How to conduct a bibliometric content analysis: Guidelines and contributions of content co-occurrence or co-word literature reviews

Abstract

Literature reviews summarize existing literature, uncover research gaps, and offer future research directions, thus aiding in theoretical and methodological development. Informetric research including bibliometric, scientometric, webometric, cybermetric, patentometric, and altmetric methods are becoming increasingly prevalent in conducting literature review studies. Looking at the common informetric literature review methods—citation, co-citation, co-author, bibliographic coupling, and content co-occurrence analyses, this study aims to serve as a guide in using content co-occurrence also known as co-word analysis to conduct literature reviews. This study outlines a variety of informetric research methods and how they are utilized to conduct review and evidence-based conceptual studies. In addition to the analyses, the study highlights different informetric software packages like Bibliometrix, Biblioshiny, Leximancer, NVivo, and CiteSpace including their comparison. The study further discusses contributions of algorithm-based content analyses including offering taxonomies, definitions, classifications, typologies, comparisons, and theoretical development to constitute integrative literature reviews. Finally, this study offers step-by-step guidelines for conducting a review study using VOSviewer content co-occurrence analysis while providing a systems view of informetric research in social science. The study also notes the emergence of generative artificial intelligence (AI) like Open AI's ChatGPT, Google's Bard, Elicit, Scite, Research Rabbit, and ChatPDF among others, and its potential in contributing to the literature review methods and, as such, being an interesting direction for future research.