Table 2.

Interpretation of the different types of indicators related to performance analysis

Type of metric	Measure entity characteristic	Interpretation
Number of documents	Productivity (Cucari et al. 2022)	Characterization of the research quantity (Song et al. 2021)
Average citations per documents/average citations per year	Scientific impact (Cucari et al. 2022)	The amount of citations and average citations which are frequently linked with the quality and impact of scholars (Song et al. 2021; Tang et al. 2018)
Author’s keywords (DE)	Identify the existing research themes (Zhang et al. 2016)	Relates to a set list of keywords that authors use to convey what their research was about (Tripathi et al. 2018)
Keywords plus (ID)	Represent the knowledge base embodied in the analyzed collection and explore the different thematics developed into the research domain (van Meter et al. 2004)	This metric refers to the total number of keywords created by Scopus, based on the titles, keywords, and abstracts of publications examined (Tripathi et al. 2018)
Number of documents per author	Productivity of authors (Arias-Ciro 2020)	The quantity of research works produced by the authors (Song et al. 2021)
Number of authors per document ratio	Authorship pattern (Aria et al. 2020)	This ratio assesses if researchers tend to produce single- or co-authored works, and it can be also used to interpret the average size of research teams (Aria et al. 2020)
Co-authors per document ratio	Information on direct and indirect connections of each author (E. Y. Li et al. 2013)	The frequency of an author’s appearances in a collection of documents (Aria et al. 2020)
Collaboration index	Trend towards multiple authorships in a discipline (Karpagam et al. 2011)	The nature and magnitude of a collaboration (Ajiferuke et al. 1988; Subramanyam 1983)