Artificial Intelligence in Management (AI)

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Research Areas   |   Research Focus


Our research is driven by the strong desire to conquer the challenges surrounding the recent wave of digitizalization, especially in health management. Individuals, firms and society as whole face an increasing abundance of data that allows to create value for the provision of care. We not only want to contribute to the managerial dimension but also the technologies themselves. Our research thus propels innovative applications in data-driven management.


»We develop, implement and evaluate AI to improve management«

Research Areas

Our group conducts research on management information systems with the objective of improving digital interactions between humans and machines. The aim is to increase the level of data-driven personalization with a positive effect on management decisions. Examples include areas that are subject to extensive digitalization (e.g., digital platforms, social media and, specifically prevention of fake news) but also stakeholders for whom benefits from digitalization have still to be developed (e.g., digital health management, public sector management).

Our group contributes to theory by advancing our understanding of digital interactions and, in particular, by shaping its future use with respect to measurable performance indicators (e.g., health outcomes). To this end, he develops novel methodological frameworks from the realm of data science that are tailored to a managerial or organizational context.

Reserch Focus

1. Digital Health

How can we leverage health apps for better treatment planning? How can health trajectory data help in personalizing care? How can wearable sensor serve as early warning systems for critical health conditions?

  • Impact: Our digital health projects develop data-driven decision support for health management in order to aid the elemental tasks of treatment planning and treatment selection. Thereby, we answer the cardinal questions of when to choose which treatment. Based on longitudinal health trajectories, we numerically quantify the expected benefits for health management in providing effective care at lower cost. We specifically construct new decision support models.
  • Innovation: Prior research in healthcare analytics has been primarily concerned with an explanatory understanding (e.g., measuring ex post how risk factors were associated with the onset of diseases) or forecasts of expected health outcomes (e.g., predicting readmission risk). However, it has largely neglected prescriptive analytics in health management. Hence, we develop strategies for data-driven treatment planning and selection.
2. Business Analytics

How can predictive analytics create value for businesses? How can firms leverage the unstructured data in emails, word-of-mouth or customer reviews? Which methodological innovations in business analytics are needed? How can we better process semantics in natural language? How can we leverage the data collected from the ubiquituous health monitoring of smart devices?

  • Methods: Descriptive analytics (what has happened?) » Predictive analytics (what will happen?) » Prescriptive analytics (how can one make it happen?)
3. AI for Good

How can AI help reaching the United Nations' Sustainable Development Goals? How can AI foster equality? How can AI improve development aid?


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