a signaling perspective on partner selection in venture capital syndicates

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A Signaling Perspective on Partner Selection in Venture Capital Syndicates Christian Hopp Christian Lukas This paper analyzes the factors impacting partnering decisions in venture capital syndicates using a unique data set of 2,373 venture capitalist (VC) transactions in Germany. We employ a signaling perspective to partner-selection strategies within VC syndicates. By including time-varying information about industry experience and cooperation patterns, we explicitly take into account not only the changing social context for partner selection, but also the dynamic nature of signals sent and received. Our analysis documents that the informative- ness of investment experience as a signal depends on the existence and frequency of previous joint deals with the lead VC. Experience becomes a much stronger signal if previous invitations to syndicates are bilateral rather than unilateral. The willingness to invite others to deals signals the ability to reciprocate through one’s own deal flow. More- over, we show how the value of signals erodes over time, that is, information from the previous year carries more informational value than signals from more distant years. In sum, the data reveal that different signals carry weight for lead VCs, and that the frequency of signals sent and the stage of development of the portfolio firm positively moderate the value and relevance of signaling behavior. While early stage investments are mainly characterized by need to diversify and to spread risks, value-added advice is necessary in later rounds, and hence, the strength of the signals sent and received gain in relevance and in value. Introduction The syndication of venture capital involves cooperation between partner venture capitalists (VCs) who jointly finance promising growth companies. Their relationship is characterized by uncertainty over the prospects of the ventures they fund, and the con- siderable time and effort they apply to guiding and advising the entrepreneurs (Ferrary, 2010; Gompers & Lerner, 2002; Manigart et al., 2005). There are various ways in which VCs can benefit from each other, in terms of sharing financial and managerial resources (Brander, Antweiler, & Amit, 2002; Lerner, 1994) or sharing risk (Manigart et al.). As VCs are mutually dependent on each other, it is important to choose the best possible Please send correspondence to: Christian Hopp, tel.: +43-1-4277-38166; e-mail: [email protected]. P T E & 1042-2587 © 2013 Baylor University 635 May, 2014 DOI: 10.1111/etap.12023

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Page 1: A Signaling Perspective on Partner Selection in Venture Capital Syndicates

A Signaling Perspectiveon Partner Selectionin Venture CapitalSyndicatesChristian HoppChristian Lukas

This paper analyzes the factors impacting partnering decisions in venture capital syndicatesusing a unique data set of 2,373 venture capitalist (VC) transactions in Germany. We employa signaling perspective to partner-selection strategies within VC syndicates. By includingtime-varying information about industry experience and cooperation patterns, we explicitlytake into account not only the changing social context for partner selection, but also thedynamic nature of signals sent and received. Our analysis documents that the informative-ness of investment experience as a signal depends on the existence and frequency ofprevious joint deals with the lead VC. Experience becomes a much stronger signal ifprevious invitations to syndicates are bilateral rather than unilateral. The willingness toinvite others to deals signals the ability to reciprocate through one’s own deal flow. More-over, we show how the value of signals erodes over time, that is, information from theprevious year carries more informational value than signals from more distant years. In sum,the data reveal that different signals carry weight for lead VCs, and that the frequency ofsignals sent and the stage of development of the portfolio firm positively moderate the valueand relevance of signaling behavior. While early stage investments are mainly characterizedby need to diversify and to spread risks, value-added advice is necessary in later rounds,and hence, the strength of the signals sent and received gain in relevance and in value.

Introduction

The syndication of venture capital involves cooperation between partner venturecapitalists (VCs) who jointly finance promising growth companies. Their relationship ischaracterized by uncertainty over the prospects of the ventures they fund, and the con-siderable time and effort they apply to guiding and advising the entrepreneurs (Ferrary,2010; Gompers & Lerner, 2002; Manigart et al., 2005). There are various ways in whichVCs can benefit from each other, in terms of sharing financial and managerial resources(Brander, Antweiler, & Amit, 2002; Lerner, 1994) or sharing risk (Manigart et al.). AsVCs are mutually dependent on each other, it is important to choose the best possible

Please send correspondence to: Christian Hopp, tel.: +43-1-4277-38166; e-mail: [email protected].

PTE &

1042-2587© 2013 Baylor University

635May, 2014DOI: 10.1111/etap.12023

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partner(s). The decision of whom to invite to participate in VC transactions lies at the heartof understanding why syndication adds value to the funded firm. However, a majorproblem associated with a lead VC’s partnering decision is uncertainty and informationasymmetry.

Generally, information asymmetry is also present in the entrepreneur’s decision ofwhom to invite to join the young firm’s team, or whom to choose as financier; and for thedecision by a financier—be it a VC, bank, or business angel—of which entrepreneur tofund. The common feature of these examples is that the information available about thequality of a potential partner or project differs between contracting partners, and ingeneral, the party making the selection decision possesses less information. However, theparty making the selection may use additional observable information to infer the part-ner’s or the project’s quality. Such a situation conforms to the typical setup analyzed insignaling theory: There is information asymmetry between contracting parties, and it canbe reduced by signals that are observable and known in advance (Certo, 2003; Certo,Daily, & Dalton, 2001; Spence, 1974). For example, Kaplan and Strömberg (2004) showthat ex-ante (i.e., within-round) staging of deals may allow very professional VC firms tosignal their type to entrepreneurs. Another signal is the presence of a VC, which helpsto verify that a young firm is not holding back initial public offering (IPO)-relevantinformation (Megginson & Weiss, 1991). In their meta-analysis, Daily, Certo, Dalton, andRoengpitya (2003) review a number of studies evaluating possible signals of IPO quality,like firm size, auditor reputation, and venture capital equity. Certo proposes board struc-ture as an important signal in that context. Following Filatotchev and Bishop (2002), shareownership of board members represents a signal of the young firm’s quality. However,depending on institutional factors, the value of otherwise-identical signals in differentIPOs may vary (Moore, Bell, & Filatotchev, 2010). When judging the prospects of youngfirms, financiers may also consider the investment behavior of entrepreneurs (Prasad,Bruton, & Vozikis, 2000) or new venture teams (Busenitz, Fiet, & Moesel, 2005), VCfunding events (Davila, Foster, & Gupta, 2003), and/or the status and reputation of VCs(Dimov & Milanov, 2009). Hence, signals can in general mitigate information asymmetryinvolved in entrepreneurial financing.

More particularly, in VC partnering decisions, the lead VC thinking about whetherto select a partner can also turn to observable signals such as records of deals, statusand reputation, or board composition to cope with and reduce information asymmetry.1

Valliere (2011) demonstrates that success in early stage financing constitutes a signalof the VC’s ability to screen investment proposals; Manigart et al. (2005) argue that aninvitation from a reputable lead investor could be a valuable signal of the quality of anonlead investor.

The theoretical literature on VC syndication places great emphasis on experienceas a signal for the selection of potential partner VCs (Booth, Orkunt, & Young, 2004;Casamatta & Haritchabalet, 2007; Cestone, White, & Lerner, 2006; Dorobantu, 2006). Inpractice, however, experience is likely to be an imperfect proxy for the quality of a VC;other signals are available, embedded in the wider investment context. The question arisesof when and how the signal “experience” can be strengthened so that it better helps to inferthe quality of a VC as a potential partner before a syndication decision is made.

In this paper, we are particularly interested in one part of the asymmetric informationthat VCs face, namely adverse selection in partnering decisions when one party lacksskills in selecting or managing deals, yet claims to possess these abilities. One way that

1. See also the review by Connelly, Certo, Ireland, and Reutzel (2010) and the references therein.

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this information asymmetry is overcome is through the emission of signals via actions thatreveal the true quality of VCs. Our approach builds upon theoretical advancements insignaling behavior by testing how market actions affect the syndication behavior of VCs(Basdeo, Smith, Grimm, Rindova, & Derfus, 2006; Clark & Montgomery, 1998; Stiglitz,2002). In line with Basdeo et al. and Milgrom and Roberts (1982), we propose that marketactions act as a signal to reveal unobservable VC attributes. By market actions, wespecifically refer to the frequency of VC investments and the sharing of deal flow, aloneand, more importantly, in combination.

We analyze the partnering decision in VC syndicates using a unique sample of 2,373venture capital transactions in Germany. The underlying units of analysis are the syndi-cates of VCs formed during the period of 1995–2005. We address the questions of whichpartner VC(s) the lead investor chooses and which characteristics of the potential partnerinfluence the likelihood of collaboration.

Our results suggest that signaling in venture capital investing comprises continuousinvestments to develop one’s own competence, subsequently engaging in relationshipswith other VC partners in syndicate transactions, being able and willing to reciprocate andact as lead investor, and lastly being able to continuously make this effort of ambidex-trously managing networking resources and individual investments. Based on our find-ings, different combinations of signals can spur cooperation between VCs. Especially acombination of signals of quality and intent—industry experience and reciprocation—strongly increases the probability of cooperation.

The remainder of the paper is structured as follows. We first introduce the theoreticalbackground and develop the hypotheses. Next, we present the data set, the variables used,and the methodology. A presentation of the regression results follows, along with adiscussion of and our findings, potential limitations and avenues for future research, anda conclusion.

Theoretical Background

Analyzing the decisive factors in partner-selection processes certainly representsa promising way to get an understanding of why syndication adds value to a VC-backedfirm. As current and desired expertise forms the basis of value creation in the VCmarket, the VCs’ strategic actions are characterized by new opportunities and thecorresponding competencies to master them. Related work argues that VCs are likelyto lack (at least to some extent) potential resources, such as technological or invest-ment expertise, that are needed to achieve long-term competitive advantages; they referto the ability to earn above-normal economic rents through the exploitation of capa-bilities or financial resources to provide adequate financing (Ferrary, 2010; Manigartet al., 2005). Accordingly, interorganizational relationships (syndicates) can createvalue by allowing VCs to combine resources and to share knowledge (Brander et al.,2002; Manigart et al.). While a VC’s internal resources are key to acquiring and sustain-ing competitive advantages, when those resources are lacking, alternative routes ofgenerating and accessing knowledge are needed to prosper (Barney, 1991; Pfeffer &Salancik, 1978).

The resource motive may lead VCs to form syndicates, thereby gaining accessto valuable resources of partners that aid in the management of financed transactions(Harrison, Hitt, Hoskisson, & Ireland, 2001; Larson, 1992; McEvily & Marcus, 2005;Rumelt, 1984). Syndication, however, can come at a cost, namely improper partnerselection (mainly due to poor quality of partner VCs and only a negligible part due to

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opportunism2) and foregone potential returns. Pichler and Wilhelm (2001) demonstratetheoretically that restricting access to syndicates may be Pareto optimal due to moralhazard concerns; Meuleman, Wright, Manigart, and Lockett (2009) analyze empiricallywhen the agency costs of syndication exceed its benefits. Syndication unavoidablyentails giving up some of the expected profits to partner VCs (Brander et al., 2002).Casamatta and Haritchabalet (2007) and Cestone et al. (2006) found that more experi-enced VCs are less likely to syndicate, while less experienced VCs would want to reapthe benefits of the formers’ more reliable evaluation of a venture’s success prospects.Yet there may be situations where inexperienced VCs never syndicate (Dorobantu,2006). Hence, despite the possible lack of resources, many firms do not enter intointerfirm relationships because the costs associated with partner selection do not out-weigh the benefits of getting access to new knowledge (Barringer & Harrison, 2000;Dimov & Milanov, 2009).

Given the earlier arguments, a major problem associated with the decision of whomto select in a syndicate is to find out exactly what resources a VC can contribute and whatthe level of the quality of those resources is. It is easy to calculate financial resources, butmuch harder to calculate nonfinancial resources such as knowledge, familiarity withsector-specific factors, and the ability to analyze, guide, and decide. These latter hard-to-measure and hard-to-communicate characteristics naturally give rise to an informationasymmetry between partners in a syndicate. Even if a VC wishes to communicate truth-fully, it may not be possible (Busenitz et al., 2005; Williamson, 1991a) because “the limitsof language are real” (Williamson, 1991b, p. 168).

Consequently, the lead VC selecting a partner has to cope with information asymme-try (Ferrary, 2010; Keil, Maula, & Wilson, 2010). The signaling theory offers a solutionto the problem. It suggests that the better-informed party provides additional information(“signals”) to better communicate its own quality to less-informed parties (Certo et al.,2001; Spence, 1973, 1974). The receiver is referred to as the external, less-informed party,and the sender is referred to as the internal, better-informed party emitting the signal. Inorder to be valuable, signals must be freely accessible (i.e., observable), understoodin advance, and costly to imitate (Certo et al.; Connelly et al., 2010). In the VC context,observability may be accomplished by communicating deal flows into the VC network(Ferrary; Hochberg, Ljungqvist, & Lu, 2007). Signals meet the costly-to-imitate criterionif they entail positive costs, and these costs or the benefits derived from the signals are typedependent (Spence, 2002). A VC’s deal record certainly cannot be replicated at zero costsbecause generating a deal flow requires effort; different VCs may incur different costsdue to differences in abilities. But even if they have identical costs, in terms of amountinvested, or labor costs for offering advice, VCs are likely to receive different returns(Cochrane, 2005; Hochberg et al.). Here the quality of advice as a resource matters, andit largely determines the possible benefits.

In our study we adopt a broad notion of VC quality and understand it as an “under-lying, unobservable ability . . . to fulfill the needs or demands of an outsider observing thesignal” (Connelly et al., 2010, p. 43), where potential partner VCs form the group of

2. Besides unobservable quality, the intent of the VC may be unobservable as well. This is usually referredto as a hidden intention in the agency framework. Opportunistic behavior by a partner VC would certainly leadto inefficiencies, and in such a case, precautionary measures like comprehensive contractual arrangements ormonitoring would seem appropriate. One could, however, argue that the entrepreneur is most likely to earna profit when each VC provides service and guidance as expected by the other VCs; hence, the threat ofopportunistic behavior may not be substantial. We do not deny that threat, but we focus on quality uncertaintyin this study. We thank an anonymous reviewer for clarifications on these issues.

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outsiders. We treat a VC financing event as the basic signal about the experience of a VCand consider it a proxy of VC quality. Obviously, the more often a VC becomes involvedin financing events, the more often he faces situations where information has to becollected and analyzed, and based on this information, decisions have to be made (Fried& Hisrich, 1994). Also, more financing events lead to more frequent interaction withentrepreneurs and partner VCs. According to Gorman and Sahlman (1989), every year alead VC spends an average of 112 hours in direct contact with the entrepreneur, either onsite or over the phone, and makes an average of 19 visits to the entrepreneur. For nonleadand late-stage VCs, these numbers are substantially lower but still significant. VCs learnfrom these experiences (Fried & Hisrich; Sahlman, 1990) so that experienced VCs arebetter able to screen investment proposals (Tyebjee & Bruno, 1984) and hence improvetheir quality. At the same time, outsiders will update their beliefs about the VC’s qualitycorrespondingly. In addition, given that financing events are informative signals about thefunded firm’s quality (Davila et al., 2003), they should indicate the VC’s quality as wellsince the two are likely to be correlated. If more signals are made observable, i.e., morefinancing events are communicated into a VC network (Ferrary, 2010; Hochberg et al.,2007), receivers can better infer the quality of the signal-emitting VC—in other words,sending signals frequently to the VC market strengthens the signal (Connelly et al.;Janney & Folta, 2003). Syndication would be another way to improve signal effectivenessand to reduce information asymmetry. Joint work offers the chance to observe or learn thecharacteristics of partner VCs through direct interaction and possibly in different roles(lead VC or nonlead VC). It provides additional valuable information beyond the merefact that a particular VC worked on a particular deal. Syndication, however, entails thedisadvantage of sharing profits with partner VCs, thereby increasing the costs of signaling.

The basic idea behind our partner selection model is that a lack of resources triggersthe quest for partner VCs. Since characteristics of VCs cannot easily be observed orcommunicated, information about them remains private, giving rise to information asym-metry. Signaling offers a way to deal with this problem. In light of the previous con-siderations, a lead VC considers the following properties of potential partners

• experience levels in different industries,• history of syndicating activity, including existence of joint deals,• willingness to provide access to deal flow,• the frequency of signal occurrence, and• the stage of development of the portfolio firm and corresponding signal relevance

to assess their quality. The more precise that assessment, the higher the likelihood of agood partnering decision, which is vital for the success of a VC syndicate. Furthermore,most of these signals result from actions and hence may be worth more than words(Busenitz et al., 2005). Figure 1 presents a stylized depiction of our theoretical modellinking various competing signals to the probability of cooperation between a given leadVC and a potential selection from a universe of partners.

We link three main underlying signals to the probability of cooperation: first, theexistence of additional industry-relevant investment experience (measured relative tothe lead VC) to proxy for value-adding advice and capabilities (Brander et al., 2002;Manigart et al., 2005); second, the existence of joint experiences on which the lead candraw (Higgins & Gulati, 2003); and third, reciprocated ties to account for deal flow(Hochberg et al., 2007). The two signals measuring previous interactions of lead investorand potential partners should positively moderate the impact of the base signal experience.Additionally, the corresponding effects are reinforced by the frequency of occurrence

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(Janney & Folta, 2003). Moreover, the development stage of the portfolio firm positivelymoderates the value of signals. In later stages that involve lower levels of uncertaintyabout the portfolio firm signals about partner ability to provide value-adding advicebecome more relevant (Connelly et al., 2010; Lester, Certo, Dalton, Dalton, & Cannella,2006). To account for potential partners conveying signals relative to each other (higherquality firms signaling at the expense of lower quality firms), we employ a case controldesign and account for relative strength of signals vis-à-vis competitors and the receiver(Connelly et al.; Sorenson & Stuart, 2008; Stiglitz, 2002).

Hypotheses

Experience LevelThe inherent resources and investment experience of VCs form the basis for strategic

value creation and address corresponding demands of entrepreneurs. In fact, betterresources allow VCs to provide better advice and/or to better screen business proposals togenerate superior long-term returns for fund shareholders (Brander et al., 2002; Lerner,1994). Investment experience within a particular industry yields valuable insights intostructuring deals and advising the funded entrepreneur, so it is crucial for understandinghow resources shape competitive advantages in entrepreneurial financing. Therefore,investment experience likely signals the ability of a potential partner VC and helps thelead VC to distinguish between high-quality and low-quality VCs in the market. Havinghigh-quality VCs in the syndicate would enhance the expected profitability of a deal byreducing the probability that partners do not live up to expectations (Lerner), and therebyincreasing the chances for cooperation among a lead VC and potential partners (Dimov &Milanov, 2009). In a similar vein, Casamatta and Haritchabalet (2007) show analyticallythat a lead VC who lacks a needed competence is more likely to choose a partner whopossesses that competence.

Figure 1

Graphical Illustration of Theoretical Model and Hypotheses

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Hence, we argue that the experience potential partners have gained within atransaction-relevant industry (possibly in excess of the lead investor’s existing knowl-edge) should be positively related to the likelihood of being chosen as a syndicatemember. This leads to the following hypothesis:

Hypothesis 1: More investment experience of a potential partner within the giventransaction-relevant industry increases the likelihood of collaboration with a given leadVC.

History of Syndicating ActivityGiven the opaqueness of the market, some VCs use their investment strategy to

obfuscate information. Less able VCs might actively engage in a large number of trans-actions to look attractive to potentially superior partners. The search for investmenttransactions not only documents what firms prefer, but also affects the belief of othersabout them. Due to the long time horizon and the difficulty of disentangling bad luck froma lack of managerial skills in VC investing, a large number of deals do not necessarilyyield full information to separate less able from more able VCs.

One of the key elements of signaling when firms have incentives to mislead others abouttheir true ability is to create a set of choices through which firms with different character-istics self-select and reveal their ability. Following this underlying logic, VC firms that areless able in sourcing, evaluating, and managing transactions will act differently from VCsthat possess the necessary skills to strive. While VC firms do reveal their track record, themarket is fairly opaque and returns are difficult to verify (Cochrane, 2005). Given that talkis literally cheap, less able VC firms do not necessarily have incentives to fully reveal theirabilities.3 Consequently, market actions are the necessary mechanisms through which betterable VCs can reveal their true abilities.4 One way to overcome these deficient signals is toengage in ongoing syndication activities with other VCs to signal quality to potentialpartners (Granovetter, 1994; Hanneman & Riddle, 2005; Podolny, 1994). Recent work byHochberg et al. (2007) reports higher returns for well-connected VCs. This finding under-scores the value of signaling in venture capital markets: More able VCs will receive higherreturns for their investments if they can establish that they are more productive.

Previous exchanges between partners mirror the joint history of deals. As arguedearlier, direct interactions allow for a more precise assessment of a partner’s quality thanthe mere observation that the partner participated in or initiated a certain financing event.The interactions may even help to get to know a VC’s founding team characteristics,which are predictors of a VC’s future success (Walske & Zacharakis, 2009). Hence, a jointdeal history should matter for upcoming partnering decisions and increase the strength ofexperience as a base signal.

Accordingly, we formulate the following hypothesis:

Hypothesis 2: A joint history of syndicating activity between the lead VC and apotential partner positively moderates the value of experience as a base signal andincreases the likelihood of collaboration with the given lead VC.

3. Incentives to misrepresent ability exist for less able VC firms when the expected gains from falselyclaiming high quality outweigh the losses suffered in the event of detection, leading to a pooling equilibrium(Kirmani & Rao, 2000; Spence, 2002; Stiglitz, 2002).4. Busenitz et al. (2005) point out that signals based on actions could be worth more than those based onverbal pronouncements.

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Deal flow and the willingness to let others participate also convey valuable informationto the market. It then seems natural to ask whether inviting or both inviting and beinginvited to a deal makes a difference. In other words, does the relative strength of thequality signal increase through reciprocation of an invitation? In the event of recipro-cation, the current lead VC has observed the potential partner in different roles, as leadVC and as nonlead VC. One could argue that seeing a partner in different roles helps toassess the quality better. Moreover, the number of deals that VCs are willing to jointlyinvest in reveals information about the quality of the deals sourced and, hence, the abilityof the corresponding VCs. In fact, the willingness to invite others to deals signals theability to reciprocate through one’s own generated deal flow and is therefore a strongersignal than the pure ability to invest in as many firms as possible. Thus, better infor-mation about the ability of a VC is disclosed not only when investments were made inthe past, but also when a willingness to let other VCs participate in deal flow was shown.In essence, less able VCs will not be willing to let other firms participate in theirtransactions, as they will reveal to partners the characteristics of the transactions chosen.Signaling through actions of VCs is costly and more importantly, more costly for somethan others.

Hence, actions of superior VCs need to be more costly or more profitable to signalability and achieve a separating equilibrium (Spence, 2002). Work by Basdeo et al. (2006)reveals that the complexity of one’s actions affects how they are perceived by the market,as they are more difficult for competitors to imitate. In general, the costs of signaling areborne by high-quality firms to separate themselves from low-quality firms. Given thetime lag between deals entered into and the point at which success/failure is discovered,signaling deals undertaken do not suffice as a strong enough signal to actually achieve aseparating equilibrium. The discrepancy between the initial signal and subsequent actionsmay lead to decoupling. While VCs signal quality through previous investments, thereluctance to provide deal flow renders the signal sent erroneous. In other words, the purepresence of experience without reciprocated ties is only a weak signal due to decouplingconcerning the abilities present and the sought-after quality of experience or deal flowwithin the VC market (Connelly et al., 2010; Hochberg, Ljungqvist, & Lu, 2010). Thisleads to the following hypothesis:

Hypothesis 3: A reciprocated history of syndicating activity between a lead VC and apotential partner positively moderates the value of experience as a signal, and increasesthe likelihood of collaboration with the lead VC.

Returning to hypothesis 1, one may ask whether the effect of experience accumulates overtime or whether experience in the VC business is subject to rather fast obsolescence,meaning that it has to be renewed by recurring activities in the VC market. Since the VCmarket deals with innovative business proposals, one could argue that experience accu-mulated years ago may not be worth so much.

Assuming that signals that were emitted previously become obsolescent, senders havethe opportunity to further the strength of the signals through repetitively signaling quality.A higher signaling frequency by making more transactions or providing more accessto deal flow would provide a much stronger signal than a once-off engagement in thesesignaling activities. Hence, being able to not only engage in the provision of varioussignals at the same time, but being able to also do this continuously increases the strengthof the underlying base signal emitted to potential partners. This way, senders can movefrom simple snapshots to a continuum of activities that provide signals, indicating theunderlying yet unobservable quality they possess. Being able to signal repetitively fosters

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differentiation from competitors and prevents the erosion of previously signaled informa-tion (Janney & Folta, 2003).

Hypothesis 4: The frequency of signaling activities positively moderates the effectof signaling strength and increases the likelihood of collaboration with a given leadVC.

Another difficulty VCs face when signaling in uncertain and ambiguous environments isthat the decision to syndicate is contingent on the underlying venture. VC financing takesplace in emerging and knowledge-intensive industries where the value of the fundedprojects is highly uncertain and future payoffs are distant. The difficulty of disentanglingthe contribution of individual activities gives substantial leeway to the entrepreneur.Hence, the characteristics of the financed firm and the corresponding uncertainty coulderode the value of a signal by creating noise so that the underlying message of VC abilitybecomes less evident in earlier rounds. Among other things, a coherent view of signalingtherefore needs to take into account the value of signals conditional on venture-levelcharacteristics. VCs usually fund ventures in several financing rounds or stages. The needfor additional partner skills is anticipated to be greater in later stages of an investment thanin earlier stages. This is mainly due to the fact that more mature firms funded already havean established management structure and market position (Brander et al., 2002; Bygrave,1987; Bygrave & Timmons, 1992; Lockett & Wright, 1999). Consequently, the advicebecomes more specific and context dependent in later rounds, while it is rather general(i.e., it addresses basic management topics) in earlier rounds. Research suggests that VCfinancing goes hand in hand with institutionalizing human resource management (Hell-mann & Puri, 2002), development of the accounting system allowing for more frequentmonitoring (Mitchell, Reid, & Terry, 1997), or internationalization strategies (Mäkelä& Maula, 2005). Arguably, all of these activities become more important in later stages.With every round, the ambiguity and uncertainty of the project decreases. This allows forimproved judgment about the managerial advice needed to support the funded firm(Lerner, 1994).

Of course, partners contribute financial resources in every round. Yet as arguedearlier the need to add specific knowledge and expertise grows over time. Consistentwith this argument, during initial rounds of funding, empirical evidence highlightsthe role of risk sharing among the VCs involved in a syndicate (Manigart et al.,2005). The value of signals provided could be lower in early rounds, as lead investorswill simply be looking for potential partners to share the financial burden, but notnecessarily for characteristics proxying quality and ability. Although information isprovided and available in the VC network, lead VCs possibly do not look for thesecharacteristics. Signals sent, alone and in combination, only have explanatory powerwhen receivers are actively looking for these signals (Lester et al., 2006). Therefore,the value of the signals is context specific and varies across the different financingstages of VC investing.

Accordingly, the various financing stages involved in VC investments positivelymoderate the strength of signals sent and received; signals are less sought after in earlystages due to pronounced risk and uncertainty and limits to add value, while VCs in laterstages place a stronger emphasis on signals and combinations of signals due to anincreased need for value-added advice. With respect to partnering decisions, we wouldexpect signals sent by potential partner VCs to become more relevant or decisive in laterrounds of financing; here, the lead VC does have a clear conception about the contribu-tions of partner VCs.

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Hypothesis 5: As the stage of development of the portfolio firms advances, thestrength of the signals emitted rises and increases the likelihood of collaboration witha given lead VC.

Data and Methods

Dataset and Summary StatisticsThe sample consists of 2,373 venture capital transactions in Germany within the

period of 1995–2005. The number of total financing events (2,373) comprises capitalinjections from 447 VCs that are made over different stages (start-up, early stage, and latestage) into 964 firms. The focus on the underlying German data has the advantage that wecan study VC decision making with respect to partner selection from the inception ofGermany’s Neuer Markt, the growth stock segment at the Frankfurt Stock Exchange in1997, and up to 300 firms that were taken public until 2000, and a paralleling increasein VC investments, until the closure of the Neuer Markt in 2003 and a correspondingdecline in VC transactions (von Kalckreuth & Silbermann, 2010). Given the growingfocus of investment into these high-risk ventures and a lack of comparable investmenthistories (unlike in more established markets in other countries), the task of disentanglingthe role of signals (alone and in combination) is not obscured by longer lasting networksof VC investments, as evidenced by the work of Hochberg et al. (2007, 2010). Hence,being able to signal ability and making use of multiple signals might be more importantin environments prone to uncertainty about market participants. And notwithstandingdifferences between the U.S. VC market and the ones in Germany and Europe as a whole,it is interesting to note that the study by Bottazzi, Da Rin, and Hellmann (2004) coveringthe years 1998–2001 (which are included in our sample) found that European VC firmswere “increasingly emulating U.S. investment practices” and had established links to theUnited States; furthermore, over a third of European VC had worked in the United Statesbefore. Therefore, the results of our study are likely to be relevant also for VC marketsbeyond Germany.

The transactions were compiled by using public sources and the Thomson VentureEconomics (TVE) Database. On average, a funded firm goes through 2.2 rounds offinancing. We identify the involved parties in each transaction and the correspondinginformation on the VCs along with the funded firms. The result is a deal survey exhib-iting who funded a new company and who was joined by which partner. Moreover, wecollect information about each financing round to infer which VC made an investmentinto a target firm at which point in time. In addition we supplement the database withinformation regarding the VCs and the funded firms, along with information specific toeach deal. The analysis is carried out on the basis of investment rounds as indicated byTVE.5

To calculate measures of investment experience acquired by the VCs, we includeinformation on the industries that the funded firms are active in. This also makes it

5. A distinction between milestone and round financing cannot be observed. Gompers and Lerner (2002)study the completeness of the TVE database, and argue that most VC investments are contained in it and thatthose missing are among the less significant ones. The sample resembles the aggregate statistics published bythe German Venture Capital and Private Equity Association and comparable representative studies in terms ofindustries and stages studied (see among others Bascha & Walz [2007], Bundesverband deutscher Kapitalan-lagegesellschaften [BVK] [2005], Mayer, Schoors, & Yafeh [2005]). The sample we studied is thereforeunlikely to suffer from sample selection bias by focusing on TVE data.

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possible to analyze the acquisition of resources motive by considering the industry of theunderlying transaction and the knowledge acquired by the VCs in previous transactions.Based on information from TVE, we identify the industry of a particular venture byapplying the Venture Economics Industry Classification (VEIC), a Venture Economicsproprietary industry classification scheme. To draw more distinct conclusions, we splitthe industries further, which results in finer industry clusters. For example, we divide themedical/health classification into two separate categories. In addition, we split the indus-trial sector into industrial products (such as chemicals and industrial equipment) andindustrial services (such as transportation, logistics, and manufacturing services). A cat-egory for Internet firms is introduced to cope with the particularities of investments intoNew Economy firms over the period. Groupings have been made based on VEIC level 1codes. Firms that were solely focusing on the Internet to sell and market products wereincluded in the separate Internet/e-commerce category.

Partner Selection Into the Syndicate as the Unit of AnalysisA syndicate is usually defined as a group of VCs that jointly invest in a certain firm.

We look at VCs that invested in the same company simultaneously (within the sameround) and in different rounds, thus employing a wider definition of a syndicate. We areless concerned whether VCs invested in the same round, as VC relationships are built byformal interaction (such as board meetings) as well as by informal interaction (Branderet al., 2002; Gompers & Lerner, 2002; Manigart et al., 2005). Thus, previous interactionscan yield insights into the decision patterns. Accordingly, a VC who invested in the firstround interacts with an investor who joins the syndicate in a subsequent round. The unitof analysis is each accepted invitation of a partner to form a new syndicate or expand anexisting syndicate further. Rather than focusing on the dyad level alone, we analyze whichpartner VC was chosen by the lead investor at which point in time.6 Hence, we employa methodology that shares many features with a case–control setup. This design isbeneficial, as selection among potential VC partners is relatively rare. Units of analysis areselected based on outcomes and not hypothesized relationships (Pennings & Harianto,1992). By studying the signaling behavior of potential partners relative to each other, weplace our analysis into the wider context of signaling models, in which the costs ofthe signal are borne by higher quality firms at the expense of lower quality firms.Hence, setting oneself apart from competitors signals ability. Accordingly, we need tostudy signaling behavior vis-à-vis the universe of potential partners to draw inferences(Connelly et al., 2010; Stiglitz, 2002).

In order to cover the dynamics of partner selection in the most comprehensive way,we place no restrictions on the size of the VCs in the sample, thus including both largeand small VCs. However, for the list of VCs from which a potential partner is chosen,we restrict the analysis to the most active VCs to ensure variation in the explanatoryvariables over time (and to avoid problems with autocorrelation). Here we choose acutoff point of at least 10 deals over the time period of 1995–2005. This reduces the listof partners to 35 among whom the lead investor can choose. In explaining the dynamicsof partner selection, we therefore make inferences about the major players withinthe market, rather than analyzing marginal ones. We do, however, include syndicatesformed between major and peripheral VCs. In this case, the dependent variable for theperipheral VC is equal to one, and the entries for the other 35 (more active) VCs are

6. A transaction example is included in the Appendix.

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equal to zero. If one of the more active VCs from the list of likely candidates is chosen,it receives an entry of one, and each nonchosen VC receives an entry of zero for thedependent variable. Our analysis therefore aims at explaining which factors impactthe zero/one collaboration variable. We measure experience and contacts on the basisof the VC level rather than for specific funds separately. Underlying this assumptionis the argument that ties and experience acquired carry over to a VC’s next funding(Hochberg et al., 2007).

The Role of the Lead InvestorThe role in managing and monitoring the underlying investment differs substantially

between lead and nonlead investors. Gorman and Sahlman (1989) find that the leadinvestor spends about 10 times more time on monitoring and managing the investment. Ina network where one VC has invited partners to assist in the transaction, it is possible toaccount for the direction of the relationship. We include a measure of a “leading role” ofVCs for each transaction. Hochberg et al. (2007) define the lead investor as the investorwho acquires the largest stake in a portfolio company. Megginson and Weiss (1991) andSorensen (2007) support this definition. As TVE reports only the total amount invested perround and does not distinguish between the sum invested by each VC involved on astand-alone basis, we proxy for the lead investor(s) using two criteria that have to befulfilled simultaneously: The maximum number of rounds and the involvement in theinitial financing round. The lead investor is defined as the VC that has participated inthe maximum number of rounds among all investing VCs and was also involved in the firstround of financing. The argument underlying this assumption is the same as in Megginsonand Weiss and Sorensen, that the lead investor usually has the largest amount of money atstake and therefore an incentive to take a more active role in managing the syndicate andadvising the portfolio company. Thus, we can account for the direction of the ties that areestablished. A VC that has a leading role within a syndicate thus invites one or more newinvestors to participate in the deal; those do not have a leading role. Correspondingly,the partnering decision by the lead investor forms the basis of analysis. As the leadinvestors appear as often as they invited new partners in the data set, we adjust the standarderrors for clustering on the lead investor level. We provide a transaction example in theAppendix to illustrate our approach.

MethodologyEach invitation record for a specific transaction includes various VC attributes for the

lead investor, as well as for the VCs from which the partner is chosen. The VC attributesare based on the cumulative cooperation behavior until the end of the year prior to thegiven year. The resulting structure is a cross-section of transactions over time, withvarying covariates (such as network status, number of deals, and funds managed) over theyears. In order to account for the fact that the sample includes a larger number ofnonevents for the dependent variable (indicating all the VCs that have not been chosen toparticipate in the syndicate), we estimate the coefficients using the rare events logisticadjustments suggested by King and Zeng (2001a).

Our sample reflects the total number of transactions that have been subject to part-nership behavior and are to some extent driven by the behavior of a distinct number ofVCs that were involved in multiple transactions over the years. Those serial VCs requirea control for clustering in the error terms and an adjustment of the standard errors for the

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rare events logistic regressions.7 Additionally, inclusion of dummies for every yearaccounts for time effects. All dummies are measured against the year 2000 dummy, whichis dropped (to avoid perfect collinearity) from all regressions.

All measures are either calculated as the cumulative number until the end of the yearprior to the year in which the deal takes place, or by just using the relevant informationfrom events happening in the year prior to the year in which the deal takes place (thevariable description clearly indicates which time horizon was used). This way, issuesof causality between the dependent and independent variables are circumvented. Forexample, the total number of transactions that a VC has made in (or until the end of theyear) 2004 is used to explain his partnering decisions in 2005. Hence, a partneringdecision in 2005 cannot influence the independent variables in 2004.

Explanatory Variables: ExperienceIn order to test our hypotheses, we calculate various VC characteristics that act as

signals to increase the chances of a potential partner VC to be invited by a given lead VC.

Industry Experience. Hypothesis 1 states that the investment experience of the potentialpartner VC positively affects the likelihood of collaboration. We compute the total numberof transactions within the industry in which the funded firms operates that the leadinvestor(s) as well as the potential partners have invested in during the year prior to theyear in which the deal takes place. To be able to better track the signaling behavior andthe quality of the signal, we investigate relative performance, which reveals more infor-mation than absolute performance (Stiglitz, 1975). Accordingly, we calculate the differ-ence between the number of transactions the lead investor and the potential partnerengaged in to proxy for additional experience that could be expected from a potentialpartner. A negative number would therefore indicate that the partner possesses moreexperience within the given industry than the lead investor.

Explanatory Variables: Direct Relationships

Lead-Invited VC. With respect to hypothesis 2 and the impact of direct previous rela-tionships between the lead investor and the potential partner, we include a measureindicating how often the lead investor previously invited the potential partners (lead-invited VC). Based on the past transactions in which the current lead investor also actedas a lead investor, we count the number of previous collaborations with each potentialpartner. We hypothesize this measure to positively moderate the effect of investmentexperience.

7. We also estimate two additional variants of all models shown. First, we estimate a model without anyclustering. Generally, if we were to neglect the underlying covariance structure due to repeated observationsof VCs that invest numerous times, we would underestimate our standard errors, and hence, we would likelymake wrong inferences concerning our hypothesis tests. Indeed, in the model without clustering, standarderrors are much lower, and significance levels are much higher. Second, we also estimate a set of models wherewe cluster the standard errors across the VCs chosen as potential partners. Given that we have an exclusionrestriction concerning which partner the lead VCs can choose, this might bias our results. The results suggestthat all of the effects reported in the paper remain significant at least at the 5% level. Results are available uponrequest from the authors.

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Reciprocated Tie (Dummy). We include a measure for reciprocated ties that originatefrom both sides. Asymmetries in relationship building are detrimental for dyadic stability,and thus we account for a reciprocated tie if the current lead investor has invited thepotential partner in the past and if the corresponding partner has also previouslyinvited the current lead VC. That is, the direction of the tie should be bilaterally sharedamong the partners (Hanneman & Riddle, 2005; Lockett & Wright, 1999; Shane & Cable,2002). The dummy takes on the value of one if bilaterally shared ties are present and zerootherwise. Again, these measures are calculated cumulatively over the past years andsolely over the previous year.

Interaction Terms

Experience ¥ Lead-Invited VC. To measure the interplay between the signal experienceand the directed tie as a potential amplifier in hypothesis 2, we interact the variables forthe direct relationships between VCs and the excess industry experience of potentialpartners. We use the interaction terms with two separate variables: one based on thecontacts during the previous year alone, and a second one using the cumulative informa-tion on partnering behavior, taking into consideration the entire past investment horizon.This way, long-term and more immediate effects (informed by hypothesis 4) can bedisentangled.

Experience ¥ Reciprocated Tie. To proxy for the effect of reciprocated ties in combina-tion with transaction-relevant industry experience on the propensity to work collabora-tively on a given deal (presented in hypothesis 3), we interact the reciprocated tie dummywith the excess industry experience of the potential partner. Here we use the presence ofa reciprocated tie during the previous year for one variable and over the previous invest-ment horizon for a second variable, and insert them in separate regression specifications.

Type of Financing Round (Dummy). In hypothesis 5, we argue that the relevance ofsignals depends on which stage the investment is made and when potential partners are tobe selected. Hence, while in early rounds VCs aim at spreading risk and therefore arereluctant to search for meaningful signals, the signals become more important in laterrounds. TVE gives information about five different stage categories: start-up/seed,early stage, expansion, later stage, and other. Similar to Gompers (1995) who labels thecategories for bridge, second, and third stage financing as “late stage” financing, wecombine the TVE categories expansion, later stage, and other to form a new category,which we also label “late stage.” As there is no clear distinction between expansionfinancing, which almost always occurs in later phases, and other financing activities fromthe “later stage” category, namely bridge financing and special purpose financing, thiscombination appears to be the most reasonable classification scheme. In the followingwe split the regressions into separate stages and compare coefficients across the differentmodels, following Hoetker (2007).

Control Variables

Funds and Capital Managed. To account for experience acquired through the manage-ment of funds and capital, we include two variables indicating the difference in capital and

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the number of funds managed between the lead investor and the potential partner. Thesemeasures are calculated using VC fund information from TVE.

Total Deals Previous Year. We control for the activity (and possible investment over-load) of VCs by summing over all transactions a VC was involved in during the pre-vious year. The control variable measures the number of total transactions financedalone and/or as a member of a syndicate. VCs that were involved in a larger number oftransactions previously are less inclined to join in on an upcoming transaction due tolimitations in management capacity. We include the number of total transactions in allregressions.

Analysis and Results

Table 1 reports the summary statistics and the correlation matrix for the independentvariables. With respect to the experience within the given industry, one can infer that onaverage, the lead investors financed about the same number of deals during the previousyear as the potential partners did. The measures of previous direct relationships showthat over the years, various relationships were formed at differing levels of intensity.Among the 35 potential partners, some .07 ties are present, which indicates that onaverage, the given lead investor has roughly two ties established over the previousinvestment horizon and about 1.5 during the previous year alone. Among the relation-ships, there is less than one tie for the previous year where the direction of establishmenthas been reciprocated with the potential partner and roughly one for the cumulativenumber of reciprocated ties. The focus of investments is concentrated into late stageinvestments with around 55%; the early and start-up stage have some 30% and 15%,

Table 1

Descriptive Statistics and Correlation Matrix

Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11

1 Invited (1/0) .05 .222 Industry experience (previous

year).46 2.16 -.01

3 Lead-invited VC .07 .41 .01 .064 Lead-invited VC (previous year) .04 .24 .02 .12 .155 Reciprocated tie (dummy) .03 .01 .06 .67 .046 Reciprocated tie previous year

(dummy).02 .02 .13 .11 .71 -.02

7 Total deals (Previous year) 3.88 5.23 -.02 -.24 .10 .22 .08 .168 Start-up (dummy) .15 -.02 -.03 .00 .00 -.01 .00 .019 Early stage (dummy) .30 .02 -.03 -.01 -.03 -.01 -.02 -.01 -.28

10 Late stage (dummy) .55 .00 .06 .01 .02 .01 .02 .00 -.47 -.7111 Capital managed 10.34 184.91 .02 .05 .06 .04 .02 .00 -.04 -.01 .01 .0012 Funds managed .81 10.49 .01 .20 .08 .01 .01 .00 -.18 .00 -.05 .04 .37

Notes: n = 23,968. All correlations above .01 are significant at least at the 5% level.SD, standard deviation.

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respectively. The correlations among the variables show a few problems of multi-collinearity. Notably, the various tie measures and the interaction terms are correlated.In order to cope with collinear variables, we include them separately into the regressions.In all regressions estimated, the variance inflation factors are, on average, around 1.2–1.4, thus showing no sign of problems with multicollinearity. Moreover, all conditionnumbers calculated have values of around 5, and are well below the critical thresholds.It is noteworthy that only when we introduce squared terms into the analysis do thevariance inflation factors rise to 3.6, but then they are still well below critical values(Hair, Anderson, Tatham, & Black, 2005).

Table 2a presents the regression output using the industry experience measure;it includes only transactions undertaken within the year prior to the year in which a dealtakes place. To contrast these results, Table 2b uses cumulative industry experience as ameasure of all transactions undertaken prior to that year. Using these two measures, wecan disentangle current effects from long-term signaling behavior. The results support

Table 2

Rare Events Logit Regression Using Robust Standard Errors

(1) (2) (3) (4) (5)H1 H2 H2 H3 H3

(a)Industry experience (previous year) -.053* -.050* -.052† -.048* -.051†

(.023) (.035) (.079) (.048) (.073)Lead-invited VC .139*

(.032)Experience ¥ lead-invited VC -.033

(.146)Lead-invited VC (previous year) .425*

(.014)Experience ¥ lead-invited VC (previous year) -.051

(.150)Reciprocated tie (dummy) .442**

(.004)Experience ¥ reciprocated tie -.143*

(.024)Reciprocated tie (previous year) (dummy) 1.012**

(.002)Experience ¥ reciprocated tie (previous year) -.145*

(.019)Total deals (previous year) -.020† -.022† -.029* -.022† -.028*

(.091) (.068) (.027) (.065) (.024)Start-up stage (dummy) -.255* -.256* -.254* -.255* -.256*

(.011) (.010) (.012) (.011) (.011)Early stage (dummy) .052 .052 .055 .054 .056

(.444) (.439) (.415) (.426) (.412)Capital managed .000** .000* .000* .000** .000**

(.007) (.013) (.021) (.008) (.008)Funds managed .005 .004 .004 .004 .004

(.192) (.222) (.250) (.191) (.219)Chi-square 165.25 174.38 202.95 198.40 195.26p > chi-square .000*** .000*** .000*** .000*** .000***Observations 23,968 23,968 23,968 23,968 23,968

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hypothesis 1, that getting access to new knowledge and resources, as measured by theexcess industry experience of potential partners, plays a significant role in explainingthe partnering decision in VC syndicates. The coefficient associated with the differencebetween the number of transactions of the lead investor (within the industry of the

Table 2

Continued

(1) (2) (3) (4) (5)H1 H2 H2 H3 H3

(b)Industry experience .005 .008 .006 .010† .007

(.368) (.154) (.310) (.086) (.296)Lead-invited VC .107†

(.068)Experience ¥ lead-invited VC -.009*

(.037)Lead-invited VC (previous year) .407*

(.034)Experience ¥ lead-invited VC (previous year) -.031*

(.034)Reciprocated tie .392*

(.017)Experience ¥ reciprocated tie -.039**

(.009)Reciprocated tie (previous year) .845*

(.018)Experience ¥ reciprocated tie (previous year) -.069*

(.027)Total deals (previous year) -.011 -.012 -.017 -.012 -.016

(.370) (.324) (.169) (.321) (.165)Start-up stage -.241* -.241* -.241* -.238* -.245*

(.018) (.017) (.018) (.019) (.018)Early stage .061 .060 .064 .061 .064

(.368) (.372) (.345) (.365) (.350)Capital managed .000** .000* .000* .000* .000**

(.009) (.013) (.021) (.010) (.009)Funds managed .002 .002 .002 .002 .002

(.462) (.586) (.547) (.554) (.511)Chi-square 143.63 147.71 170.17 164.28 176.98p > chi-square .000*** .000*** .000*** .000*** .000***Observations 23,968 23,968 23,968 23,968 23,968

† p < .1; * p < .05; ** p < .01; *** p < .001Notes: Marginal effects. Table 2a presents the results for the regressions using the rare events logistic methodologysuggested in King and Zeng (2001a) to account for the fact that the sample includes a larger number of nonevents for thedependent variable (indicating all the VCs that have not been chosen to participate in the syndicate). The first line for eachindependent variable indicates that the coefficient and the second line shows the corresponding p-values. Standard errorshave been adjusted for clustering at the lead investor level. The industry experience measure includes only transactionsundertaken within the year prior to the year in which a given deal takes place. Table 2b presents the results for theregressions using the rare events logistic methodology suggested in King and Zeng (2001a) to account for the fact that thesample includes a larger number of nonevents for the dependent variable (indicating all the VCs that have not been chosento participate in the syndicate). The first line for each independent variable indicates the coefficient and the second lineshows the corresponding p-values. Standard errors have been adjusted for clustering at the lead investor level. The industryexperience measure includes all transactions undertaken prior to the year in which a given deal takes place.VC, venture capitalist.

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funded firm) and the potential partner is positive and highly significant across all speci-fications estimated.8 We also test the absolute experience (in contrast to the relativeexperience) and find the same effects as for the relative measures. The results shown inTable 2b report that the cumulative investment experience is not significant at conven-tional levels and hence has no impact on partnering behavior, supporting the argumentsin hypothesis 4 that experience becomes obsolete over time and signals have to berenewed continuously. Only experience that is more immediate increases the chancesof collaboration. In unreported regressions, we also used a measure comprising onlyunsuccessful transactions (deals in which the portfolio company went bankrupt). Here,all coefficients reported turn insignificant. These results confirm hypothesis 1 that addi-tional experience is a predictor of collaboration among VCs. However, experience onlycarries signaling value when the underlying deals turn out successful, providing evi-dence that it is not that experience in general is the driving force, but that experiencehas to be relevant (such as previous successful transactions) for the lead VC looking forpotential partners.9

Turning to hypothesis 2, Table 2a suggests that past direct relationships affect thelikelihood of participation in a syndicate positively. However, the interaction is notsignificant at conventional levels for neither the cumulative number of directed ties,nor for the previous year variables. From Table 2b, we can infer that direct ties increasethe value of the signal when the underling base signal is cumulative industry experi-ence (which is insignificant as a single variable). The coefficient associated with theinteraction term is significant at the 5% level for both interactions terms. Hence, thereappears to be some, albeit weak, evidence supporting the arguments advanced inhypothesis 2.

Turning to hypothesis 3 and the influence of reciprocated ties on collaborationpatterns, we find that both interaction terms are positive and highly significant. In Table 2awe can infer that reciprocation acts as a strong signal in conveying information to leadVCs inviting potential partners, while the interaction with direct ties remains insignificant.Table 2b reports the robustness of the reciprocation effect, where both interaction termsare significant at the 5% level. Hence, there is strong evidence supporting hypothesis 3,that the multitude of signals is more important than the pure presence of experience as asole signal. Reciprocation presents a stronger signal than directed ties alone and positivelymoderates the effects of the previous year and cumulative investment experience. Con-cerning our argumentation in hypothesis 4, the tables show that only industry experience

8. We also include yearly dummies in all regressions that are, for reasons of brevity, not reported inTables 2–4. The dummies for all the years 1996 and 2003 are significantly different from the omitted 2000dummy and exhibit a negative sign. This indicates weak evidence that in the years where fewer transactionstake place, the chances of being invited are lower.9. In an additional analysis, we also used the size of the round as an explanatory variable. The size of theround increases the general likelihood of any partner to be collaborating. This implies that when the size offunding increases, more partners are generally involved. This is in line with the general notion that the size ofthe round is a driver for syndication patterns (Manigart et al., 2005). However, the second dimension thenaffects which partner is actually chosen when there is a larger amount of money to be brought up. We thereforealso included an interaction term in our model to test whether the size of the potential partner chosen (numberof funds managed as a signal of size and capital available) is conditional on the size of the round. We can inferthat the interaction term of differences in funds managed between lead VC and potential partner and the roundsize is negative and significant. The linear effect, however, is not significant. This suggests that when the sizeof the round is larger, the financial resources of the potential partner become more important as a signal.Unfortunately, the number of observations drops to 11,851. Hence, one should be a bit careful aboutgeneralizing the implications. The results are available upon request from the authors.

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as a base signal is relevant for collaboration when stemming from the previous year; themeasures for reciprocated ties and direct ties are all statistically insignificant. Hence, thereis support for hypothesis 4 only with respect to the base signal industry experience.10 In

10. We also use a measure of reciprocity following the methodology of Chung, Singh, and Lee (2000), whichshows the same effect. Additionally, we included a squared term for the number of previous directedcollaborations between the current lead VC and the potential partner. The squared term is negative andsignificant for the cumulative number of collaborations, indicating an inverse U-shaped effect, although thecoefficient is only significant at the 10% level. One could speculate that too many joint deals within a short

Figure 2

Interaction Effect Experience ¥ Lead-Invited Venture Capitalist (VC) (Model 2)

Figure 3

Z-Statistics Experience ¥ Lead-Invited Venture Capitalist (VC) (Model 2)

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period of time lead to a substantial knowledge transfer, so that a partner risks losing the competitive advantagethat gave rise to his or her being invited in the first place. This area clearly deserves more attention. Resultsare not tabulated but are available upon request from the authors.

Figure 4

Interaction Effect Experience ¥ Lead-Invited Venture Capitalist (VC) PreviousYear (Model 3)

Figure 5

Z-Statistics Experience ¥ Lead-Invited Venture Capitalist (VC) Previous Year(Model 3)

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unreported regression, we also included an interaction term using the number of un-successful transactions together with direct ties and reciprocated ties. Interestingly,the interaction terms using unsuccessful experience and direct ties turn insignificant.However, the interaction terms using the reciprocated ties remain highly significant andnegative. This indicates that the only way to overcome the erosion of signals is to maintain

Figure 6

Interaction Effect Experience ¥ Reciprocated Tie (Model 4)

Figure 7

Z-Statistics Experience ¥ Reciprocated Tie (Model 4)

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reciprocated ties with the current lead VC. Hence, we find again strong support for ourhypothesis 3, suggesting that a reciprocal history of syndicating activity between the leadVC and a potential partner positively moderates the value of experience as a signal andincreases the likelihood of collaboration with the given lead VC.

Ai and Norton (2003) and Hoetker (2007) document that the sign of the interactionin logit models merely suffices to draw ultimate conclusions about the effect of theinteraction on the dependent variable. They argue that depending on the covariates,the sign of the interaction term might vary. We follow their proposed methodology tocalculate the magnitude and standard errors of the estimates for the interaction term in alogit-regression specification, and depict the results along the entire range of values inFigures 1–8. We make use of the results in Table 2a, given the stronger effects of morerecent investment experience and the insignificance of the baseline (cumulative) invest-ment experience measure in Table 2b. We report two sets of figures for each interactionterm in Models 2–5 in Table 2a. The first figure plots the interaction term in percentagepoints against the predicted probability (and the incorrect marginal effect), while thesecond figure reports the z-statistics for the corresponding interaction term. Calculationsare done in accordance with formulas provided in Ai and Norton.

When we look at the interaction effect using the measures provided in Ai and Norton(2003), Figures 2–5 document a widely varying pattern for the interaction term usingdirect ties. Both effects are found to be negative, even when we split the sample intosubsamples with the greater experience of the lead on the one hand and the lesserexperience of the potential partner on the other hand, we find the same pattern. However,the levels of significance (the z-scores) vary heavily.11 While for some values of the

11. Industry experience has a significant negative effect only when the partner possesses more experience, notwhen the lead investor has more experience. However, focusing only on a sample where the potential partnerhas less experience (or more experience) is relatively meaningless, as we want to explicitly model the trade-offwith respect to choosing among different partners and consequently different levels of experience or the lack

Figure 8

Interaction Effect Experience ¥ Reciprocated Tie Previous Year (Model 5)

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interaction effect significant results can be found, the overall pattern suggests that thecoefficients are not robustly related to the partnering decisions. Hence, this suggests noevidence for our hypothesis 2.

Concerning the interaction of reciprocated ties and experience to proxy for a strongersignal comprising experience and the ability to generate deal flow (hypothesis 3), Table 2aand 2b show that the effect using the entire time elapsed, and only the previous year tocalculate reciprocated ties is negative and statistically significant. In addition, calculatingthe interaction effect of the continuous experience measure and the reciprocated tiedummy indicates that the effect is negative for both measures and statistically significant(as indicated by the z-score plot in Figures 6–9) over almost the entire range of outcomes.Hence, there is strong evidence that the combination of experience and deal flow consti-tutes a strong predictor of collaboration and that the multitude of signals matters inexplaining interaction patterns. In sum, we find evidence strongly supporting hypothesis3, that being able and willing to reciprocate and provide access to deal flow is a crucial andimportant signal for future cooperation (Figure 9).

To test hypothesis 5, we split the sample into stages of development (e.g., start-up,early stage, and later stage) to see whether the impact of signals differs across stages(Sorenson & Stuart, 2008). For a comparison across groups to be meaningful, each groupmust have the same level of unobserved variation, i.e., variation in outcomes beyond theerror term. Allison (1999) documents that if that is not the case, information drawn fromgroup comparisons could be misleading, and differences across coefficients might simplyreflect differences in samples caused by unobserved variations and need not be related to

thereof. Hence, while one needs to interpret the results with some caution, the overall interpretation (impres-sion) is that partners are chosen based on additional experience. Moreover, having less experience than thelead is not more detrimental than having the same level of experience; neither provides any additionalexperience that is important for the lead investor. Given the strong presence of nonevents in the data set, thepredicted probabilities are concentrated below the .2 level.

Figure 9

Z-Statistics Experience ¥ Reciprocated Tie Previous Year (Model 5)

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the explanatory variables chosen. We estimate the models separately for each investmentstage following Hoetker (2007) to allow for inferences across groups.12

From Tables 3 and 4, one can infer that the stage of development of the portfoliofirm positively moderates the value of signals emitted, and signal strength increases thelikelihood of collaboration. Signals are less sought after in early stages, while VCs in laterstages place a stronger emphasis on signals and combinations of signals. Excess industryexperience of the potential partner is not statistically significant at conventional levelsin any of the start-up stage regression specifications. However, the coefficient is negativeand significant for the early stage samples. Accordingly, experience signals ability forearly stage investments but not for start-up investments. In addition, Tables 3 and 4 showthat none of the other interaction terms is significant for the corresponding regressionspecifications within the start-up stage. Only the number of total deals, as a proxy for theinvestment intensity of the lead and the difference in the number of funds managed, aresignificant. In sum, the results favor risk-spreading arguments and point toward theirrelevance of signals in start-up stages. Accordingly, when syndicating in start-up stages,lead VCs aim at spreading risk rather than searching for partners to create value added forentrepreneurial firms.

When turning to the interaction terms of directed ties and reciprocated ties with excessinvestment experience in early and later stages, the interaction terms of directed ties andexperience are significant in the late stage only. The results suggest only weak supportor no support for the idea that direct ties are important for partner selection across thedifferent stages. Both variables are only marginally significant. In contrast, reciprocatedties and the interaction with experience are highly significant in the early and later stagephase. To test whether the size of the coefficients differs across the early and late stage forreciprocated ties (columns 2 and 3 in Table 4), we estimate a combined model for later andearly stage investments and subsequently test whether the interaction term for an earlystage dummy and reciprocated ties is significantly different from zero. Here we modelthe effect that is likely to differ across groups as an interaction term and test whetherthis term is significant when added to the baseline model. The significance of thisinteraction term then allows inferences about whether coefficients differ across groups(Allison, 1999; Wooldridge, 2008). We also extend this methodology to the effect ofreciprocated ties from the previous year only. The results of a likelihood ratio test showthat the coefficients for reciprocated ties (Models 2 and 3 in Table 4) do not significantlydiffer between early stage and later stage investments (likelihood ratio [LR]: .77, p = notsignificant [n.s.]). The same effect can be found for reciprocated ties for the previous yearonly (Models 5 and 6 in Table 4). Again, the difference between coefficients is notsignificant (LR: 1.08, p = n.s.). Hence, we can conclude that the effect of reciprocated tieson the likelihood of collaboration differs between the start-up stage and the later stages(comprising both early and late stage investments). Thus, the results strongly supporthypothesis 5 within early and later stages in comparison with the start-up stage. Signalsare less sought after in early stages, while VCs in later stages place a stronger emphasis

12. With respect to comparing statistically significant and nonsignificant coefficients, we follow the extantliterature on comparing coefficients within logit and probit models. Hoetker (2007, p. 338), for example, notesthat “If the model is estimated separately for each group, the researcher can—at a minimum—compare thestatistical significance of the coefficients across groups. This is possible because the coefficients and standarderrors are consistent within each group. One could report, for example, that x has a significant and positiveimpact for Group 1, but is not significant for Group 2.” We refer to the statistical significance of eachcoefficient in the different models and draw conclusions with respect to our hypotheses.

658 ENTREPRENEURSHIP THEORY and PRACTICE

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Tabl

e3

Tabl

e2a

Reg

ress

ions

Split

Into

Stag

es—

Mod

els

1,2,

and

3

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Star

t-up

Ear

lyst

age

Lat

erst

age

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t-up

Ear

lyst

age

Lat

erst

age

Star

t-up

Ear

lyst

age

Lat

erst

age

Indu

stry

expe

rien

ce(p

revi

ous

year

)-.

035

-.10

5**

-.04

2-.

040

-.09

9*-.

036

-.01

7-.

112*

-.04

0(.

206)

(.00

8)(.

119)

(.15

5)(.

011)

(.22

7)(.

629)

(.02

2)(.

208)

Lea

d-in

vite

dV

C.1

43.0

70.1

94*

(.48

6)(.

402)

(.01

6)E

xper

ienc

lead

-inv

ited

VC

.021

-.07

9-.

053*

(.82

5)(.

365)

(.04

5)L

ead-

invi

ted

VC

(pre

viou

sye

ar)

-.03

0.5

03†

.558

**(.

917)

(.08

7)(.

002)

Exp

erie

nce

¥le

ad-i

nvite

dV

C(p

revi

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year

)-.

104

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071*

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946)

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7)To

tal

deal

s(p

revi

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year

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58**

-.04

2†-.

035†

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043†

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59**

-.04

6†-.

051*

(.00

1)(.

063)

(.08

3)(.

003)

(.06

5)(.

057)

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053)

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5)C

apita

lm

anag

ed.0

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.000

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882)

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0)(.

321)

(.92

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020)

(.29

3)(.

997)

(.02

8)Fu

nds

man

aged

.029

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000)

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659May, 2014

Page 26: A Signaling Perspective on Partner Selection in Venture Capital Syndicates

on signals and a combination thereof. However, the strength does not differ furtherbetween these two stages.

In the start-up stage, there appears to be no signaling value for multiple signals. In theearly stage, investments experience alone appears to be a sufficient predictor. However,multiple signals carry more weight in later stage investments. This supports the view thatfinancial motives drive the need to spread risks among VCs engaged in seed financing andthat potential value-added motives become involved when firms are at a later stage ofinvestment (Manigart et al., 2005). Accordingly, in later stages, the relevance of signalsand the necessity for the creation of value-added advice both help explain VC syndicationpatterns (Brander et al., 2002; Manigart et al.).

We test for the strength of the impact that each explanatory variable has on thechances of collaboration. Inferences about the change in the likelihood of collaboration asa reaction to a change in one of the explanatory variables are based on the relative riskratio suggested in King and Zeng (2001b). A one standard deviation increase in excessinvestment by the potential partner increases the chances for collaboration by 10%. Anadditional direct tie (cumulative) increases the chances by 13%, while a more recent directtie increases the chances by 45%. An additional cumulative reciprocated tie raises thechances of being invited by 50%, and a recently reciprocated tie more than doublesthe likelihood of collaboration. Again, these results support the economic magnitude ofcreating signals more frequently.

Table 4

Table 2a Regressions Split Into Stages—Model 4 and 5

(1) (2) (3) (4) (5) (6)

Start-upEarlystage

Laterstage Start-up

Earlystage

Laterstage

Industry experience (previous year) -.036 -.102** -.034 -.027 -.114* -.036(.197) (.004) (.275) (.410) (.019) (.229)

Reciprocated tie .335 .590* .481**(.486) (.020) (.006)

Experience ¥ reciprocated tie .006 -.124 -.204*(.978) (.582) (.015)

Reciprocated tie (previous year) .120 .937* 1.301**(.817) (.013) (.002)

Experience ¥ reciprocated tie (previous year) -.066 -.002 -.205**(.579) (.992) (.002)

Total deals (previous year) .057** -.045* -.038† .058** -.047* -.049*(.001) (.047) (.059) (.002) (.047) (.029)

Capital managed .000 .000 .000* .000 .000 .000*(.319) (.895) (.011) (.301) (.895) (.017)

Funds managed .029*** .004 -.001 .028*** .004 -.002(.000) (.404) (.807) (.000) (.298) (.729)

Chi-square .61 23.35 36.90 33.19 35.21 56.02p > chi-square .735 .037* .000*** .000*** .000*** .000***Observations 3,750 7,107 13,111 3,750 7,107 13,111

† p < .1; * p < .05; ** p < .01; *** p < .001Notes: Marginal effects. Table 4 documents the logistic regressions carried out in Models 4 and 5 in Table 2a split intostages of development. Comparisons among coefficients are made in accordance with Hoetker (2007).

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Discussion

When investing jointly into portfolio firms, partner VCs are mutually dependenton each other. Consequently, the decision of whom to invite to participate in VC trans-actions lies at the heart of understanding why syndication adds value to the funded firm.Analyzing the decisive factors in partner-selection processes therefore represents a prom-ising way to get an understanding of why and how syndication adds value to a VC-backedfirm.

In our work, we show that different combinations of signals can spur cooperationbetween VCs. While industry experience generally signals competence and increases thechances to be invited, the combination of industry experience and previously establishedties is associated with a higher probability of cooperation. For the whole sample, we findthat industry experience represents a valuable signal to infer the ability of potential partnerVCs (although it does not hold for all stages; see later). Given that syndication offersaccess to additional expertise that the lead VC may lack, it is intuitive that experiencematters in the partnering decision. More experience simply signals higher ability. Ourresults also document that the ability to generate deal flow and the willingness to subse-quently share with partners act as strong signals for a lead investor searching for coin-vestors. Interestingly, and contrary to one of our hypotheses, directed ties do not increasethe strength of the signal experience, yet reciprocated ties do so.

More importantly, we find that combining signals of quality and intent—industryexperience and reciprocation—strongly increases the probability of cooperation. Accord-ingly, signaling the willingness to invite potential partners back in the future and docu-menting one’s own ability to generate deal flow represents the strongest signal amongpotential partners. Seeing a partner in different roles, both as lead VC and as invitedpartner, helps to assess the partner’s ability better. Another possible explanation could bethat reciprocated ties mirror positive intent or are interpreted as evidence for satisfyingcollaborations in the past. Apparently, working together in a deal or sharing a deal flowgenerates a useful signal about the partner’s ability. In light of the high returns forwell-networked VCs, enhancing one’s network position could present a vital strategicconsideration for an incumbent VC (Hochberg et al., 2007, 2010). As our results indicate,being able to syndicate with a larger number of new partners substantially improves aVC’s position, increasing the VC’s likelihood of being invited to other profitable dealsin the future. In this way, repeated relationships might transfer expectations about thepartner’s behavior and possession of competencies from a previous deal to the newtransaction. Consequently, partners regard a transaction as a situation of mutual gain.

Yet, we also show how the value of signals erodes over time; that is, signals from theprevious year carry more informational value than signals from more distant years. Thismeans that VCs must invest repeatedly in generating a deal flow to sustain their advantageover other VCs. A possible reason could be the young age of VC firms in our study, so thatfor them, there is no established history of maintaining certain ability over time. In sucha situation, turnover may be associated with increased uncertainty about ability. To reducethis uncertainty, a lead VC places more value on a recent signal—the probability of againworking with the people (and not just the same firm) that generated that signal is higherthan the one for signals from the more distant past. An alternative explanation would bethat knowledge rather quickly obsolesces in innovative industries. Experience acquiredthrough deals dating back a longer time does not represent a reliable indicator of asufficiently high ability to master future deals.

Last, by splitting the sample into the different stages of development for the portfoliofirm, we document that the contextual uncertainty and the necessity of relying on signals

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positively moderate the value and relevance of signaling behavior. Stated differently,signal strength increases the likelihood of cooperation in later stages but not in earlierstages. While early stage investments are mainly characterized by the need to diversifyand to spread risks, value-added advice is necessary in later rounds, and hence, thestrength of the signals sent and received gain in relevance and in value. In the start-upstage, syndication mainly serves the purpose of risk spreading, and the partners’ signalsare not relevant for cooperation patterns. Early stage advice concerns rather generalmanagement topics and the ability to provide it does not increase significantly with thenumber of deals. However, in later stages of development VCs place a stronger emphasison signals and a combination thereof. Hence, advice needs to be more specific, preciselytailored to the demand of the funded firm and its industry. Signaling therefore becomesvaluable in later stages, as it helps to reduce uncertainty over ability to provide therespective tailored advice. A wrong ability assessment by the lead VC or a cheap misrep-resentation by a potential partner might turn out to be costly for the lead VC—andpotentially a wrong partner selection may lead to failure. This could also be seen as anadditional cost of transacting with a potential partner. Given high uncertainty over theability of a partner, transaction costs could be substantial so that transaction costs wouldprevent cooperation. However, signaling helps to reduce the (expected) costs of coopera-tion making it viable, and needs to be borne by the potential partner VC that signals abilityand intent vis-à-vis other VCs.

In summary, our findings imply that VCs should consider investing in different signalsto convey information about their ability to the market. While industry experience gen-erally signals expertise and increases the chances of collaborating, the combination ofindustry experience and previously established ties is associated with a higher probabilityof cooperation. Accordingly, by signaling one’s willingness to invite potential partnersback in the future and by documenting one’s own ability to generate deal flow, one sendsthe strongest signal to potential partners.

Potential Limitations

Our study is not without limitations and provides room to further extend our line ofreasoning and analysis. Despite the increase in equity-based venture capital financingthroughout the late 1990s and 2000s, Germany is, by and large, a prime example of abank-based financial system that inherently differs from the prevalent market-basedAnglo-Saxon system. Accordingly, when generalizing our results across borders, oneneeds to bear in mind (and possibly control for) peculiarities of the German context andespecially, fit our findings into a wider socioeconomic perspective. Accordingly, whatworks in one country might not be generally applicable to other countries (see, forexample, Milhaupt [1997], who shows that in Japan VC financing fails to fit the prevalentcorporate governance system).

In order to establish a successful VC industry, an active IPO market is necessary butdoes not present a sufficient condition (Becker & Hellmann, 2005; Black & Gilson, 1997).Becker and Hellmann point out that in a bank-based financial system, VC investmentsneed to be complemented with appropriate corporate governance and, most importantly,with a country’s attitude toward entrepreneurship. Bottazzi, Da Rin, and Hellmann (2009)report that with an increase in a country’s legal protection, VC investors give morenoncontractible support. Too little protection for investors and too many limits to addvalue to entrepreneurs (for example through restriction on majority holdings) mightprevent VC financing to live up to its full potential. Hence, when comparing our findings

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with other countries, one needs to situate the signal relevancy into the wider governanceenvironment. For example, German employment laws limit VCs’ opportunities to imple-ment changes in executive boards, as evidenced in other countries. Hellmann and Puri(2002) study VC financing in the context of Silicon Valley firms and find that professionalmanagers replace around 50% of the managers in their portfolio firms. German numbersare comparably lower as reported in Heger and Tykvová (2009).

Moreover, the quantity and quality of entrepreneurs plays a tremendous role forpotentially value-adding advice and qualities sought after from potential syndicationpartners. Incentives for entrepreneurship need to complement the availability of financ-ing provided (Becker & Hellmann, 2005). In contrast to other innovation-driven econo-mies, Germany lacks a broad supply of latent, potential entrepreneurs (Blanchflower,Oswald, & Stutzer, 2001). Despite producing and publishing high-quality scientificoutput, German universities failed to transfer knowledge and were lacking seriousattempts for academic spin-offs (Clarysse, Wright, Lockett, Mustar, & Knockaert,2007). In fact, employment stability in the research sector combined with a relativelyhigh social status of academics and professors in particular provided little to no incen-tive to commercialize ideas.

Yet, during the time period of our investigation, one was able to witness dramaticchanges. Fiedler and Hellmann (2001), for example, report that younger Germans areprone to a much larger exposure to U.S. influences, imitating ides and processes fromSilicon Valley and the foundation of Internet start-ups. Moreover, the German governmentstrongly supported biotechnology in the mid-1990s, which led to the provision of privateas well as semiprivate funds into venture capital corporations. Adelberger (2000) arguesthat the German institutional framework (rigid employment practices for senior scientists,high capital gains taxes, and a social stigma for failed entrepreneurs) support incrementalrather than radical innovation (also evidenced by the high number of German patents inmature industries—manufacturing and mechanical engineering, as opposed to a U.S. leadin new technologies—such as information technology). The recent Global Entrepreneur-ship Monitor reports a nascent rate of 2.2%, which is comparable to other establishedinnovation-driven economies such as the United States and the U.K. (Bosma & Levie,2010).

Evidently, a better quality of entrepreneurs raises the chances to add value for VCfirms to further professionalize the gestation process. Consequently, the quality of signalssought after from potential syndication partners need to be put in perspective to theuniverse of entrepreneurs available. As such, money might not substitute a dearth of ideasin some industries. Accordingly, more complete examinations of signaling in VC financ-ing should embrace the consequences of institutional environments on how VC decisionmakers navigate within the boundaries that governments, capital market systems, and theprevalence of entrepreneurial activity in general provide.

Future Research

In our work, we do not directly test for the presence of trust; we are more con-cerned to find the ways that VCs can signal quality (and only partially good intent) ina relationship. In this respect, our analysis is distinct from research on interorganiza-tional trust. All in all, trust is a more passive construct; one actively emits signalsthat receivers can favorably interpret regardless of whether they actually see trust orability in the potential partner. Trust may or may not arise between partners because

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of or despite a cooperation that both sides entered into. Nevertheless, the pronouncedprevious-year effects for reciprocated ties could be of interest. Given that trust buildsup gradually over time, one would not assume a remarkable difference betweenexperiences from the previous year and those from more distant years (providedthat experiences were positive). However, in a situation where firms are very young,trust may arise between particular employees of different firms and not so muchbetween different firms. For that purpose, a longer company history of maintainingexpertise and norms could be necessary. An alternative interpretation would be thatopportunism does not exhibit a substantial threat in a VC deal based on an enforceablecontract, appreciable equity investments, and the need for all partners to cooperate tolead the deal to success. Therefore, the most recent signals about ability matter morethan a trusting relationship between particular partners.

More research could also be directed toward the consequences of partner selection oninvestment success. It would be interesting to analyze the impact of more intense col-laboration on the profitability of investments. Established routines with respect to decisionmaking and interaction with the funded firm could, in general, lead to higher performancedue to lower costs of cooperation. By analyzing the performance consequence of collabo-ration, one could determine under which circumstances signals emitted predict valuecreation in VC syndicates.

Conclusion

In this paper we analyze which VC characteristics influence the partnering decisionwithin the German VC market. By including time-varying information about experiencegained within industries and financial resources, we explicitly take into account not onlythe changing social context of partner selection, but also the dynamic nature of VCresources and capabilities.

Our results highlight the importance of signaling within VC environments and derivea complex interplay of signaling behavior across time that helps to illuminate under whichcircumstances potential partner VCs are chosen. The analysis documents that the infor-mativeness of investment experience as a signal depends on the existence and frequencyof previous joint deals with the lead VC. Experience becomes a much stronger signalif previous invitations to syndicates are bilateral rather than unilateral. The willingnessto invite others to deals signals the ability to reciprocate through one’s own deal flow.Moreover, we show how the value of signals erodes over time; that is, information fromthe previous year carries more informational value than signals from more distant years.Lastly, we document that the relevance of the signal that is sought explains cooperationpatterns across a venture’s life cycle. While early stage investments are mainly charac-terized by the need to diversify and to spread risks, value-added advice is necessary inlater rounds, and hence, the strength of the signals sent and received gain in relevance andin value.

Hence, our findings imply that signaling in venture capital investing comprises (1)continuous investments to develop one’s own expertise, (2) subsequent engagement inrelationships with other VC partners in syndicate transactions, (3) the ability and willing-ness to reciprocate and act as lead investor subsequently, and (4) the capability torepeatedly make this effort of ambidextrously managing networking resources and indi-vidual investments. Balancing this delicate trade-off is crucial to being able to engage inVC syndicates continuously.

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Appendix

Transaction Example

The following example clarifies the approach taken in this paper to infer the leadinvestor and syndication patterns:

On March 1, 1998, Munich-based Apax Partners invested capital into the early stagebiotech firm Wilex Biotechnology, a firm that develops novel cancer therapies. On April1, 1999, Apax and Earlybird Venture Capital provided additional expansion financing. OnOctober 20, 2000 and on May 10, 2005, two additional rounds of financing were providedby Apax, Earlybird, Julius Bär, and Merlin Bioscience, and by Apax, Earlybird, Karolin-ska Invest, and Quest Management, respectively.

Based on the previous elucidations, we infer that Apax Partners acted as the leadinvestor: it was involved in the first round of financing and financed the maximum numberof rounds (this would still be the case even if Apax had not been involved in the last roundof financing). Consequently, the underlying model of partner selection captures thedecision of which coinvestors were chosen by the lead investor. The first entry in the dataset therefore includes the decision made in 1999 to collaborate with Earlybird VentureCapital. Because Earlybird is included in the list of the top 35 investors (with at least 10deals, equal to an average of one deal per year), Earlybird receives an entry of one, and allother VCs (including 3i, TBG, and Deutsche Bank Investor) receive an entry of zero. Werestrict the explanatory variables to all information on the VCs until the end of the year1998 to avoid causal dependencies between the transactions made in 1999 and thedecisions made in the same year. Further, we calculate differences in investment experi-ence among the lead investor, the chosen partner, and the nonchosen candidates based onall transactions cumulated until the end of the year 1998. For the next decision, to inviteJulius Bär and Merlin Bioscience, the list of the top 35 (minus Earlybird, which waschosen previously) is dilated by the two chosen firms. They each receive an entry of one,and all nonchosen candidate firms receive zeros. To cope with the changed investmentcontext, we now include all information on the VCs cumulated until the end of the year1999. This exercise repeats for the last invitations made in 2005, including all informationon the VCs cumulated until the end of 2004.

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Christian Hopp is an Assistant Professor in International Personnel Management in the Department ofBusiness Administration at the University of Vienna, A-1210 Vienna, Austria.

Christian Lukas is a chair for Controlling at the University of Jena, 3. OG/Raum 3.104, Carl-Zeib-Straße 3,07743 Jena, Germany.

The authors would like to thank Benson Honig (the editor), Günter Franke, Thomas Weber, Julia Hein, OliverFabel, and three anonymous reviewers for invaluable feedback on an earlier draft of this paper.

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