Möller, Frederik

Otto, Boris

Schoormann, Thorsten

Strobel, Gero

English

ScholarSpace at University of Hawai'i at Manoa

Introduction to the Mini-Track on Designing Data Ecosystems:  Values, Impacts, and Fundamentals   Frederik Möller TU Dortmund University & Fraunhofer ISST Frederik.Moeller@tu-dortmund.de Gero Strobel University of Duisburg-Essen Gero.Strobel@paluno.uni-due.de  Thorsten Schoormann University of Hildesheim thorsten.schoormann@uni-hildesheim.de Boris Otto TU Dortmund University & Fraunhofer ISST Boris.Otto@tu-dortmund.de  Abstract Data ecosystems are a novel approach to enable data sharing on an organizational and personal level. The track ‘Designing Data Ecosystems: Values, Impacts, and Fundamentals’ invited papers on various issues exploring the phenomenon of data ecosystems from multiple angles. Themes included the fundamentals of data sharing, incentives, and barriers to data sharing, as well as data space design.  Keywords: Data Ecosystems, Data Sharing, Data Spaces, Data Marketplaces 1. Introduction Data ecosystems are a novel perspective on leveraging the potential of data drawing from the ecosystem analogy originating in biology (Oliveira et al., 2019). The fundamental activity enabling data ecosystems is sharing data between different stakeholders, such as individuals, companies, or governmental agencies (Prieelle & Reuver, 2020). Sharing data between organizations is a powerful motor to generate new business value and optimize internal processes. Also, having an appropriate set of data enables the application of emerging technologies that, for instance, can make valuable contributions to grand societal challenges (Schoormann et al., 2021). A variety of research streams explored data-driven business models from a primarily internal perspective (e.g., Schüritz et al., 2017), which we extend in this track through a broader ecosystem perspective. The track looked for papers investigating data ecosystems from multiple angles. For example, papers elaborating on the peculiarities of data sharing, broadening the mere technical view of transmitting data from a data provider to a data receiver. Explicitly, we looked for conceptualizations of data ecosystems, as well as incentive mechanisms and barriers to data sharing (Gelhaar et al., 2021). In this, we did not focus on data sharing in either an industry or personal setting but instead left it to the authors to propose interesting papers on either or both. We also left it to the authors to report on specific elements of data ecosystems, be it more technical (e.g., in data spaces) or organizational (e.g., in barriers to data sharing). We invited studies with diverse methodological backgrounds. 2. Designing Data Ecosystems: Values, Impacts, and Fundamentals The track had a total of 13 submissions with six accepted papers, resulting in an acceptance rate of 46,15%. The mini-track chair paper was handled by the track chairs of the Internet and the Digital Economy track. Papers with institutional overlap to the mini-track chairs were handled exclusively by those chairs that did not have a potential conflict of interest. The six accepted papers cover broad themes in data ecosystems, such as data sharing, data marketplaces, and open platforms. They report on various methods, including literature reviews and interview studies. Next, we provide an overview of the accepted papers from our mini-track. The first paper (1) by Jussen et al. (2023) investigates the fundamentals of data sharing and contrasts them with data exchange. Their findings suggest that data exchange mainly refers to the technical exchange of data (e.g., through data exchange formats), while data sharing embodies the social-technical construct that is also riddled with many organizational decisions and characteristics. In their work, the authors propose a definition and defining characteristics of data sharing. Proceedings of the 56th Hawaii International Conference on System Sciences | 2023Page 3683URI: https://hdl.handle.net/10125/103082978-0-9981331-6-4(CC BY-NC-ND 4.0)The second paper (2) by Fassnacht et al. (2023) proposes a set of barriers to sharing data in private organizations. They extract these barriers from an interview study with 14 experts from different industries. These barriers include fear of data misuse, legal concerns, or the lack of technical capabilities. The third paper (3) by Gelhaar et al. (2023) reports on a single case study in the Catena-X Automotive Network project extracting a set of incentives for sharing data. The authors analyze the case by interviewing experts and generating results through the lens of the Motive-Incentive-Activation-Behaviour Model. They propose eight incentives matched with seven motives.  The fourth paper (4) by Scheider et al. (2023) proposes a set of design principles for human-centric B2C data ecosystems. Their work builds on an interview study with six informants, as well as a systematic literature review. The design principles cover data sovereignty, legal compliance, economic rationale, ethical correctness, and technical implementation. The fifth paper (5) by Abbas et al. (2023) investigates issues in meta-platforms with the example of data marketplaces. The authors report on an interview study with 20 informants engaging in data sharing for their organizations. They propose three value creation archetypes: discovery aggregator, brokerage, and one-stop-shop. The sixth paper (6) by Rudmark & Lindgren (2023) on an ADR study builds on a large-scale interview and workshop study. The result is the codification of design knowledge for open platforms in a design theory.  3. Conclusion The six accepted papers show a broad spectrum of topics and issues regarding data ecosystems. They allow discussing the nature of data sharing in various domains. From this starting point, we find a promising indication of where data ecosystem research is headed: exploring the interplay of different actors in data ecosystems on multiple levels, such as technical, economic, and organizational. References Abbas, A. E., Ofe, H., Zuiderwijk, A., & Reuver, M. de (2023). Business Models for a Meta-Platform: Exploring Value Creation in the Case of Data Marketplaces. In Proceedings of the 56th Hawaii International Conference on System Sciences, Hawaii: USA. Fassnacht, M., Benz, C., Heinz, D., Leimstoll, J., & Satzger, G. (2023). Analyzing Barriers to Data Sharing among Private Sector Organizations: Combined Insights from Research and Practice. In Proceedings of the 56th Hawaii International Conference on System Sciences, Hawaii: USA. Gelhaar, J., Guerpinar, T., Henke, M., & Otto, B. (2021). Towards a Taxonomy of Incentive Mechanisms for Data Sharing in Data Ecosystems. In Proceedings of the 25th Pacific Asia Conference on Information Systems, Dubai: UAE. Gelhaar, J., Müller, P., Bergmann, N., & Dogan, R. (2023). Motives and incentives for data sharing in industrial data ecosystems: An explorative single case study. In Proceedings of the 56th Hawaii International Conference on System Sciences, Hawaii: USA. Jussen, I., Schweihoff, J., Dahm, V., Möller, F., & Otto, B. (2023). Data Sharing Fundamentals: Definition and Characteristics. In Proceedings of the 56th Hawaii International Conference on System Sciences, Hawaii: USA. Oliveira, M. I. S., Barros Lima, Glória de Fátima, & Farias Lóscio, B. (2019). Investigations into Data Ecosystems: a systematic mapping study. Knowledge and Information Systems, 61(2), 589–630. https://doi.org/10.1007/s10115-018-1323-6 Prieelle, F. de, & Reuver, M. (2020). The Role of Ecosystem Data Governance in Adoption of Data Platforms by Internet-of-Things Data Providers: Case of Dutch Horticulture Industry. IEEE Transactions on Engineering Management, 64(4), 940–950. https://doi.org/10.1109/TEM.2020.2966024 Rudmark, D., & Lindgren, R. (2023). Designing Open Platforms: An Emulation Theory. In Proceedings of the 56th Hawaii International Conference on System Sciences, Hawaii: USA. Scheider, S., Lauf, F., & Geller, S. (2023). Data Sovereign Humans and the Information Economy: Towards Design Principles for Human-Centric Data Ecosystems. In Proceedings of the 56th Hawaii International Conference on System Sciences, Hawaii: USA. Schoormann, T., Strobel, G., Möller, F., & Petrik, D. (2021). Achieving Sustainability with Artificial Intelligence—A Survey of Information Systems Research. In Proccedings of International Conference on Information Systems, Austin:USA. Schüritz, R., Seebacher, S., & Dorner, R. (2017). Capturing Value from Data: Revenue Models for Data-Driven Services. In Proceedings of the 50th Hawaii International Conference on System Sciences, Hawaii: USA.   Page 3684

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Introduction to the Minitrack on Designing Data Ecosystems: Value, Impacts, and Fundamentals

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