School students need to make decisions about their career paths after graduating. In Germany, students can choose between more than 300 vocational training programs, which can be overwhelming. Frequently, the students hesitate to talk with career counselors. The objective of this research is, therefore, to provide a recommendation system for school students to support their decision-making, which is based on their interests and provides recommendations with explanations based on a LLM. This system was developed with a social robot as the user interface to make it easy to use and appeal to the young target group. Based on user observations, preliminary findings indicate that the system is a valuable and engaging approach to support career counseling activities.

This study aims to investigate the influence of two interaction designs on user comfort and intention to use during pre-interaction phase. As part of a field experiment in a retail bank, a proactive and a passive interaction design of a social robot were compared. A standardized questionnaire was used to determine how the interaction design affects the comfort, trust and usage intention of customers and what role trust plays as a mediating factor. The data analysis shows that the proactive design was rated better in terms of psychological comfort and emotional value, but not in terms of trust and intention to use. Comfort with robots positively influenced the intention to use the social robot, with trust serving as a key mediator; in the proactive variant, the effect was only indirect via trust, while in the passive variant, both direct and indirect effects were observed. According to dual processing theory, proactive designs rely on automatic, emotion-driven processes that directly influence comfort, while passive designs encourage reflective decision-making, supporting trust and increasing usage intention despite lower comfort. A balanced integration of both approaches can enhance customer comfort and trust, facilitating the successful adoption of social robots in retail.

Nach der Corona-Pandemie haben Messeveranstaltungen als physische Interaktionsplattformen der Fachöffentlichkeit zu alter Stärke zurückgefunden. Die Digitalisierung erweitert die Angebote auf den Messen über Virtual Reality, Augmented Reality, Robotik und den Einsatz von künstlicher Intelligenz (KI). Bei der Programmierung von Messerobotern mit Unterstützung von KI stellt sich die Frage, wie emotional die Mensch-Roboter Interaktion gestaltet werden sollen. Aus der Forschung zur Robotik im Pflegebereich ist z. B. bekannt, dass emotional programmierte Roboter einen Zusatznutzen für die Anwender bringen. Aber gilt das auch für ein Messegespräch, in dem die emotional-menschliche Komponente Vertrauensfördernd wirket soll? Dazu wurde die folgende Forschungsfrage untersucht: „Inwiefern beeinflussen Emotionen als Teil der nonverbalen Interaktion im Rahmen der Mensch-Roboter-Interaktion die Akzeptanz von sozialen Robotern bei Messegesprächen?“ Zur Beantwortung der Forschungsfrage wurde ein Laborexperiment durchgeführt. Es wurde eine emotional und eine sachlich programmierte Version des Furhat-Roboters konzipiert, mit denen Probanden im Rahmen eines Messegesprächs interagiert haben. Nach Auswertung der Ergebnisse konnten kaum signifikanten Unterschiede in der Akzeptanz zwischen der emotionalen und der sachlichen Roboterversion festgestellt werden. Mögliche Investition in emotionale Programmierungselemente von Robotern im Messeeinsatz sollten nach diesen Ergebnissen hinterfragt werden.

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.

Tourism recommendation systems can mitigate the potential impact of choice overload on tourists. Social robots are a promising approach to provide recommendations to tourists through an engaging and intuitive user interface on sites like tourist information offices. This study investigates whether tourists perceive tourism recommendations provided via social robots as a satisfying and effective experience and whether tourists respond better to a more human or robotic design of social robot interactions. Therefore, an experiment is conducted at a real-world tourist information office where 60 tourists are exposed to either the more human or robotic version of the social robot recommender system. Their feedback is collected with a survey. The results show that the social robot is perceived positively across
all user-centric evaluation dimensions. This indicates that tourists accept social robots in real-world tourist recommendation situations and would also use them in the future.

Technology in football is increasingly used for decision making. Adoption, especially in Germany, has been slow. However, the benefits of data analytics for pre-, and post-match analysis have motivated decision makers to pay attention to the data science trend. Nowadays, football clubs from the third leagues or even amateur clubs are using technology to help them gain a competitive edge. Fan experience, both online and offline (home infront of the TV or at the stadium) is driving the next innovation stage in football. The study presented here is focused on testing and evaluation a facial recognition software on images from football coaches, just a few seconds after an important situation during the match has taken place (e.g., win, goal scored). We demonstrated that, in fact, emotion recognition software captures unexpected emotional reactions from coaches which could then be used to calculate interesting statistics and increase fan engagement and entertainment.

Digital ownership has gained attraction as a prospective domain for research and development of emerging technologies in recent years. A significant number of solutions, primarily blockchain-powered systems for digital ownership, have been developed and published aiming for widespread usage. However, the approach still appears uncommon to both digital creators and consumers community. While the majority of research in this field has been on technical aspects of implementing such solutions, there is an extreme deficiency regarding users’ viewpoints incorporated into the design and thus enlarging the barriers in mainstream adoption. This study picked the area of digital arts and shifted the focus to users’ perspectives in blockchain-based services for digital ownership in art. By adopting a qualitative approach to learn about digital creators’ behaviors and opinions, the study findings revealed various concerns about contemporary services that hinder creators’ use, their actual needs and expectations in a blockchain-based system for powering digital art products. Based on the study results, three design implications were identified to enhance the level of acceptance from the digital creator group.

Kundenfeedback im Online-Handel in Form von Produktrezensionen liefern wichtige Informationen über die Kundenwahrnehmung von Produkten. So beschreiben sie verwendete Materialien, Farben, die Passform, das Design und den Anwendungszweck eines Produkts. Das Kundenfeedback liegt hier in unstrukturierter Textform vor, weshalb zur Verarbeitung Ansätze aus dem Gebiet des Natural Language Processing und des maschinellen Lernens von Vorteil sind. In diesem Beitrag wird ein hybrider Ansatz zur Kategorisierung von Produktrezensionen vorgestellt, der die Vorteile des maschinellen Lernens des Word2Vec-Algorithmus und die der menschlichen Expertise vereint. Das daraus resultierende Datenmodell wird im Anschluss anhand einer Praxisanwendung zum Thema Produktempfehlungen demonstriert.