the likelihood that they will adopt 45 46 strategies that create value across markets and thus pursue an IPO. We thus contribute to research 47 48 49 on how network structure impacts organizational performance. The impact of a given type of 50 51 network structure depends, we suggest, on the appraisers, complexity, and prominence of the 52 53 outcome under consideration. Future work may explore how different types and features of 54 55 56 57 58 59 60 Page 39 of 65 Academy of Management Journal

1 2 3 network structures vary in their impact on focused and broadcast successes, in contexts ranging 4 5 6 from academic publication to joint R&D ventures and artistic productions. 7 8 Our second contribution is to research on entrepreneurship. The practice of venture 9 10 capitalists and the contribution they make to their portfolio companies has emerged as a key 11 12 research domain within entrepreneurship (see Grégoire et al., 2006: 358; Lerner & Nanda, 2020). 13 14 15 A significant stream of this research has connected VCs to start-ups’ (largely positive) 16 17 performance outcomes (e.g. Ferrary, & Granovetter, 2009; Pahnke, Katila & Eisenhardt, 2015). 18 19 But unlike prior work that treats exits in a monolithic way by collapsing them into a single 20 21 22 performance variable (e.g. Hall and Woodward 2010), we take a fine-grained approach by 23 24 distinguishing between different exit pathways (McDonald & Eisenhardt, 2020). Importantly, we 25 26 find that differences in VCs’ past collaborative experience promote different exit pathways for the 27 28 29 start-ups they support. More broadly, these findings indicate that understanding the dynamics and 30 31 structural properties of existing collaborative networks may be of value to low-power players 32 33 seeking to form ties and to embed themselves in such networks (Hallen & Eisenhardt, 2012; Ozcan 34 35 & Eisenhardt, 2009; Pahnke et al, 2015a; Pahnke et al, 2015b). 36 37 38 Theoretical Implications and Extensions 39 40 Our main findings lend credence to the central idea of the paper: different levels of relational 41 42 embeddedness prompt different kinds of collaborative success. However, the additional analyses 43 44 we performed point to several opportunities for theoretical extensions. For example, our results 45 46 highlight an underappreciated trade-off: lower levels of VC joint experience increase the 47 48 49 likelihood of exit via IPO (a lucrative and enviable outcome for many parties involved) but also 50 51 raise the chances of start-up failure before reaching any successful equity exit (see Eisenmann, 52 53 2021 for a comprehensive treatment of failure factors). This finding is consistent with recent work 54 55 56 that has emphasized the performance trade-offs entailed in receiving venture capital funding. For 57 58 59 60 Academy of Management Journal Page 40 of 65

1 2 3 example, Pahnke, et al. (2015b) showed that when a start-up’s VCs back competitors in the same 4 5 6 sector, the focal start-up’s innovation performance diminished. Similarly, Ozmel and Guler (2014) 7 8 showed that receiving funding from a VC can harm a start-up’s chances of a successful exit if it 9 10 has low relative standing in the VC’s portfolio. Finally, Kim and Park (2017) demonstrated how 11 12 start-ups that take corporate venture capital early on tend to produce more innovations, but are also 13 14 15 less likely to exit by IPO. Our results extend these findings by introducing an additional risk- 16 17 reward calculus: lower levels of relational embeddedness among VC backers might increase the 18 19 potential of reaching a more prominent IPO exit, but it also comes with the higher risk of failure. 20 21 22 Why might less embedded syndicates also be associated with start-up failure? One reason, 23 24 which resonates with our main argument, is an inability to coordinate efficiently (see Nanda & 25 26 Rhodes-Kropf, 2019). Another possibility is that less-embedded syndicates bring in a wider variety 27 28 29 of market information (Ter Wal et al., 2016), enabling them to detect flaws in a start-up’s 30 31 operations or strategy more effectively, and results in their encouraging those start-ups to shut 32 33 down rapidly. Conversely, more embedded syndicates—falling prey to commitment traps—may 34 35 support underperforming companies for longer (Guler, 2007). We conducted supplemental 36 37 38 analysis, showing that time-to-failure is indeed shorter for start-ups funded by less embedded VCs 39 40 than those funded by more embedded VCs. This finding highlights a novel mechanism that might 41 42 explain failure rates among VC-backed start-ups that, although beyond the scope of our work, 43 44 45 deserves further inquiry. 46 47 Another important theoretical implication of our analyses relates to how VCs’ relational 48 49 embeddedness affect strategic choices more broadly. Although we analyze how VCs’ prior joint 50 51 52 experiences shape a start-up’s exit pathway, our models also reveal that relational embeddedness 53 54 may influence VCs’ own strategy of which start-ups to fund. A complementary explanation for 55 56 57 58 59 60 Page 41 of 65 Academy of Management Journal

1 2 3 our results is rooted in selection effects: more relationally embedded VC syndicates might fund 4 5 6 start-ups they perceive as ‘aiming to be acquired’ whereas less embedded VC syndicates might 7 8 back start-ups perceived as ‘IPO candidates.’ Such reasoning resonates with Gulati’s (1995a: 624) 9 10 suggestion that “networks of interorganizational relations are maps both of and for strategic action” 11 12 (see also Barley, et al [1992]). Relational embeddedness among VCs may thus serve as a map— 13 14 15 guiding the strategic choice of which start-ups to fund. This insight invites further investigation 16 17 into how relational embeddedness can account for both VCs’ selection of which start-ups to fund 18 19 and their actions guiding the start-ups’ subsequent behaviors. 20 21 22 Finally, our additional analyses also surface important boundary conditions that elaborate 23 24 the precise theoretical mechanisms underlying the relationship between relational embeddedness 25 26 and collective collaborative performance. In particular, we find that greater prior joint experience 27 28 29 among VCs can lead to a higher likelihood of IPO when VCs themselves have accumulated 30 31 investment experience across a broad range of market segments in their own prior experiences. 32 33 The benefit of diverse expertise enjoyed by lower levels of relational embeddedness may therefore 34 35 be redundant when organizations themselves possess diverse expertise. Under this scenario, lower 36 37 38 levels of relational embeddedness and diverse expertise could be interpreted as substitutes. Our 39 40 findings join a chorus of recent work on organizational networks (Kwon et al., 2020; Li & 41 42 Piezunka, 2019; Ter Wal et al., 2016) that ask scholars to specify whether and how the effects of 43 44 45 inter-organizational ties on organizational success might be conditional on certain environmental 46 47 features and the content that flows through these ties. More broadly, our results point to the need 48 49 for further investigation into the interplay between network structure and informational context to 50 51 52 gain a comprehensive understanding of the relationship between embeddedness and collaborative 53 54 performance. 55 56 57 58 59 60 Academy of Management Journal Page 42 of 65

1 2 3 Though we developed our distinction between focused success and broadcast success in 4 5 6 the context of venture-capital firms’ co-investments in start-ups, the idea that embeddedness in 7 8 different network structures engenders different types of success is potentially generalizable to any 9 10 domain where appraisal, prominence, and complexity shape the environment for rewards and 11 12 recognition. As another example of a focused success, consider the annual Academy Award for 13 14 15 Best Sound Editing of a feature film. Few outside of the professional sound-editing community 16 17 will recall that the 2019 winner was Alan Robert Murray for Ford v Ferrari. Only members of the 18 19 Sound Editing branch of the Academy of Motion Picture Arts and Sciences are eligible to cast 20 21 22 votes in this category; thus interest in the award, and its impact, are restricted to a small and 23 24 narrowly focused community of experts (appraisers). By contrast, the Academy Award for Best 25 26 Picture is voted on by all Academy members, a much more diverse set of appraisers which 27 28 29 amplifies overall prominence of the award with all of the attendant increases in complexity and 30 31 coordination that the expanded process entails. Far more people, from industry insiders to casual 32 33 film fans, will recall that 2019’s Best Picture winner was Parasite. 34 35 Closer to home, an example of a focused success for management scholars is publication 36 37 38 in a respected specialty journal like The Journal of Technology Transfer (JTT). Reviewers 39 40 (appraisers) for JTT possess specialized knowledge and evaluate papers based on their potential 41 42 contribution to its subfield. JTT is likely to be read and cited by academics interested in technology 43 44 45 transfer and adjacent fields but probably not by colleagues in other disciplines. Publication in a 46 47 high-impact generalist management journal, by comparison, requires potential appeal to a broader 48 49 audience (often by making a theoretical contribution that transcends a specific context), increasing 50 51 52 both the complexity of the submission process and the likelihood of rejection but elevating the 53 54 prominence of a successful submission. 55 56 57 58 59 60 Page 43 of 65 Academy of Management Journal

1 2 3 Managerial Implications 4 5 Our findings may be useful to entrepreneurs, venture capitalists, and other participants in the start- 6 7 up ecosystem, with the caveat that our aggregate findings describe a general pattern that may have 8 9 10 limited pertinence to any particular situation or firm. For start-ups, the finding that investment by 11 12 highly embedded VCs increases the likelihood of exit by acquisition is a useful, though neutral, 13 14 insight that ultimately leads back to founder motivations. Founders with the luxury of choice may 15 16 17 favor VCs whose network position and record of exit types match their own preferences. Start-ups 18 19 in search of funding may want to investigate not only the track record of each firm that offers a 20 21 term sheet (as is standard) but also its relational embeddedness, as revealed by its relevant history 22 23 of co-investment partners. If founders learn that a venture firm is relatively highly embedded, has 24 25 26 a record of pulling in peer firms from its network, and usually exits when its portfolio companies 27 28 are sold to big-company acquirers (a focused success), they may have a better sense of the likely 29 30 road ahead with this particular investor or set of investors. Some founders, weighing the costs and 31 32 33 benefits of VC embeddedness in light of these findings, might actively seek out well-embedded 34 35 VCs in order to benefit from their sector experience, proven network of partners, and collective 36 37 eye for steering start-ups toward promising opportunities to be acquired. 38 39 40 Alternatively, founders intent on an IPO (a broadcast success) could view our findings as 41 42 good reason to be wary of highly embedded VCs; instead, they might cultivate a more diverse set 43 44 of investors that will push them to develop multiple interested audiences earlier and support 45 46 multiple visions of success. This strategy could also provide founders more room to maneuver 47 48 49 amid the influences of investors (Ewens & Marx, 2017; McDonald & Gao, 2019), if and when 50 51 acquisition and exit conversations begin. Simultaneously, of course, such entrepreneurs should 52 53 also glean from our findings that this network strategy entails a higher overall risk of failure. 54 55 56 57 58 59 60 Academy of Management Journal Page 44 of 65

1 2 3 For venture capitalists, our findings can be translated into several pieces of advice. More 4 5 6 embedded VCs with consistent records of acquisition hits but fewer IPO home runs may want to 7 8 strategically seek out more diverse co-investors in order to foster greater diversity of inputs while 9 10 increasing the percentage of portfolio firms on the IPO track. Less-embedded VCs seeking more 11 12 stable returns might pursue the opposite approach, becoming strategically embedded in a network 13 14 15 of like-minded investors that regularly invest in each other’s companies and thus achieving a 16 17 steadier stream of successful acquisition exits each year. Early-stage VCs seeking different degrees 18 19 of relative embeddedness could in turn calibrate their co-investment behavior to their long-term 20 21 22 strategic goals. 23 24 Future Research Directions and Conclusion 25 26 We subjected our empirical analyses to a number of robustness checks. However, the limitations 27 28 of our analysis highlight opportunities for future work. First, our measure of VCs’ collaborative 29 30 experience pertains only to first-round investors. Although these early investors tend to be the most 31 32 33 active in shaping start-up trajectories, later-round investors matter, too (though in an unreported 34 35 analysis, including VC investors from all rounds does not substantially alter our results). Second, 36 37 our work also does not address how VC partners and entrepreneurs reach consensus on which exit 38 39 40 pathway to pursue. Prior ties among network partners can affect the influence that a partner wields 41 42 in such a collective decision, but our data restrict our ability to study this phenomenon (Thomas- 43 44 Hunt et al., 2003). Finally, we did not scrutinize the value created by firms that went public or 45 46 were acquired. Having gone to great lengths to verify that acquisition exits were indeed positive 47 48 49 outcomes rather than fire sales or “masked failures,” we lacked data on the premiums they 50 51 generated. Future work could investigate the ultimate financial returns of VC syndicates’ 52 53 collaborative experience using acquisition premiums and IPO pricing as outcome variables. 54 55 56 57 58 59 60 Page 45 of 65 Academy of Management Journal

1 2 3 Furthermore, we study a particular type of collaboration in which successful outcomes are 4 5 6 mutually exclusive: VC-backed start-ups are either acquired or they go public. Future studies 7 8 might explore collaborations in settings where different types of success are more tightly coupled 9 10 or non-exclusive. For example, members of dissertation committees collaborate to help their 11 12 students publish in academic journals and win placement at prestigious institutions, two outcomes 13 14 15 that are often tightly coupled. In entertainment collaborations, critical acclaim and box-office 16 17 success are not mutually exclusive outcomes; in fact, they may build on each other. Additionally, 18 19 in some settings, focused and broadcast successes may not differ across all three of the dimensions 20 21 22 that we consider in this study. Future research in other domains may explore interdependencies 23 24 between focused and broadcast successes, the sequences in which they occur, and when some 25 26 dimensions of distinct types of successes overlap. 27 28 29 Finally, one of the challenges of a macro level study like ours is that we theorize about 30 31 mechanisms that we do not directly observe in our data. In particular, we theorize that factors such 32 33 as identity, ease of coordination, and shared interpretive schema positively impact the likelihood 34 35 of one kind of success or the other. However, we do not observe, nor measure how these kinds of 36 37 38 cognitive factors may shape the ex-ante preferences of investors to favor one kind of success over 39 40 the others. Future studies, particularly micro-level experimental research, may be able to better 41 42 distinguish the role that such cognitive factors may play in driving different kinds of successes. 43 44 45 Connecting structural aspects of collaborative networks to performance continues to be a 46 47 prominent focus of organizational scholarship. By theorizing about different kinds of success, we 48 49 hope to inspire future work on how the dynamics of inter-organizational networks affect the 50 51 52 performance of their members. Future appraisals of focused and broadcast success, drawing on 53 54 increasingly rich and accessible data sources like Crunchbase, may yield additional insights into 55 56 57 58 59 60 Academy of Management Journal Page 46 of 65

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1 2 3 Table 1. Summary statistics for entrepreneurial firms in sample (n = 10,879 entrepreneurial firms) 4 5 6 7 At final date of 8 At date of first funding round observation 9 Variable Mean SD Mean SD 10 Acquisition Exit 0.155 11 12 IPO Exit 0.029 0.130 0.336 13 VC Joint Collaboration Experience 2.153 13.565 8.747 39.781 14 VC Joint Collaboration Experience, 15 IPO-specific 0.011 0.081 0.011 0.081 16 17 VC Joint Collaboration Experience, 18 acquisition-specific 0.046 0.166 0.049 0.169 19 VC Joint Collaboration Experience, 20 failure-specific 0.013 0.084 0.015 0.089 21 Total quarterly acqusitions in U.S. market 15.536 5.772 18.783 4.584 22 23 Total quarterly IPOs in U.S. market 24.765 13.226 35.441 10.542 24 Years since firm founding 3.303 6.156 6.282 6.681 25 Number of rounds of funding 1.000 0.000 1.695 1.138 26 27 Total Number of VCs 2.253 2.242 3.443 3.523 28 Proportion of VCs with other portfolio 29 firms in same segment as focal start-up 0.341 0.427 0.690 0.375 30 Segment diversity of VCs' other portfolio 31 firms 0.163 0.179 0.124 0.152 32 33 Number of start-up's market segments 2.299 1.976 2.288 1.973 34 Years between start-up founding and first 35 round of funding 3.303 6.156 36 Average VC centrality (lagged) 37.658 78.284 90.241 127.458 37 38 Average number of VCs' other portfolio 39 firms (lagged) 45.617 90.493 155.890 221.654 40 Proportion of Investors who are Angels 41 (lagged) 0.147 0.317 0.146 0.293 42 Proportion of VCs' other portfolio firms 43 that go IPO (lagged) 0.054 0.129 0.041 0.080 44 45 Proportion of VCs' other portfolio firms 46 that are acquired (lagged) 0.170 0.202 0.148 0.131 47 Proportion of VCs' other portfolio firms 48 that failed (lagged) 0.062 0.114 0.062 0.082 49 50 51 52 53 54 55 56 57 58 59 60 Academy of Management Journal Page 56 of 65

1 2 3 Table 2. Correlation Matrix of selected variables used in analysis 4 5 6 Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 Acquisition Exit 7 8 2 IPO Exit 9 0.00 - 10 3 VC Joint Collaboration Experience 0.01 0.00 11 VC Joint Collaboration Experience, IPO- - 4 12 specific 0.00 0.01 0.05 VC Joint Collaboration Experience, 13 5 acquisition-specific 0.00 0.00 0.11 0.00 14 VC Joint Collaboration Experience, failure- 6 15 specific 0.00 0.00 0.03 0.01 0.04 - - - 16 7 Total quarterly acquisitions in U.S. market 17 0.00 0.00 0.04 0.02 0.05 0.03 - - - 8 Total quarterly IPOs in U.S. market 18 0.00 0.01 0.11 0.05 0.12 0.08 0.18 19 - - - 9 Years since firm founding 20 0.01 0.01 0.12 0.15 0.01 0.00 0.02 0.03 - 21 10 Number of rounds of funding 0.01 0.01 0.19 0.12 0.16 0.02 0.02 0.08 0.12 22 - - 11 Total Number of VCs 23 0.01 0.00 0.61 0.05 0.25 0.09 0.02 0.09 0.02 0.46 Proportion of VCs with other portfolio - - - 24 12 25 firms in same segment as focal start-up 0.00 0.00 0.02 0.05 0.01 0.01 0.01 0.00 0.02 0.09 0.24 Segment diversity of VCs' other portfolio ------13 26 firms 0.02 0.02 0.13 0.03 0.13 0.03 0.03 0.03 0.13 0.17 0.19 0.04 27 - - - - - 14 Number of start-up's market segments 28 0.00 0.00 0.06 0.08 0.04 0.03 0.00 0.01 0.17 0.13 0.11 0.10 0.11 Years between start-up founding and first ------29 15 round of funding 0.01 0.01 0.13 0.10 0.05 0.00 0.01 0.03 0.85 0.16 0.11 0.01 0.19 0.17 30 ------16 Average VC centrality (lagged) 31 0.01 0.01 0.18 0.01 0.03 0.02 0.09 0.26 0.00 0.02 0.06 0.30 0.29 0.00 0.07 Average number of VCs' other portfolio - - - 32 17 firms (lagged) 0.01 0.00 0.66 0.04 0.12 0.03 0.10 0.28 0.02 0.41 0.57 0.09 0.29 0.07 0.13 0.52 33 Proportion of Investors who are Angels ------18 34 (lagged) 0.00 0.01 0.32 0.12 0.12 0.03 0.01 0.06 0.26 0.07 0.50 0.31 0.10 0.17 0.22 0.10 0.18 Proportion of VCs' other portfolio firms that ------35 19 36 go IPO (lagged) 0.01 0.03 0.12 0.20 0.02 0.02 0.06 0.09 0.26 0.02 0.08 0.04 0.34 0.10 0.23 0.12 0.10 0.18 Proportion of VCs' other portfolio firms that ------20 37 are acquired (lagged) 0.01 0.00 0.02 0.04 0.23 0.07 0.10 0.27 0.06 0.02 0.05 0.02 0.06 0.00 0.06 0.13 0.13 0.03 0.11 38 Proportion of VCs' other portfolio firms that ------21 39 failed (lagged) 0.00 0.01 0.01 0.02 0.03 0.08 0.08 0.14 0.11 0.03 0.00 0.02 0.07 0.02 0.10 0.04 0.01 0.03 0.17 0.03 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 57 of 65 Academy of Management Journal

1 2 3 Table 3. First-stage probit model of VC syndicate investing in a start-up in the first round 4 5 6 7 Variable Model 1 8 9 Average Distance between VC Partners -0.356*** 10 and start-up (0.009) 11 0.064*** 12 VC Joint Collaboration Experience (0.006) 13 -0.004*** 14 Total quarterly acquisitions in U.S. market 15 (0.001) 16 -0.005*** 17 Total quarterly IPOs in U.S. market (0.001) 18 0.001 19 Years since firm founding 20 (0.001) 21 -0.087*** 22 Total number of VCs (0.003) 23 24 Proportion of VCs with other portfolio 2.226*** 25 firms in same segment as focal start-up (0.023) 26 Segment diversity of VCs' other portfolio -0.556*** 27 firms (0.032) 28 0.027*** 29 Number of start-up's market segments 30 (0.004) 31 -1.057*** 32 Intercept (0.041) 33 34 Market segment dummies Y 35 State dummies Y 36 df 40 37 Log likelihood -21516 38 39 n (Start-up syndicate pairs) 84027 40 * p < 0.05, ** p < .01, *** p < .001 (two-tailed test) 41 Note: Below each coefficient, stardard error appears in 42 parentheses. 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Academy of Management Journal Page 58 of 65

1 2 3 Table 4. GLM coefficients in numerator and denominator models for stabilized treatment 4 weight calculation in IPTW estimation 5 6 7 Dependent Variable: VC Joint 8 Collaboration Experience 9 Variable Numerator Denominator 10 Total quarterly acquisitions in 0.021*** -0.009** 11 U.S. market (0.003) (0.003) 12 Total quarterly IPOs in U.S. 0.076*** 0.032*** 13 market (0.001) (0.001) 14 -0.309*** -0.684*** Years since firm founding 15 (0.003) (0.007) -4.462*** -4.123*** 16 Number of rounds of funding 17 (0.014) (0.016) 2.230*** 2.202*** 18 Total Number of VCs (0.004) (0.005) 19 Proportion of VCs with other 8.713*** 3.765*** 20 portfolio firms in same 21 segment as focal start-up (0.065) (0.070) 22 Segment diversity of VCs' -6.238*** 5.881*** 23 other portfolio firms (0.173) (0.188) 24 Number of start-up's market -0.549*** -0.516*** 25 segments (0.009) (0.009) 26 Years between start-up 0.489** founding and first round of 27 funding (0.009) 28 Density of VCs' co-investment 7.885*** 29 network (lagged) (0.056) 30 Average VC centrality 0.028*** 31 (lagged) (0.000) 32 Average number of VCs' other -0.012*** 33 portfolio firms (lagged) (0.000) 34 Proportion of Investors who 1.033 35 are Angels (lagged) (0.083) 36 Proportion of VCs' other -3.919 portfolio firms that go IPO 37 (lagged) (0.238) 38 Proportion of VCs' other 1.251*** 39 portfolio firms that are 40 acquired (lagged) (0.152) 41 Proportion of VCs' other -5.430*** 42 portfolio firms that failed (lagged) (0.266) 43 -9.199*** -8.109*** Intercept 44 (0.105) (0.121) 45 Year of First Funding 46 Dummies Y Y 47 Market Segment Dummies Y Y 48 State Dummies Y Y 49 df 39 47 50 Log-likelihood -6307229 -6278406 51 n 1460973 1460973 52 * p < 0.05, ** p < .01, *** p < .001 (two-tailed test) 53 Note: Below each coefficient, stardard error appears in 54 parentheses. The following variables were standardized prior 55 to estimation: average VC centrality (lagged), average 56 number of VCs' other portfolio firms (lagged). 57 58 59 60 Page 59 of 65 Academy of Management Journal

1 2 3 Table 5. Estimated coefficients from proportional hazards regression of exit by acquistion and 4 IPO with and without treatment weight 5 6 s 7 8 Hypothesis 1 (DV: Acquisition Exit) Hypothesis 2 (DV: IPO Exit) 9 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 10 Unweighted, Unweighted, Unweighted, Unweighted, 11 no with IPTW no with IPTW Variable confounders confounders estimation confounders confounders estimation 12 VC Joint 0.054*** 0.050*** 0.042** -0.317* -0.297* -0.209* 13 Collaboration (0.013) (0.016) (0.015) (0.147) (0.132) (0.099) 14 Experience 15 Total -0.004 0.003 -0.004 -0.032*** -0.018** -0.034*** quarterly (0.005) (0.006) (0.004) (0.007) (0.007) (0.006) 16 acquisitions in 17 U.S. market 18 Total 0.002 0.002 0.001 0.032*** 0.027*** 0.032*** quarterly IPOs (0.002) (0.003) (0.002) (0.003) (0.003) (0.002) 19 in U.S. market 20 Years since 0.024*** 0.477*** 0.026*** 0.034*** 0.682*** 0.037*** 21 firm founding (0.003) (0.049) (0.003) (0.007) (0.074) (0.007) 22 Number of 0.075*** 0.028 0.075*** 0.351*** 0.266*** 0.358*** rounds of (0.022) (0.025) (0.022) (0.021) (0.017) (0.023) 23 funding 24 Total Number 0.040*** 0.029*** 0.040*** 0.105*** 0.139*** 0.101*** 25 of VCs (0.006) (0.007) (0.006) (0.012) (0.016) (0.011) 26 Proportion of 0.365*** 0.229** 0.349*** 1.468*** 1.286*** 1.409*** VCs with (0.080) (0.078) (0.079) (0.300) (0.219) (0.315) 27 other portfolio 28 firms in same 29 segment as focal start-up 30 Segment -0.798*** -0.440* -0.779** 1.320 0.367 1.321*** 31 diversity of (0.265) (0.216) (0.268) (0.235) (0.262) (0.271) 32 VCs' other 33 portfolio firms Number of -0.017 -0.017 -0.016 0.009 0.065*** 0.005 34 start-up's (0.015) (0.016) (0.015) (0.013) (0.017) (0.011) 35 market 36 segments Years between -0.464*** -0.656*** 37 start-up (0.049) (0.071) 38 founding and 39 first round of funding 40 Average VC 0.068* 0.616 41 centrality (0.032) (0.175) 42 (lagged) 43 Average 0.078** -0.444** number of (0.030) (0.170) 44 VCs' other 45 portfolio firms 46 (lagged) Proportion of 0.346*** -2.812*** 47 Investors who (0.098) (0.602) 48 are Angels 49 (lagged) Proportion of 0.505* 4.833*** 50 VCs' other (0.267) (0.337) 51 portfolio firms 52 that go IPO (lagged) 53 Proportion of 2.262*** 0.955* 54 VCs' other (0.206) (0.506) 55 portfolio firms 56 that are 57 58 59 60 Academy of Management Journal Page 60 of 65

1 2 3 acquired 4 (lagged) 5 Proportion of 0.373 -0.902 VCs' other (0.270) (0.915) 6 portfolio firms 7 that failed 8 (lagged) Inverse Mills 0.015 0.044 0.010 -0.241 -0.194* -0.223* 9 Ratio (0.048) (0.046) (0.049) (0.113) (0.100) (0.103) 10 Year of First 11 Funding Y Y Y Y Y Y 12 Dummies Market 13 Segment Y Y Y Y Y Y 14 Dummies State 15 Y Y Y Y Y Y Dummies 16 df 40 47 40 40 47 40 17 Log- -13743 -13554 -14212 -2328 -2186 -2368 18 Likelihood 19 n 1377404 1377404 1453668 1377404 1377404 1453668 20 * p < 0.05, ** p < .01, *** p < .001 (two-tailed test) 21 Note: Below each coefficient, stardard error appears in parentheses. The following variables 22 were standardized prior to estimation: VC Joint Collaboration Experience, VC Joint 23 Collaboration Experience (acquisition specific), VC Joint Collaboration Experience (IPO 24 specific), VC Joint Collaboration Experience (failure specific), average VC centrality (lagged), 25 average number of VCs' other portfolio firms (lagged). Confounder controls are not included in 26 IPTW models because they were used to calculate treatment weights. Note that "Density of 27 VCs' co-investment network" was dropped from the models above due to multi-collinearity as 28 assessed by an abnormally high VIF (> 10). 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 61 of 65 Academy of Management Journal

1 2 3 Table 6. Estimated coefficients from proportional hazards 4 regression of exit by closure 5 6 Hypothesis 3 7 DV: Closure Exit 8 Model 1 Model 2 Model 3 9 Unweighted, no Unweighted, with 10 Variable confounders confounders IPTW estimation 11 -0.763*** -0.693*** -0.580*** VC Joint Collaboration Experience 12 (0.168) (0.168) (0.161) -0.017*** -0.016*** -0.033*** 13 Total quarterly acqusitions in U.S. market 14 (0.004) (0.004) (0.010) 0.013*** 0.014*** 0.033*** Total quarterly IPOs in U.S. market 15 (0.002) (0.002) (0.004) 16 0.108*** -0.092*** 0.037*** Years since firm founding 17 (0.007) (0.017) (0.006) -0.320*** -0.307*** 0.361*** 18 Number of rounds of funding 19 (0.034) (0.035) (0.036) -0.034*** -0.057*** 0.152*** Total Number of VCs 20 (0.013) (0.014) (0.019) 21 Proportion of VCs with other portfolio firms 0.190*** 0.301*** 1.721*** 22 in same segment as focal EF (0.079) (0.082) (0.303) 23 Segment diversity of VCs' other portfolio -1.150*** -1.193*** 1.350*** firms 24 (0.165) (0.177) (0.296) 0.003 -0.001 0.026 Number of EF's market segments 25 (0.014) (0.014) (0.037) 26 Years between EF founding and first round -0.012 27 of funding (0.019) 0.128*** 28 Average VC centrality (lagged) 29 (0.052) Average number of VCs' other portfolio -0.291*** 30 firms (lagged) (0.052) 31 Proportion of Investors who are Angels 0.347*** 32 (lagged) (0.093) -0.003 -0.068** -0.259*** 33 Inverse Mills Ratio 34 (0.038) (0.040) (0.107) 35 Year of First Funding Dummies Y Y Y Market Segment Dummies Y Y Y 36 State Dummies Y Y Y 37 df 37 44 37 38 Log-Likelihood -17147 -17113 -2273 39 n 1377404 1377404 1371658 40 * p < 0.05, ** p < .01, *** p < .001 (two-tailed test) 41 Note: Below each coefficient, stardard error appears in parentheses. The 42 following variables were standardized prior to estimation: VC Joint 43 Collaboration Experience. Confounder controls are not included in IPTW 44 models because they were used to calculate treatment weights. 'Density of VCs' 45 co-investment network' was dropped from the models above due to multi- 46 collinearity as assessed by an abnormally high VIF (> 10). 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Academy of Management Journal Page 62 of 65

1 2 3 Table 7. Estimated coefficients from linear 4 regression, predicting the similarity between market 5 6 segments of the start-up acquirer and acquisition 7 target 8 Model 1 9 (Linear Regression) 10 DV: Jaccard similarity 11 between acquirer and 12 target's self-reported market 13 Variable segments 0.023** 14 VC Joint Collaboration Experience 15 (0.010) 16 Total quarterly acquisitions in U.S. 0.001 17 market (0.001) 18 <0.001 Total quarterly IPOs in U.S. market 19 (0.000) 20 0.002 21 Years since firm founding (0.001) 22 -0.005 23 Total Number of VCs 24 (0.003) 25 Proportion of VCs with other portfolio -0.066*** 26 firms in same segment as focal start-up (0.021) 27 Segment diversity of VCs' other -0.108** 28 portfolio firms (0.042) 29 0.023*** Number of start-up's market segments 30 (0.003) 31 0.780*** Intercept 32 (0.031) 33 Market Segment Dummies Y 34 State Dummies Y 35 36 df 41 37 R-squared .206 38 n 1692 39 * p < 0.05, ** p < .01, *** p < .001 (two-tailed test) 40 Note: Below each coefficient, stardard error appears in 41 parentheses. Model 1 uses all start-ups in our dataset that have 42 made at least one acquisition of another firm. 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 63 of 65 Academy of Management Journal

1 2 3 Table 8. Estimated coefficients from proportional hazards regression of exit by acquistion and 4 IPO on exit-specific VC Joint Collaborative Experience Variable 5 6 s 7 8 Hypothesis 1 (DV: Acquisition Exit) Hypothesis 2 (DV: IPO Exit) 9 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 10 Unweighted, Unweighted, Unweighted, Unweighted, 11 no with IPTW no with IPTW 12 Variable confounders confounders estimation confounders confounders estimation 13 VC Joint Collaboration 0.050*** 0.048*** 0.036* -0.335 -0.326 -0.219 Experience (0.012) (0.016) (0.020) (0.226) (0.227) (0.178) 14 VC Joint Collaboration -0.029 -0.034 -0.031 0.110** 0.085** 0.111* 15 Experience, IPO-specific (0.019) (0.023) (0.019) (0.044) (0.037) (0.045) 16 VC Joint Collaboration 0.038*** 0.012 0.038** -0.085 -0.078 -0.089 17 Experience, acquisition- 18 specific (0.013) (0.023) (0.014) (0.054) (0.051) (0.057) Year of First Funding 19 Dummies Y Y Y Y Y Y 20 Market Segment Dummies Y Y Y Y Y Y 21 State Dummies Y Y Y Y Y Y 22 df 43 50 43 43 50 43 23 Log-Likelihood -13741 -13554 -14210 -2321 -2181 -2359 24 n 1377404 1377404 1453668 1377404 1377404 1453668 25 * p < 0.05, ** p < .01, *** p < .001 (two-tailed test) 26 Note: Below each coefficient, stardard error appears in parentheses. The following variables were 27 standardized prior to estimation: VC Joint Collaboration Experience, VC Joint Collaboration Experience 28 (acquisition specific), VC Joint Collaboration Experience (IPO specific), average VC centrality (lagged), 29 average number of VCs' other portfolio firms (lagged). Confounder controls are not included in IPTW 30 models because they were used to calculate treatment weights. Confounder controls are included in Models 31 2 and 5, but are not shown due to space constraints. 'Density of VCs' co-investment network' was dropped 32 from the models above due to multi-collinearity as assessed by an abnormally high VIF (> 10). 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Academy of Management Journal Page 64 of 65

1 2 3 Figure 1. Calculation of VC joint collaboration experience 4 5 A) Current VC co-investors in Drop.io B) Prior joint co-investments by DFJ and RRE 6 7 Sailthru 8 RRE 9 10 DFJ Rose Tech DFJ RRE 11 12 13 14 15 SinglePlatform 16 Drop.io 17 18 19 C) Strength of prior joint co-investments D) Total joint co-investment experience 20 by DFJ and RRE among Drop.io’s VCs 21 22 Sailthru investors SinglePlatform investors RRE 23 RRE Ventures RRE Ventures .17 + .33 24 DFJ Gotham Ventures DFJ Gotham Ventures = .50 Lerer Hippeau Ventures First Round DFJ 25 Rose Tech Bowery Capital Gunderson Dettmer 26 Pilot Group NSinglePlatform = 4, 27 AOL Ventures 1/(NSinglePlatform - 1) = .33 28 Metamorphic Ventures 29 NSailthru = 7, 30 1/(NSailthru - 1) = .17 Drop.io 31 VC jVoCin Jtocinotl lCaob-oinravteisotmn enxtp Eexrpieenriceenc=e 0=. .5500 32 33 Note: See the Variables section of the text for more detail. 34 35 Figure 2. VC syndicates’ joint collaborative experience, by exit type 36 MeMane Tano tTalo tIanlv Ienstvoersstors MeMane Jaoni nJto iCnot -CInov-Ienstvmesetnmt eEnxt pEexrpieenrcien ocfe of 37 at Faitr Fsti rRsot uRnodu,n bdy, Ebyx iEt xTiyt pTeype VCV SCyn Sdyincadtiecsa taets F aitr sFti rRsot uRnodu, nbdy, Ebyx iEt Txiytp Teype

38 4 4 4 4 ) ) ) ) d s s s s ) n n n n s o o 39 o o i i i i o u n D a t a t a t a t i i i i S R

40 t e v e v e v e v n s i d d d d r ( i d d d d r r r r 3 3 3 3

41 e F a a a a c t d d d d n a n n n n e a a 42 a a t t t t s i s s s s r r o n n n n i i i i t

43 p e ( ( ( ( s x e e c e c e c e c e n n 44 n n n v n e e e e 2 2 2 2 i i i i I o r r r r i e e e e t C p p 45 p p a x x x x V E E E E o r l t t 46 t t b a n n n n a e e e e l o t m m m m t t 47 t t o l T s s s s c 1 1 1 1 e e e e v v v v o f n n 48 n n n t I I I I r - - - - e o i o o o o j b C C 49 C C t t t t C n n n n i i i i u m V o o 50 o o J J J J N 51 0 0 All fAirlml fsirms AcqAuicreqdu ifrierdm fsirmsPublPicu bfilricm fsirms FaileFda iflierdm fsirms 0 0 All fAirlml fsirms AcqAuicreqdu ifrierdm fsirmPs ublPicu bfilrimc fsirms FaileFda ifliermd fsirms 52 (n =( 1n0 =,8 1709,)879) (n =( 1n, 6=8 19,)689) (n =( 3n1 =7 )317) (n =( 2n, 3=8 28,)388) (n = (1n0 =,8 1709,)879) (n = (1n, 6=8 19,)689) (n = (3n1 =7 )317) (n = (2n, 3=8 28,)388) 53 Note: The right-hand graph standardizes VCs’ joint collaboration experience by dividing the measure by its standard 54 deviation; it is not mean-centered. 55 56 57 58 59 60 Page 65 of 65 Academy of Management Journal

1 2 3 Figure 3. Predicted hazard-ratios of exit (via acquisition, IPO), by VCs’ joint collaboration 4 experience 5 6 Exit by acquisition Exit by IPO Exit by failure 7 8 5 5 5 . . . n 2 2 2

9 o i e t r i O u s l 10 i P i u I a f q y c

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18 r d r a r - z a d 5 5 5 a . . . r 19 z 0 0 0 a a H z H 20 a H 21 0 0 0 . . . 22 0 0 0 23 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 24 VC VjoCin Stycnodlilcaabteo rJaotiinot nExepxepreierniecne c(ein (SinDsS)Ds) VC VjoCin Stycnodlilcaabteo rJaotiniot nExepxepreierniecen c(ien (SinDsS)Ds) VCVCjo Sinytndcioclaltaeb Jooriant iEoxnpeexripenecriee (ninc eSD(ins)SDs) 25 26 Note: The hazard-ratio of exit via acquisition, for example, consists of the ratio between the hazard rate of a start-up 27 exiting by acquisition and the baseline hazard rate estimated by the model. The predicted hazard ratio of exit by 28 acquisition is calculated using estimates from Table 5, Model 1 and that of exit by IPO from Table 5, Model 4 29 30 31 32 Dan Wang ([email protected]) is Associate Professor of Business and (by courtesy) 33 Sociology at Columbia Business School. He studies how social networks shape opportunities for 34 entrepreneurship, innovation, and large-scale economic and social transformation. He received 35 36 his Ph.D. in Sociology from Stanford University. 37 38 Emily Cox Pahnke ([email protected]) is the Lawrence P. Hughes Associate Professor of 39 Innovation and Entrepreneurship at the Foster School of Business, University of Washington. 40 She studies innovation, competition and funding in entrepreneurial settings. She received her 41 Ph.D. in management science and engineering from Stanford University’s Technology Ventures 42 Program. 43 44 45 Rory M. McDonald ([email protected]) is the Thai-Hi T. Lee (MBA 1985) Associate 46 Professor of Business at Harvard Business School. He studies how firms successfully navigate 47 new technology-enabled markets. He received his Ph.D. in management science and engineering 48 from Stanford University’s Technology Ventures Program 49 50 51 52 53 54 55 56 57 58 59 60