Companies and entrepreneurs that integrate generative AI into products or services are increasingly participating in generative AI platform ecosystems. Platform owners such as OpenAI, Google, and Baidu provide interfaces and development tools for this purpose. On this basis, third parties can develop innovative business models for their customers.

Participation in generative AI platform ecosystems opens up new value (co-)creation potential (Heimburg et al. 2025), but also brings with it specific challenges that are closely linked to the characteristics of generative AI (Berente et al. 2021; Heimburg et al. 2025). These challenges affect both third-party providers (complementors) that want to co-create value and gain competitive advantages (Kemp 2024) and platform owners that implement governance mechanisms to ensure the effective operation of the platform ecosystem (Parker et al. 2016).

The thesis should examine one aspect of value (co-)creation and/or governance in generative AI platform ecosystems in depth. One possible focus is on the role of third-party providers who contribute significantly to value (co-)creation in the ecosystem (Deilen & Wiesche 2021). A study focusing on the design of governance mechanisms by platform owners or the interface design through which end users interact with third-party providers' offerings is also conceivable.

Exemplary questions could include:

• What value co-creation mechanisms can third-party providers use to strengthen their role in the ecosystem?
• How do third-party providers co-create value through data they provide in generative AI platform ecosystems?
• What challenges arise when integrating generative AI into services and products, and what solutions exist?
• How do third-party providers use MLOps (or Generative AI Ops, Agent Ops) to bring transparency and observerability to the operation of AI-based business models and run them efficiently?
• What challenges arise for third-party providers and platform owners as a result of the increasing autonomy of products and services in the context of “agentic AI”?
• How are traditional platform ecosystems transforming into generative AI platform ecosystems by integrating generative AI interfaces and development tools?
• To what extent do concepts such as governance, value creation, or generativity in generative AI platform ecosystems differ from previous platform ecosystems?
• Under what conditions are generative AI platform ecosystems attractive to third-party providers and enable value creation?
• How do value (co-)creation logics in generative AI platform ecosystems change over time, and what strategic adjustments can be observed among the various actors?
• To what extent do concepts such as governance, value (co-)creation, or generativity in generative AI platform ecosystems differ from traditional platform ecosystems?
• What ethical and regulatory challenges arise in the governance of generative AI platform ecosystems, and how do platform owners and third-party providers design mechanisms to address them?

The following research methods, for example, can be used for the work:

• Primary data collection (interviews or surveys among third-party providers or end users)
• Analysis of publicly available data

Requirements:

This topic is aimed at students of economics, industrial engineering, or applied computer science who meet the following requirements:

• Interest in current research on digital platform ecosystems
• Interest in current research on the integration of generative AI into business models
• High degree of independence and personal responsibility
• Experience with scientific research methods and analytical skills

Literature:

• Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Managing artificial intelligence. MIS quarterly, 45(3).
• Deilen, M., & Wiesche, M. (2021). The Role of Complementors in Platform Ecosystems. In Wirtschaftsinformatik Conference Proceedings
• Fetzer, D., Gimpel, H., Meindl, O., & Strickmann, J. (2025). Responsible Engineering of Information Systems Based on Generative Artificial Intelligence: An Action Design Research Study at a German Premium Car Manufacturer. Business & Information Systems Engineering, 1-26.
• Heimburg, V., Schreieck, M., & Wiesche, M. (2025). Complementor value co-creation in generative AI platform ecosystems. Journal of Management Information Systems, 42(2), 491-528.
• Kemp, A. (2024). Competitive advantage through artificial intelligence: Toward a theory of situated AI. Academy of Management Review, 49(3), 618-635.
• Parker, G. G., Alstyne, M. W. V., and Choudary, S. P. 2016. Platform Revolution: How Networked Markets Are Transforming the Economy - and How to Make Them Work for You. Norton & Company