Designing Data Ecosystems: Value, Impacts, and Fundamentals

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    Towards Electronic Healthcare for All: Designing a Capability Maturity Model for Digital Healthcare Services
    (2025-01-07) Campmann, Julia; Rosenkranz, Nadine; Rosenkranz, Christoph
    European healthcare systems are becoming increasingly digital. The European Health Data Space (EHDS) proposal aims to create a single European governance for the secondary use of health data by 2025. The EHDS will introduce new regulations for data access, availability, use, protection, and standards, creating both opportunities and challenges, especially for small and medium-sized enterprises (SMEs) that rely on healthcare data. Our study aims to address these challenges through a design-oriented research approach. In this paper, we present the first version of a Capability Maturity Model (CMM) that builds on existing knowledge from the literature and insights from empirical studies to support SMEs that want to engage in activities and the role of a data provider within the EHDS. Our maturity model addresses the necessary infrastructure, service, and data requirements for SMEs to provide secure and usable data.
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    Engaging in Data Ecosystems: Propositions for Design Characteristics to Foster Value Co-Creation
    (2025-01-07) Fassnacht, Marcel; Benz, Carina; Riefle, Lara; Fromm, Hansjoerg; Satzger, Gerhard
    Data ecosystems are gaining traction as key constructs for innovation, collaboration, and co-creation of value. Existing research has predominantly focused on the design of data ecosystems (e.g., incentive mechanisms, governance, or business models) on an institutional level. Surprisingly, actor engagement as the microfoundation for value co-creation has only received very little attention. To advance our understanding of this crucial factor for ecosystem success, we conduct a multiple case study across three real-world data ecosystems. We examine the characteristics of the cases, analyze the observable engagement behavior within each case, and apply a cross-case analysis to derive six propositions for data ecosystem design characteristics that foster value co-creation. Thus, we contribute to unraveling the complex dynamics of data ecosystems with a set of hypotheses that encourage participation and collaboration for more effective value co-creation in emerging data ecosystems.
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    To Sell or Not to Sell? Drivers and Barriers to Direct Data Monetization in Incumbents
    (2025-01-07) Mirbagheri, Fatemeh
    The rapid expansion of data along with the growing significance of analytics across various fields has led to direct data monetization, where data and data-driven products are exchanged for monetary value. Although data monetization provides incumbents with significant opportunities for new revenue streams, they move slowly in monetizing data. This study aims to identify the drivers and barriers to selling data in incumbents. Through a field study, we establish two comprehensive typologies of drivers and barriers. Our results show that endogenous factors, such as strategy and resource access, have a stronger impact on data monetization in incumbents than exogenous factors. Conversely, exogenous barriers, such as lack of demand and market immaturity, negatively affect data monetization more significantly than endogenous barriers. This study provides a deeper understanding of direct data monetization. It helps practitioners see different motivations and barriers related to data monetization, aiding them in scoping their data initiatives.
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    Bad Data Quality Eats Ecosystem for Breakfast
    (2025-01-07) Dzierzawa, Fabian; Petrik, Dimitri; Stuber, Kim; Merz, Sarah; Jaensch, Lennart; Herzwurm, Georg
    This paper explores challenges in intra- organizational data ecosystems, with a focus on data quality and its impact on organizations. Data quality is essential for optimizing data integration and interoperability within large companies, which often function as independent ecosystems. However, data sharing is frequently hindered by various issues. Through expert interviews with two distinct business units at a leading global tech firm, the study identifies six data issues with technical incompatibilities as the main cause of data quality problems. It reveals that independent technical decisions by one actor can significantly affect others, highlighting the need to balance individual requirements with overall data quality improvement.
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    A Configurational Approach to Understanding Data Ecosystems
    (2025-01-07) Kernstock, Philipp; Altenkamp, Pascal; Böttcher, Timo; Hein, Andreas; Krcmar, Helmut
    In an era of data-driven decision-making, organizations increasingly build data ecosystems to share and capitalize on data within their ecosystem. With the advent of various initiatives with different goals, architectures, and governance structures, understanding the possible configurations that lead to vibrant data ecosystems is crucial for enhancing innovation and collaboration. This study investigates these configurations, focusing on the interplay between technical and social boundary resources, centralization, domain specialization, and the number of developing partners. Based on data from 26 data ecosystem initiatives, we use fuzzy-set Qualitative Comparative Analysis to identify three configurations of vibrant data ecosystems and derive two configurations associated with less success. Our findings contribute to understanding how different elements' combinations impact data ecosystems' performance, offering insights for practitioners aiming to enhance data sharing, innovation, and collaboration within their ecosystems.
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    Introduction to the Minitrack on Designing Data Ecosystems: Value, Impacts, and Fundamentals
    (2025-01-07) Schoormann, Thorsten; Möller, Frederik; Strobel, Gero; Jussen, Ilka
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    Data Sharing is Caring – A Multiple Case Study on Business Model Types in Decentralized Data Ecosystems
    (2025-01-07) Ammann, Jana
    Data spaces are paving the way for decentralized data ecosystems to emerge in practice. However, our understanding of their influence on how data providers and consumers create and capture value remains limited. Based on 22 qualitative interviews with experts from two cases, we delineate two generic business model types for data sharing enabled by the ecosystem context: bartering and marketplace. In the first case, Catena-X, participants barter data for data to capture value indirectly. Conversely, in the second case, the Mobility Data Space, the underlying marketplace model enables data providers to sell or donate data to consumers. By comparing the two cases and their generic business model types, we explore the impact of decentralization on value creation and capture.