Hybrid Networks in Venture Capital Investments

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 CVCfirms 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’sVentureXpert 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 • Multinominallogit 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