Skip to main content
SpringerLink
Log in

Crossing the hurdle: the determinants of individual scientific performance

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

An original cross-sectional dataset referring to a medium-sized Italian university is implemented in order to analyze the determinants of scientific research production at individual level. The dataset includes 942 permanent researchers of various scientific sectors for a 3-year time-span (2008–2010). Three different indicators—based on the number of publications and/or citations—are considered as response variables. The corresponding distributions are highly skewed and display an excess of zero-valued observations. In this setting, the goodness-of-fit of several Poisson mixture regression models are explored by assuming an extensive set of explanatory variables. As to the personal observable characteristics of the researchers, the results emphasize the age effect and the gender productivity gap—as previously documented by existing studies. Analogously, the analysis confirms that productivity is strongly affected by the publication and citation practices adopted in different scientific disciplines. The empirical evidence on the connection between teaching and research activities suggests that no univocal substitution or complementarity thesis can be claimed: a major teaching load does not affect the odds to be a non-active researcher and does not significantly reduce the number of publications for active researchers. In addition, new evidence emerges on the effect of researchers administrative tasks—which seem to be negatively related with researcher’s productivity—and on the composition of departments. Researchers’ productivity is apparently enhanced by operating in department filled with more administrative and technical staff, and it is not significantly affected by the composition of the department in terms of senior/junior researchers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Abramo, G., D’Angelo, C. A., & Caprasecca, A. (2009). Gender differences in research productivity: A bibliometric analysis of the Italian academic system. Scientometrics, 79, 517–539.

    Article  Google Scholar 

  • Allison, P. D., & Long, J. S. (1990). Departmental effects on scientific productivity. American Sociological Review, 55, 469–478.

    Article  Google Scholar 

  • Allison, P. D., & Stewart, J. A. (1974). Productivity differences among scientists: Evidence for accumulative advantage. American Sociological Review, 39, 596–606.

    Article  Google Scholar 

  • Anania, G., & Caruso, A. (2013). Two simple new bibliometric indexes to better evaluate research in disciplines where publications typically receive less citations. Scientometrics, 96, 617–631.

    Article  Google Scholar 

  • Barabesi, L., & Pratelli, L. (2014). Discussion of “On simulation and properties of the stable law” by L. Devroye and L: James. Statistical Methods & Applications. doi:10.1007/s10260-014-0263-x.

    Google Scholar 

  • Bayer, A. E., & Dutton, J. E. (1977). Career age and research professional activities of academic scientists. Journal of Higher Education, 48, 259–282.

    Article  Google Scholar 

  • Böhning, D., Dietz, E., Schlattmann, P., Mendonça, L., & Kirchner, U. (1999). The zero-inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology. Journal of the Royal Statistical Society Series A, 162, 195–209.

    Article  Google Scholar 

  • Bonaccorsi, A., & Daraio, C. (2003). Age effects in scientific productivity. The case of the Italian National Research Council (CNR). Scientometrics, 58, 49–90.

    Article  Google Scholar 

  • Burrell, Q. L. (2005). The use of the generalized Waring process in modelling informetric data. Scientometrics, 64, 247–270.

    Article  Google Scholar 

  • Burrell, Q. L., & Fenton, M. R. (1993). Yes, the GIGP really does work – and is workable! Journal of the American Society for Information Science, 44, 61–69.

    Article  Google Scholar 

  • Cainelli, G., de Felice, A., Lamonarca, M., & Zoboli, R. (2006). The publications of Italian economists in ECONLIT: Quantitative assessment and implications for research evaluation. Economia Politica, 23, 385–423.

    Google Scholar 

  • Carayol, N., & Matt, M. (2004). Does research organization influence academic production? Laboratory level evidence from a large European university. Research Policy, 33, 1081–1102.

    Article  Google Scholar 

  • Carayol, N., & Matt, M. (2006). Individual and collective determinants of academic scientists’ productivity. Information Economics and Policy, 18, 55–72.

    Article  Google Scholar 

  • Chambers, J. M., & Hastie, T. J. (Eds.). (1992). Statistical Models in S. London: Chapman & Hall.

    MATH  Google Scholar 

  • Cole, J. R., & Cole, S. (1973). Social Stratification in Science. Chicago: University of Chicago Press.

    Google Scholar 

  • Cole, J. R., & Zuckerman, H. (1984). The productivity puzzle: Persistence and change in patterns of publication of men and women scientists. In M. W. Steinkempt & M. L. Maehr (Eds.), Advances in Motivation and Achievement (pp. 217–258). Conn.: JAI Press, Greenwich.

    Google Scholar 

  • Dalrymple, M. L., Hudson, I. L., & Ford, R. P. K. (2003). Finite mixture, zero-inflated Poisson and hurdle models with application to SIDS. Computational Statistics & Data Analysis, 41, 491–504.

    Article  MATH  MathSciNet  Google Scholar 

  • David, P. (1994). Positive feedbacks and research productivity in science: Reopening another black box. In O. Grandstrand (Ed.), Economics and Technology (pp. 65–85). Amsterdam: Elsevier.

    Google Scholar 

  • Defazio, D., Lockett, A., & Wright, M. (2009). Funding incentives, collaborative dynamics and scientific productivity: Evidence from the EU framework program. Research Policy, 38, 293–305.

    Article  Google Scholar 

  • Diamond, A. M. (1984). An economic-model of the life-cycle research productivity of scientists. Scientometrics, 6, 189–196.

    Article  Google Scholar 

  • Diamond, A. M. (1986). The life-cycle research productivity of mathematicians and scientists. The Journal of Gerontology, 41, 520–525.

    Article  Google Scholar 

  • Fabel, O., Hein, M., & Hofmeister, R. (2008). Research productivity in business economics: An investigation of Austrian, German and Swiss universities. German Economic Review, 9, 506–531.

    Article  Google Scholar 

  • Fox, M. F. (1992). Research, teaching and publication productivity: Mutuality versus competition in academia. Sociology of Education, 65, 293–305.

    Article  Google Scholar 

  • Fox, M. F. (2005). Gender, family characteristics, and publication productivity among scientists. Social Studies of Science, 35, 131–150.

    Article  Google Scholar 

  • Fox, M. F., Fonseca, C., & Bao, J. (2011). Work and family conflict in academic science: Patterns and predictors among women and men in research universities. Social Studies of Science, 41, 715–735.

    Article  Google Scholar 

  • Hall, D. B. (2000). Zero-inflated Poisson and binomial regression with random effects: A case study. Biometrics, 56, 1030–1039.

    Article  MATH  MathSciNet  Google Scholar 

  • Hicks, D. (2004). The four literatures of social science. In F. H. Moed, W. Glaenzel, & U. Schmoch (Eds.), Handbook of Quantitative Science and Technology Research (pp. 473–496). Dordrecht, Boston and London: Kluwer Academic Publishers.

    Google Scholar 

  • Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102, 16569–16572.

    Article  Google Scholar 

  • Iglesias, J. E., & Pecharroman, C. (2007). Scaling the h-index for different scientific ISI fields. Scientometrics, 73, 303–320.

    Article  Google Scholar 

  • Irwin, J. O. (1968). The generalized Waring distribution applied to accident theory. Journal of the Royal Statistical Society. Series A (General), 131, 205–225.

    Article  MathSciNet  Google Scholar 

  • Johnson, N. L., Kemp, A. W., & Kotz, S. (2005). Univariate discrete distributions (3rd ed.). New York: Wiley.

    Book  MATH  Google Scholar 

  • Kelchtermans, K., & Veugelers, R. (2011). The great divide in scientific productivity: Why the average scientist does not exist. Industrial and Corporate Change, 20, 295–336.

    Article  Google Scholar 

  • Kossi, Y., Lesueur, J. Y., & Sabatier, M. (2013). Publish or teach? The role of the scientific environment on academics multitasking. Groupe d’Analyse et de Théorie Économique Lyon-St Étienne, GATE WP 1315.

  • Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34, 1–17.

    Article  MATH  Google Scholar 

  • Leahey, E. (2006). Gender differences in productivity: Research specialization as a missing link. Gender & Society, 20, 754–780.

    Article  Google Scholar 

  • Levin, S., & Stephan, P. E. (1991). Research productivity over the life cycle: Evidence for academic scientists. American Economic Review, 81, 114–132.

    Google Scholar 

  • Levin, S., & Stephan, P. E. (1998). Gender differences in the rewards to publishing in academe: Science in the 1970s. Sex Roles, 38, 1041–1064.

    Article  Google Scholar 

  • Lissoni, F., Mairesse, J., Montobbio, F., & Pezzoni, M. (2011). Scientific productivity and academic promotion: A study on French and Italian physicists. Industrial and Corporate Change, 20, 253–294.

    Article  Google Scholar 

  • Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Science, 16, 317–323.

    Google Scholar 

  • Mairesse J., Pezzoni M. (2013). Does gender affect scientific productivity? A critical review of the empirical evidence and a panel data econometric analysis for French physicists. Presented to AFSE Meeting, Aix en Provence, 26 June 2013.

  • Mairesse, J., Turner, L. (2006). Measurement and explanation of the intensity of co-publication in scientific research: An analysis at the laboratory level. In: Antonelli, C., Foray, D., Hall, B. H., Steinmueller, W. E. (Eds), New Frontiers in the Economics of Innovation and New Technology: Essays in Honour of Paul A. David. Edward Elgar, Cheltenham and Northampton, pp 255–295.

  • Marcheselli, M., Baccini, A., & Barabesi, L. (2008). Parameter estimation for the discrete stable family. Communications in statistics: Theory and methods, 37, 815–830.

    Article  MATH  MathSciNet  Google Scholar 

  • Martin, B. R., & Irvine, J. (1983). Assessing basic research: Some partial indicators of scientific progress in radio astronomy. Research Policy, 12, 61–90.

    Article  Google Scholar 

  • Merton, R. (1968). The Matthew effect in science. Science, 159, 56–63.

    Article  Google Scholar 

  • Minami, M., Lennert-Cody, C. E., Gao, W., & Román-Verdesoto, M. (2007). Modeling shark bycatch: The zero-inflated negative binomial regression model with smoothing. Fisheries Research, 84, 210–221.

    Article  Google Scholar 

  • Mullahy, J. (1986). Specification and testing of some modified count data models. Journal of Econometrics, 33, 341–365.

    Article  MathSciNet  Google Scholar 

  • Pezzoni, M., Sterzi, V., & Lissoni, F. (2012). Career progress in centralized academic systems: Social capital and institutions in France and Italy. Research Policy, 41, 704–719.

    Article  Google Scholar 

  • R Core Team (2012). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, from http://www.R-project.org/.

  • Rao, I. K. R. (1980). The Distribution of Scientific Productivity and Social Change. Journal of the American Society for Information Science, 31, 111–122.

    Article  Google Scholar 

  • Rathbun, S. L., & Fei, S. (2006). A spatial zero-inflated Poisson regression model for oak regeneration. Environmental and Ecological Statistics, 13, 409–426.

    Article  MathSciNet  Google Scholar 

  • Rigby, R. A., Stasinopoulos, D. M., & Akantziliotou, C. (2008). A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution. Computational Statistics and Data Analysis, 53, 381–393.

    Article  MATH  MathSciNet  Google Scholar 

  • Rivera-Huerta, R., Dutrénit, G., Ekboir, J. M., Sampedro, J. L., & Vera-Cruz, A. O. (2011). Do linkages between farmers and academic researchers influence researcher productivity? The Mexican case. Research Policy, 40, 932–942.

    Article  Google Scholar 

  • Rodríguez-Avi, J., Conde-Sánchez, A., Sáez-Castillo, A. J., Olmo-Jiménez, M. J., & Martínez-Rodríguez, A. M. (2009). A generalized Waring regression model for count data. Computational Statistics and Data Analysis, 53, 3717–3725.

    Article  MATH  MathSciNet  Google Scholar 

  • Rose, C. E., Martin, S. W., Wannemuehler, K. A., & Plikaytis, B. D. (2006). On the use of zero-inflated and hurdle models for modeling vaccine adverse event count data. Journal of Biopharmaceutical Statistics, 16, 463–481.

    Article  MathSciNet  Google Scholar 

  • Schubert, A., & Glänzel, W. (1984). A dynamic Look at a class of skew distributions. A model with scientometric applications. Scientometrics, 6, 149–167.

    Article  Google Scholar 

  • Schubert, A., & Telcs, A. (1989). Estimation of the publication potential in 50 U.S. states and in the District of Columbia based on the frequency distribution of scientific productivity. Journal of the American Society for Information Science, 40, 291–297.

    Article  Google Scholar 

  • Sibuya, M. (1979). Generalized hypergeometric, digamma and trigamma distributions. Annals of the Institute of Statistical Mathematics, 31, 373–390.

    Article  MATH  MathSciNet  Google Scholar 

  • Sichel, H. S. (1985). A bibliometric distribution which really works. Journal of the American Society for Information Science, 36, 314–321.

    Article  Google Scholar 

  • Stephan, P. E. (1996). The economics of science. Journal of Economic Literature, 34, 1199–1235.

    Google Scholar 

  • Stephan, P. E. (2012). How Economics Shapes Science. Cambridge, Mass: Harvard University Press.

    Book  Google Scholar 

  • Taylor, S. W., Fender, B. F., & Burke, K. G. (2006). Unraveling the academic productivity of economists: The opportunity costs of teaching and service. Southern Economic Journal, 72, 846–859.

    Article  Google Scholar 

  • van Arensbergen, P., van der Weijden, I., & van den Besselaar, P. (2012). Gender differences in scientific productivity: A persisting phenomenon? Scientometrics, 93, 857–868.

    Article  Google Scholar 

  • van Leeuwen, T. N., Visser, M. S., Moed, H. F., Nederhof, T. J., & van Raan, A. F. J. (2003). The Holy Grail of science policy: Exploring and combining bibliometric tools in search of scientific excellence. Scientometrics, 57, 257–280.

    Article  Google Scholar 

  • Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S. New York: Springer-Verlag.

    Book  MATH  Google Scholar 

  • Wallner, B., Fieder, M., & Iber, K. (2003). Age profile, personnel costs and scientific productivity at the University of Vienna. Scientometrics, 58, 143–153.

    Article  Google Scholar 

  • Wang, D., Song, C., & Barabási, A. L. (2013). Quantifying long-term scientific impact. Science, 342, 127–132.

    Article  Google Scholar 

  • Weiss, Y., & Lillard, L. A. (1982). Output variability, academic labor contracts, and waiting times for promotion. Research in Labor Economics, 5, 157–188.

    Google Scholar 

  • Wooton, R. (2013). A simple, generalizable method for measuring individual research productivity and its use in the long-term analysis of departmental performance, including between-country comparisons. Health Research Policy and Systems, 11, 1–14.

    Article  Google Scholar 

  • Xie, Y., & Shauman, K. A. (2003). Women in science: Career processes and outcomes. Cambridge: Harvard University Press.

    Google Scholar 

  • Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27, 1–25.

    Google Scholar 

  • Zhang, X., Lei, Y., Cai, D., & Liu, F. (2012). Predicting tree recruitment with negative binomial mixture models. Forest Ecology and Management, 270, 209–215.

    Article  Google Scholar 

  • Zuckerman, H. A., & Merton, R. K. (1972). Age, aging, and age structure in science. In M. R. Riley, M. Johnson, & A. Foner (Eds.), A sociology of age stratification: Aging and society (pp. 292–356). New York: Russell Sage Foundation.

    Google Scholar 

Download references

Acknowledgments

We would like to thank Sonia Boldrini from the Evaluation Committee of the University of Siena for her valuable assistance in data harvesting and database building. In addition, we would like to express our gratitude to two anonymous referees for their comments which have led to a truly improved version of the paper.

Author information

Authors and Affiliations

  1. Department of Economics and Statistics, University of Siena, Siena, Italy

    A. Baccini, L. Barabesi, M. Cioni & C. Pisani

Authors
  1. A. Baccini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. Barabesi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Cioni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C. Pisani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Baccini.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baccini, A., Barabesi, L., Cioni, M. et al. Crossing the hurdle: the determinants of individual scientific performance. Scientometrics 101, 2035–2062 (2014). https://doi.org/10.1007/s11192-014-1395-3

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-014-1395-3

Keywords

Search

Navigation