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The primary argument for including large numbers of known-to-be innocent fillers in lineups is that guessing-based selections are dispersed among a large number of lineup members, leading to low innocent-suspect identification rates. However, a recent study using the two-high threshold eyewitness identification model has demonstrated advantages of smaller lineups at the level of the processes underlying the observable responses. Participants were more likely to detect the presence of the culprit and less likely to select lineup members based on guessing in smaller than in larger lineups. Nonetheless, at the level of observable responses, the rate of innocent-suspect identifications was higher in smaller compared to larger lineups due to the decreased dispersion of guessing-based selections among the lineup members. To address this issue, we combined smaller lineups with lineup instructions insinuating that the culprit was unlikely to be in the lineup. The goal was to achieve a particularly low rate of guessing-based selections. These lineups were compared to larger lineups with neutral instructions. In two experiments, culprit-presence detection occurred with a higher probability in smaller compared to larger lineups. Furthermore, instructions insinuating that the culprit was unlikely to be in the lineup reduced guessing-based selection compared to neutral instructions. At the level of observable responses, the innocent-suspect identification rate did not differ between smaller lineups with low-culprit-probability instructions and larger lineups with neutral instructions. The rate of culprit identifications was higher in smaller lineups with low-culprit-probability instructions than in larger lineups with neutral instructions.

Every maneuver of a vehicle redistributes risks between road users. While human drivers do this intuitively, autonomous vehicles allow and require deliberative algorithmic risk management. But how should traffic risks be distributed among road users? In a global experimental study in eight countries with different cultural backgrounds and almost 11,000 participants, we compared risk distribution preferences. It turns out that risk preferences in road traffic are strikingly similar between the cultural zones. The vast majority of participants in all countries deviates from a guiding principle of minimizing accident probabilities in favor of weighing up the probability and severity of accidents. At the national level, the consideration of accident probability and severity hardly differs between countries. The social dilemma of autonomous vehicles detected in deterministic crash scenarios disappears in risk assessments of everyday traffic situations in all countries. In no country do cyclists receive a risk bonus that goes beyond their higher vulnerability. In sum, our results suggest that a global consensus on the risk ethics of autonomous driving is easier to establish than on the ethics of crashing.

Data is essential to better understand tourists and visitor flows as well as to better inform and guide tourists and manage visitor flows. In the age of data protection and overtourism, destination management organizations and national tourism organizations are challenged to find ways for consensual visitor tracking via mobile apps. Based on social exchange theory, it can be assumed that people are willing to provide personal location data when being compensated fairly. In this study, we find and explore a selection of potential mobile app features, ranging from utilitarian (personalized content, higher value of services, monetary rewards) to hedonic (gamified features) and social benefits. Based on the Kano model and a representative sample of 2038 panelists, we classify app features according to their ability to make people use a mobile app and thus allow for location-based tracking. Results show that gamification seems appealing to a subset of younger generations, while utility features are more broadly accepted and wanted. Social elements of travel mobile apps seem less attractive and relevant to most people. To convince a representative group of tourists to use a mobile app with tracking capabilities, we conclude that the focus should primarily be on actual utility of the app while gamified features should be implemented with care.

Das Leisure-Sickness-Syndrom ist keine anerkannte Krankheit, aber eine Realität im Leben vieler Arbeitnehmer:innen in Deutschland. Mögliche Ursachen sind laut Expertin: hohe Arbeitsbelastung, Überstunden und ständige Erreichbarkeit.

Erst die Arbeit, dann die Erholung? Das gilt für viele Menschen in Deutschland nur mit Einschränkungen: 71,9 Prozent der Arbeitnehmer:innen erleben die sogenannte Leisure Sickness (deutsch: Freizeitkrankheit), also Erschöpfung und Krankheitssymptome an freien Tagen oder im Urlaub. Das zeigt eine repräsentative Studie der IU Internationalen Hochschule.

Die am häufigsten auftretenden Symptome von Leisure Sickness sind laut den Befragten Müdigkeit bzw. Erschöpfung, Schlafprobleme, Reizbarkeit, Kopfschmerzen und Erkältungssymptome. Außerdem geben 38,4 Prozent der Arbeitnehmer:innen an, nach der Arbeit schlecht abschalten zu können. Prof.ⁱⁿ Dr.ⁱⁿ Stefanie André, Professorin für Gesundheitsmanagement an der IU Internationalen Hochschule und Expertin für Gesundheit am Arbeitsplatz, ordnet die Ursachen von Leisure Sickness ein: „Die Ergebnisse zeigen, dass Erreichbarkeit außerhalb der Arbeitszeit, hohe Arbeitsbelastung und fehlende Erholung klare Risikofaktoren für Krankheitssymptome an freien Tagen sind.“

Obwohl für 95,5 Prozent der Befragten Erholung und Freizeit wichtig und sinnvoll sind, geben 4 von 10 Arbeitnehmer:innen in Deutschland an, dass ihr Privatleben nicht ausreichend für Erholung sorgt, um den Anforderungen im Beruf gerecht zu werden. Vor allem jüngeren Menschen fehlen laut Prof.ⁱⁿ Dr.ⁱⁿ Stefanie André Strategien zur Stressbewältigung. Unternehmen sollten hier Verantwortung übernehmen und mehr Unterstützung anbieten. Das finden laut IU Studie auch 63,6 Prozent der Arbeitnehmer:innen in Deutschland.