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Review time in peer review: quantitative analysis and modelling of editorial workflows

Author

Listed:
  • Maciej J. Mrowinski

    (Warsaw University of Technology)

  • Agata Fronczak

    (Warsaw University of Technology)

  • Piotr Fronczak

    (Warsaw University of Technology)

  • Olgica Nedic

    (University of Belgrade)

  • Marcel Ausloos

    (University of Leicester
    Royal Netherlands Academy of Arts and Sciences (NKVA)
    Group of Researchers for Applications of Physics in Economy and Sociology (GRAPES))

Abstract

In this paper, we undertake a data-driven theoretical investigation of editorial workflows. We analyse a dataset containing information about 58 papers submitted to the Biochemistry and Biotechnology section of the Journal of the Serbian Chemical Society. We separate the peer review process into stages that each paper has to go through and introduce the notion of completion rate - the probability that an invitation sent to a potential reviewer will result in a finished review. Using empirical transition probabilities and probability distributions of the duration of each stage we create a directed weighted network, the analysis of which allows us to obtain the theoretical probability distributions of review time for different classes of reviewers. These theoretical distributions underlie our numerical simulations of different editorial strategies. Through these simulations, we test the impact of some modifications of the editorial policy on the efficiency of the whole review process. We discover that the distribution of review time is similar for all classes of reviewers, and that the completion rate of reviewers known personally by the editor is very high, which means that they are much more likely to answer the invitation and finish the review than other reviewers. Thus, the completion rate is the key factor that determines the efficiency of each editorial policy. Our results may be of great importance for editors and act as a guide in determining the optimal number of reviewers.

Suggested Citation

  • Maciej J. Mrowinski & Agata Fronczak & Piotr Fronczak & Olgica Nedic & Marcel Ausloos, 2016. "Review time in peer review: quantitative analysis and modelling of editorial workflows," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 271-286, April.
    • Handle: RePEc:spr:scient:v:107:y:2016:i:1:d:10.1007_s11192-016-1871-z
    DOI: 10.1007/s11192-016-1871-z
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    References listed on IDEAS

    as
    1. Flaminio Squazzoni & Károly Takács, 2011. "Social Simulation That 'Peers into Peer Review'," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 14(4), pages 1-3.
    2. Adrian Mulligan & Louise Hall & Ellen Raphael, 2013. "Peer review in a changing world: An international study measuring the attitudes of researchers," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 132-161, January.
    3. Virgina Trimble & Jose A. Ceja, 2011. "Are American astrophysics papers accepted more quickly than others?," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 281-289, October.
    4. Martijn Arns, 2014. "Open access is tiring out peer reviewers," Nature, Nature, vol. 515(7528), pages 467-467, November.
    5. Adrian Mulligan & Louise Hall & Ellen Raphael, 2013. "Peer review in a changing world: An international study measuring the attitudes of researchers," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(1), pages 132-161, January.
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    Cited by:

    1. Sultan Orazbayev, 2017. "Sequential order as an extraneous factor in editorial decision," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1573-1592, December.
    2. Ausloos, Marcel & Nedic, Olgica & Dekanski, Aleksandar & Mrowinski, Maciej J. & Fronczak, Piotr & Fronczak, Agata, 2017. "Day of the week effect in paper submission/acceptance/rejection to/in/by peer review journals. II. An ARCH econometric-like modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 462-474.
    3. Thomas Feliciani & Junwen Luo & Lai Ma & Pablo Lucas & Flaminio Squazzoni & Ana Marušić & Kalpana Shankar, 2019. "A scoping review of simulation models of peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 555-594, October.
    4. Maciej J Mrowinski & Piotr Fronczak & Agata Fronczak & Marcel Ausloos & Olgica Nedic, 2017. "Artificial intelligence in peer review: How can evolutionary computation support journal editors?," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-11, September.
    5. Sahana Roy Chowdhury, 2016. "When do referees shirk in a peer review process?," Economics and Business Letters, Oviedo University Press, vol. 5(2), pages 45-49.
    6. Xie, Yundong & Wu, Qiang & Wang, Yezhu & Hou, Li & Liu, Yuanyuan, 2024. "Does the handling time of scientific papers relate to their academic impact and social attention? Evidence from Nature, Science, and PNAS," Journal of Informetrics, Elsevier, vol. 18(2).
    7. Besim Bilalli & Rana Faisal Munir & Alberto Abelló, 2021. "A framework for assessing the peer review duration of journals: case study in computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 545-563, January.
    8. Maciej J. Mrowinski & Agata Fronczak & Piotr Fronczak & Olgica Nedic & Aleksandar Dekanski, 2020. "The hurdles of academic publishing from the perspective of journal editors: a case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 115-133, October.

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