As artificial intelligence (AI) becomes an increasingly central force in the financial technology landscape, its integration into Software-as-a-Service (SaaS) platforms promises to transform how financial institutions operate. Yet, despite a growing interest in AI-powered tools, adoption among financial institutions remains limited and inconsistent. This thesis investigates the underlying reasons for this hesitation, exploring the decision-making dynamics that lead institutions to either embrace or reject AI within fintech SaaS environments.

Drawing on a qualitative research design, this study incorporates insights from both SaaS providers and financial institutions through semi-structured interviews. The findings show that adoption is shaped by more than just technological capability: organisational inertia, regulatory ambiguity, and risk perception are major barriers on the institutional side, while SaaS providers often fail to align their solutions with institutional processes, constraints, and strategic priorities. The study identifies a set of tangible practices for both sides to address these misalignments, including collaborative onboarding strategies, modular tool development, clearer communication of AI limitations, and the institutionalisation of cross-functional technology evaluation processes.

By framing adoption as a co-owned challenge, the research contributes to both the theory and practice of AI-driven financial services. For providers, it offers actionable guidance to improve service models and product-market fit. For financial institutions, it outlines how internal processes can be adapted to engage more effectively with emerging AI solutions. Ultimately, the thesis advances a nuanced understanding of adoption not as a binary event, but as a phased, relational process requiring mutual adaptation across organisational boundaries.

The pitch-deck evaluation is a crucial first step in the Venture Capital (VC) investment process, where investors assess a startup’s potential in the first screening of the startup. Traditionally, this relies solely on manual human judgement, making the process time-consuming, subjective and prone to bias. With the rise of Artificial Intelligence (AI), new opportunities emerge to support the decision-making with data-driven insights. However, due to the qualitative nature of early-stage startup evaluations, implementing AI in this context remains challenging due to its quantitative and data-driven nature.

This research therefore explores the potential of hybrid human-AI models (combining human judgement with AI support) in early-stage VC pitch-deck evaluations. A case study was conducted at InnovationQuarter using the AI tool 'Deckmatch'. Through a combination of interviews, a pilot experiment and a follow-up workshop, this study investigates how hybrid models can impact the effectiveness and efficiency of pitch-deck evaluations. This aims to answer the following research question:

How can hybrid models, combining artificial intelligence and human judgement, improve the effectiveness and efficiency of pitch-deck evaluations in early-stage venture capital?

Throughout the research, data was obtained with guidance from the Technology Acceptance Model (TAM) and Behavioral Decision Theory (BDT). The interview dataset consists of six interviews with VC professionals and their perceptions prior-usage of hybrid models within pitch-deck analysis. This was followed by a pilot experiment and workshop including both seven participants. During the pilot experiment the efficiency and efficacy was measured, in addition to the user experience. Afterwards, perceptions on hybrid models after experience with hybrid models was gathered.

It was expected that the hybrid models would enhance both the efficiency and efficacy of the evaluations, leading to a positive attitude towards use. The actual findings suggest that hybrid models show potential to increase the effectiveness or efficiency of pitch-deck analysis depending on the hybrid model used. Here, the sequential and interactive search models show the potential for improved effectiveness, at a slight decrease of time efficiency. On the contrary, the autonomous search model shows the potential for an improved time efficiency at a lower effectiveness. For the usage and preference of hybrid model type, the trust and transparency in the AI output is highlighted to be of importance. These findings contribute to the real-world understanding of human-AI collaboration in VC decision-making and offers further practical insights for VC firms, developers and entrepreneurs.

The semiconductor industry, which has evolved over around 65 years, witnessed the emergence of the Integrated Device Manufacturer (IDM) business model, followed by the introduction of the fabless and foundry models. These three models continue to coexist and interact today. Characterised by cyclicality, capital and technology intensity, and high economic volatility, the semiconductor industry is shaped by the complicated interactions among these business models, which form the competitive dynamics of the industry. Companies must adopt strategic
adaptations in response to these dynamics, further influencing their market performance. The relationship among competitive dynamics, strategic adaptations, and market performance is not a linear causal chain but a complex, mutually influential process. This study focuses on examining and comparing these concepts’ relationships within the IDM, fabless, and foundry business models...

Carbon trading sets a price on greenhouse gases (GHG), enabling countries and companies to buy emission rights. The carbon market includes the compliant market, regulated by governments, and the voluntary carbon market (VCM), which lacks strict regulation and allows anyone to offset emissions. In the VCM, carbon credits are created through various projects and used to compensate for emissions elsewhere.

The creation of carbon credits in the VCM involves multiple stakeholders, including project developers, validation bodies, brokers, and customers. Given the differences in bargaining power between stakeholders, issues regarding fair revenue distribution exist.

Blockchain technology offers promise in addressing these challenges by providing transparency and accountability. Through blockchain-enabled platforms, customers can access real-time project information, verify emissions reductions, and ensure fair revenue distribution. This transparency fosters trust and empowers customers to make informed decisions.

Research on customer preferences regarding fairness in the VCM is lacking, highlighting the need to understand customer perspectives on fairness and how blockchain can enhance it. This study utilizes Q-methodology to explore customer views on fairness in the VCM.

Three perspectives on fairness, named factors 1, 2, and 3, emerged from the analysis. Factor 1 emphasizes community impact, Factor 2 focuses on market participation and intermediary responsibility, and Factor 3 highlights the importance of broker transparency.

Based on these findings, blockchain implementation should prioritize transparency, standardization, and credibility to increase trust and fairness in the VCM. Brokers play a crucial role in project selection, emphasizing the importance of trust and transparency. Blockchain should focus on increasing transparency throughout the project and carbon credit trading, simplifying information for brokers and enhancing market credibility. Increased transparency and understanding of project co-benefits can shift focus beyond emissions reduction, improving benefit-sharing in the VCM.

This research broadens the discussion on fairness in the VCM and underscores the role of brokers in enhancing trustworthiness and credibility. Brokers should stay actively engaged in blockchain developments in the VCM. However, challenges in blockchain implementation may arise, warranting further exploration.

Emerging technologies like artificial intelligence, big data analytics, blockchain, and the internet of things offer interesting opportunities to automate business processes. With the potential to streamline operations like claims management, underwriting, and customer service, these advancements appear to be especially promising for insurance companies. Nevertheless, digital transformation does not come without its barriers, such as IT-system legacies, regulatory demands, and business issues. One challenge for digital transformation is the rejection of innovations. Insurers, therefore, need their internal stakeholders to accept and adapt to technological change.

This study investigates this barrier to a successful digital transformation of the insurance industry. Following a literature review on innovations in this realm and technology acceptance, this thesis aims to identify factors influencing the behavioral intention to use technologies within this context....

This thesis investigates the role of critical infrastructure (CI) in disaster management, focusing on Hatay in the 2023 Turkey - Syria Earthquake. Given the extensive destruction that impacted critical infrastructure systems, this study aims to investigate both the underlying factors that contributed to infrastructure failures and the effectiveness of the long-term recovery plans for the city. Through a case study approach, this thesis provides insights that not only aid in understanding the earthquake’s impact on Hatay's infrastructure but also offers broader implications for improving resilience in other disaster-prone regions.

The earthquake devastated critical infrastructure in Hatay, delaying rescue and emergency response efforts and exacerbating loss of life and property damage. This research seeks to uncover the root causes that contributed to these failures and evaluate post-disaster recovery plans. The study’s primary question centers on the planning and management shortcomings within critical infrastructure systems and explores how these might be rectified in future disaster responses. The objectives include identifying the key critical infrastructures, analyzing root causes of the key critical infrastructure's system failures, and evaluating the long-term recovery plans’ effectiveness in mitigating risks.

A mixed-methods approach was selected for this case study to provide a comprehensive understanding of the key factors at play. The study includes qualitative analysis based on semi-structured interviews enabling the identification of key critical infrastructures based on stakeholder experiences and perspectives. To investigate the root causes of system failures, Fault Tree Analysis (FTA) is employed, offering a systematic and logical approach to identify underlying issues and contributing factors. Additionally, the study evaluates the effectiveness of long-term recovery plans through a comparative analysis of retrospective and prospective risk assessments.

In the analysis of the interviews education sector, healthcare services and transportation systems emerged as the key critical infrastructure, with their failure significantly impeding immediate response efforts and their importance in recovery. The education sector, in particular, emerged as a crucial component for long-term recovery and the restoration of societal normalcy. Meanwhile, the healthcare system struggled to manage overwhelming patient surges, while transportation bottlenecks, worsened by damage to road networks and airports, severely delayed the delivery of essential services and resources.

The FTA revealed several critical failures in CI planning and management. Healthcare services experienced capacity overload and staff shortage resulting from resource allocation and distribution failures, lack of seismic retrofitting and neglected structural faults in hospital buildings and restricted access to hospitals originated from transportation system failure. Transportation systems suffered from non-functional airport and road network failure originating from poor site location, previous inaccurate site assessments, inadequate design for site conditions, lack of maintenance and lack of redundancy, leaving many areas inaccessible post-disaster. A recurring theme across CIs was a lack of region-specific resilience planning to account for Hatay’s unique geographic context.

While the recovery plans address several CI weaknesses identified in the root cause analysis, significant gaps remain, especially in aligning strategies with the identified pre-disaster vulnerabilities. Although the recovery efforts incorporate bed capacity improvements and increased redundancy, heightened risk concerning neglected structural faults inaccurate site assessment were observed.

The increased risk suggests that the recovery plans may be addressing symptoms rather than the root causes of vulnerabilities. This points to a need for recovery strategies that go beyond immediate repairs, focusing instead on addressing foundational risks to ensure lasting resilience and reduce the likelihood of repeated failures.

This case study highlights the devastating effects of infrastructure failures during disasters. By addressing the root causes of these failures and implementing more comprehensive, resilient recovery plans, Turkey can build a disaster management framework that is robust enough to withstand future natural disasters. Although disaster management is highly context-dependent and cannot be universally applied, this study is important at a broader level, highlighting the need to identify the root causes of system failures in order to effectively mitigate future disaster risks.

This thesis delves into the ethical dimensions of stakeholder management within the growing impact investing ecosystem, with a specific focus on the Benelux region. Impact investing is distinguished by its dual objective of generating financial returns alongside measurable social and environmental benefits. Thus, it necessitates a unique approach to stakeholder management, particularly by fund managers who play a pivotal role in aligning the often-competing logics of various stakeholders. The research addresses the need for a comprehensive ethical framework that guides fund managers in their interactions with internal and external stakeholders (investors, social enterprises, beneficiaries, and regulatory bodies). The study is grounded in neo-Aristotelian virtue theory, which provides a normative foundation for exploring the moral and epistemic virtues required for effective stakeholder management in impact investing. With normative stakeholder theory and institutional theory as auxiliaries, the thesis offers a holistic view of the ethical challenges and opportunities in the field. The methodology includes a thorough literature review to establish a theoretical model of ethical stakeholder management by virtuous fund managers, which is validated by abductive qualitative research involving semi-structured interviews with fund managers and an analysis of corporate documents from impact investing firms in the Benelux. The research aims to uncover the institutional logics that influence fund managers' decisions and the character traits (virtues) that these managers embody in their professional roles. Findings from the research reveal that fund managers operate in a complex ecosystem where ethical stakeholder management is crucial to the success of impact investments.

This thesis addresses the increasing vulnerabilities in modern supply chains, particularly within the maritime industry. Emphasizing the crucial role of procurement in risk mitigation, the study challenges conventional lean management, advocating for agile supply chain strategies. It introduces the Integrated Supplier Matrix (ISM) to enhance supplier segmentation, combining aspects of the Purchasing Portfolio Matrix (PPM) and Supplier Potential Matrix (SPM).

Conducted as a case study at Allseas, the research employs the Best Worst Method (BWM) to rank risks, identifying the uniqueness of the product, rules and regulations, and external factors as significant concerns. Through graphical representations and ISM-based supplier segmentation, relationships between supplier willingness, capabilities, risks and profit impact are established. The findings stress the importance of communication and trust in supplier relationship management (SRM) trade-offs, including inventory reliance, contract durations, sourcing strategies, and addressing non-performing suppliers.

The research provides insights for maritime managers, recommending the integration of theoretical SRM approaches with multi-criteria decision-making methods for resilient supply chains. The suggested shift from lean to agile management and the ISM implementation offers practical strategies to optimize relationships, navigate complexities, and establish adaptive procurement approaches, effectively mitigating supply chain risks.

Decentralized Autonomous Organizations (DAOs) are a fast-growing group of blockchain and smart contract-based applications. DAOs aim to serve new decentralized governance and organizational needs for various business purposes, ranging from decentralized exchanges such as UniSwap and theDAO, to R&D like VitADAO, managing infrastructures like Arbitrum, to charity funds like PactDAO. DAO projects have experienced an exponential growth over the past years. Although strongly rising in number, there is no clear consensus on what a DAO exactly entails. Additionally, despite this exponential growth, various DAO projects tend to pivot away from this form of governance or organization. They either experience a mismatch in the governance setup or the business objective, threatening their viability in the long-term. Also, as DAOs are built on blockchain infrastructure, this poses new governance challenges.
The goal of this thesis is to analyze how governance elements, business objectives and infrastructural choices can influence the long-term viability of DAOs. The main research objective of this PhD thesis is to:

“Develop a theory to explain how governance and infrastructure elements influence the long-term viability of decentralized autonomous organizations on public permissionless blockchains.”

Public participation plays a crucial role in democratic governance, as it allows citizens to contribute their opinions and influence decision-making. However, traditional methods of public participation often face challenges such as lack of transparency, limited accessibility, and low citizen engagement. This master thesis explores the potential of blockchain technologies to increase citizens' involvement in public participation processes. The thesis employs a mixed-methods approach, combining qualitative and quantitative research methods to provide a comprehensive understanding of the topic. Through multiple interviews and surveys, it analyses the current pain points of the public participation methods and finds the missing pieces of the blockchain technology that still has to be developed. The research presents multiple future research areas for further development, providing starting points and ideas that could be further evaluated.

The current second quantum revolution heralds the biggest technological advancements of the twenty-first century. At the forefront of this is the development of quantum computers, which promise to bring transformations to almost all sectors, from banking to drug research. With rising investments in developing full-scale, fault-tolerant quantum computers, the quantum computing provider market is starting to saturate with incumbents. In order to succeed, firms operating in this market need to start acting competitively. However, despite vast research on the potential applications, technological development and responsible innovation, little is known about strategic factors that drive the market.

To address this issue, this work identifies, describes and contextualises the use of alliances in firms’ business strategy in the quantum computing business ecosystem. Through literature review, publications of various types of collaborations on firms's websites, and interviews with four experts in the industry, the work describes the quantum computing business ecosystem, links the cooperations to literature on strategies, and provides contextual implementations of various alliances.

First, a value proposition is constructed, containing five components: Hardware providers, cloud service providers, quantum software providers, managed services providers and end-users, each with its own set of challenges and strategies attached to it. Furthermore, two technology strategies pursued by firms can be identified: an open innovation approach and a full-stack approach. The use of various types of collaborations are described, including licensing, standards agreements, business networks, joint marketing, selling and offering agreements, consortia, strategic partnerships, mergers and acquisitions, and joint ventures.

For SMEs, alliances through bidding consortia, formed for tenders and subsidies, are the main use of cooperation. Strategies herein are the acquisition of capital, reduction of R\&D costs, early product show-off, and staying in the loop for technological decision making on standards. Other partnerships are aimed at figuring out relationships with other firms in the industry, and fostering standardisation through the release of open-source software. The former is characteristic of the nascent ecosystem's formation, as the value proposition is still unclear and the industry's architecture is not set. The use of open-source software has also played a key role in the development of the classical computer, and firms are seen to adhere to a similar approach. Furthermore, we contextualise the strategic differences between the full-stack and open innovation approaches that are seen within the ecosystem. Where firms in the former are challenged by technical inflexibility, low sales volume and vast investment costs, firms in the latter need to balance an appropriability-adoption dilemma, create sufficient alignment with their value proposition and use extensive cooperation to ensure compatibility and modularity. However, through this, they get to enjoy increased economies of scale and agility.

All in all, the strategies can be summarised in two main categories: procurement of resources and embedding into the ecosystem. The work in this thesis encompasses a multitude of related fields including competitive strategy, ecosystems and alliances, and discusses the quantum computing industry from the standpoint of the technology providers and developers, which should aid managers in evaluating their strategic position in the ecosystem and in drafting cooperation strategies. From an academic perspective, this work provides the first managerial study of the quantum computing market from the standpoint of the technology providers. In addition to strategies, it identifies a lack of alignment of end-users as a key inhibitor to the constitution of the value proposition. Moreover, the findings of this thesis support the hypothesis that pre-adaptation-phase strategies are aimed at constituting Ortt and Kamp's technological innovation system (TIS) framework's building blocks, an important step to understanding ecosystem formation dynamics.

Lastly, we identify several key areas for future studies. This includes validating and strengthening the conclusions of this work through additional expert interviews, which should also help in exposing nuances in strategy between firm sizes, position in the value chain and geolocation, as well as similarities and differences with the development of the classical computer. Furthermore, future studies are suggested to investigate the differences in efficiency and effectiveness of governmental funding in the quantum industry, characterise end-users based on their expected moment of engagement with the quantum computing market, the role of alliances in quantum computing platform formation, and the link between innovation-phase strategies and the TIS framework's building blocks.

This article explores the implementation of the Responsible Research and Innovation (RRI) framework among small and medium-sized healthcare companies in China. The RRI framework has been widely discussed in Europe and the US but is still in the early stages of diffusion in emerging countries and economies. The lack of institutional support and enabling conditions in these countries make it difficult for SMEs to fulfill their mission of socially responsible innovation. Based on a single case study at Gaush Teleon, the article identifies 24 factors for implementing RRI in Chinese SMEs. It develops questionnaires to understand the diffusion of the RRI concept and the ranking of the influencing factors. The results of the data analysis show that policy factors are considered to be the most important influencing factor on the adoption of the RRI framework by Chinese SMEs, followed by organizational and financial factors. The study suggests that examining the alignment of Chinese society with RRI and exploring the feasibility of developing the RRI concept in China can help developing countries assess how emerging technologies and society can evolve together.

The adoption of data marketplaces is lacking. Data marketplaces work differently from other information sharing platforms (such as supply chain platforms), because they are more open. This openness allows data to be shared with many more companies, bringing more potential benefits, but also increasing the risk. On data marketplaces there typically is less trust between users, however this lack of risk can be compensated by adding more control mechanisms. For these reasons I want to address the research question:

How does the interplay between control, trust and risk affect a company’s decision to share data in a data marketplace?

The objective of this research is to define and validate the relationships between control, trust, risk and willingness to share data. This will be done through a literature review and a qualitative study using structural equation modelling.

The literature review resulted in a conceptual model, which was subsequently tested using an online survey. The demographic of the survey was managers in the manufacturing industry and they were given two scenarios in which they could share data. In both scenarios their trust in other users, trust in the platform, perceived risk, perceived sensitivity of data and willingness to share data was measured.

Using structural equation modelling we found 5 significant relationships, out of the 6 that were hypothesized. Answering the research question, this study has shown that increasing trust in other users and perceived control can really help decrease the risk of sharing data, which in turn is an important predictor of willingness to share data. However, the sensitivity of data can also have an influence over risk. This insight is new in the field of data marketplaces. We now have a more clear picture of how control and trust can be used to lower the risk and in turn increase the willingness to share data. This study also provided some data that confirms these relationships in the context of the manufacturing industry. This data shows that companies are willing to share their data, if preconditions such as a fair price are met. These results contribute to the overall literature on willingness to share data in a marketplace and on the literature on control mechanisms, trust and risk. The insights from this study can be useful for people designing data marketplaces and regulators that want to encourage the sharing of data.

The financial industry has been significantly transformed by innovations, but traditional banks have managed to maintain their dominant position without implementing many innovations. However, new technologies have caused a shift and should no longer be ignored. Financial institutions are now facing competition from high-tech newcomers, also known as Fin- or BigTechs, that threaten their dominance. A recent study shows that financial institutions have a growing interest in (cloud) platform business models (Fintechs) without being handled in a responsible way. Firms are under pressure to transform, but guidance on how to design and control this transformation is hard to find. EU regulators are concerned about the speed and scale at which financial institutions are moving critical functions and market operations to a few cloud platforms and will soon have to demonstrate their ability to recover from a cyber-attack. Choosing a secure cloud platform is essential, but the complexity, risks and regulation make this decision-making process challenging. This study aims to identify the key factors influencing the adoption of cloud platforms within the Dutch financial sector and determine the most suitable Cloud platform for this sector.

Based on the knowledge gap and the aim of this research, the following main research question was formulated: What are the key influencing factors that determine the adoption of a cloud platform within the cloud platform battle within the Dutch financial sector? The study uses a qualitative approach and the Best-Worst Method to determine the key factors and the most suitable cloud platform. As the focus is on standard selection, a modified version of van der Kaa's 2011 framework was eventually chosen to identify the most important factors in this battle. From the 29 factors in the framework, the 24 most relevant were selected and five additional specific Cloud platform factors were added. This resulted in a new set of 29 factors relevant to Cloud platform adoption. Data is collected through literature review and expert interviews. Interviews were conducted with 13 IT/Cloud experts from various Dutch financial institutions, varying from the consulting, banking, insurance, government and academic sectors.

Ultimately, this research showed that the factors: 1) Security & privacy, 2) Brand reputation and credibility, 3) Pricing strategy, 4) Commitment and 5) Compatibility, are the key influencing factors determining the adoption of a cloud platform within the cloud platform battle within the Dutch financial sector. Based on these key factors and the platform comparison, it was found that among the three most dominant cloud platforms (Amazon Web Services (AWS), Microsoft Azure, and Google Cloud), Microsoft Azure is currently the most suitable cloud platform for the Dutch financial sector.

The Metaverse, although not a new concept, has had many developments recently. One of the core contributions for this is the current developments around blockchain technology and non-fungible tokens. Although it might be possible to create a metaverse on the blockchain, it would also be possible to create it on traditional technology, namely cloud computing. Regardless of the technology it will be built on, the space is expected to grow rapidly within the next few years, but still does not have any regulations attached to it within the EU. This thesis therefore looked into both technologies and compared them on 7 metrics: energy usage, security, scalability, governance, democratization, costs, and additional features. It was found that cloud computers are much more scalable and cost effective, while blockchain has more benefits when it comes to governance, democratization and additional features. Interviews were conducted with experts on blockchain, cloud computing, and policy advisors/strategist to investigate the most critical parts that must be regulated. The interviewee results showed that data privacy, IP rights, and governance & legal documentation were most important. Regulations and policies were then formulated with the help of the qualitative data obtained from the interviewees. These interviews yielded valuable solutions for the potential regulations, which the EU can use and