Abstract
Along with recent advances of team-AI collaboration, we observe the emergence of adaptive, collaborative, and explainable AI technologies that spur the creation of organizational knowledge for group decision-making. This is substantiated by the explicit and tacit knowledge that decision-makers can create with AI and by the procedural support that AI can provide for the organizational knowledge conversion processes among decision-makers. However, research on AI design for effective organizational knowledge creation is in a nascent state. This is problematic because this leaves organizations without guidance for the implementation and assessment of AI that enables effective knowledge creation. We see potential in robo-advisors, which represent a form of AI, to facilitate such organizational knowledge creation for decision-making in economic contexts. We aim to realize this potential and apply an action design research approach to identify meta-requirements and design principles for a robo-advisor prototype. The robo-advisor is contextualized in the after-sales domain of a German car manufacturer, the Dr. Ing. h.c. F. Porsche AG, where complex decision problems are informed and solved by expert groups. The robo-advisor prototype contributes to collaborative knowledge creation that informs the group’s decision-making on field measures in the event of product quality issues aimed at ensuring product safety and customer satisfaction.
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Namyslo, N., Jung, D., Sturm, T. (2024). Exploring Design Principles Promoting Organizational Knowledge Creation via Robo-Advisory: The Case of Collaborative Group Decision-Making in the After Sales Management. In: Mandviwalla, M., Söllner, M., Tuunanen, T. (eds) Design Science Research for a Resilient Future. DESRIST 2024. Lecture Notes in Computer Science, vol 14621. Springer, Cham. https://doi.org/10.1007/978-3-031-61175-9_21
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