https://opendata.uni-halle.de//handle/1981185920/13585 Sat, 02 May 2026 15:08:00 GMT 2026-05-02T15:08:00Z http://opendata.uni-halle.de:80/retrieve/c0081ab0-3797-4bdc-99ea-1bcffe3d06e5/HSB_transparent.png https://opendata.uni-halle.de//handle/1981185920/13585 https://opendata.uni-halle.de//handle/1981185920/125176 Title: Proceedings of the 13th International Conference on Applied Innovations in IT : volume 13, issue 5 : Koethen, Germany, 22 December 2025 Author(s): International Conference on Applied Innovations in IT Editors: Siemens, Eduard; Kalendar, Marija; Falfushynska, Halina; Bevrani, Hassan; Gottschalg, Ralph; Stankevych, Iryna; Hall, Douglas Wed, 01 Jan 2025 00:00:00 GMT https://opendata.uni-halle.de//handle/1981185920/125176 2025-01-01T00:00:00Z https://opendata.uni-halle.de//handle/1981185920/125158 Title: Atlas der Mobilität : Potenziale für den Ausbau des ÖPNV-Angebots im westlichen Burgenlandkreis Author(s): Rocke, Melina; Zareh, Nele; Schröder, Janine; Grötsch, Christian Thu, 01 Jan 2026 00:00:00 GMT https://opendata.uni-halle.de//handle/1981185920/125158 2026-01-01T00:00:00Z https://opendata.uni-halle.de//handle/1981185920/125127 Title: Identification of Factors Affecting Government Job Satisfaction : A Macroergonomic Perspective from the Philippines Author(s): Ramirez, Keno Brian C.; Ong, Ardvin Kester S. Abstract: This study examines the relationships among job demands, job resources, job satisfaction, organizational commitment, and job productivity within the public sector. Grounded in the Job Demands–Resources (JD-R) framework, the research investigates how work-related factors and personal resources influence employee attitudes and performance outcomes. Using survey data collected from public servants, structural equation modeling (SEM) was employed to test the proposed hypotheses. The results indicate that job demands significantly influence job satisfaction, while job resources contribute positively to both job satisfaction and organizational commitment. Organizational commitment was found to mediate the relationship between job satisfaction and job productivity, confirming its central role in translating employee attitudes into performance outcomes. In contrast, psychological safety and public service motivation did not demonstrate statistically significant effects within the final model and were excluded from further analysis. These findings suggest that, in this context, structural and organizational factors play a more substantial role than individual motivational constructs. The study highlights the importance of strengthening job resources and fostering supportive work environments to enhance employee satisfaction, commitment, and productivity in public sector organizations. Practical implications include the need for management strategies that prioritize resource availability, role clarity, and employee engagement to improve organizational performance. Mon, 01 Dec 2025 00:00:00 GMT https://opendata.uni-halle.de//handle/1981185920/125127 2025-12-01T00:00:00Z https://opendata.uni-halle.de//handle/1981185920/125126 Title: Ethical Risks and Improvement Potentials of AI Tools in Carbon Footprint Calculation: The Case of XAI Author(s): Huang, Din-Yuang Abstract: This study aims to explore the ethical risks from AI tools in carbon footprint calculation applications, especially focusing on Explainability and Transparency. Although AI provides convenient computing, the problem of “black box” may lead to a variety of ethical concerns caused by computing. This study first systematically reviews multiple documents and analyses to understand the initiatives for AI ethics and the risks arising from unexplainability and opacity. This study proposes Explainable Artificial Intelligence (XAI) as a solution to mitigate these ethical risks. The paper further explores how XAI can improve the transparency of carbon footprint calculation and its specific implementation by users through simulation of real-world scenarios. The Results show that XAI technology can transform AI's abstract predictions into mathematically rigorous and actionable evidence, enabling users to implement specific carbon reduction behaviours in their lives. Ultimately, this research contributes to the growing discourse on responsible AI by demonstrating how explainable models can foster trust, accountability, and sustainability in data-driven environmental decision-making. Mon, 01 Dec 2025 00:00:00 GMT https://opendata.uni-halle.de//handle/1981185920/125126 2025-12-01T00:00:00Z