The sheer amount of available apps allows users to customize smartphones to match their personality and interests. As one of the first large-scale studies, the impact of personality traits on mobile app adoption was examined through an empirical study involving 2043 Android users. A mobile app was developed to assess each smartphone user's personality traits based on a state-of-the-art Big Five questionnaire and to collect information about her installed apps. The contributions of this work are twofold. First, it confirms that personality traits have significant impact on the adoption of different types of mobile apps. Second, a machine-learning model is developed to automatically determine a user's personality based on her installed apps. The predictive model is implemented in a prototype app and shows a 65% higher precision than a random guess. Additionally, the model can be deployed in a non-intrusive, low privacy-concern, and highly scalable manner as part of any mobile app.

Surveillance of animal movements using electronic tags (i.e., biotelemetry) has emerged as an essential tool for both basic and applied ecological research and monitoring. Advances in animal tracking are occurring simultaneously with changes to technology, in an evolving global scientific culture that increasingly promotes data sharing and transparency. However, there is a risk that misuse of biotelemetry data could increase the vulnerability of animals to human disturbance or exploitation. For the most part, telemetry data security is not a danger to animals or their ecosystems, but for some high-risk cases, as with species’ with high economic value or at-risk populations, available knowledge of their movements may promote active disturbance or worse, potential poaching. We suggest that when designing animal tracking studies it is incumbent on scientists to consider the vulnerability of their study animals to risks arising from the implementation of the proposed program, and to take preventative measures.

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In light of digitalization, customers increasingly share private data through their online behaviors and actions. Yet, customers have become reluctant to share data due to privacy concerns. From a psychological perspective, a reduction of users' perceived risks should result in a higher willingness to share sensitive data. The development of blockchain-supported, multi-part computation thereby represents an interesting novel empirical context to study such willingness to disclose personal data, as such technologies involve a privacy-preserving approach that could not only technically solve privacy issues but also ought to address precisely the user's risk perception. Therefore, we conducted an online experiment with 420 participants to examine the willingness to disclose personal data dependent on different privacy protection mechanisms. A deception based experiment allowed to measure not only user intention, but also real user behavior. Surprisingly, our results demonstrate that participants shared similar amounts of personal data for blockchainsupported approaches and standard privacy policies. Even though an aversion to the blockchain system due to its novelty and potentially perceived complexity was not detected. Furthermore, we found that the willingness to share data increased significantly specifically for technically affine people when they were presented with the opportunity to monetize their data. We further discuss the effects of privacy awareness and whether prior knowledge of blockchain technology had a supporting effect for user acceptance.