Fraunhofer Institute for Software and Systems EngineeringDigitalization is fundamentally changing business models, value chains, and markets, opening up new opportunities for companies but also presenting them with considerable challenges. The development, evaluation, and sustainable implementation of digital business models require a deep understanding of the economic relationships, value flows, and success factors in digital ecosystems.
Economics and electronic commerce
Research on the analysis of business models, value flows, and digital transformation in data ecosystems.
Our research area analyzes the impact of data-driven innovations on business models and develops frameworks for evaluating and designing value flows in data spaces, platform economies, and digital markets. A particular focus is on the design and evaluation of business models in data spaces and digital platforms, the identification of new roles and value creation potential, and the development of sustainable and profitable business models. We support companies through market analyses, the development of process and evaluation models, the implementation of data assessments, and the support of proof-of-concepts for innovative business models. Our research provides practical insights that help companies leverage the potential of digitalization, tap into new markets, and secure their competitiveness in the digital age. Through close cooperation with partners from business, science, and politics, we contribute to the development of future-proof, resilient, and profitable business models that meet the requirements of an increasingly data-driven economy.
The results of our work show that data-driven business models not only open up new value creation potential, but can also make a decisive contribution to the sustainable development and competitiveness of companies.
Latest publications
| Jahr Year | Titel/Autor:in Title/Author | Publikationstyp Publication Type |
|---|---|---|
| 2026 | Verifiable Credentials-Based Decentralized Master Data Management in Data Spaces Gür, Inan; Schulte, Niklas; Johnson Jeyakumar, Isaac Henderson; Lux, Michael; Kubach, Michael |
Paper |
| 2026 | SLλ: A Scalable Algorithm for Register Automata Learning Dierl, Simon; Fiterǎu-Broştean, Paul; Howar, Falk; Jonsson, Bengt; Sagonas, Konstantinos; Taquist, Fredrik |
Zeitschriftenaufsatz Journal Article |
| 2026 | Enabling Non-Simulation Experts to Adapt Digital Twins during Production System Redesign using Agentic AI Schwede, Christian; Schipper, Lennart; Cirullies, Jan |
Konferenzbeitrag Conference Paper |
| 2026 | Effects of large language model-generated, patient-oriented discharge summaries on patient activation: a single-centre, single-blind, randomised controlled trial in Germany Rust, Paul; Frings, Julian; Meister, Sven; Schulte, Falko C.; Prinz, Christian; Fehring, Leonard |
Zeitschriftenaufsatz Journal Article |
| 2026 | From Values to Policies: A Value-Based Decision Model for Data Trustees in Data Spaces Steinert, Michael; Geller, Simon; Lauf, Florian |
Konferenzbeitrag Conference Paper |
| 2026 | Data Spaces for Sustainable Product Development - A Structured Analysis of Technical and Semantic Infrastructures Quernheim, Niklas; Scheerer, Hannah; Hesse, Annika; Winter, Sven; Valk, Hendrik van der; Otto, Boris; Schleich, Benjamin |
Zeitschriftenaufsatz Journal Article |
Diese Liste ist ein Auszug aus der Publikationsplattform Fraunhofer-Publica
This list has been generated from the publication platform Fraunhofer-Publica
Examples from our projects
Our research projects are developed in close collaboration with industry partners. They are market- and demand-oriented, with the aim of providing applicable solutions for the cloud edge ecosystem.
IEDS: Incentives and Economics of Data Sharing
The aim of the project "Incentives and Economics of Data Sharing" (IEDS) is to investigate the design of incentive systems for data sharing in an entrepreneurial context through interdisciplinary collaborative research. To this end, the topic of data sharing will be analyzed from technical and economic perspectives. Specifically, insights will be gained and mechanisms developed on how to incentivize organizations to participate in open and collaborative data networks.