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Influences and conflicts of federal policies in academic–industrial scientific collaboration

The Journal of Technology Transfer volume 36, pages 514–545 (2011)Cite this article

Abstract

This paper examines the role of the federal government in shaping the relationship between academics scientists and industry. There exists a potential conflict between government policies encouraging collaboration within academia and the policies encouraging collaboration between academia and industry. To test and model these potential conflicts, this paper uses data collected in a 2004–2005 survey by the Research Valuing Mapping Project (a project based at Georgia Tech and led by Barry Bozeman) of more than 2000 academically based research scientists and engineers. The major finding in this paper shows that academic scientists working with industry collaborate more (with all types of collaborators) than those that do not collaborate with industry. However, when examining only those scientist that collaborate with industry, the results reveal a negative relationship between the amount of time spent collaborating with industry and the number of collaborators; implying that increasing collaboration with industry leads to less academic–academic collaboration.

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Notes

  1. The Bayh–Dole Act of 1980 gave small and universities the right to own inventions resulting from federally funded research (Stevens 2004, p. 93). Bayh–Dole been credited with propelling scientific and technological discovery by allowed universities to own their inventions. For information on the history of Bayh–Dole see Stevens (2004). For information on Bayh–Dole public policy implications see Mowery et al. (2004) or Link (2006).

  2. Running Model 3 with an interaction term between female and married also shows no significant results (full results of that model have been omitted). Long (1990) had previously shown the influence of marriage was greater on females than males.

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    Benjamin Y. Clark

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Appendix

Appendix

Implementation of the RVM survey conformed to the tailored design method, as specified by Dillman (2000). The survey was completed over three waves. A wave analysis of responses was done, which correlated survey items with the three survey waves. The wave analysis “indicated no significant differences in response patterns by either wave or date received, indicating that non-respondents, who are theoretically more like late or third wave respondents, are not significantly different than respondents” (Bozeman and Gaughan 2007).

The survey sampling frame targeted “scientists and engineers in tenure-track academic positions” at Carnegie Doctoral/Research Universities (Research Value Mapping Program 2005). Once the doctoral/research universities were indentified, further refinement of the sample was done by determining which universities offered science and technology doctorate degrees, using NSF field definitions (resulting number of schools n = 150). The 13 academic disciplines/fields this survey drew upon for respondents include: Biology, Computer Science, Math, Physics, Earth and Atmospheric Science, Chemistry, Agriculture, Chemical Engineering, Civil Engineering, Electrical Engineering, Mechanical Engineering, Materials Engineering, and Sociology.

Two hundred responses were sought from men and 200 women for each field in the RVM survey. The sampling goal was not obtainable for some fields were it was established that fewer than 200 women were employed. Race and R&D rank stratification were not taken on in the survey design.

The lack of weighting may prove problematic because of the oversample of women in the dataset. However, unequal sample done in the survey does not affect the theoretical basis of my models, and consequently I do not believe that this will be an issue. Weighting has been shown to be unnecessary when the weights are functions of the independent variables only, which is the case in this paper (Winship and Radbill 1994). Consequently, it should not necessary to use weighted models. The oversampling of women in the survey results in half of the respondents being female, where the actual population parameter in 1995 for tenured and tenure-track women in the Carnegie Research Extensive universities was 17% (National Research Council 2001). All of this paper’s models control for this sample selection factor, producing unbiased estimates (Winship and Radbill 1994).

The survey had a response rate of 38%. The non-response bias analysis implies that male university scientists had a lower likelihood of responding than females. This analysis also showed that following disciplines were less likely to respond to the survey: computer science, mathematics, biology, and electrical engineering. By including both gender and discipline in all of the models in this paper, omitted variable bias in the results is reduced.

Data about the institutions housing the surveyed scientists was added to the original dataset from the 2003 NSF Survey of R&D Expenditures at Universities and Colleges (via the NSF WebCASPAR database).

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Clark, B.Y. Influences and conflicts of federal policies in academic–industrial scientific collaboration. J Technol Transf 36, 514–545 (2011). https://doi.org/10.1007/s10961-010-9161-z

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