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The relationship between web usage and citation statistics for electronics and information technology articles

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  • Anthony Breitzman

    (1790 Analytics, LLC)

Abstract

Forward citations are widely recognized as a useful measure of the impact of scientific papers. However, an inherent characteristic of forward citations is that they take time to accumulate. This makes them valuable for retrospective impact evaluations, but less helpful for prospective forecasting exercises. To overcome this, it would be desirable to have an indicator that forecasts future citations close to the time a scientific paper is published. In this study, we discuss scientific paper usage (full-text PDF downloads and HTML views) as a useful indicator that precedes citations. This paper represents the first large scale study of usage for IEEE Xplore papers, which account for about one-third of all Electrical Engineering and Information Technology papers. We also resolve earlier contradictory results where some studies showed high correlation between usage and citations, while others highlighted low overlap between individual highly cited papers and high usage papers. We also show that usage significantly precedes citations, such that a usage indicator can predict the future citation impact of papers six months or less after publication. Finally, we develop a practical usage indicator that is relatively simple to calculate and easy to understand.

Suggested Citation

  • Anthony Breitzman, 2021. "The relationship between web usage and citation statistics for electronics and information technology articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2085-2105, March.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:3:d:10.1007_s11192-020-03851-5
    DOI: 10.1007/s11192-020-03851-5
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    References listed on IDEAS

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    1. Muhammad Salman Khan & Muhammad Younas, 2017. "Analyzing readers behavior in downloading articles from IEEE digital library: a study of two selected journals in the field of education," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1523-1537, March.
    2. Henk F. Moed, 2005. "Statistical relationships between downloads and citations at the level of individual documents within a single journal," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 56(10), pages 1088-1097, August.
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    5. Christian Schloegl & Juan Gorraiz, 2010. "Comparison of citation and usage indicators: the case of oncology journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(3), pages 567-580, March.
    6. Christian Schloegl & Juan Gorraiz, 2011. "Global usage versus global citation metrics: The case of pharmacology journals," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(1), pages 161-170, January.
    7. Tim Brody & Stevan Harnad & Leslie Carr, 2006. "Earlier Web usage statistics as predictors of later citation impact," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(8), pages 1060-1072, June.
    8. Vicente P. Guerrero-Bote & Félix Moya-Anegón, 2014. "Relationship between downloads and citations at journal and paper levels, and the influence of language," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1043-1065, November.
    9. Liwen Vaughan & Juan Tang & Rongbin Yang, 2017. "Investigating disciplinary differences in the relationships between citations and downloads," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1533-1545, June.
    10. Henk F. Moed & Gali Halevi, 2016. "On full text download and citation distributions in scientific-scholarly journals," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(2), pages 412-431, February.
    11. Juan Gorraiz & Christian Gumpenberger & Christian Schlögl, 2014. "Usage versus citation behaviours in four subject areas," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1077-1095, November.
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    Cited by:

    1. Jiang, Zhuoren & Lin, Tianqianjin & Huang, Cui, 2023. "Deep representation learning of scientific paper reveals its potential scholarly impact," Journal of Informetrics, Elsevier, vol. 17(1).
    2. János József Tóth & Gergő Háló & Manuel Goyanes, 2023. "Beyond views, productivity, and citations: measuring geopolitical differences of scientific impact in communication research," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5705-5729, October.

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