The modern economy relies heavily on data as a resource for advancement and growth. Data marketplaces have gained an increasing amount of attention, since they provide possibilities to exchange, trade and access data across organizations. Due to the rapid development of the field, the research on business models of data marketplaces is fragmented. We aimed to address this issue in this article by identifying the dimensions and characteristics of data marketplaces from a business model perspective. Following a rigorous process for taxonomy building, we propose a business model taxonomy for data marketplaces. Using evidence collected from a final sample of twenty data marketplaces, we analyze the frequency of specific characteristics of data marketplaces. In addition, we identify four data marketplace business model archetypes. The findings reveal the impact of the structure of data marketplaces as well as the relevance of anonymity and encryption for identified data marketplace archetypes.

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Data-driven technologies enable organizations to innovate new services and business models and thus hold the potential for new sources of revenue and business growth. However, such new data-driven business models impose new ways for unwanted knowledge spillovers. Current research on datadriven business models and knowledge risks provides little help to identify and discuss such novel risks within the innovation process. We have developed a network-based representation of data-driven business models within one case organization, where it helped to identify knowledge risks in the design process of data-driven business models. In this paper, we further evaluated the artifact through 17 interviews with experts from the domain of business models, data analytics and knowledge management. We found that the network-based representation is suitable to visualize, discuss and create awareness for knowledge risks and see types of data-related value objects and quantification of risks as two recommendations for further research.

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Digitalization is a game changer. It enables the move from a single expertise toward interdisciplinary innovation. It thus enables technical innovation by making it easier to acquire and connect system-related information thus enabling the generation of a digital twin. Experts can feed their knowledge into different and connected models. This now structured store of information can be used for holistic value creation. Furthermore, different application domains can also be mapped together creating an environment where new solutions for new markets can emerge. An example of this is predictive maintenance where information derived during vehicle operation is mapped with component knowledge from the design phase. The result is a new service for the user and a new source of revenue for the vehicle manufacturer in a new market (services during vehicle operation). This increases productivity through the optimization of the entire supply chain and the emergence of new services where different application domains converge. At the same time, major automotive trends such as electrification, automated driving, connectivity, and the diversification of mobility are fundamentally reshaping the market in terms of customer needs, the skills required, and business logic. These trends demonstrate that a vehicle is no longer a monolithic system but has instead become a highly customizable system able to adapt itself to its customer and environment. The goal of this chapter is to analyze the opportunities for digitalization in the automotive domain as well as the respective needs for systems engineering including processes, methods, organization, and tools.