Usage-Based Insurance (UBI) is an application of Intelligent Transportation Systems (ITS) in the context of car insurance. UBI refers to insurance models in which insurers collect driving data using a telematics device. Based on the collected information, insurers can offer individual discounts depending on driving behaviour and provide feedback about each trip. Although there are plenty of advertising materials about the benefits of UBI, its user acceptance and usability have not received much research attention so far. To cover this gap, we conducted two user studies: semi-structured interviews with UBI users and a qualitative analysis of 186 customer inquiries concerning a UBI program from a web forum of a German insurer. We found that UBI can benefit drivers, insurers and society. Moreover, the country driving conditions, the policy conditions, the users’ perceived driving style, the perception of UBI, and the premium reduction influence UBI acceptance. Regarding traffic safety, some of our participants were concerned that UBI may provoke dangerous driving behaviour under certain circumstances. Finally, we make recommendations for insurers derived from users’ views, such as to provide to drivers more control over the user interface and over the way driving feedback is given to them. Concerning the driving scores, the ways in which they are calculated should be more transparent.

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The novelty of the Internet of Things (IoT) as a trend has not given society sufficient time to establish a clear view of what IoT is and how it operates. As such, people are likely to be unaware of the privacy implications, thus creating a gap between the belief of what a device does and its actual behaviour. The responses collected in our online survey indicate that participants tend to see IoT as computer-like devices, rather than appliances, though there are some important misconceptions about the way these devices function. We also find that privacy is a primary concern when it comes to IoT adoption. Nevertheless, participants have a propensity to keep using IoT devices even after they find out that the device abuses their trust. Finally, we provide recommendations to IoT vendors, to make their products more transparent in terms of privacy.

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We present a "privacy facts" label, which aims at helping non-experts understand how an Internet of Things (IoT) device collects and handles data. We describe our design methodology, and detail the results of our user study involving 31 participants, assessing the efficacy of the label. The results suggest that the label was perceived positively by the participants, and is a promising solution to help users in making informed decisions.