Digital media have become an integral part of everyday life, education and work. However, intensive and problematic media use, and in particular problematic smartphone use has been shown to reliably predict reduced well-being and increased stress. Therefore, it is essential to investigate the factors that lead to problematic smartphone use and respective negative well-being outcomes and to develop interventions that effectively address these factors. Considering self-control and fear of missing out (FoMO) as key psychological factors promoting problematic smartphone use, we present a conceptual model explaining the emergence of digital stress due to problematic smartphone use, upon which we built a blended training intervention against digital stress. A controlled trial evaluation of the training intervention provided data at multiple time points for multilevel regression analyses on a sample of 175 university students. The results indicated that the intervention was effective in reducing FoMO (fear of missing out) and increasing self-control. Indirect effects suggested that both the reduction in FoMO and the gain in self-control effectively reduced emotional irritation and perceived stress via a reduction in problematic smartphone use. Conclusively, the findings identify key psychological factors that promote problematic smartphone use and demonstrate that these factors can be effectively addressed through appropriate psychological interventions.

This study examines how Large Language Models (LLMs) can reduce biases in text-to-image generation systems by modifying user prompts. We define bias as a model's unfair deviation from population statistics given neutral prompts. Our experiments with Stable Diffusion XL, 3.5 and Flux demonstrate that LLM-modified prompts significantly increase image diversity and reduce bias without the need to change the image generators themselves. While occasionally producing results that diverge from original user intent for elaborate prompts, this approach generally provides more varied interpretations of underspecified requests rather than superficial variations. The method works particularly well for less advanced image generators, though limitations persist for certain contexts like disability representation. All prompts and generated images are available at https://iisys-hof.github.io/llm-prompt-img-gen/

This paper considers three fundamental approaches to software development, namely manual coding, modeldriven software engineering, and code generation by large language models. All of these approaches have their individual pros and cons, motivating the desire for an integrated approach. We present MoProCo, a technical solution to integrate the three approaches into a single tool chain, allowing the developer to split a software engineering task into modeling, prompting or coding sub-tasks. From a single input file consisting of static model structure, natural language prompts and/or source code fragments, Java source code is generated using a two-stage approach. A case study demonstrates that the MoProCo approach combines the desirable properties of the three development approaches by offering the appropriate level of abstraction, determinism, and dynamism for each specific software engineering sub-task.

Similar to LLMs, the development of vision language models is mainly driven by English datasets and models trained in English and Chinese language, whereas support for other languages, even those considered high-resource languages such as German, remains significantly weaker. In this work we present an analysis of open-weight VLMs on factual knowledge in the German and English language. We disentangle the image-related aspects from the textual ones by analyzing accuracy with jury-as-a-judge in both prompt languages and images from German and international contexts. We found that for celebrities and sights, VLMs struggle because they are lacking visual cognition of German image contents. For animals and plants, the tested models can often correctly identify the image contents according to the scientific name or English common name but fail in German language. Cars and supermarket products were identified equally well in English and German images across both prompt languages.

Nach einer Studie des spanischen Center of the Governance of Change würde ein Viertel der Befragten Europäer es bevorzugen, dass politische Entscheidungen eher von einer KI als von Politikern aus Fleisch und Blut getroffen werden würden. Damit würde der Endpunkt einer Entwicklung zunehmender Mechanisierung gesellschaftlicher Prozesse beschrieben, die vor allem durch den Rückgriff auf Technologien wie Maschinelles Lernen und Blockchain möglich wird. Führt dies zu mehr in Verwaltung und Staat oder befinden wir uns damit auf dem Weg in eine Dystopie?

KI eröffnet für das Marketing und die Mediaplanung neue Möglichkeiten. Marken, die die analytische und operative Effizienz intelligenter Systeme nutzen wollen, sollten aber einiges beachten. Was, das erläutert Andreas Wagener, Professor für Digitales Marketing an der Hochschule Hof.

Städte sehen sich durch den Klimawandel einer zunehmenden Komplexität und einer Vielzahl an Krisen gegenüber. Steigende Temperaturen und häufigere Extremwetterereignisse belasten die urbane Infrastruktur erheblich. Verstädterung und Bevölkerungswachstum verschärfen diese Herausforderungen, indem sie den Flächenbedarf erhöhen und die Ressourcennutzung intensivieren. Versiegelte Flächen verhindern die natürliche Wasserversickerung und verschärfen Hochwassergefahren. Zudem führen urbane Wärmeinseln zu erhöhtem Energieverbrauch und Gesundheitsrisiken.

Dieser Band rekapituliert empirische Projekte der Forschungsgruppe "Empirical Research and User Experience (ERUX)“ am Institut für Informationssysteme (iisys) der Hochschule Hof aus den Jahren 2023 bis 2024.

Im Fokus steht die Usability von Features im Fahrzeuginnenraum, nämlich Fahrerassistenzsysteme, Optionen für die Klimaanlage und Situational Awareness Interfaces.
Während im ersten Band dieser Reihe neben einer großzahligen Erhebung vor allem die apparative Beobachtung mittels Eyetracking Anwendung fand, verwenden die im aktuellen Band referierten Forschungsprojekte ein eher qualitatives
empirisches Methodenspektrum, von Expertenratings über die Analyse von Use Cases bis zu Konzepttests mit zahlenmäßig begrenzten Usergruppen. Daneben ist eine Conjoint-Studie enthalten.
Im Automobilsektor hat die „Markt“- oder „Kundenorientierung“ in den letzten Jahrzehnten kaum Fortschritte gemacht, die Zufriedenheit der Kunden mit dem Automobil ist eher rückläufig. Im Zeitablauf haben zuerst die Kosten-
einsparungswelle und nachfolgend die Erfordernisse und die Präferenzen der Softwareentwicklung die HMI im Fahrzeuginnenraum viel stärker determiniert als die Usability für die Autofahrer, die das fertige Produkt letztlich bezahlen müssen.
Und weiterhin haben bei einigen OEM mächtige Designabteilungen das letzte Wort bei allen Oberflächen, mit denen der Autofahrer in Kontakt kommt. Als Fazit einer Vielzahl von Detailerkenntnissen aus den hier berichteten Studien lässt
sich feststellen, dass für die Berücksichtigung von Useranforderungen in der Entwicklung weiterhin viele Ansatzpunkte bestehen. Zugleich zeigt sich, dass die Auskunftsfähigkeit von Usern in Bezug auf zukünftige Fahrsituationen begrenzt ist.

Dieser Band redet somit keineswegs einer einseitigen Dominanz „des Marketings“ oder „der Marketingforschung“ im Entwicklungsprozess das Wort. Richtig bleibt vielmehr, was schon seit Jahrzehnten bekannt ist: Es kommt darauf an, ingenieurbasierte Innovations-
leistungen, kreative Designentwürfe, kundenseitige Nutzenerwartungen und marktgegebene Kostenvorgaben in ein ausgewogenes Verhältnis zu bringen. Das Know-how über den dafür erforderlichen, laufenden Abstimmungsprozess entscheidet über den Markterfolg

This volume recaps selected empirical projects from the research group Empirical Research and User Experience (ERUX) at the Institute for Information Systems (iisys) at Hof University from 2023 to 2024. The focus is on the usability of features inside the vehicle, specifically driver assistance systems, climate control options, and situational awareness interfaces, which are designed to provide drivers with orientation and safety when transitioning from manual to automated driving modes. While the first volume of this series included a large-scale survey and primarily relied on apparatus observation using eye tracking, the research projects referenced in the current volume employ a more qualitative empirical method spectrum, ranging from expert ratings to the analysis of use cases and concept testing with numerically limited user groups. As in the first volume, a conjoint study is also included. In the automotive sector, the focus on "market" or "customer orientation" has made little noticeable progress in recent decades, and customer satisfaction with automobiles is declining. Over time, the cost-saving wave and, later, the demands and preferences of software development have determined HMI in the vehicle interior much more strongly than usability from the perspective of the drivers, who ultimately pay for the final product. Furthermore, at some OEMs, powerful design departments still have the final say on all surfaces that the driver interacts with. As a conclusion from a variety of detailed findings, it can be stated that there are still many areas for the consideration of user requirements in development. At the same time, the studies reported here show that users' ability to provide meaningful feedback on future driving situations and associated requirements is limited. This volume does not advocate for the one-sided dominance of "marketing" or "marketing research" in the development process. Rather, what has been known for decades remains true: It is essential to strike a balance between engineering-based innovations, creative design concepts, customer expectations of value, and market-driven cost requirements. The expertise required for the ongoing coordination process is what ultimately determines market success.