TY - INPR A1 - Koch, Timo A1 - Pargent, Florian A1 - Kleine, Anne-Kathrin A1 - Lermer, Eva A1 - Gaube, Susanne T1 - A Tutorial on Tailored Simulation-Based Power Analysis for Experimental Designs with Generalized Linear Mixed Models T2 - PsyArXiv Preprints N2 - When planning experimental research, determining an appropriate sample size and using suitable statistical models are crucial for robust and informative results. However, the recent replication crisis underlines the need for more rigorous statistical methodology and well-powered designs. Generalized linear mixed models (GLMMs) offer a flexible statistical framework to analyze experimental data with complex (e.g., dependent and hierarchical) data structures. Yet, available methods and software for a priori power analyses for GLMMs are often limited to specific designs, while data simulation approaches offer more flexibility. Based on a practical case study, the current tutorial equips researchers with a step-by-step guide and corresponding code for conducting tailored a priori power analyses to determine appropriate sample sizes with GLMMs. Finally, we give an outlook on the increasing importance of simulation-based power analysis in experimental research. KW - data simulation KW - generalized linear mixed model KW - power analysis KW - sample size Y1 - 2023 U6 - https://doi.org/10.31234/osf.io/rpjem ER - TY - INPR A1 - Kleine, Anne-Kathrin A1 - Kokje, Eesha A1 - Hummelsberger, Pia A1 - Lermer, Eva A1 - Gaube, Susanne T1 - AI-Enabled Clinical Decision Support Tools for Mental Healthcare: A Product Review T2 - OSF Preprints KW - AI-CDSS KW - artificial intelligence KW - clinical decision support KW - mental healthcare Y1 - 2023 U6 - https://doi.org/10.31219/osf.io/ez43g ER - TY - JOUR A1 - Wieschke, Johannes A1 - Schacht, Diana D. A1 - Spensberger, Florian A1 - Klinkhammer, Nicole A1 - Grgic, Mariana T1 - Establishing Multi-Perspective Instruments in Early Education during COVID-19: Measuring the Implementation of Protective Measures and the Subjective Level of Information about Pandemic-Related Regulations JF - Measurement Instruments for the Social Sciences Y1 - 2022 U6 - https://doi.org/10.1186/s42409-022-00033-2 SN - 2523-8930 IS - 2022,4 ER - TY - THES A1 - Geh, Simon T1 - Motivations and Implications of the Lufthansa Bailout during the COVID-19 Pandemic N2 - The bailout of Lufthansa in June 2020 was subject to controversial debate due to its size and conditions. To better understand the situation, this paper aims to answer the question "What were the motivations and implications of the Lufthansa bailout during the COVID-19 pandemic?" by analyzing Lufthansa's economic environment before and after the crisis. On the basis of projected hypotheses, the impact on the German economy, Lufthansa's operations, as well as similar bailouts during the same period were examined. The findings suggest that engaging in the bailout was the best option for both Lufthansa and the German government with both parties profiting considerably from the deal. More precisely, it was found that: (1) the bailout was realized because Lufthansa was "too big to fail", (2) its employees criticized the cost-saving measures and conditions of the bailout, (3) Lufthansa was able to offset its losses with its cargo and maintenance business and recovered well with the financial support, (4) Lufthansa's performance aided the German economy in its recovery by supporting various economic sectors, (5) public perception of Lufthansa deteriorated following the bailout and travel chaos in 2022, (6) Lufthansa gained a competitive advantage over its competitors who did not receive government support. KW - aviation KW - Covid-19 pandemic KW - corporate bailout KW - competitive advantage Y1 - 2023 U6 - https://doi.org/10.60524/opus-1479 ER - TY - JOUR A1 - Kaplan-Rakowski, Regina A1 - Gruber, Alice T1 - The Impact of High-Immersion Virtual Reality on Foreign Language Anxiety JF - Smart Learning Environments KW - virtual reality KW - foreign language anxiety KW - virtual assistants KW - English as a foreign language KW - pedagogical agents Y1 - 2023 U6 - https://doi.org/10.1186/s40561-023-00263-9 IS - 2023, 10:46 ER - TY - INPR A1 - Diel, Sören A1 - Doctor, Eileen A1 - Reith, Riccardo A1 - Buck, Christoph A1 - Eymann, Torsten T1 - Examining Supporting and Constraining Factors of Physicians’ Acceptance of Telemedical Online Consultations: A Survey Study KW - telemedicine KW - online consultation KW - acceptance KW - UTAUT KW - structural equation modeling Y1 - 2023 U6 - https://doi.org/10.21203/rs.3.rs-3129155/v1 N1 - This preprint is Under Review at BMC Health Services Research. (19.07.2023) ER - TY - JOUR A1 - Stahl, Bastian A1 - Häckel, Björn A1 - Leuthe, Daniel A1 - Ritter, Christian T1 - Data or Business First? - Manufacturers’ Transformation Toward Data-driven Business Models JF - Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung KW - data analytics KW - data-driven business models KW - data-driven services KW - enterprise architecture KW - L60 KW - manufacturing KW - O14 KW - O32 Y1 - 2023 U6 - https://doi.org/10.1007/s41471-023-00154-2 SN - 0341-2687 ER - TY - THES A1 - Fischer, Alicia T1 - Ecosystem Building in Industry 4.0 – A Value (Co-) Creation Process for Business Model Innovation N2 - Industrie 4.0 revolutioniert derzeit die Fertigungsindustrie, welches durch neue digitale Technologien wie beispielsweise KI ermöglicht wird. Insbesondere das industrielle Internet der Dinge (IIoT) verändert die Geschäftsmodelle in der Industrie 4.0 durch den Austausch großer Datenmengen zwischen der physischen und der virtuellen Welt, was zu neuen digitalen Lösungen führt. Geschäftsmodellinnovationen sind erforderlich, um diese neuen Wertschöpfungsmöglichkeiten zu nutzen. Das komplexe digitalisierte Geschäftsumfeld schafft einen Bedarf an neuen Partnern. Die Zusammenarbeit zwischen Unternehmen wird genutzt, um gemeinsam digitale Lösungen zu schaffen, was zum Entstehen von KI-gestützten Ökosystemen oder genauer gesagt IIoT-Ökosystemen führt. Derzeit ist nur wenig über diesen neuartigen Ansatz zur Entwicklung und Umgestaltung von Geschäftsmodellen bekannt. Daher ist es für Wissenschaftler und Praktiker von großer Bedeutung, ein Konzept für den Aufbau eines Ökosystems in der Industrie 4.0 unter Nutzung von KI und IIoT zu entwickeln. Zur Analyse des Aufbaus von IIoT-Ökosystemen wurde das 6C-Modell von Rong et al. (2015) verwendet, und anhand von drei Experteninterviews weiterentwickelt, um ein detailliertes Modell zur Entwicklung von IIoT-Ökosystemen zu schaffen. Der Rahmen für den Aufbau eines IIoT-Ökosystems führt Forscher und Praktiker systematisch durch fünf strukturelle Dimensionen, sieben Schlüsselaufgaben und entsprechende strukturelle und unterstützende Elemente zum Aufbau eines IIoT-Ökosystems in dessen Entstehungsphase, der ersten Phase des Ökosystems. Diese Aufgaben bestehen aus der Festlegung der entsprechenden Strategie für den Aufbau eines IIoT-Ökosystems, der Identifizierung von potenziellen Partnern, der Erstellung eines kundenorientierten Wertversprechens, der Implementierung einer IIoT-Plattform, der Anpassung der Rollen und Aktivitäten der involvierten Akteure, der Prozessumstrukturierung und der Entwicklung gemeinsamer Fähigkeiten. KW - Internet of Things KW - Ecosystem KW - business model innovation KW - value creation KW - Industry 4.0 Y1 - 2022 U6 - https://doi.org/10.60524/opus-838 ER - TY - CHAP A1 - Ricker, Marcus A1 - Rempel, Sergej A1 - Feiri, Tania A1 - Schulze-Ardey, Jan A1 - Hegger, Josef T1 - Statistical Characterisation of Reinforcement Properties for Textile-Reinforced Concrete: A Novel Approach T2 - Acta polytechnica CTU proceedings / International Probabilistic Workshop 2022 KW - AR-glass reinforcement KW - carbon concrete KW - carbon reinforcement KW - design provisions KW - standardised tensile test for fibre strands KW - textile-reinforced concrete Y1 - 2022 SN - 978-800107035-2 U6 - https://doi.org/10.14311/APP.2022.36.0175 SN - 2336-5382 VL - 36 SP - 175 EP - 184 ER -