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Short-term effects of non-competitive funding to single academic researchers

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Research funding is essential to promote the scientific activity of researchers and the dissemination of their results. Simplifying, funding schemes can be classified in two categories—competitive and non-competitive—with several corresponding advantages and shortcomings, which are widely discussed in the scientific literature. The researchers of Politecnico di Torino (i.e., one of the major Italian technical universities) have recently been funded through a non-competitive research funding, consisting of 14k€ for every single researcher in each of the last three years (i.e., from 2017 to 2019), for a total of 42k€. This somewhat unusual initiative—also called “diffused funding” (DF)—represents an important opportunity to investigate the effects of the relatively large allocation of non-competitive funding to single researchers. In this regard, this paper investigates the effects of the DF on the researchers’ scientific output, according to four dimensions of analysis: publishing productivity, publishing diffusion/impact, journal reputation, and international research relations. Preliminary results do not indicate any improvement in the publication output, at least in the short term.

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  1. As a rule, salaries are progressively increased depending on the length of service, with two-yearly increments.

  2. In Italian, “Consiglio di Amministrazione”.

  3. In Italian, “finanziamento diffuso”.

  4. In Italian, “Settore Scientifico Disciplinare”, which means “Scientific and Disciplinary Sector”.

  5. The “natural performance” may be defined as the performance that PoliTO researchers tended to exhibit before benefiting from the DF.

  6. As for dimension (2), this phenomenon is partly hidden since the so-called “citation inflation” is compensated by the lower time available for citation accumulation of the more recent journal articles.


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The authors acknowledge that this paper extends and completes the research presented by the authors at ISSI2019 (17th International Conference on Scientometrics and Informetrics) in Rome (Italy), 2–5 September 2019 (Maisano et al. 2019).

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Correspondence to Domenico A. Maisano.

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This paper is dedicated to the memory of Judit Bar-Ilan (1958–2019), an outstanding scholar and an inimitable friend and colleague.

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Supplementary file1 (DOC 199 kb)



Additional statistical tests

Table 7 contains the results of an additional Kendall’s Turning Point test for each of the four dimensions considered (Kendall 1973; Brockwell and Davis 2016). This other test is performed to check the randomness of the time series, using the totality of the data (i.e., from 2008 to 2019). For the first three time series, the null hypothesis of randomness cannot be rejected at a confidence level of 95% (p-values > 0.05); for the last one, randomness is doubtful (p-value = 0.013), due to the relatively high number of turning points; this result can somehow be affected by the relatively low number of data, therefore it should be considered with prudence (Ross 2009).

Table 7 Results of Kendall’s Turning Point test for testing the randomness of the four time series in Fig. 2, considering the totality of the data (i.e., from 2008 to 2019)

Figures 3 and 4 illustrate two Anderson Darling normality tests at 95%, for each of the four time series in Table 6, considering respectively (1) the 2008–2016 data (i.e., excluding the effects of DF), and (2) the 2008–2019 data (i.e., including the effects of DF). The authors are aware that the power of these tests is not very high, due to the relatively limited number of data (i.e., 9 in the first case and 12 in the second one) (Ross 2009). Nevertheless, it is interesting to note that for all the considered time series the null hypothesis of normal distribution cannot be rejected.

Fig. 3
figure 3

Results of the Anderson Darling normality test applied to the time series in Table 6 (data included in the period from 2008 to 2016)

Fig. 4
figure 4

Results of the Anderson Darling normality test applied to the time series in Table 6 in their totality (i.e., considering all of the annual data in the period from 2008 to 2019)

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Maisano, D.A., Mastrogiacomo, L. & Franceschini, F. Short-term effects of non-competitive funding to single academic researchers. Scientometrics 123, 1261–1280 (2020).

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