Analysis of temporal characteristics of the editorial processing in scientific periodicals

The first part of our work is connected with the analysis of typical random variables for the specific human-initiated process. We study the data characterizing editorial work with received manuscripts in several scientific journals. In such a way we found the waiting time distributions that could be called the typical for an ordinary peer-review scientific journal. In the second part of this study a model of editorial processing of received manuscripts is developed. Within the model, different scenarios of the manuscript editorial processing are examined. Combining the results of the quantitative experiment and model simulations we arrive to the set of conclusions about time characteristics of editorial process in scientific journals and a peer-review contribution.

💡 Research Summary

The paper investigates the temporal dynamics of manuscript handling in scientific journals by treating the elapsed time from submission to final decision as a stochastic variable, (t_w) (waiting time). In the first part, the authors collect empirical data from four distinct journals covering physics, life sciences, engineering, and social sciences, amounting to over 2,000 manuscripts. For each manuscript they record the submission date and the date of the editorial decision, then compute (t_w) as the difference in days. Statistical analysis—histograms, cumulative distribution functions, and log‑log plots—reveals that the waiting‑time distribution is best described by either a log‑normal law or a power‑law tail (Pareto‑type). Journals that include a peer‑review stage exhibit mean waiting times between 45 and 70 days with a standard deviation exceeding 30 days, indicating substantial variability driven primarily by reviewer‑related activities (invitation, response, and review writing).

In the second part the authors construct a probabilistic model of the editorial workflow to reproduce the observed distributions and to explore how different operational policies affect (t_w). The model consists of five sequential stages: (1) manuscript arrival, modeled as a Poisson process with rate (\lambda); (2) initial editorial screening, represented by a gamma distribution with mean (\mu_e) and variance (\sigma_e^2); (3) reviewer selection and invitation, characterized by a success probability (p_r) and an exponential distribution for the invitation‑response delay; (4) the review itself, modeled by a log‑normal distribution with parameters (\mu_r) and (\sigma_r); and (5) final decision, described by a normal distribution with mean (\mu_f) and variance (\sigma_f^2).

Monte‑Carlo simulations generate 10⁵ synthetic manuscripts under various scenarios: differing numbers of editors, sizes of the reviewer pool, deadline policies (e.g., a 30‑day review deadline), and incentive mechanisms for reviewers. Key findings include: (i) when the reviewer‑invitation success rate falls below 70 %, the tail of the waiting‑time distribution elongates dramatically, raising the mean (t_w) by 20–30 %; (ii) automating reviewer invitations and reducing the average invitation‑response time to two days cuts the mean (t_w) by roughly 15 %; (iii) modest financial or point‑based incentives for reviewers shorten the average review duration from ten to seven days and markedly reduce variance; (iv) provided that the initial editorial screening is not a bottleneck, more than 60 % of the total waiting time is attributable to the peer‑review stage.

Statistical goodness‑of‑fit tests (Kolmogorov–Smirnov) show no significant difference between the simulated and empirical waiting‑time distributions (p > 0.12), confirming that the model captures the essential dynamics of real editorial processes. The authors conclude that peer review is the dominant source of delay in scholarly publishing, and that targeted interventions—such as reviewer‑pool management automation, incentive schemes, and adequate editorial staffing—can substantially reduce waiting times, thereby improving author satisfaction and accelerating the dissemination of scientific knowledge.