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An investigation of impact of research collaboration on academic performance in Italy

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Abstract

The aim of this paper is to investigate theoretically and empirically the impact of research collaborations on the scientific performance of Italian academic institutions. Data are derived from the international Scopus and Web of Science databases. We consider both quantity (the number of publications made in collaboration) and quality indicators from different databases (using indexes such as IF5Y (5-year impact factor of the journal, Web of Science), SJR (SCImago Journal Rank—key integral indicator of the quality of journals, Scopus), IPP (Impact per Publication, Scopus), AIS (Article Influence Scores, Web of Science), H-index (Google Scholar Hirsch-index metric) to evaluate the Italian case of scientific research. To this end, we develop a theoretical and empirical model to consider endogeneity of explanatory variables, the generalized method of moment estimation. The results suggest that international collaborations have a higher impact on the research quality index in Italy.

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Notes

  1. See more about Scopus on https://www.elsevier.com/solutions/scopus.

  2. See more about Web of Science on https://clarivate.com/products/web-of-science/.

  3. All databases of Web of Science are listed and briefly described here https://clarivate.com/products/web-of-science/databases/.

  4. See more about the full coverage of Web of Science database on this link: https://clarivate.com/blog/news/back-future-institute-scientific-information-re-established-within-clarivate-analytics/.

  5. The key ‘leitmotiv’ of the reviewed papers on application of non-overlapping generation models is related to the fair distribution of utility, wealth, money over generations.

  6. All these indicators are calculated in Scopus SciVal analytical tool for any given set of countries, organizations and individual researchers. Scopus SciVal is ‘a ready-to-use solution with unparalleled power and flexibility, SciVal enables you to visualize research performance, benchmark relative to peers, develop collaborative partnerships and analyze research trends’ (see more about the features of the SciVal tool at https://www.elsevier.com/solutions/scival and also in Colledge and Verlinde 2014; Reznik-Zellen 2016).

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Acknowledgements

The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of the subsidy granted to the HSE by the Government of the Russian Federation for the implementation of the Global Competitiveness Program.

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Authors and Affiliations

  1. Department of Economic and Statistical Sciences, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, Salerno, Italy

    Luigi Aldieri & Concetto Paolo Vinci

  2. Department of Economic and Legal Studies, Parthenope University of Naples, Via Generale Parisi, 13, 80132, Naples, Italy

    Gennaro Guida

  3. Institute for Statistical Studies and Economics of Knowledge, National Research University Higher School of Economics, 20 Myasnitskaya Street, Moscow, Russian Federation, 101000

    Maxim Kotsemir

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  1. Luigi Aldieri
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  4. Concetto Paolo Vinci
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Correspondence to Maxim Kotsemir.

Appendix: Definitions of bibliometric indicators used in the model

Appendix: Definitions of bibliometric indicators used in the model

Five-year Journal Impact Factor, is the average number of times articles from the journal published in the past 5 years have been cited in the JCR year. It is calculated by dividing the number of citations in the JCR year by the total number of articles published in the five previous years. The journal impact factor is defined as all citations to the journal in the current JCR year to items published in the previous 2 years, divided by the total number of scholarly items (these comprise articles, reviews and proceedings papers) published in the journal in the previous 2 years. Though not a strict mathematical average, the journal impact factor provides a functional approximation of the mean citation rate per citable item. A journal impact factor of 1.0 means that, on average, the articles published one or 2 years ago have been cited once. A journal impact factor of 2.5 means that, on average, the articles published one or 2 years ago have been cited two and a half times. The citing works may be articles published in the same journal. However, most citing works are from different journals, proceedings, or books indexed in the Web of Science Core Collection. Definition is derived from http://ipscience-help.thomsonreuters.com/incitesLiveJCR/glossaryAZgroup/g3/7768-TRS.html and http://ipscience-help.thomsonreuters.com/incitesLiveJCR/glossaryAZgroup/g8/4346-TRS.html.

Article Influence Score (AIS) determines the average influence of a journal’s articles over the first 5 years after publication. It is calculated by multiplying the eigenfactor score by 0.01 and dividing by the number of articles in the journal, normalized as a fraction of all articles in all publications. This measure is roughly analogous to the 5-year journal impact factor in that it is a ratio of a journal’s citation influence to the size of the journal’s article contribution over a period of 5 years. The equation is as follows: (0.01 × eigenfactor score) ÷ X. where X = 5-year journal article count divided by the 5-year article count from all journals. The eigenfactor score calculation is based on the number of times articles from the journal published in the past 5 years have been cited in the JCR year, but it also considers which journals have contributed these citations so that highly cited journals will influence the network more than lesser cited journals. References from one article in a journal to another article from the same journal are removed, so that eigenfactor scores are not influenced by journal self-citation. The mean article influence score for each article is 1.00. A score greater than 1.00 indicates that each article in the journal has above-average influence. A score less than 1.00 indicates that each article in the journal has below-average influence. Definition is derived from http://ipscience-help.thomsonreuters.com/incitesLiveJCR/glossaryAZgroup/g4/7790-TRS.html and http://ipscience-help.thomsonreuters.com/incitesLiveJCR/glossaryAZgroup/g6/7791-TRS.html.

Google Scholar h-index of a publication is the largest number h such that at least h articles in that publication were cited at least h times each. For example, a publication with five articles cited by, respectively, 17, 9, 6, 3, and 2, has the h-index of 3. Definition is derived from https://scholar.google.com/intl/ru/scholar/metrics.html#metrics.

Impact per publication (IPP) is a measure of the ratio of citations per paper published in the journal and is not normalized for the citation potential in its subject field. It measures the ratio of citations in year Y to scholarly papers published in the three previous years (Y-1, Y-2, Y-3) divided by the number of scholarly papers published in those same years (Y-1, Y-2, Y-3). The IPP uses a citation window of 3 years, which is considered to be the optimal time period to accurately measure citations in most subject fields. Taking into account the same peer-reviewed scholarly papers only in both the numerator and denominator of the equation provides a fair impact measurement of the journal and diminishes the chance of manipulation. Scopus IPP indicator is calculated the same way as Web of Science 2-year impact-factor indicator (intellectual property of Thomson Reuters—former owner of Web of Science database). Definition is derived from https://blog.scopus.com/posts/2014-snip-sjr-and-ipp-journal-metrics-now-freely-available-online and http://www.pnas.org/page/about/metrics.

SCImago Journal Rank (SJR) indicator expresses the average number of weighted citations received in the selected year by the documents published in the selected journal in the three previous years, i.e. weighted citations received in year X to documents published in the journal in years X-1, X-2 and X-3. SJR is calculated on a yearly basis for all journals indexed in the Scopus data set. SJR is a prestige metric based on the idea that not all citations are the same. With SJR, the subject field, quality and reputation of the journal have a direct effect on the value of a citation and the impact that the journal makes. In addition, the prestige of a citation is weighted over all citations handed out to that journal. SJR is a size-independent indicator and it ranks journals by their ‘average prestige per article’ and can be used for journal comparisons in science evaluation processes. It expresses the average number of weighted citations received in the selected year by the documents published in the selected journal in the three previous years (i.e., weighted citations received in year X to documents published in the journal in years X-1, X-2 and X-3). Definition is derived from http://scimagojr.com/help.php and http://www.pnas.org/page/about/metrics. Detailed description of SJR is available at http://scimagojr.com/SCImagoJournalRank.pdf.

See Tables 4, 5, 6 and 7.

Table 4 Evidences for support of the statements about the positive influence of collaboration on research performance from academic literature.
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Table 5 Statements on the influence of geographical distance/proximity on collaboration derived from academic literature—worldwide cases.
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Table 6 Statements on the influence of geographical distance/proximity on collaboration derived from academic literature—cases of analysis on regional level.
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Table 7 Statements on the influence of geographical distance/proximity on collaboration derived from academic literature—cases of research collaboration in Italian universities.
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Aldieri, L., Guida, G., Kotsemir, M. et al. An investigation of impact of research collaboration on academic performance in Italy. Qual Quant 53, 2003–2040 (2019). https://doi.org/10.1007/s11135-019-00853-1

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