Introducing AI to German SMEs – practical insights into challenges and future research directions

Rapid changes in the business environment require adopting new technologies to maintain the pace of change and improve business results. This can be achieved through the adoption of artificial intelligence (AI). Small and medium-sized enterprises (SMEs) often face challenges regarding AI adoption. However, SMEs are often marginalized in research. For us as a regional AI lab, it is important to understand the challenges of regional SMEs to better help them adopt AI. Therefore, an expert study was conducted. Six categories of challenges were identified. The most frequently identified category was “lack of experience”. The study shows practitioners need clear guidelines and a safe testing environment to implement AI projects

An unsupervised multi-level fusion domain adaptation method for transfer diagnosis under time-varying working conditions

Unsupervised multi-source domain adaptation can overcome the limitations associated with insufficient information diversity in single-source domain adaptation for intelligent transfer diagnosis. However, the challenges of time-varying working conditions in practical industrial applications, limitation in single-level information fusion along with lack of multi-level information fusion restrict effective applications of unsupervised multi-source domain adaptation in transfer diagnosis. To address these challenges, this research presents a novel unsupervised multi-level fusion domain adaptation methodology for transfer diagnostics under time-varying working conditions, which employs a multi-level fusion domain adaptation network (MLFDAN). Firstly, a multi-sensor data enhancement and fusion module is proposed by combining continuous wavelet transform with an RGB information fusion, which integrates time–frequency and spatial information from multi-sensors. Then, a squeeze and excitation feature fusion module is designed for feature fusion across both time–frequency and spatial domains, which effectively emphasizes domain-invariant features while suppressing less relevant ones. Subsequently, an adaptive collaborative decision module is developed, which employs a weighted fusion strategy to address strong conflicts among multi-subnet predictions and utilizes consensus-based fusion strategy when multi-subnet predictions align, thus ensuring reliable and robust diagnostics decisions. Finally, a promising MLFDAN framework for transfer diagnosis is proposed by incorporating a dual-component domain adaptation approach that integrates a domain discriminator and multi-kernel maximum mean discrepancy. Numerous experiment results show that the presented MLFDAN methodology effectively adapts to transfer diagnosis scenarios from steady to time-varying working conditions, achieving impressive performances and outperforming several prominent unsupervised transfer diagnosis methodologies

Dynamische Netzentgelte und ihre mögliche Ausgestaltung für Deutschland

Die aktuelle Netzentgeltsystematik wird den Anforderungen eines zunehmend von erneuerbaren Energien geprägten Stromsystems immer weniger gerecht und steht daher derzeit auf dem Prüfstand. Dynamische Netzentgelte werden in dieser Debatte häufiger als wichtiger Reformbaustein genannt, allerdings fehlt es bislang an konkreten Konzepten für deren Ausgestaltung in Deutschland. Das vorliegende Papier schließt an diese Lücke an, indem es notwendige Voraussetzungen analysiert und mögliche Ausgestaltungsformen dynamischer Netzentgelte erörtert. Es soll damit einen fundierten und konstruktiven Beitrag zur Weiterentwicklung des Netzentgeltsystems in Deutschland leisten

Overcoming data shortage in critical domains with data augmentation for natural language software requirements

Natural language processing (NLP) offers the potential to automate quality assurance of software requirement specifications. In particular, large‐scale projects involving numerous suppliers can benefit from this improvement. However, due to privacy restrictions especially in highly restrictive industries, the availability of software requirements specification documents for training NLP tools is severely limited. Also, domain‐ and project‐specific vocabulary, as such in the aerospace domain, require specialized models for processing effectively. To provide a sufficient amount of data to train such models, we studied algorithms for the augmentation of textual data. Four algorithms have been investigated by expanding a given set of requirements from the European Space projects generating correct and incorrect requirements. The initial study yielded data of poor quality due to the particularities of the domain‐specific vocabulary, yet laid the foundation for the algorithms' improvement, which, eventually, resulted in an increased set of requirements, which is 20 times the size of the seed set. A complementing experiment demonstrated the usability of augmented requirements to support AI‐based quality assurance of software requirements. Furthermore, a selected improvement of the augmentation algorithms demonstrated notable quality improvements by doubling the number of correctly augmented requirements

Does socially non-compliant corporate behavior lead to underperformance? Event analysis related to the Russia–Ukraine conflict and the CELI list

Following the escalation of Russia’s invasion of Ukraine in February 2022, the conflict prompted extensive economic sanctions against the Russian Federation. This study examines the stock performance of companies across various countries and continents that either continued business operations in Russia or chose to withdraw. Using signaling theory in the context of military conflict, we apply multiple event study methodologies. Companies are categorized based on their engagement in Russia, using the CELI list as an ESG-equivalent signal to construct distinct portfolios. These are tested against several EU sanctions packages, accounting for different estimation windows. Results show that European companies that exited Russia outperformed those that remained, suggesting that markets reward compliance-oriented behavior. However, industrial and geographical factors also influence the results. Our findings emphasize the importance of strategic decision-making under geopolitical risk and encourage investors to consider conflict risk. Furthermore, we recommend regulators incorporate socially responsible investing into policy frameworks to promote ethical conduct

From ghost games to the return of the crowd: effects of increased fan attendance during and following COVID-19 on home advantage in the German Soccer Bundesliga

While prior studies have analyzed the effects of COVID-19-induced ghost games on home advantage in Germany’s Soccer Bundesliga, none have examined the 2021/22 season—the period immediately following spectator-free matches. This study addresses that gap by analyzing the impact of fan return on home advantage. Using a dataset of all Bundesliga matches from 2017/18 to 2021/22 (N = 1,530), we performed regression analyses incorporating a categorical variable to represent different phases of attendance restrictions. In the 2021/22 season, after a brief phase of stricter capacity limits, we find evidence of a significantly greater home advantage compared to pre-pandemic levels. However, this elevated effect diminishes over time despite rising spectator numbers. The findings highlight the psychological impact of fans on home team players as the primary mechanism driving home advantage. This suggests that fan presence influences mental rather than physical performance. Accordingly, clubs should prioritize players’ physical and mental well-being, even outside extraordinary contexts such as the pandemic

The geopolitics, government-business relations, and triangular cooperation of ‘Africa+1’ conferences

Various countries have adopted Africa strategies and established high-level conferences with African nations over the past decades. We contribute to the interdisciplinary literature at the intersection of geopolitics and business by revealing the historical trajectory of government-business relations in the various Africa initiatives. We particularly aim to explore and compare how governments shape international business through their geopolitical initiatives. We conduct a qualitative analysis of 34 policy documents of the Forum on China–Africa Cooperation, the Tokyo International Conference on African Development, and the Türkiye-Africa Partnership conferences held between 1993 and 2022. Based on our longitudinal analysis, we find a complementarity of government-business relations: governments primarily act as facilitators and channelizers of business engagement, while firms are considered crucial enablers for government policies. We further discuss differences in the identified role and ownership of companies in the three Africa initiatives against the backdrop of the respective geopolitical agendas

Emotional dynamics in semi-clinical settings: speech emotion recognition in depression-related interviews

The goal of this study was to utilize a state-of-the-art Speech Emotion Recognition (SER) model to explore the dynamics of basic emotions in semi-structured clinical interviews about depression. Segments of N = 217 interviews from the general population were evaluated using the emotion2vec+ large model and compared with the results of a depressive symptom questionnaire. A direct comparison of depressed and non-depressed subgroups revealed significant differences in the frequency of happy and sad emotions, with participants with higher depression scores exhibiting more sad and less happy emotions. A multiple linear regression model including the seven most predicted emotions plus the duration of the interview as predictors explained 23.7 % of variance in depression scores, with happiness, neutrality, and interview duration emerging as significant predictors. Higher depression scores were associated with lesser happiness and neutrality, as well as a longer interview duration. The study demonstrates the potential of SER models in advancing research methodology by providing a novel, objective tool for exploring emotional dynamics in mental health assessment processes. The model’s capacity for depression screening was tested in a realistic sample from the general population, revealing the potential to supplement future screening systems with an objective emotion measurement

Kleine Ausführungen zur Vertriebsethik

Vertriebsmitarbeiter werden häufig unethischen Verhaltens beschuldigt. Laut Jung (2019) sind Bluffen, Lügen, Täuschung und Falschdarstellung in Verhandlungen zwischen Unternehmen gängige Praktiken. Eine internationale Studie zu Lügen in Vertragsverhandlungen ergab, dass zwar Falschdarstellungen über den Inhalt eines Vertrags allgemein als inakzeptabel angesehen wurden, die Mehrheit der Befragten jedoch Bluffen über Fristen und Budgetbeschränkungen als moralisch akzeptabel betrachtete (Jung, 2019)

Wirksames Projektcontrolling durch die Earned Value Analyse

In diesem Beitrag wird zunächst das Instrument der Earned Value Analyse (EVA) mit ihren zentralen Kennzahlen beschrieben. Sodann werden die Bearbeitungsschritte der EVA sowie die Beschreibung des empirischen Vorgehens erläutert. Darauf aufbauend werden aus Experteninterviews abgeleitete praxisorientierte Ansätze zur Ermittlung des Projektaufwands, des Fertigstellungsgrads und des Projektfortschritts vorgestellt