Investigating Knowledge Flows in Scientific Communities: The Potential of Bibliometric Methods.
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| Title: | Investigating Knowledge Flows in Scientific Communities: The Potential of Bibliometric Methods. |
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| Authors: | Aman, Valeria1 (AUTHOR) aman@dzhw.eu, Gläser, Jochen2,3 (AUTHOR) jochen.glaeser@tu-berlin.de |
| Source: | Minerva: A Review of Science, Learning & Policy. Mar2025, Vol. 63 Issue 1, p155-182. 28p. |
| Subject Terms: | *Bibliometrics, *Scientific knowledge, *Social media, *Qualitative research, Scientific community, Knowledge transfer |
| Abstract: | In their everyday work, scholars constantly acquire and transfer knowledge. Many of these knowledge flows are difficult to observe, not least because scholars are often not aware of them. This may be the reason why the attention to knowledge flows is very unevenly distributed across science studies, with bibliometric citation-based studies contributing the most research. Starting from the premise that bibliometric methods can be more readily exploited in the study of knowledge flows, this review explores the potential of bibliometric methods for the investigation of knowledge flows. Bibliometrics provides a portfolio of data and methods that can be used alone or in combination with qualitative methods to study knowledge flows. We organise contributions to the study of knowledge flows according to their object of study—formal, informal, or tacit knowledge—and according to the mode of flow—impersonal or interpersonal knowledge flow. The review shows that bibliometrics is strongly focused on the use of citation data for the investigation of impersonal flows of formal knowledge and has recently turned to the impersonal flow of informal knowledge via social media. In contrast, there are only few bibliometric studies that address interpersonal flows of knowledge. The review identifies an under-utilised potential of bibliometric methods and suggests some directions for future methodological development. [ABSTRACT FROM AUTHOR] |
| Copyright of Minerva: A Review of Science, Learning & Policy is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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