A Taxonomy of Business Models for Data Intermediaries

Background: Both the rising dominance of large platforms in data markets and the increasing exchange of personal data among organizations within data ecosystems have led to significant asymmetries, as individuals have limited control over data-related decisions and restricted access to the value generated from data use. Scholars have called for data intermediaries to help address these asymmetries, yet little research has examined how their business models function within data ecosystems that facilitate personal data exchange. Aim: This study investigates the key characteristics and archetypes of business models of data intermediaries in data ecosystems that facilitate the exchange of personal data. Method: Using Nickerson’s taxonomy methodology and the Al-Debei business model ontological structure, a comprehensive taxonomy of data intermediary business models is developed. Hierarchical clustering is then applied to identify distinct archetypes. Results: The research develops a taxonomy of data intermediary business models in data ecosystems centered on personal data exchange and derives archetypes through hierarchical clustering. The identified taxonomy dimensions clarify how these business models operate and create value within personal data ecosystems from a network-level business model perspective. Based on this framework, eight archetypes are identified, illustrating how data intermediaries prioritize interests of different ecosystem actors. Dataset: This dataset contains: A summary of the 107 cases analyzed in this research, and how the researchers mapped the dimensions and characteristics from the taxonomy onto these cases. Definitions of the dimensions and characteristics in the taxonomy. A cross-table analysis showing how frequently the dimensions and characteristics appear in the eight identified archetypes.