Prester, Julian and Wagner, Gerit and Schryen, Guido and Hassan, Nik Rushdi (2021) Classifying the ideational impact of Information Systems review articles: A content-enriched deep learning approach. DECISION SUPPORT SYSTEMS, 140: 113432. ISSN 0167-9236, 1873-5797
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Ideational impact refers to the uptake of a paper's ideas and concepts by subsequent research. It is defined in stark contrast to total citation impact, a measure predominantly used in research evaluation that assumes that all citations are equal. Understanding ideational impact is critical for evaluating research impact and understanding how scientific disciplines build a cumulative tradition. Research has only recently developed automated citation classification techniques to distinguish between different types of citations and generally does not emphasize the conceptual content of the citations and its ideational impact. To address this problem, we develop Deep Content-enriched Ideational Impact Classification (Deep-CENIC) as the first automated approach for ideational impact classification to support researchers' literature search practices. We evaluate Deep-CENIC on 1256 papers citing 24 information systems review articles from the IT business value domain. We show that Deep-CENIC significantly outperforms state-of-the-art benchmark models. We contribute to information systems research by operationalizing the concept of ideational impact, designing a recommender system for academic papers based on deep learning techniques, and empirically exploring the ideational impact in the IT business value domain.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | DECISION-SUPPORT-SYSTEMS; CITATION ANALYSIS; INTELLECTUAL STRUCTURE; SOCIAL NETWORK; ANALYSIS CCA; SCIENCE; ANALYTICS; KNOWLEDGE; FRAMEWORK; JOURNALS; Ideational impact; Citation classification; Academic recommender systems; Natural language processing; Deep learning; Cumulative tradition |
| Subjects: | 000 Computer science, information & general works > 004 Computer science 300 Social sciences > 330 Economics |
| Divisions: | Business, Economics and Information Systems > Institut für Wirtschaftsinformatik |
| Depositing User: | Petra Gürster |
| Date Deposited: | 12 Jan 2023 10:43 |
| Last Modified: | 12 Jan 2023 10:43 |
| URI: | https://pred.uni-regensburg.de/id/eprint/47008 |
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