Networks, Regions, and Knowledge Communities

Jason Owen-Smith Walter W. Powell

University of Michigan Stanford University/SFI

For presentation at conference on Advancing Knowledge and the Knowledge Economy at the National Academies, 10-11 January, 2005

Regions and Networks

Why does agglomeration spur innovation?• Economic Geography

Increasing returns – concentrated supply of skilled labor, support services etc.

Information spillovers – “the secrets of industry are in the air”

• Economic SociologyRelational Density – networks channel information and

resources, contributing to the generation of novel ideas

Institutional Diversity – for profit, non-profit, public and private organizations create a robust community ecology

Biotechnology firms Biotechnology Firms & Universities

Biotechnology Firms, Universities, Research Institutes & Hospitals

The Boston Biotechnology Knowledge Community

In Boston, organizational diversity drives innovation networks.

Knowledge Communities

Proximity, diversity, and long term relationships forge communities with unique characteristics

• Forbearance and relational contracting• Local norms for collaboration and knowledge sharing• Information rich markets for labor, technology, services

Knowledge communities are the synergy between proximity and relationships

But the institutional anchors and growth trajectories of networks stamp the character of innovative communities and the knowledge they produce.

Consider two key biotechnology regions

Boston and the San Francisco Bay Area are the most successful and widely emulated biotechnology regions in the world

Both are home to regionally bounded but relationally defined communities

Webs of collaboration connecting firms grew from a substrate of ties to other types of organizations

In Boston Public Research Organizations (PROs) including Universities, Research Hospitals & Non-Profit Research institutes anchor the community

In the Bay Area early Venture Capitalists link the community

Boston and Bay Area Networks, 1988, 1994, 1999

1988

Harvard

MIT

1994

Harvard

Genzyme

Autoimmune

1999

MIT

Harvard

Bay Area

Stanford

UCSF

Genentech

Stanford

Genentech

Chiron

Stanford

ChironGenentech

1988 1994 1999

Boston

Boston and Bay Area knowledge communities evolved along different paths from disparate starting points

Their trajectories stamp the character of the regions and of the knowledge they produce

Mix of technologies and impact of discoveries is the same but approach to innovation varies dramatically

Differences hold in the aggregate and for a matched pair of drugs for the same indication

Boston Bay AreaExploration – high variance discovery strategy increases diversity among research programs

Exploitation – low variance discovery strategy increases convergence among research programs

Research trajectories tied to academe

Research trajectories tied to other firms

Lesser reliance on internal R&D

Greater reliance on internal R&D

More patient centered, ‘clinical’ approach

More market centered, ‘home run’ approach

Boston Bay Area

# DBFs 57 82

# Patents 1,376 3,806

# PROs 19 3

# VCs 29 64

Mean Impact (standardized) 1.113 0.979

Impact Variance 30.270 14.150

# Citations made 12,659 41,389

% Non-DBF cites 71% 55%

% Self Cites 12% 35%

FDA Approved Drugs 18 40

Orphan Products 60 51

Boston & Bay Area R&D Outputs Differ, 1988-99

A Quick Look at the Numbers

Implications for Discussion The paradox of university engagement

• formal connections to academe generate a more ‘open’ research trajectory in Boston, informal involvement in the Bay Area allows VC model to emerge

• but overly tight connections to firms limits the distinctiveness of university innovation

• an unintended danger of corporate capture for academe?

The dangers of late stage emulation• innovation intensive regions require more than an ‘add

institutions and stir’ policy• similar end states can be reached from diverse starting points

and the paths regions take matter • imitating end states may fail to produce desired results

Matching formal and informal networks and institutions• Do inter-organizational ties grow from or catalyze social

connections?• How can more ‘diffuse’ organizations (e.g. patient groups, social

movements, professional associations) be included in knowledge communities?

National and International Policy• Will seeding disparate types of knowledge communities

strengthen national innovation systems, or increase regional disparities?

• How do diverse regional approaches diffuse?• Can distant organizations benefit from ties to knowledge

communities?

Implications for Discussion

Related papers

Owen-Smith et. al. (2002) “A Comparison of U.S. and European University-Industry Relations in the Life Sciences.” Management Science. 48(1): 24-43.

Owen-Smith & Powell (2003) “The Expanding Role of University Patenting in the Life Sciences: Assessing the Importance of Experience and Connectivity.” Research Policy 32(9): 1695-1711.

Owen-Smith & Powell (2004) “Knowledge Networks as Channels and Conduits: The Effect of Formal Structure in the Boston Biotechnology Community.” Organization Science. 15(1): 5-21.

Powell et al. (2005) “Network Dynamics and Field Evolution: The Growth of Interorganizational Collaboration in the Life Sciences.”

American Journal of Sociology 110,4 (Jan.)

Owen-Smith & Powell (Forthcoming) “Accounting for Emergence and Novelty in Boston and Bay Area Biotechnology.” in P. Braunerhjelm & M. Feldman (eds.) Cluster Genesis: The Emergence of Technology Clusters and Their Implications for Government Policy