Understanding the structure, characteristics, and future of collective intelligence using local and global bibliometric analyses

• Published in: Technological forecasting & social change 2022-05, Vol.178, p.121561, Article 121561
• Main Authors: Calof, Jonathan, Søilen, Klaus Solberg, Klavans, Richard, Abdulkader, Bisan, Moudni, Ismail El
• Format: Article
• Language: English
• Subjects: Bibliometric analysis; Bibliometrics; Collective intelligence; Community; Global local relationship; Intelligence; Intelligence (information); Research communities
• ISSN: 0040-1625; 1873-5509; 1873-5509
• DOI: 10.1016/j.techfore.2022.121561

•The most prolific authors in the field of “collective intelligence”.•How the topic evolves.•Using data in the SCOPUS database we have identified the most extensive research communities that deal with this topic.•Support for the Kuhnian idea of research communities as a useful concept in bibliometric analysis.•The complexity of the birth and the sustainability of multidisciplinary research areas such as “collective intelligence”.•That the greatest increases in publication share were for the discipline of business management and provide a basis for claiming that CI is shifting from a more theoretical base (driven by math and computer science) to diverse areas of application (driven by social science).•What this means for authors’ research strategy and management practitioners.•Both the local and global bibliometric approaches are starting to address fundamental issues of prediction.•Future research. “Collective Intelligence” has been a popular area of research for more than a decade. We apply two different analytical approaches (local and global bibliometric analysis) to describe how this literature is organized and how it has evolved. A local approach focuses on the 3,138 articles indexed in the Scopus database where ‘collective intelligence’ is in the title, abstract, or keyword. A global approach reclassifies all of the Scopus documents into research communities using all (1.28 billion) citations in the database and proceeds to identify which research communities are populated by the 3,138 Collective Intelligence (CI) articles. These two approaches provide significantly different perspectives on how CI is structured, who the leaders of the field are, and how it is evolving. A synthesis of these two perspectives provides ideas for those who wish to contribute to the collective intelligence field. Our findings support the Kuhnian idea of research communities as a useful concept in bibliometric analysis.