Smart cars learn from gathered operating data to add value to the users’ driving experience and increase security. Thereby, not only users benefit from these data- driven services; various actors in the associated ecosystem are able to optimize their business models based on smart car related information. Continuous collection of data can defy users’ privacy expectations, which may lead to reluctant usage or even refusal to accept services offered by smart car providers. This paper investigates users’ privacy expectations using a vignette study, in which participants judge variations of smart car applications, differing with respect to factors such as data transmission and the type of information transferred. We expect to identify application dependent privacy expectations, that eventually yield insights on how to design smart car applications and associated business models that respect users’ privacy expectations.
Ostern, Nadine; Eßer, Arved; and Buxmann, Peter, "Capturing Users’ Privacy Expectations To Design Better Smart Car Applications" (2018). PACIS 2018 Proceedings. 97.