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Revisiting size effects in higher education research productivity

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Abstract

The potential occurrence of variable returns to size in research activity is a factor to be considered in choices about the size of research organizations and also in the planning of national research assessment exercises, so as to avoid favoring those organizations that would benefit from such occurrence. The aim of the current work is to improve on weaknesses in past inquiries concerning returns to size through application of a research productivity measurement methodology that is more accurate and robust. The method involves field-standardized measurements that are free of the typical distortions of aggregate measurement by discipline or organization. The analysis is conducted for 183 hard science fields in all 77 Italian universities (time period 2004–2008) and allows detection of potential differences by field.

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Notes

  1. As an example of such style, Italy’s Triennial Research Evaluation exercise (2001–2003) subdivided research organizations into three groups based on size of their research staff (VTR 2006).

  2. Local regression (i.e. non-linear) method.

  3. Since MIUR financing composes 55.5% of the total, the share distributed on the basis of the VTR represents 3.9% of total income.

  4. The complete list is accessible on http://cercauniversita.cineca.it/php5/settori/index.php. Last accessed on July 20, 2011.

  5. http://cercauniversita.cineca.it/php5/docenti/cerca.php. Last accessed on July 20, 2011.

  6. www.orp.researchvalue.it. Last accessed on July 20, 2011.

  7. In the Italian academic system, the hard sciences are matched in nine UDAs: mathematics and computer sciences; physics; chemistry; earth sciences; biology; medicine; agricultural and veterinary sciences; civil engineering; industrial and information engineering.

  8. Observed as of June 30, 2009.

  9. Standardizing citations to the median value rather than to the average, as frequently observed in literature, is justified by the fact that distribution of citations is highly skewed (Lundberg 2007).

  10. The average number of publications per year by a physicist is 2.3 times what a mathematician produces.

  11. Although members of the same SDS may publish in different WoS subject categories their publication rate is not greatly affected by this. Differences in SS continue to reflect differences in productivity.

  12. Because in the life sciences, the different position in the authors’ list of publications reflects the different contribution of the authors to the work, the following algorithm has been proposed by Italian scientists in the life sciences. It can be adapted to reflect different national contexts. If first and last authors belong to the same university, 40% of citations are attributed to each of them; the remaining 20% are divided among all other authors. If the first two and last two authors belong to different universities, 30% of citations are attributed to first and last authors; 15% of citations are attributed to second and last author but one; the remaining 10% are divided among all others. This algorithm has been proposed by Italian scientists in the life sciences. It can be adapted to reflect different national contexts.

  13. With individual top scientists defined as those positioning above 80th percentile of national performance, a value of “top scientists” greater than 20% at a university indicates that their percentage in the staff is higher than the national average.

  14. Kendall’s τb assumes values between −1 (perfect inversion) and +1 (perfect concordance), with a value of 0 in cases of absence of association.

  15. DEA methodology seems particularly suited to comparing efficiency of research institutions, especially with the increasing availability of quantitative indicators for input and output. Abramo et al. (2011b) is an example of a field-standardized application to national assessment.

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Authors and Affiliations

  1. Institute for System Analysis and Computer Science, National Research Council of Italy IASI-CNR, Viale Manzoni 30, 00185, Rome, Italy

    Giovanni Abramo

  2. Department of Management, Laboratory for Studies of Research and Technology Transfer, School of Engineering, University of Rome “Tor Vergata”, Via del Politecnico 1, 00133, Rome, Italy

    Giovanni Abramo, Tindaro Cicero & Ciriaco Andrea D’Angelo

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  1. Giovanni Abramo
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  2. Tindaro Cicero
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  3. Ciriaco Andrea D’Angelo
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Correspondence to Giovanni Abramo.

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Abramo, G., Cicero, T. & D’Angelo, C.A. Revisiting size effects in higher education research productivity. High Educ 63, 701–717 (2012). https://doi.org/10.1007/s10734-011-9471-6

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