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How to boost scientific production? A statistical analysis of research funding and other influencing factors

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Abstract

This paper analyzes the impact of several influencing factors on scientific production of researchers. Time related statistical models for the period of 1996 to 2010 are estimated to assess the impact of research funding and other determinant factors on the quantity and quality of the scientific output of individual funded researchers in Canadian natural sciences and engineering. Results confirm a positive impact of funding on the quantity and quality of the publications. In addition, the existence of the Matthew effect is partially confirmed such that the rich get richer. Although a positive relation between the career age and the rate of publications is observed, it is found that the career age negatively affects the quality of works. Moreover, the results suggest that young researchers who work in large teams are more likely to produce high quality publications. We also found that even though academic researchers produce higher quantity of papers it is the researchers with industrial affiliation whose work is of higher quality. Finally, we observed that strategic, targeted and high priority funding programs lead to higher quantity and quality of publications.

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Notes

  1. For more information about NSERC see http://www.nserc-crsng.gc.ca/index_eng.asp.

  2. For more information, see the review by Ebadi and Schiffauerova (2013).

  3. They found no impact of private funding but positive impact of public funding.

  4. Scopus is a commercial database of scientific articles that has been launched by Elsevier in 2004. It is now one of the main competitors of Thomson Reuter‘s Web of Science.

  5. According to NSERC policies and regulations, researchers are required to acknowledge the source of funding in their publications. We held more than 30 interviews with randomly selected funded researchers and almost all of them approved such assumption and confirmed that they do acknowledge NSERC in their papers.

  6. Machine learning technique for topic modeling, first introduced by Blei et al. (2003).

  7. www.scimagojr.com.

  8. For more information see: http://www.stata.com/stata12/.

  9. The NSERC database originally contains both scholarships and grants.

  10. The rich get richer.

  11. NSERC discovery grants program mainly supports the long term research projects. For more information, see: http://www.nserc-crsng.gc.ca/Professors-Professeurs/Grants-Subs/DGIGP-PSIGP_eng.asp.

  12. As an example of the students’ contribution, please refer to Larivière (2012) who particularly studied the research activities of the Ph.D. students and their contribution to the advancement of knowledge.

  13. We call Researcher in this paper anybody who is funded by NSERC while Professional Researchers are only the ones that are not counted as students.

  14. Measured by the date of his first publication that is available on SCOPUS.

  15. They only found a positive impact in the United States.

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Authors and Affiliations

  1. Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, Canada

    Ashkan Ebadi & Andrea Schiffauerova

  2. Department of Engineering Systems and Management, Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates

    Andrea Schiffauerova

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  1. Ashkan Ebadi
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  2. Andrea Schiffauerova
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Correspondence to Ashkan Ebadi.

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Ebadi, A., Schiffauerova, A. How to boost scientific production? A statistical analysis of research funding and other influencing factors. Scientometrics 106, 1093–1116 (2016). https://doi.org/10.1007/s11192-015-1825-x

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