Organizational and Institutional Genesis: Organizational and - - PowerPoint PPT Presentation

Organizational and Institutional Genesis: Organizational and Institutional Genesis: Why do life science clusters form in some Why do life science clusters form in some locales but not others? locales but not others?

Walter W. Powell Walter W. Powell Stanford University Stanford University ~

*This paper builds on work done jointly with Kjersten Whittington, Kelley Packalen, and Jason Owen-Smith. To appear in J. Padgett and W. Powell, The Emergence of Organizations and Markets, Ch. 14. Earlier versions were presented at the University of San Andrés, Center for Advanced Study in the Behavioral Sciences, Nobel Symposium on Foundations of Organizations, the Academy of Management distinguished scholar lecture, and Max Planck summer conference on Economy and Society. Sincere thanks to the diverse audiences for very generative feedback.

September 2009

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Organizational and Institutional Emergence: unaddressed questions

• What factors make distinctive institutional configurations

possible at particular points in time (history, sequence) and space (geography)? How does a collection of diverse organizations emerge and form a field?

– The origins of institutions remain largely opaque. Most research works backward from successful cases to fashion an account of why an outcome solved a particular problem or advanced some group’s or entrepreneur’s project.

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Draw on our several decades of data on the life sciences to analyze institutional emergence and reproduction: The leading sources of knowledge and expertise in the life sciences in the late 1970s and early 1980s were widely distributed across U.S. and globally. In the U.S., public policy and political muscle were flexed to support this field. Many regions had a deep stock of endowments - - Philadelphia, New Jersey, Washington, New York, in particular, but arguably Atlanta, Seattle, Houston, and LA as well. But today, nearly 50% of firms and more than 50% of the outputs (patents, employment, medicines) are from just three regions - - Bay Area, Boston, San Diego. Why such a pronounced pattern of spatial agglomeration? Geographic propinquity: a critical feature of the emergence and institutionalization of the life sciences field. It was not anticipated, given initial founding conditions, nor an obvious outcome but became self-reinforcing and highly resilient. What do I mean by self-reinforcing? An increasing number of participants were attracted, common expectations developed to guide interactions and these were sustained by shared cognitive beliefs.

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Biotech companies in United States, 1978 (n=30)

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Biotech companies in United States, 1984 (n=130)

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Potential candidates for formation of biomedical clusters (early 1980s)

Los Angeles CA - - largest early biotech firm – Amgen, Cal Tech, UCLA, City of Hope Hospital

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Boston - - MIT and to lesser extent Harvard (commercial involvement by faculty was initially precluded there), numerous research hospitals

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Bay Area CA - - UCSF, Stanford, venture capital…but crowding from ICT industries?

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San Diego CA - - sleepy Navy and tourist town, but UCSD, Scripps, Salk, and Burnham Institutes

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Seattle WA - - Fred Hutchinson Cancer Center, U Washington…large investments by Bill Gates and others in biomedicine in 1990s

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Houston TX - - U Texas Medical Center, Rice University, MD Anderson Hospital

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Research Triangle NC - - three universities, major state public policy initiative to build a cluster

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Washington DC metro area- - home of National Institutes of Health, Johns Hopkins University Medical School

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Philadelphia - - “the cradle of pharmacy” - - strong pharmaceutical presence, U Penn, Wistar Institute, Fox Chase Cancer Center

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Northern New Jersey - - home of major U.S. and foreign pharmaceutical companies, Princeton University

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New York City - - extraordinary array of research hospitals, elite universities and medical schools, venture capital and investment banks

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Ranking in number of biomedical patents, 1980

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Biotech companies in United States, 2002 (n=368)

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Why did clusters form in some places and not others?

• A diversity of organizational forms and an anchor tenant are critical factors as they increase

the possibility and salience of transposition, such that the consequences are linked to, but more consequential than, the initial conditions.

• Multiple organizational forms - - a rich soup in which diverse practices, and rules can
• emerge. There are divergent criteria for evaluating success. Some organizations have a foot

in several doors. (This is not unleashed, instrumental action --the MacGyver fetish in org theory-- but cognition in the wild.)

• Anchor tenant - - a position that affords access to several domains, but not directly
• competitive. Having different principles of evaluation enables the anchor to recombine and

repurpose diverse activities. The anchor institution protects the values of the local community.

• Diversity and Connectivity are not sufficient. The mechanism is cross-network

transposition, a form of brokerage that allows ideas to move from one domain to another.

• Most cross-network transpositions are selected against because they are likely to fail from at

least one perspective. The more multi-purpose an idea or activity is, the more perspectives from which it can be shot down. In those few cases where cross-network transposition is absorbed by the social system, it creates a new channel for activities from one domain to cascade into others, possibly with reorganizing or tipping potential.

• Central to my argument is not just statistical reproduction in the sense that something unusual

diffused and became accepted, but transposition: the initial participants brought the status and experience garnered in one realm and converted those assets into energy in another domain, for good or bad.

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‘Cognition in the wild’ or ‘on the hoof category construction’: UC – Berkeley Bancroft Oral History Library Interview in early 1990s,

• n Recombinant DNA Research at UCSF and its Commercial

Application at Genentech Interviewer (Sally Smith Hughes): The next step as I see it is the formation of Genentech in 1976. Had Bob Swanson (the eventual co-founder) approached anybody before he came to your laboratory? Herbert Boyer, UCSF Professor and Co-founder of Genentech: He said he took a list of names associated with the publicity on Asilomar and went through it alphabetically, which means (Nobel Laureate) Paul Berg must have turned him down. I suppose I was next on the list. It was a telephone introduction. He wanted to talk, so I had him come to my lab on a Friday afternoon at quarter to five. He introduced himself, talked about what he wanted to do. Did I think the technology was ready to be commercialized? He said he had access to some money, and I thought it would be a good way to fund some postdocs and some work in my laboratory, because we always needed some money to do that. We spent a good deal of time that evening talking about it.

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Data sources:

• The contemporary life sciences, including dedicated biotech firms,

large multinational corporations, research universities, government labs and institutes, research hospitals, nonprofit research centers, and venture capital firms.

• Data set covers all the above organizations, as well as their formal

inter-organizational collaborations, from 1988-2004. Includes data

• n earlier years, but is left censored so that we were only able to

collect ‘early’ data on firms that were alive in 1988.

• Two-mode network: 691 dedicated biotech firms, 3,000 plus

collaborators, 11,000 plus collaborations - - both local and global ties

• Field work, archival records, interviews with 100s of scientists and

managers in DBFs, universities, pharma cos., govt. institutes, technology licensing offices, VC and law firms.

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Method: Network Visualization, with Pajek

• Pajek (Slovenian for ‘Spider’) is a freeware package for the analysis and

visualization of large networks created by Vladimir Batagelj and Andrej Mrvar and available online at http://vlado.fmf.uni-lj.si/pub/networks/pajek/

• In Pajek, ‘spring-embedded’ network drawing algorithms enable meaningful

representation of social networks in Euclidean space.

• ‘Particles’ repel one another, ‘springs’ draw attached particles together
• Drawing algorithms seek a ‘solution’ where the energy of the entire system

is minimized

• In these representations, the positions of nodes are generated by the pattern of

ties connecting the entire system

• We draw on two such algorithms
• Fruchterman-Reingold (FR) (1991) optimizes network configurations without

reference to graph-theoretic conceptions of distance

• Kamada-Kawai (KK) (1989) positions connected nodes adjacent to one

another and makes euclidean distances proportional to geodesic path length in the network

Figure 1. Boston Network, 1988

Node Key: Circles = DBFs Triangles = PROs Squares = VCs Diamonds = Pharma

MIT BU TUFTS DANA FARBER HARVARD MGH NEMC

MAIN COMPONENT PROs REMOVED

42.9% Of Boston DBFs Reachable 0.0% of Boston DBFs Reachable Source: Owen-Smith and Powell, Organization Science 2004

Public Science anchors Boston Network, 1988 Tie Key: R&D Finance Commercialization Licensing

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Results from Analysis of Boston Biotechnology Community

• Local public research organizations (PROs) were the foundation on

which the Boston commercial biotech community was built. R&D ties to local PROs increased rates of DBF patenting.

• The Boston network changed to become more anchored by for-profit
• firms. Ties to orgs. outside of Boston grew rapidly. As network

expanded, the majority of ties became commercial. The importance of local PROs receded, but the footprint remained. Centrality in the local network continued to have big impact on firm’s patenting.

• Ties to local PROs are leaky (spillovers), while external commercial

ties are closed, and contractually restricted.

• Public research organizations contribute to cluster formation precisely

because they perform commercially important research under academic institutional arrangements.

• Active commercial participation by PROs catalyzes life science

innovation, but may carry the danger of capture by industrial interests.

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1988

Harvard MIT

1994

Harvard Genzyme Autoimmune

1999

MIT Harvard

Bay Area

Stanford UCSF Genentech Stanford Genentech Chiron Stanford Chiron Genentech

1988 1994 1999

Boston

Note: Thickness of line indicates multiple ties. Source: Owen-Smith and Powell, 2006.

Boston and Bay Area Local Networks, 1988, 1994, 1999

Node Key: DBF PRO VC Pharma

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San Diego - - A sleepy Navy and tourist town became a high tech cluster in biotech and wireless (Casper, 2007; Simard, 2004; Walshok, 2002). 1978 - - Hybritech founded by Ivor Royston, an asst prof at UCSD and former Stanford postdoc, and Howard Birndorf, a lab tech, who secured backing from Kleiner, Perkins and got Brooks Byers as their manager. Developed diagnostic tests based on monoclonal antibody technology, no need for lengthy clinical trials or FDA approval, generated revenue within months of invention…one of few firms to become profitable early, had a successful IPO in 1981. Royston & Birndorf become serial founders - - GenProbe (1983), IDEC (1985) 1986 - - Hybritech acquired by pharma giant Eli Lilly for $300 million and 100 million in shares. “Animal House meets the Waltons” Huge failure! But ex-Hybritech scientists and managers stayed in San Diego and started more than 40 biotech firms (Ligand, Gensia, Genta, Nanogen, Amylin) and several VC firms (Biovest, Forward Ventures, Kingsbury Partners). This failed merger seeded the San Diego biotech cluster.

Boston, Bay Area, and San Diego, 1990, 1996, and 2002 n=69 (37) n=79 (40) n=40 (16) n=106 (65) n=131 (70) n=66 (27) n=105 (73) n=139 (77) n=65 (33) Boston Bay Area San Diego

Node Key: ■ DBFs ■ Fin. Institutions ■ Gov’t Institutes ■ Pharma Corps ■ Public Research Orgs ■ Biomed Suppliers Tie Key: ■ R&D ■ Finance ■ Commercialization ■ Licensing Note: n = all nodes, number in brackets = connected nodes

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Comparison of Boston, Bay Area, and San Diego:

• Different types of organizations serve as anchor tenants, but each operates to

foster interaction among disparate parties and provide means for local information sharing. Organization-forming organizations that spark the mixing of practices across domains (whatever one may think of the consequences for public science or corporate governance). No standard solution, instead a topology of the possible.

• Boston: Public Research Organizations were Anchors
• Bay Area: Venture Capital, multidisciplinary model of UCSF, technology transfer
• ffice at Stanford focused on relationships with startups, first-generation

companies pursued invisible college model

• San Diego: Spinoffs from failed acquisition of Hybritech by Eli Lilly (“like working

for your grandfather”), Salk, Scripps, & Burnham Institutes, UCSD, and Connect, a local nonprofit incubator.

• In all three regions: considerable inter-org job mobility, local competitors

collaborated, public and private science interwoven, all independent from

• verweening control of a dominant organization. Moreover, these three clusters

(and so far only these) combine dense local connectivity with extensive outside linkages. Let’s look at the nascent clusters that didn’t take off 

New York, New Jersey, and Philadelphia, 1990, 1996, and 2002 n=52 (7) n=44 (18) n=20 (7) n=79 (18) n=54 (26) n=34 (13) n=74 (14) n=47 (18) n=28 (8) New York New Jersey Philadelphia

Node Key: ■ DBFs ■ Fin. Institutions ■ Gov’t Institutes ■ Pharma Corps ■ Public Research Orgs ■ Biomed Suppliers Tie Key: ■ R&D ■ Finance ■ Commercialization ■ Licensing Note: n = all nodes, number in brackets = connected nodes

Washington-Baltimore, Research Triangle, NC, and Houston, 1990, 1996, and 2002 n=42 (15) n=6 (3) n=16 (8) n=58 (22) n=14 (4) n=17 (6) n=53 (18) n=20 (8) n=13 (5) Washington- Baltimore Research Triangle, NC Houston

Node Key: ■ DBFs ■ Fin. Institutions ■ Gov’t Institutes ■ Pharma Corps ■ Public Research Orgs ■ Biomed Suppliers Tie Key: ■ R&D ■ Finance ■ Commercialization ■ Licensing Note: n = all nodes, number in brackets = connected nodes

Seattle and Los Angeles, 1990, 1996, and 2002 n=16 (6) n=35 (9) n=25 (17) n=35 (8) n=22 (11) n=32 (8) Seattle Los Angeles

Node Key: ■ DBFs ■ Fin. Institutions ■ Gov’t Institutes ■ Pharma Corps ■ Public Research Orgs ■ Biomed Suppliers Tie Key: ■ R&D ■ Finance ■ Commercialization ■ Licensing Note: n = all nodes, number in brackets = connected nodes

Figure 9: Anchor tenant vs. 800-lb. gorilla: % of all ties by organizational form of partners, 1990, 1996, and 2002

Type of Partner:

Figure 10: Transposition: % of local ties by organizational form of partners, 1990, 1996, and 2002

Type of Partner:

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Why did clusters form communities in some locales but not

• thers?
• All of the regions had considerable local endowments, but resources

alone were insufficient.

• The anchor tenants in three ‘successful’ regions catalyzed further
• rg. and network formation, rather than acting as a hegemonic
• power. The norms that characterized inter-org. relations in the three

clusters bore the institutional stamp of the anchors. Moreover, the anchor institutions became guardians of these norms.

• Cross-network transposition:

– DBFs collaborated with other local DBFs; DBF scientists published in scientific journals – Universities and research institutes became active in commercialization and licensing – VCs became executives in DBFs and donors to universities – Serial founders of DBFs became VCs – Three clusters with widespread mutual awareness, a community of common fate, & mixing of practices from multiple domains (for better

• r worse!)

24 The story is not about differential access to money: Research funding was abundant in nascent clusters National Institutes of Health Extramural Awards, 1996, Top 50 Recipients

91,249,662.00 SAN DIEGO, CA SCRIPPS RESEARCH INSTITUTE 25 91,642,586.00 LOS ANGELES, CA UNIV OF SOUTHERN CALIFORNIA 24 94,291,478.00 ITHACA, NY CORNELL UNIV 23 99,967,195.00 BOSTON, MA BRIGHAM AND WOMEN'S HOSPITAL 22 109,955,960.00 BOSTON, MA MASSACHUSETTS GENERAL HOSPITAL 21 114,109,079.00 SAN DIEGO, CA SCIENCE APPLICATION INTERNATL CORP 20 118,292,038.00 BIRMINGHAM, AL UNIV OF ALABAMA-BIRMINGHAM 19 121,990,782.00 MADISON, WI UNIV OF WISCONSIN 18 124,180,639.00 CLEVELAND, OH CASE WESTERN RESERVE 17 131,077,595.00 MINNEAPOLIS, MN UNIV OF MINNESOTA 16 135,469,556.00 SAN DIEGO, CA UNIV OF CALIFORNIA SAN DIEGO 15 136,204,607.00 PITTSBURGH, PA UNIV OF PITTSBURGH 14 137,815,335.00 NEW YORK, NY COLUMBIA UNIV 13 140,140,193.00 CHAPEL HILL, NC UNIV OF NORTH CAROLINA 12 143,358,921.00 DURHAM, NC DUKE UNIV 11 153,205,664.00 STANFORD, CA STANFORD UNIV 10 156,574,520.00 LOS ANGELES, CA UNIV OF CALIFORNIA LOS ANGELES 9 166,727,904.00 CAMBRIDGE, MA HARVARD UNIV 8 172,774,071.00

• ST. LOUIS, MO

WASHINGTON UNIV 7 174,741,782.00 NEW HAVEN, CT YALE UNIV 6 179,651,361.00 ANN ARBOR, MI UNIV OF MICHIGAN 5 186,727,955.00 PHILADELPHIA, PA UNIV OF PENNSYLVANIA 4 212,281,915.00 SEATTLE, WA UNIV OF WASHINGTON 3 212,877,232.00 SAN FRANCISCO, CA UNIV OF CALIFORNIA SAN FRANCISCO 2 $ 279,185,690.00 BALTIMORE, MD JOHNS HOPKINS UNIV 1

KEY: BLUE: established clusters GREEN: nascent clusters BLACK: other locales

56,620,062.00 BOSTON, MA DANA-FARBER CANCER INST 50 57,047,488.00 DAVIS, CA UNIV OF CALIFORNIA DAVIS 49 57,665,548.00 CORAL GABLES, FL UNIV OF MIAMI 48 59,167,175.00 CAMBRIDGE, MA MASSACHUSETTS INSTITUTE OF TECHNOLOGY 47 59,289,524.00 CHARLOTTESVILLE, VA UNIV OF VIRGINIA 46 60,604,497.00 ROCHESTER, MN MAYO FOUNDATION 45 61,933,250.00 SALT LAKE CITY, UT UNIV OF UTAH 44 62,127,860.00 NEW YORK, NY MOUNT SINAI SCHOOL OF MEDICINE 43 63,312,861.00 BALTIMORE, MD UNIV OF MARYLAND BALT 42 63,345,228.00 BERKELEY, CA UNIV OF CALIFORNIA BERKELEY 41 63,809,470.00 HOUSTON, TX UNIV OF TEXAS HEALTH SCIENCES CTR 40 67,131,615.00 BLOOMINGTON, IN INDIANA UNIV 39 68,165,506.00 EVANSTON, IL NORTHWESTERN UNIV 38 69,918,952.00 BOSTON, MA BOSTON UNIV 37 70,978,006.00 ROCHESTER, NY UNIV OF ROCHESTER 36 71,294,949.00 NEW YORK, NY NEW YORK UNIV MEDICAL CTR 35 76,639,587.00 NEW YORK, NY YESHIVA UNIV 34 78,133,179.00 SEATTLE, WA FRED HUTCHINSON CANCER RESEARCH CTR 33 78,300,389.00 ATLANTA, GA EMORY UNIV 32 82,900,672.00 DALLAS, TX UNIV OF TEXAS SW MED CTR 31 83,423,416.00 DENVER, CO UNIV OF COLORADO-HEALTH SCI CTR 30 83,480,815.00 IOWA CITY, IA UNIV OF IOWA 29 87,150,662.00 NASHVILLE, TN VANDERBILT UNIV 28 89,200,036.00 CHICAGO, IL UNIV OF CHICAGO 27 $ 90,895,535.00 HOUSTON, TX BAYLOR COLLEGE OF MEDICINE 26

KEY: BLUE: established clusters GREEN: nascent clusters BLACK: other locales

Top 50 NIH Awardees, cont.

Figure 11: Sample selection on networks? Count of partner ties by location, 1990, 1996, and 2002

Location of DBF:

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How do fields evolve and change?

Starting points and sequences matter - - what types of organizations are involved and where you begin shapes where you can go (A process story not a recipe!) Windows of opening can be brief - - opportunities are ephemeral, and institutionalization may depend on catalyzing those ingredients at specific

• moments. (This is not a linear story!)

Multiple logics always present - - but how you work, whom you work with, and what you work on are conditioned by micro patterns of partner choice and local norms that sustain the evolving field structure Multiple Autocatalytic Networks: Multivocality - - actions can be interpreted from diverse perspectives simultaneously; multivocal actions are moves in multiple games at once Change does not necessarily entail uprooting of incumbents and replacement by

• challengers. Elements of the old guard may find new tools to retain position, or

forge alliances with new entrants, or co-opt them. Multiple network transposition does insure reshuffling of relations and identities, and altering of criteria of evaluation For ex., Pharma corps. move R&D labs to Kendall Square and La Jolla; Novartis creates nonprofit Genomic Institute in La Jolla; Harvard endowment fund invests as VC

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Conclusion:

• Relational density and transposition foster multiple

criteria for evaluation, offering catalytic possibilities that no broker or strategic actor could ever anticipate. Cross- network transpositions were the means by which ideas and skills were transferred into new domains, where they were put to new purposes. Such cascades are unusual, but when they occur and are reinforced by multiconnected organizations, the potential for institutional change is considerable.

• Transposition can have potent consequences: Diversely

anchored, multiconnected networks are much less likely to unravel than networks that are reliant on a few elite

• rganizations, and the organizing practices of

multiconnected networks are more likely to become institutionalized.

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Next Steps:

• Emergence (with Kurt Sandholtz): In the late ‘70s and early ‘80s,

academic entrepreneurship was not venerated; such activity was questionable, if not illegitimate. A handful of world-class scientists and restless executives created science-driven companies (transposition: convert scientific status into new domain). No available template in the world of business; what models did they draw on to create these new entities?

• Operationalizing transposition (with Kjersten Whittington): How do

we capture this mechanism? Passing a practice or rule to a different set of organizations or using the currency of one realm in a new setting for a different purpose?

• “Open Elite” (with Jason Owen-Smith): What forces catalyze

restlessness and preclude lock-in? Put differently, when do highly central organizations prospect for novelty rather than defend their positions? Is “open-ness” an inherently conservative strategy - - welcoming and sponsoring newcomers, adopting new routines, etc., in order to retain dominant position? Or does maintaining centrality in a high-velocity field require fidelity to certain practices and relationships?