Research Areas

We investigate the prerequisites, mechanisms, and impacts of digital technologies in socio-technical systems in order to improve user outcomes while considering economic framework conditions. Our work focuses on data-driven platforms, AI-powered analytics, and the socio-economic evaluation of digital innovations, particularly in complex, highly regulated domains such as healthcare. We combine information systems perspectives with empirical, quantitative, and qualitative research methods.

Research Area 1: Digital Platforms and Infrastructures

Digital platforms and infrastructures constitute the foundation for data-driven innovation in business, science, and society. In this research area, we analyze how platforms emerge, evolve, and maintain long-term viability, especially under conditions of regulatory, technical, and organizational complexity.

Our research examines mechanisms such as generativity, system embeddedness, and governance to better understand the dynamics of digital ecosystems. We distinguish between social and technical forms of platform openness and demonstrate how tensions between user groups, data providers, and developers shape innovation trajectories. We develop theoretical models to explain growth limits, infrastructure evolution, and economic sustainability, and we draw on quantitative and qualitative research methods including panel data analysis, case studies, and systematic reviews. Particular attention is given to cross-sector platforms, for example in healthcare, whose long-term sustainability must be secured not solely through network effects but also through sustainable value creation.

• Fürstenau, D., Baiyere, A., Schewina, K., Schulte-Althoff, M., & Rothe, H. (2023). Extended generativity theory on digital platforms. Information Systems Research, 34(4), 1686–1710. https://doi.org/10.1287/isre.2023.1209
• Fürstenau, D., Baiyere, A., & Kliewer, N. (2019). A dynamic model of embeddedness in digital infrastructures. Information Systems Research, 30(4), 1319–1342. https://doi.org/10.1287/isre.2019.0864
• Akbari, K., Fürstenau, D., & Winkler, T. J. (2024). Governance and longevity of architecturally embedded applications. Journal of Management Information Systems, 41(1), 266–296. https://doi.org/10.1080/07421222.2023.2301169
• Kilgus, T., et al. (2024). Creating value from the secondary use of health data. Communications of the Association for Information Systems, 55, 507–534. https://doi.org/10.17705/1CAIS.05520

Research Area 2: AI & Analytics

In this area, we design and evaluate data-driven approaches that integrate classical machine learning techniques with current advances in natural language processing (NLP) and large language models (LLMs). A central principle of our empirical work is user- and outcome-centeredness. By combining experimental designs, real-world evaluations, and explainability methods, we aim to make the potential benefits transparent and ensure the trustworthiness of decision support.

Our focus includes the curated integration of heterogeneous datasets as well as the systematic assessment of data quality and fairness. Special attention is given to multimodal AI systems that jointly analyze structured and unstructured data to enable robust services in healthcare and nursing care.

• Seibert, K., et al. (2021). Application scenarios for artificial intelligence in nursing care: Rapid review. Journal of Medical Internet Research, 23(11), e26522. https://doi.org/10.2196/26522
• Schulte-Althoff, M., Fürstenau, D., & Moo Lee, G. (2021). A Scaling Perspective on AI Startups. In Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS), 6515–6524. Hawaii International Conference on System Sciences (HICSS). https://doi.org/10.24251/HICSS.2021.784 
• Nanevski, I., et al. (2025). Evaluating the quality of synthetic data in health care [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-6320382/v1
• Seibert, K., et al. (2023). Exploring needs and challenges for AI in nursing care. BMC Digital Health, 1, Article 13. https://doi.org/10.1186/s44247-023-00015-2
• Nowak, A., et al. (2024). AI for care. In Herrmann et al. (Eds.), The digitalization of healthcare for older adults. https://doi.org/10.14279/depositonce-20430

Research Area 3: Socio-Economic Evaluation

Not every digital solution in healthcare is equally worthwhile. But which metrics matter in addition to clinical outcomes? In this research area, we develop and evaluate methods that assess digital solutions and implementation initiatives with regard to costs and promised benefits.

Our empirical work emphasizes both social and economic outcomes. By combining experimental designs, patient-reported outcomes, and simulation methods, we aim to make the benefit potential transparent and ensure the feasibility of various digital transformations. This includes the curated integration of patient-generated quality metrics, the systematic evaluation of costs and effectiveness, as well as ethical, legal, and social implications (ELSI). We also place special emphasis on the market introduction of AI systems that jointly leverage structured and unstructured data to enable robust services in healthcare and nursing.

• Fürstenau, D., Gersch, M., & Schreiter, S. (2023). Digital therapeutics (DTx). Business & Information Systems Engineering, 65(3), 349–360. https://doi.org/10.1007/s12599-023-00804-z
• Doctor, E., et al. (2023). A maturity model for assessing the digitalization of public health agencies. Business & Information Systems Engineering, 65(5), 539–554. https://doi.org/10.1007/s12599-023-00813-y
• Vogel, A., et al. (2021). The social construction of the patient–physician relationship. Social Science & Medicine, 289, Article 114420. https://doi.org/10.1016/j.socscimed.2021.114420
• Zuber, A., et al. (2024). The impact of adhering to a quality indicator. PLOS ONE, 19(8), e0308948. https://doi.org/10.1371/journal.pone.0308948