The inter-organizational processes in procurement remain burdened by media discontinuity, inefficiencies, and a lack of trust among trading partners. Blockchain-based information systems are frequently proposed as a remedy because they enable shared, tamper-evident records. However, existing instantiations rarely scale beyond small consortia because they fail to address the extended blockchain trilemma, which requires simultaneously achieving decentralization, scalability, security, and strict privacy requirements for sensitive commercial information. In contrast to prior blockchain procurement prototypes that manage the extended blockchain trilemma primarily through permissioned architectures, this study investigates how a blockchain-based information system can be designed to reconcile the trade-offs inherent in the extended trilemma, achieving a viable balance through architectural allocation and cryptographic enforcement. Following the design-science research paradigm, an empirically validated problem statement is synthesized from a structured literature review and expert interviews. Five design objectives are derived, evaluated, and used to guide a prototype design, which is then iteratively refined and evaluated through quantitative and formative assessments and sixteen semi-structured expert interviews. Reflection on the build–evaluate cycles yields two design principles: (1) Balancing decentralization, scalability, and security by using a public chain as a trust anchor, a Layer 2 for scaling, and decentralized communication between layers. (2) Maintaining that balance when privacy is required by integrating efficient, resilient cryptography and minimizing control points. These principles extend existing procurement research, linking business requirements to infrastructural choices, providing a transferable foundation for scholars and practitioners aiming to deploy secure, scalable, and privacy-preserving blockchain solutions in inter-organizational contexts. • Procurement processes suffer from media discontinuity, inefficiencies, and low trust. • DSR study derives five design objectives via literature review and expert interviews. • Prototype designed, tested, evaluated using public trust anchor and second-layer roll-up. • Two design principles guide blockchain design for inter-organizational settings.

Sharing and reusing data across organisations is central to the European data strategy and its transformation towards creating a data-driven economy. Currently, data sharing, when it occurs, is typically facilitated through centralised platforms or bilateral integration solutions, which are increasingly failing to meet requirements for flexibility, trustworthiness, and self-determination over shared data. Data spaces have emerged as a promising solution, built on distributed and shared infrastructure and governance frameworks. However, widespread adoption requires consensus on the necessary organisational and technical capabilities. This paper proposes a data service model and design principles for data services to clarify choices and guide decision-making. We adopt a novel perspective by conceptualising data spaces as meta-organisations to guide an Action Design Research study, utilising privileged access to key large-scale European data space projects in the mobility and automotive sectors. This paper not only develops a nascent design theory for data spaces, but also offers a practical framework, breaking down complex challenges into actionable, scientifically validated components – for further growth and adoption of data spaces.

209

Data ecosystems are a novel inter-organizational form of cooperation. They require at least one data provider and one or more data consumers. Existing research mainly addresses generativity mechanisms in this relationship, such as business models or role models for data ecosystems. However, an essential prerequisite for thriving data ecosystems is high data quality in the shared data. Without sufficient data quality, sharing data might lead to negative business consequences, given that the information drawn from them or services built on them might be incorrect or produce fraudulent results. We tackle this issue precisely since we report on a multi-case study deploying data quality tools in data ecosystem scenarios. From these cases, we derive generalized prescriptive design knowledge as a design theory to make the knowledge available for others designing data quality tools for data sharing. Subsequently, our study contributes to integrating the issue of data quality in data ecosystem research and provides practitioners with actionable guidelines inferred from three real-world cases.