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Co-authorship networks and scientific performance: an empirical analysis using the generalized extreme value distribution

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  • Domenico De Stefano
  • Susanna Zaccarin

Abstract

This paper aims to explore the effects of collaborative behaviour on scholar scientific performance. Individual network measures related to scholar centrality as well as attitude to collaborate with others are derived from co-authorship networks in a given scientific community (i.e. Italian academic statisticians). Co-authorship information have been collected from three data sources of national-based, discipline-based, and international-based high-impact publications. Both network and individual covariates are used to model individual h -index by generalized extreme value distribution. Results show a positive association between performance and actors' central position in the network. Having a large number of co-authors and occupying central positions are likely to positively affect scientific performance.

Suggested Citation

  • Domenico De Stefano & Susanna Zaccarin, 2016. "Co-authorship networks and scientific performance: an empirical analysis using the generalized extreme value distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(1), pages 262-279, January.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:262-279
    DOI: 10.1080/02664763.2015.1017719
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    Cited by:

    1. Silvia Bacci & Bruno Bertaccini & Alessandra Petrucci, 2023. "Insights from the co-authorship network of the Italian academic statisticians," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4269-4303, August.
    2. Zhai, Li & Yan, Xiangbin, 2022. "A directed collaboration network for exploring the order of scientific collaboration," Journal of Informetrics, Elsevier, vol. 16(4).
    3. Fabio Zagonari, 2019. "Scientific Production and Productivity for Characterizing an Author’s Publication History: Simple and Nested Gini’s and Hirsch’s Indexes Combined," Publications, MDPI, vol. 7(2), pages 1-30, May.
    4. Vittorio Fuccella & Domenico De Stefano & Maria Prosperina Vitale & Susanna Zaccarin, 2016. "Improving co-authorship network structures by combining multiple data sources: evidence from Italian academic statisticians," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 167-184, April.

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