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.