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.

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Returnable transport items (RTIs) are key elements for enabling a smooth flow of goods throughout supply chains. Despite their importance, RTIs can be prone to high loss and breakage rates. Today's RTI management processes are rather inefficient and are based on estimates about when, where and how RTIs are utilised. This limited visibility inevitably causes the involved parties to feel less responsible for the proper management of RTIs. As a consequence, inefficiencies created by a single party can result in a significant cost burden for the whole supply chain. The goal of this paper is therefore to explore the impact of increased asset visibility on the RTI management process. We describe a solution based on Radio Frequency Identification (RFID) technology and quantify its financial impact from each individual stakeholder's perspective. Our findings suggest that RFID can provide a powerful means to counter inefficiencies in the RTI management process and improve the overall effectiveness of the RTI supply chain network.

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