| Summary: | Accelerated by the increasing digitalization, the connection of humans and machines has exponentially grown in recent years and the trend continues to gain momentum. Connected cars follow this course of connecting everyday objects to the internet and as contemporary vehicles are becoming more networked, services tailored around connectivity functions form a central value proposition of modern OEM business models. Yet, while connectivity features extend and improve a vehicle’s functional portfolio, the dependency on personal user data fosters the relevance of informational privacy in vehicles. This thesis aims at examining the moderating role of data privacy risks in the adoption process of connected car features. Building on Davis’ Technology Acceptance Model, an adapted acceptance framework which accounts for data privacy risks of connected car features is proposed. In a split-test experiment, 440 participants were exposed to different levels of connected car related data privacy risks and subsequently responded to an acceptance survey. Interaction based regression modelling revealed a statistically significant and practically meaningful attenuating moderation effect of data privacy risks on the perceived usefulness and perceived ease of use of connected car features, which in turn influenced usage intention. The obtained results underscore the importance of data privacy for the adoption of connected car features and contribute to the existing research by showing the importance of combining data privacy risks of connected cars with classical TAM constructs.
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