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Information Systems at TU Dortmund University
Panoramic view of the North Campus © Roland Baege​/​TU Dortmund
Research

The Chair of Enterprise Computing was represented at DESRIST 2026

© Drechsler (2026)
From left to right: Maximilian Nebel, Alexander van der Staay, Niklas Schulte, Christian Janiesch, Pauline Speckmann
The department was on a conference trip to Münster

The Chair of Enterprise Computing is pleased to have participated in DESRIST 2026 – The 21st International Conference on Design Science Research in Information Systems and Technology, held this year from June 8 to June 10. By presenting four research papers, we were able to showcase our current research in the field of Design Science Research to an international audience of experts.

DESRIST is one of the leading international conferences for Design Science Research in information systems and serves as a key platform for the exchange of innovative research findings, methodological approaches, and practice-relevant insights. It brings together researchers and practitioners from around the world and promotes the further development of the discipline.

We are particularly pleased that Alexander van der Staay had the opportunity to moderate a session as session chair during the conference, thereby actively contributing to the shaping of the scientific program.

For more information about the conference, please click here.

Here are the papers we presented:

  • Speckmann, P., van der Staay, A., Markic, M., Kelch, Y., Nebel, M., Poeppelbuss, J., Janiesch, C. (2026). “"Do AI Yourself": Designing a Toolbox to Empower Small- and Medium-Sized Enterprises to Embrace AI-as-a-Service.”
    In this paper, we develop and evaluate an interactive toolbox within a design science framework that supports small and medium-sized enterprises in independently identifying, evaluating, and implementing AI-as-a-Service applications. The toolbox combines building blocks such as AI literacy, strategic evaluation, archetypal use cases, business model development, and regulatory classification to support companies in a reflective and value-adding AI adoption.
  • Dasmann, L., van der Staay, A., Janiesch, C. (2026). “Design Knowledge for Ethical Use of AI in Emergency Medical Dispatch Systems.”
    The paper examines the use of artificial intelligence in emergency medical dispatch systems and analyzes the associated ethical challenges in a safety-critical context. The focus is on developing practical and scientifically sound guidelines for the responsible use of AI, particularly with regard to transparency, fairness, and accountability. The goal is to enable the trustworthy integration of AI into the decision-making processes of emergency dispatch centers.
  • Demircan, F., Nebel, M., Janiesch, C. (2026). “Design Principles for Ethical Automated Mental Workload Monitoring in the Industrial Internet of Things.”
    Automated systems for monitoring mental workload in the Industrial Internet of Things raise numerous ethical questions. Using a design science approach, we developed design principles that translate key requirements such as data protection, transparency, and human autonomy into concrete design mechanisms for such systems. The results show how organizations can use technologies to support decision-making processes without compromising privacy, self-determination, and the long-term well-being of employees.
  • Schulte, N., Jeyakumar, I.H.J., Kubach, M., Janiesch, C. (2026). “Beyond the Golden Record: Toward a Design Theory for Trustworthy Master Data Management with Self-Sovereign Identity.”
    The paper addresses the persistent challenge of unreliable master data in enterprises, which is still frequently mitigated through strategically risky dependencies on commercial data brokers. To this end, we have developed a design theory for trustworthy master data management based on self-sovereign identity, grounded in a hermeneutic literature review and expert interviews. The theory is instantiated in a reference architecture for data spaces and provides a framework for reliable, sovereign, and traceable master data management.
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