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Research collaboration and productivity: is there correlation?

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Abstract

The incidence of extramural collaboration in academic research activities is increasing as a result of various factors. These factors include policy measures aimed at fostering partnership and networking among the various components of the research system, policies which are in turn justified by the idea that knowledge sharing could increase the effectiveness of the system. Over the last two decades, the scientific community has also stepped up activities to assess the actual impact of collaboration intensity on the performance of research systems. This study draws on a number of empirical analyses, with the intention of measuring the effects of extramural collaboration on research performance and, indirectly, verifying the legitimacy of policies that support this type of collaboration. The analysis focuses on the Italian academic research system. The aim of the work is to assess the level of correlation, at institutional level, between scientific productivity and collaboration intensity as a whole, both internationally and with private organizations. This will be carried out using a bibliometric type of approach, which equates collaboration with the co-authorship of scientific publications.

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Notes

  1. Mathematics and computer sciences, physics, chemical sciences, earth sciences, biological sciences, medical sciences, agricultural and veterinary sciences, industrial and information engineering.

  2. See http://www.miur.it/atti/2000/alladm001001_01.htm for a comprehensive list. Note that the 8 selected areas include 183 sectors, but for the two of the sectors there were no scientific publications recorded during the period 2001–2003.

  3. The distribution of impact factors of journals is remarkably different from one sector to another. Normalization to the sector average makes it possible to contain the distortions inherent in measurements from different sectors.

  4. This operation makes it possible to contain the bias typical of comparisons performed at high aggregation levels. Different sectors show different scientific prolificacy rates: robust comparisons are only possible through normalization of the data to the sector average and weighting by number of staff members in each sector. See Abramo et al. (2007) on this issue.

  5. Concentration Index is a measure of association between two variables based on frequencies data and varying around the neutral value of 1. Referring to the first cell of Table 1, the value of 1.33 derives from this ratio (2,974/7,481)/(16,011/53,420) and indicates that intramural articles tend to concentrate more (+33%) in the last quartile of quality as compared with all publications.

  6. The coefficient of variation is a normalized measure of dispersion of a distribution. It is defined as the ratio of the standard deviation to the mean and its dimensionless nature renders comparable different data sets with wildly different means. The larger is its value the broader is the data dispersion of the distribution.

  7. Hereinafter we’ll make an extensive use of association analysis by means of:

    • correlation coefficient: it measures the strength and direction of a linear relationship between two variables on a 0–1 scale, where “0” represents no association and “1” perfect association.

    • regression statistics: they measure the relationship between an independent variable (X) and a dependent variable (Y). In our case we show the coefficient (?) of such relationship and the coefficient of determination (R2) which measures (on a 0–1 scale) the variability of Y explained by X.

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Correspondence to Giovanni Abramo.

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Abramo, G., D’Angelo, C.A. & Di Costa, F. Research collaboration and productivity: is there correlation?. High Educ 57, 155–171 (2009). https://doi.org/10.1007/s10734-008-9139-z

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