A multidimensional journal evaluation framework based on the Pareto‐dominated set measured by the Manhattan distance

Abstract Journal evaluation is a multifaceted issue, and multidimensional information cannot be conflated into one metric due to the inability of a single indicator to reflect the quality of a journal. The goal of this paper is to develop a multidimensional journal evaluation framework based on the Pareto‐dominated set through integrating information measured by the Manhattan distance related to article performance, academic communities, and publishing platforms. This paper identifies 29 related indexes to form a three‐dimensional (3D) journal evaluation framework with metrics involving stakeholders in journal publication. To reduce multicollinearity among related indexes, a factor analysis‐based entropy weight method is proposed to integrate the multidimensional information into five aggregated indicators and then transform them into a 3D‐weighted influence factor coordinate system. A journal evaluation framework is defined based on the Pareto‐dominated set of a journal in the 3D‐coordinate system measured by the Manhattan distance to assess journal impact. A case study has been implemented based on 124 journals selected from the “Statistics & Probability” category in the 2019 Journal Citation Report to demonstrate the validity of the proposed method.