Understanding a learner’s resources as a system of interacting components, the success of a learning process is determined by the effectiveness of their interactions. Theoretical assumptions and empirical findings clearly show the importance of resource availability in learning systems but do not sufficiently consider the individuality or the temporal and situational aspects of resource regulation. Therefore, the current study addresses the complex interplay between learning resources (educational and learning capitals) in an individual learner (N = 1) by utilizing multivariate time series data of a 50-day vocabulary learning process with daily assessments of learning resource availability, performance, learning duration, and stress. We draw on methods of psychometric network analysis, modeling all variables in simultaneous interaction and allowing predictions between all variables from measuring point to measuring point (temporal dynamics). Specifically, using a Graphical Vector Autoregressive (graphicalVAR) model, yielding a contemporaneous and a temporal dynamics network model, we identified pivotal resources in regulating the student’s learning processes and outcomes, including resources with strong connections to other variables, intermediary resources, and resources maintaining the system’s homeostasis. This innovative approach has possible applications as a diagnostic tool that lays the foundation for tailored interventions.

This paper systematically investigates how large language models (LLMs) encode moral reasoning across six moral dimensions: care, fairness, loyalty, authority, sanctity, and liberty. We propose a novel interpretability pipeline that combines differential activation analysis, automated neuron description, and ablation experiments to identify specialized neurons aligned with each moral dimension. Our curated dataset of 240 validated moral and immoral statement pairs guides this exploration and reveals that certain neurons consistently exhibit increased activation in response to morally aligned statements. Notably, the care and sanctity dimensions show the largest sets of specialized neurons, whereas fairness and loyalty show fewer. We further demonstrate that ablating these neurons can causally modulate ethical decision-making, supporting the presence of discrete sub-circuits that influence moral outputs. Our findings not only advance the theoretical understanding of moral reasoning in LLMs, but also highlight avenues for targeted interventions and alignment. 

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.

AI-based technologies are becoming increasingly significant while transforming human-machine interactions. Yet, many important questions remain unanswered. One example is the research question of the present contribution, regarding the effects of artificial intelligence (AI) on users’ perceptions of dependency on chatbots and smart home systems. The objective is to analyze to what extent users perceive dependencies on these technologies and which factors influence this perception. Based on a survey of 325 users, it was found that dependency perception is currently low but increases with more frequent usage. The results show a substantial association between perception of dependence and frequency of use, with a stronger effect among users of chatbots compared to users of smart home systems. Furthermore, a slight negative effect was observed for attitudes toward AI on dependency perception for both chatbots and smart home systems. To test the hypotheses, linear regression analyses were employed, revealing substantial associations between usage frequency and dependency perception. Despite limitations such as gender imbalance in the sample—even though gender had a negligible effect on PoD –, this study provides valuable insights into the societal impacts of AI and lays the groundwork for future research in this area. Future studies should include larger and more representative samples and develop validated measurement instruments to enhance the generalizability of findings. The results emphasize the necessity of critically evaluating the deployment and integration of AI technologies to identify potential dependencies at an early stage.

Photovoltaic (PV) energy production in Western countries increases yearly. Its production can be carried out in a highly distributed manner, not being necessary to use large concentrations of solar panels. As a result of this situation, electricity production through PV has spread to homes and open-field plans. Production varies substantially depending on the panels’ location and weather conditions. However, the integration of PV systems presents a challenge for both grid planning and operation. Furthermore, the predictability of rooftop-installed PV systems can play an essential role in home energy management systems (HEMS) for optimising local self-consumption and integrating small PV systems in the low-voltage grid. In this article, we show a novel methodology used to predict the electrical energy production of a 48 kWp PV system located at the Campus Feuchtwangen, part of Hochschule Ansbach. This methodology involves hybrid time series techniques that include state space models supported by artificial intelligence tools to produce predictions. The results show an accuracy of around 3% on nRMSE for the prediction, depending on the different system orientations.

The nature of corporate communication has undergone significant changes in recent years. One notable trend is the increasing use of employees as brand ambassadors, as evidenced by the proliferation of corporate influencer programs. However, a critical question is often overlooked: under what conditions are employees genuinely willing to participate in such programs? This predicament poses a substantial challenge to companies, who must devise compelling strategies to attract and engage employees in these initiatives. This study aims to address this gap by examining the critical factors influencing employee participation in corporate influencer programs on LinkedIn through a conjoint analysis. More than 100 employees, representing a range of company types from start-ups to large corporations, were surveyed. The findings reveal that a modern and actively cultivated corporate culture is essential for employees, while external recognition and occasional support (such as social media guidelines) play only a minor role.

Purpose

For decades, the concept of Shared Service Centers (SSCs) has been recognized in management discourse as a strategic approach to restructuring organizational support functions. Scholars have extensively examined this organizational trend, consistently emphasizing cost savings as the primary rationale behind SSC implementation. However, a comprehensive synthesis of the existing research – encompassing both qualitative and quantitative evidence – to substantiate this critical claim is still lacking. The purpose of this study is to fill this gap by systematically analyzing the SSC literature and assessing the empirical support for cost savings as a central implementation motive.

Design/methodology/approach

A systematic literature review was conducted, involving a screening of scientific databases for SSC-related publications. Following a structured review procedure, 89 articles were identified as containing information on cost savings. These prioritized publications were subjected to a detailed framework-based analysis employing a Theories-Characteristics-Contexts-Methods scheme to extract both qualitative and quantitative data. Ultimately, 306 relevant commentaries were gathered, with 40 publications offering author-generated evidence that underwent an in-depth analysis.

Findings

The structured evaluation of the evidence highlights a significant research gap: the lack of quantitative evidence based on objective financial metrics to substantiate the reduction of administrative costs achieved by SSC organizations within a company-wide profit-and-loss context. Existing research predominantly emphasizes qualitative commentary, often referenced from non-scientific third-party sources. When quantitative evidence is presented, it is frequently derived from single case studies and lacks detailed information on the calculation methodology and the associated baseline.

Originality/value

This paper represents the inaugural publication providing a comprehensive mapping of cost-saving evidence within the research domain. The findings underscore the urgent need for the development of a standardized approach capable of effectively capturing the cost-saving contributions of SSCs. Greater transparency regarding the concrete effects of SSCs in this context would significantly enhance top management’s decision-making processes regarding the implementation and expansion of such concepts. Enhanced quantitative rigor would constitute a pivotal advancement in the scientific field, addressing longstanding debates and establishing a novel approach to validating such contributions.

Phasen hoher wirtschaftlicher Unsicherheit können das Interesse an Weiterbildung spürbar verringern. Dies zeigte sich während der Covid-19-Pandemie sehr deutlich. Zugleich nahm aber das Interesse an Online-Weiterbildungsmöglichkeiten in dieser Zeit stark zu.  Zu diesem Ergebnis kommt eine Auswertung einschlägiger Suchanfragen bei Google.

School Shootings sind gezielte Gewalttaten an Schulen bzw. Bildungseinrichtungen, die langfristig geplant sind. Obwohl der Begriff „Amoklauf“ häufig verwendet wird, trifft er nicht den Kern dieser Taten, da sie nicht impulsiv, sondern strategisch durchgeführt werden und daher Ähnlichkeit mit Terroranschlägen haben. Die Täter sind überwiegend männlich und Schüler oder ehemalige Schüler der betroffenen Bildungseinrichtung, oder es liegt ein direkter Bezug dazu vor. In den letzten Jahrzehnten hat die Zahl der School Shootings stark zugenommen, insbesondere seit den 1990er-Jahren. Das Columbine-Shooting von 1999 gilt als eine Art Vorbild für viele spätere Täter und war das erste School Shooting, über das die Medien weltweit live berichteten und dessen Bilder eine Art ikonischen Status erreichten. Die Täter von School Shootings haben oft wenig soziale Bindungen, aber ein starkes Interesse an Waffen und gewalthaltigen Medieninhalten. Sie bereiten ihre Taten über Monate, teilweise bis zu einem Jahr hinweg vor. Viele haben schulische Probleme, erleben soziale Zurückweisung und zeigen psychische Auffälligkeiten wie narzisstische oder depressive Züge. Ein entscheidender Faktor ist die Fantasie der Täter, die sich oft in Rachegedanken oder dem Wunsch nach Ruhm äußert. Zugang zu Waffen ist ein weiterer Risikofaktor. Medien tragen zur Verbreitung des Phänomens bei, da Täter sich an früheren Fällen orientieren und ihre eigene Tat medial inszenieren. Prävention erfordert Maßnahmen auf verschiedenen Ebenen: Ein frühzeitiges Erkennen von Warnsignalen, strengere Waffengesetze, eine gezielte medienpädagogische Arbeit und Veränderungen an Schulen können das Risiko senken. Schutzfaktoren auf individueller Ebene liegen in stabilen sozialen Beziehungen, emotionaler Unterstützung und sinnvollen Freizeitaktivitäten.

Maintaining the quality and reliability of selective soldering processes in printed circuit board (PCB) manufacturing is crucial for economic efficiency and sustainability. This study explores the application of autoencoders, a neural network-based approach, for automatic anomaly detection in soldering nozzles, emphasizing the economic benefits of predictive maintenance. Traditional methods, such as visual inspections and rule based algorithms, are limited by subjectivity, delay, and susceptibility to false readings, leading to increased costs and production downtime. Autoencoders, on the other hand, use unsupervised learning to identify deviations from normal operational states by reconstructing input data and detecting anomalies based on reconstruction errors. This predictive maintenance approach can significantly reduce unexpected failures and maintenance costs, ensuring continuous production and enhancing sustainability. This research highlights the potential of autoencoder-based systems to automate and enhance the reliability of selective soldering processes, ultimately leading to significant economic benefits. The findings pave the way for real-time monitoring solutions, reducing dependency on manual inspections and rule-based algorithms, and improving production efficiency and sustainability in the electronics manufacturing industry.

With the recent advancements in Large Language Models (LLMs), web developers increasingly apply their code-generation capabilities to website design. However, since these models are trained on existing designerly knowledge, they may inadvertently replicate bad or even illegal practices, especially deceptive designs (DD). This paper examines whether users can accidentally create DD for a fictitious webshop using GPT-4. We recruited 20 participants, asking them to use ChatGPT to generate functionalities (product overview or checkout) and then modify these using neutral prompts to meet a business goal (e.g., “increase the likelihood of us selling our product”). We found that all 20 generated websites contained at least one DD pattern (mean: 5, max: 9), with GPT-4 providing no warnings. When reflecting on the designs, only 4 participants expressed concerns, while most considered the outcomes satisfactory and not morally problematic, despite the potential ethical and legal implications for end-users and those adopting ChatGPT’s recommendations.

Dynamical Component Analysis (DyCA) and Dynamic Mode Decomposition (DMD), both data-driven dimension reduction methods, are introduced. After application to multivariate simulated signals the techniques of mode selection and the resulting amplitudes are compared with respect to noise robustness and sampling periods. The results indicate that DyCA is a useful alternative to DMD and outperforms DMD under certain conditions. These conditions are based on the underlying dynamics in the terms of differential equations and on the noise ratios of the signals.