Requirements For Incentive Mechanisms In Industrial Data Ecosystems

In the increasingly interconnected business world, economic value is less and less created by one company alone but rather through the combination and enrichment of data by various actors in so-called data ecosystems. The research field around data ecosystems is, however, still in its infancy. In particular, the lack of knowledge about the actual benefits of inter-organisational data sharing is seen as one of the main obstacles why companies are currently not motivated to engage in data ecosystems. This is especially evident in traditional sectors, such as production or logistics, where data is still shared comparatively rarely. However, there is also consensus in these sectors that cross-company data-driven services, such as collaborative condition monitoring, can generate major value for all actors involved. One reason for this discrepancy is that it is often not clear which incentives exist for data providers and how they can generate added value from offering their data to other actors in an ecosystem. Fair and appropriate incentive and revenue sharing mechanisms are needed to ensure reliable cooperation and sustainable ecosystem development. To address this research gap and contribute to a deeper understanding, we conduct a literature review and identify requirements for incentive mechanisms in industrial data ecosystems. The results show, among other things, that technical requirements, such as enabling data usage control, as well as economic aspects, for instance, the fair monetary valuation of data, play an important role in incentive mechanisms in industrial data ecosystems. Understanding these requirements can help practitioners to better comprehend the incentive mechanisms of the ecosystems in which their organisations participate and can ultimately help to create new data-driven products and services

Hunting the Treasure: Modeling Data Ecosystem Value Co-Creation

Data ecosystems are an emerging theme in IS research. They represent the complex dynamics of inter-organizational value co-creation based on data sharing. Interestingly, empirical research on the value that the various actors can extract from participating in a data ecosystem is still sparse. We address this issue by analyzing 64 Gaia-X use cases, each representing a data ecosystem. From them, we derive roles relevant to data ecosystems and describe them according to typical ontological business model elements (value proposition, value creation, value delivery, and value capture). To visualize the value co-creation in data ecosystems, we use the modeling language e3-value. We illustrate this approach by modeling the specific Agri-Gaia use case. Our work contributes to understanding value co-creation in data ecosystems more in-depth as we extract roles, demonstrate how to model actors and their value co-creation, and discuss the implications of a service ecosystems perspective

A Framework to Identify Data Governance Requirements in Open Data Ecosystems

Open data and open data ecosystems (ODEs) are important for stakeholders from science, businesses, and the broader society. However, concerns about data sharing and data handling are significant adoption barriers of ODEs that reduce stakeholder participation and thus the success of the initiative. Data governance (DG) is proposed as solution, but requirements of the three stakeholder groups combined are not clear and especially how they can be integrated in one DG concept. This paper develops a framework, supporting elicitation of DG requirements in ODEs. The framework builds on a series of stakeholder workshops and literature research resulting in DG requirements and DG mechanisms. The resulting framework includes five main dimensions: (1) data usability, (2) ethical and legal compliance, (3) data lineage, (4) data access and specified data use, and (5) organizational design

Characterization of Relationships in Data Ecosystems

The importance of data as a strategic resource for the development of innovation is steadily growing. Data-driven value creation increasingly requires cross-company collaboration between various actors with different roles in so called data ecosystems. So far, however, the existing knowledge in the research field around data ecosystems is still relatively limited. In particular, the relationships and interdependencies between the different actors in a data ecosystem are not well understood yet. To address this research gap, we conduct a structured literature review and interview eleven experts from practice to identify characteristics of relationships between actors in data ecosystems. Among other things, the results show that both tangible characteristics, such as a clear exchange of values, and intangible characteristics, such as trust, are distinguishing features of the relationships between actors in data ecosystems. These study results can serve as a tool for both researchers and practitioners to better understand data ecosystems in general and the relationships and interactions that occur within them

Towards a Framework for Enterprise & Platform Ecosystem Data Governance

Platform ecosystems offer great potential for enterprises by sharing data. However, the convergence of different data from multiple data sources results in large efforts to use this data in a valuable way. Data governance for platform ecosystems has the potential to tackle this problem. However, the data governance of individual enterprises differs significantly from the data governance for platform ecosystems. In this paper a systematic literature review was used to identify the differences between enterprise data governance and data governance for platform ecosystems. On this basis, a conceptual framework that demonstrates the design elements that need to be added to an enterprise data governance in order to be able to function as a platform ecosystem was created. Therefore, a framework for enterprise data governance was extended with 24 factors of platform ecosystem data governance

Data Ecosystem Governance: A Conceptual Framework

Data need to be created, collected, stored, exchanged, integrated, and processed among a diverse set of actors and infrastructures to create value. Such reliance on other actors leads to the emergence of data ecosystems. Despite the focus on data ecosystems in the literature, little is known about who governs what data activities and how. Data ecosystem governance aims to ensure the alignment of activities with different goals and strategies of ecosystem actors. We contribute to the understanding of data governance by expanding the conceptual model for data ecosystem governance. The framework draws on an extensive review of data governance and ecosystems. We show governance is multi-layer, multi-actor and multi- dimension which creates complexity and interdependencies. The conceptual framework provides a guide for managers to fully understand and implement data ecosystem governance

Design Principles for Institutionalized Data Ecosystems – Results from a Series of Case Studies

Sharing and collaborating on data across organizational boundaries is increasingly important for building a comprehensive data foundation for a variety of relevant analytical models and reports. We argue that a formalized set of rules and responsibilities - data governance - is needed to guide such data sharing ac-tivities and thus provide the foundation for an institutionalized data ecosystem. To this end, we propose a set of design principles. Based on three case studies from different application domains, we derive the design principles using Ser-vice-Dominant Logic as our theoretical lens. We distinguish between dynamic and static design principles. Our approach supports the delineation and specifi-cation of data governance structures for data ecosystems

UNDERSTANDING DATA TRUSTS

Finding ways to share data while upholding data sovereignty is a key issue to succeed in building the digital economy. One way to achieve this is to install data intermediaries – so-called data trusts – that facilitate this process. Their role is to orchestrate data sharing for organizations and individuals by ensuring that data sovereignty (i.e., the right to decide how data can be used) remains with the data provider. However, while the concept is promising, research on it is still in its infancy. The paper tackles exactly that issue as we collect data on data trusts (interviews and publically available information) and construct a general solution space for designing data trusts. With our research, we provide an overview as well as options for designing data trusts

Trust me, I’m an Intermediary! Exploring Data Intermediation Services

Data ecosystems receive considerable attention in academia and practice, as indicated by a steadily growing body of research and large-scale (industry-driven) research projects. They can leverage so-called data intermediaries, which are mediating parties that facilitate data sharing between a data provider and a data consumer. Research has uncovered many types of data intermediaries, such as data marketplaces or data trusts. However, what is missing is a ‘big picture’ of data intermediaries and the functions they fulfill. We tackle this issue by extracting data intermediation services decoupled from specific instances to give a comprehensive overview of how they work. To achieve this, we report on a systematic literature review, contributing data intermediation services

Data Sharing in the German Food Industry - Empirical Insights

Big data are collected along the entire food industry value chain, but remain mostly unused. Data sharing in data ecosystems could lead to efficiency gains and new revenue streams. We investigate data sharing within food industry and derive challenges and opportunities for data sharing in this context. We conducted interviews with ten qualified experts from the German food industry. The results reveal that mainly trust, usefulness and value influence users’ attitude towards data sharing. Our results confirm social exchange theory in conjunction with technology acceptance model as relevant underlying IS theories of data sharing