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 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.

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.

Geometric deviations play a crucial role in the quality of additive manufacturing, particularly in parts made with biodegradable resins. Accurately controlling dimensional and geometric variations in manufactured components is critical for achieving defect-free production and meeting functional standards. However, defining a final quality score can be challenging due to numerous dimensional and geometric deviations associated with a part. An innovative metric for evaluating geometric performance was created to measure dimensional precision in components produced through VAT photopolymerization. The index measures the dimensional and geometrical deviations, revealing that external surfaces exhibit greater precision than internal ones. This difference is likely due to internal surfaces overcoming heat dissipation challenges during the cooling process, resulting in less shrinkage for external surfaces. This index is essential in various stages of the manufacturing process, including part design, design for manufacturing and assembly, quality assurance, and process planning, helping to select the appropriate additive manufacturing technology and optimal process parameters.

This paper discusses the challenges and methods for source reconstruction of evoked potentials using deep learning in the context of electroencephalography (EEG). We propose the use of deep learning to address known challenges and improve traditional approaches. We explain the creation of a suitable dataset for solving the inverse problem, including the simulation of neural activity and the use of lead field matrices for the forward solution. Furthermore, we undertake a comparative analysis of some initial deep learning models with similar classical methods.

The manifold of essential matrices is equipped with a one-parameter family of (pseudo-)Riemannian metrics. For the whole family, explicit formulas for geodesics are derived. Moreover, specific curves, so-called quasi-geodesics, are studied and a closed form expression for a quasi-geodesic connecting two given essential matrices is obtained.

In this paper, we show an application of spatiotemporal persistence landscapes to real world time series. Spatiotemporal persistence landscapes are a recent extension of persistence landscapes to time series that capture features of the data that are persistent with respect to time and space. We perform our analysis on EEG data to detect absence epileptic seizures. Further, we compare two dimension reduction techniques (DyCA and PCA) with no dimension reduction and show that the combination of DyCA and persistent landscapes yields the best results.

Influencer-Marketing ist eine etablierte Strategie der digitalen Markenkommunikation, die auf die Authentizität und Überzeugungskraft von Influencern setzt. Mit der zunehmenden Verbreitung von Künstlicher Intelligenz (KI) verändert sich dieser Bereich durch den Einsatz von KI-Influencern. Immer häufiger vermitteln virtuelle, computergenerierte Persönlichkeiten Markenbotschaften und beeinflussen Kaufentscheidungen. Ein zentrales Merkmal von KI-Influencern ist ihre digitale Perfektion, die durch idealisierte ästhetische Merkmale und makellose Darstellung charakterisiert ist. Dieser Beitrag untersucht die Wirkung dieser digitalen Perfektion auf die Wahrnehmung von KI-Influencern. Basierend auf dem Uncanny-Valley-Effekt und der Theorie der para-sozialen Interaktion wird ein konzeptionelles Wirkungsmodell abgeleitet und empirisch überprüft. In einer quantitativen Studie (n=100) wurden Konsumenten KI-Influencer mit unterschiedlichem Perfektionsgrad (unperfekt, moderat perfekt, perfekt) präsentiert. Die Ergebnisse zeigen, dass eine höhere digitale Perfektion zu einer geringeren wahrgenommenen Kompetenz, Menschlichkeit und Glaubwürdigkeit führt. Diese Befunde unterstreichen die Bedeutung gezielter Imperfektion bei der Gestaltung von KI-Influencern, um Konsumentenvertrauen zu stärken und eine positive Markenwahrnehmung zu fördern. Abschließend werden Empfehlungen und Implikationen für Forschung und Praxis aufgezeigt.

Media organizations, journalism, and social media play a crucial role in the coverage of terrorism attacks including lone wolf/school shootings. Those crisis events involve strategic communication by perpetrators who seek (media) attention and public awareness and lead to widespread international impact. This chapter analyzes how media organizations, journalism, and social media respond to these events and how journalists shape their coverage. It reviews extant research and guidelines on how media can deal with the ethical and professional challenges in this field. It also discusses the challenges and dilemmas that media organizations face, introducing concepts such as labeling, framing, censorship, and contagion effects. The chapter concludes with recommendations for future research and practice in this field.

Splined assemblies ensure precise torque transmission and alignment in mechanical systems. Three-dimensional printing, especially FDM, enables fast production of customized components with complex geometries, reducing material waste and costs. Optimized printing parameters improve dimensional accuracy and performance. Dimensional accuracy is a critical aspect in the additive manufacturing of mechanical components, especially for splined shafts and hubs, where deviations can impact assembly precision and functionality. This study investigates the influence of key FDM 3D printing parameters—layer thickness, infill density, and nominal diameter—on the dimensional deviations of splined components. A full factorial experimental design was implemented, and measurements were conducted using a high-precision coordinate measuring machine (CMM). To optimize dimensional accuracy, artificial neural networks (ANNs) were trained using experimental data, and a genetic algorithm (GA) was employed for multi-objective optimization. Three ANN models were developed to predict dimensional deviations for different parameters, achieving high correlation coefficients (R2 values of 0.961, 0.947, and 0.910). The optimization process resulted in an optimal set of printing conditions that minimize dimensional errors. The findings provide valuable insights into improving precision in FDM-printed splined components, contributing to enhanced design tolerances and manufacturing quality.