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.

Digitalization concepts and methods have, in recent times, drawn the interest of researchers and academics, especially in design and manufacturing. Modern product design integrates advanced CAD/CAE/CAPP/CAM/PLM and computational tools as a means to automate tasks and increase the quality of the designed product, while reducing manufacturing cost and the time-to-market. The same is true that in the era of artificial intelligence, digital manufacturing is a fast-expanding focus of research that includes not only pure manufacturing topics but also additive manufacturing, reverse engineering, simulations of material properties for strength, manufacturability, and optimization of manufacturing processes.

The book aims to cover digital tools and methods that offer additional advantages for designing and manufacturing products in a variety of industries. Furthermore, the book reviews green digital design and green digital manufacturing in the context of a circular economy.

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.

A systematic sequence of materials was investigated to develop phytic-acid (Phyt)–based flame retarded poly(lactide acid) (PLA), while factoring in molecular weight (MW), crystallinity and mechanical properties. Synergistic approaches were developed based on combinations with lignin and expandable graphite (EG), as well as by applying different Phyt salts of melamine (Mel), piperazine (Pip), and arginine (Arg). Compounds were twin screw extruded, injection molded, hot pressed and investigated with thermal analysis, size exclusion chromatography, infrared spectroscopy, tensile testing, limited oxygen index (LOI), UL 94, cone calorimeter, and scanning electron microscope. 16.7 wt.% flame retardant (FR) slightly enhances crystallization while MW remains unchanged in PLA Phyt Arg and PLA Phyt Mel. LOI was improved to 43.7 vol.% for PLA Phyt Arg, UL 94 V0 achieved for PLA Phyt Pip. Cone calorimeter results show total heat evolved reduced by 14 %, maximum average rate of heat emission 43 % lower, and peak heat release rate reduced by 50 % for PLA Phyt Mel. Phyt Mel combined with EG increased the char yield of PLA to 20 wt.% and 15.5 wt.% at 600 and 900 °C, respectively. Phyt is exploited to enhance char yield, stabilize the intumescent char, and lower the apparent effective heat of combustion. The combination of Phyt Mel and EG was proposed as an efficient FR for PLA via an evidence based developing route.

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.

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.

A systematic sequence of materials was investigated to develop phytic-acid (Phyt)–based flame retarded poly (lactide acid) (PLA), while factoring in molecular weight (MW), crystallinity and mechanical properties. Synergistic approaches were developed based on combinations with lignin and expandable graphite (EG), as well as by applying different Phyt salts of melamine (Mel), piperazine (Pip), and arginine (Arg). Compounds were twin screw extruded, injection molded, hot pressed and investigated with thermal analysis, size exclusion chromatography, infrared spectroscopy, tensile testing, limited oxygen index (LOI), UL 94, cone calorimeter, and scanning electron microscope. 16.7 wt.% flame retardant (FR) slightly enhances crystallization while MW remains unchanged in PLA Phyt Arg and PLA Phyt Mel. LOI was improved to 43.7 vol.% for PLA Phyt Arg, UL 94 V0 achieved for PLA Phyt Pip. Cone calorimeter results show total heat evolved reduced by 14 %, maximum average rate of heat emission 43 % lower, and peak heat release rate reduced by 50 % for PLA Phyt Mel. Phyt Mel combined with EG increased the char yield of PLA to 20 wt.% and 15.5 wt.% at 600 and 900 ◦C, respectively. Phyt is exploited to enhance char yield, stabilize the intumescent char, and lower the apparent effective heat of combustion. The combination of Phyt Mel and EG was proposed as an efficient FR for PLA via an evidence based developing route.

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.

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.

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.