TY - JOUR A1 - Hartmann, Julia A1 - Heckner, Markus A1 - Plach, Ulrike T1 - Future Skills bei Studierenden – Messung und Einflussfaktoren JF - Die Neue Hochschule N2 - Eine sich stetig verändernde Arbeitswelt erfordert neue Kompetenzen von unseren Studierenden. In der Lehre gilt es nicht nur, diese Zukunftskompetenzen (sog. Future Skills) zu fördern, sondern diese auch zu messem und sichtbar zu machen. KW - Future Skills KW - Studierende Y1 - 2023 UR - https://www.yumpu.com/de/document/read/68553002/die-neue-hochschule-heft-6-2023 IS - 6 SP - 24 EP - 27 ER - TY - GEN A1 - Hartmann, Julia A1 - Heckner, Markus A1 - Plach, Ulrike T1 - Einfluss studierendenzentrierter Lehrelemente auf die Entwicklung von Zukunftskompetenzen bei Studierenden KW - Future Skills KW - Lehrkonzepte Y1 - 2023 ER - TY - JOUR A1 - Findeisen, Vivien A1 - Lang, Gabriele A1 - Beckmann, Wilhelm T1 - Gibt es die richtige Transferindikatorik? BT - Ein Überblick über bestehende Kennzahlenvorschläge und Empfehlungen zur Findung und Anwendung passender Indikatoren zur Steuerung von Wissens- und Technologietransferprozessen JF - Wissenschaftsmanagement, Entscheiden.Führen.Gestalten 2021 N2 - Wissens- und Technologietransfer (WTT) hat innerhalb der letzten Jahrzehnte stark an Bedeutung gewonnen und reiht sich nicht nur im Hochschulbereich als Third Mission neben Lehre und Forschung ein (vergleiche Roessler et al. 2015, 4). Über den Hochschulbereich hinaus bewegt dieses Thema auch außerakademische Sektoren. Zur Bewertung und Steuerung der Transferaktivitäten und vor allem der Transferleistung von Transferprozessen bedarf es einer adäquaten Auswahl an Transferindikatoren zur Erreichung der Transferziele (vergleiche Molas-Gallert 2002; Hachmeister et al. 2016; Frondizi et al. 2019). Somit sollte das Interesse an einer sinnvollen Transferindikatorik bei jedem Akteur liegen, der aktiv am Transfermanagement teilnimmt. Doch welches Set an Kennzahlen ist geeignet, um die eigenen Transferaktivitäten zu beobachten und deren Leistung zu steuern? KW - Transferforschung KW - Transferindikatoren KW - Wissens- und Technologietransfer Y1 - 2021 UR - https://shop.lemmens.de/produkt/gibt-es-die-richtige-transferindikatorik/ SP - 322 EP - 329 PB - Lemmens Medien GmbH CY - Bonn ER - TY - GEN A1 - Braun, Carina N. A1 - Wilson, Edith E. T1 - Berufswahl Professur an einer Hochschule für angewandte Wissenschaften BT - Forschungsstand und Forschungsagenda T2 - 17. Jahrestagung der Gesellschaft für Hochschulforschung, 14. bis 16. September 2022, Wien N2 - Der Vortrag präsentiert die heute vorliegenden, wissenschaftlichen Erkenntnisse zur Gewinnung und Entwicklung von Professor:innen an HAW. Darauf aufbauend wird eine Forschungsagenda abgeleitet, die u.a. Ansätze aus relevanten Literaturströmungen verknüpft. KW - HAW-Professur KW - Hochschule für angewandte Wissenschaften KW - Hochschulforschung KW - Fachhochschulen KW - Karriereforschung Y1 - 2022 ER - TY - GEN A1 - Braun, Carina N. A1 - Wilson, Edith E. T1 - Learning from German “Pracademics" BT - Professors’ Career Paths at German Universities of Applied Sciences T2 - ICHEM Conference - The Future of Higher Education: Competition, Collaboration and the Global Good, 19-10 June, 2023, Bath (UK) N2 - The presentation explains the conditions and problems regarding the recruitment of professors at German universities of applied sciences (UAS). Based on a qualitative study, preliminary findings about the career paths to professorships at a UAS can be shown. KW - HAW-Professur KW - Hochschulen für angewandte Wissenschaften KW - Hochschulforschung KW - Karrierewege KW - Universities of applied sciences Y1 - 2023 PB - International Centre for Higher Education Management (ICHEM) ER - TY - CHAP A1 - Kellner, Florian A1 - Otto, Andreas A1 - Lienland, Bernhard T1 - Cost Assignment Paradox: Indirect Tooling Costs and Production Orders T2 - Advances in Management Accounting N2 - Purpose Tooling is a common component of an industrial product’s manufacture. Specific tooling is devised to serve the fabrication of a particular product, while generic tooling can be used in the manufacture of multiple products. In the latter case, companies are confronted with the problem of fairly allocating the indirect costs of the tooling. This article studies how to allocate costs of generic tooling to single production orders. Methodology Ten allocation methods (AMs) are described that are in principle suited to the distribution of generic tooling costs to production orders. Since the presented methods have for the most part been discussed in differing contexts, we apply them to a specified generic tooling problem for comparison. Evaluation of the various methods is based on 16 criteria. Reasoning is supported by a computational Monte Carlo simulation. Furthermore, we suggest using the Analytical Hierarchy Process (AHP) to elaborate one final proposition concerning the most preferable allocation scheme. Findings The article reports the single allocation rules’ performances for different allocation scenarios. The described characteristics refer to fairness, efficiency, and simplicity as well as to empty-core performance. Using AHP analysis allows for the aggregation of the rules’ criteria ratings. Thus, especially suitable allocation schemes for the problem at hand are identified. Practical implications An allocation is required for budgeting reasons and also for the definition of projects’ bottom-up sales prices. Selecting the “right” AM is important, as a suboptimal AM can result in unfair allocation vectors, which will act as incentives to stop using the common resource, potentially leading to higher total costs. Originality/value of the article Research on the comparison of AMs is typically performed for certain purposes, such as enterprise networks, horizontal cooperative purchasing scenarios, or municipal service units. This article will augment the research evaluating AMs by introducing a novel set of evaluation criteria and by providing an in-depth comparison of AMs suited for the allocation of generic tooling costs. KW - Tooling KW - Cost allocation KW - Indirect cost KW - Overhead cost KW - Joint cost KW - Fairness criteria Y1 - 2014 U6 - https://doi.org/10.1108/S1474-787120140000023007 VL - 23 SP - 211 EP - 251 PB - Emerald ER - TY - GEN A1 - Pöhlmann, Kendra T1 - Similarities and Differences of Research-based and Corporate Spin-offs: Theoretical Thoughts, Research Design and Expected Implications N2 - This presentation explores the similarities and differences between research-based spin-offs and corporate spin-offs, addressing a key gap in the entrepreneurship and innovation literature. While both types of ventures aim at commercializing knowledge and generating economic value, they emerge from distinct institutional contexts and may therefore rely on different success factors. Building on existing research on research-based spin-offs, the study develops a comparative conceptual framework that integrates perspectives from the resource-based view and industrial economics. It examines dimensions such as founder characteristics, motivation structures, knowledge bases, and relationships with parent organizations. The proposed research design aims to systematically contrast both spin-off types and assess the transferability of established success factors. The paper contributes to a more differentiated understanding of spin-off heterogeneity and derives implications for improving commercialization processes in both academic and corporate contexts. KW - Spin-offs; Knowledge Transfer; Commercialization Process Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-90198 ER - TY - GEN A1 - Pöhlmann, Kendra T1 - Research Based Spin-Offs: Understanding the specifics of Best Performers N2 - This presentation investigates the success factors of research-based spin-offs, with a particular focus on the differences between best and worst performers. Based on an empirical study of German spin-offs originating from public research institutions, key influencing factors such as team size, degree of innovation, market conditions, and the role of the parent organization are analyzed. The findings indicate that spin-off success is shaped by a combination of entrepreneurial, technological, and contextual factors, and varies depending on the applied performance measure (e.g., growth, financial performance, or technological output). The study provides nuanced insights into the mechanisms of successful knowledge and technology transfer and derives implications for both research institutions and founders. KW - Research-based spin-offs; technology transfer; success factors Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-90181 ER - TY - GEN A1 - Pöhlmann, Kendra T1 - Diversity: Important, Fair, and Sustainable – But Often Overlooked in AI Applications N2 - In the era of digital transformation, the intersection of sustainability and AI has emerged as a critical area of research. My poster delves into the intricate relationship between these two domains, highlighting their potential to shape a more equitable and sustainable future. One significant facet of this is diversity. But diversity, as a vital component of the social dimension of sustainability, is frequently sidelined in the discourse surrounding digitalization and AI, despite its potential to synergize with these technologies in forging a fairer and more sustainable world. Digital solutions frequently lack inclusivity and emancipation, exacerbating disparities in access and opportunity. AI tools, likewise, often perpetuate bias and inequality by being trained on homogeneous datasets, overlooking factors such as gender, ability, and ethnicity. To address this oversight, it is imperative to incorporate diversity and sustainability education into the training of future IT specialists and AI engineers. Developing ethical awareness and the ability to critically reflect on biases within AI systems is essential to avoid reproducing biases and stereotypical thinking. This is why it is imperative to emphasizes the urgency of recognizing diversity as a key enabler of fairness and sustainability within the digital transformation landscape. KW - Digital Transformation; Diversity; Sustainability Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-90156 ER - TY - GEN A1 - Pöhlmann, Kendra T1 - Bildung für Nachhaltige Entwicklung – Achtsamkeit als Future Skill N2 - Das Poster beleuchtet die Rolle von Achtsamkeit als zentrale Zukunftskompetenz im Kontext von Bildung für nachhaltige Entwicklung (BNE). Ausgangspunkt ist die Annahme, dass nachhaltige Transformation nicht allein durch Wissen über die Sustainable Development Goals (SDGs) gelingt, sondern maßgeblich innere Kompetenzen erfordert, wie sie in den Inner Development Goals (IDGs) beschrieben werden. Achtsamkeit wird dabei als grundlegender Baustein von Selbst- und Reflexionskompetenz verstanden und bildet die Voraussetzung für Fähigkeiten wie Perspektivenwechsel, Ambiguitätstoleranz und verantwortungsvolles Handeln. Das Poster integriert verschiedene Kompetenzrahmen (Future Skills, IDGs, Rounder Sense of Purpose) und zeigt deren konzeptionelle Schnittmengen auf. Es argumentiert, dass Achtsamkeit nicht nur individuelle Entwicklung unterstützt, sondern als Hebel für kollektive Transformationsprozesse wirkt. Für die Hochschullehre ergibt sich daraus die Implikation, Achtsamkeit systematisch in fachliche Kontexte zu integrieren, um Studierende zur aktiven Mitgestaltung nachhaltiger Entwicklung zu befähigen. KW - Bildung für nachhaltige Entwicklung; Transformationskompetenz; innere Entwicklung Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-90168 ER - TY - GEN A1 - Pöhlmann, Kendra T1 - Diversity: Important, fair, and sustainable – but often overlooked in AI applications N2 - In the era of digital transformation, the intersection of sustainability and AI has emerged as a critical area of research. My research delves into the intricate relationship between these two domains, highlighting their potential to shape a more equitable and sustainable future. One significant facet of this is diversity. But diversity, as a vital component of the social dimension of sustainability, is frequently sidelined in the discourse surrounding digitalization and AI, despite its potential to synergize with these technologies in forging a fairer and more sustainable world. Digital solutions frequently lack inclusivity and emancipation, exacerbating disparities in access and opportunity. AI tools, likewise, often perpetuate bias and inequality by being trained on homogeneous datasets, overlooking factors such as gender, ability, and ethnicity. To address this oversight, it is imperative to incorporate diversity and sustainability education into the training of future IT specialists and AI engineers. Developing ethical awareness and the ability to critically reflect on biases within AI systems is essential to avoid reproducing biases and stereotypical thinking. This is why my research emphasizes the urgency of recognizing diversity as a key enabler of fairness and sustainability within the digital transformation landscape. KW - Diversity; Digital Transformation; Sustainability Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-90170 ER -