1	Scientific and Technical Human Capital as an Indicator of Capacity in R&D Organizations Juan D. Rogers Barry Bozeman School of Public Policy Georgia Institute of Technology
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3	Intensive Case Studies 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
4	Models for R&D Evaluation Output/Impact Evaluation: Focus on the “Knowledge Product” (e.g. article, technological device) Capacity Evaluation: Focus on resources and ability to produce
5	Capacity Evaluation is Different Capacity Evaluation Operates at individual and project/program level, but also institutional, scientific fields, disciplines, and “knowledge value community” Both quantitative and qualitative Focuses on resources assessment rather than commensurate metric Key concept: “Scientific and Technical Human Capital”
6	What is “S&T Human Capital?” S&T human capital is the reservoir of knowledge and resources, both technical and social, scientists bring to their work. It determines scientists’ and engineers’ capacity to produce and disseminate knowledge products.
7	What is “S&T Human Capital?” S&T human capital includes not only the formal educational endowments, but also the skills, know-how, "tacit knowledge," and experential knowledge embodied in individual scientists. S&T human capital also includes the social capital that shape scientists' work: networks, “invisible colleges,” gatekeeping institutions, inter-organizational relationships. These networks (the “social capital”) integrate and shape scientific work, providing knowledge of scientists' and engineers' work activity, framing “important” research problems; helping with career mobility, providing contacts to users such as industrial partners.
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10	Example 1:Differential Impacts of Grants on Women Monica Gaughan and Barry Bozeman (2003 Data source: Curriculum Vita from 1,080 Researchers at NSF Science Centers, ERC’s and DOE Facilities Method: Event history analysis/hazard models with career data Question: How do women and men compare in grants acquisition
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13	Example 2: Patterns of Collaboration and Impacts on Productivity Sources: Bozeman and Corley (in Press); Bozeman and Lee (2002) Data sources: Curriculum Vita from 1,080 Researchers at NSF Science Centers, ERC’s and DOE Facilities Data from Survey of Same Set of Researchers Questions: What are Patterns of Collaboration What are Collaboration Strategies? What is the effect on research productivity?
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16	% Female Collaborators Correlated with “I think I am or soon will be a leader in my field” (-,p=.009) Goal: Best university (-,p=.001) Goal: Best research group (-,p=.004) Gender [1=Male] (-,p=.001) Native Citizen (p=.01) Doctoral students now supported (-,p=.007) Research time with Industry (-,p=003) Spouse full time homemaker (-,p=.05) Not correlated: age, tenure, job satisfaction, grants “batting average,” marital status
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19	Summary of Collaboration Style Nationalists are native born Mentors are older and married PI’s likely to be Mentors and Followers Tacticians support more students Mentors have: More collaborators More Female collaborators A higher percentage of female collaborators Taskmasters and Nationalists do not Support students
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22	STHC and SC/HC Theories Social Capital Theories: Facilitation of collective action Friends, contacts Embedded resources Human Capital Theories: Costs and rewards of training Metaphorical uses in human resources discourse
23	STHC and SC/HC Theories (II) Embedded Resources Mobilized for Purposive Action Expressive action (minimize loss) Instrumental action (maximize gain) Quality of Social Capital Social strata in the R&D system Reach and range of social capital
24	STHC and SC/HC Theories (III) We need more than credential numbers Tacit knowledge/skills Cognitive abilities in context Learning after terminal degree Especially relevant in context of R&D teams and organizations
25	STHC Indicators: A Suggestion for Centers and Teams So far, two components measured separately: SCI: Position in “ladder” plus a factor on mobilized resources in time window HCI: Ratio of Number of researchers on a team w/r to a factor on context dependant skills and tacit knowledge Don’t yet include interaction terms
26	STHC Indicators: Data Gathering Teams centered on PIs Life course plus recent time period Curriculum Vitae Semi-structured interviews Network questionnaires
27	The Overview of RVM Research Drew from the Following Studies: E. Corley, B. Bozeman and M. Gaughan (2003). “Evaluating the Impacts on Grants on Women Scientists’ Careers: The Curriculum Vita as a Tool for Research Assessment,” In P. Shapira and Stefan Kuhlmann, Learning from Science and Technology Policy Evaluation: Experiences from the U.S. and Europe, Cheltenham, UK: Edward Elgar Publishing. Barry Bozeman and Elizabeth Corley (in press), “Research Collaboration Strategies among Scientists and Engineers,” Research Policy. Barry Bozeman and Dan Sarewitz, “Public Failure in Science Policy,” Science and Public Policy, (in press). Pablo Saavedra and B. Bozeman. (In press) “The ‘Gradient Effect’ in Federal Laboratory-Industry Technology Transfer,” Policy Studies Journal. Barry Bozeman and Sooho Lee (2003). “The Effects of Scientific Collaboration on Productivity,” paper presented at the annual meeting of the AAAS, January, 2003. Barry Bozeman and Juan Rogers (2002). “A Churn Model of Scientific Knowledge Value: Internet Researchers as a Knowledge Value Collective,” Research Policy, vol. 31, pp. 769-794. Monica Gaughan and Barry Bozeman (2002). “Using Curriculum Vitae to Compare Some Impacts of NSF Research Center Grants with Research Center Funding.” Research Evaluation, 11, 1, pp. 17-26. B. Bozeman and D. Wittmer (2001). "Technical Roles and Success of US Federal Laboratory-Industry Partnerships." Science and Public Policy, 28, 4, June 2001, pp. 169-178. Barry Bozeman, Juan Rogers, and Ivan Chompolov, “Knowledge Value Alliances and Networks,” Research Evaluation (December 2001). Juan Rogers and Barry Bozeman, “Knowledge Value Alliances: An Alternative to R&D Project Evaluation,” Science, Technology and Human Values, January 2001.
28	And… Barry Bozeman, “Technology Transfer Research: A Review and Assessment,” Research Policy, vol. 29 (2000), pp. 627-655. J. Dietz, I.Chompolov, B, Bozeman, E. Lane, and J. Park, “Using the curriculum vita to study the career paths of scientists and engineers: An exploratory assessment,” Scientometrics, vol. 49, no. 3 (2001), pp. 419-442. Barry Bozeman, James Dietz, and Monica Gaughan, “Models of Scientific Careers: Using Network Theory to Explain Transmission of Scientific and Technical Human Capital,” International Journal of Technology Management, vol. 22 (2001), pp. 716-740. Barry Bozeman and Juan Rogers, “Strategic Management of Government-Sponsored R&D Portfolios: Lessons from Office of Basic Energy Sciences Projects,” Environment and Planning C: Government and Policy, vol.19 (2001), pp. 413-442. J. Youtie, Barry Bozeman, P. Shapira “Methods of Evaluating Technology-Based Economic Development Programs,” Evaluation and Program Planning, January, 1999. Barry Bozeman and Gordon Kingsley, “The Research Value Mapping Approach to R&D Assessment,” Journal of Technology Transfer, vol. 22, no. 2 (1997), pp. 33-42. Juan Rogers and Barry Bozeman, “Basic Research and the Success of Federal Lab-Industry Partnerships,” Journal of Technology Transfer, vol. 22, no. 3 (1997), pp. 37-48. G. Kingsley, Barry Bozeman, and K. Coker, “Technology Transfer and Absorption: An R&D Value Mapping Approach,” Research Policy, vol.25 (1996), pp. 967-995. Barry Bozeman and Maria Papadakis, “ Firms’ Objectives in Industry-Federal Laboratory Technology Development Partnerships,” Journal of Technology Transfer, December, 1995. Barry Bozeman, “The Cooperative Technology Paradigm: An Evaluation of U.S. Government Laboratories’ Technology Transfer Activities,” Policy Studies Journal, vol. 22, no. 2 (1994). Barry Bozeman and S. Pandey, “Cooperative R&D in Government Laboratories: Comparing the U.S. and Japan,” Technovation, vol.14, no. 3 (1994), pp. 145-159. Barry Bozeman and David Coursey, "Benefits and Problems in Technology Transfer: A National Survey of U.S. University and Government Laboratories," IEEE Transactions in Engineering Management, 1993.