Motives and Incentives for Data Sharing in Industrial Data Ecosystems: An Explorative Single Case Study

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

Barriers to the Development of Data-Driven Services: An ISM Approach for SMEs

Data is nowadays considered as a key resource and represents the most valuable asset of our technology-driven world. However, the ability to use this resource in a value-adding way requires a holistic perspective. Small- and medium-sized enterprises in particular face major challenges in the innovation and development process. Despite preliminary research in the area of data-driven services (DDS), there is a lack of methodological analysis of the key barriers for SMEs in the context of DDS development. To address this shortcoming, we have developed an interpretive structural model based on a two-stage mixed-method approach by combining a structured literature review with practice-oriented focus group interviews to identify key barriers and their interdependencies and interactions. Our paper strengthens the knowledge of DDS development through a methodological barrier analysis and provides a guide for practitioners to eliminate the most relevant barriers to DSS development

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

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

Challenges in the Emergence of Data Ecosystems

The importance of data as a resource for innovation and value creation is increasing steadily. As a result, organizations must adapt their strategies and develop methods for integrating data into their value creation processes. At the same time, data-driven value is less and less created by one company alone but rather through the sharing of data in so-called data ecosystems. The data ecosystem field is, however, not well understood yet. For example, it is unclear how data ecosystems emerge and evolve further. To address this research gap, this paper focuses on the challenges in the emergence phase of data ecosystems. By conducting a multiple case study of eleven use cases, we find that, among other challenges, building trust between the ecosystem participants is one of the major challenges a data ecosystem has to overcome in its emergence phase

A Taxonomy for 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. With this study, we want to address this issue and contribute to a deeper understanding of data ecosystems. Therefore, we develop a taxonomy for data ecosystems which is grounded both theoretically through the linkage to the scientific knowledge base and empirically through the analyses of data ecosystem use cases. The resulting taxonomy consists of key dimensions and characteristics of data ecosystems and contributes to a better scientific understanding of this concept. Practitioners can use the taxonomy as an instrument to further understand, design and manage the data ecosystems their organizations are involved in

Towards a taxonomy of incentive mechanisms for data sharing in data ecosystems

In the increasingly connected business world, economic value is created less and less by one company alone but rather through the combination and enrichment of data by various actors in so-called data ecosystems. However, one of the main obstacles to why actors are currently not motivated to engage in data ecosystems is that they are often not aware of the actual benefits of cross-organisational data sharing. This is partly because it is in many cases unclear what the incentives and ultimately the added value are for data providers when they share their data with others. To address this research gap we develop a taxonomy of incentive mechanisms for data sharing in data ecosystems which is based on a structured literature review. The resulting taxonomy consists of key dimensions and characteristics of incentive mechanisms for data sharing in data ecosystems and contributes to a better scientific understanding of these concepts