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Quantifying Scholarly Impact: IQp Versus the Hirsch h

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  • John Antonakis
  • Rafael Lalive

Abstract

Hirsch's (2005) h index of scholarly output has generated substantial interest and wide acceptance because of its apparent ability to quantify scholarly impact simply and accurately. We show that the excitement surrounding h is premature for three reasons: h stagnates with increasing scientific age; it is highly dependent on publication quantity; and it is highly dependent on field‐specific citation rates. Thus, it is not useful for comparing scholars across disciplines. We propose the scholarly “index of quality and productivity” (IQp) as an alternative to h. The new index takes into account a scholar's total impact and also corrects for field‐specific citation rates, scholarly productivity, and scientific age. The IQp accurately predicts group membership on a common metric, as tested on a sample of 80 scholars from three populations: (a) Nobel winners in physics (n = 10), chemistry (n = 10), medicine (n = 10), and economics (n = 10), and towering psychologists (n = 10); and scholars who have made more modest contributions to science including randomly selected (b) fellows (n = 15) and (c) members (n = 15) of the Society of Industrial and Organizational Psychology. The IQp also correlates better with expert ratings of greatness than does the h index.

Suggested Citation

  • John Antonakis & Rafael Lalive, 2008. "Quantifying Scholarly Impact: IQp Versus the Hirsch h," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(6), pages 956-969, April.
  • Handle: RePEc:bla:jamist:v:59:y:2008:i:6:p:956-969
    DOI: 10.1002/asi.20802
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    Cited by:

    1. Fiorenzo Franceschini & Domenico Maisano, 2011. "Proposals for evaluating the regularity of a scientist’s research output," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 279-295, July.
    2. Anne-Wil Harzing & Satu Alakangas & David Adams, 2014. "hIa: an individual annual h-index to accommodate disciplinary and career length differences," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(3), pages 811-821, June.
    3. Fiorenzo Franceschini & Domenico Maisano, 2011. "Bibliometric positioning of scientific manufacturing journals: a comparative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(2), pages 463-485, February.
    4. Asma Hammami & Nabil Semmar, 2022. "The simplex simulation as a tool to reveal publication strategies and citation factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 319-350, January.
    5. Eleni Fragkiadaki & Georgios Evangelidis, 2014. "Review of the indirect citations paradigm: theory and practice of the assessment of papers, authors and journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 261-288, May.
    6. Bornmann, Lutz & Mutz, Rüdiger & Hug, Sven E. & Daniel, Hans-Dieter, 2011. "A multilevel meta-analysis of studies reporting correlations between the h index and 37 different h index variants," Journal of Informetrics, Elsevier, vol. 5(3), pages 346-359.
    7. Fiorenzo Franceschini & Domenico Maisano & Anna Perotti & Andrea Proto, 2010. "Analysis of the ch-index: an indicator to evaluate the diffusion of scientific research output by citers," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 203-217, October.
    8. Brandão, Luana Carneiro & Soares de Mello, João Carlos Correia Baptista, 2019. "A multi-criteria approach to the h-index," European Journal of Operational Research, Elsevier, vol. 276(1), pages 357-363.
    9. Elisabeth Maria Schlagberger & Lutz Bornmann & Johann Bauer, 2016. "At what institutions did Nobel laureates do their prize-winning work? An analysis of biographical information on Nobel laureates from 1994 to 2014," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 723-767, November.
    10. James C. Ryan, 2016. "A validation of the individual annual h-index (hIa): application of the hIa to a qualitatively and quantitatively different sample," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 577-590, October.
    11. Franceschini, Fiorenzo & Maisano, Domenico A., 2010. "Analysis of the Hirsch index's operational properties," European Journal of Operational Research, Elsevier, vol. 203(2), pages 494-504, June.
    12. Lorna Wildgaard & Jesper W. Schneider & Birger Larsen, 2014. "A review of the characteristics of 108 author-level bibliometric indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 125-158, October.
    13. Franceschini, Fiorenzo & Maisano, Domenico, 2010. "The Hirsch spectrum: A novel tool for analyzing scientific journals," Journal of Informetrics, Elsevier, vol. 4(1), pages 64-73.
    14. Siying Li & Huawei Shen & Peng Bao & Xueqi Cheng, 2021. "$$h_u$$ h u -index: a unified index to quantify individuals across disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3209-3226, April.
    15. J. E. Hirsch, 2010. "An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(3), pages 741-754, December.
    16. Chen, Chaomei & Chen, Yue & Horowitz, Mark & Hou, Haiyan & Liu, Zeyuan & Pellegrino, Donald, 2009. "Towards an explanatory and computational theory of scientific discovery," Journal of Informetrics, Elsevier, vol. 3(3), pages 191-209.

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