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Ages of cited references and growth of scientific knowledge: an explication of the gamma distribution in business and management disciplines

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  • Anthony G. Stacey

    (University of the Witwatersrand)

Abstract

The purpose of this study was to assess the gamma distribution as a model of the distribution of ages of cited references in corpora of scientific literature, and to derive inferences from the parameters of the distributions. The ages of cited references in 2867 articles published in 40 distinguished journals in the fields of accounting, economics, finance, management, marketing, operations and information systems, organisation behaviour and human resources were analysed. The distributions of ages of cited references in each subject area were fitted to gamma distributions with the parameters estimated using minimum distance estimation. In contrast to extant literature, it is shown for all subject areas in the study that the goodness-of-fit statistics for gamma distributions were superior to those for lognormal distributions. The rate of growth of knowledge and the temporal profile of the growth of knowledge are derived from the parameters of the gamma distributions and differentiate the subject areas. Longitudinal analysis demonstrates that the gamma distribution models are stable but illustrate the evolution of specific corpora of literature over time. The gamma distribution parameters and derived metrics can be applied diagnostically and descriptively to characterise corpora of literature, or prospectively to set norms, expectations and criteria for new research. The results have implications for future bibliometric studies, authors, editors, reviewers, and knowledge researchers. Opportunities for further research and verification of prior research are created from this novel bibliometric approach.

Suggested Citation

  • Anthony G. Stacey, 2021. "Ages of cited references and growth of scientific knowledge: an explication of the gamma distribution in business and management disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 619-640, January.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:1:d:10.1007_s11192-020-03761-6
    DOI: 10.1007/s11192-020-03761-6
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    References listed on IDEAS

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    1. L. Egghe, 1997. "Price index and its relation to the mean and median reference age," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 48(6), pages 564-573, June.
    2. Ivan Jarić & Jelena Knežević-Jarić & Mirjana Lenhardt, 2014. "Relative age of references as a tool to identify emerging research fields with an application to the field of ecology and environmental sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 519-529, August.
    3. Marc Bertin & Iana Atanassova & Yves Gingras & Vincent Larivière, 2016. "The invariant distribution of references in scientific articles," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(1), pages 164-177, January.
    4. Pan, Raj K. & Petersen, Alexander M. & Pammolli, Fabio & Fortunato, Santo, 2018. "The memory of science: Inflation, myopia, and the knowledge network," Journal of Informetrics, Elsevier, vol. 12(3), pages 656-678.
    5. Stacey, Anthony G, 2020. "Robust parameterisation of ages of references in published research," Journal of Informetrics, Elsevier, vol. 14(3).
    6. Yin, Yian & Wang, Dashun, 2017. "The time dimension of science: Connecting the past to the future," Journal of Informetrics, Elsevier, vol. 11(2), pages 608-621.
    7. Daniel S. Hamermesh, 2018. "Citations in Economics: Measurement, Uses, and Impacts," Journal of Economic Literature, American Economic Association, vol. 56(1), pages 115-156, March.
    8. Thijs Pollman, 2000. "Forgetting and the Ageing of Scientific Publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 47(1), pages 43-54, January.
    9. Tadeusz K. Krauze & Claude Hillinger, 1971. "Citations, references and the growth of scientific literature: A model of dynamic interaction," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 22(5), pages 333-336, September.
    10. Quentin L. Burrell, 2002. "Modelling citation age data: Simple graphical methods from reliability theory," Scientometrics, Springer;Akadémiai Kiadó, vol. 55(2), pages 273-285, August.
    11. Quentin L. Burrel, 2001. "Stochastic modelling of the first-citation distribution," Scientometrics, Springer;Akadémiai Kiadó, vol. 52(1), pages 3-12, September.
    12. Mingfeng Lin & Henry C. Lucas & Galit Shmueli, 2013. "Research Commentary ---Too Big to Fail: Large Samples and the p -Value Problem," Information Systems Research, INFORMS, vol. 24(4), pages 906-917, December.
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