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Technology-Induced Stress and Employee Resistance in the Context of Digital Transformation and Identification of Countermeasures

Bausch, David; Krämer, Tobias; Mauroner, Oliver (2024)

International Journal of Innovation and Technology Management.
DOI: 10.1142/S0219877024500299


Peer Reviewed
 

In the face of increasing digitization, companies must make significant changes to their offerings and operations to remain competitive. This digital transformation of organizations includes a digital transformation of the workplace, which is often met with resistance from employees. While it is recognized that reducing employee resistance is crucial for organizations, there is a limited understanding of the antecedents of employee resistance in the context of digital transformation, different resistance behaviors, and potential countermeasures. Drawing on technostress and employee resistance theories, we address these research gaps. Results from two empirical studies support our central prediction that digital transformation of the workplace causes technostress, which in turn promotes passive and active resistance behaviors among employees. Additionally, we highlight that organizations can use digital literacy facilitation to reduce employee technostress and resistance.

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Unterstützungsleistungen von Krankenkassen zugunsten Versicherter bei vermuteten Behandlungsfehlern und Aufklärungsmängeln (Anmerkung zu LSG Niedersachsen-Bremen, Beschl. v. 25. 5. 2023 – L 16 KR 432/22)

Finn, Markus (2024)

Medizinrecht (MedR) 42 (3), S. 208-212.
DOI: 10.1007/s00350-024-6704-0


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Digitizing Complex Tasks in Water Management with Multilevel Analysis

Müller-Czygan, Günter (2024)

Prof. Eduard Babulak (Hrsg.). Advances in Digital Transformation. 2024.


Open Access Peer Reviewed

Maschinelles Lernen und „generative KI“: Wie ChatGPT&Co. Wirtschaft und Gesellschaft verändern

Wagener, Andreas (2024)

HAC Investmentkonferenz 2024, Hamburg 2024.


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Evaluation of Medium-Sized Language Models in German and English Language

Peinl, René; Wirth, Johannes (2024)

International Journal on Natural Language Computing (IJNLC) 2024 (1).


Open Access
 

Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative question answering in German and English language, which requires models to provide elaborate answers without external document retrieval (RAG). The paper introduces an own test dataset and presents results from human evaluation. Results show that combining the best answers from different MLMs yielded an overall correct answer rate of 82.7% which is better than the 60.9% of ChatGPT. The best English MLM achieved 71.8% and has 33B parameters, which highlights the importance of using appropriate training data for fine-tuning rather than solely relying on the number of parameters. The best German model also surpasses ChatGPT for the equivalent dataset. More fine-grained feedback should be used to further improve the quality of answers. The open source community is quickly closing the gap to the best commercial models.

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Characterization of Microstructural and Mechanical Properties of 17-4 PH Stainless Steel by Cold Rolled and Machining vs. DMLS Additive Manufacturing

Molenda, Paul; Moreno-Garibaldi, Pablo; Alvarez-Vera, Melvyn...

Journal of Manufacturing and Materials Processing 2024 (8).
DOI: 10.3390/jmmp8020048


Open Access Peer Reviewed
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Wie es mit der TI weitergeht

Wolff, Dietmar (2024)

Altenpflege-online.net 03.2024 2024, S. 56-58.


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Bei der Pflege erleben wir eine hohe Aufgeschlossenheit

Wolff, Dietmar; Eckhardt, Thordis; Klingbeil, Darren (2024)

Interview mit D. Wolff, T. Eckhardt veröffentlicht in: D. Klingbeil: „Bei der Pflege erleben wir eine hohe Aufgeschlossenheit“, Häusliche Pflege plus, erschienen am 26.02.2024 2024.


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AI-Based Recognition of Sketched Class Diagrams

Buchmann, Thomas; Fraas, Jonas (2024)

Proceedings of the 12th International Conference on Model-Based Software and Systems Engineering, MODELSWARD 2024, Rome, Italy, S. 227-234.
DOI: 10.5220/0012421900003645


Peer Reviewed
 

Class diagrams are at the core of object oriented modeling. They are the foundation of model-driven software engineering and backed up by a wide range of supporting tools. In most cases, source code may be generated from class diagrams which results in increasing productivity of developers. In this paper we present an approach that allows the automatic conversion of hand-drawn sketches of class diagrams into corresponding UML models and thus can help to speed up the development process significantly.

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Profiling with trust: system monitoring from trusted execution environments

Eichler, Christian; Röckl, Jonas; Jung, Benedikt; Schlenk, Ralph; Müller, Tilo...

Design Automation for Embedded Systems 2024.
DOI: 10.1007/s10617-024-09283-1


Open Access Peer Reviewed
 

Large-scale attacks on IoT and edge computing devices pose a significant threat. As a prominent example, Mirai is an IoT botnet with 600,000 infected devices around the globe, capable of conducting effective and targeted DDoS attacks on (critical) infrastructure. Driven by the substantial impacts of attacks, manufacturers and system integrators propose Trusted Execution Environments (TEEs) that have gained significant importance recently. TEEs offer an execution environment to run small portions of code isolated from the rest of the system, even if the operating system is compromised. In this publication, we examine TEEs in the context of system monitoring and introduce the Trusted Monitor (TM), a novel anomaly detection system that runs within a TEE. The TM continuously profiles the system using hardware performance counters and utilizes an application-specific machine-learning model for anomaly detection. In our evaluation, we demonstrate that the TM accurately classifies 86% of 183 tested workloads, with an overhead of less than 2%. Notably, we show that a real-world kernel-level rootkit has observable effects on performance counters, allowing the TM to detect it. Major parts of the TM are implemented in the Rust programming language, eliminating common security-critical programming errors.

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Die treibhausgasneutrale Liegenschaft in öffentlicher Hand am Beispiel der Hochschule Hof

Stark, Oliver; Dölz, Michael; Kluck, Johannes; Plessing, Tobias (2024)

RET.Con Tagungsband 2024, S. 115-128.



Direktes Feedback erhalten

Leuoth, Sebastian (2024)

DUZ Wissenschaft & Management Ausgabe 01.2024 2024 (1), S. 19-21.



Erhebliche Unterschiede

Wolff, Dietmar (2024)

care konkret 2024 (5), S. 6.


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Clinical and immunological benefits of full primary COVID-19 vaccination in individuals with SARS-CoV-2 breakthrough infections: a prospective cohort study in non-hospitalized adults

Prelog, Martina; Jeske, Samuel D.; Asam, Claudia; Fuchs, André; Wieser, Andreas...

Journal of Clinical Virology 170, 105622.
DOI: 10.1016/j.jcv.2023.105622


Open Access Peer Reviewed
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Strukturierte Auswahl von Pflegesoftware

Wolff, Dietmar (2024)

Leitungskräfte Akademie 2024 (2), S. 42-50.


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Blitzlicht

Wolff, Dietmar (2024)

SOZIALwirtschaft aktuell 2024, S. 5 .


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A new legal framework for digital building passports in a sustainable building life cycle, ECPPM 2024

Weber, Beatrix; Achenbach, Marcus (2024)

ECPPM 2024.


Peer Reviewed

A template for the design review processes for building in Germany using BIM, ECPPM 2024

Achenbach, Marcus; Weber, Beatrix; Rivas, Paul (2024)

ECPPM 2024.


Peer Reviewed

Haben DiGA keinen Nutzen?“,

Hamann, K. (2024)

Interview mit D. Wolff veröffentlicht in: K. Hamann: „Haben DiGA keinen Nutzen?“, care konkret 3/19.01.2024 2024 (3), S. 3 .


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Resilience Balanced Scorecard: Measuring Resilience of Manufacturing Companies at Multiple Levels

Molenda, Paul; Groneberg, Hajo; Schötz, Sebastian; Döpper, Frank (2024)

Procedia CIRP, 56th CIRP International Conference on Manufacturing Systems 2023 2023, 2212-8271 (120), S. 189-194.
DOI: 10.1016/j.procir.2023.08.034


Peer Reviewed
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Forschung und Entwicklung

Hochschule für Angewandte Wissenschaften Hof

Alfons-Goppel-Platz 1
95028 Hof

T +49 9281 409 - 4690
valentin.plenk[at]hof-university.de

Betreuung der Publikationsseiten

Daniela Stock

T 09281 409 – 3042
daniela.stock.2[at]hof-university.de