Dutch municipalities are highly digitized and experience an increased digital dependency. Between 2017 and 2025, municipal reliance on Software as a Service (SaaS) for their main tasks has increased from 16% to 70%. Municipalities are highly dependent on a small number of US cloud service providers, which together have a 75-90% EU cloud services market share. This dependency raises democratic and geopolitical concerns. US legislation, including the Foreign Intelligence Surveillance Act (FISA) Section 702, grants the US government access to data stored by US cloud service providers, regardless of where it is stored. Furthermore, US sanctions can terminate access to cloud services. Simultaneously, private cloud service providers can implement unilateral changes to services used by the municipalities, without democratic oversight or explicit municipal consent. Current dependencies are large and run deep. Therefore, any approach should be strategic: full autonomy is unfeasible and undesirable.

This thesis addresses these concerns through the lens of digital strategic autonomy, defined as "the capabilities, capacities, and control to decide and act autonomously on essential digital aspects of our economy, society and democracy", following Timmers. Unlike the binary concept of digital sovereignty, digital strategic autonomy acknowledges degrees of control and better captures the nuanced reality of contemporary digital dependencies.

The research employs a Design Science Research Approach to develop both an analytical theory of digital strategic autonomy and an artefact for municipal application. The methodology combines literature review, semi-structured interviews, actor analysis and legal doctrinal research. The study conceptualizes digital infrastructure through a layered ‘stack’ framework, focusing specifically on the cloud, data & AI, and application layers where municipal vulnerabilities are most pronounced. The research identifies two critical dimensions of digital strategic autonomy: the geopolitical dimension, where foreign jurisdiction poses risks to data confidentiality (the panopticon effect) and service availability (the chokepoint effect), and the public/private dimension, highlighting private actors imposing private values on public service delivery.

Based on this conceptualization, the study develops a self-assessment tool that enables municipalities to evaluate their digital strategic autonomy for specific processes supported by SaaS applications. This tool operationalizes the analytical theory by providing concrete indicators across multiple dimensions. Applying self-assessment tool and an institutional analysis results in technical, institutional and governance measures.

The European Central Bank (ECB) is developing the digital euro, a retail central bank digital currency (CBDC) aimed at providing a secure and efficient digital payment option. While the ECB prioritizes financial stability through features like holding limits and no remuneration, the success of the digital euro largely depends on consumer adoption. This thesis examines what design choices the ECB can make to enhance consumer adoption of the digital euro.
Using a two-phased approach, design science research was first applied to map the digital euro’s design space, followed by a discrete choice experiment assessing Dutch consumers’ adoption preferences. Findings reveal that holding limits are the most significant factor in adoption, with higher limits increasing appeal. Interest rates and privacy protections also play important roles, while the issuing institution (ECB vs. commercial banks) has little effect.
Results suggest that current design choices may limit adoption, raising concerns about the digital euro’s viability. The study recommends increasing flexibility in holding limits, exploring financial incentives and improving communication on privacy and offline functionality. It also shows how commercial banks influenced the design, leading to restrictive features. This highlights the need for more balanced governance in future policy decisions. The thesis provides recommendations for enhancing the digital euro’s design and adoption potential while highlighting broader implications for designing consumer-facing financial innovations.

We live in the era of data. Data stands as the new essential resource, proving crucial not only for sophisticated mathematical models but also for commonplace applications. Regardless the field or domain, data needs to be shared in order to improve these tools, which means that systems must speak the same language. To address this challenge, there is a growing popularity in utilizing ontologies. These are formal structures of knowledge that enable a common understanding of concepts related to a specific domain, both by humans and by machines. But their generation is a complex task that requires knowledge from both the domain and the ontology engineering field. Many ontologies already exist, and extending those with new concepts and paradigms is a common practice. It is also essential to promote reusability of standards and standardization, and to avoid “reinventing the wheel”.
In parallel, the fast evolution of Artificial Intelligence is leading the emergence of infinite possibilities in terms of automation of tasks that have hitherto been completely manual. In particular, the use of Large Language Models is booming, though its use to help in the extension of existing ontologies has not been exploited yet. This research proposal aims to answer the question of: How can LLMs be integrated into a semi-automated and domain-independent process for the extension of existing ontologies?
This master’s thesis, carried out in collaboration with TNO, follows the Design Science Research Approach to analyze the problems associated with ontology engineering and the integration of LLMs in the ontology extension process by interviewing 11 ontology engineers and experts in the field. The analysis of the interviews is used to generate a model of the current ontology extension process and to produce a set of 22 high-level design requirements to guide the design a process framework for human-LLM collaboration for the ontology extension process. The design prototype proposed consists of an extended version of the current ontology extension process model augmented by the assistance of LLMs on various ontology extension tasks and incorporating additional ontology engineering tools and stakeholders in the process. The design includes a set of prompt templates that can be customized by the ontology engineer to extend an ontology in any domain. The final design is demonstrated and evaluated with a real use case, which consists of extending the Common Greenhouse Ontology (CGO), developed by TNO, with the use case Semantic Explanation and Navigation System (SENS), previously executed by TNO. The demonstration and evaluation is performed using OpenAI’s model GPT-4 Omni, through the creation of a GPT Assistant.
The generated ontology extension using the process framework and the GPT assistant is similar to the manually crafted extension. Remarkably good results are achieved in tasks such as defining business scenarios and creating a glossary of terms, generating Competency Questions, formalizing the ontology (in OWL) and the CQs (in SPARQL queries), and verifying the generated ontology extension using mock data and CQs. An end-user (ontology engineer) is asked to use the proposed process framework prototype and qualitatively evaluate the tasks, concluding that this approach is very useful for ontology engineers with various level of expertise to structure and increase the quality of the current ontology extension process.
Although the proposed design holds a great potential to be implemented and integrated with the current methodologies and tools used by ontology engineers, several challenges need to be tackled before we see a wide adoption and integration of LLMs in the ontology extension process. These challenges go beyond the technical performance of the LLMs, but revolve around the societal implications of the use of this technology instead. The loss of enriching human interactions and expertise, and the environmental and ethical impact are big concerns of the ontology engineers. These must be addressed before the potential of LLMs for the ontology extension process can be unveiled.

This thesis investigates the impact of Environmental, Social, and Governance (ESG) risk factors on the risk and return dynamics of equity investment portfolios, addressing a critical gap in both academic and industry research. The study utilizes Principal Component Analysis and Fama-MacBeth regression to quantify the material impact of ESG risk factors on financial performance, integrating these factors into an extended Fama-French model for asset pricing. The findings reveal that ESG risk factors can mitigate negative impacts on investment returns, underscoring the importance of incorporating ESG considerations into investment strategies. Despite challenges such as inconsistent data quality and limitations of multi-factor models, the research contributes to the understanding of ESG integration and suggests avenues for future exploration, including the development of more precise models and the assessment of long-term effects across different market conditions.

Proactivity in e-government and service design aims to revolutionize digital public services by having the government reach out to citizens based on data, life changes, or predicted events. This could streamline internal processes and enhance citizen well-being. However, the success of proactive services depends on their incorporation, design, and implementation. Values play a crucial role in these processes and are influenced by politics, governance, design processes, social factors, legislation, and technical possibilities, potentially leading to value conflicts.

This thesis examines the contextual conditions shaping the management of value conflicts in digital proactive public services, focusing on the Dutch Persoonlijke Regelingen Assistent (PRA) project, which aims to centralize Dutch regulations and provide proactive notifications and assistance. The project, still in its design phase, offers insights into how contextual conditions impact proactive application development. These findings can be extrapolated to broader proactive public services contexts.

The thesis uses existing literature to identify crucial values and assess their manifestation in proactive applications. It employs the Value Sensitive Design (VSD) approach to construct value hierarchies, identifying conflicting and enabling values. Strategies for handling value conflicts are evaluated and extended with public policy design strategies, highlighting the influence of contextual conditions on value conflicts and their resolution.

A framework of conditions was established to illustrate their impact on value conflicts and strategies. Political conditions, such as ideology-based values and funding priorities, influence dominant values. Governance conditions affect practical matters like funding, project timelines, and stakeholder involvement. Procedural conditions in the design process involve key trade-offs. Legal and technical conditions are crucial for data processing applications, impacting privacy and proactivity conflicts. Social conditions determine the application's reception and effectiveness, with sensitivity to expectations, stigma, and readiness being vital.

Public organizations must set realistic objectives, consider contextual conditions, and anticipate outcomes at each step. A chosen strategy for managing value conflicts from the start can ease decision-making. Evaluated strategies have varying practicality and benefits, complicated by the context-dependency of value conflicts. A learning strategy is recommended, involving multiple strategies and stakeholder coordination to gather practical insights.

To formalize this process, appointing an organization at the meso-level of governance is suggested. This organization would evaluate project queries based on past cases and value conflict consequences, bridging the gap between macro-level government legislation and micro-level standards. This body of knowledge would guide responsible innovation in the public sector, enabling designers, policymakers, and scientists to learn from each other’s cases and contribute collectively to progress.

In summary, proactivity in digital public services holds promise but requires careful consideration of contextual conditions and value conflicts. A meso-level organization can provide the necessary guidance and knowledge to navigate these challenges effectively.

This study examines the challenges faced by the Customs Administration of the Netherlands in supervising cross-border e-commerce imports under the current European customs legislation, the Union Customs Code (UCC). Through a mixed-method approach, including qualitative interviews, document analysis, and simulation modelling, the study identifies potential supervisory actions to strengthen e-commerce import supervision, particularly in anticipation of the proposed UCC reform. The findings indicate the complexities of customs operations and emphasise the need for efficient, fair, and effective enforcement while fostering trust and accountability. The uncertainties surrounding the UCC reform, trade markets, and technological advancements are addressed, with recommendations that focus on strategic automation, stimulating curiosity among customs officers, clear communication, and further research to develop adaptive supervision strategies. These recommendations aim to address core challenges and exploit opportunities for improvement while considering feasibility constraints and potential uncertainties.

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.

The digital society increasingly demands for integrated ICT transformations. Societal, technical, and institutional developments enable interorganisational and cross-sectoral integration of digital services. However, a lack of trust and legal certainty impedes electronic transactions and the development and adoption of innovative digital services. Digital trust services counter this by providing solutions for secure and trustworthy electronic transactions, which benefits the electronic society. As the development of digital trust services have the risk to develop in isolated manner, interoperability is required to allow wide adoption of these services. Collaborative ecosystems are dynamic and co-evolving communities of interdependent stakeholders, and could support valorisation of digital trust services. Nonetheless, stakeholders participating in such an ecosystem pursue different interests while envisioning different ecosystem operations. Governance contributes to efficient and effective ecosystem orchestration by providing rules on decision-making processes. In addition, the governance needs to fit with the technical architecture of an ecosystem, meanwhile, it assesses external factors and the development through time. The challenges of designing a governance model for collaborative ecosystems are relatively unexplored, as literature is sparse. Therefore, this thesis aims to design a framework which analyses and mitigates the challenges in a current collaborative ecosystem by prescribing a fitting governance model which adapts to its context through time. By following Hevner’s Design Science Research approach, the following main research question is aspired to be answered: ”How to design a governance model for a collaborative ecosystem which facilitates electronic transactions?”. By applying this framework to the current collaborative ecosystem of the Trusted Information Partners (TIP) initiative in the Netherlands, the framework is evaluated and validated. The study affirms that a framework should cogitate three components when designing a governance model for a collaborative ecosystem facilitating electronic transactions: I) Identifying the governance tensions enables context setting of factors affecting the ecosystem, II) Prescribing governance mechanisms boosts the mitigation of power dynamics, and III) Pursuing governance consistency checks whether proper governance takes place in decision-making. The framework contributes to the knowledge base of governance networks, because the research provides building blocks for establishing a governance model for collaborative ecosystems. To boost the service delivery of collaborative ecosystems, the framework could be validated by applying it to an operative collaborative ecosystem. As where the framework currently focusses on a developing ecosystem, the insights of an operative ecosystem can increase the generalisability of the framework. Future research on this can provide further alignment between the functioning of ecosystems and the governance model. Applying improved framework versions to other digital ecosystems, could also stimulate the valorisation of trusted services in society as a whole.

Worldwide, machine learning is increasingly used to achieve optimal analytics and outcomes in business and governmental organisations. However, it has become evident that structural guidance for creating machine learning projects is needed. Machine learning projects are dependent on multiple factors. This thesis focuses on three factors influencing machine learning projects: the technical aspects of machine learning, the organisational aspects of successful implementation of machine learning projects and last the ethical aspects, since difficulties coming with meeting ethical standards contribute to the suboptimal use of machine learning projects. The research was done in accordance with Design Science Research Methodology (DSRM). Ten steps to setup machine learning projects were identified and a method has been designed, demonstrated and evaluated. The research included an extensive literature search which provided the main contributing theories of Knowledge Discovery in Databases and Ethical Impact Assessment. Furthermore, multiple experiments in Standard Business Reporting (SBR) context were done. Expert interviews provided input for optimisation and evaluation. In this thesis a method for setting up creating machine learning projects, including ten steps with accompanying sub steps, was created. A single method combining and integrating technical, ethical and organisational aspects was not yet available. At this moment, it is unique in comparison to other methods, as it combines interdisciplinary aspects into one method and provides guidance for management and engineers. Apart from the developed method, this thesis also provides structured insights in the use of machine learning in SBR context.