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The relation between Eigenfactor, audience factor, and influence weight

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  • Ludo Waltman
  • Nees Jan van Eck

Abstract

We present a theoretical and empirical analysis of a number of bibliometric indicators of journal performance. We focus on three indicators in particular: the Eigenfactor indicator, the audience factor, and the influence weight indicator. Our main finding is that the last two indicators can be regarded as a kind of special case of the first indicator. We also find that the three indicators can be nicely characterized in terms of two properties. We refer to these properties as the property of insensitivity to field differences and the property of insensitivity to insignificant journals. The empirical results that we present illustrate our theoretical findings. We also show empirically that the differences between various indicators of journal performance are quite substantial.

Suggested Citation

  • Ludo Waltman & Nees Jan van Eck, 2010. "The relation between Eigenfactor, audience factor, and influence weight," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(7), pages 1476-1486, July.
    • Handle: RePEc:bla:jamist:v:61:y:2010:i:7:p:1476-1486
    DOI: 10.1002/asi.21354
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    Cited by:

    1. Damien Besancenot & Abdelghani Maddi, 2019. "Should citations be weighted to assess the influence of an academic article?," Economics Bulletin, AccessEcon, vol. 435(1), pages 435-445.
    2. P. Dorta-González & M. I. Dorta-González, 2013. "Comparing journals from different fields of science and social science through a JCR subject categories normalized impact factor," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(2), pages 645-672, May.
    3. Antonin Mac'e, 2017. "The Limits of Citation Counts," Papers 1711.02695, arXiv.org, revised Sep 2023.
    4. Gabriel-Alexandru Vîiu & Mihai Păunescu, 2021. "The citation impact of articles from which authors gained monetary rewards based on journal metrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4941-4974, June.
    5. Carmen Herrero & Antonio Villar, 2013. "On the Comparison of Group Performance with Categorical Data," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-7, December.
    6. Tahamtan, Iman & Bornmann, Lutz, 2018. "Creativity in science and the link to cited references: Is the creative potential of papers reflected in their cited references?," Journal of Informetrics, Elsevier, vol. 12(3), pages 906-930.
    7. Waltman, Ludo & van Eck, Nees Jan & van Leeuwen, Thed N. & Visser, Martijn S., 2013. "Some modifications to the SNIP journal impact indicator," Journal of Informetrics, Elsevier, vol. 7(2), pages 272-285.
    8. Liwei Cai & Jiahao Tian & Jiaying Liu & Xiaomei Bai & Ivan Lee & Xiangjie Kong & Feng Xia, 2019. "Scholarly impact assessment: a survey of citation weighting solutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(2), pages 453-478, February.
    9. Tom Z. J. Fu & Qianqian Song & Dah Ming Chiu, 2014. "The academic social network," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 203-239, October.
    10. Walters, William H., 2014. "Do Article Influence scores overestimate the citation impact of social science journals in subfields that are related to higher-impact natural science disciplines?," Journal of Informetrics, Elsevier, vol. 8(2), pages 421-430.
    11. Waltman, Ludo & van Eck, Nees Jan, 2013. "A systematic empirical comparison of different approaches for normalizing citation impact indicators," Journal of Informetrics, Elsevier, vol. 7(4), pages 833-849.
    12. J. M. Calabuig & A. Ferrer-Sapena & E. A. Sánchez-Pérez, 2016. "Vector-valued impact measures and generation of specific indexes for research assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1425-1443, September.
    13. Vanclay, Jerome K., 2012. "Publication patterns of award-winning forest scientists and implications for the Australian ERA journal ranking," Journal of Informetrics, Elsevier, vol. 6(1), pages 19-26.
    14. Vaccario, Giacomo & Medo, Matúš & Wider, Nicolas & Mariani, Manuel Sebastian, 2017. "Quantifying and suppressing ranking bias in a large citation network," Journal of Informetrics, Elsevier, vol. 11(3), pages 766-782.

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