Abstract
The Marispace-X project aims to create a digital ecosystem of providers and users of data from the oceans. For this purpose, different use cases (munitions detection, biological climate protection, the construction of offshore wind platforms and the testing of IoT sensors) are used as examples to model the benefits of the planned ecosystem. One of the main focuses of the project is to analyze and build emerging business models. Most research in this sector is focussed on singular business models, while industry specific business models are underrepresented. The problems in building such an ecosystem are not only technical, but also social in nature. Therefore, this work aims to illustrate how academia and industry can work together to address problems and develop possible solutions for Digital Business Ecosystems (DBE). This conjunction of problems, i.e., the view of an industry domain as a business model and the view of digital business ecosystems, motivate this research. This work shows how to start building an ecosystem or platform and how to sensitize relevant stakeholders to existing challenges in designing ecosystems. Furthermore, some solution approaches are presented to address sub-problems in business model design, especially in the area of value creation through data. Lessons learned and further open challenges can be found at the end of the paper. The contributions of this paper are: (1) an emerging digital business ecosystem with different roles, (2) a problem/goal model for science-communication with relevant stakeholders, (3) the application of an analysis tool for business models with a FAIR-Data Value Chain, (4) remaining challenges, especially in the areas of ecosystem governance and modelling.
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Hellmanzik, B., Sandkuhl, K. (2024). Challenges in Building a Digital Business Ecosystem in the Maritime Domain. In: Malinova Mandelburger, M., Guerreiro, S., Griffo, C., Aveiro, D., Proper, H.A., Schnellmann, M. (eds) Advances in Enterprise Engineering XVII. EDEWC 2023. Lecture Notes in Business Information Processing, vol 510. Springer, Cham. https://doi.org/10.1007/978-3-031-58935-5_1
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