Towards Global Academic Performance Rankings: A Dynamic and Integrated Decision Support System Based on Scientometric Indicators in Different Databases

This study aims to propose a decision support system based on multi-criteria decision-making (MCDM) methodologies in order to reach individual and global-scale academic performance, which is a neglected subject. Unlike previous classical applications and past studies, in this study, different science indicators (citation counts, article counts, and field-based impact) taken from different databases (Scopus, Web of Science, InCites, Google Scholar) were combined, and objective weights were assigned to each criterion. These indicators, weighted with the entropy method, were analyzed with CRADIS and other alternative methods. The analysis results showed that the Q1 article count and field-based impact scores were of high importance, whereas Google Scholar citations had lower weight. In accordance with the recommendation of the Leiden manifesto, which had a great impact on the academic community, to take into account multi-indicator and being field-based, the system proposed in this study also allows for the dynamic (updatable) and comprehensive evaluation of individual researcher performance. Compared to one-sided and limited performance measurements in literature or applications, this study fills a serious gap. Moreover, this system will help the parties to make accurate and updatable strategic decisions.