Search

Data intermediation services are a novel class of services mitigating various aspects of inter-organizational data sharing. Facilitating data intermediation services is a pivotal activity that leverages the benefits of the data economy. These services are usually provided by so-called data intermediaries, who organize data sharing between one or multiple data provider(s) and data consumer(s). Against the background of prominent legislation, these data intermediaries (e.g., data marketplaces or data trusts) receive increasing attention in research, practice, and politics. For instance, the Data Governance Act regulates data intermediation services, prompting organizations sharing data to comply with various regulations, including registration with government bodies or acquiring a trusted label. However, to date, little research has been done about the nature of data intermediation services, motivating us to tackle this issue in a two-fold strategy. First, we derive a visualization of a data intermediary and concept subsuming its potential functions. Second, we explore archetypical configurations of co-existing services of data intermediaries. With this, we contribute to understanding the data intermediaries as a concept and enrich this understanding with more detailed service configurations.

Industrial data ecosystems are inter-organizational forms of cooperation emerging around sharing data. They arise from a digital infrastructure, giving data providers and data users a platform to share and (re-)use data. Data spaces are among the digital infrastructures frequently associated with data ecosystems, as they supply a shared digital space for its participants to share data in a sovereign way. Data spaces aim to close a gap in the digital infrastructure landscape, addressing concerns of organizations when sharing data, such as data misappropriation or a lack of control of shared data. They do this by implementing data sovereignty—typically through Usage Control Policies—that give data providers the means to formalize semantically and technically how data users are allowed to use their data. In this fundamentals article, we address the following issues: (1) contextualizing and demarcating data spaces and data ecosystems, (2) systematizing data spaces in the research and policy landscape, and (3) elaborating on a research agenda for Information Systems (IS) research.

Classification is an essential approach in business model research. Empirical classifications, termed taxonomies, are widespread in and beyond Information Systems (IS) and enjoy high popularity as both stand-alone artifacts and the foundation for further application. In this article, we focus on the study of empirical business model taxonomies for two reasons. Firstly, as these taxonomies serve as a tool to store empirical data about business models, we investigate their coverage of different industries and technologies. Secondly, as they are emerging artifacts in IS research, we aim to strengthen rigor in their design by illustrating essential design dimensions and characteristics. In doing this, we contribute to research and practice by synthesizing the diffusion of business model taxonomies that helps to draw on the available body of empirical knowledge and providing artifact-specific guidance for building taxonomies in the context of business models.

Since the emerging information economy relies heavily on data for advancement and growth, data markets have gained increasing attention. However, while global data economies are evolving and data are increasingly shared among organizations in various data ecosystems, marketplaces for personal data (PDMs) exhibited considerable start-up difficulties, which doomed their majority to either fail quickly or to operate in legal grey zones. Apparently, in recent times, novel PDMs have arisen which seem economically and technically viable. The study investigates this “new generation” from both an economic and a technological perspective. Adhering to a rigorous methodology for taxonomy building and evaluation, 18 dimensions and 59 characteristics are presented alongside which these new PDMs can be designed. Additionally, archetypes are derived. The findings reveal that PDMs tend to follow certain design commonalities holding for data markets generally but comprise specific design elements distinguishing them both from conventional data markets and among each other.