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

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

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  1. 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

  2. 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

  3. 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

  4. 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”

  5. 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

  6. 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

  7. 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.

  8. 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

  9. Current Components Of U.S. NIS • 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

  10. Three Competing R&D Policy Models

  11. 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 R&D • Developing cooperative research centers and consortia • Altering guidelines for disposition of government owned intellectual property

  12. 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.”

  13. The Cooperative Technology Paradigm: Major U.S. Technology Transfer Policy Legislation

  14. 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

  15. A “Micro Profile of the US NIS: Illustration: Portfolio Assessment- Algorithms

  16. Illustration: Portfolio Assessment- Patents

  17. 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 Difference Factors

  18. Academic Staff In S&T Higher Education 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

  19. South Africa* United States** Institutions in the Science and Technology System *NACI, South African Science and Technology: Key Facts and Figures 2002 **Approximate Figures.

  20. The Key to the Success of the U.S. NIS Figure: R&D Funding, by Source NSF (2000)

  21. 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

  22. 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

  23. 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

  24. 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

  25. What were the characteristics of “product developers?” • 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

  26. 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

  27. Number of Roles: Did More Roles= More Product?

  28. Relation of Technical Strategy to Product: INCONCLUSIVE [1] If n=<10, not reported.

  29. 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

  30. 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)

  31. How about the $? What is the estimated benefit-cost? BenefitCostNet Benefit $925,975 $419,355 $487,588 --BUT, Mean net benefit = $1.2 million Median net benefit = ZERO

  32. 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.

  33. 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

  34. 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

  35. 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

  36. 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.

  37. 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

  38. 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)

  39. ResearchProject 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

  40. 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

  41. Results of S&THC Studies • 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)

  42. Differential Impacts of Grants on WomenMonica 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

  43. Median Grant Award by Gender, in Dollars 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

  44. Impact of Grants and Gender on Time to Tenure: 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.

  45. Lessons Learned S&THC • 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

  46. 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

  47. Conceptual Model for Use/Non-use Study Product Uses, 1…n Knowledge Product Developed Product Used Investigate: Determinants of Use-Non-use Product not used Unaware Aware but rejected

  48. 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 Africa’s National Research and Development Strategy, section 5.6 “Science and technology for poverty reductionAugust, 2002, pp. 42-44.

  49. Biological + Capacity- Impact Political + Opportunity - Individual Impact Basic Needs - Basic Needs + Social Impact Consumption Impact Opportunity + Political - Fig. One: S&T Social Impact Model Biological -

  50. What Does the Model Imply? • 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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