Creative insight is critical to adaptive leadership and innovation in increasingly complex environments. This article introduces Inspirational Intuition as a conceptual framework that integrates intuitive perception and knowledge imagination. Drawing on creativity theory, unconscious cognition, and leadership research, the article outlines four foundational dimensions: Self-Confident Acting, Creative Silence, Emotional Awareness, and Visionary Openness. These form the basis of the IKII-10, a reflective inventory designed for conceptual engagement with intuitive Human Resource Development (HRD) competencies. Rather than an empirical tool, the IKII-10 serves as a scaffold for further theory building, supporting a more holistic understanding of learning, leadership, and organisational development in the face of uncertainty.

Künstliche Intelligenz (KI) kann im Prozess der Leistungsbewertung assistieren und diesen transformieren. Besonders lohnend scheint eine KI-Assistenz bei der Bewertung von komplexem, geschriebenem Text. Jedoch ist der Einsatz von KI im Bewertungsprozess «hochriskant» (EU 2024) und bedarf umfangreicher Analysen. Die vorliegende Studie untersucht, inwiefern ChatGPT-4o die Auswertung und Interpretation von Lerntagebucheinträgen objektiv vornehmen kann. Dafür werden 757 Lerntagebucheinträge aus der geförderten Weiterbildung in Deutschland von Mensch und Maschine bewertet. Sowohl Mensch als auch Maschine erhalten hierzu Kriterien, nach denen die Bewertung vorzunehmen ist; ChatGPT-4o wird diesbezüglich mit einem Prompt unterstützt. Die Übereinstimmung der Bewertungen wird anhand der Masse Sensitivität und Spezifität gemessen. Die Ergebnisse zeigen, dass die Bewertungsvorschläge von ChatGPT-4o eine moderate bis hohe Übereinstimmung mit den menschlichen Bewertungen aufweisen; gleichzeitig neigt ChatGPT-4o jedoch zu einer optimistischen Bewertung der Lerntagebucheinträge. Die Ergebnisse weisen darauf hin, dass eine hybride Intelligenz, also eine Kombination der Stärken von Mensch und Maschine, gewinnbringend für Bewertungsprozesse sein kann. Künftig denkbar sind halbautomatisierte Bewertungsprozesse von Lerntagebucheinträgen, in denen die KI die Bewertung der Lerntagebucheinträge übernimmt und Lehrkräfte bei kritischen Fällen regulierend eingreifen. So könnte die Korrektureffizienz ohne bedeutende Qualitätsverluste gesteigert werden.

The European Union and many governments of its (former) membership states demand more expertise on China, including Chinese language skills. Fostering Chinese language education requires a thorough understanding of the target group. This article aims to lay the foundation for a systematic and comprehensive analysis of the profile of at least one subgroup of Chinese learners, namely adult learners at universities in German-speaking countries. It presents the results of a preliminary meta-study comprising data from 217 Chinese learners. Among the information that can be extracted from various studies are personal data (age, gender, field of study, type of educational institution, first and, where applicable, second language) and data on individual learning biographies (number and specification of languages learned to date, use of learning strategies, and degree of language awareness). Results show that these learners have, on average, a comparable number of previously learned foreign languages. There are more multilingual speakers than, for example, among learners of Spanish. Experienced learners have certain preconditions and needs according to current L3 acquisition theories, which should be paid special attention to when teaching Chinese as a foreign language. Some didactic suggestions are put forward on how to integrate the results into the teaching of Chinese. 

The exponential rise in global plastic consumption, coupled with its heavy dependence on fossil fuels, has resulted in severe environmental and ecological consequences—ranging from greenhouse gas emissions to widespread marine pollution. In response, bioplastics derived from renewable sources have emerged as a promising solution, particularly those synthesized from algae. This study explores the potential of algae-based bioplastics—produced from macroalgae (e.g., Ulva lactuca) and microalgae (e.g., Chlorella, Spirulina) - as a sustainable and circular alternative to conventional plastics. Owing to their rapid growth, high biomass yield, and non-reliance on arable land, algae offer distinct ecological advantages over traditional biomass sources like corn or starch. The research examines the types of algal bioplastics, key production processes, and the integration of genetic engineering techniques to enhance yield and biodegradability. It also addresses scalability, economic feasibility, and degradation by-product limitations. Furthermore, the study evaluates policy and regulatory frameworks that support the development and adoption of bio-based materials. The findings suggest that algae-derived bioplastics reduce reliance on fossil resources and align with circular economy principles by offering biodegradable, renewable, and low-impact alternatives. As global environmental concerns intensify, algae-based bioplastics may play a vital role in reshaping the future of sustainable materials and reducing plastic pollution at scale. 

Learning diaries are reflective tools, often used as formative assessments in adult education with the aim to promote cognitive and metacognitive learning strategies. As grading of and feedback on learning diaries is effortful for teachers, artificial intelligence (AI) may assist teachers in evaluating learning diaries. A prerequisite is that AI's ratings show high accordance with the teachers' ratings. AI accuracy, measured via absolute accuracy and bias, is the focus of the current study with N = 540 learning diary entries focusing on learning strategies, seven teachers, and ChatGPT-4o. Findings revealed that AI evaluations align closely with teacher assessments, indicated by high overall accuracy and low bias. Interestingly, the accuracy varied based on the types of learning strategies assessed in the diaries. Additionally, individual teacher assessments influenced the alignment between human and AI evaluations, suggesting that teachers applied their profession-specific expertise to the assessment process while AI produced somewhat generic evaluations. Overall, the study results indicate that AI can enhance the efficiency of formative assessments while providing timely feedback to learners.

For successful learning, monitoring one's learning behavior and outcomes is essential. While both the use of monitoring strategies and monitoring judgment accuracy are regarded as indicators for metacognitive monitoring, their joint contribution to metacognitive monitoring and their interrelationship is unclear. The current study investigated how monitoring strategies and monitoring judgments (spontaneous as well as prompted judgments) change micro-genetically and influence each other reciprocally during an exam preparation phase. In this diary study, N = 273 higher education students completed knowledge tests and surveys on five consecutive days. Results indicated interindividual differences in dependence of prior performance and gender for the accuracy of prompted judgments but not for monitoring strategies or spontaneous judgments. Random intercept cross-lagged panel models revealed that higher quality monitoring strategies led to the spontaneous formation of monitoring judgments, while the quality of monitoring strategies and prompted judgment accuracy did not influence each other significantly.

Entrepreneurs benefit significantly from resources within their entrepreneurial ecosystems, but under what conditions do they decide to contribute back (a mechanism called downward causation) and thus revitalize the ecosystem they originated from? Drawing on social exchange theory, we develop a set of drivers of such contribution behaviors and test their influence through a metric-conjoint experiment involving 234 entrepreneurs. Our findings confirm the impact of social exchange theory constructs on entrepreneurs' contribution behaviors and highlight the moderating effects of personal traits—in particular, self-interest and other-orientation—on these dynamics. The key insight of our study is that social exchange structures and entrepreneurs’ relational contexts shape contribution behaviors that underlie the microfoundational dynamics of ecosystem development. This investigation underscores the importance of social structures within entrepreneurial ecosystems and enhances our understanding of the micro-level mechanisms that sustain ecosystem health and development. Furthermore, it offers practical insights that transcend traditional policy approaches, focusing on tailored strategies for cultivating entrepreneur-centered ecosystems.

Abstract. Topological Interlocking Elements (TIEs) offer a novel approach to self-supporting and damage-resistant structures, relying on their geometric constraints rather than traditional adhesives or fasteners. While cubic-based TIEs provide ease ways of fabrication and modularity, their limited contact surface areas reduce load transfer efficiency and structural stability. This study introduces a new cubic-based TIE configuration with enhanced contact surfaces, improving interlocking efficiency and mechanical performance. By employing parametric modeling and geometric analysis, the study evaluates the proposed design against conventional cubic TIEs. This research advances interlocking structural systems by presenting a more efficient and stable cubic-based TIE, contributing to improved structural performance and broader applications in engineering and architecture.

Context plays a critical role in talent development, yet most national analyses continue to rely on individual-centered talent concepts. This paper highlights the limitations of traditional models for assessing how countries support talent and proposes a resource-oriented, systemic alternative. Building on the Educational and Learning Capital Approach (ELCA), this study argues that national talent development depends on the availability, accessibility, and orchestration of both endogenous and exogenous learning resources across systemic levels. By analyzing the clumping patterns of excellence in STEM, the arts, sports, and innovation, this paper illustrates the unequal global distribution of talent-supportive environments. Seven key principles for effective resource orchestration are outlined, offering a framework for evaluating and strengthening national talent ecosystems. The paper concludes that systematic assessment and strategic enhancement of national resource landscapes are critical for sustainable talent development and for ensuring that human potential can flourish more equitably across countries.

Dieses essential bietet eine fundierte Analyse und praktische Hinweise zur erfolgreichen Implementierung von Corporate-Influencer-Programmen auf LinkedIn. Es beleuchtet die entscheidenden Einflussfaktoren, die Mitarbeitende motivieren, als digitale Markenbotschafter für ihr Unternehmen zu agieren und zeigt auf, wie Unternehmen diese Potenziale optimal nutzen können.

In einer detaillierten Conjoint-Analyse werden die Rahmenbedingungen für erfolgreiche Programme untersucht – von besonderer Bedeutung ist dabei eine moderne und gelebte Unternehmenskultur. Für alle Verantwortlichen in der Unternehmenskommunikation, HR und Marketing, die ihre Strategien im Bereich Corporate Influencing optimieren und langfristig eine positive Arbeitgebermarke aufbauen wollen.

Digital platform business models are reshaping the market through new forms of value creation and require robust methodologies like the SPDS – Smart Platform Design Sprint for design and validation. Generative AI can support innovation processes by accelerating ideation, yet the SPDS lacks a framework to incorporate it. To address this gap, this study investigates how generative AI tools can be embedded into the SPDS to enhance creativity and decision-making. Building on a case study, several AI tools were implemented and evaluated. The findings reveal that generative AI can optimize SPDS processes by assisting with problem analysis, text generation, and visualization. While ChatGPT excelled at analysis and text generation, Napkin AI produced concise visualizations. This study advocates a hybrid approach combining the strengths of both tools, providing a guide for teams and innovation coaches to reduce effort, foster creativity, and improve time efficiency.

Der Softwaremarkt zählt zu den wachstumsstärksten Sektoren der deutschen Wirtschaft. Unternehmen wie Celonis und Personio zeigen, welches Potenzial in Software-Startups steckt – sowohl im Hinblick auf Umsatz als auch auf die internationale Wettbewerbsfähigkeit. Dennoch ist die Ausfallquote hoch: Viele Startups scheitern frühzeitig, häufig am fehlenden Product-Market-Fit. Gerade Software-Startups stehen vor besonderen Herausforderungen. Sie entwickeln digitale, häufig komplexe Produkte, deren Nutzen sich schwer kommunizieren lässt. Der Irrtum „Build it and they will come“ führt dazu, dass Produkte ohne fundiertes Kundenverständnis entwickelt werden. Um dieses Risiko zu minimieren, gibt es nutzer- und marktzentrierte Methoden. Sie helfen dabei, frühzeitig Rückmeldung aus dem Markt einzuholen, den Produktnutzen klar zu definieren und ein tiefes Verständnis der Zielgruppe zu gewinnen. Wer als Startup transparent kommuniziert und seine Zielgruppe aktiv einbindet, kann nicht nur Produktideen validieren, sondern auch erste loyale Kundengruppen gewinnen. Dieser Beitrag zeigt, dass die Integration nutzer- und marktorientierter Methoden die Erfolgschancen von Software-Startups deutlich erhöhen kann. Eine praxisorientierte Übersicht am Ende des Beitrags zeigt, wie zentrale Methoden je nach Entwicklungsphase gezielt eingesetzt werden können.

This book reports on innovations and engineering achievements of industrial relevance, with a special emphasis on industrial engineering developments aimed at improving the quality of processes and products in the context of a sustainable economy. It gathers peer-reviewed papers presented at the 4th International Conference “Innovation in Engineering”, ICIE 2025, held on June 18-20, 2025, in Prague, Czech Republic. All in all, this third volume of a three-volume set provides engineering researchers and professionals with a timely snapshot of technologies and strategies that should help shaping different industrial sectors to improve production efficiency, industrial sustainability, and human well-being.

 

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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.

Seam visibility remains a significant challenge in Fused Filament Fabrication (FFF) 3D Printing, particularly on curved and complex geometries. Traditional seaming methods, such as the butt-joint, often produce visible artifacts, reducing the surface quality and the mechanical characteristics of parts. This study explores the effectiveness of the Scarf Seam method, a new seam management technique integrated into open-source slicing software, to improving surface finish. While previous research has addressed seam visibility and strength, limited studies have systematically analyzed the impact of seam parameters. Using a Taguchi L16 design, this study evaluates key parameters influencing Scarf Seam performance, including Scarf Joint Speed, Scarf Steps, Scarf Start Height, and Scarf Length. Results indicate that parameter optimization significantly enhances part’s shells concealment. Despite improvements, inconsistencies in seam start localization across different configurations remain unexplained, highlighting the need for further investigation. This research provides valuable insights into seam optimization strategies, contributing to better surface aesthetics and structural performance in FFF-printed parts.

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.