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

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

• Barry Bozeman
• Research Value Mapping Program
• Georgia Institute of Technology
• Presentation to the National Advisory Commission
  on Innovation, South Africa, October 2003

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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 Bozemans 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 Africas 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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• Study 1 RVM project on U.S. NIS (summarized in
  Crow and Bozeman 1998)
• Attempt to profile The 16,000
• Questionnaires from panel survey of 1,600 labs
• Case studies of more than 100 by type
• Focus Environment, output, design and structure,
  management

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Current Components Of U.S. NIS

• Civilian technology programs at both the national
  and state levels that are designed to promote
  economic growth
• Defense RD with an emphasis on dual-use
  technologies and conversion of defense RD from
  military to civilian applications
• Federal Governments national laboratories,
  industry-university research centers,
  multidisciplinary multipurpose research centers

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Three Competing RD Policy Models
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Cooperative Technology Policy Paradigm

• 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 RD
• 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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The Cooperative Technology Paradigm Major U.S.
Technology Transfer Policy Legislation
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Intensive Case Studies
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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A Micro Profile of the US NIS Illustration
Portfolio Assessment- Algorithms
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Illustration Portfolio Assessment- Patents
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Difference Factors

• United States
• 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

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Academic Staff In ST Higher Education
South Africa, 1999
United States, 2001
20,000 university faculty, top heavy
lt5000 university faculty, bottom 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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Institutions in the Science and Technology System

• South Africa

• United States

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
Figure RD 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
  CRADAs and Technology Transfer, Report to the
  National Science Foundation, 1996.
• Study of 239 cooperative RD 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?

• Smaller firms (500-1000 employees)
• Project initiated by the firm
• Low RD 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?
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Relation of Technical Strategy to
Product INCONCLUSIVE

1 If nlt10, 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?

• 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?

• Benefit Cost Net 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

• 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 ST 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 CVs

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Technology Walks on Two Legs

• 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
(STHC)?

• STHC is the amalgamation of
• The individuals endowments and abilities-
• Formal training
• Craft knowledge and tacit knowledge
• Cognitive skills
• Intelligence
• Creativity
• (i.e. capacity to produce knowledge)

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
(STHC)?

• 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)

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Illustration Basic Model of ST 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 STHC 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

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Results of STHC Studies

• University Research Centers differ enormously in
  level of STHC 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 STHC but most
  collaborations (55) within ones own research
  group (Bozeman and Corley, in press)
• Women and minorities have very different
  experiences with grants than men have (Gaughan
  and Bozeman, 2002)

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Differential Impacts of Grants on Women Monica
Gaughan and Barry Bozeman

• Data source Curriculum Vita from 1,080
  Researchers at NSF Science Centers, ERCs and DOE
  Facilities
• Questions
• How do women and men compare in grants
  acquisition
• Likelihood?
• Amount

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(No Transcript)
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Policy implication to involve women and
minorities in the NIS, give more grants even if
it means smaller average grants.
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Lessons Learned STHC

• STHC must be nurtured and understood with
  inadequate STHC the NIS cannot adequately
  produce, absorb or utilise scientific and
  technical knowledge
• Institutions and policies matter, after STHC
  adequacy is achieved
• Different NIS has different STHC requirements at
  different times and stages

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What next?

• 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
Product Uses, 1n
Knowledge Product Developed
Product Used
Investigate Determinants of Use-Non-use
Product not used
Unaware Aware but rejected
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What next?

• Kellogg Foundation
• Science and the Maldistribution of Benefits How
  Can the Disadvantaged before the Advantaged?
• Viz., Government of the Republic of South
  Africa, South Africas National Research and
  Development Strategy, section 5.6 Science and
  technology for poverty reduction August, 2002,
  pp. 42-44.

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Biological
Capacity- Impact
Political
Opportunity -
Individual Impact
Basic Needs -
Social Impact
Basic Needs
Consumption Impact
Opportunity
Political -
Fig. One ST Social Impact Model
Biological -
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What Does the Model Imply?

• There are different dimensions of maldistribution
  of ST costs and benefits
• Biological
• Basic needs
• Political
• Opportunity
• All driven by interaction of ST and Economics

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What Does the Model Imply?

• ST 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

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For More Information http//rvm.gatech.edu/ Res
earch Value Mapping Program School of Public
Policy Georgia Tech Atlanta, GA 30032
Dave Gardner, testing coiled resonator on
thermoacoustic engine, Los Alamos