Designing Data Ecosystems: Value, Impacts, and Fundamentals

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    Barriers to Data Sharing among Private Sector Organizations
    ( 2023-01-03) Fassnacht, Marcel ; Benz, Carina ; Heinz, Daniel ; Leimstoll, Jannis ; Satzger, Gerhard
    In today’s digital world, sharing data among private sector organizations to realm mutual benefits, such as innovation and value co-creation, is considered a promising yet barely explored and realized approach. Although private sector organizations are pursuing data sharing, successful real-world examples are sparse due to a multitude of barriers. However, knowledge on barriers to data sharing among private sector organizations is scarcely existent in scientific literature. Therefore, we apply an exploratory research approach by triangulating insights from fourteen expert interviews and a systematic literature review to identify barriers which we group along five distinct perspectives. By exploring the multi-faceted barriers to data sharing among private sector organizations, our work contributes to a better understanding of data sharing in this field and lays the foundation for future studies. For practitioners, we identify key challenges to successful data sharing among private sector organizations and, hence call for additional endeavors in data sharing.
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    Motives and Incentives for Data Sharing in Industrial Data Ecosystems: An Explorative Single Case Study
    ( 2023-01-03) Gelhaar, Joshua ; Müller, Paul ; Bergmann, Nils ; Dogan, Rojda
    The increasing connectivity of the business world leads to economic value being created less and less by one company alone, but rather through the exchange and combination of data by various actors in so-called data ecosystems. However, many companies are not yet willing to participate in data ecosystems because they do not see the added value of their participation. This is partly because the motives of data providers do not match the incentives offered to share their data. So far, there are only very few studies that deal with this issue in detail. Therefore, we close this research gap by adopting a conceptual model to the issue of motives and incentives for data sharing and applying it to the industrial data ecosystem Catena-X in a single case study. Through the case study analysis, we can identify seven different motives and eight incentives for data sharing.
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    Data Sharing Fundamentals: Definition and Characteristics
    ( 2023-01-03) Jussen, Ilka ; Schweihoff, Julia ; Dahms, Valentin ; Möller, Frederik ; Otto, Boris
    The importance of data as a key resource is a universal theme dominating social and business life. In this regard, inter-organizational data sharing shines in a new light prompting businesses to leverage their potential. However, it is still unclear what data sharing actually entails, i.e., what it means, what its potentials are, and what barriers one must overcome. In short, it lacks conceptual clarity and a clear description of its characteristics. The conceptual ambiguity and the synonymous use with data exchange in the literature are particularly problematic, which prevents a targeted conceptualization and use. The paper starts precisely at this point as it proposes a unifying definition and characteristics of data sharing. We report on a systematic literature review characterizing data sharing and delineating it from data exchange.
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    Toward Business Models for a Meta-Platform: Exploring Value Creation in the Case of Data Marketplaces
    ( 2023-01-03) Abbas, Antragama Ewa ; Ofe, Hosea ; Zuiderwijk, Anneke ; De Reuver, Mark
    Investigating meta-platforms has been a continuing concern within information system literature due to the increasingly complex constellations of platforms in ecologies of ecosystems. A meta-platform is a platform built on top of two or more platforms, hence connecting their respective ecosystems. One promising case to benefit from meta-platforms is data marketplaces: a particular type of platform that facilitates responsible (personal and non-personal) data sharing among companies. Given that business models for meta-platforms are largely unexplored in this emerging case, how they can create value for data marketplaces remain speculative. As a starting point toward business model investigations, this paper explores value creation of a meta-platform in the case of data marketplaces. We interviewed fourteen data-sharing consultants and six meta-platform experts. We identify three potential value creation archetypes of a meta-platform. The discovery aggregator archetype emphasizes searching and dispatching value, while the brokerage one focuses on promoting and supporting value. Finally, the one-stop-shop archetype creates value by standardizing, regulating, sharing, and experimenting. This study is among the first that explore value creation archetypes for a meta-platform, thus identifying core value as a base for further business model investigations.
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    Data Sovereign Humans and the Information Economy: Towards Design Principles for Human Centric B2C Data Ecosystems
    ( 2023-01-03) Scheider, Simon ; Lauf, Florian ; Geller, Simon
    The ever-growing amounts of data offer companies many opportunities for data-driven-value generation which, in turn, can be multiplied by leveraging data across company boundaries in evolving data ecosystems. However, while such systems increasingly emerge in B2B environments enabling systematic sharing and utilization of “industrial data”, comparable concepts in B2C ambits have not yet prevailed. Despite the rising importance of personal data in the information economy, B2C data ecosystems represent a widely unexplored research area. To remedy this gap, the study generates design principles for human centric B2C data ecosystems to aid in their development. For this purpose, a qualitative interview study with experts of interdisciplinary domains and a structured literature review are conducted both embedded into a methodology for generating design principles. On this basis, derived design principles help to understand peculiarities of data ecosystems in B2C ambits and provide solutions to overcome their obstacles identified in the empirical investigation.
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    The Design of Open Platforms: Towards an Emulation Theory
    ( 2023-01-03) Rudmark, Daniel ; Lindgren, Rikard
    The enrolment of third-party developers is essential to leverage the creation and evolution of data ecosystems. When such complementary development takes place without any organizational consent, however, it causes new social and technical problems to be solved. In this paper, we advance platform emulation as a theoretical perspective to explore the nature of such problem-solving in the realm of open platforms. Empirically, our analysis builds on a 10-year action design research effort together with a Swedish authority. Its deliberate change agenda was to transform unsolicited third-party development into a sanctioned data ecosystem, which led to a live open platform that is still in production use. Theoretically, we synthesize and extend received theory on open platforms and offer novel product and process principles for this class of digital platforms.
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    Introduction to the Minitrack on Designing Data Ecosystems: Value, Impacts, and Fundamentals
    ( 2023-01-03) Strobel, Gero ; Schoormann, Thorsten ; Möller, Frederik ; Otto, Boris