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

Maciej J. Mrowinski (), Agata Fronczak (), Piotr Fronczak (), Olgica Nedic () and Marcel Ausloos
Additional contact information
Maciej J. Mrowinski: Warsaw University of Technology
Agata Fronczak: Warsaw University of Technology
Piotr Fronczak: Warsaw University of Technology
Olgica Nedic: University of Belgrade

Scientometrics, 2016, vol. 107, issue 1, No 15, 286 pages

Abstract: 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.

Keywords: Peer review; Editorial process; Weighted directed graph (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-016-1871-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:107:y:2016:i:1:d:10.1007_s11192-016-1871-z

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-016-1871-z

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().