Communities Maxim Sytch Ross School of Business University of - - PowerPoint PPT Presentation

Dynamics of Network Communities

Maxim Sytch Ross School of Business University of Michigan msytch@umich.edu

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Network Communities (Global Computer Industry)

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(3) global-network level (2) network-community level (1) ego-network level (3) global-network level (2) network-community level (1) ego-network level

Dynamics of Network Communities and a Firm’s Invention

• Interorganizational ties are

instrumental for the diffusion of tacit knowledge, information, and other resources

• Greater heterogeneity in

knowledge and information between communities, rather than within communities

• Homophilous selection
• Homogenization
• Knowledge and other

resources in network communities are more accessible than more distant inputs

• Shorter network distances
• Multiple redundant pathways

connecting residents

• Lower transaction costs

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time t-1 time t

Membership Turnover & Movement Across Communities

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4 4.2 4.4 4.6 4.8 5 .2 .4 .6 .8 1

Membership turnover

Community Membership Turnover and a Firm’s Invention

A typical member of a moderately dynamic community (i.e., one that retains about 55% of its members from year t-1 to t) tends to file for 19.5% more patents than a member of a static community (i.e., one that retains all of its members), and for 4.2% more patents than a member of a highly dynamic community (i.e., one that retains just 30% of its members).

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3 4 5 6 7 8 1 2 3 4 5 6 7 8 9

Prior community affiliations

Movement across Communities and a Firm’s Invention

A typical member of a moderately dynamic community (i.e., one that retains about 55% of its members from year t-1 to t) tends to file for 19.5% more patents than a member of a static community (i.e., one that retains all of its members), and for 4.2% more patents than a member of a highly dynamic community (i.e., one that retains just 30% of its members). A typical firm with a moderate rate of movement across different network communities (i.e., one with about 5 prior community affiliations) files for approximately twice as many patents as a firm with no prior community

• affiliations. It also files for about 50% more patents than a firm with 9 prior

community affiliations.

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3.5 4 4.5 5 5.5 .2 .4 .6 .8 1

Membership turnover

Low coreness (mean - 1SD) High coreness (mean + 1SD) 2 4 6 8 2 4 6 8 1 3 5 7 9

Prior community affiliations

Low avg. coreness (mean - 1SD) High avg. coreness (mean + 1SD)

Dynamics of Network Communities and a Firm’s Invention

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Spanning Community Boundaries and Diffusion

Diffusion Model: (a) No hoarding of knowledge (b) Probability of knowledge transfer:

• the existence of a network

relationship between firms

• the level of familiarity and trust

between them (fraction of ties linking them to the same third parties)

(c) Network formation & diffusion (d) Outcome: scale of diffusion (%

• f adopters)

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Spanning Community Boundaries and Diffusion

Diffusion Model: (a) No hoarding of knowledge (b) Probability of knowledge transfer:

• the existence of a network

relationship between firms

• the level of familiarity and trust

between them (fraction of ties linking them to the same third parties)

(c) Network formation & diffusion (d) Outcome: scale of diffusion (%

• f adopters)

Community Network

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Communities and Diffusion Clan Network Convention Network

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Clan network

Community Network Convention Network

Network Behaviors and Diffusion

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Ube Industries

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Transient Bridges

Daihatsu

Balkancar