--- _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. Decision Support Systems. 2021;140(January).' apa: '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, 140(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.” Decision Support Systems 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,” Decision Support Systems, 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.” Decision Support Systems, 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' ... --- _id: '16285' abstract: - lang: eng 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. author: - first_name: Philipp full_name: zur Heiden, Philipp id: '64394' last_name: zur Heiden - first_name: Carsten Ingo full_name: Berendes, Carsten Ingo id: '53344' last_name: Berendes - first_name: Daniel full_name: Beverungen, Daniel id: '59677' last_name: Beverungen citation: ama: 'zur Heiden P, Berendes CI, Beverungen D. Designing City Center Area Recommendation Systems . In: Proceedings of the 15th International Conference on Wirtschaftsinformatik. Potsdam; 2020. doi:doi.org/10.30844/wi_2020_e1-heiden' apa: zur Heiden, P., Berendes, C. I., & Beverungen, D. (2020). Designing City Center Area Recommendation Systems . In Proceedings of the 15th International Conference on Wirtschaftsinformatik. Potsdam. https://doi.org/doi.org/10.30844/wi_2020_e1-heiden bibtex: '@inproceedings{zur Heiden_Berendes_Beverungen_2020, place={Potsdam}, title={Designing City Center Area Recommendation Systems }, DOI={doi.org/10.30844/wi_2020_e1-heiden}, booktitle={Proceedings of the 15th International Conference on Wirtschaftsinformatik}, author={zur Heiden, Philipp and Berendes, Carsten Ingo and Beverungen, Daniel}, year={2020} }' chicago: Heiden, Philipp zur, Carsten Ingo Berendes, and Daniel Beverungen. “Designing City Center Area Recommendation Systems .” In Proceedings of the 15th International Conference on Wirtschaftsinformatik. Potsdam, 2020. https://doi.org/doi.org/10.30844/wi_2020_e1-heiden. ieee: P. zur Heiden, C. I. Berendes, and D. Beverungen, “Designing City Center Area Recommendation Systems ,” in Proceedings of the 15th International Conference on Wirtschaftsinformatik, Potsdam, 2020. mla: zur Heiden, Philipp, et al. “Designing City Center Area Recommendation Systems .” Proceedings of the 15th International Conference on Wirtschaftsinformatik, 2020, doi:doi.org/10.30844/wi_2020_e1-heiden. short: 'P. zur Heiden, C.I. Berendes, D. Beverungen, in: Proceedings of the 15th International Conference on Wirtschaftsinformatik, Potsdam, 2020.' conference: end_date: 2020-03-11 location: Potsdam name: 15th International Conference on Wirtschaftsinformatik start_date: 2020-03-07 date_created: 2020-03-13T07:05:24Z date_updated: 2022-01-06T06:52:48Z ddc: - '000' department: - _id: '526' doi: doi.org/10.30844/wi_2020_e1-heiden file: - access_level: closed content_type: application/pdf creator: pzh date_created: 2020-03-13T06:58:54Z date_updated: 2020-03-13T06:58:54Z file_id: '16286' file_name: E1_City_recommendations_Master_final.pdf file_size: 1370273 relation: main_file success: 1 file_date_updated: 2020-03-13T06:58:54Z has_accepted_license: '1' keyword: - Town Center Management - High Street Retail - Recommender Systems - Geospatial Recommendations - Design Science Research language: - iso: eng main_file_link: - open_access: '1' url: https://library.gito.de/open-access-pdf/E1_City_recommendations_Master_final.pdf oa: '1' place: Potsdam project: - _id: '35' grant_number: ​02K15A073 name: ​Interaktive Einkaufserlebnisse in Innenstädten durch digitale Dienstleistungen publication: Proceedings of the 15th International Conference on Wirtschaftsinformatik publication_status: published quality_controlled: '1' status: public title: 'Designing City Center Area Recommendation Systems ' type: conference user_id: '64394' year: '2020' ...