Data Ecosystems: Design, Innovation, and Impacts
Permanent URI for this collectionhttps://hdl.handle.net/10125/112504
Browse
Recent Submissions
Item type: Item , Generating Value in Data Ecosystems: The Ontological Characteristics of Data as Enablers of Data-driven Value Propositions(2026-01-06) Toorajipour, Reza; Eaton, Ben; Oghazi, PejvakThe unique characteristics of data require rethinking how firms develop new value propositions. Viewing data as a static resource, as current information systems (IS) literature does, is insufficient; instead, it must be seen as a dynamic, co-constructed asset embedded in evolving data ecosystems. This study explores how data characteristics enable such value propositions through a qualitative case study of a healthcare infrastructure provider. Findings reveal a dual model: a platform supporting care delivery, and a data-driven proposition offering insights from patient-generated data. Thematic analysis identifies four core data characteristics (recontextualization, editability, portability, and non-rivalry) each linked to empirical patterns that explain how data underpins value generation. The study contributes to the literature on data ecosystems by revealing the ontological characteristics of data as enablers of data-driven value propositions, while offering practical guidance for firms leveraging digital data for strategic advantage.Item type: Item , Balance is Key—Insights on the Governance Design of Data Space-Based Ecosystems(2026-01-06) Liebert, Pauline; Oswald, Robin; Hess, ThomasData space-based ecosystems (DSEs) rely on decentralized infrastructures and promote broad member participation to counteract the monopolistic tendencies of platform-based data ecosystems. While this approach enhances fairness for DSE members, it also creates complexity that poses a challenge for effective governance. In this study, we investigate how DSEs can design their governance to integrate these dual goals of fairness and effectiveness. Drawing on a case study of Catena-X, a DSE in the automotive sector, we adopt an exploratory qualitative approach to analyze DSE governance in depth. Our findings reveal four governance dimensions: structures, decisions, relations, and behavior. Across these dimensions, the two goals manifest in different design choices that need to be balanced with each other. This study contributes to the nascent literature on DSEs by providing a nuanced understanding of how they can incorporate both normative principles of fairness and operational aspects fostering effectiveness into their governance.Item type: Item , Why Data Spaces Are Not (Yet) Emerging - a Manufacturing Intralogistics Study(2026-01-06) Toroi, Teemu; Turpeinen, MarkoData sharing is critical to industrial digitalization. Data spaces are socio-technical infrastructures that enable data sharing activities between organizations. Although data spaces are regarded as promising enablers for smart manufacturing, their uptake has remained limited. Drawing on empirical evidence, this study demonstrates that the key factors influencing manufacturing intralogistics companies’ adoption decisions hinge on the relevance of specific data sharing use cases and the perceived viability of data spaces in addressing them. Companies must first recognize tangible business benefits and assess the technical applicability of data spaces before progressing toward adoption. The findings suggest that clear articulation and communication of data space capabilities, enabled use cases, and associated business value are critical to fostering adoption. Otherwise, the emergence of industrial data spaces is likely to remain slow.Item type: Item , Introduction to the Minitrack on Data Ecosystems: Design, Innovation, and Impacts(2026-01-06) Schoormann, Thorsten; Jussen-Lengersdorf, Ilka; Strobel, Gero; Möller, Frederik