XIAODAN ABBY WANG, B.S., M.B.A. A DISSERTATION BUSINESS ADMINISTRATION MANAGEMENT

THREE STUDIES ON THE ROLE OF VENTURE CAPITAL FIRMS IN FUNDED VENTURES INTER-FIRM COLLABORATION by XIAODAN ABBY WANG, B.S., M.B.A. A DISSERTATION IN BUSINESS ADMINISTRATION MANAGEMENT Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Approved Dr. William P. Wan Co-chairperson of the Committee Dr. G. Tom Lumpkin Co-chairperson of the Committee Dr. G. Tyge Payne Dr. Jeremy C. Short Dr. Peter H. Westfall Accepted Peggy Gordon Miller Dean of the Graduate School August, 2011

Copyright 2011, Xiaodan Abby Wang

ACKNOWLEDGMENTS I would like to express my appreciation to a number of people who provided valuable help and support in reaching this milestone in my life. I first would like to thank the faculty in the Area of Management at Texas Tech University, especially the members of my dissertation committee. Dr. William P. Wan and Dr. G. Tom Lumpkin, who served as the dissertation co-chairpersons, provided extensive hours of counsel, direction and support for the creation and completion of this dissertation. It was a great learning experience to work with them. Likewise, Dr. G. Tyge Payne and Dr. Jeremy C. Short committed their time and expertise to help, providing many unique insights and suggestions on theory building. Dr. Peter H. Westfall provided additional strength in the area of research methodology, which was of great importance to me. In addition to my committee, the other faculty in the Management Area at Texas Tech University was also instrumental in my completing the Ph.D. program. I am deeply grateful to each of you. In addition, I am grateful to my friends and fellow Ph.D. students. My friends Yang Yu, Xiaodong Cai, Dongri Meng and Yun Shen will likely never know how invaluable their friendship and technical support were to me as I went through the daunting process of completing my dissertation. Also, I want to thank my fellow Ph.D. students in the management Ph.D. program. Without your friendship and support, my time in the Ph.D. program would not be as enjoyable as what I experienced. Finally, my dear parents, Tiejun Wang and Fengzhen Zou, were always supportive throughout the process. Their love and support were priceless treasure in any time of my life. I love you. ii

TABLE OF CONTENTS ACKNOWLEDGMENTS... II ABSTRACT... V LIST OF TABLES... VII LIST OF FIGURES... VIII CHAPTER ONE... 1 CHAPTER TWO... 7 INTRODUCTION... 7 THEORETICAL BACKGROUND... 11 HYPOTHESES... 20 METHODOLOGY... 26 DISCUSSION AND CONCLUSION... 34 REFERENCES... 45 CHAPTER THREE... 53 INTRODUCTION... 53 THEORETICAL BACKGROUND... 57 HYPOTHESES... 65 METHODOLOGY... 73 DISCUSSION AND CONCLUSION... 82 REFERENCES... 93 CHAPTER FOUR... 101 INTRODUCTION... 101 THEORETICAL BACKGROUND... 104 iii

HYPOTHESES... 111 METHODOLOGY... 122 DISCUSSION AND CONCLUSION... 130 REFERENCES... 142 iv

ABSTRACT Despite the beneficial effects of alliances in facilitating survival, growth and performance of entrepreneurial startups, it is in general difficult for entrepreneurial startups to form alliances due to their poor resource endowments and lack of established social networks. Venture capital firms are active investors that support the development of entrepreneurial startups by providing both funding and a variety of non-pecuniary value-added services. In this dissertation, I examine the influence of VC firms on alliance formation by entrepreneurial startups in their investment portfolios (portfolio firms), which is a unique value-added service of VC firms that has been largely ignored in extant literature. In this three-paper dissertation, I posit that VC firms can influence portfolio firms alliance formation by providing reliable information about potential alliance opportunities. I investigate, respectively in three papers, the relationship between alliance formation by a portfolio firm and 1) composition of VC syndication, 2) alliance formation by firms in the same VC portfolio, and 3) network positions of VC firms. Specifically, in study one I argue that VC syndications with different compositional characteristics, such as size, experience and heterogeneity, offer different levels of information benefits for portfolio firms with respect to alliance opportunities, which influence the likelihood of alliance formation by portfolio firms. Strength of the relationship between VC firms and their portfolio firms is found to positively moderate the relationship between VC syndication composition and the alliance formation by portfolio firms. In study two, I examine the influence of VC interlocks on alliance formation by portfolio firms, as well as the cross-level v

moderating effects of tie strength and TMT size. In study three I propose that VC firms positions in their syndication networks network centrality and network constraint are positively associated with alliance formation by portfolio firms, and such relationship is weaker when the VC firms are corporate subsidiaries. To test the hypotheses of these three papers of the dissertation, I assembled three distinct data sets from multiple archival data sources. Conditional logit regression, longitudinal multilevel logit regression, and logit regression were employed respectively. These studies contribute to the strategy and entrepreneurship literatures in general and the alliance and VC literatures in particular. vi

LIST OF TABLES 1.1 Variable Operationalization (Study 1)... 39 1.2 Descriptive Statistics and Correlations (Study 1)... 40 1.3 Conditional Logistic Regression Results (Study 1)... 41 2.1 Variable Operationalization (Study 2)... 87 2.2 Descriptive Statistics and Correlations (Study 2)... 88 2.3 Multilevel Logit Regression Results (Study 2)... 89 3.1 Variable Operationalization (Study 3)... 134 3.2 Descriptive Statistics and Correlations (Study 3)... 135 3.3 Logit Regression Results (Study 3)... 137 vii

LIST OF FIGURES 1.1 The Framework (Study 1)... 42 1.2 Significant Moderating Effects Full Range (Study 1)... 43 1.3 Significant Moderating Effects ± 1 Standard Deviation (Study 1)... 44 2.1 The Framework (Study 2)... 91 2.2 Significant Moderating Effects (Study 2)... 92 3.1 The Framework (Study 3)... 139 3.2 Significant Moderating Effects Full Range (Study 3)... 140 3.3 Significant Moderating Effects ± 1 Standard Deviation (Study 3)... 141 viii

CHAPTER ONE VENTURE CAPITAL FIRMS AND ENTREPRENEURIAL STARTUPS ALLIANCE FORMATION This dissertation, entitled Three Studies on the Role of Venture Capital Firms in Funded Ventures Inter-Firm Collaboration, is completed in a three-paper format, with each paper addressing a distinct aspect of venture capital (VC) firms influence on the alliance formation of portfolio firms that receive VC financing. The research questions of this dissertation are: (1) how the network resources (e.g., information) portfolio firms gain from their VC investors influence their likelihood of alliance formation; (2) whether or not alliance formation of portfolio firms in the same VC investment portfolio increases the likelihood of alliance formation by the focal portfolio firm; and (3) whether or not VC firms positions in the syndication network influence the likelihood of alliance formation by portfolio firms. Firms can enjoy a number of benefits from alliances, such as acquiring complementary resources, reducing environment uncertainty, increasing market power, and entering new markets (e.g., Das & Teng, 2000; Eisenhardt & Schoonhoven, 1996; Hennart, 1988; McEvily & Zaheer, 1999; Rothaermel & Boeker, 2008; Uzzi, 1997). Particularly, alliances substantially benefit entrepreneurial startups in resource endowments improvement and in network expansion (Stuart, 2000; Stuart, Hoang & Hybels, 1999). However, it is in general difficult for startups to identify alliance opportunities and attract potential partners due to their lack of stable social and business networks, their low visibility and attractiveness, and the high uncertainty associated with their products or business models (Hsu, 2006). As such, how entrepreneurial startups 1

overcome the obstacles in expanding their networks (e.g., form alliances) becomes relevant and of great interest. The structure of the networks in which firms are embedded and the pattern of interaction with current social contacts have been found to be important catalysts of alliance formation (e.g., Gulati, 1995; Gulati, 1998; Walker, Kogut & Shan, 1997). However, the antecedents of alliances identified in studies based on established firms may not be readily applicable to the cases of entrepreneurial startups due to the latter s limited networks. For entrepreneurial startups, their affiliation with VC firms may be the most important basis to build their various social relationships, such as strategic alliances. In addition to financial resources, VC firms provide a variety of value-added services to portfolio firms to facilitate the latter to proceed toward a profitable exit. By leveraging their extensive network relationships, VC firms help build and expand portfolio firms networks by referring them to potential suppliers, customers, service providers and job candidates. I suggest that VC firms, which are deemed relationship investors (Fried & Hisrich, 1995), are also capable of facilitating portfolio firms alliance formation. Toward this end, the three studies in this dissertation collectively explore the role of VC firms in portfolio firms alliance formation by examining the influence of several types of network relations involving VC firms and portfolio firms. The first study, which is entitled Composition of VC Syndication and Portfolio Firms Alliance Formation: A Network Resource Perspective, explores the quantity and quality of informational benefits a portfolio firm can get access to through its affiliation with VC firms as well as the influence of these informational benefits on its alliance formation. The availability and reliability of alliance information have a direct bearing 2

on a firm s likelihood of alliance formation. Network composition is a key factor that influences the network benefits a firm can derive from its network (Gulati, 1998, 2007). The overall network benefits portfolio firms can gain from VC affiliation vary in accordance with the composition of the VC syndication. In other words, the quantity and quality of network resources (e.g., information) that portfolio firms gain from VC affiliation depend on the aggregative characteristics of their VC investors including syndication size, experience and diversity. Using a matched sample of 322 independent, private U.S. portfolio firms between 2001 and 2007 and the conditional logit regression, we found that portfolio firms financed by 1) more VC firms, 2) younger VC firms and 3) diverse VC firms are more likely to form alliances. Results also indicated that the strength of the relationship between portfolio firms and VC firms has a moderating effect. The findings of this study both support and extend those of previous research. In line with recent research (e.g., Hsu, 2006; Lindsey, 2008), this study illustrated the informational benefits VC firms provide in facilitating portfolio firms alliance formation. However, previous studies have merely examined the presence or absence of VC ownership, implicitly assuming that portfolio firms funded by different VC firms (or different groups of VC firms) enjoy the same opportunity to form alliances. This study challenges such assumption by explicitly examining the effects of VC firm heterogeneity on this dyadic relationship, which have been largely ignored in extant literature (Lockett, Ucbasaran & Butler, 2006). In addition, this study explicitly investigated the joint effects of the quality of nodes and the strength of ties on generation of network benefits, integrating the two primary elements of network. 3

Study Two, which is entitled VC Interlocks and Alliance Formation of Entrepreneurial Startups: A Longitudinal Cross-level Study, examines the role of VC firms as informational conduits among their portfolio firms, which may increase the likelihood of interfirm imitation of the latter. In the management literature, it has been found that interfirm relational ties represent an effective channel through which information about a practice s utility or value spreads (Haunschild, 1993; Marsden & Friedkin, 1993; Williamson & Cable, 2003). Drawing on social contagion theory and the structural equivalence perspective, I argue that portfolio firms connected by their shared VC firm are well aware of one another s strategic actions (e.g., alliance formation). The likelihood of a portfolio firm to form alliance increases when portfolio firms in the same VC portfolio have formed more alliances. Although VC interlocks are an influential source of interfirm information, they are unlikely to influence outcomes uniformly across different portfolio firms. I argue that the influence of VC interlocks on a specific portfolio firm depends on 1) the extent to which the portfolio firm views the information conveyed by VC interlocks as reliable and trustful, and 2) the extent to which the portfolio firm relies on VC interlocks as key information sources. Using a sample of 2145 firm-year observations (independent, private U.S. portfolio firms) between 2003 and 2007, I used three-level logit regression analysis to test the hypotheses and results provided general support to this study s core arguments. This study contributes to both the strategy and entrepreneurship literatures in three ways. First, this study demonstrates the special role of VC firms as information conduits among portfolio firms. Second, this study contributes to the strategy literature by responding to Hough s (2006: 60) recent call for re-examination of the results in any 4

area of strategy research based upon hierarchical data structures that has not explicitly modeled the inherent nesting of the data. Third, this study illustrates the contingent nature of the effects of VC interlocks on portfolio firms alliance formation. The last study, which is entitled Effects of VC Firms Network Positions on Alliance Formation of Portfolio Firms, addresses the relationship between a VC firm s position in the syndication network and the likelihood of alliance formation by its portfolio firms. I argue that a VC firm s capability of facilitating portfolio firms alliance formation critically depends on its position in the syndication networks different types of network positions imply different informational advantages. Syndication partners share industrial information, knowledge and wisdom. VC firms can rely on their syndication networks to identify alliance opportunities for their portfolio firms to cooperate with established corporations or other entrepreneurial startups. I suggest that a central and constrained position in the syndication network is the most favorable in terms of seeking alliance opportunities for portfolio firms. However, syndication network is not the only source of information for VC firms. I argue that the availability of alternative network relations that provide similar information also influences the effects of VC firms positions in the syndication networks. Using a sample of 417 independent, private U.S. portfolio firms, I found supports for the core arguments of this study. This study contributes to the network and VC literatures in several ways. First, by highlighting the valuable supports VC firms can provide to their portfolio firms in networking building and expansion, this study sheds light on an interesting but largely ignored issue in the network literature the transfer of social capital across firms. Second, this study identifies the most favorable network structure for VC firms to seek 5

relational benefits for their portfolio firms. Third, this study demonstrates that the effects of positions in a given network are contingent on the availability of alternative networks. I formatted each study in accordance with the Academy of Management Journal style guidelines. Because all three studies are intended for future publication, the tables, figures and references were placed at the end of each study. 6

CHAPTER TWO COMPOSITION OF VC SYNDICATION AND PORTFOLIO FIRMS ALLIANCE FORMATION: A NETWORK RESOURCE PERSPECTIVE INTRODUCTION Strategy and organization scholars have emphasized the benefits that accrue to a firm from entering alliances in terms of reduced transaction costs (Hennart, 1988), strengthened resource configuration (Das & Teng, 2000), mitigated environmental uncertainty (Uzzi, 1997), and improved competitive position (McEvily & Zaheer, 1999). Particularly, strategic alliance is an effective way to solve the problem of resource constraint. By allying with resource-rich partners, firms are able to get access to valuable resources that contribute to better outcome and greater success. Given these benefits associated with alliances, resource poor entrepreneurial startups are typically eager to form alliances so as to reduce their resource constraint and increase their visibility and status (Eisenhardt & Schoonhoven, 1996; Stuart, Hoang & Hybels, 1999). However, it is usually difficult for them to identify alliance opportunities and attract desired partners due to their lack of stable networks and reliable track records (Hsu, 2006; Lindsey, 2008). In this regard, research examining how such obstacles are reduced and alliance formation is facilitated for entrepreneurial startups should become relevant and of great interest to researchers. In recent years, the application of network perspectives for studying interorganizational relationships, such as cooperative alliances, has proliferated. The structure of the networks in which firms are embedded and the pattern of interaction with current social contacts have been found to be important catalysts of alliance formation (e.g., Gulati, 1995; Gulati, 1998; Walker, Kogut & Shan, 1997). However, startups are 7

typically lack stable social and economic networks and are often poorly embedded. The initial network of a startup may have to limited to its founder s personal network of contacts. However, for a VC-backed startup, its relationship with VC investors expands its networks, thus representing an important conduit of external resources. Through their extensive networks, VC firms substantial social capital can be leveraged to offer crucial supports to their portfolio firms. For example, portfolio firms may obtain valuable alliance opportunities from VC investors, who obtain such information through their various network contacts. In addition, due to the signaling or certification effects of VC ownership (Hsu, 2006; Megginson and Weiss, 1991), VC-backed startups become more attractive in the eyes of potential alliance partners than do non-vc-backed startups and therefore are more likely to realize alliance opportunities. Extant knowledge on strategic alliances has primarily come from studying established, large firms. Research on alliance formation by startups or poorly embedded firms only emerge in recent years (e.g., Ahuja, Polidoro & Mitchell, 2009; Hsu, 2006; Park, Chen & Gallagher, 2002). Although this line of research has suggested that affiliation with VC firms is likely to increase the likelihood of portfolio firms to form alliances (Hsu, 2006), the focus centers on the role of VC firms serving as information intermediaries in portfolio firms alliance formation. Little attention has been paid to the heterogeneity in the informational advantages VC firms can provide to their portfolio firms that facilitate the alliance formation of the latter. Because many portfolio firms are jointly invested by multiple VC firms, through syndication, that differ in their capabilities of providing useful information and valueadded services, it is therefore important to distinguish between the informational 8

advantages that different portfolio firms can obtain from their VC investors. Syndication is a common practice in the VC industry, whereas a lead VC firm initiates a new investment and invites other VC firms to join (Fried & Hisrich, 1995; Wright & Lockett, 2003). Startups financed by multiple VC firms have access not only to the resources of the lead firm but also those of the syndication partners. In other words, VC syndication offers a larger source of network resources than does a single VC firm. The question of how and to what extent portfolio firms can benefit from this enlarged source of network resources thus deserves exploration. Given the differences among VC firms, the implicit assumption that startups funded by different VC firms enjoy the same chance to form desirable alliances is unlikely to be valid. Furthermore, prior studies on this topic have primarily treated the informational benefits provided by VC firms as a single dimensional construct, ignoring the various forms informational benefits can take (Koka & Prescott, 2002). In this paper, I argue that portfolio firms differ in the informational benefits they obtain from their VC investors in terms of information volume, information relevance and information diversity, depending on the composition of VC syndication. In addition, the network resource perspective suggests that quality of both the nodes and the ties constituting a network decides the benefits a firm can actually realize from the network (Gulati, 2007). To date, the VC literature has primarily focused on examining what benefits VC firms can provide to its portfolio firms (e.g., Amit, Brander & Zott, 1999; Sapienza, 1992), but has only paid little attention to the effects of the strength of investor-investee relationship. Given that tie strength is an important determinant of realized network benefits, the neglect of examining the role of tie strength 9

may lead to an incomplete understanding of how and when VC firms support is most facilitative. To fill this gap in the VC literature, I incorporate tie strength in this study as a moderator, suggesting that portfolio firms that are strongly connected to their VC investors are likely to realize more network benefits. Building on and extending prior studies, this study explores the effects of compositional attributes of VC syndication on portfolio firms likelihood to form alliances, as well as the moderating effect of the strength of ties defined by the equity relationship (see Figure 1 for the framework). A sample of U.S. VC-backed entrepreneurial startups between year 2000 and 2004 will be assembled to test the relationship between VC syndication composition and portfolio firms likelihood to form alliances. This study intends to contribute to the entrepreneurial literature, particularly VC literature, in three major ways. First, this study explores VC firms role in facilitating portfolio firms alliance formation, which is a unique value-added service of VC firms that has been largely ignored in extant literature. Particularly, this study highlights the multidimensional nature of informational benefits portfolio firms can obtain from their relationship with VC firms. Second, this study demonstrates how the composition of VC syndication affects portfolio firm level outcomes. Different compositional attributes have different implications on the informational benefits portfolio firms obtain, which influence portfolio firms likelihood to form alliances. Third, by examining the moderating role of tie strength that has received little research attention, this study illustrates the joint effects of the quality of nodes and the strength of ties on determining the network benefits firms can obtain from their network connections. 10

THEORETICAL BACKGROUND Antecedents and Consequences of Strategic Alliances A strategic alliance is commonly defined as a voluntarily initiated cooperative agreement between independent firms, aiming to achieve mutually beneficial goals by resource exchange, knowledge sharing and co-specialization (Gulati, 1999). Strategic alliances may take several forms based on the amount of equity involved in the inter-firm relationship, such as non-equity alliances and equity alliances (e.g. joint ventures) (Das & Teng, 1996; Hennart, 1988; Teece, 1992). Examples of non-equity alliances include joint R&D projects, joint manufacturing, technology exchange, marketing agreements, to name a few. The structure and governance of non-equity and equity alliances differ substantially. Researchers have provided many theoretical and empirical accounts for strategic alliances, including both internal motives and external catalysts. Resource scarcity and market uncertainty are two key motives for alliance formation (Park, Chen & Gallagher, 2002). The major benefits include obtaining access to complementary resources, gaining legitimacy, increasing market power and reducing environmental uncertainty (Baum & Oliver, 1991; Burgers, Hill & Kim, 1993; Kogut, 1988). The economic rationale for resource needs and the sociological justification for status seeking represent two major streams of research on internal benefit-seeking motives of strategic alliances (Lin, Yang & Arya, 2009). Adopting the resourced-based view (RBV) (Wenerfelt, 1984; Barney, 1991), researchers argue that firms form alliances to acquire valuable resources (Das & Teng, 2000; Eisenhardt & Schoonhoven, 1996; Kogut, 1988; Park, Mezias & Song, 2000). Resources of particular interest in alliance formation include financial resources, technologies, and managerial capabilities, to name 11

a few (Hitt et al., 2000). By partnering with firms that can provide missing or complementary resources, the focal firm can strengthen its resource configuration and generate more value. Researchers subscribing to the social embeddedness and institutional perspective argue that firms establish alliances to conform to social justification and social obligation (Zukin & Dimaggio, 1990), or to improve their images or status in the eyes of key constituents (Dacin, Oliver & Roy, 2007). In this sense, firms with a positive image and high social status are viewed as desired partners. By partnering with reputable firms, the focal firm may enhance its own status and image and improve its economic performance in the marketplace (Dacin, Oliver & Roy, 2007; Podolny, 1994). In addition, some researchers have made efforts to integrate these two theoretical arguments and examined the interaction between resource needs and status seeking to explain alliance formation and partner selection (e.g., Lin, Yang & Arya, 2009). Increasing market power and decreasing environmental uncertainty also represent major motives for alliance formation. Alliances have been found to be an effective means to deal with market changes in terms of both structure and dynamics (Beckman, Haunschild & Phillips, 2004). Particularly, strategic alliances can be viewed as a firm s adaptive response to environmental changes. Prior research has indicated that industry level factors such as competition intensity and industrial alliance formation rates have direct bearings on firms strategic choice to form alliances (Ang, 2008; Garcia-Pont & Nohria, 2002). Studies on external catalysts of strategic alliances, or the enabling conditions, have largely been built on network theory. Research has shown that preexisting network structure influence the formation of new inter-firm alliances (e.g., Gulati, 1995; Walker, 12

Kogut & Shan, 1997). The network perspective on inter-firm collaboration contends that alliance activities are embedded in a wider social network resulting from prior exchanges and collaborative activities (Granovetter, 1985). Structural embeddedness has been found to be the key enabling condition or catalyst of inter-firm collaboration. The network structure in which a firm is embedded influences the availability of and access to alliance opportunities (Nahapiet & Ghoshal, 1998). While richly connected firms are more likely to form new alliances due to their access to a wider array of information about potential alliance opportunities and their high network status attributable to their central position, poorly connected firms have much less inter-firm collaboration opportunities due to their lack of reputational and informational advantages. Relational embeddedness has also been found to be positively associated with the likelihood of a firm s alliance formation (Gulati, 1998). Relational embeddedness concerns the properties of the relational ties developed through a history of interactions (Granovetter, 1985). Relational embeddedness has often been used to explain repeated collaborative activities - the inclination of a firm to partner with firms with which it has prior experience of cooperation (Gulati, 1998). While research on strategic alliances has traditionally focused on established, large firms, studies on the strategic and performance implications of alliances for entrepreneurial, small firms have only captured academic attention in recent years (e.g., Alvarez & Barney, 2001; Ireland, Hitt & Webb, 2006). Startups are usually assumed to be typified by a lack of sufficient resources and stable relational networks (Baum, Calabrese & Silverman, 2000). By allying with established, reputable firms, startups can get access to valuable tangible and intangible resources, which help mitigate the liability 13

of newness (Freeman, Carroll & Hannan, 1983), increase their chance of survival (Baum, et al, 1998; Eisenhardt & Schoonhoven, 1996) and increase their IPO valuation and overall IPO success (Higgins & Gulati, 2003; Stuart et al., 1999). Given the uncertain market conditions they face and their resource constraints, entrepreneurial startups typically have strong incentive to form alliances, but often do not have the necessary capabilities to do so. This is especially true for startups that rely on uncertain technologies, that have unclear market prospects, or whose early stage development may take a protracted period to develop and involve substantial R&D expenditures. Entrepreneurial startups rely on different networks for seeking different supports (Hanlon & Saunders, 2007). None of these networks can provide all the resources and supports entrepreneurial startups need. For example, entrepreneurs look for seed capital, basic advices and emotional supports from their personal networks including families, friends and prior colleagues. They seek other supports in terms of professional network referrals and strategic information from business partners and professional advertisers such as VC firms, service providers and public agencies. Particularly, by affiliating with VC firms, startups can get access to a number of valuable resources, such as funding, industrial knowledge and managerial capabilities. However, these resources are necessary but not sufficient for their survival and success. Although VC firms can provide a variety of value-added services at the strategic level, they cannot help much at the operational level, such as new product design and development. For entrepreneurial startups, especially the ones in industries where collaborative R&D activities are common, strategic alliances with industrial partners can generate benefits at the operational level that beyond VC firms capability to bring about. 14

However, resource poor startups have limited abilities to form alliances (Park et al., 2002). Prior studies have revealed the important roles of existing social connections of startups in their network development and expansion (e.g., Higgins & Gulati, 2003; Kim & Higgins, 2007). Startups rely heavily on the social contacts of their founders and VC investors to build their network connections. Particularly, affiliations with VC firms have been found to increase startups likelihood of inter-firm collaborations (Hsu, 2006; Lindsey, 2008). VC Network and VC Syndication The role of VC in facilitating the development of entrepreneurial startups has been widely accepted. VC-backed startups have been found to perform better and are more likely to reach the IPO stage than startups financed by other sources of capital (Gompers & Lerner, 2002). In addition to financial capital, VC firms provide their portfolio firms with a variety of other benefits in the post-investment stage, such as recruiting key personnel, access to business contacts, general business knowledge, and financial and strategic discipline (Fried & Hisrich, 1995; Gorman & Sahlman, 1989). By offering these value-added services, VC firms play an active role in shaping portfolio firms strategic direction and action, such as professionalization (Hellmann & Puri, 2002; Wijbenga, Postma & Stratling, 2007), internationalization (Fernhaver & McDougall- Covin, 2009) and cooperative commercialization (Hsu, 2006). VC firms capabilities of providing value-added services are closely related to their capabilities of leveraging network resources. VC firms are embedded in multiple networks defined by the specific relational ties. Bygrave (1987) viewed VC firms as having extensive networks that include their fund providers, portfolio firms and other VC 15

firms. An even broader definition of VC networks encompasses various service providers such as head hunters, lawyers, and investment bankers. These broad VC networks are called VC constellations (Echols, 2000). Indeed, one prominent VC firm goes so far as describing itself as a venture keiretsu (Lindsey, 2005; Hsu, 2004), alluding to a Japanese business group. When VC firms make new investments, they draw on the members in the constellation or keiretsu to help their portfolio firms succeed. VC firms rely on their overall networks as the sources of most of the value-added services they provide, such as collecting strategic information, identifying candidates for employment, locating service providers and potential customers and finding acquisitions or corporate partners (Fried & Hisrich, 1995). Syndication is a common practice in VC industry, through which a VC firm builds its network with other VC firms (Lockett, Ucbasaran & Butler, 2006). In 2000, 63.6 percent of VC investments in the United States were syndications (Wright & Lockett, 2003). VC syndication is a voluntary arrangement among independent VC firms to co-invest in a portfolio firm for a joint payoff (Bygrave, 1987; Wilson, 1968), either in the same investment round or at different points in time (Brander, Amit, & Antweiler, 2002). Typically, VC investment syndications are initiated by a lead VC firm that often holds the largest VC equity stakes in the portfolio firm (Wright & Lockett, 2003). Cooperating with the portfolio firm, the lead VC firm then invites other VC firms to participate in the financing rounds. VC syndications involve information sharing and syndication partners often engage in co-development of the portfolio firms. According to the extant research on motivation of VC syndication, syndication activities were either driven by individual deal management motives such as deal selection and resource 16

seeking (e.g., Brander, Amit & Antweiler, 2002) or by portfolio management motives such as risk sharing and portfolio diversification (e.g., Manigart et al., 2006). It has been found that syndicated VC investments generate higher returns than stand-alone VC investments (Brander, Raphael & Werner, 2002). One of the most critical factors explaining such higher returns is the improve value-added supports in the form of a larger variety of social and economic contacts received by the portfolio firms funded by syndicated VC firms than a single VC firm. VC Syndication as Important Source of Network Resources of Portfolio Firms The adaptation and adoption of social network approaches for studying interorganizational relationships have proliferated in the past few decades. Social network perspectives have now dominated research on inter-organizational relationships, anchoring a large number of studies examining the antecedents and consequences of inter-organizational networks (e.g., Gulati, 1998; Stuart, 1998; Tsai & Ghoshal, 1998). Network resources refers to tangible and intangible assets, such as technology, information and reputation, that reside outside of a firm s boundaries but can be accessed or leveraged through relational ties (Gnyawali & Madhavan, 2001; Lavie, 2006). By this conceptualization, network resources are external resources possessed by the firm s partners. The ties between the firm and its partners serve as conduits of resource flows. Network resources perspective contends that both ties and partners resource endowments are critical for the firm to generate value from its network (Gulati, 2007). If a firm is embedded in a network whose members have poor resource endowments (e.g., outdated 17

technologies, low reputation), it faces few options to generate value from this network no matter what the structural and relational properties of the network are. Startups typically lack valuable network connections (Eisenhardt & Schoonhoven, 1996). As parts of the limited networks of portfolio firms, VC firms represent a major, stable source of network resources. Portfolio firms can rely on VC firms to obtain a number of resources necessary for development and success, such as follow-on financial inputs and connection to potential suppliers and customers. The investor-investee relationships represent channels for information and resource flows. The strength of the equity ties between VC firms and portfolio firms is considered strong (Stuart, Hoang & Hybels, 1999). The investor-investee relationship is stable compared with many nonequity based relational ties. Such equity relations are akin to strong ties (Granovetter, 1973) because of VC firms intensive interactions with portfolio firms and their active monitoring activities. Also, there is often a shared vision between VC firms and portfolio firms that embodies the collective goals and aspirations. All these features of relationships between VC firms and portfolio firms indicate the reliability of VC firms as a source of network resources. However, the effectiveness of VC firms as a source of network resources depends also on their specific capabilities and resource endowments. For a portfolio firm jointly funded by multiple VC firms, the total benefits it can obtain by virtue of the relationship with its VC investors are determined collectively by these VC investors. Viewed from the network resources perspective (Gulati, 2007; Lavie, 2006), the composition of VC syndication determines the quality and quantity of network resources a portfolio firm can obtain, which have direct implications on its strategy and performance. 18

Expanding the Investor Side in Examining the Investor-Investee Relationship Examining the strategic implications of VC syndication as a whole on portfolio firms is of great importance. The relationship between VC firms and portfolio firms has received considerable attention in the entrepreneurship literature. Prior studies have sought to understand this dyadic relationship by examining the interaction between the two parties, such as mutual learning (Clercq & Sapienza, 2005) and venture capitalist- CEO interaction (Arthurs & Busenitz, 2003; Cable & Shane, 1997; Sapienza & Gupta, 1994), and by investigating the impacts of VC firms on portfolio firms development and performance (e.g., Dushnitsky & Lenox, 2006; Wijbenga, Postma & Stratling, 2007). These studies have primarily treated the dyadic investor-investee relationship as one involving just two players: one VC firm and one portfolio firm (Lockett, Ucbasaran & Bulter, 2006). However, both side of the investor-investee relationship may involve multiple players (Lockett & Wright, 2001). Multiple VC firms may co-invest in a single startup through syndication, and a VC firm typically invests in a number of startups constituting an investment portfolio. In recent years, some research efforts have been made to broaden the investorinvestee dyad by expanding the investee side from individual portfolio firms to the whole investment portfolio. This stream of studies have examined the impacts of VC firm level factors on portfolio size (Bernile, Cumming & Lyandres, 2007), portfolio composition (Patzelt, Knyphausen-Aufseb & Fischer, 2009) and portfolio performance (Dimov & De Clercq, 2006). However, in studies examining portfolio firms strategies and performance, the theoretical focus on the investor side is still primarily on the lead firm only. Although VC syndication is a topic that has attracted substantial research attention 19

(e.g., Bygrave, 1987; Manigart et al., 2006; Wright & Lockett, 2003), extant studies on VC syndication have primarily conducted from the lenses of VC firms. Major streams of research on VC syndication include the motives for syndication, the management of syndication, and the effects of syndication on investment performance (Lockett, Ucbasaran & Bulter, 2006). However, a lead VC firm typically select syndication partners based on its own interests and strategic considerations. A syndication comprised of partners that is most beneficial to the lead VC firm may not necessarily be the most favorable to the portfolio firm. To date, our knowledge with respect to the topic of how VC syndication partners jointly influence portfolio firm level outcomes is still limited. Although non-lead VC firms are typically not involved in the day-to-day management of the portfolio firm, they can benefit the portfolio firm in other ways such as the increased legitimacy resulting from mere affiliation and information sharing in board meetings. I contend that, in studying portfolio firms strategic actions in which external information plays a key role (e.g., alliance formation), ignoring the role of non-lead VC firms is likely to lead to an incomplete understanding, or even an incorrect conclusion. As such, this study examines how the composition of VC syndication influences the alliance formation of portfolio firms. HYPOTHESES Effects of Compositional Characteristics of VC Syndication Optimal strategic actions of a firm are the results of a match between the firm s competencies and the availability of new opportunities in the environment (Andrews, 1971). In the case of alliance formation, the presence or lack of certain competencies 20

propel firms to enter into new alliances while the identification of and the access to alliance opportunities determine the realization of new alliances. In other words, in addition to internal motives, the likelihood of a firm s alliance formation also depends on its engagement in identifying and responding to partnering opportunities (Sarkar, Echambadi & Harrison, 2001). By virtue of the relationship with VC firms, portfolio firms can get valuable network resources supporting the opportunity identification process. One of the key network resources firms can gain by virtue of their network of social relationships is information (Gulati, 1999; Koka & Prescott, 2002). Researchers have demonstrated the multidimensional nature of information benefits derived from social network and the differential effects of information dimensions on firm performance (e.g., Koka & Prescott, 2002). Adopting the multidimensional view of information benefits, I suggest that a firm s likelihood to form alliances depends on both the quantity and the quality of alliance information it can have access to. Specifically, the volume of information, the relevance of information and the diversity of information contribute to the identification of alliance opportunities and potential partners. With extensive social networks and in-depth understanding of portfolio firms capabilities and needs, VC firms enjoy advantageous positions in collecting information that are useful for portfolio firms. Through formal and informal communications with VC firms, portfolio firms gain access to an enlarged alliance opportunity set. Compared with portfolio firms financed by a single VC firm, portfolio firms funded by VC syndications consisting of multiple VC partners have more information channels to collect alliance information. Equipped with larger information pools, they are more likely to identify 21

partnering opportunities. As such, I predict that portfolio firms receiving financing from larger number of VC investors enjoy a higher chance of alliance formation. Hypothesis 1. VC syndication size is positively associated with the likelihood of a portfolio firm to form alliances. Both the quantity and quality of network resources supporting alliance formation are important in determining the likelihood of a firm to form alliances. As Adler and Kwon (2002) highlighted, the competencies and resources at the nodes of the network influence the benefits a focal firm can obtain from the network it is embedded. A focal firm can benefit more by connecting to quality nodes. No two VC firms are identical in terms of resource endowments. Therefore, VC syndications differ in their resource aggregate. Characteristics of VC syndication partners jointly determine the syndication s quality as a source of network resources. As investment experience is accumulated, VC firms become better at detecting and evaluating portfolio firms true needs and potential. Based on prior experience of financing similar portfolio firms, VC firms may be able to visualize the appropriate network structure of alliances for their portfolio firms in the future and work backwards to help build these portfolio firms current alliance strategy. Experienced VC firms not only provide relevant and useful alliance information, but also provide this information at a right time. Therefore, I suggest that a VC syndication consisting of experienced VC partners is better at facilitating portfolio firms alliance formation. Hypothesis 2. The experience of VC syndication partners is positively associated with the likelihood of a portfolio firm to form alliances. 22

The diversity of information refers to the variety or range of information, or the extent to which the information is not redundant. While syndication size determines the volume of information and experience of syndication partners decides the relevance of information, the diversity of information is proportional to the heterogeneity of VC syndication partners. VC firms with different backgrounds typically have different social networks. For example, there are more established large corporations in corporate VC firms networks while VC firms affiliated to financial institutions have more contacts with investment banks and other financial service providers. The heterogeneity of VC syndication partners determine the extent to which additive or redundant information may be provided. In other words, the more diverse the syndication partners are, the richer the information available to the portfolio firm is. Irredundant information means a larger set of effective alliance opportunities, which increases the likelihood of the portfolio firm to find ideal potential partners. Hypothesis 3. The heterogeneity of VC syndication partners in terms of type is positively associated with the likelihood of a portfolio firm to form alliances. Moderating Effects of Tie Strength According to the network resource perspective, both ties and partners resource endowments are critical for the firms to generate value from its network (Gulati, 2007). In essence, the strength of relational ties between partners and their resources endowments jointly determine the benefits stemmed from inter-firm relations. As such, a firm may enjoy more network benefits from a tightly connected partner with rich resources than from a loosely connected one. However, compared with partners resource 23

endowments, tie strength has received less research attention in the network literature, particularly in firm level studies. Furthermore, few studies have examined the joint effects of tie strength and partner s resource endowments on network benefits generated. The concept of tie strength was initiated by Granovetter s (1973) paper titled The Strength of Weak Ties, whereby he conceptualized tie strength as the combination of the amount of time spent in the relationship, emotional intensity, intimacy and reciprocal services associated with the relational tie. The concept of tie strength has then been used in a number of studies in both management and sociology areas treating it as a predictor of outcomes at both individual and organization level (e.g., Lechner, Frankenberger & Floyd, 2010). The strength of the tie connecting two actors (e.g., individuals, organizational units or firms) has been found to influence the levels of trust and efficiency of information sharing/transfer between them (Krackhardt, 1992; Szulanski, 1996). Stronger ties often mean higher levels of trust and moral obligation as an underlying support mechanism to reduce the risk of opportunism and cheating (Johanson & Mattson, 1987; Powell, 1990). The effectiveness of a relational tie in channeling network benefits is contingent on its strength. Extant literature examining effects of tie strength on information sharing or knowledge transfer has largely focused on relational ties between individuals or teams, measuring the strength of ties by assessing the closeness, duration and frequency of the relationship through five- or seven-point scale survey questions (e.g., Lechner, Frankenberger & Floyd, 2010; Perry-Smith, 2006; Pil & Leana, 2009; Reagans & McEvily, 2003). In firm level studies, researchers typically use repeated relational ties to capture the strength of the relationship between firms, arguing that prior network 24

connections between a pair of firms are important catalysts of new alliance formation between them (e.g., Gulati, 1999). I argue that a higher level of strength of the ties between VC firms and portfolio firms are likely to enhance the effectiveness of communication between the two parties and increase the network benefits portfolio firms can obtain from the relationship. The primary relationships between VC firms and their portfolio firms are built on equity holding. The strength of such equity ties is determined by the amount of equity stakes held by VC firms. The larger the amount of VC financing that portfolio firms receive, the stronger the investor-investee ties are. VC firms make substantial investment in a portfolio firm only if they trust its founding team and have confidence on its growth potential and prospects. Large equity stakes held by VC firms are thus often associated with high level of commitment to portfolio firms. When VC firms make large amounts of investment in their portfolio firms, such significant financial commitment will motivate them to work even harder to facilitate the latter to succeed so that they can harvest the resulting financial returns. Such commitment is also likely to increase portfolio firms trust on VC firms and deem the information they convey more reliable. In addition, more equity holding means more powerful position on the board, through which VC firms exert their influence on portfolio firms. Therefore, I hypothesize that the strength of equity ties moderates the relationships between compositional characteristics of VC syndication and the alliance formation of portfolio firms, making the relationships stronger. 25

Hypothesis 4a. The amount of total VC equity stakes positively moderates the relationship between syndication size and a portfolio firm alliance formation. Hypothesis 4b. The amount of total VC equity stakes positively moderates the relationship between syndication experience and a portfolio firm alliance formation. Hypothesis 4c. The amount of total VC equity stakes positively moderates the relationship between syndication diversity and a portfolio firm alliance formation. METHODOLOGY Sample Selection The dependent variable of interest in this study is the alliance formation of portfolio firms. Unlike alliance formation of established, pubic firms, alliance formations of private startups are relatively rare events (approximately 3 percent base rate). Random sampling is not the best choice in studies on alliance formation by startups due to the low base rate. The sample selected randomly may contain no or few events, which means inadequate variation on the dependent variable (King & Zeng, 2001). Matched pair design provides a means for investigating phenomena with a low base rate. It has been commonly used in studies on rare events such as bankruptcy (e.g., Daily & Dalton, 1994a, 1994b; & D Aveni & MacMillan, 1990; Hambrick & D Aveni, 1988) and fraudulent financial reporting (e.g., O Connor, et al. 2006). Following these prior studies, I used a matched-pair sampling design instead, identifying alliances formed by VCbacked startups in medical care/biotechnology industry between 2001 and 2007 and then, 26

for each such startup, identifying a matching firm that did not form any alliance in the same time period. A notable advantage of matched pair sampling is that it provides controls for important compound factors by using these variables as matching criteria (McKinlay, 1977). It has been found that matched pair design do not cause qualitatively different conclusions from those arrived at by using random sampling design in studies of financial predictors of bankruptcy (Zmijewski, 1984). Alliances formed by portfolio firms between 2001and 2007. I identified 1681 U.S. portfolio firms in medical care/biotechnology industry between 2001 and 2007. 276 out of the 1681 firms formed 325 alliances within the sever-year time frame. After excluding the alliances that were formed before receiving the first VC investment and the ones after IPOs or mergers/acquisitions, I have a sample consisting of 178 alliances formed by 144 private portfolio firms in medical care/biotechnology industry during the sample period. Matching firms. I introduced a number of controls through my matching processes, employing six matching variables: firm independence (not acquired by the time of alliance formation), private ownership (not had IPO by the time of alliance formation), headquarter in the U.S., 2001-2007 time period, industrial segment and firm age. Two key variables that may influence the likelihood of alliance formation by portfolio firms industrial segment and firm characteristics are controlled through the matching procedures. Research has found that industrial conditions and trends influence alliance formation (Ang, 2008; Beckman, Haunschild & Phillips, 2004). Different industries, as well as different segments within an industry, have different value propositions and sources of growth and competitive advantages, which lead to differences 27

in alliance formation rates across industries/segments. These potential sources of variation are controlled by focusing on a single industry (medical care/biotechnology) and matching by segments within the industry, defined by the default industrial segment classification used in the SDC database. Firm age is an important factor that can influence the likelihood of alliance formation. Older, more established firms are in general more likely to form alliances given the increase in visibility and legitimacy over time (Gulati, 1995). I controlled for this source of variation through matching firms by age (with range of ± 1 year when matching firm of the exact same age was not available). This helps decouple the alliance formation due to the increasing visibility and legitimacy associated with the passage of time from that resulting from affiliation with VC firms. The final sample size was 322 (161 matched pairs) after 17 pairs were dropped due to missing data and lack of variation between the paired firms. Data Source and Collection I assembled the data set from multiple electronic and printed data sources, including the Alliance and Joint Venture database and the Venture Xpert database from Security Data Company (SDC), Pratt s Guide to Private Equity & Venture Capital and The Directory of Venture Capital and Private Equity Firms. These data were supplemented with information from public filings and other public sources as needed. Data for VC firm specific variables and portfolio firm specific variables were largely retrieved and manually coded from SDC VentureXpert database. This database and its predecessor (Venture Economics) have been used in many studies of VC firms and VC activities (e.g., Gompers, 1995; Lerner, 1995; Kaplan & Schoar, 2005, Gompers et al. 2007), and has been found to be generally free from bias (Kaplan, Sensoy & 28

Strömberg, 2002). Alliances data (from year 2001-2007) were retrieved and manually coded from SDC s Alliance and Joint Venture database. The SDC database reports complete data for new alliances announced each year. It is one of the most commonly used alliance databases in empirical studies published in top management journals (Schilling, 2009). Measures Dependent variables. In studying inter-firm relationships, researchers have adopted two different levels of analysis: the firm and the dyad (Stuart, 1998). Firm level models are used when the research focus is on the likelihood of firms to form inter-firm relationships, regardless of the identities of their affiliates (e.g., Ang, 2008; Beckman, Haunschild & Phillips, 2004). Dyadic level models are used when the dependent variables is a pair of firms (e.g., Podolny, 1994; Stuart, 1998). Dyadic models are appropriate in examining how relationships (e.g., similarities, differences, prior relational ties) between a pair of firms influence the chance that the pair members form a relationship of a different type such as alliances. Given the specific research question of this study, I used firm level modes. The dependent variable of this study is the likelihood of alliance formation by portfolio firms, with 1 denoting an alliance formed in a given year and 0 denoting a matched firm with no alliance. Independent variables. This study examines how the composition of VC syndication influences portfolio firms likelihood to form alliances. I defined VC syndication as consisting of all VC firms providing financing for a portfolio firm. Three independent variables measuring different aspects of VC syndication composition were 29

used to test the hypotheses. Syndication size is measured by the number of VC firms (including business angels) co-investing in the portfolio firm. I followed prior VC studies to measure VC experience by the age of VC firms (e.g., Lindsey, 2008). Experience of VC syndication partners is measured by average age (number of years since foundation) of syndication partners. VC investors were categorized into thirteen types by SDC database based on the major sources of capital: individuals, private equity firms, corporate PE/venture, bank affiliated, insurance firm affiliate, endowment, foundation or pension fund, investment management firm, SBIC, service provider, incubator, government affiliate, university and others. I used the counted number of VC types represented by the syndication partners to capture the heterogeneity of syndication in terms of capabilities and resource endowments. The more types of VC investors co-investing in a portfolio firm, the more diverse the informational benefits it can gain access to. Besides the three main effects, I also examined the moderating effect of tie strength. The strength of the equity ties between VC investors and portfolio firms was measured by the total amount of VC investment portfolio firms had received since its inception. To avoid potential collinearity, I mean-centered the continuous variables involved in the interaction terms by subtracting the mean from each value before generated the interaction terms (Aiken & West, 1991). Statistical control variables. I have introduced several key controls through the matching processes such as firm age and industry segments. The empirical model also includes a number of statistical control variables for alternative explanations. I controlled for several firm attributes that prior studies have pointed out as factors that influence the 30

likelihood of alliance formation. Research has demonstrated the effects of firm size on alliance formation (e.g., Hagedoorn & Schakenraad, 1994). Firms with larger scale and scope have more opportunities to cooperate with partners in different areas. Large firms typically have more divisions and correspondingly more managers. I used the total number of executives as the measure of firm size (logged). Firm performance is also an important attribute that influence alliance formation. Some scholars argued that poorly performing firms have a stronger incentive to enter alliances so as to deal with environmental uncertainty (Burgers et al., 1993). Others suggested that firms with superior performance are more likely to attract alliance partners and capitalize on the identified alliance opportunities (Gulati, 1998). Given the difficulty to obtain financial performance information of privately held startups such as revenue, net income and sales, I used the total number of VC financing rounds a portfolio firm had received since its inception as the proxy of performance (logged). VC firms reduce investment risk by providing fund to portfolio firms in a sequence of financing rounds. It is rarely specified contractually that VC firms must invest in subsequent rounds (Wright & Lockett, 2003). Follow-on financing will be provided only if they figure that their portfolio firms have performed well. Thus the number of financing rounds can be seen as reflecting portfolio firms performance. I included in the model the time elapsed since a portfolio firm received its first VC investment as a control variable (measured in year). The longer the time VC firms had worked with it, the more alliance information the portfolio firm might gain access to, which increases its likelihood of alliance formation. The development stage of portfolio firms at the time of alliance formation was also included in the model to control for 31

unobservable firm level effects associated with development stage on alliance formation. Startups in their later stage of development may be more likely to form alliances than their early stage counterparts due to the increased visibility and legitimacy. I followed the way SDC database classifies the development stage of portfolio firms. The six dummy stage variables included in the model are: seed, early, expansion, later, buyout/acquisition and other. Table 1 summarizes the operationalization of the dependent variable, independent variables and control variables. Data Analysis and Results Statistical model. Given the dichotomous dependent variable, I tested the hypotheses using the conditional (fixed effects) logistic regression model (Agresti, 2002; Hosmer & Lemeshow, 2000). In the matched-pair design (also called matched casecontrol design), each pair contains one portfolio firm that formed an alliance in the given year (coded as 1) and one that did not (coded as 0). The matched-pair method employs a conditional distribution, where the distribution of the dependent variable is fixed (each matched pair has one 1 and one 0) and therefore subject specific, instead of marginally distributed and population-averaged. The conditional logit model I used is specified as: π ij Logit( π ij ) = log = xij β + ε i π 1 ij where i denotes the groups (in this study there are only two groups the matched pairs) and j denotes the firms in the ith group; π ij is the probability of alliance formation; xij is a vector of independent variables; ε i captures the unobserved heterogeneity across the groups. Conditional logistic regression does not contain an overall intercept term because 32

such a term would interfere with the case-based estimates of the other parameters (Agresti, 2002). Table 2 presents the descriptive statistics and the zero-order correlations between the variables included in the statistical model. I computed variance inflation factors (VIFs) to ensure that multicollinearity did not influence the results. The mean VIF is 2.16. The maximum VIF is 4.03, which is well below the guideline of 10 that was advocated by Chatterjee and Price (1991). Thus multicollinearity is not a problem for this study. To avoid potential colinearity among the interaction terms, I mean-centered the continuous variables involved in the interaction terms by subtracting the mean from each value before generated the interaction terms (Aiken & West, 1991). Results. I used Stata 10 to conduct the statistical analysis. Table 3 shows the results of conditional logistic regression analysis. Model 1 is the baseline model that only includes control variables. Model 2 incorporated the three key independent variables. Model 3 is the full, unrestricted model, which includes all statistical control variables, three main effects and three interaction terms. I reported robust standard errors that are robust to departures from homoscedasticity. Hypothesis 1 states that number of VC investors co-investing in a portfolio firm (syndication size) is positively associated with the portfolio firm s likelihood of involving in inter-firm collaborations. This hypothesis was strongly supported (p<0.05). Hypothesis 2 asserted that portfolio firms financed by syndication partners with more accumulated experiences (measured by average age) are more likely to form alliances. The coefficient of this independent variable is moderately significant (p<0.1) 33

but the sign is the opposite of what I hypothesized, which indicates that portfolio firms financed by younger VC investors are more likely to form alliances. Hypothesis 3 argues that the heterogeneity of syndication partners in terms of type increases the likelihood of alliance formation by portfolio firms. Results show strong support for this hypothesis (p<0.01). When a portfolio firm receives financing from multiple types of VC investors, they can get access to diverse information about potential alliance opportunities, which increase its chance to form alliances. Hypotheses 4a 4b suggested that tie strength measured by total amount of VC investment a portfolio firm receives moderates the relationships between the three VC syndication compositional characteristics and the portfolio firm s alliance formation. The stronger the tie between a portfolio firm and its VC investors, the more informational benefits it can obtain from the latter. Model 3 is the full model including all the interaction terms. In this model, the three main effects were still found to be significantly related to portfolio firms alliance formation. The interaction effect of tie strength with syndication size was negative and nonsignificant. Hypothesis 4a was not supported. The interaction effect of tie strength with experience of VC syndication partners was moderately significant (p<0.1) but had the opposite sign to what was predicted in Hypothesis 4b. The interaction effect of tie strength with type heterogeneity of VC syndication partners was positive and significant (p<0.05), supporting Hypothesis 4c. Figures 2 and 3 illustrate the significant moderating effects on logarithmic odds scale. DISCUSSION AND CONCLUSION Anchored in the network resource theory (Gulati, 2007; Lavie, 2006), this study provides empirical evidence that the compositional characteristics of VC syndications 34

influence portfolio firms likelihood of alliance formation and the moderating effects of tie strength on these relationships. While most hypotheses in this study received empirical support, the findings suggested that portfolio firms financed by younger VC investors are more likely to form alliances, which is opposite to what I predicated in Hypothesis 2. This result may be attributed to several reasons. For example, seasoned VC investors with richer industrial experience and stronger internal competencies are more capable of providing various supports for their portfolio firms. Portfolio firms affiliated to such experienced VC firms may thus less intend to seek other external sources of supports and benefits, such as strategic alliances. It is also possible that younger VC firms, which are eager to establish reputation of capable investors, put forth more efforts to build industrial connections and cooperative relations for their portfolio firms. Another potential explanation for this result may be the sample used for this study. Medical care/biotechnology industry has been a hot investment target for VC firms only since about two decades ago. The VC firms founded in the 1970s or 1980s built their success primary on investments in computer hardware and software startups. Their experiences accumulated over time in financing the computer science related businesses may not be readily transferred to the investments in medical care/biotechnology firms because of the substantial differences between these industries. In contrast, VC firms that were founded more recently may have a better understanding about the trends and dynamics in medical care/biotechnology industry and therefore be more capable of providing relevant and effective alliance information. The results of regression analysis also indicated that tie strength negatively moderates the negative relationship between experience of VC syndication partners and 35

alliance formation by portfolio firms. This finding confirmed the argument that tie strength enhances the original relations between partners resource endowments or capabilities and the network benefits a focal firm can obtain. As the statistical result for Hypothesis 2 indicated, older VC investors decrease the likelihood of portfolio firms alliance formation. The strength of the ties between VC investors and portfolio firms make this negative relationship stronger. The findings of this study both support and extend those of previous research. First, the findings confirm the important roles of VC firms as sources of network resources such as information. Prior studies have demonstrated that VC investors help develop networks for their portfolio firms (e.g., Hsu, 2006; Lindsey, 2008). The information benefits they bring about for portfolio firms are critical value they added to the latter. Through collecting information about individual firms for screening or mornitoring purposes, VC firms not only gain an indepth understanding about their portfolio firms true capabilities and needs but also accumulate volume information of various firms that may serve as good allainces partners of their portfolio firms. In addition, via their extensive social networks, which include contacts with establihsed corporations, VC firms are able to identify potential alliance partners that may have synergy with their portfolio firms. Second, this study explicitly examined the joint effects of the quality of nodes and the strength of ties on generation of network benefits. The results provided some support for the moderating effects of tie strength, which confirm Gulati s (2007) arguments on the collective role of partners resources endowments and tie strength in determining the benefits a firm can obtain from its social network. 36

Third, this paper sheds light on an important issue that has received little attention in the alliance literature and entrepreneurship literature, namely, the collective effect of VC syndication partners on portfolio firms alliance formation. There has been ample anecdotal and empirical evidence that VC firms can influence portfolio firms strategy and performance (e.g., Baum & Silverman, 2004; Davila, Foster & Gupta, 2003). Despite the fact that portfolio firms often receive financing from more than one VC investors, extant studies on VC influence have largely focused on the mere presence or absence of VC ownership or the characteristics of the lead VC investor, ignoring the differences between VC firms making co-investments and the resulting aggregate differences between VC syndications. This study filled this cap in the VC literature by examining three different compositional characteristics of VC syndication, their respective implications on the information benefits associated with VC financing in facilitating alliance formation. This study has several limitations that must be taken into consideration when evaluating the results. First, the relative infrequency of alliance formation by VC-backed private ventures and the limited data availability for these privately held ventures kept the sample size small: only 158 alliances formed by 1681 companies with available data of key variables had been identified from the whole population of approximately 1600 U.S. VC-backed private new ventures in medical care/biotechnology industry (at the time of data collection). This resulted in a matched-pair sample of 316 firms in total. However, the matched-pair design is relatively powerful and increasingly so as matching accuracy increases (O Connor et al., 2006; Sheskin, 2000). Through deploying a number of 37

stringent matching criteria, the level of matching accuracy of this study should be able to ensure the reliability of the findings. Second, the sample consists of only U.S. VC-backed private ventures in medical care/biotechnology industry. It has been found that VC-backed ventures in different industries have different base rates of alliance formation (e.g., Lindsey, 2008). The dynamics of inter-firm collaborations in these industries may differ substantially. Thus whether the findings of this study are generalizable to other industries needs further investigation. In addition, future studies incorporating foreign samples may product additional insights. In conclusion, this paper contributes to both the strategy literature and the entrepreneurship literature by providing empirical evidence of significant relationships between three key compositional characteristics of VC syndications and alliance formation of portfolio firms. These relationships appear to be stronger when the portfolio firms receive larger amounts of VC investments. Future studies may further examine the influence of different aspects of VC syndication composition on portfolio firms behavior and performance. 38

TABLE 1.1 Variable Operationalization (Study 1) Variable name Variable type Measures DV: Alliance formation Dichotomous 1 = alliance formed the given year; 0 = otherwise IVs: Syndication size Count The number of VC partners co-investing in the portfolio firm Experience of VC Calculated AGE i syndication partners n I used age as the proxy of experience. Age of individual VC partners is measured by the number of years since their foundation. Type diversity Count The number of types of VC partners coinvesting in a portfolio firm I followed the type classification used in SDC database, which divides VC investors into 13 different categories. Moderator: Tie strength Continuous Total amount of VC financing a portfolio firm received Statistical Controls: Count Total number of executives Firm size Firm performance Count Total number of financing rounds received Time since 1 st VC investment Count Number of years since the portfolio firm received the 1 st VC financing Firm development stage Dummy I followed the way SDC database classifies the development stage of portfolio firms. The six dummy stage variables are: seed, early, expansion, later, buyout/acquisition and other. Controls via Matching: Count Number of years since founding Firm age Firm industry Dummy Segments within medical care/biotechnology industry as defined by SDC database 39

TABLE 1.2 Descriptive Statistics and Correlations (Study 1) Variables Mean S.D. Min Max 1 2 3 4 1 alliance formation 0.500 0.50 0 1 1 2 number of financing rounds 0.37 0.28 0 1.11 0.19* 1 3 number of executives 0.99 0.25 0 1.41 0.20* 0.26* 1 4 time since 1st investment 3.83 2.49 0 17 0.10 0.59* 0.14* 1 5 seed 0.09 0.29 0 1-0.09-0.65* -0.23* -0.17* 6 early 0.26 0.44 0 1 0.00-0.30* -0.0-0.23* 7 expansion 0.40 0.49 0 1 0.05 0.15* 0.10 0.11 8 later 0.14 0.35 0 1 0.08 0.31* 0.11* 0.23* 9 buyout/acquisition 0.02 0.12 0 1 0.03 0.06 0.05 0.04 10 other 0.09 0.28 0 1-0.10 0.18* -0.07 0.05 11 syndication size 4.72 3.60 1 22 0.32* 0.56* 0.40* 0.44* 12 average VC age 17.04 7.64 0 44.33-0.04 0.18* -0.06 0.24* 13 type diversity 2.04 1.07 1 6 0.34* 0.40* 0.29* 0.31* 14 total VC investment 28.03 45.73 0.01 572.25 0.31* 0.23* 0.34* 0.16* Variables 5 6 7 8 9 10 11 12 1 alliance formation 2 number of financing rounds 3 number of executives 4 time since 1st investment 5 seed 1 6 early -0.19* 1 7 expansion -0.26* -0.49* 1 8 later -0.13* -0.24* -0.33* 1 9 buyout/acquisition -0.04-0.07-0.10-0.05 1 10 other -0.10-0.18* -0.25* -0.12* -0.04 1 11 syndication size -0.26* -0.23* 0.16* 0.28* 0.14* -0.06 1 12 average VC age -0.12* -0.07 0.08 0.06 0.07-0.02 0.09 1 13 type diversity -0.23* -0.07 0.10 0.19* -0.03-0.05 0.68* 0.06 14 total VC investment -0.16* -0.15* 0.16* 0.14* 0.05-0.07 0.45* -0.07 Variables 13 14 1 alliance formation 2 number of financing rounds 3 number of executives 4 time since 1st investment 5 seed 6 early 7 expansion 8 later 9 buyout/acquisition 10 other 11 syndication size 12 average VC age 13 type diversity 1 14 total VC investment 0.37* 1 * p < 0.05 40

TABLE 1.3 Conditional Logistic Regression Results (Study 1) Model 1 Model 2 Model 3 number of financing rounds 1.50 * 0.08 0.66 (0.65) (0.88) (0.97) number of executives 1.49 * 0.73 0.54 (0.61) (0.62) (0.73) time since 1st investment 0.03 0.05 0.02 (0.06) (0.07) (0.08) seed -0.33 0.95 0.83 (0.97) (0.93) (1.16) early -0.23 0.63 0.35 (0.85) (0.82) (1.08) expansion -0.56 0.28-0.52 (0.87) (0.82) (1.11) later -0.54 0.12-0.69 (0.96) (0.94) (1.26) other -1.23-0.27-0.91 (0.97) (0.98) (1.25) syndication size 0.22 ** 0.16 * (0.07) (0.09) average VC age -0.03-0.05 41 (0.02) (0.03) type diversity 0.48 ** 0.58 ** (0.19) (0.24) total VC investment 0.02 * (0.01) total VC investment syndication size -0.00 (0.00) total VC investment average VC age -0.00 (0.00) total VC investment type diversity 0.02 * (0.01) Log Pseudo Likelihood -97.06-79.11-71.65 Pseudo R 2 0.11 0.28 0.35 Wald Chi 2 17.70 52.19 52.74 N 316 316 316 Significance levels (two-tailed test for control variables; one-tailed test for hypotheses): p < 0.1 * p < 0.05 ** p < 0.01 *** p < 0.001

FIGURE 1.1 The Framework (Study 1) 42

FIGURE 1.2 Significant Moderating Effects Full Range (Study 1) 43

FIGURE 1.3 Significant Moderating Effects ± 1 Standard Deviation (Study 1) 44

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CHAPTER THREE VC INTERLOCKS AND ALLIANCE FORMATION OF ENTREPRENEURIAL STARTUPS: A LONGITUDINAL CROSS-LEVEL STUDY INTRODUCTION Research has well established the strategic and organizational benefits of interfirm collaboration (e.g., Ang, 2008; Beckman, Haunschild & Phillips, 2004; Chung et al., 2000; Dacin, Oliver & Roy, 2007). The beneficial effects of alliances in facilitating survival, growth and performance of entrepreneurial startups have also been well examined (e.g., McGee, Dowling & Megginson, 1995; Stuart, 2000; Stuart et al., 1999). Network theory has often been used to explain the access to alliance opportunities and partner selection (e.g., Granovetter, 1973; Gulati, 1998; Gulati, 1999). However, relational networks (e.g. board interlocks) can also be a source of behavioral model for interfirm imitation. Interfirm imitation occurs when one or more firms execution of a practice increases the likelihood that other firms adopt the same practice (Haunschild & Miner, 1997). By modeling their action on referent others, firms can obtain a number of benefits such as reducing uncertainty, increasing legitimacy and avoiding competitive disadvantage (DiMaggio & Powell, 1983; Fulk, 1993; Gimeno, 2005). However, a simple awareness of a practice (e.g., alliance formation by others) may not be sufficient to account for its spread because of the ambiguity surrounding the value of the practice. Imitation occurs only if the utility or value of the practice is appreciated, either based on technical or legitimacy consideration. Interfirm relational ties represent an effective channel through which information about a practice s utility or value spreads. Through the interaction and communication with tied-to firms, a firm may obtain detailed 53

information about the initiation, management and outcomes of their alliance activities. Given the quality and reliability of information flowing from connected peers, the focal firm is likely to act upon the information by forming similar alliances so as to capture the potential benefits. In addition, imitation is particularly meaningful in environments that are characterized by ambiguity and uncertainty (Lieberman & Asaba, 2006). For entrepreneurial startups, the uncertainty they face is particularly high due to the underdeveloped market segments/niches in which they are operating. Thus startups are likely to imitate the behavior of referent others perceived to be feasible and valuable for them so as to reduce uncertainties. Venture capital (VC) firms invest simultaneously in a portfolio of entrepreneurial startups and provided a variety of value-added services during the investment period to help the latter progress toward a profitable exit (Sahlman, 1990; Gompers & Lerner, 2004). Prior research has well established the role of VC firms as an important source of external resources and social capital for portfolio firms (Fired & Hisrich, 1995; Gorman & Sahlman, 1989). In particular, VC firms serve as the information intermediary between portfolio firms and various external constituents such as industrial actors, service providers and potential investors. However, VC firms role as information intermediaries among their portfolio firms has largely been ignored in the literature. Similar to firms linked through board interlocks (Mizruchi, 1996), portfolio firms connected by their common VC firm are well aware of one another s strategic actions. From a social contagion perspective (Marsden & Friedkin, 1993), exposure to such information via 54

VC interlocks is likely to influence portfolio firms behaviors and actions, such as alliance formation. Toward this end, this study intends to contribute to both the strategy and entrepreneurship literatures by exploring the cross-level influence of VC interlocks on alliance formation of portfolio firms (see Figure 1 for the framework). I argue that firms in the same VC portfolio tend to view one another as models to imitate or emulate because of both the connection through their common VC firm and their underlying similarities implied by the portfolio membership. The cohesion model and the structural equivalence model are two major mechanisms that are responsible for the diffusion of organizational practices (e.g., Burt, 1987; Davis, 1991; Marsden & Friedkin, 1993). These models are built on direct interaction and vicarious observation, respectively, which are two major ways by which managers identify models of action. In cohesion models, the social contagion theory highlights the role of interfirm relational ties as sources of models to be imitated. In structural equivalence models, imitation is not a result of direct contact between early adopters and late adopters, but is the consequence of environmental scanning and analysis. VC interlocks influence the alliance formation by portfolio firms through both mechanisms. Also, examining the above relationship can highlight the multilevel nature of the relationship between a VC firm and its multiple portfolio firms, which has been largely ignored in previous research. The emphasis on using multilevel frameworks and analytical tools to study complex organizational phenomena has increased in recent years (Hitt et al., 2007). Researchers have adopted multilevel perspectives and cross-level 55

analyses in recent studies on management team behavioral integration (Simsek et al., 2005), firm performance (Misangyi et al., 2006), team member boundary-spanning behavior (Marrone, Tesluk & Carson, 2007), bribery activity (Martin et al., 2007), foreign direct investment ownership structure (Mani, Antia & Rindfleisch, 2007) and expatriate effectiveness (Chen et al., 2010). Particularly, firms are embedded in multiple network relationships and nested in various contexts such as strategic groups, industries and nations, which collectively influence firm behavior and performance. This multilevel nature of social embeddedness has gained increasing research attention (Brass et al., 2004; Hagedoorn, 2006; Payne et al., 2010). Given the nature of the relationship between a VC firm and its portfolio firms, whereby the latter are nested within the former, multilevel modeling is an appropriate and desirable. Such a multilevel study can help advance our understanding about drivers and catalysts of entrepreneurial startups partnership building. Furthermore, although VC interlocks are an influential source of interfirm information, they are unlikely to influence outcomes uniformly across different portfolio firms. I argue that the influence of VC interlocks on a specific portfolio firm depends on the extent to which the portfolio firm views the information conveyed by VC interlocks as reliable and trustful, as well as the extent to which the portfolio firm relies on VC interlocks as key information sources. Research has suggested that firms can obtain more benefits from stronger ties because of the mutual trust accumulated over time (Granovetter, 1973). As such, the strength of relationship between a portfolio firm and a VC firm is likely to influence the chance of information conveyed through VC interlocks 56

to be acted upon. In addition, a firm s reliance on an information source decreases when alternative sources of information present (Haunschild & Beckman, 1998). Personal social relations of top management teams (TMTs), or founding teams, are critical sources of external information and other social capital for startups that have yet established stable firm-level networks (Eisenhardt & Schoonhoven, 1996). A startup having larger TMT thus enjoy more informational advantages and may rely less on VC interlocks as an information source. Therefore, this study can demonstrate the interactions of variables at different levels by examining how portfolio firm level factors influence the effects of VC interlocks on portfolio firms alliance formation. In the following sections, I will first briefly review the literature of interfirm imitation and then explore specifically how VC interlocks influence portfolio firms mimetic behavior in terms of alliance formation and examine the cross-level moderating effects of tie strength and TMT size. A sample of U.S. portfolio firms and their lead VC firms between 2003 and 2007 were assembled for empirical testing. Longitudinal multilevel logistic regression models were used to test the hypotheses. THEORETICAL BACKGROUND Interorganizational Imitation Firms determine appropriate courses of action not simply on the basis of their private information or calculated self-interest, because firms actions are not independent of the social context in which they are embedded (Granovetter, 1985). The actions of other firms have direct influence on a focal firm s decision and strategy making. Interorganizational imitation occurs when one or more organizations execution of a 57

practice increases the likelihood that others adopt the same practice (Haunschild & Miner, 1997). Interorganizational imitation is a common form of behavior that can be observed in a variety of business domains. Firms imitate one another in the introduction of new products/services and processes, in the timing and mode of market entry (e.g., Delios, Gaur & Makino, 2008; Gimeno et al, 2005; Haveman, 1993) and in the adoption of organizational forms, governance structures and managerial methods (e.g., Davis, 1991; Haunschild, 1993). Prior studies have identified a number of motives of mimetic behavior. First, institutional theory suggests that organizations imitate practices adopted by others so as to acquire legitimacy (DiMaggio & Powell, 1983). Firms judge a practice s legitimacy by the frequency of adoption, the status of the early adopters and the outcomes of the practice. Specifically, a firm is more likely to imitate actions that have been taken by a large number of firms, activities of large or prominent firms and practices perceived to have produced positive outcomes for other firms (Haunschild & Miner, 1997; Williamson & Cable, 2003). Second, social learning theories argue that organizational actors learn vicariously by copying the actions of similar actors in the face of uncertainty (Bandura, 1986; Fulk, 1993). For example, research has found that firms in automobile industry tend to mimic behavior of those they consider strategically similar as defined as belonging to the same strategic groups (Garcia-Pont & Nohria, 2002). Third, competitive dynamics theories contend that firms imitate their competitors actions in order to avoid competitive disadvantage (Gimeno, 2004). For example, after Star Alliance, the first true global airline alliance, was established in 1997, airline companies around the world began 58

joining international airline collaborative programs actively to avoid being left behind by their competitors. Alongside the large volume of studies focusing on the benefits associated with interorganizational imitation, researchers have also made substantial efforts to identify and investigate the contextual catalysts or conditions of imitation. Two social structural mechanisms that have been identified to be responsible for interoganizational imitation are direct communication based mechanism and symbolic communication based mechanism (e.g., Davis, 1991; Marsden & Friedkin, 1993). Imitation models based on direct communication, also called cohesion models, are primarily built on social/interorganizational contagion theory (Marsden & Friedkin, 1993). This theory highlights the role of interfirm relational ties as sources of models to be imitated. For example, it has been found that firms tied via common board directors are more likely to mimic one another s actions and practices (e.g., Haunschild, 1993; Williamson & Cable, 2003). In contrast, imitation models based on symbolic communication draw on multiple theories including structural equivalence perspective (Burt, 1987), institutional theory (DiMaggio & Powell, 1983), social learning theory (Bandura, 1986) and competitive dynamics theory (Chen, 1996). Under the symbolic models, imitation is not resulted from direct contact between early adopters and late adopters, but is the consequence of environmental scanning and analysis firms mimic what the salient referent others under observation are doing. The two mechanisms highlight different ways of model identification. Through direct communication, the focal firm learns about the technical utility or value of the actions or practices adopted by the tied-to firms. Direct communication with early 59

adopters of a practice helps resolve the ambiguity surrounding the value of the practice to the extent that the rationale behind the practice is communicated (Davis, 1991). Through observing and monitoring similar others in the environment, the focal firm infers the social value of an action or practice from the frequency of adoption and the traits of early adopters (Haunschild & Miner, 1997; Williamson & Cable, 2003). Modeling actions upon actors occupying similar roles is primarily based on the consideration of avoiding embarrassment, losses, as well as competitive advantages rather than on that action s technical merit (Burt, 1987, Gimeno et al, 2005). Faced with ambiguity, firms observe and monitor the actions of similar others in an effort to find viable solutions to organizational problems (DiMaggio & Powell, 1983). In addition, the likelihood that a focal firm will model its action upon the tied-to firms is higher when the latter share similarities with the former (Bandura, 1986). Technical merits communicated via direct ties, combined with the social value stemmed from similarities, increase the likelihood of imitation. VC Interlocks The cohesion model of diffusion emphasizes the direct contact and communication between firms that are at risk of adoption and those that have already adopted. In this view, simple awareness of others adoption of a practice is not sufficient to account for the spread of the practice. The practice s value is understood through direct communication between firms. Diffusion is explained by the evaluation resulting from communication with one s direct contacts. The homogeneity resulting from this mechanism is generally called social/interorganizational contagion (e.g., Galaskiewicz & 60

Burt, 1991; Marsden & Friedkin, 1993). Social contagion perspective highlights the role of interfirm network ties in social influence. Board interlocks (also called interlocking directorates) is one of the most examined cohesion model of imitation and diffusion. It is refers to the ties between firms through a member of one firm sitting on the board of another (Mizruchi, 1996). From an informational perspective, researchers have viewed interlocks as an effective means for firms to reduce uncertainties and share information about corporate practices. Interlock has been found to facilitate the spread of poison pills a defense against unwanted corporate takeovers (Davis, 1991), affect the adoption of the multidivisional form (Palmer, Jennings & Zhou, 1993), promote imitation of acquisition activities (Haunschild, 1993) and influence organizational hiring patterns (Williamson & Cable, 2003). Managers are exposed to the strategic and non-strategic actions of other firms when they sit on these firms boards. Through the interaction between these managers and their respective firms for which they serve as board members, information of these actions is transferred between interlocked firms. The actions taken by the firms to which a focal firm is tied thus serve as its models to imitate. In such an imitation and diffusion process, managers sitting on boards of multiple firms play the role of information conduits. According to social network theory (Nahapiet & Ghoshal, 1998) and social contagion theory (Marsden & Friedkin, 1993), interfirm ties generate comfort and trust between connected firms that encourage the transmission of high-quality, reliable information, which has direct influence on managers decision making. Interlocked firms 61

are likely to mimic one another s actions due to the dissemination of detailed information about the referent others actions. The effects of interlocks in interorganizational imitation have been primarily examined in the case of large, established corporations (e.g., Davis, 1991; Haunschild, 1993; Williamson & Cable, 2003). Startups and small ventures, whose direct social contacts are limited due to their narrow networks, have long been ignored in studies on social mechanisms of imitation. Such ignorance may lead to an incomplete understanding of the effect of network connections on firm behavior in entrepreneurial settings. Research has suggested that the information sharing and uncertainty reducing benefits of interlocks are especially important in uncertain environments (Carpenter & Westphal, 2001; Geletkanycz & Hambrick, 1997). Given the high levels of uncertainties associated with the new technologies or business models entrepreneurial startups rely on, interlocks may play critical roles in shaping the behavior of startups. VC firms are financial investors that typically specialize in high growth but high risk investments, such as early stage ventures in unproven, high technology industries (Black & Gilson, 1998; Sahlman, 1990). The prevailing organizational form in the VC industry is the limited partnership, whereby venture capitalists serve as general partners and raise funds from limited partners and manage the fund over a fixed period of time (typically 5-10 years) (Gompers & Lerner, 2004). Limited partners include wealthy individuals and institutional investors such as pension funds, large foundations, universities and insurance companies. VC firms manage funds by investing in a portfolio of entrepreneurial ventures. Firms receive VC financing are typically referred to as 62

portfolio firms. At the end of the fund duration, VC firms must generate financial returns to limited partners by exit via IPO or trade sales. Established corporations and large financial institutions, which usually invest through their wholly-owned VC subsidiaries, constitute the other major groups of players in the VC industry (Gaba & Meyer, 2008). The role of VC firms in supporting the development of entrepreneurial ventures has been widely recognized and acknowledged by researchers from various academic disciplines including strategic management, entrepreneurship and financial economics (e.g., Amit, Brander & Zott, 1998; Bottazzi, Rin & Hellmann, 2008; Fried & Hisrich, 1995). The VC industry has helped develop many successful entrepreneurial ventures. Notable examples include Apple, Google, Federal Express, Compaq and Digital Equipment Corporation. As a highly desired source of financing for start-ups, especially for those operating on the frontier of novel technologies and emerging markets, VC firms plays a critical role in the entrepreneurial process. Prior studies have demonstrated that support from VC firms can increase a new venture s chance of survival and reaching the IPO stage (Macmillan, Kulow & Khoylian, 1989; Sapienza, 1992). VC firms have been generally viewed as value-added investors who play a significant role in the development of entrepreneurial startups (Bygrave & Timmons, 1992). In addition to providing financial capital, VC firms often contribute to the management of portfolio firms (Fried & Hisrich, 1995). They provide monitoring and value-added services, such as raising additional capital, hiring key personnel, professionalizing the organization, providing general business knowledge and information on competition, assisting in formulating strategy, and seeking potential 63

acquirers (Fired & Hisrich, 1995; Gorman & Sahlman, 1989). These services are useful to entrepreneurial startups, ensuring them to respond quickly and effectively to the identified entrepreneurial opportunities. Through intensively monitoring portfolio firms behavior and providing them with a variety of value-added services, VC firms help them progress toward a successful, profitable exit. Despite the large volume of studies on the value-added services VC firms provide to portfolio firms, an important but largely ignored role of VC firms is that they can serve as information conduits between their portfolio firms and facilitate practice diffusion within the portfolio. VC firms invest in a portfolio of entrepreneurial startups and monitor their progress often by sitting on the board of directors in these portfolio firms (Lerner, 1995; Gompers & Lerner, 2001). In this sense, firms in a given VC firm s investment portfolio can be seen as being connected to one another via VC interlocks the relational ties based on their common VC investor. These portfolio firms are thus exposed to one another s strategic and non-strategic actions and are likely to view one another as models of action. Similar to interlocked firms in general, the communication between two portfolio firms connected by their common VC investor takes place largely in board meetings. However, VC interlocks differ from general board interlocks in that VC firms are independent from connected portfolio firms and present a higher level influence. In this sense, the relational tie between two portfolio firms based on their common VC investor can be seen as an indirect, third party tie. A multilevel framework for studying the influence of VC interlocks is appropriate given the fact that VC firms 64

invest and serve as board members in a portfolio of firms portfolio firms are nested within VC firms. HYPOTHESES Imitation through VC Interlocks The core argument of interorganizational contagion theory is that the likelihood of a firm to take a certain action or adopt a certain practice increases when the firm is tied to other firms that have already taken the action or adopted the practice (Marsden & Friedkin, 1993). A common VC investor sitting in boards of two portfolio firms facilitates the communication between them through the VC investor s respective interaction with them. The common VC investor functions as the information conduit by which valuable information is exchanged between the tied portfolio firms. The information exchanged through the common VC investor is reliable in that the VC investor serves as an insider of both portfolio firms. The direct consequence of such transmission of high quality information is the increased likelihood that the information two tied portfolio firms share will be acted upon by their respective decision makers. While general board interlocks affect firms behavior primarily through the relational aspect of social influence, VC interlocks have both relational effects and structural effects. As Granovetter (1985) argued, firms are embedded in a larger socioeconomic context that influences their behaviors. A firm directly interacts with various economic actors through transactions, partnerships, trade associations, etc. The firm can learn about new practices through such relational embeddedness. Learning also occurs when firms are not in direct contact. A firm may take strategic actions based on 65

the information it collected through scanning and monitoring activities of salient or similar firms. In the latter case, structural embeddedness comes into effect. In imitation based on symbolic communication, firm characteristics such as size and status are often used as the criteria in determining whether or not to copy the referent others actions. Salient firms are more likely to be the model to imitate. However, although large, established corporations are often viewed as salient reference in general (DiMaggio & Powell, 1983; Haunschild & Miner, 1997), imitating these firms may not be the most appropriate for entrepreneurial startups because of the substantial differences in scale, scope and resource endowments. The major difficulties encountered by entrepreneurial startups and established corporation are different, which reduces the relevance and salience of the latter as behavior reference for the former. For entrepreneurial startups, the behavior and practice of other startups are often more relevant for learning and imitation purpose. VC ownership can serve as a central characteristic by which portfolio firms distinguish themselves from non-vc-backed startups. Receiving VC financing is a cornerstone in the development of an entrepreneurial startup and is viewed as a signal of increased chance of future success. Every year, VC firms receive a large number of business plans but invest in only a small fraction of the deals come to their attention (Tyebjee & Bruno, 1984). Portfolio firms can thus be viewed as outstanding from the population of entrepreneurial startups. Such a stereotype of portfolio firms is likely to render them to act cohesively as an outstanding group in the population of startups, treating one another as key reference to imitate. 66

We can view firms in a VC firm s portfolio as being similar to one another to the extent that the VC firm describes themselves as focusing on a specialized domain such as industry or region. Moreover, firms in the same VC investment portfolio are often selected by the VC firm using the same or similar criteria in business philosophy, managerial vision and core competency. Being certificated by a common VC investor, portfolio firms are likely to perceive one another as promising peers sharing a number of non-visible, underlying attributes. As such, these visible and invisible similarities among portfolio firms of the same VC firm increase their salience as models of action in one another s eyes and they are therefore more likely to act upon the information transmitted though VC interlocks. Hypothesis 1. The likelihood of a portfolio firm to form alliances is positively associated with the total number of alliances formed by firms in the same VC portfolio. However, although VC interlocks represent an influential source of interfirm information that increases the likelihood of interfirm imitation, they are unlikely to influence outcomes uniformly across different portfolio firms. The strength of the proposed effect of VC interlocks is likely to be influenced by other factors. I suggest that the effect of VC interlocks on a given portfolio firm is contingent on the extent to which the portfolio firm deems the information conveyed by VC interlocks reliable and trustful and the extent to which the portfolio firm relies on VC interlocks as primary information sources. 67

Moderating Effect of Tie Strength While board interlocks have been widely acknowledged as important information conduits among otherwise unconnected firms, few research attention has been paid to the factors influence the effectiveness of such information channels (see Haunschild & Beckman, 1998 for an exception). Actors and the relational ties between them are the two elementary components of a network, which jointly determine the benefits stemmed from network relations (Gulati, 2007). As actors with different resource endowments and social status provide different levels of benefits to their network contacts, different ties are generally assumed to function differently (Borgatti & Foster, 2003). Ties linking two actors can be measured as either dichotomous or valued. When a relational tie is measured on a scale, the value captures the strength of the tie (e.g., number of collaborative projects between a pair of firms). Research has found that a firm enjoys more network benefits from a tightly connected quality partner than from a loosely connected one because of the shared interests and trust accumulated in the course of relationship development (Coleman, 1990; Granovetter, 1985). The concept of tie strength was initiated by Granovetter s (1973) paper titled The Strength of Weak Ties, whereby he conceptualized tie strength as the combination of the amount of time spent in the relationship, emotional intensity, intimacy and reciprocal services associated with the relational tie. The concept of tie strength has then been used in a number of studies in both management and sociology areas treating it as a predictor of outcomes at both individual and organization level (e.g., Lechner, Frankenberger & Floyd, 2010). The strength of the tie connecting two actors (e.g., individuals, 68

organizational units or firms) has been found to influence the levels of trust and efficiency of information sharing/transfer between them (Krackhardt, 1992; Szulanski, 1996). Stronger ties often mean higher levels of trust and moral obligation as an underlying support mechanism to reduce the risk of opportunism and cheating (Johanson & Mattson, 1987; Powell, 1990). The effectiveness of a relational tie in channeling network benefits is contingent on its strength. Extant literature examining effects of tie strength on information sharing or knowledge transfer has largely focused on relational ties between individuals or teams, measuring the strength of ties by assessing the closeness, duration and frequency of the relationship through five- or seven-point scale survey questions (e.g., Lechner, Frankenberger & Floyd, 2010; Perry-Smith, 2006; Pil & Leana, 2009; Reagans & McEvily, 2003). In firm level studies, researchers typically use repeated relational ties to capture the strength of the relationship between firms, arguing that prior network connections between a pair of firms are important catalysts of new alliance formation between them (e.g., Gulati, 1999). I argue that higher levels of strength of interlock ties between a VC firm and its portfolio firms are likely to enhance the effectiveness of the former as an information intermediary between the latter. The familiarity and trust between a VC firm and its portfolio firm, which have direct influence on the effectiveness of information sharing and exchange, are enhanced as the strength of the relationship between them increases. On the one hand, the intensive pre-investment due diligence and post-investment monitoring enable VC firms to learn about their portfolio firms. As the amount of time 69

spent in the relationship increases, VC firms gain better understanding about their portfolio firms strengths and weaknesses, which enhances their effectiveness in providing relevant and useful information. Information with strategic relevance is more likely to influence portfolio firms behavior and strategy than general information. On the other hand, with higher levels of trust on their VC investors, portfolio firms will treat more seriously the information conveyed by their VC investors. When a portfolio firm learned the alliance benefits enjoyed by other firms in its trusted VC investor s portfolio, it is more likely to form similar alliances to capture those benefits because of the confidence on the benefits stated by the VC investor. Hypothesis 2. Tie strength positively moderates the relationship between the likelihood of a portfolio firm to form alliance and the alliance formation of other firms in the same VC portfolio. Moderating Effect of Top Management Team Size A major task of a firm s top management team (TMT) is to align strategies and internal operations with the current and anticipated external environment through monitoring market trends, competitor actions, changes in regulations and so on (Hambrick, 1989). As such, TMT can be viewed as a firm s center of informationprocessing, dealing with its relationship with its environment (Thompson, 1967). TMT size is a key determinant of the information-processing capabilities of a firm s top management (Haleblian & Finkelstein, 1993), which have direct implications on firm performance. Research has found that TMTs of firms nearing bankruptcy tend to be smaller than are those of surviving counterparts (Hambrick & D Aveni, 1992). For 70

entrepreneurial startups, the role of TMTs, or founding teams, is particularly important, given the high levels of uncertainties derived from the novel technologies or business models they use and the limited human and social capital they have in the early stage of development. The size of founding teams in high-tech ventures has been found to be positively associated with growth (Cooper & Bruno, 1977; Eisenhardt & Schoonhoven, 1990). The TMT of an entrepreneurial startup also represents a major source of social capital and the foundation of network building (e.g., Higgins & Gulati, 2003; Kim & Higgins, 2007). Firms are embedded in multiple networks of personal, social, professional and exchange relationships with various organizational actors (Granovetter, 1985; Gulati, 1998; Gulati, Nohria & Zaheer, 2000). An actor s different social relations have different utilities and can be used for different purposes (Hansen, Mors, & Lovas, 2005; Podolny & Baron, 1997). Besides the network benefits derived from the affiliation to VC firms, TMT members personal web of relationships constitutes major parts of startups social capital (Bamford, Bruton & Hinson, 2006; Davidsson & Honig, 2003). Such social capital is important in performing many tasks like dealing with suppliers, obtaining new customers, hiring new employees and navigating relevant regulations. TMT members personal networks are also critical basis for startups network building and expansion (Kim & Higgins, 2007) and significantly contribute to startups survival and success (Bosma et al., 2004; Brüderl & Preisendörfer, 1998; Hallen, 2008). Firms identify external opportunities and threats through environmental scanning and monitoring. Networks play key roles in channeling salient and trusted information 71

that is likely to affect decision making and behavior (Brass et al., 2004). For entrepreneurial startups that have not established stable networks yet, personal networks of TMT members are particularly important for seeking external supports and collecting strategic information (Dubini & Aldrich, 1991). The social contacts of TMT members can thus be viewed as channels through which information and other external resources flow into the firm. As such, a greater number of TMT members means more boundary spanners connecting to the external environment and gathering environmental information. In other words, larger TMTs are more likely to have more network connections from which information about strategic actions of other firms may be collected. As suggested by resource dependence theory (Pfeffer & Salancik, 1978), a firm s reliance on one type of information conduits decreases as more alternative channels are available. Greater numbers of executives do not only imply higher levels of external information accessibility, but also better capabilities of environmental scanning and monitoring. Startups with more executives may rely less on their lead VC firms to collect and convey information of strategic actions by industrial peers. In other words, the importance or weight of information conveyed by a startup s lead VC firm in decision making decreases as alternative information sources or information collection capabilities of the startup itself increases. Hypothesis 3. TMT size negatively moderates the relationship between the likelihood of a portfolio firm to form alliance and the alliance formation of other firms in the same VC portfolio. 72

METHODOLOGY Sample Selection The data used for this study are an unbalanced panel of observations capturing alliance formation by portfolio firms in the medical care/biotechnology industry between 2003 and 2007. The selection of research setting for this study was based on both the nature of the phenomenon of interest and the trends in VC industry. The phenomenon of interest in this study is the alliance formation of entrepreneurial startups. In recent years, entrepreneurial initiatives and new venture creation activities in medical care/biotechnology industry have been proliferating. The medical care/biotechnology sector has become a prominent recipient of U.S. VC funding (second only to the software industry as of the second quarter of 2010) since the Internet bubble burst in early 2000s and commanded a consistently increasing portion of all U.S. VC investments (Dooley, 2010). Given the substantial capital and time needed for R&D and product commercialization, collaborative projects are common in this industry, and thus provide an appropriate setting for studying entrepreneurial startups alliance formation. In order to ensure enough variance within VC portfolios, I started the sampling process with selection of VC firms. I selected the top 100 U.S. VC firms that were active in making investments in medical care/biotechnology industry in 2007 as defined by the total number of financing rounds participated as of the end of year 2007. I limited the sample frame to the most active VC firms to ensure sizeable portfolios can be observed over the sample time period. These VC firms have invested in 2368 portfolio firms in 73

total in the medical care/biotechnology industry and served as the lead VC firm in 1774 out of these 2368 portfolio firms. I followed prior studies to treat the VC firm with the largest stake in the portfolio firm as the lead VC firm (e.g., Barry et al., 1990; Hochberg, Ljungqvist & Lu, 2007). I excluded portfolio firms that had exited (e.g. through IPO or acquisition) before the sample time period and the ones that received the first investment from their lead VC firms after the sample time period. These screening processes resulted in a sample of 939 portfolio firms of 91 VC firms. VC firms that had a portfolio consisting of less than three portfolio firms during 2003-2007 were excluded from the sample, which further reduces the sample size to 899 portfolio firms of 73 VC firms. The 18 excluded VC firms do not differ substantially from the 73 VC firms in key characteristics such as capital under management, preferred roles in investment, geographic preference and stage preference. The final sample used for statistical analysis is comprised of 2145 firm-year observations (745 portfolio firms of 69 VC firms over 5 years) after removing observations with missing data for key variables at either VC level or portfolio firm level. Data Source and Collection To test the hypotheses of this study, I assembled the data set from multiple archival data sources, including the Alliance and Joint Venture database and the Venture Xpert database from Security Data Company (SDC), Pratt s Guide to Private Equity & Venture Capital and The Directory of Venture Capital and Private Equity Firms. These data were supplemented with information from public filings and other public sources as needed. 74

Data for VC firm specific variables and portfolio firm specific variables were largely retrieved and manually coded from SDC VentureXpert database. This database and its predecessor (Venture Economics) have been used in many studies of VC firms and VC activities (e.g., Gompers, 1995; Lerner, 1995; Kaplan & Schoar, 2005, Gompers et al. 2007), and it has been found to be generally free from bias (Kaplan, Sensoy & Strömberg, 2002). Alliances data (from year 2002-2007) were retrieved and manually coded from SDC Alliance and Joint Venture database. The SDC database reports complete data for new alliances announced each year. It is one of the most commonly used alliance databases in empirical studies published in top management journals (Schilling, 2009). Measures Dependent variables. The likelihood of a portfolio firm to form alliances, the dependent variable of this study, was coded as 1 if the portfolio firm forms at least one alliance in a given year (t) and 0 otherwise. Independent variables. The VC level independent variable portfolio alliances was measured by the total number of alliances formed by firms in a VC portfolio in t-1. Two portfolio firm level moderators were included in the model. The first moderator is tie strength. Time spent in a relationship is an important indicator of the strength of the relationship (Granovetter, 1973). The duration of a relationship has been found to result in greater trust, enhanced effectiveness of information sharing, and increased interfirm learning (Gulati, 1995; Simontin, 1999). I measured tie strength by the number of years since the portfolio firm received the first investment from its lead VC firm by the end of 75

year t. The other moderator is TMT size. I measured this variable by the total number of executives the portfolio firm has, given the fact that executives personal social relationships are important sources of social capital for a poorly embedded startup (Higgins & Gulati, 2003; Kim & Higgins, 2007). Control variables. To minimize alternative explanations and isolate the marginal effects of the independent variables, I controlled for a number of variables at different levels that may confound the influence of independent variables on the dependent variable. Two VC level control variables were included in the model. First, I controlled for VC firms industry association membership. Industrial associations provide VC firms with an open platform to sharing industry information, whereby a VC firm may learn the alliance activities of portfolio firms of other VC firms. The VC firm may convey this information to its portfolio firms through formal and information interactions, which may influence portfolio firms strategic decision making. This control variable was measured by the total number of industrial association a VC firm joint. Second, I controlled for VC firms stage specialization the extent to which firms in a VC firm s lead medical care/biotechnology portfolio are similar in terms of stage of development. This variable is calculated as P 2 i, where P i is the proportion of portfolio firms in the ith stage category. This index varies between 0 and 1. Values close to 0 indicate lower specialization while values close to 1 indicate higher specialization. Following the classifications used in the SDC database, portfolio firms were categorized into six stages of development: seed, early, expansion, later, buyout/acquisition and other. 76

The model also includes several portfolio firm level control variables. Firm performance is an important attribute that influences alliance formation. Given the difficulty to obtain financial performance information of privately held startups such as revenue, net income and sales, I used the total number of VC financing rounds a portfolio firm had received since its inception as the proxy of performance. VC firms will provide follow-on financing only if they figure that their portfolio firms perform well. Thus the number of financing rounds can be seen as reflecting portfolio firms performance. Firm age is also an important factor that can influence the likelihood of alliance formation. Older, more established firms are in general more likely to form alliances given the increase in visibility and legitimacy over time (Gulati, 1995). I operationalized firm age as the number of years from the founding year to year t. Research has found that industrial conditions and trends influence alliance formation (Ang, 2008; Beckman, Haunschild & Phillips, 2004). Different industries, as well as different segments within an industry, have different value propositions and sources of growth and competitive advantages, which lead to differences in alliance formation rates across industries/segments. I controlled for these potential sources of variation by including industry segments dummies (biotechnology, medical care). I also controlled for some factors that may affect VC firms influence on portfolio firms, including the amount of equity held by a portfolio firm s lead VC firm and a dummy variable indicating whether the portfolio firm and its lead VC firm were located in the same state. 77

Model Specification This study is a longitudinal study of VC interlocks influence on alliance formation of portfolio firms in medical care/biotechnology industry. The data have a three-level structure with repeated observations on 745 portfolio firms of 69 VC firms between 2003 and 2007: Level 1 (year) - t Year: year identifier Alliance formation: 1 if at least one alliance was formed in year t, otherwise 0 ( y tij ) Level 2 (PF) - i PF ID: portfolio firm identifier Performance ( x 1, ij ): number of financing rounds received Age ( x 2, ij ): number of years since founding Same state ( x 3, ij ): a dummy variable indicating whether a portfolio firm and its lead VC firm were located in the same state Lead VC equity ( x 4, ij ): amount of equity held by the lead VC firm Industrial segment ( x 5, ij ): a dummy variable indicating the portfolio firm s industrial segment (medical care or biotechnology) Tie strength ( x 6, ij ): number of years since the first investment made by the lead VC firm Alternative information sources ( x 7, ij ): number of executives 78

Level 3 (VCF) - j VCF ID: VC firm identifier Alliances portfolio ( x 8, j ): number of alliances formed by portfolio firms in a VC portfolio in t-1 Association membership ( x 9, j ): number of VC or private equity association joint Stage specialization ( x 10, j ): the extent to which portfolio firms in a VC portfolio are similar in development stage Simmel (1950) suggested that social relations should be analyzed in cross-level settings so as to better understand their complexity. Given the research questions this study intending to address, a cross-level study may better reveal how relational factors influence portfolio firms alliance formation. I specify a three-level random-intercept logit model for alliances formation with year t nested in PC i that belong to VC j: (2) (3) logit{pr ( y = 1 X, ζ, ζ,) } = tij tij ij j β + β x + + β x + ζ + ζ (2) (3) 0 1 1, ij... 10 10, j ij j (2) (3) = ( β 0 ζ ij + ζ j ) + β1x 1, ij +... + β10 x10, j + (1.1), where X x,..., x ) is a vector containing all covariates, ( tij = 1, ij 10, j (2) (3) (2) ζ ij X tij, ζ j ~ N(0, ϕ ) is a random intercept varying over portfolio firms (level 2), (3) (3) and ζ ~ N(0, ϕ ) is a random intercept varying over VC firms (level 3). The j X tij random effectζ (2) ij andζ (3) j are assumed independent of each other and across clusters, and (2) ζ ij is assumed independent across units as well. The model can alternatively be written as a latent-response model: 79

* (2) y tij = β 0 β1x1, ij +... + β10 x10, j + ζ ij + ζ j (3) + tij + ε (1.2) (2) whereε tij X tij, ζ ij, ζ (3) j has a logistic distribution with variance 2 π /3. Analogous to single-level logistic regression, the observed dichotomous responses are then presumed to be generated from the threshold model y tij = 1 if y * tij > 0 0 otherwise Retaining the distributional assumptions for the random intercepts, the same models as in (1.1) can be specified using a three stage formulation. Level 1 model: logit{pr ( y = 1 η 0ij } = η 0 ij tij where the intercept Level 2 model: η0ij varies between portfolio firm i and VC firm j. 0ij = π 00 j π 01 η + x, ij 1 + 02 x 2, ij π + π ij + 07 x 7, (2) ζ ij where the intercept π 00 j varies between VC firm j. Level 3 model: π + + + ζ 00 j = γ 000 γ 001 x 8, j + γ 002 x 9, j γ 003 x 10, j (3) j Table 2 presents the descriptive statistics and the zero-order correlations between the variables included in the statistical model. I computed variance inflation factors (VIFs) to ensure that multicollinearity did not influence the results. The mean VIF is 1.25. The maximum VIF is 1.78, which is well below the guideline of 10 suggested by Chatterjee and Price (1991). Thus multicollinearity is not a problem for this study. To 80

avoid potential collinearity among the interaction terms, I mean-centered the continuous variables involved in the interaction terms by subtracting the mean from each value before generated the interaction terms (Aiken & West, 1991). Results Table 3 shows the results of the multilevel logit regression models estimated using Stata 10 (xtmelogit). The coefficients represent the effects of independent variables on the logarithmic odds of alliance formation. Model 1 is the baseline model that only contains control variables. Model 2 examines the main effects of the VC level independent variable prior alliances in the VC portfolio. Model 3 investigates the crosslevel moderating effect of tie strength. Model 4 tests the cross-level moderating effects of alternative information sources. Model 5 is the full, unrestricted model that includes all control variables, the main effect and all interaction terms to ensure a rigorous test of the hypothesized effects. Results show the robustness of the estimated coefficients across model 2-5. I reported the robust standard errors that are robust to departures from homoscedasticity. I reported the likelihood statistics and measures of overall model fit at the bottom of the table. Hypothesis 1 asserts that interlocked portfolio firms are likely to act upon the information about alliance formation of one another. The coefficient of the independent variable in model 2 was positive and moderately significant (p<0.1). This finding supports hypothesis 1, indicating that the number of prior alliances formed by firms in the same VC portfolio is positively associated with the likelihood of the focal portfolio firm s alliance formation. 81

Hypothesis 2 argues that the strength of tie between a portfolio firm and its lead VC firm enhances the effects of prior alliances formed by firms in the VC portfolio on the likelihood of the portfolio firm s alliance formation. The interaction coefficient in model 3 was positive and significant (p<0.05), supporting hypothesis 2. Model 4 tested the moderating effect of alternative information sources. Like what I predicted in Hypothesis 3, the number of executives the portfolio firm has was found to mitigate the effects of prior alliances formed by firms in the VC portfolio on the likelihood of the portfolio firm s alliance formation. The negative and significant (p<0.05) interaction coefficient in model 4 provides support for Hypothesis 3. Figures 2 illustrates the significant moderating effects on logarithmic odds scale. The sign and significance of the coefficients of the main effect and interaction terms in the full model (model 5) are consistent with those in model 2-4, providing further support for the hypotheses. DISCUSSION AND CONCLUSION In this study, I examined the role that VC firms play as conduits of information of behavior models to imitate, which influence the likelihood of alliance formation by portfolio firms. I argue that firms in the same VC portfolio are interlocked through their common VC investor and thus are exposed to the strategic actions of one another. Empirical findings supported my hypothesis on the relationship between the number of alliances formed by firms in the same VC portfolio and the likelihood of alliance formation by the focal portfolio firm. This study also explored cross-level moderating effects of tie strength and alternative information sources. Results indicate that VC 82

interlocks impact is stronger when the focal portfolio firm has longer relationship with its lead VC firm and that the more executives the focal portfolio firm has, the less is its reliance on the information conveyed by its lead VC firm. This study contributes to both the strategy literature and the entrepreneurship literature in three ways. First, as a special type of board interlocks, VC interlocks have long been ignored in the management literature. While the literature has well established the role of board interlocks in the diffusion of strategic and non-strategic practices among established corporations (e.g., Davis, 1991; Haunschild, 1993; Palmer, Jennings & Zhou, 1993), little research attention has been paid to the mimetic behaviors of entrepreneurial startups. For entrepreneurial startups lacking stable social networks, their behavior are more likely to be influenced by the information available through existing relational ties such as VC interlocks. Entrepreneurial startups provide an interesting setting to examine the diffusion of beneficial practices. This study helps fill this gap by examining this special role of VC firms as information conduits among portfolio firms. Second, this study contributes to the strategy literature by responding to Hough s (2006: 60) recent call for re-examination of the results in any area of strategy research based upon hierarchical data structures that has not explicitly modeled the inherent nesting of the data. In the case of VC investment, VC firms can serve as the lead investor in multiple portfolio firms. Portfolio firms in the same VC portfolio are thus interlocked to one another. While VC interlocks are similar to general board interlocks in the role of information channels between interlocked firms, VC interlocks present a higher level influence in that the portfolio firms are nested in VC firms and that VC firms 83

are entities independent from the interlocked portfolio firms. Therefore, multilevel modeling is needed to study VC firm s cross-level influence on portfolio firms. In addition, given the fact that VC firms are often closely involved in portfolio firms decision making and strategic planning through intensive formal and information interactions, it is inappropriate to assume that a portfolio firm s decision to form alliances is completely independent from the decision of other firms in the same VC portfolio. This further entails the use of multilevel modeling, which relaxes the independence assumption of standard estimation techniques and allows for correlated error structures (Luke, 2004). Using standard techniques to analyze data with multilevel structure appear to overstate the influence of some explanatory variables because these techniques do not account for unobserved heterogeneity across higher level units. Multilevel modeling accounts for this unobserved heterogeneity and thus can provide estimates that are not inflated (Raudenbush & Bryk, 2002). Third, I investigated factors that affect the effects of VC interlocks on portfolio firms alliance formation. I demonstrated that tie strength enhances VC interlocks role as important information conduits between portfolio firms. Like board interlocks in general, VC interlocks are in essence interfirm social network ties. As argued by Gulati (2007), the benefits firms can derive from their networks depend on both the quality of network resources they get access to via relational ties and the strength of these ties. In line with this argument, I examined the cross-level moderating effect of tie strength. Furthermore, while different information channels are facilitative in practice diffusion if examined separately, this study found that the positive effect of one information channel is 84

mitigated by the presence of alternative information channels. As one of the most important sources of social capital, a portfolio firm s executives collect strategic information through environmental scanning and monitoring, which may reduce the weight or importance of the information conveyed by VC firms in its decision making. This study adds to our understanding of the dynamics of alliance formation by highlighting the role of VC interlocks in alliance formation by portfolio firms. However, this study has some limitations that must be kept in mind in interpreting the results. First, the generalizability of the findings needs further investigation. I only studied the medical care/biotechnology industry. It is possible that different industries have different underlying alliance dynamics. Therefore, the results must be interpreted keeping the specific industrial context in mind. Second, this study only studies the likelihood of alliance formation by portfolio firms but not the specific purposes or contents of alliances and the characteristics of alliance partners. Important insights may be achieved by distinguishing between types of alliances, such as R&D, marketing and sales, technology license, etc. Portfolio firms may be more likely to imitate alliance activities of certain types than others. In addition, it will be interesting to examine the characteristics of the alliance partners. Whether portfolio firms seek collaborative opportunities more with other entrepreneurial startups than with established corporations? Whether portfolio firms sharing a same VC investor tend to cooperative with one another? In conclusion, by highlighting the multilevel nature of VC interlocks influence on portfolio firms alliance formation and examining relevant cross-level interactions, 85

this study uniquely contributes to the strategy and entrepreneurship literature in general, and the alliance literature and the VC literature in particular. 86

TABLE 2.1 Variable Operationalization (Study 2) Variable name Variable type Measures DV: Alliance formation Dichotomous 1 = the portfolio firm formed at least one alliance in year t; 0 = otherwise IV: Alliances portfolio Count The number of alliances formed by firms in a VC portfolio in year t-1 Tie strength Count Number of years since the portfolio firm received the 1 st investment from its lead VC firm by t Alternative information Count Number of executives the portfolio firm has sources Control (VC level) Count Number of VC or private equity Association membership Stage specialization Calculated 2 i associations joint by the VC firm P P i is the proportion of firm in a VC portfolio in the ith stage I followed the way SDC database classifies the development stage of portfolio firms. The six dummy stage variables are: seed, early, expansion, later, buyout/acquisition and other. Control (PC level) Count Number of years since founding Age Performance Count Total number of financing rounds received Same state Dichotomous 1 = the portfolio firm and its lead VC firm were located in the same state; 0 = otherwise Lead VC equity Continuous Total amount of investment made by the lead VC firm in the portfolio firm Industrial segment Dummy 1 = biotechnology; 2 = medical care 87

TABLE 2.2 Descriptive Statistics and Correlations (Study 2) Variables Mean Std. Dev. Min Max 1 2 3 1 Alliance formation 0.040 0.20 0 1 1 2 Investment by lead VC 15251.31 27552.30 4 344150 0.08* 1 3 Number of rounds 3.34 2.60 1 25 0.06* 0.19* 1 4 Industry segment 1.65 0.48 1 2-0.11* 0.10* -0.03 5 Same state 0.52 0.50 0 1-0.02-0.15* -0.05* 6 PC age 7.13 7.97 0 83-0.01-0.04 0.14* 7 Association membership 1.61 1.38 0 8 0.01 0.08* 0.05* 8 Stage similarity 0.38 0.14 0.18 1-0.01-0.10* -0.13* 9 Alliances in portfolio 0.49 0.85 0 5 0.06* 0.02 0.09* 10 Time spent with lead VC 3.99 4.84 0 47-0.00-0.04* 0.39* 11 Number of executives 8.81 5.26 1 27 0.09* 0.26* 0.19* Variables 4 5 6 7 8 9 10 11 1 Alliance formation 2 Investment by lead VC 3 Number of rounds 4 Industry segment 1 5 Same state -0.13* 1 6 PC age 0.13* -0.17* 1 7 Association membership 0.11* -0.17* 0.06* 1 8 Stage similarity -0.06* 0.17* 0.01-0.10* 1 9 Alliances in portfolio -0.04* -0.05* -0.06* 0.10* -0.22* 1 10 Time spent with lead VC 0.10* -0.11* 0.55* 0.02-0.01-0.04* 1 11 Number of executives -0.03-0.15* -0.14* 0.08* -0.22* 0.13* -0.16* 1 * p < 0.05 88

TABLE 2.3 Multilevel Logit Regression Results (Study 2) Model 1 Model 2 Model 3 Constant -2.23 ** (0.66) -2.37 *** (0.66) -2.28 ** (0.66) PC age -0.00 (0.02) -0.00 (0.02) -0.00 (0.03) Industry segment -1.38 *** (0.29) -1.35 *** (0.28) -1.36 *** (0.28) Number of rounds 0.01 (0.05) 0.09 (0.05) 0.08 (0.06) Investment by lead VC 0.00 ** (0.00) 0.00 ** (0.00) 0.00 ** (0.00) Same state -0.46 (0.29) -0.45 (0.28) -0.46 (0.28) Association membership 0.02 (0.10) 0.02 (0.10) 0.02 (0.10) Stage similarity 0.24 (0.96) 0.47 (0.96) 0.33 (0.97) Alliances in portfolio (A) 0.17 (0.12) 0.15 (0.12) Time spent with lead VC (T) -0.00 (0.05) A T 0.06 * (0.03) Number of executives (E) A E Log Pseudo Likelihood -387.28-386.35-384.50 Wald Chi 2 34.34 37.58 40.4 N 2415 2415 2415 Random-effects Parameters sd(_cons) - vcid: Identity 0.00 *** (0.29) 0.00 *** (0.28) 0.00 *** (0.41) sd(_cons) - pcid: Identity 1.30 *** (0.23) 1.30 *** (0.23) 1.33 *** (0.24) Chi 2 18.11 18 18.59 89

TABLE 2.3 continued Multilevel Logit Regression Results (Study 2) Model 4 Model 5 Constant -3.25 *** (0.74) -3.17 *** (0.74) PC age 0.01 (0.02) 0.00 (0.03) Industry segment -1.35 *** (0.28) -1.36 *** (0.28) Number of rounds 0.07 (0.05) 0.05 (0.06) Investment by lead VC 0.00 * (0.00) 0.00 * (0.00) Same state -0.36 (0.28) -0.36 (0.28) Association membership 0.00 (0.10) 0.00 (0.10) Stage similarity 0.95 (0.97) 0.79 (0.98) Alliances in portfolio (A) 0.23 * (0.12) 0.21 * (0.12) Time spent with lead VC (T) 0.02 (0.05) A T 0.05 (0.03) Number of executives (E) 0.07 ** (0.03) 0.07 ** (0.03) A E -0.05 * (0.02) -0.04 (0.02) Log Pseudo Likelihood -380.99-379.77 Wald Chi 2 44.88 46.76 N 2415 2415 Random-effects Parameters sd(_cons) - vcid: Identity 0.00 *** (0.38) 0.00 *** (1.17) sd(_cons) - pcid: Identity 1.32 *** (0.24) 1.38 *** (0.23) Chi 2 18.04 21.30 Significance levels (two-tailed test for control variables; one-tailed test for hypotheses): p < 0.1 * p < 0.05 ** p < 0.01 *** p < 0.001 90

FIGURE 2.1 The Framework (Study 2) 91

FIGURE 2.2 Significant Moderating Effects (Study 2) 0.5 Interaction of Tie Strength and Alliances-P Log Odds of Alliance Formation 0-0.5-1 -1.5-2 -2.5-3 -3.5 0 1 2 3 4 5 Low level of tie strength High level of tie strength Alliances - Portfolio Interaction of NoE and Alliance-P Log Odds of Alliance Formation -2.1 0 1 2 3 4 5-2.3-2.5-2.7-2.9-3.1-3.3 Smaller number of executives Larger number of executives Alliances - Portfolio 92

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CHAPTER FOUR EFFECTS OF VC FIRMS NETWORK POSITIONS ON ALLIANCE FORMATION OF PORTFOLIO FIRMS INTRODUCTION Research has well established the strategic and organizational benefits of inter-firm collaboration, including getting access to complementary resources (Chung et al., 2000), improving competitive position (Ang, 2008), reducing uncertainties (Beckman, Haunschild & Phillips, 2004), facilitating knowledge transfer and creation (Powell, Koput & Smith-Doerr, 1996), gaining legitimacy and status (Dacin, Oliver & Roy, 2007). The beneficial effects of alliances in facilitating survival, growth and performance of entrepreneurial startups have also received strong empirical support (e.g., McGee, Dowling & Megginson, 1995; Stuart, 2000; Stuart et al., 1999). Particularly, alliances can provide entrepreneurial startups with unique benefits at the operational and technological levels, which may not be easily available from their networks with families, friends and VC firms. Due to their limited social and economic networks, lack of resources and unproved technologies and business models, as well as the high search costs in identifying appropriate cooperative partners, it is more difficult for entrepreneurial startups to form strategic alliances compared with established firms. Extant research has established the critical role of prior experience of interfirm cooperation in network building and expansion (e.g., Gulati, 1995a, 1995b, 1999). However, with few prior alliance experiences, entrepreneurial startups have to rely on other channels to identify and realize alliance opportunities. Among entrepreneurial startups limited social 101

contacts, VC firms serve an especially important role of bridging them with potential alliance opportunities that are otherwise beyond their reach (Lindsey, 2008). Through intensive due diligence and monitoring, VC firms have a strong understanding about their portfolio firms needs and capabilities. Such idiosyncratic knowledge in combination with their extensive networks, VC firms hold an advantageous position for identifying appropriate alliance partners for their portfolio companies. Also, by active involvement in portfolio firms management and strategy making, VC firms can provide a variety of value-added services to help portfolio firms locate potential partners and to facilitate collaboration between partners. Recent studies have found that VCbacked startups are more likely to form strategic alliances than are their non-vc-backed peers (Hsu, 2006, Lindsey, 2008). VC firms facilitate portfolio firms alliance formation primarily by providing them with information of potential partners and opportunities and increasing their legitimacy or attractiveness in the eyes of potential partners (Hsu, 2006). I suggest that the informational and reputational benefits portfolio firms can gain access to critically depend on VC firms positions in their networks. Network positions determine the quantity and quality of social capital VC firms can obtain, which have direct bearing on VC firm performance (Abell & Nisar, 2007; Hochberg, Ljungqvist & Lu, 2007). However, research has not examined the relationship between VC firms network positions and outcomes at portfolio firm level. I argue that research examining the relationship between VC firms network positions and portfolio firms network building can help us better understand the benefits associated to affiliations with VC firms. 102

This study explores how VC firms positions in syndication networks influence their portfolio firms alliance formation (see Figure 1 for the framework). Drawing from network theory (Gulati, 1998, 1999; Stuart, 1998), I seek to examine the effects of two types of network positions that imply different informational advantages: network centrality and network constraint, as well as their interaction effects. While syndication networks are critical channels for information sharing in the VC industry (Hochberg, Ljungqvist & Lu, 2007; Sorenson & Stuart, 2001), some VC firms also have access to other networks that are particularly useful in seeking alliance opportunities for portfolio firms. For example, corporate VC firms can access their corporate parents extensive industrial networks encompassing quality potential alliance partners for portfolio firms, which are not available to other types of VC firms. To investigate the influence of such alternative information sources, this study examines the moderating effects of corporate VC on the relationship between VC firms positions in syndication networks and portfolio firms alliance formation. The hypotheses will be tested using a sample of 417 VC-backed entrepreneurial startups. This study makes several key contributions to the strategy literature and the entrepreneurship literature. First, this study highlights the valuable supports VC firms can provide to their portfolio firms in networking building and expansion, which have long term beneficial effects that won t evaporate even after VC exits. Second, this study sheds some light on an interesting but largely ignored issue in network literature: the transfer of social capital across populations. This study illustrated how the relations and interactions within one organizational population (e.g., VC firms) influence activities of actors in a 103

tied-to population (e.g., portfolio firms). Third, this study identified the most conductive network structure for VC firms to seek relational benefits for their portfolio firms. High levels of network centrality, high levels of network constraint and their combination have been found to have positive effects. Forth, by examining the moderating effect of corporate VC, this study demonstrates that the effects of positions in a network are contingent on the availability of alternative network relations that provide similar benefits. THEORETICAL BACKGROUND Entrepreneurial startups typically face the liabilities of newness resulted from the lack of critical resources and network connections (Freeman & Carroll, 1983). Without extensive social and economic networks, startups rely heavily on founders personal contacts (e.g., families, friends, ex-colleagues) for a broad range of supports such as money, advice and emotional backup. They also look for supports in terms of network referrals and strategic information from business partners and professional advisers such as VC firms, service providers and public agencies. Startups seek different resources from different networks, thus all their contacts cannot be treated as a single undifferentiated network (Hanlon & Saunders, 2007). VC firms have long been viewed as one of the most important providers of resources and social capital for entrepreneurial startups (Gompers & Lerner, 2004). However, although VC firms can provide a variety of value-added services to portfolio firms, they are less capable of providing supports at the technological level. Collaborations with business partners that can provide important 104

strategic and technological resources are also critical for portfolio firms development and success. The academic interest in strategic alliances emerged in the late 1970s and early 1980s and has gained widespread interest subsequently. Studies on alliances have examined topics as diverse as motives and catalysts of alliance formation (e.g., Ang, 2008; Gulati, 1999; Chung, Singh & Lee, 2000; Stuart, 1998), partner selection (e.g., Beckman, Haunschild & Phillips, 2004; Shah & Swaminathan, 2008; Sorenson & Stuart, 2008), learning in strategic alliances (e.g., Doz 1996; Khanna, Gulati & Nohria, 1998) and interaction between competition and cooperation (e.g., Gimeno, 2005; Silverman & Baum, 2002), among others. However, the majority of studies on strategic alliances focus on established, large firms. Much less attention has been paid to alliance activities of new, entrepreneurial ventures, notwithstanding the important role of alliances in facilitating startup survival, growth and success. To date, the interaction between alliance research and the entrepreneurial literature is still limited (Alvarez, Ireland & Reuer, 2006). Alliance strategy of young, small firms is likely to be different from established firms. For instance, on the one hand, anecdotal evidence has suggested that small firms are often more eager to form alliances without clear substantial gains than are established firms, in the hope of avoiding potential losses resulting from being left out of alliances (Park & Zhou, 2005). On the other hand, startups sometimes are hesitant to engage in collaborative activities due to the concern that their entrepreneurial ideas could be expropriated by partners (Hsu, 2006). In light of the crucial contribution of entrepreneurial ventures to economic development and wealth creation, as well as the 105

intricacies and complexities of strategic alliance formation, it will be of great interest to examine strategic alliances in entrepreneurial settings. Affiliation with established, reputable partners may bring many benefits. By allying with prominent partners, startups can gain legitimacy and overcome liabilities of newness (Deeds & Hill, 1996). Startups with prominent alliance partners have been found to perform better (Stuart, 2000; Stuart et al., 1999). Quality partners are always the most desired. However, startups typically face highly constrained alliance opportunities due to their underdeveloped social networks. For most startups, they are not yet sufficiently developed to be attractive to these established potential alliance partners. Because established firms may not be willing to form collaborative relationships with startups, startups may have to cooperate with other privately held startups. The direct benefits from allying with other startups may be very limited, but in some circumstances, alliance formation is necessary to avoid competitive disadvantages even when such activity cannot generate tangible benefits (Park & Zhou, 2005). However, even when two startups are inclined to collaborate so as to reduce competitive pressures and increase chances of survival, their collective goals may not be realized due to information asymmetries. Information asymmetry is particularly intensive in the case of alliances between privately held startups due to the low transparency of internal information. For VC-backed startups, these disadvantages are less severe because VC firms can leverage their network relations to collect critical information about potential partners and serve as information intermediaries between the two parties to reduce information asymmetries. 106

VC Syndication Networks Syndication is a common practice in the VC industry, whereby a lead VC firm initiates a deal and invites other VC firms to join for the joint pay-off (Wright & Lockett, 2003). The lead VC firm typically has large equity stakes and plays an active role in monitoring the portfolio firm s progress and providing value-added services (Gorman & Sahlman, 1981). VC firms tend to prefer syndicated investments with other VC firms rather than making stand-alone investments (Lerner, 1994). About 70% of all the VC investments between 1980 and 2009 were syndicated in the U.S. (Toldra, 2010). VC firms syndicate their investments for a number of reasons. First, in line with traditional financial theory, VC firms use syndication as a means to secure a well diversified portfolio so as to reduce the overall unsystematic investment risks (Markowitz, 1952; Wilson, 1968). Second, syndication helps improve the selection of deals by improving the effectiveness of screening, due diligence and decision-making (Bygrave, 1987; Lerner, 1994). Third, syndication often involves expectations of reciprocity (Abell & Nisar, 2007; Ozdemir, 2006). When a VC firms initiates a deal and invites other reputable VC firms to joint, it implicitly expects the latter to share quality deals with it in the future. Besides these ex-ante motives of syndicating VC investments, there are also expost benefits associated with syndications. Connected by the joint investments, VC syndication partners can share complementary skills or knowledge, which help them to provide better value-added services to their portfolio firms (Brander Raphael & Werner, 2002). Syndication also results in a multitude of social relationships that span the 107

syndication partners coordination and a variety of service providers, such as R&D institutions, investment bankers, headhunters and lawyers, to name a few (Gorman & Sahlman, 1989; Sahlman, 1990). To some extent, such diverse sets of relationships with different agents explain the variations in the quality of value-added supports VC firms provide to portfolio firms (Abell & Nisar, 2007). VC Firms Role in Facilitating Alliance Formation of Portfolio Firms Given the benefits entrepreneurial startups can obtain from strategic alliances and the limited network base they have, factors that facilitate their identification, access and realization of alliance opportunities become of research interest. Research has found that a firm s embeddedness in a collaborative network affects its propensity to form future relational ties in the same network (Baum et al., 2005). Prior relational ties often serve as important sources of information about partner s needs and capabilities, which is a major antecedent of repeated cooperative arrangements (Gulati, 1995). However, de-novo startups rarely have access to this information channel that is built on prior cooperative experiences. Without such relational foundation of alliance formation, entrepreneurial startups have to seek ways to open the door to the alliance network. Particularly, VC firms have been found to play an important role in facilitating portfolio firms network expansion activities. The way VC firms facilitate network building of portfolio firms illustrates the transfer of relational capital between connected groups. Firms are embedded in multiple networks defined by the specific contents of relational ties. Information and other network benefits stemmed from the membership of one network can facilitate the 108

development of another network (Jensen, 2003). The network benefits stemmed from relationship with VC firms play nontrivial roles in portfolio firms network expansion including formation of strategic alliances. Given the fact that VC firms present an important portion of portfolio firms extended networks that provide critical resources (Dubini & Aldrich, 1991), their network positions and the associated information advantages have direct bearing on portfolio firms likelihood of alliance formation. Research has long recognized firms embeddedness in multiple networks (Gulati, 1999; Baker & Faulkner, 2002), but has largely ignored the transferability of relationships or relational capital across networks. In recent years, scholars have begun to examine whether and how partnering experiences in a network spill over into another network and whether and how relational benefits accumulated in one area (e.g., geographic region, market segment, etc.) can be used to facilitate network building in another area. For example, Li and Shipilov (2005) examined the effect of prior ties between two banks in the merger and acquisition network on the likelihood of these two banks to become public offering syndication partners. Guler and Gulen (2010) found that social status advantages accumulated in home country network is transferable to foreign markets and shape firm s foreign expansion and network building. In this sense, in order to gain a comprehensive understanding about network dynamics and evolution, it is necessary to look into the cross-network influence of social relations in the focal firm s various networks. In line with this argument, I contend that portfolio firms can enjoy a number of benefits transferred from VC firms syndication network that can enhance their chance to form strategic alliances. 109

Prior studies have found that VC-backed startups are more likely to involve in interfirm collaborations than new ventures relying on other sources of financing (e.g., Hsu, 2006; Lindsey, 2002). VC firms have long asserted their roles as value-added investors rather than mere fund providers. One such value-added service is to facilitate key contacts for their portfolio firms as well as promote the interactions within their networks (Lindsey, 2008). In the alliance formation of portfolio companies, VC firms often play two key roles. First, as important information sources, VC firms help expand the set of alliance opportunity available to their portfolio companies. VC firms can serve as information intermediaries that help startups to get access to the information of potential cooperative partners. Second, VC firms also facilitate portfolio companies to form cooperative relational ties by increasing their legitimacy in potential partners eyes. Researchers have found that startups affiliation with well-know third parties increases potential partners confidence in their viability and prospects. VC firms can rely on their syndication networks to identify potential partners for their portfolio firms. Searching potential partners for portfolio firms through syndication networks can be very effective. First, VC firms know their portfolio firms needs and capabilities well through the intensive due diligence and monitoring processes (Tyebjee & Gruno, 1984). Second, VC firms with prior or current cooperative relationships are more likely to share sensitive information due to the trust built in their previous or ongoing collaborations. Through the communication and interaction with syndication partners, a VC firm can get access to reliable information of portfolio firms funded by other VC firms. By looking for quality portfolio firms of other VC firms, the VC firm can 110

seek alliance opportunities for its portfolio firms. The combination of knowledge about its portfolio firms and information about other portfolio firms gives the VC firm unique advantages in matching the needs and complementarities of these firms and facilitate collaboration between them. Third, syndication partners can provide referrals to established corporations with which the focal VC firm has no prior connection. Valuable alliance opportunities emerge, if synergies are identified between the established corporations and the VC firm s portfolio firms. A firm s position in an inter-firm network influences its access to information and other resources that circulate among networks members. To the extent that a VC firm s syndication network influences portfolio firms chance to form alliance, the size and quality of alliance opportunities set are dependent on the VC firm s position in its syndication network. HYPOTHESES Firms are embedded in networks of social relationships, which provide opportunities for and constraints on firm behavior. Such network perspectives focus on relations among actors or structured patterns of interaction rather than attributes of isolated individual actors. Key dimensions of network characteristics include size of networks, ways of connection, patterns of interaction, dynamics/evolution of networks, strength of ties and resources exchanged within networks (Dubini & Aldrich, 1991; Reagans & McEvily, 2003). The overall pattern of network connections and a firm s position within this network structure influence the resources available to the firm. Centrality and brokerage are two major measures of a firm s position in a network that have been found to have 111

important performance implications in network research (e.g., Podolny, 2001; Stam & Elfring, 2008). Centrality refers to the extent to which a firm is connected to other firms in the network (Powell et al., 1996) while network position brokering many structural holes means that the firm s network contacts are largely unconnected to one another (Burt, 1992). Both types of network positions provide firms with access to distinct network resources and social benefits (Geletkanycz & Hambrick, 1997). For example, firms holding a central network position are recipients of large volume of information flows, while firms filling many structure holes enjoy the diverse information from unconnected partners. Network positions of firms have been found to have multifaceted consequences such as survival (Uzzi, 1996), growth (Ingram & Roberts, 2000), innovation rates (Ahuja, 2000) and reaching performance milestones (Stuart, Hoang & Hybels, 1999). However, despite the large volume of network literature, there has yet been an agreement on what network position is most conductive to firm performance and other organizational level outcomes (Ahuja, 2000). Some scholars argued that a central position within a network is most beneficial (e.g. Tsai, 2001), while others emphasized the benefits associated with bridging ties (e.g., Perry-Smith & Shalley, 2003). It has been found that the effects of different aspects of network positions on firm level outcomes are contingent on a variety of internal and external factors, such as industrial conditions and dynamics, environmental change and firm strategy (Koka & Prescott, 2008). For example, central position in the network has been found to facilitate innovation in the biotechnology industry (Powell, Koput & Simth-Doerr, 1996) but result 112

in poorer performance in the steel industry (Madhavan, 1996). While network positions bridging many structural holes lead to higher project performance in TV production (Soda, Usai & Zaheer, 2004), increases in structural holes have been found to have a negative impact on innovation output in the international chemical industry (Ahuja, 2000). In this sense, different network positions have different strategic or performance implications for firms in different industries. As such, the assessment of effects of network positions must take into account the specific contexts, examining the impacts of different types of network positions on firm behavior and performance in the specific industrial background. A VC firm may diversify its portfolio by participating in deals initiated by other VC firms. However, a VC firm tends to serve as the lead VC firm only in deals within its domain specialization so as to provide effective value-added services to its portfolio firms. As such, network position that is most conductive to this purpose should be one channeling large volume of information relevant to the VC firm s domain specialization. Central Network Position Network centrality refers to the extent to which a VC firm is connected to other VC firms in the syndication network. Prior studies found that firms having central network positions enjoy a number of network based advantages, which directly contribute to firm performance (Brass et al., 2004). High network centrality means the accessibility to multiple sources of information and other network resources (Powell et al., 1996; Tsai, 2001). Being positioned at the confluence of information and resource flows, a central VC firm enjoys early exposure to emerging market trends, innovative industrial practices, 113

new investment opportunities, as well as partnership opportunities for both itself and its portfolio firms. A central VC firm privileged access to this valuable information lead to sophisticated advices for its portfolio firms, including presenting them selected sets of business and alliance opportunities. Central VC firms have advantages in identifying potential alliance partners for their portfolio firms from both VC-backed ventures and established corporations. First, through formal and inform interactions and communications with its syndication partners, a central VC firm can get access to the basic information of a large number of portfolio firms financed by its syndication partners. Equipped with such a rich information pool, it is easier for the VC firm to identify quality potential alliance partners that may have synergies with its own portfolio firms. Second, besides looking for potential alliance partners from VC-backed ventures, a central VC firm is also likely to facilitate alliance formation between its portfolio firms and established industrial corporations. The central VC firm can develop social relations with more established corporations through its syndication partners referrals, which means an even larger set of alliance opportunities for its portfolio firms. In addition, VC firms that sustain central network positions enjoy reputational advantages. Central VC firms are more likely to be perceived as industry leaders (Stam & Elfring, 2008). Being viewed as well connected enhances VC firms reputation, given the fact that VC firms are essentially relationship investor[s] (Fried & Hisrich, 1995). The reputation of being central makes VC firms more visible and trustworthy to both other VC firms in the syndication network and potential social contacts or resource providers 114

outside the syndication network. A central VC firm s reputational advantage may transfer to it portfolio firms, increasing their attractiveness in the eyes of established corporations and other VC-backed ventures seeking cooperative partners. In other words, when a firm (either established or VC-backed) looks for potential alliance partners, central VC firms portfolio firms are likely to come into its consideration first, when other conditions are the same. Hypothesis 1. Portfolio firms funded by VC firms that hold central positions in their syndication networks are more likely to form alliances than the ones funded by VC firms that hold peripheral positions in their syndication networks. Constrained Network Position Syndication networks are important sources of information and other resources for VC firms. The structure of a VC firm s ego syndication network determines the quantity and quality of information it can get access to. A VC firm bridging many structural holes is said to have information advantage in terms of diverse information flows (Burt, 1992). However, there is often a trade-off between the quantity and quality of resources obtained from a given source. Resources or supports from sources with an abundant supply tend to have lower quality than those from sources that are hard or costly to access (Hanlon & Saunders, 2007). For example, free information or advice that is easy to get may be less relevant and useful than that is from relatively exclusive sources. Therefore, it is more important to maximize the relevance or effectiveness of the obtained resources or supports. 115

VC investments are typically very specialized. VC firms tend to have clear specialization in terms of geographical, industry and development stage preferences (Bygrave 1987, 1988). For VC firms, the most useful information is that relevant to their specializations and conductive to their investment strategies or philosophies. That a VC firm s partners are relatively unconnected to one another in its syndication network may indicate either that the VC firm has a rather diversified portfolio or that the VC firm tends to partner with peripheral or poorly embedded VC firms. Though it can enjoy diverse information flows on such a brokerage position, this information may be less relevant to its specialization or be lower in quality. For VC generalists with highly diversified portfolio, their syndication partners may be largely unconnected with one another due to the difference in investment specialization. Although a VC firm can obtain a large amount of diverse, irredundant information from such a sparse network structure, most of this information may not be relevant to its specialization. In other words, the effectiveness of the information from syndication partners specializing on unrelated areas may not be proportional to the volume. In VC industry where specialization is emphasized, connections with syndication partners having similar specializations can be more beneficial. Furthermore, sparse networks may include many inexperienced VC firms that are not well connected due to limited financial and social resources. Connections with such VC firms will not add much value to the focal VC firm s investments. For VC firms having many ties with poorly embedded syndication partners, the informational benefits they can obtain from the latter is very limited. 116

In contrast, for VC specialists focusing on a narrow group of investment targets, their syndication partners are more likely to link to one another through joint investments in portfolio firms in the same industry. Information flows in such a constrained or concentrated VC syndication network tend to be more industry specific and relevant to the focal VC firm s specialization. Research have indicated that local clusters built on embedded ties with past partners and partner s partners, which are characterized by density and stability, are infused with information useful for fostering trust and reducing uncertainty in future exchange (Walker, Kogut & Shan, 1997; Guilati & Gargiulo, 1999). Given the high levels of trust between partners in a densely connected network, the communication and information sharing within this network will be more efficient. Also, if the information collected through different paths appears to be consistent, VC firms can have higher levels of confidence on the reliability of this information. Actually, a small amount of trustful and relevant information is more useful and effective in facilitating critical decision making than a large volume of irrelevant or imprecise information. In this sense, the effectiveness is higher of information flowing into VC firms whose syndication partners are densely connected. As such, VC firms occupying constrained network positions are more likely to effectively and efficiently identify the best alliance opportunities for their portfolio firms given the relevance and usefulness of information flows in their networks. Therefore, I predict that portfolio firms financed by VC firms holding constrained network positions are more likely to form alliances. 117

Hypothesis 2. Portfolio firms funded by VC firms whose syndication partners are densely connected to one another are more likely to form alliances than the ones funded by VC firms whose syndication partners are sparsely connected. Interaction of Network Centrality and Constraint Network centrality and network constraint have been the two mostly examined aspects of network position in contemporary social relationship and network research. However, while acknowledging the interrelations between these two measures of network position (e.g., Stam & Elfring, 2008), most extant studies have examined their effects separately and largely ignored the potential interaction effect of these two terms. Recent network studies have suggested that the effects of network position on performance and other firm level outcomes are actually contingent on a number of internal and external factors, including firm strategy, configurations of different social capital conduits, and industrial dynamics (Koka & Prescott, 2008; Mehra et al., 2006; Oh et al., 2004). As such, different combinations of levels of network centrality and network constraint may have different effects on different organizational outcomes in different industries. VC industry emphasizes both network building and domain specialization, which are important sources of competitive advantages. Given the nature of VC firms as relationship investor[s] (Fried & Hisrich, 1995), extensive networks are necessary for VC firms to select promising startups and facilitate their development by leveraging relational capital. Connecting to many VC firms that are also well connected increases a focal VC firm s capability of supporting its portfolio firms to expand their collaborative 118

network. Most VC firms specialize on investments in a specific market segment or industry, such as software, internet and biotechnology, with which they are most familiar (Bygrave 1987, 1988). The specialized industrial knowledge, including whether, when and how alliances generate value for businesses in the specific industry, is another major benefits portfolio firms can obtain through their affiliation with VC firms. I argue that when it sustains a network position characterized by both ample information conduits and specialized and relevant information flows, the VC firm is most advantageous in identifying quality alliance opportunities for its portfolio firms and facilitating the alliance formation by the latter. Accordingly, I predict that VC firms network centrality and network constraint jointly influence the likelihood of alliance formation by portfolio firms in a mutually strengthening manner. Hypothesis 3. The interaction of VC firms network centrality and network constraint is positively related to the likelihood of alliance formation by their portfolio firms. Moderating Effects of Corporate VC While syndication networks are critical for VC firms to collect information about potential alliance opportunities for their portfolio firms, they are not the only, or even the most important, source of such information for some VC firms. When a VC firm has alternative information sources other than its syndication network, its position in the syndication network may become less important or relevant. Among the whole family of VC firms, VC subsidiaries of established corporations enjoy a unique advantage in identifying appropriate alliance opportunities for their portfolio firms given their 119

corporate parents extensive vertical and horizontal industrial connections, which is not available to other types of VC firms. I argue that for corporate VC firms, which have access to corporate parents industrial networks, the importance of syndication networks as information sources may decrease. In the past decades, established corporations have invested substantially in entrepreneurial startups. Examples of such active corporate VC investors include Intel, Microsoft and UPS. Through their VC subsidiaries, these corporations have gained substantial strategic benefits as well as financial returns. Prior studies have found that corporate VC firms facilitate the survival and success of portfolio firms by providing a number of unique non-pecuniary resources such as manufacturing capabilities, bargaining power with suppliers and access to distribution channels (Chesbrough, 2000; Maula & Murray, 2001; Teece, 1986). By leveraging their rich industry knowledge, strong technology expertise and broad social and professional networks, established corporations can effectively enhance their portfolio firms value. Particularly, portfolio firms benefit most from corporate VC investors when there are similarities or complementarities between the former and the latter s business lines (Gompers & Lerner, 2002; Hellmann, 2002). Corporate VC firms are different from independent, private VC firms in a number of ways (Chesbrough, 2002; Gompers & Lener, 1998; Maula & Murray, 2001). First, they differ in organizational structure. Independent, private VC firms are typically organized as limited partnerships whereby VC firms serve the role of general partners and raise fund from limited partners such as wealth individual, large financial institutions, 120

pension funds, etc. In contrast, corporate VC firms receive funding directly from their corporate parents and their performance directly influences the achievement of corporate parents overall goals. Second, corporate VC firms have different resources endowments from independent, private VC firms. Corporate VC firms can provide key resources of high strategic and operational value such as R&D capabilities, in-depth industrial specific knowledge and direct assistance or supports at the operational level, which are beyond independent VC firms ability to offer. Third, rather than just seeking financial returns, corporate VC firms often strategically oriented and pursue strategic gains such as access to new technologies. Besides these basic structural and resource related differences, corporate VC firms also differ from independent, private VC firms in their social and economic networks. Compared with independent, private VC firms, corporate VC firms have more extensive networks consisting of both the social and professional contacts accumulated through VC activities and the broad industrial connections of their corporate parents. According to the resource dependence theory (Pfeffer & Salancik, 1978), the availability of multiple accessible sources of resources reduces a firm s reliance on a single source. Given the dual networks they have, corporate VC firms are less dependent on syndication networks to collect information of potential alliance partners for their portfolio firms. In addition, keeping strategic goals in mind, corporate VC firms may even primarily rely on their corporate parents industrial connections in identifying potential alliance partners for their portfolio firms. Therefore, for corporate VC firms, the effect of positions in 121

syndication networks on portfolio firms alliance formation is not as strong as for independent, private VC firms. Hypothesis 4a. Portfolio firms funded by corporate VC firms that are central in syndication networks are less likely to form alliances than those funded by non-corporate VC firms that are central in syndication networks. The effect of network centrality of VC firms on portfolio firms likelihood of alliance formation is weaker for corporate VC firms. Hypothesis 4b. Portfolio firms funded by corporate VC firms that are constrained in syndication networks are less likely to form alliances than those funded by non-corporate VC firms that are constrained in syndication networks. The effect of network constraint of VC firms on portfolio firms likelihood of alliance formation is weaker for corporate VC firms. METHODOLOGY Sample Selection The proposed hypotheses were tested with a sample of U.S. VC-backed startups that reached IPO stage between 1997 and 2000. The sample selection was based on a number of rationales and justifications. First, the base rate of alliance formation by private startups is low. It is in general much harder for poorly embedded startups with unknown quality to outsiders to build social connections and develop inter-firm collaborations (Hsu, 2006). I focused on the most successful VC-backed startups in the overall population of entrepreneurial startups as defined by experiencing IPO events, in 122

order to ensure enough observations of the variable of interest. Portfolio firms ending up with IPO were typically stronger when they were still privately held compared to the ones that never reach the IPO stage and the rate of alliance formation for them should be higher correspondingly. Prior studies on alliance formation have used a similar sampling strategy of focusing on the leading or largest firms in the population to ensure data availability and reliability (e.g., Ahuja, Polidoro & Mitchell, 2009; Gulati, 1995; Rosenkopf, Metiu & George, 2001). In this study, I examined the alliance formation by the sampled firms between the time they received their first VC financing and the time of IPO, when they were still privately held. Second, I used 1997-2000 as the sample selection period because 1997-2000 was a golden time period for VC investments and VC-backed IPOs before the Internet bubble burst in 2001. Focusing on this period may further ensure adequate observations of alliance formation by VC-backed startups, which is the phenomenon of interest in this study. I defined a firm under 15 years old as a startup and excluded portfolio firms that were more than 15 years old at IPO. The final sample size for this study was 417 after excluding observations with missing data. Data Sources and Collection I assembled the data set from multiple archival data sources, including the Alliance and Joint Venture database and the Venture Xpert database from Security Data Company (SDC), Pratt s Guide to Private Equity & Venture Capital and The Directory of Venture Capital and Private Equity Firms. These data were supplemented with information from public filings and other public sources as needed. 123

Data for VC firm specific variables and portfolio firm specific variables were largely retrieved and manually coded from SDC VentureXpert database. This database and its predecessor (Venture Economics) have been used in many studies of VC firms and VC activities (e.g., Gompers, 1995; Lerner, 1995; Kaplan & Schoar, 2005, Gompers et al. 2007), and it has been found to be generally free from bias (Kaplan, Sensoy & Strömberg, 2002). Alliances data (from year 2001-2007) were retrieved and manually coded from SDC Alliance and Joint Venture database. The SDC database reports complete data for new alliances announced each year. It is one of the most commonly used alliance databases in empirical studies published in top management journals (Schilling, 2009). Measures Dependent variable. The phenomenon of interest in this study is the likelihood of private startups to form alliances after receiving VC financing. I operationalized the dependent variable as a dichotomous variable. It is coded as 1 if the portfolio firm involved in at least one alliance formation between the year when it received its first VC investment and the year of IPO, and 0 otherwise. Independent variables. This study examines how the network positions of a portfolio firm s lead VC firm influences its alliance formation. While there have been several means to identify the lead VC firm in the literature (e.g., Barry et al., 1990; Gompers, 1996; Lee & Wahal, 2004), in this study a portfolio firm s lead VC firm was defined to be the first identifiable VC firm to invest in it (Lee & Wahal, 2004). Two types 124

of network positions that have different structural features were examined: central network position and constrained network position. In line with prior studies, I measured each lead VC firm s position in the network using Bonacich s (1987) eigenvector measure. Bonacich s eigenvector centrality takes into account the centrality of VC firms to which the focal VC firm is connected. This centrality measure is calculated as the weighted sum of the centralities of the focal VC firm s partners, capturing the extent to which the focal VC firm is linked to other central VC firms. Therefore, higher centrality scores are generated for VC firms that are connected to many VC firms, which are in turn connected to many other VC firms. The eigenvector centrality has been acknowledged to be a superior measure of network position or social status than alternative centrality measures (Jensen, 2003; Nerkar & Paruchuri, 2005; Podolny, 1994). To compute network centrality scores, I constructed the VC network for each year between 1990 and 1999, considering the investment syndications of the sampled VC firms. The network between VC firms in a given year is represented by a square adjacency matrix. The cells reflect ties among VC firms in the network. I consider a network tie exist when two VC firms co-invested in the same portfolio firms in the same financing round. I follow the same procedure used by prior work (e.g., Castilla, 2005; Hochberg et al., 2007; Piskorski & Anand, 2005) to construct the network structure for a given year based on the syndication ties formed between U.S. VC firms in that year (1459 1459). In calculating centrality scores for each year t, I took into consideration the syndications in prior three year. That is, the final adjacency 125

matrices used for computation of centrality scores represent the relationships between the 1459 VC firms in the previous three years. I calculated the centrality scores using UCINET 6 (Borgatti, Everett, & Freeman, 2002). The centrality score for VC firm i in year t was modeled as follows: c α i = Aijc j Where α is the reciprocal of an eigenvalue and A is the adjacency matrix denoting the syndication ties between VC firms i and j. The centrality of each VC firm i is a function of the centrality of the other VC firms to which it is connected. To measure the extent to which a VC firms syndication partners are connected to one another, I used the adjacency matrices to calculate Burt s (1992) network constraint score by UCINET 6. The aggregate constraint score for a certain VC firm is given by: cij = pij + 2 ( p p ) (q i, j), j j q iq qj where p ij is the proportion of VC firm i s ties invested in the relationship with VC firm j, p iq is the proportion of VC firm i s ties invested in the relationship with VC firm q, and pqj is the proportional strength of VC firm q s relationship with VC firm j. The constraint score is a reverse measure of structural holes. High constraint scores indicate that the firm s partners are densely connected to one another (e.g., fewer structure holes) while low constraint scores mean a sparsely connected network. The moderator, corporate VC, is a dummy variable. It is coded as 1 if the lead VC firm is a corporate VC firm as defined by SDC database (otherwise 0). 126

Control variables. I included a number of control variables in the empirical models for alternative explanations. I controlled for a number of portfolio firm attributes that prior research has pointed out as factors that influence the likelihood of alliance formation such as age (time since founding), industry group dummies, size (number of executives), performance (number of financing rounds received), prior alliance experience (the number of prior alliances before receiving the first VC investment), time since the first VC investment (as of the time of IPO), and total number of VC investors, The model included two VC firm characteristics as control variables: age (time since founding) and size (number of executives). I also controlled for the effects of tie strength by including the total VC equity portfolio firms received and a dummy variable indicating whether a portfolio firm and its lead VC firm were located in the same state. Table 1 summarizes the operationalization of the dependent variable, independent variables and control variables. Data Analysis and Results Statistical model. Given the binary dependent variable, I test the hypotheses using a logit regression model (Long, 1997). The logit model that fits the log odds by a linear function of the independent variables is given by: pi Logit( pi ) = log = α + xiβ + ε i p 1 i where i denotes the firms; pi is the probability of alliance formation; xi is a vector of independent variables; ε i captures the unobserved heterogeneity across the groups. The 127

logit model was estimated with the Stata10 statistical package. The model can also be specified as a logistic function on the scale of probabilities: exp( α + βx) Pr( y = 1 x) =, 1+ exp( α + βx) where y is the dichotomous dependent variable (alliance formation) and x is a vector of independent variables. Table 2 shows the descriptive statistics and the zero-order correlations between the variables included in the statistical model. I computed variance inflation factors (VIFs) to ensure that multicollinearity did not influence the results. The mean VIF is 1.46. The maximum VIF is 2.38, which is well below the guideline of 10 that was advocated by Chatterjee and Price (1991). Thus multicollinearity is not a problem for this study. To avoid potential collinearity due to the presence of interaction terms, I meancentered the continuous variables involved in the interaction terms by subtracting the mean from each value before generated the interaction terms (Aiken & West, 1991). Results. Table 3 presents the results of the logit regression models. The coefficients represent the influence of independent variables on the logarithmic odds of alliance formation. Model 1 is the baseline model that only includes control variables. Model 2 tests the main effects of key independent variables, network centrality and network constraint. Model 3 examines the interaction between network centrality and constraint. Model 4 investigates the moderating effects of corporate VC. Model 5 is the full, unrestricted model that includes all control variables, two main effects and all interaction terms to ensure a rigorous test of the hypothesized effects. Results show the robustness of the estimated coefficients across model 2-5. I reported the robust standard 128

errors that are robust to departures from homoscedasticity. I reported the likelihood statistics and measures of overall model fit at the bottom of the table. Hypothesis 1 asserts that portfolio firms backed by VC firms with higher levels of network centrality are more likely to form alliances. The coefficient of network centrality in model 2 was positive and significant (p<0.05). This finding supports hypothesis 1, indicating that network centrality of lead VC firm is positively related to the probability of alliance formation by a portfolio firm. Hypothesis 2 states that portfolio firms backed by VC firms with higher levels of network constraint are more likely to form alliances. The coefficients of network constraint in model 2 was positive and significant (P<0.01), supporting hypothesis 2. These two main effects were consistent in all of the models. Hypothesis 3 argues that the interaction of VC firms network centrality and network constraint is positively associated with portfolio firms alliance formation. Model 3 shows the results of the test of this hypothesis. The interaction coefficient was positive and significant (p<0.1), supporting hypothesis 3. Model 4 tested the interaction of type of VC firms (corporate VC vs. non-corporate VC) with the two key independent variables. Hypothesis 4a states that when the lead VC firm is a corporate VC firm, the effects of network centrality on the likelihood of alliance formation by portfolio firms are weaker. The interaction coefficient was negative and significant (p<0.05). Therefore, hypothesis 4a was supported. Hypothesis 4b states that when the lead VC firm is a corporate VC firm, the effects of network constraint on the likelihood of alliance formation by portfolio firms are weaker. The interaction coefficient was negative and 129

significant (p<0.1), supporting hypothesis 4b. Figures 2 and 3 illustrate the significant moderating effects on logarithmic odds scale. DISCUSSION AND CONCLUSION This study examined the effects of VC firms network centrality and network constraint on the likelihood of portfolio firms to form strategic alliances. Network theory suggests that these two aspects of network structure provide VC firms with different informational advantages (Gulati, 1999; Lazer & Friedman, 2007; Phelps, 2010). VC firms that sustain central positions in syndication networks enjoy informational advantages in terms of information volume, especially when they are connected to other central actors. More contacts mean more conduits of valuable information, early exposure to new information more opportunities for exchange and more referrals. VC firms whose syndication network is constrained where their syndication partners are densely connected to one another enjoy information advantages in terms of information quality. Information from densely connected partners is more relevant to a focal VC firms domain specialization than from sparsely connected partners. In addition, information reliability increases if there is a consistency between information from multiple channels. In this study, I predicted and found that both VC firms network centrality and network constraint positively influence portfolio firms alliance formation and their effects are weaker for corporate VC firms. While positions bridging many structural holes (open network structure) have been found to be favorable in entrepreneurial settings (Stam & Elfring, 2008), this study found that a closed ego network structure where the focal VC firms syndication partners 130

are densely connected to one another is beneficial and facilitative for portfolio firms alliance formation. This finding confirmed Ahuja s (2000) argument that effects of structural holes are contingent on specific contexts. Given the emphases on domain specialization and relationship building in VC industry, the benefits of reliable and relevant information that provided by a group of densely connected partners outweigh the disadvantages of having less diverse information. In line with resource dependence theory (Pfeffer & Salancik, 1978), the results of this study illustrated the decreased reliance of corporate VC firms on syndication networks as sources of information and other network benefits. Unlike other types of VC firms, corporate VC firms enjoy unique resource advantages in that they can get access to their corporate parents whole bunch of internal resources and extensive industrial connections accumulated over time. Thus corporate VC firms can seek alliance opportunities for their portfolio firms through various types of information conduits and are more capable of facilitating alliance formation between their portfolio firms and established firms. This study contributes to the network literature and VC literature in three ways. First, this study addressed a largely ignored issue in network literature of the transfer of social capital across networks. Despite the considerable number of studies on effects of network positions, few studies have examined how network positions of firms in an organizational population influence the relationship building activities of firms in a tiedto organizational population. Second, this study examined a special value-added service of VC firms that has received scant research attention in VC literature. As relationship 131

investor[s] (Fried & Hisrich, 1995), VC firms can provide valuable supports for portfolio firms in network building and expansion, which have long term beneficial effects that won t evaporate even after VC exits. Third, this study identified the most conductive network structure for VC firms to seek relational benefits for their portfolio firms. High levels of network centrality, high levels of network constraint and their combination have been found to have positive effects. This find lead to an important practical implication for entrepreneurial startups. For entrepreneurial startups valuing alliance opportunities, affiliations with VC firms is particularly beneficial whose wellnetworked partners are densely connected to one another. As last, the results and contributions of this study should be considered in light of its limitations. First, I only examined the likelihood of alliance formation operationalized as a dichotomous variable but not taking into consideration the purposes or contents of alliances (e.g., exploitation or exploration). It is possible that the most facilitative VC network structure differs for different types of alliances (e.g., R&D, marketing and sales, technology licensing). For example, positions bridging many structure holes may be more effective in facilitating R&D alliances than marketing alliances. Future studies are needed on this aspect for developing a better understanding of the most favorable network positions of VC firms in regard with portfolio firms alliance network building. Second, the sample consists of only U.S. leading VC-backed startups as defined by reaching IPO stage. Whether the findings of this study are generalizable to the whole population of portfolio firms needs further investigation. In addition, future studies incorporating foreign samples may product additional insights. 132

In conclusion, this paper contributes to both the strategy literature and the entrepreneurship literature by providing empirical evidence of significant relationships between two key aspects of VC firms network positions and alliance formation of portfolio firms. These relationships appear to be weaker for corporate VC firms. The results of this study reinforce the contingency view of network positions impacts. The results suggest that VC firms with central positions in closed networks where members are densely connected to one another are most facilitative for portfolio firms alliance formation. 133

Variable name DV: Alliance formation IVs: Network centrality TABLE 3.1 Variable Operationalization (Study 3) Variable Measures type Dichotomous 1 = at least one alliance was formed in the studied time period; 0 = otherwise Calculated c α A c i = ij j Network constraint Calculated cij = pij + 2 ( p p ) (q i, j) j j q iq qj Moderator: Corporate VC Dichotomous 1 = the lead VC firm is a corporate VC firm; 0 = otherwise Controls: Count Total number of executives PC executives PC performance Count Total number of financing rounds received PC age (logged) Count Number of years since founding PC industry Dummy Industry dummies as defined by SDC database PC development stage Dummy I followed the way SDC database classifies the development stage of portfolio firms. The six dummy stage variables are: seed, early, expansion, later, buyout/acquisition and other. Time since 1 st VC investment Count Number of years since the portfolio firm received the 1 st VC financing Prior Alliances Count Total number of alliances formed as of the year when the portfolio firm received the first VC financing Number of VC Investors Count Total number of VC firms that have ever invested in the portfolio firm Total VC Investment by IPO Continuous Accumulated amount of VC investments by the time of IPO ($billions) VC age (logged) Count Number of years since founding VC executives Count Total number of executives Same State Dummy 1 = the portfolio firm and its lead VC firm were located in the same state; 0 = otherwise 134

TABLE 3.2 Descriptive Statistics and Correlations (Study 3) Variables Mean S.D. Min Max 1 2 3 1 Alliance Formation 0.34 0.47 0 1 1 2 Alliance Experience 0.09 0.37 0 3 0.18* 1 3 PC age a 1.59 0.40 0 2.4 0.02 0.04 1 4 Industry Dummy2 0.16 0.37 0 1-0.04 0.04 0.07 5 Industry Dummy3 0.10 0.30 0 1-0.13* -0.06-0.06 6 PC Executives 13.25 5.04 1 40 0.14* -0.07-0.04 7 Time since First VC Investment 2.83 1.35 1 7 0.01-0.12* 0.50* 8 Number of VC Investors 13.87 10.41 1 68 0.08-0.12* 0.11* 9 Total VC Investment b 56.38 153.42 0.1 3257.4 0.06 0.09* -0.01 10 Number of Rounds 3.96 1.88 1 12 0.09* -0.11* 0.19* 11 Same State 0.42 0.49 0 1 0.14* -0.03 0.03 12 VC age a 2.29 1.00 0 4.47 0.00-0.07 0.10* 13 VC Executives 28.35 35.92 1 233-0.01 0.01-0.04 14 Centrality 0.06 0.05 0 0.19 0.05-0.05 0.08 15 Concentration/Constraint 0.19 0.24 0.02 1.13 0.06 0.04-0.05 16 Corporate VC 0.07 0.26 0 1 0.02 0.05-0.02 Variables 4 5 6 7 8 9 10 1 Alliance Formation 2 Alliance Experience 3 PC age a 4 Industry Dummy2 1 5 Industry Dummy3-0.15* 1 6 PC Executives 0.02-0.07 1 7 Time since First VC Investment 0.18* -0.08 0.09* 1 8 Number of VC Investors 0.08-0.16* 0.19* 0.44* 1 9 Total VC Investment b -0.05 0.01 0.12* 0.06 0.08 1 10 Number of Rounds 0.08-0.12* 0.12* 0.58* 0.68* 0.14* 1 11 Same State -0.09* -0.07 0.01 0.11* 0.15* -0.04 0.08 12 VC age a 0.01 0.04 0.05 0.16* 0.11* 0.06 0.07 13 VC Executives -0.05 0.07-0.03-0.09-0.16* -0.02-0.13* 14 Centrality 0.05-0.08 0.07 0.12* 0.26* 0.00 0.11* 15 Concentration/Constraint -0.07 0.13* -0.11* -0.04-0.17* -0.09-0.04 16 Corporate VC 0.00-0.04 0.02-0.02-0.04-0.02-0.01 135

TABLE 3.2 Continued Descriptive Statistics and Correlations (Study 3) Variables 11 12 13 14 15 16 1 Alliance Formation 2 Alliance Experience 3 PC age a 4 Industry Dummy2 5 Industry Dummy3 6 PC Executives 7 Time since First VC Investment 8 Number of VC Investors 9 Total VC Investment b 10 Number of Rounds 11 Same State 1 12 VC age a 0.06 1 13 VC Executives -0.03 0.20* 1 14 Centrality 0.22* 0.42* 0.15* 1 15 Concentration/Constraint -0.13* -0.40* -0.06-0.61* 1 16 Corporate VC -0.01-0.21* -0.06-0.15* 0.12* 1 * p < 0.05 136

TABLE 3.3 Logit Regression Results (Study 3) Variables Model 1 Model 2 Model 3 Model 4 Model 5 Constant -2.43 *** -2.90 *** -4.90 ** -2.95 *** -5.07 ** (0.57) (0.67) (1.46) (0.68) (1.53) Alliance Experience 1.46 *** 1.56 *** 1.56 *** 1.57 *** 1.57 *** (0.36) (0.41) (0.41) (0.41) (0.42) PC age (log) 0.36 0.37 0.38 0.37 0.38 (0.30) (0.33) (0.33) (0.33) (0.33) PC Executives 0.06 ** 0.07 ** 0.07 ** 0.07 ** 0.07 ** (0.02) (0.02) (0.02) (0.02) (0.02) Time since First VC Investment -0.11-0.14-0.18-0.13-0.18 (0.12) (0.13) (0.13) (0.13) (0.14) Number of VC Investors -0.01-0.01-0.01-0.01-0.01 (0.01) (0.01) (0.01) (0.01) (0.01) Total VC Investment 0.00-0.00-0.00-0.00-0.00 (0.00) (0.00) (0.00) (0.00) (0.00) Number of Rounds 0.16 0.15 0.15 0.14 0.15 (0.09) (0.09) (0.09) (0.09) (0.09) Same State 0.61 ** 0.55 * 0.53 * 0.56 * 0.53 * (0.21) (0.23) (0.23) (0.23) (0.23) VC age (log) -0.03-0.03-0.01-0.03-0.01 (0.13) (0.16) (0.16) (0.16) (0.16) VC Executives -0.00-0.00-0.00-0.00-0.00 (0.00) (0.00) (0.00) (0.00) (0.00) Industry Dummy2-0.24-0.42-0.46-0.40-0.44 (0.28) (0.31) (0.31) (0.31) (0.31) Industry Dummy3-1.09-0.97 * -1.00 * -0.89 * -0.93 * (0.49) (0.46) (0.46) (0.45) (0.46) Centrality 5.27 * 31.16 * 5.84 * 33.17 * (2.69) (16.99) (2.73) (17.70) Constraint 1.68 ** 9.75 * 1.83 ** 10.33 * (0.61) (5.23) (0.64) (5.43) Centrality Concentration 140.80 148.40 (91.68) (95.25) Corporate VC 2.23 2.34 * (1.41) (1.17) Corporate VC Centrality -34.58 * -38.72 ** (16.76) (13.92) Corporate VC Concentration -4.59-4.21 * (2.95) (2.43) Log Pseudo Likelihood -266.45-246.22-245.05-245.28-244.02 Pseudo R 2 0.09 0.10 0.10 0.10 0.10 Wald Chi 2 47.15 46.70 47.02 49.44 50.64 N 452 417 417 417 417 137

TABLE 3.3 continued Logit Regression Results (Study 3) Significance levels (two-tailed test for control variables; one-tailed test for hypotheses): p < 0.1 * p < 0.05 ** p < 0.01 *** p < 0.001 138

FIGURE 3.1 The Framework (Study 3) 139

FIGURE 3.2 Significant Moderating Effects Full Range (Study 3) 140

FIGURE 3.3 Significant Moderating Effects ± 1 Standard Deviation (Study 3) 141

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