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Scientific and technological (human) social capital formation and Industry–University Cooperative Research Centers: a quasi-experimental evaluation of graduate student outcomes

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Abstract

In the current paper, we attempt to contribute to a more comprehensive understanding of science, technology and innovation (STI) outputs and outcomes through the application of a Scientific and Technical Human Capital (STHC) evaluation framework. We do this by describing a study that focuses on a type of STI initiative that appears ripe with potential to affect STHC impacts—Industry–University Cooperative Research Centers (IUCRCs). In doing so we summarize relevant theory related to the STHC framework and social capital formation more generally. We also define IUCRCs and highlight the program mechanisms that appear likely to impact the STHC outcomes. Finally, we narrow our focus to a relatively neglected research target of the STI evaluation—science and engineering (S&E) doctoral students. We compare social capital and other students’ outcomes by employing a rare quasi-experimental design with two training modalities: IUCRC and more traditional, non-center training. We show that our results demonstrate strong evidence for positive effects of IUCRC training on graduate S&E students’ outcomes. We also explain significant moderating effect of citizenship status on some of our results where international students, who account for 50% of this population, do not receive the same social capital outcomes as students with US citizenship or permanent resident status. In addition, we describe patterns in international students’ intentions to stay in the US and how they are affected by students’ training modality. Finally, we discuss the results and implications in the context of graduate training, STHC evaluation framework and STI and immigration policy.

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Notes

  1. Bozeman et al. (2001) would probably take exception with this assertion since they offer a more inclusive definition of human capital that includes “tacit knowledge, craft knowledge and know-how.”

  2. Johnson and Bozeman (2012) proposed the way to use the STHC model in helping minority students to succeed in academic medicine and science, but their approach was not empirical.

  3. For detailed information on the number of IUCRC students visit https://www.ncsu.edu/iucrc/NatReports.htm. For the detailed information on the number of ERC students see individual ERCs’ reports and publications (Huang 2009) or websites (ERC ASSIST: https://assist.ncsu.edu/; ERC FREEDM: https://www.freedm.ncsu.edu/).

  4. The Kauffman report is based on online survey sent to current STEM graduate students at ten US universities with largest total number of enrolled international students.

  5. The data are based on the Survey of Doctoral Recipients’ that asks recent graduates about their intentions to stay in the US and availability of a job or postdoctoral training upon graduation.

  6. Following the recommendation of a priori power analyses of MANOVA with special effect and interactions, the objective was established to meet the minimum requirement of total 190 participants to achieve 90% power for a small size effect employing the traditional .05 significance criterion.

  7. Based on a 2012–2013 IUCRC Structural report’s data, the total population of the site directors (N = 191) was contacted with the request to provide their university site’s current students. Almost half of site directors (49%, N = 94) responded and provided their students’ contact information. In addition, directors were asked to provide emails for students of different degree levels, but the study focused on Ph.D. students only.

  8. All procedures were approved by the North Carolina State University’s IRB.

  9. It was important to compare the IUCRC students to students trained traditionally in order for the findings to be meaningful and generalizable to the pros and cons of different training modalities that exist today. While we acknowledge that our sampling criteria did not eliminate students who might, for instance, have had industry experience in other than CRC form, we think we reached our main objective by excluding students who had collaborative experiences similar to CRC. There is a good chance that a subset of traditional students had one-on-one experience with industry, but our objective was to eliminate students who had consortium-type of experience where they work as part of a team of scientists as supposed to being part of a more limited in collaboration contractual relationship that are more typical in the US academia.

  10. US academic professionals included: faculty from students’ departments (1), graduate students and post-docs from department (2), faculty from other disciplines (3) and graduate students/postdocs (4) from the same university, and faculty (5) and graduate student/post-docs (6) from other universities in the US. International academic professionals included two categories: (1) faculty and (2) graduate students/post-docs outside of the US. Industry professionals combined five categories: representatives of (1) large companies, of small companies (2), of the US Federal or local government (3), nonprofit organizations (4), and associations/foundations (5). All categories were checked for extreme outliers that were recoded into the closest values on the distribution. Note, the industry professional network consisted primarily of representatives of industry (71%) while other types of representatives had smaller proportion: non-profit (9%), governmental organizations (10%) and entrepreneurs (10%).

  11. The network dimension of social capital includes the professional connections excluding students’ academic advisor(s).

  12. Traditional students had the same question, but it was worded differently to reflect their non-involvement with IUCRCs: “In comparison with other students in your department, … .”

  13. Three measures of size of each type of group (US academics, international academics and industry) were a continuous measure of total number of professionals in each group. Three measures of strength of connection to each type of group were the sum of two Likert-type measures with five response choices. The measure of norms and values was a scale (sum of eight Likert-type items with five response choices).

  14. The assumption of equality of variance was violated because of the large difference in variance between US citizens (MI-UCRC = 3.01, SD = 4.13; Mtrad. = 5.32, SD = 9.88) and international students (MI-UCRC = 16.03, SD = 21.80; Mtrad. = 19.78, SD = 25.05) on the outcome. Since transformation of variables with non-normal distribution and large variance was not successful, the non-parametric one-way ANOVA was used to test whether immigration status predicts the number of the international academics. The three non-parametric ANOVAs tests were performed to see whether the number of international academics differs based on citizenship status. All three were significant thus, the tests rejected the null hypothesis, Mann–Whitney U Test p < .001, Kolmogorov–Smirnov p < .001, and Kruskal–Wallis p < .001.

  15. The assumption of equal variance was violated. Three non-parametric tests of one-way ANOVA were performed for each of the significant independent variables. The tests rejected the null hypothesis for both variables, Mann–Whitney U Test p < .001, Kolmogorov–Smirnov p < .001, and Kruskal–Wallis p < .001 (immigration status) and Mann–Whitney U Test p < .001, Kolmogorov–Smirnov p < .001, and Kruskal–Wallis p < .001 (the type of training).

  16. The assumption of equality of the variance was violated, so one-way non-parametric ANOVA was performed to see if the center and non-center students differ on this outcome. The tests results rejected the null hypothesis, Mann–Whitney U Test p < .001, Kolmogorov–Smirnov p < .001, and Kruskal–Wallis p < .001.

  17. Assumption of equality of variance was violated and the non-parametric one-way ANOVA was performed with three significant tests. The three non-parametric tests indicated that the null hypothesis can be rejected, Mann–Whitney U Test p < .001, Kolmogorov–Smirnov p < .001, and Kruskal–Wallis p < .001.

  18. Examples are NSF’s Partnership for International Research and Education (https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505038) and NASA’s Intern and Fellow Opportunities for International Students (https://intern.nasa.gov/non-us-opportunities/index.html).

  19. 13% of variance in size of the international academic network; 8% of variance in strength international academic network; 8% in variance in strength of the industry network variable; 6% of variance in the norms and values; 7% variance in satisfaction; 12% of variance in students’ preparedness.

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    Olena Leonchuk & Denis O. Gray

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Appendices

Appendix 1: Screening question

See Table 4.

Table 4 Responses to screening question by traditionally-trained studentsa
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Appendix 2: Response rate

See Table 5.

Table 5 Response rate
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Appendix 3: Results summary

See Table 6.

Table 6 Summary of the two-way ANOVAs
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Leonchuk, O., Gray, D.O. Scientific and technological (human) social capital formation and Industry–University Cooperative Research Centers: a quasi-experimental evaluation of graduate student outcomes. J Technol Transf 44, 1638–1664 (2019). https://doi.org/10.1007/s10961-017-9613-9

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