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RVM TECHNOLOGY TRANSFER AND KNOWLEDGE DIFFUSION: LESSONS LEARNED FROM A 15-YEAR RESEARCH PROGRAM Barry Bozeman Research Value Mapping Program Georgia Institute of Technology Presentation to the National Advisory Commission on Innovation, South Africa, October 2003 Objective RVM

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Technology transfer and knowledge diffusion lessons learned from a 15 year research program l.jpg

RVM

TECHNOLOGY TRANSFER AND KNOWLEDGE DIFFUSION: LESSONS LEARNED FROM A 15-YEAR RESEARCH PROGRAM

Barry Bozeman

Research Value Mapping Program

Georgia Institute of Technology

Presentation to the National Advisory Commission on Innovation, South Africa, October 2003


Objective l.jpg
Objective

RVM

  • To review chief findings of Research Value Mapping Program during a 15-Year projects on technology transfer, knowledge utilisation

    • Considering salient differences between the South African and the U.S. Context

    • Distilling general lessons for public policy


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Sponsored by:

RVM

U.S. Department of Energy

Office of Basic Energy Sciences

National Science Foundation

Societal Dimensions of Engineering,

Science, Technology

National Institute of Health,

National Institute of Child Health and Human Development


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RVM Projects on Technology Transfer and Knowledge Diffusion

RVM

  • Canada Council of Science and Technology Agencies (CSTA) study of “Determinants of Effective Inter-Institutional Partnerships

  • Department of Energy study of the Impact of Research Centers Design and Management on Innovation

  • IBM study of Government-Sponsored University-Based Research Centers

  • NIH study of Diffusion of “Translational” Research from NIH Science Centers

  • National Science Foundation study of Technology Transfer from Government Laboratories to Industry

  • Department of Energy study of the Economic and Technological Impacts of Basic Science

  • National Science Foundation study of Diffusion of Knowledge through “Scientific and Technical Human Capital”


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What is Technology Transfer and Knowledge Utilisation?

  • Many different definitions, little agreement

  • Disaggregated view less possible as science evolves every more quickly into application (e.g. biotech)

  • One view: technology is physical embodiment of knowledge


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Adapted Bozeman’s model of knowledge utilisation*

  • KNOWLEDGE PRODUCER

  • Universities

  • Science Councils

  • National research

  • facilities

  • DEMAND ENVIRONMENT

  • Existing demand for knowledge

  • Potential or induced demand

  • KNOWLEDGE USERS

  • Government

  • Business / Industry

  • Civil society

  • Scientific community

  • DISSEMINATION MODE

  • Journals

  • Conference

  • Patents / Licenses

  • KNOWLEDGE PRODUCTS

  • Scientific knowledge

  • Tacit knowledge

  • Technologies

* National Advisory Council on Innovation. ‘‘Utilisation of Research Findings: Extent, Dynamics and Strategies.” South Africa, July 2003, p. 9


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Knowledge Utilization Occurs with the National Innovation System

  • NIS = intricate network of agents, policies and institutions supporting the process of technical advance in an economy

Viz., Government of the Republic of South Africa, South Africa’s

National Research and Development Strategy, August, 2002, pp. 25-34

Viz., National Advisory Council on Innovation, Utilisation of Research Findings Extent, Dynamics, and Strategies, Draft, July 2003.


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Current Components Of U.S. NIS Bozeman 1998)

  • Civilian technology programs at both the national and state levels that are designed to promote economic growth

  • Defense R&D with an emphasis on dual-use technologies and conversion of defense R&D from military to civilian applications

  • Federal Government’s national laboratories, industry-university research centers, multidisciplinary multipurpose research centers



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Cooperative Technology Policy Paradigm Bozeman 1998)

  • Premise: relies on networks and cooperation between government, industry, and universities as well as inter-firm cooperation resulting in increased technology development and transfer

  • Governmental Policies: aid research by

    • Changing patent policy to expand use of government technology

    • Relaxing anti-trust regulations to promote cooperative R&D

    • Developing cooperative research centers and consortia

    • Altering guidelines for disposition of government owned intellectual property


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The Major Change in U.S. Policy for Tech Transfer and Utilisation:

Pre-1980: “If the public pays anyone may use”

Post-1980: “If it belongs to everyone it belongs to no one.”



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Intensive Case Studies Transfer Policy Legislation

[Using this pulsed laser deposition system,

D. Norton and C. Park deposit buffer layers of

cerium oxide]

  • California Institute of Technology

    Center for Neuromorphic Systems Engineering

  • Carnegie Mellon University

    Center for Light Microscope Imaging and Biotechnology

  • Georgia Institute of Technology

    Interconnect Focus Center

  • Iowa State University

    Center for Nondestructive Evaluation

  • Lawrence Berkeley National Laboratory

    National Center for Electron Microscopy

  • Ohio State University

    Network for Research on Plant Sensory Systems

  • University of Michigan

    Center for Ultrafast Optical Science




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United States Assessment- Algorithms

“Developed” country

Knowledge production

Strong university tradition that aids in capacity building

Institutional history of NIS

South Africa

“Developing” country

Reliance on imported know-how

Recent commitment to educational reform

Focus since mid-1990s

Difference Factors


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Academic Staff In S&T Higher Education Assessment- Algorithms

South Africa, 1999*

United States, 2001**

<5000 university faculty, bottom heavy

20,000+ university faculty, top heavy

*NACI, South African Science and Technology: Key Facts and Figures 2002

**NSF/Division of Science Resource Statistics, 2001 Survey of Doctoral Recipients


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South Africa* Assessment- Algorithms

United States**

Institutions in the Science and Technology System

*NACI, South African Science and Technology: Key Facts and Figures 2002

**Approximate Figures.


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The Key to the Success of the U.S. NIS Assessment- Algorithms

Figure: R&D Funding, by Source

NSF (2000)


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What can South Africa NSI and U.S. NSI Learn from One Another?

  • Not details, not a template, but concepts and institutional innovations

  • More focus on state governments’ technology-based economic development programs


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Study 2: RVM Project on Federal Lab-Industry Technology Transfer

  • B. Bozeman, M. Papadakis, K. Coker. Federal Laboratory-Industry Technical Partnerships: CRADA’s and Technology Transfer, Report to the National Science Foundation, 1996.

    • Study of 239 cooperative R&D projects with 214 companies and 18 national laboratories

    • Objective: assess technology transfer effectiveness


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Study 2: RVM Project on Federal Lab-Industry Technology Transfer

  • B. Bozeman and D. Wittmer. "Technical Roles and Success of US Federal Laboratory-Industry Partnerships." Science and Public Policy, 28, 4, June 2001, pp. 169-178.

  • Barry Bozeman and Maria Papadakis, “ Firms’ Objectives in Industry-Federal Laboratory Technology Development Partnerships,” Journal of Technology Transfer, December, 1995.

  • J. Rogers and B. Bozeman, “Basic Research and the Success of Federal Lab-Industry Partnerships,” Journal of Technology Transfer Vol. 22, 1999, (3): 37-48.

  • P. Savaanda and B. Bozeman, “Industry-Federal Laboratory Technology Development: The ‘Gradient Effect,’ 2003


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Question: Who Developed a Product as a Result of the Partnership?

  • 46 of 155 (who had product development among their objectives) actually developed a product

  • Companies developing products generally did so within 1.5 years of the conclusion of the project


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What were the characteristics of “product developers?” Partnership?

  • Smaller firms (500-1000 employees)

  • Project initiated by the firm

  • Low R&D intensity

  • Geographic location NOT important (Coker, 1998)

  • Used federal laboratories’ specialized equipment


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Business strategy: What coupling of technical roles is optimal for product development?

  • Companies played the following technical roles:

    • None (federal lab did all technical work)

    • Basic research

    • Pre-commercial applied

    • Applied

    • Development

    • Testing


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Number of Roles: Did More Roles= More Product? optimal for product development?


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Relation of optimal for product development?

Technical

Strategy to

Product:

INCONCLUSIVE

[1] If n=<10, not reported.


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Business strategy and technical roles: the “Gradient Effect”

  • It is not the combination of roles but their proximity; there is a “gradient effect” (Saavandra and Bozeman, 2003)

  • That is,

    • The combination that works best (in terms of product and cost benefit) is for the federal laboratory to perform a role one step ahead of the company with respect to the research continuum. (If the company performs development, the federal lab performs applied; if the company performs applied, the federal lab performs basic


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How about the $? What is the estimated benefit-cost? Effect”

  • Reported net benefit correlated with product development and pursuing a product– both NEGATIVELY correlated

  • Little relation between reported satisfaction with project (96%!!) and estimated benefit cost

  • No relation to job creation (only .5 per project)


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How about the $? What is the estimated benefit-cost? Effect”

BenefitCostNet Benefit

$925,975 $419,355 $487,588

--BUT,

Mean net benefit = $1.2 million Median net benefit = ZERO


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Net Benefit Greater if Products Based on Basic Research Performed by Lab

Source: J. Rogers and B. Bozeman, BASIC RESEARCH AND THE SUCCESS

OF FEDERAL LAB-INDUSTRY PARTNERSHIPS, Journal of Technology Transfer Vol. 22(3): 37-48.


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Source: B. Bozeman and D. Wittmer, “Technical Roles and Success of Federal Laboratory Partnerships, Science and Public Policy, 2001

Illustration

CRADA

Technical

Activity Type

And Level of

Benefit


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Major Implications Success of Federal Laboratory Partnerships,

  • Chief motivation is access, capacity, and, training, not product development.

  • Those with highest satisfaction rating valued capacity and training, those with lowest sought to develop a product

  • Geography matters relatively little

  • Business technology strategy and performance role is key: “gradient effect”

  • It is not an important means of job creation, at least not in the near term


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Study 3: RVM Project on “Scientific and Technical Human Capital and Knowledge Diffusion”

  • Objectives:

    • Understand the relation of institutional setting, program funding, and the ability to create capacity through S&T human capital

    • Examine impacts on scientists’ careers and knowledge diffusion, comparing URC affiliates and sample of academic researchers

  • Methods:

    • Case studies, surveys and—

    • Analysis of CV’s


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Technology Walks on Two Legs Capital and Knowledge Diffusion”

  • KNOWLEDGE PRODUCER

  • Universities

  • Science Councils

  • National research

  • facilities

  • DEMAND ENVIRONMENT

  • Existing demand for knowledge

  • Potential or induced demand

  • KNOWLEDGE USERS

  • Government

  • Business / Industry

  • Civil society

  • Scientific community

  • DISSEMINATION MODE

  • Journals

  • Conference

  • Patents / Licenses

  • KNOWLEDGE PRODUCTS

  • Scientific knowledge

  • Tacit knowledge

  • Technologies

* National Advisory Council on Innovation. ‘‘Utilisation of Research Findings: Extent, Dynamics and Strategies.” South Africa, July 2003.


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What is “Scientific and Technical Human Capital” (S&THC)?

  • S&THC is the amalgamation of:

    • The individual’s endowments and abilities-

      • Formal training

      • Craft knowledge and tacit knowledge

      • Cognitive skills

      • Intelligence

      • Creativity

  • (i.e. capacity to produceknowledge)

Source: B. Bozeman, J. Dietz and M. Gaughan (2001) “Scientific and technical human capital,”

International Journal of Technology Management, 22, 7/8, 2001, 716-740


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What is “Scientific and Technical Human Capital” (S&THC)?

And…,

2. Social ties and network linkages

  • Formal social linkages (e.g. professional

    Association relations)

  • Informal linkages (e.g. acquaintances, professional friends)

  • (i.e. capacity to disseminate and utilize knowledge)


  • Slide39 l.jpg

    Research (S&THC)?Project

    Cognitive Skills

    Knowledge

    Cognitive Skills

    Cognitive Skills

    Craft Skills

    Knowledge

    Knowledge

    Craft Skills

    Craft Skills

    Team Member (t - 1)

    Team Member (t)

    Team Member (t + 1)

    Legend

    Weak Tie

    Strong Tie

    Project Boundary

    Institutional Settings

    (e.g., academia, industry, government)

    Roles (e.g., entre-preneur, funding agent, colleague)

    Illustration: Basic Model of S&T Human Capital- Research Project Contributions over Time

    Source: B. Bozeman, J. Dietz and M. Gaughan (2001) “Scientific and technical human capital,”

    International Journal of Technology Management, 22, 7/8, 2001, 716-740


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    How does S&THC relate to Technology Transfer and Utilisation?

    • It provides the social and institutional glue required for linkages

    • It is the “stuff of knowledge,” its human embodiment

    • It ensures knowledge mobility

    • It ensures knowledge mutability

    • It links individual career trajectories to corporate and NIS technology trajectories


    Results of s thc studies l.jpg
    Results of S&THC Studies Utilisation?

    • University Research Centers differ enormously in level of S&THC creation; the best ones create graduates highly valued by industry (Bozeman and Boardman, 2003)

    • There are multiplier effects of institution building programs, EPSCOR, HBCU (Bozeman and Rogers, 2002)

    • Collaboration is vital in S&THC but most collaborations (55%) within one’s own research group (Bozeman and Corley, in press)

    • Women and minorities have very different experiences with grants than men have (Gaughan and Bozeman, 2002)


    Differential impacts of grants on women monica gaughan and barry bozeman l.jpg
    Differential Impacts of Grants on Women Utilisation?Monica Gaughan and Barry Bozeman

    • Data source: Curriculum Vita from 1,080 Researchers at NSF Science Centers, ERC’s and DOE Facilities

    • Questions:

      • How do women and men compare in grants acquisition

        • Likelihood?

        • Amount


    Slide43 l.jpg

    Median Grant Award by Gender, in Dollars Utilisation?

    Grant

    All

    Men

    Women

    Ratio

    M/F

    First

    110,000

    140,000

    47,000

    3.0:1

    Second

    125,000

    130,000

    70,000

    1.9:1

    Third

    94,530

    94,600

    58,368

    1.6:1

    Fourth

    80,000

    89,400

    63,214

    1.4:1

    Fifth

    90,000

    85,000

    87,964

    .97:1


    Slide44 l.jpg

    Impact of Grants and Gender on Time to Tenure Utilisation?: It is whether one has had a grant, and not the value of grants, that predicts the length of time to tenure. Women and men are equally likely to be tenured.

    Cox Proportional Hazard Model:

    Beta

    Sig

    Odds

    Time since Ph.D.

    0.08

    0.001

    1.08

    Time squared

    -0.001

    0.001

    0.99

    Male

    0.01

    0.95

    1.01

    Ever had Grant

    0.35

    0.001

    1.41

    Policy implication: to involve women and minorities in the NIS, give more grants

    even if it means smaller average grants.


    Lessons learned s thc l.jpg
    Lessons Learned S&THC Utilisation?

    • S&THC must be nurtured and understood; with inadequate S&THC the NIS cannot adequately produce, absorb or utilise scientific and technical knowledge

    • Institutions and policies matter, after S&THC adequacy is achieved

    • Different NIS has different S&THC requirements at different times and stages


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    What next? Utilisation?

    • Knowledge utilisation study for NIH science centers program

      • Problem: Seek to fund “bench to bedside,” moving basic research to clinical research to clinical application; not happening

      • Approach: “User/Non-User Survey”

        • Identifies knowledge products, projected audiences, surveys about awareness and use


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    Conceptual Model for Use/Non-use Study Utilisation?

    Product Uses, 1…n

    Knowledge

    Product

    Developed

    Product

    Used

    Investigate: Determinants of Use-Non-use

    Product not used

    Unaware Aware but rejected


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    What next? Utilisation?

    • Kellogg Foundation:

      • “Science and the Maldistribution of Benefits: How Can the Disadvantaged before the Advantaged?

        Viz., Government of the Republic of South Africa, South Africa’s National Research and Development Strategy, section 5.6 “Science and technology for poverty reductionAugust, 2002, pp. 42-44.


    Slide49 l.jpg

    Biological + Utilisation?

    Capacity-

    Impact

    Political +

    Opportunity -

    Individual

    Impact

    Basic Needs -

    Basic Needs +

    Social

    Impact

    Consumption

    Impact

    Opportunity +

    Political -

    Fig. One:

    S&T Social Impact Model

    Biological -


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    What Does the Model Imply? Utilisation?

    • There are different dimensions of maldistribution of S&T costs and benefits

      • Biological

      • Basic needs

      • Political

      • Opportunity

    • All driven by interaction of S&T and Economics


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    What Does the Model Imply? Utilisation?

    • S&T Products can be characterized in terms of:

      • Social vs. Individual Impact

      • Consumption impact vs. Capacity-Building Impact

    • Developing hypotheses about interaction of dimensions, case studies of technology development and use


    Slide52 l.jpg

    For More Information: Utilisation?

    http://rvm.gatech.edu/

    Research Value Mapping Program

    School of Public Policy

    Georgia Tech

    Atlanta, GA 30032

    [Dave Gardner, testing coiled resonator

    on thermoacoustic engine, Los Alamos]


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