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 Fundamentals: Definition and Characteristics

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

How to Share Data Online (fast) – A Taxonomy of Data Sharing Business Models

Data is an integral part of almost every business. Sharing data enables new opportunities to generate value or enrich the existing data repository, opening up new potentials for optimization and business models. However, these opportunities are still untapped, as sharing data comes with many challenges. First and foremost, aspects such as trust in partners, transparency, and the desire for security are issues that need to be addressed. Only then can data sharing be used efficiently in business models. The paper addresses this issue and generates guidance for the data-sharing business model (DSBM) design in the form of a taxonomy. The taxonomy is built on the empirical analysis of 80 DSBMs. With this, the primary contributions are structuring the field of an emerging phenomenon and outlining design options for these types of business models

Towards a Taxonomy of API Services in Logistics

Data are a valuable asset for companies in the logistics sector to optimize internally and develop new business models. They can be like a magnifying glass and make previously opaque logistical processes transparent and find previously hidden potentials for optimization. Typical applications are tracking of the transport status, route optimization, or monitoring of pharmaceutical products, or monitoring shocks for fragile cargo along the trade lanes. One way to use data is to tap into publicly or commercially available Application Programming Interfaces. Hereby, logistics service providers can get or provide data automatically via a machine-to-machine interface. However, the landscape of API service providers is vast, unstructured, and intransparent in terms of potential data that companies can leverage. Given their high potential for the logistics industry, the paper proposes a taxonomy of API services in logistics based on the inductive analysis of three API databases