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Early firm engagement, government research funding, and the privatization of public knowledge

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Abstract

Early firm engagement in the scientific discovery process in public institutions is an important form of science-based innovation. However, early firm engagement may negatively affect the academic value of public papers due to firms’ impulse to privatize public knowledge. In this paper, we crawl all patent and paper text data of the Distinguished Young Scholars of the National Science Foundation of China (NSFC) in the chemical and pharmaceutical field. We use semantic recognition techniques to establish the link between scientific discovery papers and patented technologies to explore the relationship between the quality of public knowledge production, government research funding, and early firm engagement in the science-based innovation process. The empirical results show that, first, there is a relatively smooth inverted U-shaped relationship between government research funding for scholars and the quality of their publications. An initial increase in government research funding positively drives the quality of public knowledge production, but the effect turns negative when research funding is excessive. Second, government research funding for scholars can act as a value signal, triggering the firm’s impulse to privatize high-value scientific discoveries. Hence, early firm engagement moderates the inverted U-shaped relationship such that at low levels of research funding, early firm engagement can improve the quality of public knowledge production, and at high levels of research funding, early firm engagement can further reduce the quality of public knowledge production.

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Notes

  1. The allocation in favor of star scholars has the potential to improve performance in research “tournaments” with clear research goals, but also makes it much less likely that many other scholars will make major original scientific discoveries by chance (National Science Board 2007; Wahls 2019).

  2. In fact, because of diminishing marginal returns, the positive impact itself decreases as the single capital input increases. The inverted U-shape of the overall effect is therefore likely to be more pronounced.

  3. The UCSF Student Newspaper, Volume 41, Number 31, May 22, 1997, provided a detailed follow-up report on the events. Source: https://synapse.library.ucsf.edu/?a=d&d=ucsf19970522-01.2.5

  4. The output technical parameters of government research funding are represented by b and b' respectively in the input–output function (see Fig. 1).

  5. Blumenthal et al. (1997) finds that nearly 20% of the academics surveyed indicated that their papers had been delayed due to requests from sponsoring companies.

  6. Resnik (1998) traces typical cases of industry-sponsored interventions in academic research, such as the nicotine addiction study conducted by two academics, Victor DeNobel and Paul Mele, for the Philip Morris company in the early 1980s. They discovered a way to enhance the addictive effects of nicotine in cigarettes and the aim of their research was to develop a nicotine substitute that would make cigarettes less harmful. They submitted a paper to the journal summarizing their findings. However, Philip Morris asked the two academics to withdraw their paper and to stop publicly releasing the results of the research obtained using the company’s funds.

  7. Blumenthal et al. (1997) point out that in the life sciences, sponsoring companies often prevent academics from disclosing their experimental materials involving knockout mice, cell lines, and macromolecules.

  8. As shown in Fig. 3, the moderating effect of early firm engagement on the inverted U-shaped relationship between research funding and the quality of public knowledge production is the result of two offsetting effects. On the one hand, early firm engagement enhances the positive effect of research funding on the quality of public knowledge production through the “industry-university collaboration effect” (Fig. 3(a)). On the other hand, early firm engagement exacerbates the negative effect of research funding on the quality of public knowledge production through “commercially biased interventions” (Fig. 3(b)). Combining these two effects, we can obtain the overall effect of early firm engagement on the inverted U-shaped relationship (Fig. 3(c)). With early firm engagement, when the research funding is low, the inhibiting effect is smaller and the facilitating effect is dominant. Thus, the slope of the inverted U-shaped curve is positive and steeper with firm engagement. However, as research funding increases, the inhibiting effect becomes more pronounced until it reaches a threshold at which the inhibiting effect completely cancels out the facilitating effect. In the last column, we do not show a shift in the turning point of the curve, but in fact, the overall effect of the moderator may cause the turning point to shift to the left or right. This will be verified in the empirical section of this paper.

  9. A small number of papers may be paired with multiple patents. We deal with the situation of multiple paired patents when constructing variables. See “Variable” section for detailed illustrations.

  10. See demo data at https://doi.org/10.5281/zenodo.6585940.

  11. The NSFC and MSTC are the primary agencies of the Chinese government that support scientific research. Some of the scholars in our sample undertake both NSFC and MSTC research projects.

  12. The VIF is computed as \(VIF_{k} = \left( {1 - R_{i}^{2} } \right)^{ - 1}\), where \(R_{i}^{2}\) is obtained by regressing the ith predictor on all the other predictors.

  13. In other words, when an IRR is greater than 1, that indicates the coefficient of the regressor of the negative binomial regression is positive; and when an IRR is less than 1, that indicates that the coefficient of the regressor of the negative binomial regression is negative.

  14. According to Haans et al. (2016), \(C = {{\partial \;{\text{Research}}\_{\text{fund}}_{i}^{*} } \mathord{\left/ {\vphantom {{\partial \;{\text{Research}}\_{\text{fund}}_{i}^{*} } \partial }} \right. \kern-\nulldelimiterspace} \partial }\;{\text{Firm}}\_{\text{engage}}_{i}\), where \(Research\_fund_{i}^{*}\) is the turning point of equation (2):

    \({\text{Research}}\_{\text{fund}}_{i}^{*} = {{\left( { - \beta_{21} - \beta_{23} \;{\text{Firm}}\_{\text{engage}}_{i} } \right)} \mathord{\left/ {\vphantom {{\left( { - \beta_{21} - \beta_{23} \;{\text{Firm}}\_{\text{engage}}_{i} } \right)} {\left( {2\beta_{22} + 2\beta_{24} \;{\text{Firm}}\_{\text{engage}}_{i} } \right)}}} \right. \kern-\nulldelimiterspace} {\left( {2\beta_{22} + 2\beta_{24} \;{\text{Firm}}\_{\text{engage}}_{i} } \right)}}.\)

  15. Haans et al. (2016) demonstrate in Appendix 4 of the paper that flattening or steepening does not depend on any other coefficient than \(\beta_{24}\), nor on specific values of the moderating variable. Therefore, testing for flattening or steepening is equivalent to testing whether \(\beta_{24}\) is significant.

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Acknowledgements

The paper is supported by the National Natural Science Foundation of China (No: 71572034 and No: 71772038) and the National Social Science Foundation of China (No: 14CGL002).

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Correspondence to Zhang Yujie.

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Mo, Z., Yujie, Z., Jiasu, L. et al. Early firm engagement, government research funding, and the privatization of public knowledge. Scientometrics 127, 4797–4826 (2022). https://doi.org/10.1007/s11192-022-04448-w

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