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Influence of omitted citations on the bibliometric statistics of the major Manufacturing journals

Author

Listed:
  • Fiorenzo Franceschini

    (Politecnico di Torino)

  • Domenico Maisano

    (Politecnico di Torino)

  • Luca Mastrogiacomo

    (Politecnico di Torino)

Abstract

Bibliometrics is a relatively young and rapidly evolving discipline. Essential for this discipline are bibliometric databases and their information content concerning scientific publications and relevant citations. Databases are unfortunately affected by errors, whose main consequence is represented by omitted citations, i.e., citations that should be ascribed to a certain (cited) paper but, for some reason, are lost. This paper studies the impact of omitted citations on the bibliometric statistics of the major Manufacturing journals. The methodology adopted is based on a recent automated algorithm—introduced in (Franceschini et al., J Am Soc Inf Sci Technol 64(10):2149–2156, 2013)—which is applied to the Web of Science (WoS) and Scopus database. Two important results of this analysis are that: (i) on average, the omitted-citation rate (p) of WoS is slightly higher than that of Scopus; and (ii) for both databases, p values do not change drastically from journal to journal and tend to slightly decrease with respect to the issue year of citing papers. Although it would seem that omitted citations do not represent a substantial problem, they may affect indicators based on citation statistics significantly. This paper analyses the effect of omitted citations on popular bibliometric indicators like the average citations per paper and its most famous variant, i.e., the ISI Impact Factor, showing that journal classifications based on these indicators may lead to questionable discriminations.

Suggested Citation

  • Fiorenzo Franceschini & Domenico Maisano & Luca Mastrogiacomo, 2015. "Influence of omitted citations on the bibliometric statistics of the major Manufacturing journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(3), pages 1083-1122, June.
    • Handle: RePEc:spr:scient:v:103:y:2015:i:3:d:10.1007_s11192-015-1583-9
    DOI: 10.1007/s11192-015-1583-9
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    References listed on IDEAS

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    2. Thelwall, Mike, 2018. "Microsoft Academic automatic document searches: Accuracy for journal articles and suitability for citation analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 1-9.
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    5. Franceschini, Fiorenzo & Maisano, Domenico & Mastrogiacomo, Luca, 2016. "The museum of errors/horrors in Scopus," Journal of Informetrics, Elsevier, vol. 10(1), pages 174-182.
    6. Maulana Noveri, 2020. "Research Trends in Marketing Science Before COVID-19 Outbreak: A Literature Review," Management & Marketing, Sciendo, vol. 15(s1), pages 514-533, October.
    7. Franceschini, Fiorenzo & Maisano, Domenico & Mastrogiacomo, Luca, 2016. "Empirical analysis and classification of database errors in Scopus and Web of Science," Journal of Informetrics, Elsevier, vol. 10(4), pages 933-953.
    8. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    9. Philipp Korom, 2019. "A bibliometric visualization of the economics and sociology of wealth inequality: a world apart?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 849-868, March.
    10. Raminta Pranckutė, 2021. "Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World," Publications, MDPI, vol. 9(1), pages 1-59, March.

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