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