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In this paper, we analyze the research productivity of faculty entrepreneurs at 15 research institutes using a novel database combining faculty characteristics, licensing information, and journal publication records. We address two related research questions. First, are faculty entrepreneurs more productive researchers (“star scientists”) compared to their colleagues? Second, does the productivity of faculty entrepreneurs change after they found a firm? We find that faculty entrepreneurs in general are more productive researchers than control groups. We use multiple performance criteria in our analysis: differences in mean publication rate, skewness of publication rate, and impact of publications (journal citation rate). These findings bring together previous work on star scientists by Zucker, Darby, and Brewer [Zucker, L. G., Darby M. R., & Brewer M. B. (1998). The American Economic Review, 88, 290–306.] and tacit knowledge among university entrepreneurs by Shane [Shane, S. (2002). Management Science, 48, 122–137.] and Lowe [Lowe, R. A. (2001). In G. Libecap (Ed.) Entrepreneurial Inputs and Outcomes. Amsterdam: JAI Press, Lowe, R.A. (2006). Journal of Technology Transfer, 31(4), 412–429]. Finally, we find that faculty entrepreneurs’ productivity not only is greater than their peers but also does not decrease following the formation of a firm. Copyright Springer Science+Business Media, LLC 2007Entrepreneurship, University technology transfer, Research productivity, M13, O31, O34,

This paper analyzes science productivity for nine developing countries. Results show that these nations are reducing their science gap, with R&D investments and scientific impact growing at more than double the rate of the developed world. But this “catching up” hides a very uneven picture among these nations, especially on what they are able to generate in terms of impact and output relative to their levels of investment and available resources. Moreover, unlike what one might expect, it is clear that the size of the nations and the relative scale of their R&D investments are not the key drivers of efficiency.

Considering that modern science is conducted primarily through a network of collaborators who organize themselves around key researchers, this research develops and tests a characterization and assessment method that recognizes the particular endogenous, or self-organizing characteristics of research groups. Instead of establishing an ad-hoc unit of analysis and assuming an unspecified network structure, the proposed method uses knowledge footprints, based on backward citations, to measure and compare the performance/productivity of research groups. The method is demonstrated by ranking research groups in Physics, Applied Physics/Condensed Matter/Materials Science and Optics in the leading institutions in Mexico, the results show that the understanding of the scientific performance of an institution changes with a more careful account for the unit of analysis used in the assessment. Moreover, evaluations at the group level provide more accurate assessments since they allow for appropriate comparisons within subfields of science. The proposed method could be used to better understand the self-organizing mechanisms of research groups and have better assessment of their performance.