A scientometric analysis of the effect of COVID-19 on the spread of research outputs

Gianpaolo Zammarchi (), Andrea Carta, Silvia Columbu, Luca Frigau and Monica Musio
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Gianpaolo Zammarchi: University of Cagliari
Andrea Carta: University of Cagliari
Silvia Columbu: Università degli Studi di Cagliari
Luca Frigau: University of Cagliari
Monica Musio: Università degli Studi di Cagliari

Quality & Quantity: International Journal of Methodology, 2024, vol. 58, issue 3, No 13, 2265-2287

Abstract: Abstract The spread of the COVID-19 pandemic in 2020 had a huge impact on the life course of all of us. This rapid spread has also caused an increase in the research production in topics related to different aspects of COVID-19. Italy has been one of the first countries to be massively involved in the outbreak of the disease. In this paper, we present an extensive scientometric analysis of the research production both at global (entire literature produced in the first 2 years after the beginning of the pandemic) and local level (COVID-19 literature produced by authors with an Italian affiliation). Our results showed that US and China are the most active countries in terms of number of publications and that the number of collaborations between institutions varies depending on geographical distance. Moreover, we identified the medical-biological as the field with the greatest growth in terms of literature production. As regards the analysis focused on Italy, we have shown that most of the collaborations follow a geographical pattern, both externally (with a preference for European countries) and internally (two clusters of institutions, north versus center-south). Furthermore, we explored the relationship between the number of citations and variables obtained from the data set (e.g. number of authors). Using multiple correspondence analysis and quantile regression we shed light on the role of journal topics and impact factor, the type of article, the field of study and how these elements affect citations.

Keywords: Scientometric analysis; COVID-19; MCA; Quantile regression; Citations (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11135-023-01742-4

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