Research Areas
Our research focuses on the design, use, and impact of artificial intelligence (AI)-based information systems in business and society. Our primary objectives are to (1) understand the impact of AI on individuals, organizations, and society as a whole, and to (2) design explainable AI-based information systems in a human-centered way.
We are particularly interested in two key areas of research on AI-based business information systems:
Customer–AI Interaction
As AI and LLM technology continue to advance, customers increasingly find themselves interacting with chatbots, voice assistants, and physical robots instead of human employees. This shift offers many opportunities for automating business processes and enhancing customer experience in areas such as customer service, e-commerce, and healthcare. However, it also introduces new challenges for businesses and their customers. Therefore, our research focuses on understanding how individuals interact with AI and exploring how these customer–AI interactions impact business outcomes.
Gnewuch, U., Morana, S., Hinz, O., Kellner, R., Maedche, A. (2023). "More than a Bot? The Impact of Disclosing Human Involvement on Customer Interactions with Hybrid Service Agents". Information Systems Research (forthcoming). [LINK]
Gnewuch, U., Morana, S., Adam, M. T. P., Maedche, A. (2022). "Opposing Effects of Response Time in Human-Chatbot Interaction: The Moderating Role of Prior Experience," Business & Information Systems Engineering, 64, 773–791. [LINK]
Gnewuch, U., Hanschmann, L., Kaiser, C., Schallner, R., Maedche, A. (2024). "Robot Shopping Assistants: How Emotional versus Rational Robot Designs Affect Consumer Trust and Purchase Decisions," in Proceedings of the 32nd European Conference on Information Systems (ECIS 2024). [LINK]
Interactive BI&A Systems
The success of business intelligence & analytics (BI&A) systems, including dashboards, forecasting tools, and data visualization platforms, largely depends on their effective use by the target audience. When decision-makers rely solely on intuition rather than leveraging data-driven insights for business decisions, even the most advanced BI&A systems and data science projects struggle to deliver value. Our research addresses this challenge by investigating the design of interactive BI&A systems that help business users explore data, extract insights, and become more data-driven in their decision-making.
Ruoff, M., Gnewuch, U., Maedche, A, Scheibehenne, B. (2023). "Designing Conversational Dashboards for Effective Use in Crisis Response". Journal of the Association for Information Systems, 24(6), 1500–1526. [LINK]
Schelhorn, T.C., Gnewuch, U., Maedche, A. (2024). "Designing a Large Language Model Based Open Data Assistant for Effective Use," in Proceedings of the 19th International Conference on Design Science Research in Information Systems and Technology (DESRIST 2024). [LINK]
Schloß, D., Gutierrez Espitia, J. D., Gnewuch, U. (2023). "Designing a Conversation Mining System for Customer Service Chatbots," in Proceedings of the 31st European Conference on Information Systems (ECIS 2023). [LINK]
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