A two-dimensional journal classification method based on output and input factors: perspectives from citation and authorship related indicators

Journal assessment indicators have been widely investigated to improve the journal ranking and classification. However, most of the previous studies mainly focused on citation related indicators, while the authorship side was rarely explored. This paper studies the mechanism between the citation and authorship related indicators and defines new concepts of output factor and input factor. A framework of two-dimensional journal classification method is developed by combining the output and input factors together through comprehensive weighting methods. A two-dimensional journal performance value (TJPV) is defined to measure the performance of journals in a two-dimensional typology. A journal performance criterion is defined to divide the journals into four different levels according to the TJPV equipotential arcs. The journals in the plane are also classified into three groups, i.e., Input Factor-Oriented ones, Balanced ones, and Output Factor-Oriented ones. A balance criterion is developed to determine whether these journals are uniformly distributed or not. The ranking method based on TJPV performance criterion is only applicable for uniformly distributed journals. Thus, a Pareto non-dominated set-based sorting method is proposed for addressing the scenario of unevenly distributed journals. To demonstrate the effectiveness and validity of the proposed method, the data of output and input factors from 84 initially selected journals in Operation Research & Management Science category are collected to conduct a case study. New insights are obtained which are helpful to guiding the development of future journal classification criteria.

Automated summary

This paper investigates the mechanism between citation and authorship related indicators in journal assessment, introducing new concepts and a two-dimensional classification method to classify journals into different levels and balance them accordingly. A case study with data from 84 selected journals validates the effectiveness of the proposed method in guiding future journal classification criteria.