This research explores the use of Artificial Intelligence (AI), specifically Large Language Models (LLMs), into the operationalization of Government Technology (GovTech) benchmarks to increase their utility for policymakers. Research and practice consistently highlight persistent challenges in GovTech benchmarking, such as resource-intensive methodologies that provide retrospective rather than real-time analysis, a lack of complexity that overlooks digital infrastructures and emerging technologies in favor of simpler metrics, and improper levels of aggregation that can render results less useful. Considering that benchmarks can significantly influence political outcomes and shape the development of GovTech services, refining benchmarking methodologies using LLMs can mitigate these issues and potentially improve the responsiveness and relevance of government actions that better serve societal needs. Using Design Science Research Methodology and Activity Theory, an artefact is developed that combines an LLM with Retrieval-Augmented Generation (RAG), fine-tuning and prompt-engineering. The artefact is used to operationalize the GTMI-benchmark by the World Bank. The development of a benchmark specifically tailored for operationalization by LLMs is proposed, with a preliminary design for an AI-Supported GovTech Index (AGTI) outlined.

Governments are increasingly dependent on GovTech, which is the technology that facilitates processes in the public sector. Benchmarking the state of GovTech is done by governments and yields indispensable insights, which are used for optimising resource utilisation, identifying areas for improvement, and facilitating evidence-based policy prioritisation. Current benchmarking efforts are resource-intensive, time-intensive, and have limited scope, resulting in an inefficient assessment of GovTech innovation.

This thesis explores the potential of Large Language Models (LLMs) to overcome the practical limitations of existing GovTech benchmarking methods, analysing the GovTech Maturity Index of the World Bank as a case study. Using an LLM and leveraging state-of-the-art techniques, including fine-tuning, Retrieval Augmented Generation, and Prompt Engineering, the usability of these models as an artefact for GovTech benchmarking is assessed.

The results show that the best-performing model outperforms the random chance accuracy, indicating that the LLM not only understands the question and data format, but also contains the information to correctly answer the benchmark questions. The research concludes that the created artefact has the potential to improve GovTech benchmarking, resulting in more informed policy decision making.

The thesis contributes valuable insights to the field of the GovTech research field by making GovTech benchmarking more efficient, leading to a better analysis of the current GovTech market, and contributing to the academic debate on GovTech market analysis. Furthermore, by streamlining the Dutch benchmarking process, resulting in more accurate insights, the study contributes to the advancement of GovTech solutions within the Netherlands, ultimately benefiting society as a whole.

The artefact created in this research has significant policy relevance, as more efficient benchmarking allows policymakers to better optimise resource allocation, identify key areas for investment, and improve evidence-based policy changes regarding GovTech.

The limitations of this research include model inaccuracies, challenges in handling long contexts, and the potential for incorrect answers. An ethical analysis is performed, from which it can be concluded that the relevance of the information used by the models is the most apparent ethical concern.

Future research may focus on improving the models to better handle long contexts, reducing inaccuracies, and improving the overall performance in populating GovTech benchmarks by incorporating additional data sources and improving the models. The research design is limited by the lack of environmental aspects in the process and the narrow scope of using a single benchmark for one country.

As next steps, this research proposes a roadmap that includes the continuation of the development of the artefact, the extension of ethical analysis, the performance of trials, and the beginning of a wider application of the artefact.

This thesis explores the sociotechnical challenges surrounding the implementation of black-box AI in the Dutch healthcare sector, with a focus on aligning AI implementation frameworks to the needs of key stakeholders. The research addresses the growing demand for AI in healthcare as a potential solution to challenges like staff shortages, operational inefficiencies, and resource limitations. Despite its potential, the adoption of black-box AI, characterized by its opaque decision-making processes, faces resistance due to issues such as trust, lack of transparency, and difficulties in technical integration. To better understand these barriers, the study conducted a series of interviews with Dutch healthcare professionals, including technical, clinical, and business stakeholders, and analyzed existing AI implementation frameworks from the literature. The research identifies three primary challenges: trust in AI systems, technological integration with existing workflows, and organizational readiness, particularly regarding AI literacy.

In response to these findings, the study proposes a combined framework that bridges the sociotechnical gap in AI implementation, drawing from established implementation models and stakeholder insights. Key propositions include the use of Computational Reliabilism (CR) to build trust in AI systems without requiring full transparency, and the introduction of explainability tools to help end-users, particularly clinicians, trust the AI's outputs and ensure responsible use. Additionally, a maturity model is suggested to assess and improve organizational readiness for AI adoption, focusing on enhancing AI literacy and preparing healthcare institutions for more complex AI integrations. This framework and its propositions aim to foster a collaborative approach to AI adoption in Dutch healthcare, addressing both social and technical barriers to pave the way for ethical, reliable, and effective use of black-box AI.

Water, an essential resource for diverse purposes like environmental protection, urban water supply, energy generation, and agriculture, faces intensifying demand amid depleting supplies. Multi-Objective Optimization (MOO)-models are vital for addressing complex water system challenges with limited resources. However, varying approaches to distributive justice in these models introduce normative bias, leading to uncertainty in the derived implications from the model. This thesis is the first approach to understanding how the operationalization of distributive justice shapes the implications drawn from the 'optimal' outcomes of MOO-models. This thesis studied: 'How do different operationalization formulations for inequality in existing multi-objective optimization models shift the Pareto front?'. A rival framings approach acknowledges diversity in perspectives, for which it is suitable to contrast the operationalization formulation. The rival framing focused on the inequality metric and the aggregation method over time for this metric, both used for the formulation of inequality in the objective formulation. The case study revolves around the Conowingo Reservoir in the Lower Susquehanna River Basin. Utilizing an Evolutionary Multi-Object Direct Policy Search (EMODPS)-model, the research optimizes water allocation by incorporating an equality objective alongside baseline efficiency goals. Results underscore that the trade-off between equality and efficiency is highly dependent on the chosen operationalization for equality. Moreover, for higher levels of equality, the Pareto front will shift drastically in terms of strength and direction of trade-offs. By unravelling the complexities of justice's integration into MOO-models, advances are made in the comprehension of how distributive justice can inform decision-making. Through the elaboration of justice formulations, a future is reachable where justice is not only considered but reached.

In a rapidly evolving digital landscape, where information is the currency of progress, universities play a vital role in fostering innovation, research, and knowledge dissemination. However, this invaluable role also exposes universities to significant cybersecurity challenges. Cybersecurity is an increasingly important topic for organisations in all sectors, including universities. As repositories of valuable research data and other sensitive information, universities are attractive targets for cyber attacks.
Addressing these challenges is crucial not only to protect intellectual property and sensitive data but also to maintain the trust and integrity of academic institutions. Despite the importance of cybersecurity for universities, there is a lack of research on how to effectively implement cybersecurity policies and practices in this context. The lack of a standardised approach to cybersecurity can leave universities vulnerable to cyber threats and hinder the sharing of best practices. This study is expected to identify key challenges and measures for cybersecurity policy in Dutch universities. It will provide insights into the implementation of effective cybersecurity policies and contribute to the development of an approach to cybersecurity in the higher education sector. The research question is: "How should the cybersecurity policies of Dutch universities be designed to mitigate cyber threats to ensure knowledge security?".
To accomplish this research, a multidimensional approach was adopted. Extensive literature review provided a foundation for understanding cybersecurity standards and cyber ethics, while interviews were conducted with various Dutch universities to gain insights into their experiences and perspectives. Additionally, a comprehensive survey was administered to students and staff members of Dutch universities, enriching the study with diverse viewpoints. This work aligns with the objective of examining the quality of decision-making concerning grand societal challenges within the context of their socio-economic and political environments. It aspires to inform decision-makers in the public (policy) domain or at the intersection of the public and private spheres. By shedding light on the barriers faced by universities in implementing cybersecurity norms, this research aims to contribute to the ongoing discourse on securing knowledge assets in the face of emerging cyber threats. The thesis is structured as follows: it begins with an introduction that provides a comprehensive overview of the topic, delineating the problem at hand and outlining the proposed approach. Subsequently, the literature review section presents the findings of the extensive research conducted, exploring various subjects such as cybersecurity standards and cyber ethics, among others. The expected outcomes of this study are an overview of current cybersecurity challenges for Dutch universities, an understanding of the opinions and experiences of university staff and students, and recommendations for developing and implementing cybersecurity policies for Dutch universities.