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Resource typeDissertation
Title(s)Realization of Data-Driven Business Models in Incumbent Companies
CreatorLange, Hergen Eilert  1128250934 (Institut für Wirtschaftsinformatik (IIS), Leuphana Universität Lüneburg  02w2y2t16)
RefereeDrews, Paul  0000-0002-9845-5024  1026308550
Reihlen, Markus  0000-0002-9863-9472  142446890
Legner, Christine  0000-0001-8891-3813  17334545X
AdvisorDrews, Paul  0000-0002-9845-5024  1026308550
AbstractData as a business fundament has become an essential element for company strategy, operations, and decisions. Originated in the digital and technology industry of Silicon Valley, data monetization is currently a topic for many industries to stay competitive in the market. In recent years, researchers have investigated manifold approaches to understanding data-driven businesses. Data monetization is a complex field, so data-driven business models (DDBMs) have become an important ideation tool for business research and managers. While the focus of recent research was on the design of DDBMs, research is still struggling to understand the challenges and strategies for realizing DDBMs. Current research has not yet investigated the realization of DDBM, although it is a phenomenon of increasing importance for practice. To improve the understanding and knowledge in this research field, this work seeks to advance the knowledge about (1) the existing connection between DDBM literature and the realization of business models in general; (2) the understanding of periods for the realization of DDBM cases in incumbent companies; (3) the identification of required resources and capabilities through the DDBM realization (DDBMR) process; (4) the development of a DDBM realization tool to support the execution in practice; and (5) the utilization of data-driven business ventures (DDBV) for realizing DDBMs in incumbent companies. To advance knowledge about DDBMR, this study applied a mixed-method approach. This work draws upon a systematic literature review, qualitative empirical research, and a design science research (DSR) approach. A systematic literature search was applied to summarize existing knowledge from research about DDBMs and business model realization. This review provides the foundation for planning and conducting qualitative semi-structured interviews with multiple DDBM experts. Through qualitative content analysis and open coding, these experts provided knowledge into how companies execute DDBM cases in practice and identified required periods, capabilities, and resources. To provide an artifact supporting DDBM realization, this work developed the “DDBM realization board” in two design iterations following the DSR process and principles. This work provides multiple contributions to theory and practice. Previous studies have revealed a strong focus on the ideation, development, and strategies of DDBMs compared with the thesis at hand, which concentrates on the implementation and realization of DDBMs. The results of the qualitative-empirical study provide an improved understanding of the required periods, resources, and capabilities through the DDBM realization process. Furthermore, this work linked the research field of DDBM to the field of business model realization and digital ventures. The identified DDBM realization periods, capabilities, and resources improve the understanding of the iterative realization process of DDBMs. The "DDBM realization board" de-signed in this thesis adds a useful tool for DDBMR validation, and DDBVs are a data-driven enhancement of digital venture research. For practice, the results offer managers and companies a better understanding of how the DDBM realization process is conducted in other companies. Current DDBM projects are executed mostly under high uncertainty, and the identified challenges and enablers will help make the realization more structured and problem-focused. The “DDBM realization board” will help companies through the realization process and validate its progress. The identified activities and capabilities required for DDBVs will help managers con-struct the right organizational forms in incumbent companies to realize DDBMs. This thesis is not without limitations. With the regional focus of experts and companies in Germany, it might bear cultural or region-specific limitations. For further studies, it would be valuable to examine whether different cultural settings lead to different results. The interviewed ex-perts were mostly from the operational rather than the higher company management levels. For further research, it would be valuable to connect experts from different hierarchical levels within one company to develop a richer picture of the DDBM strategy and execution. Moreover, it would be beneficial to construct a quantitative research design to enable a number-driven perspective on DDBMR and its influence on company performance. Future research should focus on three research streams. First focus should be on an agile-oriented approach to DDBMR cases rather than on a traditional waterfall-like project execution of DDBMR cases. For this reason, this thesis recommends making a stronger connection be-tween DDBMs and research on digital entrepreneurship and agile software development. The identified DDBMR periods offer a first approach to how these elements fit together, but future research could take a closer look at individual DDBM realization trajectories over time. The second research stream that should be developed is the concept of DDBVs. Digital ventures are a construct that has already been established in research. DDBV can be the right construct for research to understand how incumbent companies realize DDBM ideas. Third, it would be an important next step to focus on single-company case studies from practice in which the developed ideas, concepts, and tools are used. The results of this study were collected from multiple companies and experts with different levels of experience. Observation of a complete DDBMR case from start to market launch would provide an additional fine-grained understanding of DDBMR.
KeywordsData-Driven Business; Business Transformation; Data Science; Business Model
Date of defense2024-06-19
Year of publication in PubData2024
Publishing typeFirst publication
Date issued2024-06-24
Creation contextResearch
Granting InstitutionLeuphana Universität Lüneburg
Published byMedien- und Informationszentrum, Leuphana Universität Lüneburg
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