@inproceedings{63754,
  abstract     = {{Data spaces are receiving an emerging interest in Information Systems Research and industry practice. They are central to many European research initiatives and shape the data economy in Industry 4.0. Generally, they aim to create secure environments for cross-organizational data management and sharing. Currently, there is considerable interest in developing new data spaces in Industry 4.0, also accelerated through regulatory changes. However, key questions about what precisely characterizes a data space in Industry 4.0 remain unresolved. Against this backdrop, we build a taxonomy of data spaces in the Industry 4.0 context. We identified nine distinctive dimensions and 40 corresponding characteristics among the 19 data spaces analyzed. The taxonomy enables clearer classification and nomenclature of data spaces in this context. This short paper will ignite planned further research on data spaces in Industry 4.0 and contribute to a conceptualization of a taxonomic theory for interested researchers.}},
  author       = {{Werth, Oliver and Koldewey, Christian and Uslar, Mathias and Zerbin, Julian}},
  booktitle    = {{Lecture Notes in Business Information Processing}},
  isbn         = {{9783032145178}},
  issn         = {{1865-1348}},
  keywords     = {{Industry 4.0, Taxonomy, Data spaces, Characterization}},
  location     = {{Stuttgart, Germany}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{What Characterizes Data Spaces in Industry 4.0? Towards a Better Understanding}}},
  doi          = {{10.1007/978-3-032-14518-5_3}},
  year         = {{2026}},
}

@inbook{65835,
  author       = {{Koldewey, Christian and Avermeyer, Celina Maleen and van der Valk, Hendrik and Zerbin, Julian and Dumitrescu, Roman}},
  booktitle    = {{Lecture Notes in Computer Science}},
  isbn         = {{9783032283122}},
  issn         = {{0302-9743}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Fundamental Patterns – A Taxonomy for Archetype Development in Information Systems}}},
  doi          = {{10.1007/978-3-032-28313-9_18}},
  year         = {{2026}},
}

@article{66070,
  abstract     = {{Der Beitrag stellt einen Online-Quick-Check vor, der KMU einen niedrigschwelligen Einstieg in industrielle Datenräume ermöglicht. Der Check kombiniert eine zweidimensionale Einschätzung der Readiness (strategisch/technologisch) mit einer Mehrwert-Priorisierung zur Ableitung passender Use Cases. In einer Pilotierung (n = 19) zeigen sich v. a. Defizite bei der Vernetzung, den datenbasierten Entscheidungen und Datenrollen; priorisiert werden Mehrwerte in Planung, Qualität und Effizienz.}},
  author       = {{Zerbin, Julian and Grünwoldt, Max and Koldewey, Christian and Dumitrescu, Roman}},
  issn         = {{2511-0896}},
  journal      = {{Zeitschrift für wirtschaftlichen Fabrikbetrieb}},
  keywords     = {{KMU, Data Space, Data Sharing, Industrie 4.0, Datensouveränität, Readiness}},
  number       = {{6}},
  pages        = {{458--462}},
  publisher    = {{Walter de Gruyter GmbH}},
  title        = {{{Ein Quick-Check für Industrielle Datenräume – Voraussetzungen und Mehrwerte}}},
  doi          = {{10.1515/zwf-2026-1077}},
  volume       = {{121}},
  year         = {{2026}},
}

@inproceedings{66071,
  abstract     = {{Data ecosystems offer new opportunities for value creation by enabling companies to combine and reuse
data beyond their own boundaries. Coopetition, i.e., simultaneous cooperation and competition, is an
important dynamic within data ecosystems that must be understood and managed well to leverage the
non-rivalrous nature of data. A systematic literature review of 38 peer-reviewed studies on ecosystems
and coopetition was conducted to clarify how coopetition affects data ecosystems. Using qualitative
content analysis, the study summarizes how coopetition is calibrated, what results it produces, and what
challenges are associated with it. The resulting Data Ecosystem Coopetition (DECo) model specifies
four management levers, as well as the associated outcomes and common challenges. In addition, six
cycles were identified that link levers, challenges, and outcomes. These cycles provide concise guidance
on elements that tend to vary together and highlight areas that are prone to specific tensions.}},
  author       = {{Zerbin, Julian and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{ECIS 2026 Proceedings}},
  keywords     = {{data sharing, interfirm relations, paradox theory, strategic alliances, network governance}},
  location     = {{Mailand}},
  title        = {{{Towards An Understanding Of Coopetition In Data Ecosystems: A Systematic Literature Review}}},
  year         = {{2026}},
}

@inproceedings{66073,
  abstract     = {{Data-driven business models are transforming industries, yet small and medium-sized enterprises (SMEs) often lack the resources to participate in emerging data sharing initiatives. Data spaces provide a federated infrastructure for sovereign data sharing, but SME participation remains limited due to complex onboarding processes involving technical, organizational, legal, and economic barriers. Data trustees have been proposed as intermediaries to facilitate data sharing, but their specific role in supporting SME onboarding has not been systematically explored. Here we present a reference process for SME data space onboarding and derive four design principles, structured along the TELO framework, describing how data trustees can address these barriers. Our findings reveal that SMEs require comprehensive support throughout the onboarding journey, with particular emphasis on business case development. The results offer actionable guidance for data space operators and policymakers seeking to broaden ecosystem participation as the European Union advances its data strategy.}},
  author       = {{Hupperz, Marius Johannes and Zerbin, Julian and Kunert, Julia}},
  booktitle    = {{AMCIS 2026 Proceedings}},
  keywords     = {{Data spaces, data trustees, SME Onboarding, data sharing}},
  location     = {{Reno - Nevada}},
  title        = {{{Toward Design Principles for Data Trustee-Supported SME Onboarding in Data Spaces}}},
  year         = {{2026}},
}

@inproceedings{66072,
  abstract     = {{Data Spaces are establishing themselves as a decentralized alternative for sovereign data sharing across organizations. However, their adoption faces significant challenges, particularly the lack of proven business models that encourage participation and value creation. A data space business model captures value differently than either traditional business models or platform business models. This paper addresses this challenge by developing the Data Space Canvas, a structured framework for designing and aligning business models in data spaces consisting of nine building blocks. The canvas was developed following the design science research methodology. The modular and role-specific structure of the Data Space Canvas facilitates shared understanding and supports business model development across participants. This research contributes a practical tool that enables organizations to conceptualize, communicate, and refine their role and business model in data spaces.}},
  author       = {{Zerbin, Julian and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{ECIS 2026 Proceedings}},
  keywords     = {{data ecosystems, business model, design science, resource orchestration theory, data sharing}},
  location     = {{Mailand}},
  title        = {{{The Data Space Canvas: A Framework For Structuring Business Models In Data Spaces}}},
  year         = {{2026}},
}

@inproceedings{58763,
  abstract     = {{Utilizing data is crucial for economic success, but a lack of interoperability and concerns about the misuse of ones own data are hindering the cross-organizational use of data. Dataspaces provide the infrastructure necessary to integrate heterogeneous data sources within an organization or ecosystem, enabling seamless data interaction and interoperability. In addition, data spaces strengthen data sovereignty through their decentralized nature, which enables organizations to effectively control and manage their data. However, challenges persist in managing the complexity and dynamic nature of dataspaces, requiring significant resources and technical expertise. The decentralized nature leads to a large and diverse number of stakeholders, who need to agree on the use and scope of a dataspace. Modeling is a common approach to cope with technical complexity and heterogeneous stakeholders. In this paper, we propose a version of SysML and a corresponding method that focus on the modelling of data spaces. We provide a dataspace modelling method to unify the understanding of dataspaces and scope among all stakeholders to simplify the design and development process.}},
  author       = {{Kulkarni, Pranav Jayant and Zerbin, Julian and Koldewey, Christian and Bernijazov, Ruslan and Dumitrescu, Roman}},
  booktitle    = {{2024 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)}},
  keywords     = {{Dataspaces, Modelling, SysML, Gaia-X, System Specification}},
  location     = {{Sharjah, United Arab Emirates }},
  publisher    = {{IEEE}},
  title        = {{{Using SysML as a Modelling Language for Dataspaces}}},
  doi          = {{10.1109/ictmod63116.2024.10878227}},
  year         = {{2025}},
}

@inproceedings{65038,
  abstract     = {{The rapid advancements in digital transformation have led to the emergence of dataspaces as a pivotal element for industry-wide and cross-industry data integration and interoperability across various businesses. Despite their potential, the adoption and effective utilization of dataspaces by business stakeholders remain challenging. This paper aims to address this gap by developing a comprehensive learning environment tailored for business stakeholders. Through an interview study and an analysis of the current state of research, we identify problem fields and derive key requirements for the development of the learning environment. The proposed environment includes a demonstrator and a training concept designed to enhance stakeholders' understanding and capabilities in managing and leveraging dataspaces. Our findings contribute to the body of knowledge by providing practical guidance through learning environments for the deployment of dataspaces in business contexts and highlighting areas for future research.}},
  author       = {{Lick, Jonas and Lamarz, Jessica and Dohmann, Friederike and Kulkarni, Pranav Jayant and Zerbin, Julian and Koldewey, Christian}},
  booktitle    = {{2024 6th International Conference on Control and Robotics (ICCR)}},
  location     = {{Yokohama, Japan }},
  publisher    = {{IEEE}},
  title        = {{{Guidance on Dataspaces: Development of a Learning Environment for Industrial SMEs}}},
  doi          = {{10.1109/iccr64365.2024.10927580}},
  year         = {{2025}},
}