Advertisement

Scientometrics

, Volume 106, Issue 3, pp 1093–1116 | Cite as

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

  • Ashkan Ebadi
  • Andrea Schiffauerova
Article

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.

Keywords

Statistical analysis Funding Research output NSERC Canada 

References

  1. Adams, J. D., Black, G. C., Clemmons, J. R., & Stephan, P. E. (2005). Scientific teams and institutional collaborations: Evidence from US universities, 1981–1999. Research Policy, 34(3), 259–285.CrossRefGoogle Scholar
  2. Adler, R., Ewing, J., & Taylor, P. (2009). Citation statistics. Statistical Science, 24(1), 1.CrossRefMathSciNetGoogle Scholar
  3. Arora, A., & Gambardella, A. (1998). The impact of NSF support for basic research in economics. Economics Working Paper Archive at WUSTL.Google Scholar
  4. Beaudry, C., & Allaoui, S. (2012). Impact of public and private research funding on scientific production: The case of nanotechnology. Research Policy, 41(9), 1589–1606.CrossRefGoogle Scholar
  5. Beaudry, C., & Clerk-Lamalice, M. (2010). Grants, contracts and networks: What influences biotechnology scientific production? (pp. 16–18). London: Danish Research Unit for Industrial Dynamics (DRUID) Conference.Google Scholar
  6. Beaver, D. D. (2001). Reflections on scientific collaboration (and its study): Past, present, and future. Scientometrics, 52(3), 365–377.CrossRefGoogle Scholar
  7. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993–1022.zbMATHGoogle Scholar
  8. Boyack, K. W., & Börner, K. (2003). Indicator-assisted evaluation and funding of research: Visualizing the influence of grants on the number and citation counts of research papers. Journal of the American Society for Information Science and Technology, 54(5), 447–461.CrossRefGoogle Scholar
  9. Campbell, D., & Bertrand, F. (2009). Bibliometrics as a performance measurement tool for the evaluation of research: The case of Canadian forest service. Science-Metrix, 2009 Annual CES Conference.Google Scholar
  10. Campbell, D., Picard-Aitken, M., Côté, G., Caruso, J., Valentim, R., Edmonds, S., & Bastien, N. (2010). Bibliometrics as a performance measurement tool for research evaluation: The case of research funded by the national cancer institute of Canada. American Journal of Evaluation, 31(1), 66–83.CrossRefGoogle Scholar
  11. Carayol, N., & Matt, M. (2006). Individual and collective determinants of academic scientists’ productivity. Information Economics and Policy, 18(1), 55–72.CrossRefGoogle Scholar
  12. Centra, J. A. (1983). Research productivity and teaching effectiveness. Research in Higher Education, 18(4), 379–389.CrossRefGoogle Scholar
  13. Cole, S. (1979). Age and scientific performance. American Journal of Sociology, 84, 958–977.CrossRefGoogle Scholar
  14. Coleman, J. S., & Lazarsfeld, P. F. (1981). Longitudinal data analysis. New York: Basic Books.Google Scholar
  15. Crespi, G. A., & Geuna, A. (2008). An empirical study of scientific production: A cross country analysis, 1981–2002. Research Policy, 37(4), 565–579.CrossRefGoogle Scholar
  16. De Solla Price, D. J., & Beaver, D. (1966). Collaboration in an invisible college. American Psychologist, 21(11), 1011.CrossRefGoogle Scholar
  17. Ebadi, A., & Schiffauerova, A. (2013). Impact of funding on scientific output and collaboration: A survey of literature. Journal of Information and Knowledge Management, 12(04), 1350037.Google Scholar
  18. Ebadi, A., & Schiffauerova, A. (2015a). On the relation between the small world structure and scientific activities. PLoS ONE, 10(3), e0121129.CrossRefGoogle Scholar
  19. Ebadi, A., & Schiffauerova, A. (2015b). How to receive more funding for your research? get connected to the right people! Accepted in PLoS ONE.Google Scholar
  20. Ebadi, A., & Schiffauerova, A. (2015c). How to become an important player in scientific collaboration networks? Journal of Informetrics, 9(4), 809–825.CrossRefGoogle Scholar
  21. Falagas, M. E., Kouranos, V. D., Arencibia-Jorge, R., & Karageorgopoulos, D. E. (2008). Comparison of SCImago journal rank indicator with journal impact factor. The FASEB Journal, 22(8), 2623–2628.CrossRefGoogle Scholar
  22. Gingras, Y. (1996). Bibliometric analysis of funded research. A feasibility study.Google Scholar
  23. Godin, B. (2003). The impact of research grants on the productivity and quality of scientific research. No. 2003. INRS Working Paper.Google Scholar
  24. Gulbrandsen, M., & Smeby, J. (2005). Industry funding and university professors’ research performance. Research Policy, 34(6), 932–950.CrossRefGoogle Scholar
  25. Hausman, J. A., Hall, B. H., & Griliches, Z. (1984). Econometric models for count data with an application to the patents-R&D relationship. Econometrica, 52(4), 909–938.CrossRefGoogle Scholar
  26. Heinze, T., & Kuhlmann, S. (2008). Across institutional boundaries? Research collaboration in German public sector nanoscience. Research Policy, 37(5), 888–899.CrossRefGoogle Scholar
  27. Huffman, W. E., & Evenson, R. E. (2005). New econometric evidence on agricultural total factor productivity determinants: Impact of funding composition. Iowa State University, Department of Economics, Working Paper, 3029.Google Scholar
  28. Jacob, B., & Lefgren, L. (2007). The impact of research grant funding on scientific productivity.Google Scholar
  29. Jacob, B. A., & Lefgren, L. (2011). The impact of research grant funding on scientific productivity. Journal of Public Economics, 95(9), 1168–1177.CrossRefGoogle Scholar
  30. Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18.CrossRefGoogle Scholar
  31. King, J. (1987). A review of bibliometric and other science indicators and their role in research evaluation. Journal of Information Science, 13(5), 261–276.CrossRefGoogle Scholar
  32. Kyvik, S., & Olsen, T. B. (2008). Does the aging of tenured academic staff affect the research performance of universities? Scientometrics, 76(3), 439–455.CrossRefGoogle Scholar
  33. Larivière, V. (2012). On the shoulders of students? The contribution of PhD students to the advancement of knowledge. Scientometrics, 90(2), 463–481.CrossRefGoogle Scholar
  34. Latour, B., & Woolgar, S. (1979). Laboratory life: The social construction of scientific facts. Princeton: Princeton University Press.Google Scholar
  35. Lawani, S. M. (1986). Some bibliometric correlates of quality in scientific research. Scientometrics, 9(1–2), 13–25.CrossRefGoogle Scholar
  36. Lee, S., & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673–702.CrossRefGoogle Scholar
  37. Lehman, H. C. (1953). Age and achievement.Google Scholar
  38. Lewison, G., & Dawson, G. (1998). The effect of funding on the outputs of biomedical research. Scientometrics, 41(1), 17–27.CrossRefGoogle Scholar
  39. Leydesdorff, L., & Wagner, C. (2009). Macro-level indicators of the relations between research funding and research output. Journal of Informetrics, 3(4), 353–362.CrossRefGoogle Scholar
  40. Martín-Sempere, M. J., Rey-Rocha, J., & Garzón-García, B. (2002). The effect of team consolidation on research collaboration and performance of scientists. Case study of Spanish university researchers in geology. Scientometrics, 55(3), 377–394.CrossRefGoogle Scholar
  41. McAllister, P. R., & Narin, F. (1983). Characterization of the research papers of US medical schools. Journal of the American Society for Information Science, 34(2), 123–131.CrossRefGoogle Scholar
  42. Melin, G. (2000). Pragmatism and self-organization: Research collaboration on the individual level. Research Policy, 29(1), 31–40.CrossRefGoogle Scholar
  43. Merton, R. K. (1968). The Matthew effect in science. Science, 159(3810), 56–63.CrossRefGoogle Scholar
  44. Merton, R. K. (1973). The sociology of science: Theoretical and empirical investigations. Chicago: University of Chicago press.Google Scholar
  45. Moed, H. F. (2006). Citation analysis in research evaluation. Dordrecht: Springer.Google Scholar
  46. Niosi, J. (2000). Canada’s national system of innovation. Montreal: McGill-Queen’s Press-MQUP.Google Scholar
  47. NSERC. (2013). Report on plans and priorities, 2013–2014. Retrieved from http://www.nserc-crsng.gc.ca/NSERC-CRSNG/Reports-Rapports/RPP-PPR/2013-2014/index_eng.asp.
  48. Okubo, Y. (1997). Bibliometric indicators and analysis of research systems: Methods and examples. No. 1997/1. Paris: OECD Publishing.CrossRefGoogle Scholar
  49. Payne, A. A., & Siow, A. (2003). Does federal research funding increase university research output? Advances in Economic Analysis and Policy, 3(1), 1–24.CrossRefGoogle Scholar
  50. Peritz, B. C. (1990). The citation impact of funded and unfunded research in economics. Scientometrics, 19(3–4), 199–206.CrossRefGoogle Scholar
  51. Plume, A., & van Wiejen, D. (2014). Publish or perish? The rise of the fractional author. Trends Journal of Sciences Research. Google Scholar
  52. Salter, A. J., & Martin, B. R. (2001). The economic benefits of publicly funded basic research: A critical review. Research Policy, 30(3), 509–532.CrossRefGoogle Scholar
  53. Sandström, U. (2009). Research quality and diversity of funding: A model for relating research money to output of research. Scientometrics, 79(2), 341–349.CrossRefGoogle Scholar
  54. Schilling, M. A., & Phelps, C. C. (2007). Interfirm collaboration networks: The impact of large-scale network structure on firm innovation. Management Science, 53(7), 1113–1126.CrossRefzbMATHGoogle Scholar
  55. Shapira, P., & Wang, J. (2010). Follow the money. Nature, 468(7324), 627–628.CrossRefGoogle Scholar
  56. Tahmooresnejad, L., Beaudry, C., & Schiffauerova, A. (2015). The role of public funding in nanotechnology scientific production: Where Canada stands in comparison to the United States. Scientometrics, 102, 753–787.CrossRefGoogle Scholar
  57. Thorsteinsdóttir, O. H. (2000). External research collaboration in two small science systems. Scientometrics, 49(1), 145–160.CrossRefGoogle Scholar
  58. Wray, K. B. (2003). Is science really a young man’s game? Social Studies of Science, 33(1), 137–149.CrossRefGoogle Scholar
  59. Wray, K. B. (2004). An examination of the contributions of young scientists in new fields. Scientometrics, 61(1), 117–128.CrossRefGoogle Scholar
  60. Zucker, L. G., Darby, M. R., Furner, J., Liu, R. C., & Ma, H. (2007). Minerva unbound: Knowledge stocks, knowledge flows and new knowledge production. Research Policy, 36(6), 850–863.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2015

Authors and Affiliations

  1. 1.Concordia Institute for Information Systems Engineering (CIISE)Concordia UniversityMontrealCanada
  2. 2.Department of Engineering Systems and ManagementMasdar Institute of Science and TechnologyAbu DhabiUnited Arab Emirates

Personalised recommendations