Abstract Future social robots will act autonomously in the world. Autonomous behavior is usually realized by using AI models built with real-world data, which often reflect existing inequalities and prejudices in society. Even if designers do not intend it, there are risks that robots will be developed that discriminate against certain users, e. g. based on gender. In this work, we investigate the implications of a gender-biased robot that disadvantages women, which unfortunately is a bias in AI that is often reported. Our experiment shows that both men and women perceive the gender-biased robot to be unfair. However, our work indicates that women are more aware that a gender bias causes this unfairness. We also show that gender bias results in the robot being perceived differently. While the gender bias resulted in lower likability and intelligence ratings by women, men seem to lose trust in the robot if it behaves unfairly.

Abstract Video tutorials are an effective method of knowledge transfer and learning. However, they are often time-consuming to create and difficult to access during work. This paper introduces an approach that simplifies the creation of video tutorials in the workplace and promotes their use during work. By utilizing smart glasses, practitioners can record video tutorials during their daily work processes and utilize them for knowledge transfer to other employees without much additional effort. This offers the advantage of directly and easily sharing expertise in the workplace without being constrained by time or location. Given the intended simplicity of this approach, the question arises whether it creates video tutorials that help people in their learning. The paper presents a study that compares the effectiveness of knowledge transfer using these video tutorials against traditional personal training methods in the workplace. With 18 participants from the nursing and production sectors, we observed the training and learning outcomes of using video tutorials on smart glasses over multiple sessions, comparing them with personal training, which is considered the standard for practical onboarding. The study results indicate that learning with video tutorials does not significantly differ in terms of learning outcomes from traditional personal training methods. Overall, this study highlights the potential of video tutorials with smart glasses for knowledge transfer in workplaces, while also identifying challenges and opportunities for optimizing onboarding processes for employees.

ABSTRACT Trust is important for collaboration. In hybrid teams of humans and robots, trust enables smooth collaboration and reduces risks. Just as collaboration between humans and robots differs from interpersonal collaboration, so does the nature of trust in human-robot interaction (HRI). Therefore, further investigations on trust formation and dissolution in HRI, factors affecting it, and means for keeping trust on an appropriate level are needed. However, our knowledge of interpersonal trust and trust in autonomous agents cannot be transferred directly to HRI. In this paper, we present a study with 32 participants on trust formation and dissolution as well as forecasting to influence trust in an industry robot. Results show differences in dynamics and factors of trust formation and dissolution. Additionally, we find that the effect of forecasting on trust depends on task success. These findings support the design of trustful human-robot interaction and corresponding robotic team members.

Abstract Sensory processing sensitivity (SPS) is a personality trait characterized by a high sensitivity to sensory stimuli (Aron & Aron, 1997). On the basis of environmental sensitivity theory (Pluess & Boniwell, 2015) as well as the job characteristics model (Hackman & Oldham, 1976), we investigated the moderating impact of SPS (HSP Scale; Aron & Aron, 1997; Konrad & Herzberg, 2019) on the relationship between job characteristics (Work Design Questionnaire; Morgeson & Humphrey, 2006; Stegmann et al., 2010) and organizational citizenship behavior (OCB Scale; Podsakoff et al., 1990). The results of our two-wave survey study with 199 employees from a broad range of industries and students indicate that SPS strengthens the relationship between feedback as well as task significance and OCB, but SPS weakens the relationship between autonomy (work methods) as well as task variety and OCB.

In der öffentlichen Debatte wird die Generation Z häufig als zentrale Herausforderung für die zukünftige Arbeitswelt dargestellt. Es wird behauptet, dass diese Generation, geprägt durch digitale und gesellschaftliche Umbrüche, andere Erwartungen an Arbeitgeber hat als ihre Vorgänger. Unsere Studie untersucht, ob diese Annahme wissenschaftlich haltbar ist und ob sich die Relevanz von Arbeitgeberattraktivitätsfaktoren zwischen den Generationen Baby Boomer, Generation X, Y und Z signifikant unterscheidet. Basierend auf einem neuen empirischen Ansatz analysieren wir mit einer umfangreichen deutschen Stichprobe (N=1133) mittels multipler Regressionen die Vorhersagekraft von 19 Arbeitgeberattraktivitätsfaktoren für die allgemeine Arbeitgeberattraktivität. Dabei prüfen wir, ob und inwieweit die Generationszugehörigkeit moderierende Effekte auf die Bedeutung dieser Faktoren hat. Die Ergebnisse zeigen, dass wesentliche Attraktivitätsfaktoren wie Identifikation mit dem Unternehmen, Management und Führung sowie die Arbeitsaufgabe generationsübergreifend relevant sind. Unterschiede zeigen sich lediglich in einzelnen Dimensionen wie Arbeitsplatzsicherheit, Corporate Social Responsibility, Work-Life-Balance und Unternehmensimage, wobei die Varianzaufklärung durch Generationsunterschiede äußerst gering ist (<2 %) und die signifikanten Regressionskoeffizienten im Durchschnitt nur bei .23 liegen. Die Studie liefert eine empirische Basis für die Diskussion über Generationsunterschiede bei arbeitsrelevanten Einstellungen. Unsere Ergebnisse stellen die gängige Praxis von generationsspezifischem Employer Branding und eine Überschätzung von Generationsunterschieden, die auf stereotypen Annahmen beruhen, infrage. Selbst Alters- und Lebensphaseneffekte lassen sich in unseren Ergebnissen kaum nachweisen. Zukünftige Forschung sollte verstärkt longitudinale Designs verwenden, um die Dynamik von Alter, Lebensphasen und Generationen besser zu verstehen.