As a consequence of the increasing digitization, massive amounts of data are created every day. While scholars and practitioners suggest that organizations can use this data to develop new data-driven business models, many organizations struggle to systematically develop such models. A fundamental challenge in this regard is presented by the limited research on data-driven business models. Accordingly, the goal of this research is to better understand data-driven business models by identifying key dimensions that can be used to distinguish them and to develop a taxonomy. As our taxonomy aims to guide future studies in a way that ultimately serves organizations, it is based on dimensions regarded to be most relevant from the practitioners’ perspective. To develop this taxonomy, we utilize an established empirical approach based on a combination of multidimensional scaling (MDS), property fitting (ProFit), and qualitative data. Our results reveal that the most important dimensions distinguish data-driven business models based on the data source utilized, the target audience, and the technological effort required. Based on these dimensions, our taxonomy distinguishes eight ideal-typical categories of data-driven business models. By providing an increased understanding regarding the topic, our results form the foundation for subsequent investigations in this new field of research.
Engelbrecht, Adrian; Gerlach, Jin; and Widjaja, Thomas, "UNDERSTANDING THE ANATOMY OF DATA-DRIVEN BUSINESS MODELS – TOWARDS AN EMPIRICAL TAXONOMY" (2016). Research Papers. 128.