Traditional financial companies play a central role in ensuring financial stability and reliability. However, these companies face institutional problems due to inefficiencies, lack of transparency and limited innovation. For this reason, the use of distributed ledger technology, or blockchain, has been explored as an alternative and its integration into traditional finance towards a 'hybrid' approach. The Dutch mortgage market was chosen as the focus of this research and is further analyzed due to its high transaction costs, including the high cost of trust. This led to the research objective of designing a Hybrid Mortgage Lending System (HMLS) that reduces the cost of trust.

A design science research methodology was used for this research where two interwoven artefacts were developed. The first artefact was a contextualized trust model based on three trustworthiness factors: integrity, ability and benevolence. The second artefact was a demonstration for the HMLS using a technological model.

The trust model was validated through expert interviews and enriched with additional information. Based on the feedback from the expert interviews, nine meta-requirements for the technological model were developed. The proposed solution for the design of the HMLS is to automatically distribute fractional ownership of mortgages via Non-Fungible Tokens (NFTs) through an ecosystem with the traditional finance company as originator and public investors as lenders. The ownership of the NFTs is transparently tracked via the blockchain to monitor the fractional ownership of a home. These NFTs exist in mixed token pools and are controlled by the traditional finance companies. These pools are supplemented with other digital assets to create different risk profiles to suit different types of investors. A demonstration of these token pools is shown, with an overview of the architecture explaining the mortgage application and transaction processes through the different layers. Finally, a stakeholder map is shown and their roles within the ecosystem are explained. The research provided new insights by making the second model dependent on the first. The findings highlight new challenges for further research, such as prototyping or improving the versatility of the model.

Executive Summary
Current Status The aviation industry is transitioning from traditional maintenance practices, typically scheduled after a specified number of flight hours, to more advanced, data-driven approaches. These predictive maintenance techniques leverage machine learning models to enhance the accuracy of assessments. These models can reduce the frequency of unscheduled maintenance and optimize inventory management. However, such models rely heavily on large datasets, which are challenging to compile in the aviation sector due to the rarity of specific operational incidents and the diverse types of data collected by different companies. When collaborating, a tradeoff arises between the level of security measures, trust in partners, and ensuring the system still functions. Federated Learning emerges as a promising solution to these challenges. Federated Learning is a novel form of machine learning that allows multiple entities to collaboratively develop a shared model while keeping their data localized, thus maintaining privacy and data sovereignty. Question The primary objective of this research is to identify and validate the critical architectural features necessary for the acceptance of Federated Learning in the aviation maintenance industry. Architectural features refer to design choices such as security protocols and governance frameworks. By focusing on these aspects, this study addresses the technical and collaborative challenges that must be overcome to develop a Federated Learning system for predictive maintenance in aviation. Resulting in the following research question:
‘What architectural features should be included in the design of the ‘Federated Learning for aircrafts’ predictive maintenance system’ to be accepted by the stakeholders in the aviation industry?’ Approach This study employs a Design Science Research methodology. The sub-questions for this research follow the steps in the Design Science Research methodology. This is not the case for the demonstration phase which was deemed not possible due to the conceptual nature of the design.
1.
What are the challenges in sharing maintenance data, and how do they impact data safety and collaboration? Through a literature review and interviews with stakeholders, the study identified concerns about data privacy, security, and competition. These challenges significantly limit data-sharing initiatives. (Problem Identification and Motivation)
2.
What specific technical requirements should the Federated Learning system have to address these challenges? Based on the interviews and thematic analysis, a list of requirements was developed. These include robust privacy mechanisms, transparent governance, ensuring model explainability, and clear accountability mechanisms. (Defining the Objectives for a Solution)
3.
What architectural features should be included in the Federated Learning design to meet these requirements? ArchiMate modeling was used to design a system incorporating these requirements. Federated Learning, combined with encryption techniques and a consortium-managed system, was the result. (Design and Development)
4.
How do stakeholders perceive the acceptability of the designed system? After the validation interviews and the expert session, three changes were implemented: Switching from
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differentiating on the model version to differentiating on the usages. Improving explainability
by switching to Trusted Executive Environments and removing differential privacy. Traditional contracts to increase trust towards each other were also added. (Evaluation)
5.
What lessons can be learned from the development and evaluation of the Federated Learning system for future improvements? The research highlights the importance of continuously building trust among stakeholders. Furthermore, the token-based reward system based on contribution is a good incentive to be adaptable and develop long-term collaboration. (Communication) Results
To build trust, this study employs traditional methods like legal contracts and appoints a consortium as a neutral party. Transparent governance and involving a neutral, trusted entity is necessary to gain stakeholder acceptance and ensure long-term collaboration. Blockchain technology enhances the transparency of the consortium's operations, ensuring all transactions and data exchanges are recorded on an immutable database...

The Paris Agreement seeks to limit global temperature rise and achieve climate neutrality by 2050. The UN's Sustainable Development Goal 7 emphasizes sustainable energy access, with smart grids playing a crucial role. These grids adapt to user behavior and interconnect various components. While smart grids address challenges in integrating renewable energy, their complex socio-technical nature demands a multidisciplinary approach. Institutions are vital in smart grid development, but their importance often goes unnoticed. The interplay of stakeholders, technology, and institutions complicates smart grid implementation. Although existing research largely focuses on technical aspects, a holistic approach considering social, economic, environmental, and institutional dimensions is essential. This study aims to bridge this gap by employing the Institutional Analysis & Development (IAD) framework to enhance the Netherlands' electricity/energy system design, particularly at the community level, concerning the impacts of institutional rules on smart grid development and implementation.

The chosen research approach is a comparative case study, focusing on smart grid integration in the Netherlands. The Institutional Analysis and Development framework aids in analyzing complex socio-technical systems, particularly energy transitions and smart grids. Three cases were examined: Schoonschip Amsterdam, GridFlex Heeten, and Groene Mient The Hague. Data collection involved literature review and 14 semi-structured interviews with experts and stakeholders. Interview data was deductively coded using the IAD framework, facilitating comparative case analysis and cross-case analysis using the ASI diagram (Actors, System & Institutions).

The transition of the Dutch electricity system into a competitive liberalized market has increased the importance of smart grids for sustainability. Decentralized energy production, facilitated by smart grids, requires technological advancements and institutional changes at various levels. Successful smart grid implementation in the Netherlands involves obtaining exemptions, feasibility studies, and engaging legal entities like homeowners' associations and energy cooperatives. Cases emphasized the significance of pre-exemption feasibility studies, technological requirements, stakeholders like Distribution System Operators, and energy management systems.

The findings highlight the uniqueness of each case and the role of institutional rules. In Schoonschip, boundary, position, choice, and payoff rules were pivotal. GridFlex highlighted position, choice, information, and scope rules. Groene Mient showcased boundary, position, choice, payoff, and scope rules. These case studies underscore complexities in decision-making and governance within smart grid initiatives. Successful implementation requires considering technology, institutions, and stakeholders as indicated by the ASI diagram. The IAD framework analysis reveals the impact of biophysical conditions, community attributes, and rules-in-use on smart grid outcomes.

Recommendations include further research on evolving rules, understanding actor positions, interdisciplinary research, integrated decision-making frameworks, empirical case studies, and policy recommendations such as financial support, clear policy roles, local smart energy systems, positive framing, standardized data exchange, and umbrella organizations for cohesive local smart grid projects.

In conclusion, the study underscores the intricate nature of smart grid implementation, emphasizing the need for comprehensive consideration of technology, institutions, and stakeholders. The IAD framework sheds light on the complex dynamics, guiding effective decision-making and policy formulation for successful local smart grid development.

Wind energy is deemed important in the Netherlands in sustainable energy transitions. While Commercial Wind Energy Project Developers (CWEPDs) bring professionalism, Local Energy Cooperatives (LECs) promote local renewable initiatives. The Dutch Klimaatakkoord aims for 50% community-owned renewable electricity, but real-world implementation of community involvement is unclear. The Institutional Analysis and Development (IAD) framework helps understand 'rules-in-use' for decision-making. This study explores how LECs and CWEPDs interact with these rules, focusing on Dutch onshore wind farm projects. Using 14 case studies, the research compares organisational forms in project outcomes, employing interviews and written sources for data collection and statistical testing and QCA for analysis. Key findings include LECs completing projects faster and with fewer objections than CWEPDs and the importance of transparent information sharing. The study suggests policy and development implications, highlighting the need for more inclusive and transparent decision-making in wind energy projects.

The banking sector is currently dealing with Anti-Money Laundering (AML) issues, with stricter legislation in Know Your Customer (AML) being the instigator. De Nederlandsche Bank is pursuing fully explainable and transparent models with a risk-based approach. Councyl provides such a model: Behavioural Artificial Intelligence Technology (BAIT). This study aims to analyse the current environment BAIT would be implemented, to see if the tool fits the requirements of the banking sector. The inherent features of BAIT, adding to the four functions make it an interesting tool for banks to consider.

Blockchaintechnologyhasintroducedthepossibilitytoexchangevalueandinformationthroughtheinternet without the supervision or permission from any third party intermediary. While many private and public entities have seen this as a chance to increase their operational efficiency, other ventures have been utilizing this sameedgeasameantodevelopnewinstitutionalregimesgiventheimpactontransactioncostreduction, The theories central to institutional economics claim that transaction costs are the ultimate driver for shifts in institutional settings. The purpose of the following research was to investigate whether the reduction in transaction costs allowed by blockchain technology is effectively causing new forms of institutions to arise. Given that blockchain is still in its infancy, there is a current lack of academic literature addressing its governance edge. Furthermore, the few studies available adopt a comparative approach, hence failing to explore the entirety of new institutional possibilities. For this reason, the following research utilized the Institutional Analysis and development framework developed by Elinor Ostrom as a holistic and institutional agnostic methodofanalysis. The particular application analyzed was the one of financial product exchanges. Today, the exchange of financial products as securities, is coordinated by hierarchical organizations, namely firms. As a matter of fact, multiple different firms are involvedintheexchangeoftheseproducts. Exchangesareresponsibleformatching buyers and sellers, clearing houses assume counter party risks and make sure the trading parties can meet their obligations, and custodians and central security depositories are responsible for the final settlement of transactions. Furthermore, reconciliation costs are incurred by all participants when updating their respective databases. Given that blockchains are fundamentally public distributed records of transaction, they effectively undermine the economic efficiency and logic of the current systems of clearing, settlement andreconciliation. Decentralized exchanges are tackling these issues today. For the purpose of the analysis a total of three decentralized exchanges were shortlisted from the over 200 existing. The first filter of choice was to choose between exchanges native to the Ethereum blockchain. This because, being the oldest in the space, they present the most well developed and resilient communities apt for the analysis. Next, exchanges were further filtered by total liquidity in the protocol, or total value locked, and trading volume. The three exchanges selected for the analysis in the end were Uniswap, Sushiswap and Curve. Given that these protocols run on a public and permissionless blockchain, it was possible to retrieve large amounts of data on their relative performance. A number of data acquisition and analytics platforms were utilized for data analysis and visualization namely, Dune, Tally and Sybil