Re-Engineering a Knowledge-Intensive Decision Process to support consistent decision making
Mehli Carlo, 2020
Betreuende Dozierende: Knut Hinkelmann
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Knowledge and knowledge work are essential for the success of companies nowadays. Since decisions are based on knowledge and better knowledge leads to more informed decisions, the management of knowledge and support of decision making has increasingly become a source of competitive advantage for organizations. While previous research proposes many different approaches such as Ontologies, Knowledge Management, Artificial Neural Networks, Case Based Reasoning etc. to support various decision making, there is not one single best solution outlined by research that applies for all kind of knowledge intensive processes and its decisions. Therefore, there are no specific decision support methods outlined by research which fit best for specific knowledge or decision patterns. The aim of the research was to determine appropriate methods to improve decision making; identify decisions and analyze knowledge used for those decisions in a specific process scenario; propose a system design to improve knowledge management in said scenario; evaluate the impact of the proposed system design on the knowledge intensive process. This thesis contains a profound literature framework and follows the design science research (DSR) strategy. In the awareness phase of the DSRprocess, the application scenario of student admission process was acquired using the case study research method....
Studiengang: Business Information Systems (Master)
Fachbereich der Arbeit: Wirtschaftsinformatik & IT-Management