Evaluation Of Investments In Science, Technology And Innovation: Applying Scientific and Technical Human Capital Framework For Assessment of Doctoral Students In Cooperative Research Centers

摘要

This dissertation builds on an alternative framework for evaluation of science, technology and innovation (STI) outcomes -- the scientific & technical (S&T) human capital which was developed by Bozeman, Dietz and Gaughan (2001). At its core, this framework looks beyond simple economic and publication metrics and instead focuses on scientists' social capital. The premise of the framework is that science does not happen in vacuum and that resources embedded in scientists' social networks are important and enduring outcomes of the scientific process that were not being captured by traditional metrics.;This dissertation examines social capital of science and engineering (S&E) graduate students, an underrepresented group of stakeholders in STI evaluations. S&E graduate students are unique for several reasons. In comparison with students in other disciplines, S&E graduate students have a greater proportion of international students; are widely employed by industry in numbers exceeded only by business graduates. And, most importantly, S&E graduates pursue education in fields that contribute the most to the US innovation capacity.;This dissertation introduces a multidimensional measure of social capital based on the network theory of social capital proposed by Nan Lin (1999). According to Lin, social capital consists of three components: availability of resources and social embeddedness in one's network and mobilization of these resources. In order to address these elements, the dissertation employs two studies that focus on different components of social capital. Study 1 looks at accessibility of resources in students' social networks and whether students would be likely to mobilize them by using a proxy measure of norms and values about collaborations. The study also addresses the effect of social capital on students' experiences and outcomes, specifically, on their satisfaction and perceived career preparedness. The researcher investigates the mechanisms that explain other students' outcomes by employing data from a matched sample of S&E doctoral students trained at the Industry/University Cooperative Research Centers, I/UCRCs (N=173), and doctoral students from the same universities and disciplines who were trained more traditionally (N=87). Two exploratory path models demonstrate the important role of availability of network resources and proxy for mobilizing them on students' perceived career preparedness and satisfaction with their training.;Study 2 is a case study of one I/UCRC's whole social network. The researcher attempts to provide a better understanding of the embeddedness components of students' social capital in their I/UCRC network. The case study has significant limitations in that findings cannot be generalized to the population of I/UCRC students. Nevertheless, findings are interesting for the one I/UCRC. The students scored significantly higher on preparedness when they had higher out-degree centrality, indicator of how much they reach out to other center's personnel. Also, a visual representation of the whole I/UCRC social network could be used to understand better students' embeddedness.;Both studies show that social capital is a very hard concept to measure mainly because of its different dimensions. Nevertheless, they also show that social capital is a useful tool for comparing students' outcomes in different STI programs. A focus on students and social capital is one of the ways the S&T human capital model can be applied in evaluation of the STI programs. Such focus provides a considerable contrast to linear STI metrics that focus on long-term outcomes and often exclude students all together. It is important to provide information about the human side of science in its current state including students' graduate training, experiences and social networks. In addition, inclusion of students provides a view into the future - an opportunity to look at science of tomorrow as the same students will be part of the scientific elite networks in the near future.