Knowledge and knowledge work are essential for the success of companies nowadays.
Decisions are based on knowledge and better knowledge leads to more informed decisions.
Therefore, the management of knowledge and support of decision making has increasingly
become a source of competitive advantage for organizations. The current research uses a
design science research approach (DSR) with the aim to improve the decision making of a
knowledge intensive process such as the student admission process, which is done manually
until now. In the awareness phase of the DSR process, the case study research method is applied
to analyze the decision making and the knowledge that is needed to derive the decisions. Based
on the analysis of the application scenario, suitable methods to support decision making were
identified. The resulting system design is based on a combination of Case-Based Reasoning
(CBR) and Machine Learning (ML). The proposed system design and prototype has been
validated using triangulation evaluation, to assess the impact of the proposed system on the
application scenario. The evaluation revealed that the additional hints from CBR and ML can
assist the deans of the study program to improve the knowledge management and increase the
quality, transparency and consistency of the decision-making process in the student application
process. Furthermore, the proposed approach fosters the exchange of knowledge among the
different process participants involved and codifies previously tacit knowledge to some extent
and provides relevant externalized knowledge to decision makers at the required moment. The
designed prototype showcases how ML and CBR methodologies can be combined to support
decision making in knowledge intensive processes and finally concludes with potential
recommendations for future research.