Li, MaheiLöfflad, DeniseReh, CorneliusOeste-Reiß, Sarah2022-12-272022-12-272023-01-03978-0-9981331-6-4https://hdl.handle.net/10125/102669Rapid adoption of innovative technologies confront IT-Service-Management (ITSM) to incoming support requests of increasing complexity. As a consequence, job demands and turnover rates of ITSM support agents increase. Recent technological advances have introduced assistance systems that rely on hybrid intelligence to provide support agents with contextually suitable historical solutions to help them solve customer requests. Hybrid intelligence systems rely on human input to provide high-quality data to train their underlying AI models. Yet, most agents have little incentives to label their data, lowering data quality and leading to diminishing returns of AI systems due to concept drifts. Following a design science research approach, we provide a novel Human-in-the-Loop design and hybrid intelligence system for ITSM support ticket recommendations, which incentivize agents to provide high-quality labels. Specifically, we leverage agent’s need for instant gratification by simultaneously providing better results if they improve labeling automatically labeled support tickets.10engAttribution-NonCommercial-NoDerivatives 4.0 InternationalCollaboration with Intelligent Systems: Machines as Teammatesfrontline service technologyhuman-in-the-loophybrid intelligenceitsmservice supporttext10.24251/HICSS.2023.041