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A Markov-switching approach to the study of citations in academic journals

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  • Delbianco, Fernando
  • Fioriti, Andrés
  • Hernandez-Chanto, Allan
  • Tohmé, Fernando

Abstract

In this paper, we introduce a Markovian approach to study the stability and growth of citations in academic journals by featuring a regime-switching analysis. We characterize the regime structure exhibited by the series of citations available in the Web of Science database and determine the series’ transition probabilities across regimes. Our main result shows that for most of the journals studied, the series of citations exhibit a stable growth, remaining majorly in a dominant regime identified as one of low or high number of citations.

Suggested Citation

  • Delbianco, Fernando & Fioriti, Andrés & Hernandez-Chanto, Allan & Tohmé, Fernando, 2020. "A Markov-switching approach to the study of citations in academic journals," Journal of Informetrics, Elsevier, vol. 14(4).
    • Handle: RePEc:eee:infome:v:14:y:2020:i:4:s1751157720301851
    DOI: 10.1016/j.joi.2020.101081
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    References listed on IDEAS

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    Cited by:

    1. Fernando Delbianco & Andrés Fioriti & Fernando Tohmé, 2023. "Markov chains, eigenvalues and the stability of economic growth processes," Empirical Economics, Springer, vol. 64(3), pages 1347-1373, March.
    2. Mrowinski, Maciej J. & Gagolewski, Marek & Siudem, Grzegorz, 2022. "Accidentality in journal citation patterns," Journal of Informetrics, Elsevier, vol. 16(4).
    3. Agnieszka Geras & Grzegorz Siudem & Marek Gagolewski, 2022. "Time to vote: Temporal clustering of user activity on Stack Overflow," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(12), pages 1681-1691, December.

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