---
_id: '20212'
abstract:
- lang: eng
  text: "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.\r\n"
article_number: '113432'
author:
- first_name: Julian
  full_name: Prester, Julian
  last_name: Prester
- first_name: Gerit
  full_name: Wagner, Gerit
  last_name: Wagner
- first_name: Guido
  full_name: Schryen, Guido
  id: '72850'
  last_name: Schryen
- first_name: Nik Rushdi
  full_name: Hassan, Nik Rushdi
  last_name: Hassan
citation:
  ama: 'Prester J, Wagner G, Schryen G, Hassan NR. Classifying the Ideational Impact
    of Information Systems Review Articles: A Content-Enriched Deep Learning Approach.
    <i>Decision Support Systems</i>. 2021;140(January).'
  apa: 'Prester, J., Wagner, G., Schryen, G., &#38; Hassan, N. R. (2021). Classifying
    the Ideational Impact of Information Systems Review Articles: A Content-Enriched
    Deep Learning Approach. <i>Decision Support Systems</i>, <i>140</i>(January),
    Article 113432.'
  bibtex: '@article{Prester_Wagner_Schryen_Hassan_2021, title={Classifying the Ideational
    Impact of Information Systems Review Articles: A Content-Enriched Deep Learning
    Approach}, volume={140}, number={January113432}, journal={Decision Support Systems},
    author={Prester, Julian and Wagner, Gerit and Schryen, Guido and Hassan, Nik Rushdi},
    year={2021} }'
  chicago: '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.” <i>Decision Support Systems</i> 140, no. January (2021).'
  ieee: '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,” <i>Decision Support Systems</i>, vol. 140, no. January, Art. no. 113432,
    2021.'
  mla: 'Prester, Julian, et al. “Classifying the Ideational Impact of Information
    Systems Review Articles: A Content-Enriched Deep Learning Approach.” <i>Decision
    Support Systems</i>, vol. 140, no. January, 113432, 2021.'
  short: J. Prester, G. Wagner, G. Schryen, N.R. Hassan, Decision Support Systems
    140 (2021).
date_created: 2020-10-27T13:28:21Z
date_updated: 2022-06-10T06:55:32Z
ddc:
- '000'
department:
- _id: '277'
file:
- access_level: open_access
  content_type: application/pdf
  creator: hsiemes
  date_created: 2020-10-27T13:31:01Z
  date_updated: 2020-10-27T13:31:01Z
  file_id: '20213'
  file_name: DECSUP-D-20-00312 - PREPUBLICATION.pdf
  file_size: 440903
  relation: main_file
file_date_updated: 2020-10-27T13:31:01Z
has_accepted_license: '1'
intvolume: '       140'
issue: January
keyword:
- Ideational impact
- citation classification
- academic recommender systems
- natural language processing
- deep learning
- cumulative tradition
language:
- iso: eng
oa: '1'
publication: Decision Support Systems
status: public
title: 'Classifying the Ideational Impact of Information Systems Review Articles:
  A Content-Enriched Deep Learning Approach'
type: journal_article
user_id: '72850'
volume: 140
year: '2021'
...