The research group combines theory construction on business model innovation with the empirical analysis of sectoral data-led innovation processes, e.g. in education, open data, mobility or the creative industries. Today’s production of infinite amounts of data by humans has a lasting impact on how business models are designed, even forcing well-established market participants to rethink and alter their business models. Data-driven business model innovation is therefore highly relevant for corporate practice and policy.
In science, business model research has been of increasing relevance since the 1990s. However, the previous research in the course of digitization and the associated networked society must be rethought and researched. In today's society, people produce endless amounts of data that have a lasting impact on business model design. The high agility and innovative power of start-ups forces established market players to rethink their business models. In some industries, such as the automotive sector, the development of digital business models may even change entire value chains and have a huge impact on employment. Data-driven business model innovation is therefore highly relevant for corporate practice and policy.
Consequently, Research Group 5 combines theory construction on business model innovation and empirical analysis of sectoral data-driven innovation processes in an interdisciplinary team. The focus is on the analysis of data-driven business models from different sectors, such as Education, Open Data / Open Governance, Mobility and Creative Industries, aiming to derive definitions, taxonomies or patterns as a first step. This implies exploring the data-driven business model innovation process itself and analyzing how companies develop data-driven business models in their own or in other industries. Innovative digital methods and tools are designed to empower companies to create radical, data-driven business models or to incrementally optimize certain areas with digital data.
As part of our research, we use a mixed-method approach that integrates both qualitative and quantitative research methods. In addition, we use digital data analysis tools to develop and establish new digital research approaches.
Research Group Lead
Research Group Assistant
Etsiwah, B.; Hilbig, R. (2019): “What is Data Strategy? An Analysis of an ambiguous concept”. In Proceedings of ISPIM Innovation Conference – Celebrating Innovation: 500 Years Since DaVinci (16-19.06.2019), Florence (Italy), S. 1-7.
Etsiwah, B.; Hecht, S.; Hilbig, R. (2019): „ An Interdisciplinary Exploration of Data Culture and Vocational Training”. In Proceedings of Weizenbaum-Conference (15-16.05.2019), Berlin (Germany), S. 1-8.
Hecht, S. (2019): “Improving UX of Open Government Data Platforms”. In Proceedings of ISPIM Innovation Conference – Celebrating Innovation: 500 Years Since DaVinci (16-19.06.2019), Florence (Italy), S. 1-27.
Hilbig, R.; Nirenberg, N. (2019): „Becoming International – The Business Model Innovation Process of VET Providers”. In Proceedings of the 19th EURAM (26-28.06.2019), Lisbon (Portugal), S. 1-40
Hilbig, R.; Renz, A.; Schildhauer, T. (2019): „Data Analytics – The Future of Innovative Teaching and Learning”. In Proceedings of ISPIM Innovation Conference – Celebrating Innovation: 500 Years Since DaVinci (16-19.06.2019), Florence (Italy), S. 1-16.
Hilbig, R.; Hecht, S.; Etsiwah, B. (2019): „Aufstieg datenbasierter Geschäftsmodelle in Berlin“, online verfübar: https://www.bildung-forschung.digital/de/aufstieg-datenbasierter-geschaeftsmodelle-in-berlin-2426.html.
Hilbig, R.; Etsiwah, B.; Hecht, S. (2018): “Berlin Start-ups - The Rise of Data-Driven Business Models”, In Proceedings of ISPIM Connects Fukuoka – Building on Innovation Tradition, (2-5.12.2018) Fukuoka (Japan), S.1-19.
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