Citations optimal growth path: A tool to analyze sensitivity to citations of h-like indexes

Meiqian Chen, Zhaoxia Guo, Yucheng Dong, Francisco Chiclana and Enrique Herrera-Viedma

Journal of Informetrics, 2021, vol. 15, issue 4

Abstract: The h-index is a citation-based metric with extensive applications, and several variants have been developed to complement it. This study formulates the optimal growth path (OGP) models of selected h-like indexes, that is, the h-index, g-index, A-index, R-index, and e-index, and analyzes their OGP-allocated strategies of citations. It is argued that the OGP is a useful tool for analyzing the sensitivity of these h-like indexes to citations. Through simulation experiments with both real and random data, the sensitivity of the selected h-like indexes to citations is compared. Interestingly, it is found that the h-index performs the worst according to the OGP. Further, it is shown that combining the h-index with the A-index decreases the sensitivity to the citations of the h-index. In summary, this study provides new insights into how to evaluate scientific outputs based on h-like indexes.

Keywords: bibliometrics; sensitivity analysis; optimal growth path; h-index (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:15:y:2021:i:4:s1751157721000869

DOI: 10.1016/j.joi.2021.101215

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