Hybrid Networks in Venture Capital InvestmentsJung-Chin Shen

Theories of network formationFamiliarity 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

Homophily as an organizing principleYet, 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

Homophily in networksSimmelian 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?

Homophily as an interorganizational networking principleMotives

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

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)

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.

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.

Interaction between industry distance and experienceThe 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.

MethodsContext: 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

MeasurementHybrid 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

Multinomial Logit regression for IVC lead investments

Multinomial Logit regression for CVC lead investments

ConclusionThe “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