Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach

J. Prester, G. Wagner, G. Schryen, N.R. Hassan, Decision Support Systems (2021).

Download
OA 440.90 KB
Journal Article | English
Author
; ; ;
Abstract
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 1,256 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 of the IT business value domain.
Publishing Year
Journal Title
Decision Support Systems
LibreCat-ID

Cite this

Prester J, Wagner G, Schryen G, Hassan NR. Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach. Decision Support Systems. 2021.
Prester, J., Wagner, G., Schryen, G., & Hassan, N. R. (2021). Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach. Decision Support Systems.
@article{Prester_Wagner_Schryen_Hassan_2021, title={Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach}, journal={Decision Support Systems}, author={Prester, Julian and Wagner, Gerit and Schryen, Guido and Hassan, Nik Rushdi}, year={2021} }
Prester, Julian, Gerit Wagner, Guido Schryen, and Nik Rushdi Hassan. “Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach.” Decision Support Systems, 2021.
J. Prester, G. Wagner, G. Schryen, and N. R. Hassan, “Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach,” Decision Support Systems, 2021.
Prester, Julian, et al. “Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach.” Decision Support Systems, 2021.
Main File(s)
Access Level
OA Open Access
Last Uploaded
2020-10-27T13:31:01Z


Export

Marked Publications

Open Data LibreCat

Search this title in

Google Scholar