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Exploring the dynamics of journal citations: Modelling with s-curves

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  • J Mingers

    (University of Kent)

Abstract

This paper reports on an exploratory analysis of the behaviour of citations for management science papers over a 14-year period. Citations often display s-curve type behaviour: beginning slowly, rising in response to previous citations, and then declining as the material becomes obsolete. Within the context of citation research such functions are known as obsolescence functions. The paper addresses three specific questions: (i) can collections of papers from the same journal all be modelled using the same obsolescence function? (ii) Can we identify specific patterns of behaviour such as ‘sleeping beauties’ or ‘shooting stars’? (iii) Can we predict the number of future citations from the pattern of behaviour in the first few years? Over 600 papers published in six leading management science journals are analysed using a variety of s-curves.

Suggested Citation

  • J Mingers, 2008. "Exploring the dynamics of journal citations: Modelling with s-curves," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(8), pages 1013-1025, August.
    • Handle: RePEc:pal:jorsoc:v:59:y:2008:i:8:d:10.1057_palgrave.jors.2602428
    DOI: 10.1057/palgrave.jors.2602428
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    2. Kyle Siler, 2013. "Citation choice and innovation in science studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 385-415, April.
    3. J Mingers, 2009. "Measuring the research contribution of management academics using the Hirsch-index," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1143-1153, September.
    4. Mingers, John & Leydesdorff, Loet, 2015. "A review of theory and practice in scientometrics," European Journal of Operational Research, Elsevier, vol. 246(1), pages 1-19.
    5. Osterloh, Margit & Frey, Bruno S., 2020. "How to avoid borrowed plumes in academia," Research Policy, Elsevier, vol. 49(1).
    6. Giovanni Abramo & Ciriaco Andrea D’Angelo, 2021. "A bibliometric methodology to unveil territorial inequities in the scientific wealth to combat COVID-19," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6601-6624, August.
    7. Stegehuis, Clara & Litvak, Nelly & Waltman, Ludo, 2015. "Predicting the long-term citation impact of recent publications," Journal of Informetrics, Elsevier, vol. 9(3), pages 642-657.
    8. Guoliang Lyu & Ganwei Shi, 2019. "On an approach to boosting a journal’s citation potential," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1387-1409, September.
    9. Mingers, John & Xu, Fang, 2010. "The drivers of citations in management science journals," European Journal of Operational Research, Elsevier, vol. 205(2), pages 422-430, September.
    10. Merigó, José M. & Yang, Jian-Bo, 2017. "A bibliometric analysis of operations research and management science," Omega, Elsevier, vol. 73(C), pages 37-48.
    11. Thelwall, Mike & Nevill, Tamara, 2018. "Could scientists use Altmetric.com scores to predict longer term citation counts?," Journal of Informetrics, Elsevier, vol. 12(1), pages 237-248.
    12. 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.
    13. Battiston, Pietro & Sacco, Pier Luigi & Stanca, Luca, 2022. "Cover effects on citations uncovered: Evidence from Nature," Journal of Informetrics, Elsevier, vol. 16(2).
    14. Ho Fai Chan & Malka Guillot & Lionel Page & Benno Torgler, 2015. "The inner quality of an article: Will time tell?," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 19-41, July.
    15. Abramo, Giovanni & D’Angelo, Ciriaco Andrea & Felici, Giovanni, 2019. "Predicting publication long-term impact through a combination of early citations and journal impact factor," Journal of Informetrics, Elsevier, vol. 13(1), pages 32-49.
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