Crossing the hurdle: the determinants of individual scientific performance

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 three-year time-span (2008)(2009)(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 confirm 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.

Assessment of Researchers Through Bibliometric Indicators: The Area of Information and Library Science in Spain as a Case Study (2001–2015)

“…With regard to their average level of scientific activity (0.92 articles/year), it is conspicuous that only 65% of the full professors, and 37% of the associate professors, surpass the mean value. Future lines of research that may be of interest to shed light on the contours of scientific activity include the factors associated with high research productivity, barriers that constrain or even prevent some researchers from publishing (Baccini et al, 2014;Horodnic and Zait, 2015), and the characteristics and contributions of less productive researchers in terms of visibility and impact on a given research topic.…”

Different recipes for the same dish: Comparing policies for scientific excellence across different countries

“…The three areas that can be funded include: 6  Clusters of excellence: they should, inter alia, '[…] enable German university locations to establish internationally visible, competitive research and training facilities, thereby enhancing scientific networking and cooperation among the participating institutions', and should ' […] form an important part of a university's strategic and thematic 6 The application in all three lines has to go through leadership. Therefore, there is always an institutional strategic orientation .…”

“…Individual policy instruments have been evaluated, but the effectsparticularly on the relationship between policy design and effectiveness -have not been compared across policies or across nations. Scientific studies of the level and determinants of excellence (e.g., Baccini et al 2014 Noyons et al 2003 for institutions) often take an endogenous perspectivedefining performance as a result of internal resources, leadership, network management, funding or institutional and field characteristics -and ignore or keep implicit the exogenous policy environment.…”

Impact of Subsidies for Researchers on the Gender Scientific Productivity Gap: Evidence from Paraguay

“…While most researchers have an accumulated number of publications below 50, there are some rare instances where the number of total publications is over 100, 8 typical for scientific production (Lotka, 1926;Baccini et al, 2014;Kelchtermans et al, 2011).…”