The explanatory power of citations: a new approach to unpacking impact in science

Matthias Sebastian Rüdiger (), David Antons () and Torsten-Oliver Salge ()
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Matthias Sebastian Rüdiger: Institute for Technology and Innovation Management, RWTH-Aachen University
David Antons: Institute for Technology and Innovation Management, RWTH-Aachen University
Torsten-Oliver Salge: Institute for Technology and Innovation Management, RWTH-Aachen University

Scientometrics, 2021, vol. 126, issue 12, No 24, 9779-9809

Abstract: Abstract Citation analysis has been applied to map the landscape of scientific disciplines and to assess the impact of publications. However, it is limited in that it assumes all citations to be of equal weight. Doing away with this assumption could make such studies even more insightful. Current developments in this regard focus on the evaluation of the syntactic and semantic qualities of the text that surrounds citations. Still lacking, however, are computational techniques to unpack the thematic context in which citations appear. It is against this backdrop that we propose a text clustering approach to derive contextual aspects of individual citations and the relationship between cited and citing work in an automated and scalable fashion. The method reveals a focal publication’s absorption and use within the scientific community. It can also facilitate impact assessments at all levels. In addition to analyzing individual publications, the method can also be extended to creating impact profiles for authors, institutions, disciplines, and regions. We illustrate our results based on a large corpus of full-text articles from the field of Information systems (IS) with the help of exemplary visualizations. In addition, we provide a case study, the scientific impact of the Technology acceptance model. This way, we not only show the usefulness of our method in comparison to existing techniques but also enhance the understanding of the field by providing an in-depth analysis of the absorption of a key IS theoretical base.

Keywords: Citation analysis; Content-based citation analysis; Topic modeling; Information systems (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11192-021-04103-w

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