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.

This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations

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.

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.

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.

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.