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    <dc:date>2026-05-12T16:14:32Z</dc:date>
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    <title>Proceedings of the 13th International Conference on Applied Innovations in IT : volume 13, issue 5 : Koethen, Germany, 22 December 2025</title>
    <link>https://opendata.uni-halle.de//handle/1981185920/125176</link>
    <description>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</description>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
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  <item rdf:about="https://opendata.uni-halle.de//handle/1981185920/125127">
    <title>Identification of Factors Affecting Government Job Satisfaction : A Macroergonomic Perspective from the Philippines</title>
    <link>https://opendata.uni-halle.de//handle/1981185920/125127</link>
    <description>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.</description>
    <dc:date>2025-12-01T00:00:00Z</dc:date>
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  <item rdf:about="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</title>
    <link>https://opendata.uni-halle.de//handle/1981185920/125126</link>
    <description>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.</description>
    <dc:date>2025-12-01T00:00:00Z</dc:date>
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  <item rdf:about="https://opendata.uni-halle.de//handle/1981185920/125125">
    <title>Sustainable Route Selection Using Fuzzy MCDM Techniques</title>
    <link>https://opendata.uni-halle.de//handle/1981185920/125125</link>
    <description>Title: Sustainable Route Selection Using Fuzzy MCDM Techniques
Author(s): Al Hamami, Muqdad; Abdulsaeed, Ali A.; Muhsen, Yousif Raad; Husin, Nor Azura; Aldahhan, Abdalmhiman
Abstract: Effective transportation route assessment is critical to transportation mobility, safety, and sustainability between urban cities. However, selecting the optimal route with consideration of sustainability is still a fresh challenge. This study aims to define the best pathway to utilize, taking into consideration several performance measures such as travel efficiency, safety, impact on the environment, and the quality of infrastructure between Baghdad and Fallujah. The methodology involves three phases, starting with creating an initial decision matrix that compares three assigned road routes with 12 criteria. The weight fuzzy judgment method (WFJM) was used to establish the relative significance of the twelve criteria for the assigned peak periods, considering the differences in the traffic patterns and priorities in the operations throughout the day. The roads were then ranked using the Multi-Objective Optimization by Ratio Analysis plus Full Multiplicative Form (MULTIMOORA) approach, which combines the Ratio System, Reference Point, and Multiplicative Form, followed by a Borda count to get the final ranking. Weighting outcomes suggested that speed limit, road condition, fuel consumption, and pollution were the most important variables during the AM period, whereas during the PM period, lighting and road condition were more significant. The MULTIMOORA findings showed that the Old Abu Ghuraib Route performed better in AM and PM versions and thus had a balanced score across all evaluation systems. The Karmah route and the Expressway No.1 segment had time-varying performance, whereby the Expressway had greater performance in the PM conditions. A sensitivity analysis provided validation of the robustness of the results. This paper provides information to policymakers and motorists on the best driving route selection between Baghdad and Fallujah for management and planning considerations.</description>
    <dc:date>2025-12-01T00:00:00Z</dc:date>
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