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Bibliometric indicators versus expert opinion in assessing research performance

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  • Michael E. D. Koenig

Abstract

This study compares bibliometric indicators versus expert judgment as indicators of the research performance of major pharmaceutical companies, a context which may be uniquely capable of permitting such a comparison. For each company, a refined composite research output score was calculated based on that company's drug output (1965–1976). These research production scores, normalized by research budget as an indicator of research organizational size, produced an indicator of research productivity, an output/input ratio. The best and most consistent predictors of drug research success in general were the number of clinical articles, and in particular highly cited clinical articles—both their absolute value and their proportionate occurrence among publications. In general, there was a slight but consistent tendency for bibliometric indicators to perform better than expert judgments in predicting research performance as measured by a third and independent indicator of research output. This consistent trend was composed of three aspects. First, the subject‐specific bibliometric indicators correlated more highly with the drug output performance measures than did general bibliometric measures or the expert judgments. Second, when the drug output measures were subjected to a regression analysis, the expert judgment variables were conspicuous by their absence, even in secondary or tertiary positions. Third, the expert judgment variables appeared to be very predictable from the bibliometric measures, while no such converse relationship existed.

Suggested Citation

  • Michael E. D. Koenig, 1983. "Bibliometric indicators versus expert opinion in assessing research performance," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 34(2), pages 136-145, March.
  • Handle: RePEc:bla:jamest:v:34:y:1983:i:2:p:136-145
    DOI: 10.1002/asi.4630340207
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    Cited by:

    1. Soo-Ryun Cho, 2008. "New evaluation indexes for articles and authors’ academic achievements based on Open Access Resources," Scientometrics, Springer;Akadémiai Kiadó, vol. 77(1), pages 91-112, October.
    2. Hu, Xiaojun & Rousseau, Ronald, 2016. "Scientific influence is not always visible: The phenomenon of under-cited influential publications," Journal of Informetrics, Elsevier, vol. 10(4), pages 1079-1091.
    3. Martin, Ben R., 2012. "The evolution of science policy and innovation studies," Research Policy, Elsevier, vol. 41(7), pages 1219-1239.
    4. Alberto Baccini & Giuseppe De Nicolao, 2016. "Do they agree? Bibliometric evaluation versus informed peer review in the Italian research assessment exercise," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1651-1671, September.
    5. Chi-Yen Yin & Hsiao-Hsin Chang, 2022. "What Is the Link between Strategic Innovation and Organizational Sustainability? Historical Review and Bibliometric Analytics," Sustainability, MDPI, vol. 14(11), pages 1-23, June.
    6. Siluo Yang & Feng Ma & Yanhui Song & Junping Qiu, 2010. "A longitudinal analysis of citation distribution breadth for Chinese scholars," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(3), pages 755-765, December.
    7. Jeppe Nicolaisen & Tove Faber Frandsen, 2019. "Zero impact: a large-scale study of uncitedness," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 1227-1254, May.
    8. Peter Vinkler, 2010. "The πv-index: a new indicator to characterize the impact of journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(3), pages 461-475, March.
    9. Hu, Zewen & Wu, Yishan, 2014. "Regularity in the time-dependent distribution of the percentage of never-cited papers: An empirical pilot study based on the six journals," Journal of Informetrics, Elsevier, vol. 8(1), pages 136-146.

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