Fruhwirth Michael, Pammer-Schindler Viktoria, Müller Christiana
Designing for data-driven business model innovation: a structured literature review
Big data and analytics have a transformative effect on organizations – but how should organizations approach the challenge of identifying and implementing data-driven business model opportunities? This paper constitutes a structured literature review that synthesizes what is known about artefacts that support the process of business model innovation towards data-driven business, i.e. business in which data and data analytics play a key role. By artefacts, we understand very broadly concepts, tools, as well as organizational measures. We analyse known artefacts, the environments in which they were used and tested, and generated transferrable knowledge and theories, i.e. we analyse lit-erature using the conceptual framework of Design Science Research.We find that the majority of knowledge stems from large organizations rather than SMEs, thus point-ing out SME-specific research on data-driven business model innovation as one avenue of interesting future research. Secondly, we see very little design-oriented research overall, i.e. very little re-search that is focussed on developing, rather than understanding artefacts that support the process of data-driven business model innovation. In particular, we see a largely un-explored research di-rection the design and experimentation with IT-based artefacts to support the very information tech-nology-driven field of data-driven business model innovation. Thirdly, information systems literature at the moment cannot yet give an integrated conceptual or practical framework of how different arte-facts could work together in the full business model innovation process. Finally, we find that there is little convergence of which theories are useful to inform data-driven business model innovation, with the underlying reason potentially being the novelty of the field.Overall, the present paper constitutes an overview that on the one hand maps out what is known about artefacts that support the process of business model innovation towards data-driven business, and analyses the gaps that are relevant for practitioners and are avenues for further research.