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Exploring scientific publications by firms: what are the roles of academic and corporate partners for publications in high reputation or high impact journals?

Abstract

Recent research suggests that firms, particularly in science-based industries, may publish scientific articles in order to achieve strategic goals. This paper explores whether the reputation seen as publications in journals with high impact factors and the impact seen as citations of such scientific publications originating in firms benefit from R&D alliances with different types of partners. Our empirical analysis is based on a unique dataset in pharmaceutical cancer research. We analyze publications originating in biotechnology and pharmaceutical firms, with a comparison of the results to publications that do not involve a firm-based author. Our results indicate that the returns to the number of partners are decreasing and are negative after a turning point. More surprisingly, our results suggest that biotechnology and pharmaceutical firms should focus on establishing R&D alliances with pharmaceutical firms in order to increase the probability of publishing in journals with a high reputation. However, in terms of scientific impact, i.e., forward citations, publications originating in firms do not benefit from having access to different types of alliance partners.

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Fig. 1
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Notes

  1. 1.

    It should be noted that biotechnology and pharmaceutical firms can adapt their publication strategy to support their drug candidates under development. Consequently, a large proportion of clinical research has historically remained unpublished. Lee et al. (2008) argue that one reason is that firms are not interested in publishing results that do not support their claims concerning the safety and efficacy of drug candidates and that might negatively affect regulatory authorities’ approval decisions.

  2. 2.

    While this study focuses on strategic alliances as a form of formal collaborations, it should be noted that more informal forms of collaboration such as co-authorships or other forms of professional interaction may be simultaneously present (Liebeskind et al. 1996). These reinforce and complement formal collaborations, since informal collaborations may increase the likelihood of exchanging valuable knowledge. In addition, informal collaborations provide opportunities to access knowledge that is complementary to the firm’s knowledge base and to knowledge obtained through strategic alliances. It is also possible that individuals use informal collaborations to get access to redundant knowledge that enables them to cross-check and to verify knowledge obtained internally or through strategic alliances.

  3. 3.

    http://www.biopharminsight.com/index.html. A list of the respective medical indications can be found in Table 6 in the “Appendix”.

  4. 4.

    We focus on articles published between 2001 and 2008 due to the availability of the alliance data form the ReCap database used to construct independent variables.

  5. 5.

    It has to be noted that impact factor distributions differ across scientific disciplines. Since our study refers to one disease area, we did not introduce impact factor adjustments.

  6. 6.

    Using the same time window for all publications in our sample avoids the problem that articles published earlier have more time to receive citations.

  7. 7.

    Following this rule, alliances reporting, e.g. the Dana–Farber Cancer Institute as an affiliation are assigned to Harvard University as well as articles reporting the affiliation as Harvard Medical School.

  8. 8.

    Consequently, the different research institutes of the German Max Planck Society are summarized to one institution.

  9. 9.

    It should be noted that Num. Partners is not necessarily the sum of Num. Academic Partners, Num. Pharma Partners, and Num. Biotech Partners as there is a diverse set of other partner types, such as foundations and non-academic healthcare providers, which are not a focus of this study.

  10. 10.

    An overview of the results of the test suggested by Lind and Mehlum (2010) for all regression models with squared terms can be found in the “Appendix”.

  11. 11.

    As the computation of marginal effects is based on derivatives, it is not possible to report marginal effects for squared terms.

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Acknowledgements

We thank Matthew J. Higgins for helping us with the ReCap alliance data. We thank Guido Buenstorf, Pablo D’Este, Mark Flynn, Carolin Haeussler, Daniel Ljungberg, Bart van Looy, Elena Mas Tur, Toke Reichstein, Joachim Schnurbus, the participants of the Brown Bag Seminar at the University of Passau in June 2014, the participants of the 15th ISS Conference in Jena, Germany, the participants of the research seminar at INGENIO, Polytechnic University of Valencia in September 2014, the participants of the Workshop “Innovation and Entrepreneurship in Health Care & Life Sciences” in Gothenburg, Sweden in December 2014, the participants of the 75th Annual Meeting of the Academy of Management in Vancouver, Canada, as well as the participants of the conference on “Deciphering the New Challenges to Universities” in Gothenburg, Sweden in September 2017 for their valuable feedback and their expressed interests and concerns. The usual caveats apply. We acknowledge funding from the Sten A. Olsson Foundation for Research and Culture in supporting the research program ‘Radical Innovation for Enhancing Swedish Competitiveness”, led by Professor McKelvey. Professor McKelvey acknowledges funding from the Swedish Research Council, Research Programme: “Knowledge-intensive Entrepreneurial Ecosystems: Transforming society through knowledge, innovation and entrepreneurship”, VR DNR 2017-03360.

Author information

Correspondence to Bastian Rake.

Appendix

Appendix

Medical indications

See Table 6.

Table 6 List of medical indications

Variables, descriptive statistics and correlations

See Tables 7, 8 and 9.

Table 7 Variable description
Table 8 Summary statistics and correlations (no-firm sample)
Table 9 Summary statistics and correlations (firm sample)

Regression tables reporting marginal effects

See Tables 10, 11, 12, 13, 14, 15, 16 and 17.

Table 10 Marginal effects partner types and publications in journals with a high reputation (no-firm sample)
Table 11 Marginal effects partner types and publications in journals with a high reputation (firm sample)
Table 12 Marginal effects partner types and forward citations (no-firm sample)
Table 13 Marginal effects partner types and forward citations (firm sample)
Table 14 Utest for partner types and publications in journals with a high reputation (no-firm sample)
Table 15 Utest for partner types and publications in journals with a high reputation (firm sample)
Table 16 Utest for partner types and forward citations (no-firm sample)
Table 17 Utest for partner types and forward citations (firm sample)

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McKelvey, M., Rake, B. Exploring scientific publications by firms: what are the roles of academic and corporate partners for publications in high reputation or high impact journals?. Scientometrics (2020). https://doi.org/10.1007/s11192-020-03344-5

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Keywords

  • Research reputation
  • Research impact
  • Strategic alliances
  • Bio-pharmaceutical firms
  • Corporate publications
  • Research collaboration

JEL-Classifications

  • L65
  • O32