Open Access

The need to develop tailored tools for improving the quality of thematic bibliometric analyses: Evidence from papers published in Sustainability and Scientometrics


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Purpose

The aim of this article is to explore up to seven parameters related to the methodological quality and reproducibility of thematic bibliometric research published in the two most productive journals in bibliometrics, Sustainability (a journal outside the discipline) and Scientometrics, the flagship journal in the field.

Design/methodology/approach

The study identifies the need for developing tailored tools for improving the quality of thematic bibliometric analyses, and presents a framework that can guide the development of such tools. A total of 508 papers are analysed, 77% of Sustainability, and 23% published in Scientometrics, for the 2019-2021 period.

Findings

An average of 2.6 shortcomings per paper was found for the whole sample, with an almost identical number of flaws in both journals. Sustainability has more flaws than Scientometrics in four of the seven parameters studied, while Scientometrics has more shortcomings in the remaining three variables.

Research limitations

The first limitation of this work is that it is a study of two scientific journals, so the results cannot be directly extrapolated to the set of thematic bibliometric analyses published in journals from all fields.

Practical implications

We propose the adoption of protocols, guidelines, and other similar tools, adapted to bibliometric practice, which could increase the thoroughness, transparency, and reproducibility of this type of research.

Originality/value

These results show considerable room for improvement in terms of the adequate use and breakdown of methodological procedures in thematic bibliometric research, both in journals in the Information Science area and journals outside the discipline.

eISSN:
2543-683X
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Information Technology, Project Management, Databases and Data Mining