Distinctive author ranking using DEA indexing

The productivity and impact of a researcher can be measured by considering the total number of articles authored by him/her and corresponding citations. Several techniques exist to evaluate the cumulative impact of the author’s scholarly output & performance by comparing publications to citations. However, all of them fail to rank each author uniquely, resulting in the same index value assigned to two or more authors, although they have diverse citation patterns. In some indexing, beyond a certain number of citations of a particular article, the subsequent citations do not add any value to the overall indexing. In this paper, a new indexing scheme, based on data envelopment analysis, is proposed which ensures the unique ranking by identifying the different index values of the authors who have even a minimal difference in the citation pattern. Furthermore, the proposed scheme ensures that every citation will have impact without any ceiling. The index is applied to a consistent data set having publications data of the last 40 years in the field of Computer Science. The outcome, when compared with the existing metrics, confirms that the proposed index provides more effective results by ranking authors distinctively. © 2020, Springer Nature B.V.