Research Focus

Artificial Intelligence (AI) is a concept that is constantly being refined as technology evolves while its reference point  - human intelligence - remains relatively static. It fuels describing intelligent systems that are nowadays mainly based on machine learning and deep learning technology. Our research focuses on two key aspects of AI: the performance of process prediction and the explainability of AI decisions.

For example, state-of-the-art deep learning algorithms for predicting next events in process execution have been studied to determine which features of AI models are appropriate for which type of process event protocol.

With respect to explainable AI, we are investigating the impact of white-box and (explainable) black-box AI models on human users' perceptions of their willingness to use the recommendations and on actual problem-solving performance.

Publications

• Feuerriegel, S., Hartmann, J., Janiesch, C. & Zschech, P. (2023). Generative AI. Business & Information Systems Engineering. doi: 10.1007/s12599-023-00834-7
• Herm, L.-V., Steinbach, T., Wanner, J. & Janiesch, C. (2022). A Nascent Design Theory for Explainable Intelligent Systems. Electronic Markets, 32, 2079-2102. doi: 10.1007/s12525-022-00606-3
• Herm, L.-V., Heinrich, K., Wanner, J. & Janiesch, C. (2023). Stop Ordering Machine Learning Algorithms by their Explainability! A User-Centered Investigation of Performance and Explainability. International Journal of Information Management, 69, 102538. doi:10.1016/j.ijinfomgt.2022.102538
• Janiesch, C., Zschech, P. & Heinrich, K. (2021). Machine Learning and Deep Learning. Electronic Markets, 31, 685-695. doi:10.1007/s12525-021-00475-2
• Wanner, J., Herm, L.-V., Heinrich, K. & Janiesch, C. (2022). The Effect of Transparency and Trust on Intelligent System Acceptance: Evidence from a User-based Study. Electronic Markets, 32, 2185-2205. doi: 10.1007/s12525-022-00593-5

Business Process Management looks at people, organizations, and software that perform tasks in a process-oriented manner. In the past, our focus was on problems of architectures and languages for process analysis or process mining - especially for operational BPM and BPM in the cloud.

Most recently, our research has tended to focus on the transfomational aspects of digitization, for example how the long tail of business processes can be tapped using innovative digital technology to challenge established methods and organizational behavior, and how AI technology can improve process-oriented information systems, for example by predicting the next event.

Robotic Process Automation, the automation with intelligent software robots, and Hyperautomation, the rapid and business-driven automation concept, are centerpieces of this approach.

Publications

• Fischer, M., Hofmann, A., Imgrund, F., Janiesch, C. & Winkelmann, A. (2021). On the Composition of the Long Tail of Business Processes: Implications from a Process Mining Study. Information Systems, 97, 101689. doi:10.1016/j.is.2020.101689
• Fischer, M., Imgrund, F., Janiesch, C. & Winkelmann, A. (2020). Strategy Archetypes for Digital Transformation: Defining Meta Objectives using Business Process Management. Information & Management, 57, 103262. doi:10.1016/j.im.2019.103262
• Heinrich, K., Zschech, P., Janiesch, C. & Bonin, M. (2021). Process Data Properties Matter: Introducing Gated Convolutional Neural Networks (GCNN) and Key-Value-Predict Attention Networks (KVP) for Next Event Prediction with Deep Learning. Decision Support Systems, 143, 113494. doi:10.1016/j.dss.2021.113494
• Schulte, S., Janiesch, C., Venugopal, S., Weber, I. & Hoenisch, P. (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, 46, 36-50. doi: 10.1016/j.future.2014.09.005

Information systems are socio-technical systems that consider not only information technology, but also the tasks to be solved with it and the associated human users. Thus, the development of information systems involves more than just the development of software or new processes. It takes a holistic view of changes and innovations and introduces them in a manner appropriate to the situation.

One of our specialties is the development of complex intelligent systems, i.e. systems with human-like decision-making competence fueled by machine learning. We take a design-oriented approach and follow the principles of Design Science Research.

Likewise, our research is concerned with the conceptual design of systems using conceptual modeling. We use purpose-built abstractions to represent system behavior. They serve as a basis for discussion and as implementation blueprints.

Publications

• Janiesch, C., Rosenkranz, C. & Scholten, U. (2020). An Information Systems Design Theory for Service Network Effects. Journal of the Association for Information Systems, 21, 1402-1460. doi: 10.17705/1jais.00642
• Zschech, P., Horn, R., Höschele, D., Janiesch, C. & Heinrich, K. (2020). Intelligent User Assistance for Automated Data Mining Method Selection. Business & Information Systems Engineering, 62, 227–247. doi: 10.1007/s12599-020-00642-3