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Future publication success in science is better predicted by traditional measures than by the h index

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  • Johannes Hönekopp

    (Northumbria University)

  • Julie Khan

    (Northumbria University)

Abstract

Although the use of bibliometric indicators for evaluations in science is becoming more and more ubiquitous, little is known about how future publication success can be predicted from past publication success. Here, we investigated how the post-2000 publication success of 85 researchers in oncology could be predicted from their previous publication record. Our main findings are: (i) Rates of past achievement were better predictors than measures of cumulative achievement. (ii) A combination of authors’ past productivity and the past citation rate of their average paper was most successful in predicting future publication success (R 2 ≈ 0.60). (iii) This combination of traditional bibliographic indicators clearly outperformed predictions based on the rate of the h index (R 2 between 0.37 and 0.52). We discuss implications of our findings for views on creativity and for science evaluation.

Suggested Citation

  • Johannes Hönekopp & Julie Khan, 2012. "Future publication success in science is better predicted by traditional measures than by the h index," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(3), pages 843-853, March.
    • Handle: RePEc:spr:scient:v:90:y:2012:i:3:d:10.1007_s11192-011-0551-2
    DOI: 10.1007/s11192-011-0551-2
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    Cited by:

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    3. Marcel Clermont & Johanna Krolak & Dirk Tunger, 2021. "Does the citation period have any effect on the informative value of selected citation indicators in research evaluations?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1019-1047, February.
    4. Giovanni Abramo & Ciriaco Andrea D’Angelo & Fulvio Viel, 2013. "The suitability of h and g indexes for measuring the research performance of institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 555-570, December.
    5. Danielle H. Lee, 2019. "Predicting the research performance of early career scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1481-1504, December.
    6. Dimitris Bertsimas & Erik Brynjolfsson & Shachar Reichman & John Silberholz, 2015. "OR Forum—Tenure Analytics: Models for Predicting Research Impact," Operations Research, INFORMS, vol. 63(6), pages 1246-1261, December.
    7. Jan Schulz, 2016. "Using Monte Carlo simulations to assess the impact of author name disambiguation quality on different bibliometric analyses," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1283-1298, June.

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