Emerging Topics in Digital Government
Permanent URI for this collectionhttps://hdl.handle.net/10125/112462
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Item type: Item , Bridging NASSS Domains and Resilience Pillars to Mitigate LLM Risk in Digital Government(2026-01-06) Siougioudi, Vasiliki; Stamati, TetaPublic sector workflows are rapidly moving to deploy Large Language Models (LLMs). However, today’s governance standards stop at high-level duties without concrete escalation logic. This study introduces NASSS-RE-LLM, a socio-technical governance schema that maps LLM risks to NASSS domains, while pairing each with a pillar of Resilience Engineering. Using a scenario-based methodology, we test mitigation strategies in simulated public hospital settings. Results from expert panels show that the framework effectively surfaces risks, and experts perceive it as clear and adaptable. Practical implementation may be limited by resource constraints and future research is needed to test the framework in real-world pilots. The findings suggest that embedding governance logic into public sector workflows can enhance LLM adoption, with potential broader applicability across digital government contexts.Item type: Item , Law Meets GenAI: Using Artificial Intelligence to Derive Conceptual Models from Legal Regulations(2026-01-06) Nguyen, Binh An; Scholta, Hendrik; Roth-Isigkeit, David; Djeffal, Christian; Chasin, FriedrichArtificial intelligence (AI) and conceptual models are both important to public organizations. AI and generative AI (GenAI) can help to cope with an increasing resource shortage, workload, and requirements, while conceptual models are essential for the design of IT systems. However, the combination of both, the creation of conceptual models using GenAI tools in public organizations, has been barely addressed in extant research. Thus, we investigate (1) how legal experts use GenAI tools when deriving conceptual models for public services from legal regulations and (2) what their experiences are in this use. In a qualitative study with 18 administrative legal experts we obtained various insights. For instance, we show that the participants either submitted strict instructions or conducted open conversations and they followed a top-down, bottom-up or combined approach in their analysis. The GenAI tools performed better in generating text-based models (forms) than graphic-based models (process models, decision trees).Item type: Item , Analyzing Data from Urban Citizen Participation by Applying the Retrieval Augmented Generation Architecture(2026-01-06) Borchers, Marten; Milutzki, Enrico; Oelgeschläger , Simon; Magdych, Valeria; Semmann, Martin; Bittner, EvaThis study explores the application of the retrieval-augmented generation architecture for large language models in analyzing citizens' contributions from urban participation. Existing literature highlights the potential of large language models to streamline analytical processes. However, challenges regarding required functions, domain expertise, and transparency remain underexplored. This research addresses these issues through a design science research approach. We identified eleven issues with a systematic literature review and twelve expert interviews, formulated twelve meta-requirements, and derived four design principles on which we developed a web prototype. We evaluated it with 42 experts from a crowdsourcing platform. Our findings demonstrate that retrieval-augmented generation models can enhance efficiency in automated categorization, sentiment analysis, and summarization by focusing the model's attention. However, transparency limitations persist as an ongoing challenge. Our findings contribute to existing knowledge by illustrating how hybrid intelligent systems can improve urban experts' ability to analyze and interpret participation data in smart cities.Item type: Item , Connecting the Dots: Towards a Holistic Data Standardization Methodology in Public E-Procurement (HoDS)(2026-01-06) Pauken, Cedric; Schmitz, Andreas; Kottmann, Renzo; Wimmer, Maria A.The use of isolated ICT solutions in public e-procurement has led to poor interoperability between organizations, processes, and systems. Consequently, data inconsistencies arise, and data cannot be reused throughout the procedure. While data standardization is recognized as a key enabler to overcome these issues, existing approaches often operate in isolation and thus fail to ensure holistic interoperability. This paper harmonizes four complementary methodological components—the European Interoperability Framework (EIF), the Framework for Interoperable Service Architecture Development (FISAD), Requirement Engineering in complex public sector structures, and Simple Semantic Data Modeling in XML (SeMoX)—into a holistic data standardization methodology (HoDS). Using Design Science Research, HoDS is grounded in rigorous and relevant foundations and developed iteratively in coordination with European and German standardization bodies. Emphasizing modularity and interdependency, it supports agile adaptation to dynamically changing requirements. The application of HoDS to the specification of an e-catalogue standard demonstrates its usefulness in practice.Item type: Item , Modeling Digital Repression: A Machine Learning Analysis of Shutdowns as Governance Signals(2026-01-06) Forner, DentonThis study advances Digital Government research by applying machine learning to analyze internet shutdowns as structured signals of digital repression. Using a dataset of 566 shutdown events (1995–2011) and regime attributes from the Polity 5 project, the study builds interpretable models to estimate shutdown severity and classify regime type. A decision tree regressor and bootstrapped logistic classifier reveal strong associations between shutdown characteristics and political context, achieving over 93% accuracy. While not designed for real-time prediction, these models demonstrate how event-level data can inform early warning, policy evaluation, and digital rights monitoring. By modeling shutdowns as governance decisions embedded in digital infrastructure, this research shows how computational methods can support accountability in opaque information environments.Item type: Item , Introduction to the Minitrack on Emerging Topics in Digital Government(2026-01-06) Gil-Garcia, J. Ramon; Prentza, Andriana; Wimmer, Maria A.