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.

This paper presents the development and evaluation of a first prototype of an ai-based study progress forecast. This service is integrated within a conversational agent and can be used by students to show them their current study progress. First, implications for the set-up of a forecast application from the literature are described. Based on the requirements identified in the literature and from the project itself, a lightweight formula was created that enables calculating the remaining study time. In order to assess preliminary feasibility and perception of the model prototype, a qualitative focus group discussion was conducted with five participants. Overall, the study progress forecast was well received by the participants, especially the offer itself as well as the promptness of the service were highlighted.

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Angesichts des wachsenden globalen Wettbewerbsdrucks, der Arbeit 4.0, des demografischen Wandels und der rasant steigenden Dynamik von Wissen nimmt die Bedeutung des strukturierten Wissensmanagements in allen Bereichen zu. Dabei befasst sich Wissensmanagement mit der Planung, der Durchführung und dem Controlling aller auf den Produktionsfaktor Wissen bezogenen Kernprozesse aus Sicht von Technik, Organisation und Mensch. Der Fokus dieses Beitrags liegt insbesondere darauf, wie Wissen von Menschen mit Wissen aus Daten synergetisch verknüpft werden kann und welche entsprechenden Trends sich daraus ergeben.

Finding the required information to succeed in the organisation of everyday study life is not always easy for a student. Ontologies are an instrument to define a domain by illustrating its concepts and thereby presenting knowledge in a structured way. In this paper, our aim is to design an ontology that is suitable for the higher education environment of a German university to build a Knowledge Graph for a conversational AI. As a research context, the Ansbach University of Applied Science is used. The paper is organised into five sections. After a brief introduction in Section 1, Section 2 reviews previous work of conducted ontologies within the higher education environment, whereas Section 3 outlines the methodology for developing the ontology and presents the final result. The development procedure is thereby partly based on the ontology framework provided by Stanford University (Noy & McGuinness, 2001). The presented ontology, which delivers possible classes for the development, and transferability to other universities will then be discussed in Section 4. Finally, the conclusion and approaches for future work with ensuring a constant up-to-dateness of the classes are given in Section 5.

MöglichMacher²³ – Bayerischer Hochschulpreis Entrepreneurship ist eine Auszeichnung, die das BayZiel – Bayerisches Zentrum für Innovative Lehre gemeinsam mit dem Bayerischen Staatsministerium für Wissenschaft und Kunst, der vbw - Vereinigung der Bayerischen Wirtschaft e.V. und Hochschule Bayern e.V. am 19.9.2023 zum ersten Mal vergeben hat.

Damit der Wirtschaftsstandort Bayern weiter erfolgreich bleibt, braucht er Innovationskraft und Gründergeist. Insbesondere Hochschulen spielen in Bezug auf die Förderung und Vermittlung unternehmerischer Kompetenzen und beim Gründungsgeschehen eine zentrale Rolle. Das Bayerische Hochschulinnovationsgesetz hebt die Bedeutung der Unternehmensgründung an Hochschulen hervor und fördert Entrepreneurship durch verschiedene Maßnahmen wie Gründungszentren und Gründungsfreisemester für Professorinnen und Professoren. Mit dem erstmalig ausgelobten Hochschulpreis MöglichMacher werden besondere Leistungen ausgezeichnet, die sich mit hohem Engagement für die Stärkung des unternehmerischen Denkens bei Studierenden und die Förderung des aktiven Gründungsgeschehen an den Hochschulen einsetzen.

Das Team der Gründungsberatung der Hochschule Ansbach, Prof. Dr. Carolin Durst und Johannes Hähnlein, beeindruckte die Jury mit seinen innovativen Ansätzen und den in nur zwei Jahren erzielten Erfolgen. Diese umfassen nicht nur erste spannende Ausgründungen und Startups, sondern insbesondere einen spürbar gestiegenen Gründungsgeist im Umfeld der Hochschule Ansbach.

Die Gründungsberatung der Hochschule Ansbach wurde hierfür mit dem 2. Platz ausgezeichnet sowie mit einem Preisgeld in Höhe von 10.000 € honoriert und nimmt damit laut Jury eine Vorbildrolle für ganz Bayern ein.

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.