The explanatory power of citations
A new approach to unpacking impact in science
Bibliographic Data
| ID | 21441527 |
|---|---|
| Authors | Matthias Sebastian Rüdiger (0000-0002-7086-1370, RWTH Aachen University, corresponding author), David Antons (0000-0002-2392-0374, RWTH Aachen University), Torsten-Oliver Salge (RWTH Aachen University) |
| Year | 2021 |
| Volume | 126 |
| Issue | 12 |
| Pages | 9779-9809 |
| Publication date | 2021-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Scientometrics (JOURNAL) |
| Journal identifiers | ISSN: 0138-9130 • E-ISSN: 1588-2861 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11192-021-04103-w |
| OpenAlex | W3191745493 |
| Language | EN |
| Citations received | 1 |
| References cited | 53 |
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
Citation · Citation analysis · Cluster analysis · Context (archaeology) · Data science · Field (mathematics) · Focus (optics) · Information retrieval · Linguistics · Thematic map · Unpacking · World Wide Web · Artificial Intelligence · Complex Network Analysis Techniques · Computer Science · Open Source Software Innovations · scientometrics and bibliometrics research
The sociology of science
The psychological origins of perceived usefulness and ease-of-use
Structural Equation Modeling and Regression
Information Technology Adoption Across Time
What do citation counts measure? A review of studies on citing behavior
Validation Guidelines for Is Positivist Research
LDAvis
Validating Instruments in MIS Research1
A vector space model for automatic indexing
What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018
A Practical Guide To Factorial Validity Using PLS-Graph
Bibliometric Methods in Management and Organization
From Intentions to Actions
The theory of planned behavior
User Acceptance of Information Technology
User Acceptance of Computer Technology
Content Analysis of References
Some Results on the Function and Quality of Citations
Software survey
Normative versus Social Constructivist Processes in the Allocation of Citations
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |