[{"title":"Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach","date_created":"2020-10-27T13:28:21Z","year":"2021","issue":"January","ddc":["000"],"keyword":["Ideational impact","citation classification","academic recommender systems","natural language processing","deep learning","cumulative tradition"],"language":[{"iso":"eng"}],"abstract":[{"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","lang":"eng"}],"file":[{"creator":"hsiemes","date_created":"2020-10-27T13:31:01Z","date_updated":"2020-10-27T13:31:01Z","access_level":"open_access","file_id":"20213","file_name":"DECSUP-D-20-00312 - PREPUBLICATION.pdf","file_size":440903,"content_type":"application/pdf","relation":"main_file"}],"publication":"Decision Support Systems","oa":"1","date_updated":"2022-06-10T06:55:32Z","author":[{"first_name":"Julian","full_name":"Prester, Julian","last_name":"Prester"},{"full_name":"Wagner, Gerit","last_name":"Wagner","first_name":"Gerit"},{"last_name":"Schryen","full_name":"Schryen, Guido","id":"72850","first_name":"Guido"},{"last_name":"Hassan","full_name":"Hassan, Nik Rushdi","first_name":"Nik Rushdi"}],"volume":140,"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).","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.","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} }","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)."},"intvolume":"       140","has_accepted_license":"1","article_number":"113432","file_date_updated":"2020-10-27T13:31:01Z","_id":"20212","user_id":"72850","department":[{"_id":"277"}],"status":"public","type":"journal_article"},{"abstract":[{"text":"To  decide  in  which  part  of  town to  open  stores,  high  street  retailers consult  statistical  data  on  customers  and  cities,  but  they  cannot  analyze  their customers’  shopping  behavior  and  geospatial  features  of  a  city  due  to  missing data.  While  previous  research  has  proposed  recommendation  systems  and decision  aids  that  address  this  type  of  decision  problem –  including  factory location  and  assortment  planning –  there  currently  is no design  knowledge available  to  prescribe  the  design  of  city  center  area  recommendation  systems (CCARS).   We   set   out   to   design   a   software   prototype   considering   local customers’  shopping  interests  and  geospatial  data  on  their  shopping  trips  for retail site selection.  With real data on 500 customers and 1,100 shopping trips, we demonstrate and evaluate our IT artifact. Our results illustrate how retailers and public town center managers can use CCARS for spatial location selection, growing retailers’ profits and a city center’s attractiveness for its citizens.","lang":"eng"}],"file":[{"file_size":1370273,"file_name":"E1_City_recommendations_Master_final.pdf","file_id":"16286","access_level":"closed","date_updated":"2020-03-13T06:58:54Z","creator":"pzh","date_created":"2020-03-13T06:58:54Z","success":1,"relation":"main_file","content_type":"application/pdf"}],"publication":"Proceedings of the 15th International Conference on Wirtschaftsinformatik","ddc":["000"],"keyword":["Town Center Management","High Street Retail","Recommender Systems","Geospatial Recommendations","Design Science Research"],"language":[{"iso":"eng"}],"year":"2020","quality_controlled":"1","title":"Designing City Center Area Recommendation Systems ","date_created":"2020-03-13T07:05:24Z","status":"public","type":"conference","file_date_updated":"2020-03-13T06:58:54Z","project":[{"name":"​Interaktive Einkaufserlebnisse in Innenstädten durch digitale Dienstleistungen","_id":"35","grant_number":"​02K15A073"}],"_id":"16285","user_id":"64394","department":[{"_id":"526"}],"place":"Potsdam","citation":{"chicago":"Heiden, Philipp zur, Carsten Ingo Berendes, and Daniel Beverungen. “Designing City Center Area Recommendation Systems .” In <i>Proceedings of the 15th International Conference on Wirtschaftsinformatik</i>. Potsdam, 2020. <a href=\"https://doi.org/doi.org/10.30844/wi_2020_e1-heiden\">https://doi.org/doi.org/10.30844/wi_2020_e1-heiden</a>.","ieee":"P. zur Heiden, C. I. Berendes, and D. Beverungen, “Designing City Center Area Recommendation Systems ,” in <i>Proceedings of the 15th International Conference on Wirtschaftsinformatik</i>, Potsdam, 2020.","ama":"zur Heiden P, Berendes CI, Beverungen D. Designing City Center Area Recommendation Systems . In: <i>Proceedings of the 15th International Conference on Wirtschaftsinformatik</i>. Potsdam; 2020. doi:<a href=\"https://doi.org/doi.org/10.30844/wi_2020_e1-heiden\">doi.org/10.30844/wi_2020_e1-heiden</a>","apa":"zur Heiden, P., Berendes, C. I., &#38; Beverungen, D. (2020). Designing City Center Area Recommendation Systems . In <i>Proceedings of the 15th International Conference on Wirtschaftsinformatik</i>. Potsdam. <a href=\"https://doi.org/doi.org/10.30844/wi_2020_e1-heiden\">https://doi.org/doi.org/10.30844/wi_2020_e1-heiden</a>","bibtex":"@inproceedings{zur Heiden_Berendes_Beverungen_2020, place={Potsdam}, title={Designing City Center Area Recommendation Systems }, DOI={<a href=\"https://doi.org/doi.org/10.30844/wi_2020_e1-heiden\">doi.org/10.30844/wi_2020_e1-heiden</a>}, booktitle={Proceedings of the 15th International Conference on Wirtschaftsinformatik}, author={zur Heiden, Philipp and Berendes, Carsten Ingo and Beverungen, Daniel}, year={2020} }","mla":"zur Heiden, Philipp, et al. “Designing City Center Area Recommendation Systems .” <i>Proceedings of the 15th International Conference on Wirtschaftsinformatik</i>, 2020, doi:<a href=\"https://doi.org/doi.org/10.30844/wi_2020_e1-heiden\">doi.org/10.30844/wi_2020_e1-heiden</a>.","short":"P. zur Heiden, C.I. Berendes, D. Beverungen, in: Proceedings of the 15th International Conference on Wirtschaftsinformatik, Potsdam, 2020."},"publication_status":"published","has_accepted_license":"1","main_file_link":[{"open_access":"1","url":"https://library.gito.de/open-access-pdf/E1_City_recommendations_Master_final.pdf"}],"doi":"doi.org/10.30844/wi_2020_e1-heiden","conference":{"end_date":"2020-03-11","location":"Potsdam","name":"15th International Conference on Wirtschaftsinformatik","start_date":"2020-03-07"},"oa":"1","date_updated":"2022-01-06T06:52:48Z","author":[{"first_name":"Philipp","last_name":"zur Heiden","full_name":"zur Heiden, Philipp","id":"64394"},{"last_name":"Berendes","full_name":"Berendes, Carsten Ingo","id":"53344","first_name":"Carsten Ingo"},{"id":"59677","full_name":"Beverungen, Daniel","last_name":"Beverungen","first_name":"Daniel"}]}]