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Knowledge-based information and the effectiveness of R&D in small firms

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Abstract

This paper explores the impact that external sources of information have on the effectiveness of R&D in small, entrepreneurial firms. The effectiveness of R&D is measured in terms of two probabilities: the probability that a firm that received and completed a Phase I SBIR-funded research project is invited to submit a proposal for a Phase II award, and given such an invitation, the probability that a firm receives the Phase II award. Information from competitors is an important, in a statistical sense, covariate with the probability of being asked to submit a Phase II proposal, whereas information from suppliers and customers is an important covariate with the probability of receiving a Phase II award.

Plain English Summary

Tweetable headline: Market information, especially from suppliers, customers, and competitors, increases the effectiveness of publicly funded R&D among small, entrepreneurial firms. The R&D considered in this paper came from research awards from the National Institutes of Health’s Small Business Innovation Research (SBIR) program. The analysis presented shows a relationship between the effective use of the SBIR research awards and the use of several external sources of information—namely, suppliers, customers, and competitors—related to the market demand for the technology resulting from the funded research. This finding has a policy implication. For government agencies that participate in the SBIR program to be diligent stewards of public resources, small, entrepreneurial firms who receive such funding should be advised on how to identify and use relevant external source of market information.

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Notes

  1. The notion of a systematic relationship between scientific ideas (i.e., knowledge) and inventive or innovative behavior traces at least to Siegel (1962), but small, entrepreneurial firms were not emphasized much less distinguished from large, mature firms.

  2. For a comprehensive survey of the literature around scientific discovery more generally, see Schickore (2018).

  3. In terms of the rest of this paper, senses might be thought of in terms of scientific discovery, and experiences might be thought of in terms of customer discovery and commercial merit.

  4. Audretsch and Link (2019) developed a framework, based on the ideas of Locke and Hume, to conceptualize the relationship between experience, knowledge, and entrepreneurial behavior. See also, Audretsch et al. (2021).

  5. See Okasha (2002) and Rosenberg (2008) for modern empiricist views of epistemology. Musgrave (1993, pp. 85–106) discuss and referred to the Lockean-Humean theory of “sense-data” as “idea-ism.”.

  6. Machlup (1980) also discussed Polanyi’s concept of tacit knowledge. Hess and Ostrom (2007, p. 8) have argued, citing Polanyi: “Acquiring and discovering knowledge is both a social process and a deeply personal process” (Polanyi, 1974, original 1958). See also Polanyi (1966) for further discussion of tacit knowledge.

  7. The utilization of knowledge varies across individuals who are endowed with varying abilities to utilize resources, be they homogeneous or heterogeneous resources. One might draw a relationship between this Hayekian idea and the later writings of Machlup (1980, p. 182) who stressed that the acquisition of knowledge is related to an individual’s differential abilities: “Some alert and quick-minded persons, by keeping their eyes and ears open for new facts and theories, discoveries and opportunities, perceive what normal people of lesser alertness and perceptiveness, would fail to notice. Hence new knowledge is available at little or no cost to those who are on the lookout, full of curiosity, and bright enough not to miss their chances.”.

  8. These are the same general categories of sources of knowledge that are asked about on the Eurostat 2010 and 2012 Community Innovation Survey.

  9. See https://www.sbir.gov/about.

  10. See https://www.sbir.gov/about.

  11. See, https://seedfund.nsf.gov/fastlane/definitions/. Socially or economically disadvantages individuals are defined to be a “member of any of the following groups: Black Americans, Hispanic Americans, Native Americans, Asian-Pacific Americans, Subcontinent Asian Americans, other groups designated from time to time by the Small Business Administration (SBA) to be socially disadvantaged, and any other individual found to be socially and economically disadvantaged by SBA pursuant to Sect. 8(a) of the Small Business Act, 15 U.S.C.; 637(a).”.

  12. A descriptive overview of the SBIR program is in Leyden and Link (2015) and Link and Van Hasselt (forthcoming).

  13. We chose to study Phase I NIH awards rather than Phase I DOD awards in an effort to avoid survey respondents claiming that requested information was, for national purposes, confidential.

  14. See https://www.sbir.gov/sbirsearch/award/all.

  15. It is difficult to interpret a 14.3 percent response rate for several reasons. First, there is no comparable information on academic surveys of Phase I SBIR projects. In fact, to the best of our knowledge, there has not been a systematic study of Phase I awards from any agency. Two, the survey was requesting information about research projects conducted as many as 6 years ago. Three, although COVID concerns about when and how individuals were to return from their virtual work locations to their physical work location had diminished by December 2021, the due diligence of PIs to deal with older emails is unknown.

  16. The variance inflation factors (VIFs) in the models considered in this paper are all less than 2.0. This suggest that collinearity among the independent variables is minimal.

  17. Award is measured in $2018. The 2016 and 2017 award information were inflated using the GDP deflator (2018 = 100).

  18. The relevant survey question asked if the Phase I award in question was the first Phase I award that the firm received. Here, that variable is recoded to equal 1 if the Phase I award was not the first the firm had received and 0 otherwise.

  19. We also considered that the choice to use particular external sources of information might be related to the firm characteristics in our models. Insufficient observations, and a limited number of independent variables, prohibited us to undertake a series of, say, bivariate Probit models. However, as we noted in footnote 16, collinearity among the independent variables was not an issue.

  20. We thank an anonymous reviewer for pointing out that the effectiveness of R&D might also be due to agglomeration issues. Future research into the effectiveness of R&D, be it SBIR-funded R&D or not, should include locational variables in the specification of the empirical models.

  21. For example, see the SEED program within NIH: https://seed.nih.gov/.

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Authors and Affiliations

  1. RTI International, Research Triangle Park, NC, USA

    Joshua C. R. Fletcher & Alan C. O’Connor

  2. University of North Carolina at Greensboro, Greensboro, NC, USA

    Eric S. Howard & Albert N. Link

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  1. Joshua C. R. Fletcher
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  2. Eric S. Howard
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  3. Albert N. Link
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  4. Alan C. O’Connor
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Correspondence to Albert N. Link.

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Fletcher, J.C.R., Howard, E.S., Link, A.N. et al. Knowledge-based information and the effectiveness of R&D in small firms. Small Bus Econ 60, 891–900 (2023). https://doi.org/10.1007/s11187-022-00630-9

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