Azzam, Mark und Beckmann, Rasmus (2022) How AI Helps to Increase Organizations’ Capacity to Manage Complexity – A Research Perspective and Solution Approach Bridging Different Disciplines. IEEE Transactions on Engineering Management, 71, Seiten 2324-2337. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TEM.2022.3179107. ISSN 0018-9391.
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Offizielle URL: https://dx.doi.org/10.1109/TEM.2022.3179107
Kurzfassung
A major challenge for organizations and their strategic management today is the increasingly complex internal and external information and knowledge environment. To tackle this problem, we want to provide a fresh perspective on how to construct a modern AI-based socio-technical support system for strategic management. Methodologically, we provide a deep problem analysis and use the resulting insights to provide a solution approach drawing on different disciplines and bridging the gap between social science and computer science. Starting from challenges for strategic analysis in the age of information-overload, we analyze organizations as social communication systems. This yields a deeper understanding of organizations processing strategic information and their complex information environment. Based on the resulting insights, we propose our framework design by building on text mining and knowledge graph technology and give some guidance for its implementation. The novel system generates strategic intelligence by tapping into qualitative data: defining constantly changing information needs, interpreting information in its context as well as in reflection to the organization and making new knowledge promptly available to the right people. Throughout, we give illustrating examples from the domain of strategic research management.
elib-URL des Eintrags: | https://elib.dlr.de/205589/ | ||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||
Titel: | How AI Helps to Increase Organizations’ Capacity to Manage Complexity – A Research Perspective and Solution Approach Bridging Different Disciplines | ||||||||||||
Autoren: |
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Datum: | 29 Juni 2022 | ||||||||||||
Erschienen in: | IEEE Transactions on Engineering Management | ||||||||||||
Referierte Publikation: | Ja | ||||||||||||
Open Access: | Ja | ||||||||||||
Gold Open Access: | Nein | ||||||||||||
In SCOPUS: | Ja | ||||||||||||
In ISI Web of Science: | Ja | ||||||||||||
Band: | 71 | ||||||||||||
DOI: | 10.1109/TEM.2022.3179107 | ||||||||||||
Seitenbereich: | Seiten 2324-2337 | ||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||
ISSN: | 0018-9391 | ||||||||||||
Status: | veröffentlicht | ||||||||||||
Stichwörter: | Artificial intelligence, knowledge graph, natural language processing, research management, social systems theory, strategic intelligence, strategic management | ||||||||||||
HGF - Forschungsbereich: | keine Zuordnung | ||||||||||||
HGF - Programm: | keine Zuordnung | ||||||||||||
HGF - Programmthema: | keine Zuordnung | ||||||||||||
DLR - Schwerpunkt: | keine Zuordnung | ||||||||||||
DLR - Forschungsgebiet: | keine Zuordnung | ||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | keine Zuordnung | ||||||||||||
Standort: | Köln-Porz | ||||||||||||
Institute & Einrichtungen: | Vorstandsbereich Innovation, Transfer und wissenschaftliche Infrastrukturen > Technologietransfer | ||||||||||||
Hinterlegt von: | Beckmann, Rasmus | ||||||||||||
Hinterlegt am: | 09 Mai 2025 10:09 | ||||||||||||
Letzte Änderung: | 09 Mai 2025 10:09 |
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