Die Herausforderung beim Einsatz von generativer Text-KI, wie ChatGPT, besteht darin, die Potenziale effizient zu nutzen und im Hinblick auf die Erreichung von Qualitätszielen optimal einzusetzen. Dabei ist die menschliche Eingabe in die Künstliche Intelligenz (KI) – der Prompt – entscheidend. Der vorliegende Beitrag widmet sich der Frage, wie die KI-basierte Textausgabe bei ChatGPT durch Prompt Engineering gezielt gesteuert werden kann, damit die Textqualität der generativen KI den Erfolgskriterien für Content Marketing Texte entspricht. Die Ergebnisse identifizieren eine effektive Prompt-Struktur für qualitativ hochwertige Content Marketing Texte mit ChatGPT. Insbesondere das Zero-shot Chain-of-Thought und das One-shot bzw. Few-shot Prompting erweisen sich als erfolgreich, da diese Techniken eine gezielte Steuerung des ChatGPT-Outputs in Richtung der Erfolgskriterien ermöglichen. Darüber hinaus werden die aktuellen Schwächen von KI-generierten Texten beschrieben. Dabei werden auch die Grenzen von ChatGPT deutlich, die durch eine kollaborative Wertschöpfung von Mensch und KI zur gemeinsamen Erreichung von Qualitätszielen überwunden werden können. Die theoretisch und praktisch fundierten Ergebnisse und Implikationen der Untersuchung bieten eine Orientierungshilfe für Content Marketer zur effizienten Nutzung von ChatGPT.

Tourism recommendation systems have the potential to alleviate choice overload for travelers. Social robots offer a promising avenue for delivering recommendations in tourist information settings, presenting an engaging and intuitive interface. This research explores tourists’ perceptions of the effectiveness and satisfaction of tourism recommendations provided by social robots as well as their preferences for human-like versus robotic interactions. An experiment was conducted at a tourist information office involving 60 participants exposed to either a human-like or robotic version of the social robot recommender system. Feedback was collected via survey, revealing that the participants responded positively to the social robot across various evaluation criteria. These findings suggest that tourists are receptive to social robots in real-world tourism contexts and would consider using them in the future.

Organizational Knowledge Loss (OKL) is a significant concern for companies as the loss of knowledge and experience can hinder progress and innovation. This study aims to understand the social structure of OKL. For this purpose, a bibliometric analysis consisting of performance and science mapping analyses was conducted. The results indicate different patterns of influence and cooperation, with Durst emerging as the most influential author. In addition, institutions such as University of Skövde, the University of Hong Kong, Northwestern Polytechnical University, Asian Centre for Organisation Development, and Southwest Jiaotong University are central to promoting cooperation between different research institutions. Understanding the dynamics of research collaboration networks and the role of individual researchers and institutions is crucial for shaping the landscape of knowledge production and dissemination. Future research should consider additional aspects, such as the conceptual and intellectual structure of OKL research. This will allow a more coherent picture to emerge.

Hate speech, particularly on social media channels, is a pressing cybersecurity concern and can even threaten the very foundations of societal stability. While there is a growing body of literature on how to detect and mitigate hate speech, applied researchers lack a state-of-the-art yet easily accessible infrastructure to build their own hate speech detection pipelines. We aim to provide an example of such an infrastructure that can serve as a template for other researchers. The infrastructure we present is based on the latest machine learning technologies available in the R environment: The Tidymodels framework and its extension Tidytext, plus the Targets project management approach, are the building blocks of our proposed infrastructure. In short, our data pipeline starts with downloading and preprocessing tweets, using various methods to convert text into numerical information. We then apply state-of-the-art supervised machine learning pipelines, drawing on a range of learning algorithms and incorporating new tuning capabilities. The focus of this paper is to explain the setup and rationale of the infrastructure. Our infrastructure is freely available on Github at https://github.com/sebastiansauer/hate-speech-barometer.

To make data visualization more attractive, not just color or different shapes are used, but also animations are nowadays added to graphs. Software tools for data visualization use different representation styles or display features and include animation in the data visualization process. Until now, the positive effect of different representation styles has been taken for granted in practice. However, this raises the question of the extent to which such representation styles are effective and what results can be seen in research in this regard. A literature review on data visualization is given to examine this in more detail. It was considered to what extent there are already findings on whether animated graphs have an effect on consumers’ behaviors. In the following work, we mean by an animated graph, a graph that is not visible from the beginning. Instead, the graph builds up over time. This way, motion is added to the graph. The literature review shows that data visualization has been studied more frequently in some areas. In marketing, this has received less consideration so far. Animations in graphs have not been studied much in the literature.

In the Project HAnS nine universities and three cross-institutional initiatives have teamed up on behalf of the German Federal Ministry of Education and Research (BMBF) to design and implement an intelligent university assistance system as open source. The goal is to develop an artificial intelligence (AI) tutor for higher education that transcribes video-based teaching material and enables keyword searches via indexing, but furthermore automatically generates exercises. As a result, students will be digitally assisted in their self-study. According to the design-based research idea, technological development is accompanied by an interdisciplinary approach and is checked and continuously altered throughout the developmental process in terms of user knowledge and values, pedagogical knowledge, ethics, acceptability and data protection.

Im BMBF-Verbundprojekt HAnS entwickeln und implementieren neun Hochschulen sowie drei hochschulübergreifende Einrichtungen ein intelligentes Hochschul-Assistenz-System als Open-Source-Lösung. Videobasierte Lehrmaterialien werden verschriftlicht und durch eine Indexierung Stichwortsuchen ermöglicht; geplant ist, über einen KI-Tutor automatisiert Übungsaufgaben zu generieren. Studierende sollen so in ihrem Selbststudium digital unterstützt werden. Die technische Entwicklung wird interdisziplinär – auch sozialwissenschaftlich und pädagogisch – begleitet und in einem iterativen Vorgehen evidenzbasiert entsprechend Design-Based-Research angepasst. Wissen und Wertesystem der Anwender*innen, Didaktik, Ethik, Akzeptanz und Datenschutz werden dabei im Entwicklungsprozess einbezogen.

This study highlights the importance of pre-testing social media ads and explores the challenges of objectively assessing ad performance. It outlines how a multi-method approach combining facial coding and eye-tracking can help identify successful ads before launch. The findings emphasize the growing importance of user experience research (via digital empathy) for social media advertising and performance marketing.