Performance Behavior Patterns in Author-Level Metrics: A Disciplinary Comparison of Google Scholar Citations, ResearchGate, and ImpactStory

The main goal of this work is to verify the existence of diverse behavior patterns in academic production and impact, both among members of the same scientific community (inter-author variability) and for a single author (intra-author variability), as well as to find out whether this fact affects the correlation among author-level metrics (AutLMs) in disciplinary studies. To do this, two samples are examined: a general sample (members of a discipline, in this case Bibliometrics; n = 315 authors), and a specific sample (only one author; n = 119 publications). Four AutLMs (Total Citations, Recent Citations, Reads, and Online mentions) were extracted from three platforms (Google Scholar Citations, ResearchGate, and ImpactStory). The analysis of the general sample reveals the existence of different performance patterns, in the sense that there are groups of authors who perform prominently in some platforms, but exhibit a low impact in the others. The case study shows that the high performance in certain metrics and platforms is due to the coverage of document typologies, which is different in each platform (for example, Reads in working papers). It is concluded that the identification of the behavior pattern of each author (both at the inter-author and intra-author levels) is necessary to increase the precision and usefulness of disciplinary analyses that use AutLMs, and thus avoid masking effects.