1. Hybrid Networks in Venture Capital Investments Jung-Chin Shen

2. Theories of network formation  Familiarity and similarity  Familiarity: social embeddedness theory  Three network formation mechanisms:  Repetitivity (Podolny, 1994; Gulati, 1995)  Transitivity (Baker 1990; Uzzi, 1996)  Reciprocity (Powell, 1990; Dyer and Chu, 2003)  Familiarity  Lower transaction costs  Increase flexibility  Encourage knowledge sharing  Allow role specialization

3. Homophily as an organizing principle  Yet, if network formation is solely driven by familiarity, network will evolve toward dense, unconnected clusters with familiar actors.  Similarity: homophily (Similarity breeds connections)  Homophily is the strongest single factor to predict various types of interpersonal relations  Geographic proximity  Family ties  Organizational foci  Isomorphic positions  Homophily characterizes network system, and homogeneity characterizes personal networks

4. Homophily in networks  Simmelian sensibility vs. actor attributes  Homophilous vs. heterophilous networks  High density and closure vs. sparse networks  Similar vs. diversified characteristics and resources  Trust and norm vs. information and control  Why important? Self-production: Strengthen social stratification and damper innovation  From interpersonal to interorganizational networks  Can it be an interorganizational networking principle?  Conditions for networking with dissimilar actors?

5. Homophily as an interorganizational networking principle  Motives  Homophilous network: market power (collusion, economies of scale)  Heterophilous network: risk reduction, complementary resources and capabilities  The choice between a hybrid network and a homogeneous network depends on  the information, resources and capabilities necessary for achieving common goals, and  cooperation and coordination difficulties arising from spatial uncertainty and behavioral uncertainty

6. Why hybrid network?  Costly to communicate, hard to cooperate and coordinate actions  Hybrid network and network effectiveness  The need for diverse resources and capabilities for achieving common goal  Informational problems pertaining to network formation:  Cooperation: information asymmetry (incentive)  Coordination: information incompleteness (action)

7. Spatial uncertainty  Information asymmetry between VC and invested company  Localized investments and syndication network  Industry distance:  CVC: technology and complementary knowledge  help reduce information asymmetry between lead IVC and entrepreneur  Geographic distance: local IVC  H1a: The probabilities of hybrid network formation are negatively related to geographic distance between lead IVC firms and target companies.  H1b: The probabilities of hybrid network formation are positively related to industry distance between lead IVC firms and target companies.

8. Behavioral uncertainty  The problem of information incompleteness exists between lead VCs and their partners  Improve information being used in partner selection  Past working experience  Repeated interactions  Threat of termination  First-hand observation  Mutual understanding  Shared code  The shadow of the future  H2a: The probabilities of hybrid network formation are positively related to IVC firms which have previous hybrid network experience.  H2b: The probabilities of hybrid network formation are positively related to CVC firms which have previous hybrid network experience.

9. Interaction between industry distance and experience  The value of experience is higher when VC firms confront spatial uncertainty and behavioral uncertainty concurrently  For example, free-riding problem in collective action  Mutual understanding  Shared code  Collective norm  H3: The higher the intensity of past hybrid network experience, the stronger the relationship between industry distance and hybrid network formation.

10. Methods  Context: US Venture Capital Industry  Data availability  Defining a hybrid network  Incorporating actor attributes  Data  Source: Thomson Financial’s VentureXpert database  Target companies-VC funds-rounds of 105,685 observations for all IVC and CVC funds between 1980 and 2003.  567 CVC lead portfolio companies and 7,836 IVC lead portfolio companies  Method  Multinominal logit model

11. Measurement  Hybrid network  Sole investment, homogeneous network, hybrid network  Geographic distance  Cross-state investment  Industry distance  the percentage of previous investments that the venture capitalist has made in industries other than the one in which the target firm operates  Hybrid experience

12. Multinomial Logit regression for IVC lead investments

13. Multinomial Logit regression for CVC lead investments

14. Conclusion  The “cost” of embeddedness  Homophily as an interorganizational networking principle  Spatial uncertainty and behavioral uncertainty as determinants of hybrid networks  Disentangling network formation mechanisms (e.g., common third party)  Trust and similarity  Discrete model and performance implication