Airports are under increasing pressure to use existing capacity more efficiently while maintaining operational reliability, safety, and service quality. A critical process in this context is the aircraft turnaround, where multiple time-sensitive activities must be coordinated between airport operators, airlines, ground handlers, air traffic control, and other stakeholders. Digital twins offer potential to support this coordination by improving monitoring, prediction, and decision-making. However, these benefits depend on the maturity of the digital twin. Existing digital twin maturity models provide useful foundations, but they are not specifically designed for the multi-actor, time-critical, and safety-sensitive context of airport turnaround operations.

This thesis addresses the following research question: Which maturity assessment framework can guide the development of digital twins in airport turnaround operations?

To answer this question, this thesis develops a digital twin maturity assessment framework for airport turnaround operations. The research follows a Design Science Research approach, in which the problem is first explicated, requirements are derived from literature, and a framework is developed, refined, demonstrated, and evaluated through expert feedback. The resulting framework combines a baseline category and five maturity levels: Digital Model, Digital Shadow, Monitoring Twin, Predictive Twin, and Optimizing Twin. It assesses internal maturity across six dimensions: Data Infrastructure, System Integration, Predictive and Analytical Capability, Operational Decision Optimisation, Operational Situation Visibility, and Human Interaction and Trust. In addition, the framework distinguishes these internal capabilities from external implementation constraints, including data access, commercial sensitivity, decision rights, workflow integration, accountability, and safety.

The framework was demonstrated in the Schiphol context and evaluated with feedback from Schiphol and Vancouver International Airport. The demonstration showed that the framework can support a structured discussion about current capabilities, limitations, and development priorities. It also showed that maturity can differ across dimensions and may be limited by external implementation conditions. The main contribution of this thesis is therefore a context-specific assessment framework that helps airports move beyond the question of whether they have a digital twin, towards understanding what the digital twin can support, what limits its use in practice, and what should develop next. ... Read more

The Dutch Government pursues a digital-by-default strategy, with 83% of citizens possessing the necessary digital skills, but 17% of the population is at risk of exclusion. Here, Information points Digital Government (IDOs) in libraries serve as a crucial physical safety net. While IDO employees effectively resolve individual incidents locally, most signals about structural system errors do not reach the system owners and policymakers. Because of this, this research aims to find governance arrangements within the IDO-network to improve systemic learning through these signals to bridge the Integration Gap. This qualitative research applies the 4I Framework for organisational learning to a multi-actor context, following the processes of Intuiting, Interpreting, Integrating, and Institutionalising. Filling a theoretical gap by examining how learning processes behave when executional and system levels are separated across different organisations. In total, 13 semi-structured interviews were conducted, strategically divided into three phases for problem specification, network examination, and scenario validation. The analysis reveals a mismatch between qualitative human experiences and the quantitative dashboard, fragmented network structures and a lack of central coordination. As a socio-technical design intervention, two developed scenarios, the ‘Qualitative Intermediary’ and ‘Broadening to Essential Services’, show how the feedback loop could be structurally closed. By proposing these mechanisms, this study contributes to safeguarding digital inclusion and protecting citizens’ fundamental rights in an increasingly digital society. It is recommended to transform the IDO network into a goal-oriented learning system with binding agreements between public and private actors, supported by a centrally maintained social map and formalised feedback mechanisms. ... Read more

Steel manufacturers are under increasing pressure to reduce emissions and operate more sustainably. This transition requires strategic decisions based on reliable, consistent, and traceable data. This thesis investigates how large industrial companies like Tata Steel Nederland (TSN) can improve data governance to support that goal. The study focuses on improving reporting and decision-making processes that rely on shared data across departments.

The thesis begins with a literature review that highlights key gaps in current research. Eight core data governance criteria were derived from the challenges to structure the findings: data lifecycle management, interoperability, data classification, data security & compliance, data unit responsibility, version control, data storage, and performance monitoring. These criteria were drawn from academic literature and refined based on the steel industry context. While the steel industry has used data to optimize processes and monitor performance for decades, little attention has been paid to how this data is governed. Studies often focus on real-time production data but overlook the governance of supporting documents for sustainability disclosures, regulatory reports, and investment proposals. The role of data governance in aligning technical, regulatory, and operational reporting remains underexplored. Especially with new regulations like the Corporate Sustainability Reporting Directive, the need for structured, transparent data governance has become urgent.

To explore this, the study uses a qualitative case study at TSN’s Transformation Office. This team coordinates strategic reporting, including the Green Book, a report used to secure investment for the transition to green steel. Data was collected through interviews, document analysis, and observations during a five-month internship. The analysis showed that governance practices at TSN are often inconsistent, with unclear document versions, missing responsibilities, and scattered file storage. Most issues are not technical, but organizational.

To address these challenges, the thesis presents practical recommendations for each of the eight criteria. These include using consistent versioning, labeling files with classification levels, assigning responsibility for key data elements, and creating shared templates. The recommendations are designed to work within TSN’s existing systems and routines. They aim to enhance clarity, coordination, and accountability across teams without requiring the implementation of major new technologies. Importantly, the recommendations emphasize that lasting change depends on three key factors: top-down prioritization, practical bottom-up training, and alignment among the eight data governance criteria.

To test the recommendations, a focus group was held at the end of the study. One recommendation per criterion was discussed. Most were confirmed as useful, especially when aligned with team behavior and leadership support. Six out of eight recommendations were seen as transferable to other industries with similar data challenges. However, data unit responsibility and performance monitoring were not confirmed as broadly applicable.

This thesis contributes to the growing conversation about how data governance can support sustainability in heavy industry. It shows that better governance improves data quality, which in turn strengthens strategic reporting and decision-making. While the recommendations are tailored to TSN, many of the insights also apply to other data-intensive organizations. The findings highlight that successful change depends not only on tools and frameworks but on leadership, shared responsibilities, and practical skills.
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The thesis, titled Bit by Byte: Unlocking Digital Inclusion for Migrants in the Netherlands, examines the challenges and opportunities migrants face in engaging with the Netherlands’ advanced digital infrastructure.

Conducted as part of the Complex Systems Engineering and Management program at Delft University of Technology, the research employs a qualitative methodology, including literature reviews, conceptual analysis, and semi-structured interviews with migrants and experts. It focuses on three migrant groups—economic migrants, students, and forced migrants—and addresses three key research questions: (1) What are the digital inclusion needs of migrants? (2) What are their positive and negative experiences? (3) What strategies and technologies can enhance their digital inclusion?

Key findings reveal that migrants encounter significant barriers, including unclear government information, language limitations, and a digital landscape that assumes high technical proficiency. Negative experiences often stem from the complexity of Dutch digital systems, a lack of translations, and feelings of exclusion due to cultural differences. Conversely, positive experiences highlight the accessibility of digital services, the availability of help through informal networks, and the advanced nature of the Dutch digital ecosystem. The study identifies strategies such as clear and concise communication, community-based support, and the development of unified digital platforms to address these challenges. Technologies like AI-driven chat support and multilingual interfaces are proposed to enhance accessibility. And a contribution is made to the existing digital inclusion framework.

The societal relevance of this research lies in its potential to inform policies that reduce digital exclusion, thereby improving social cohesion, economic participation, and well-being among migrants. Academically, it fills a gap in the literature by focusing on underrepresented migrant groups (economic and student migrants) and the Dutch context, contributing to interdisciplinary fields such as ICT system design, policy analysis, and migration studies. Recommendations include improving government communication, investing in community-based digital literacy programs, and designing inclusive digital tools tailored to diverse migrant needs. This thesis underscores the importance of focusing on understanding the needs of migrants to allow them to fully participate in the Netherlands’ digital society, offering insights that can guide future policy and technological interventions. ... Read more

In the last few years, e-commerce has experienced exponential growth, increasing the pressure, especially on last-mile delivery. This thesis, building on the freight-on-transit (FOT) concept, investigates whether mobile parcel lockers, when integrated into public transport vehicles, can alleviate the pressure and negative consequences of last-mile delivery.
In this research, a mixed-methods design was adopted, incorporating both qualitative and quan- titative analysis. Eleven semi-structured interviews with public transport operators, logistics firms, municipalities, and academics have been conducted to understand whether this idea was feasible from operational, economic, and governance points of view. Additionally, to capture the end-user’s acceptance, trust, and design preferences, a questionnaire (n = 122) was deliv- ered. Moreover, built on the initial idea of combining freight and public transport for last-mile delivery, six scenarios were created that explore possible alternatives. Regarding the scenarios developed, they have been described as possible ideas which, if combined, can have the most significant effect on emission and cost reduction.
The findings reveal that the original idea of ”locker-on-board” is technically feasible but econom- ically unproven and operationally fragile when applied in dense urban areas; this is because lockers remove seats that generate revenue or wheelchair bays, complicate the duties of drivers, and clash with regulations. However, this idea has been sustained for rural areas, specifically using busses, since this would save many km for delivery vans which, if they have to reach a remote area for just a few deliveries, emit many emissions. In addition, the structure of the busses to rural areas that have a luggage space which is often empty supports, even more, the transportation of packages without disrupting the comfort of passengers. ... Read more

In 2006 already, Clive Humby said: “Data is the New Oil!” and like oil, data needs “infrastructure” to be gathered, analyzed and used. This infrastructure is called data governance and it is essential in today’s data-driven era to ensure availability, quality and security of an organization’s data. This is especially true for financial services firms, which deal with massive amounts of highly-sensitive personal data, such as names, dates of birth and bank details, and operate in a highly regulated environment. Therefore, it is essential that any new data governance policies, such as a transition to a cloud-based data governance policy, are implemented as quickly and efficiently as possible. Research thus far has primarily focused on the importance of data governance and developing data governance models. However, the implementation of data governance proves to be not without its barriers. Some research has been done into what barriers organizations encounter when attempting to implement new data governance policies, but strategies to deal with these barriers have not been found in existing literature. Therefore, this research seeks to answer the question:

“How do managers at financial services firms in the Netherlands deal with the barriers to successfully implement new data governance?”

This thesis used a literature study, twelve individual interviews with PwC employees who were heavily involved in data governance implementation processes at financial services firms in the Netherlands and a focus group interview with experts from PwC to determine what barriers financial services firms face when they are implementing new data governance, which strategies they use to deal with these barriers and what key factors influence the decision-making in this implementation process. These three elements were then used to find the answer to how managers at financial services firms in the Netherlands successfully implement new data governance.

The research attempts to close the gap in the literature surrounding the general strategies that are used to navigate the barriers that inhibit (new) data governance implementation. Furthermore, it can help further identify which barriers (financial services) firms face when attempting to implement new data governance and aid in the development of more effective data governance framework. Additionally, the improved understanding of how financial services firms navigate the barriers that inhibit data governance implementation can help maintain trust in financial services firms and the financial system as a whole and it can aid in the development of more effective regulatory frameworks to increase how fast financial services firms are able to comply to them.

The barriers financial firms face to implementing data governance that were found in this thesis were sorted into four broad categories: “Organizational culture/structure”, ”Senior management priority”, ”IT performance” and ”Lack of information”. Examples of these barriers are: a “restrictive mindset”, “unfocused strategy”, “incompatible IT systems” or a “lack of information on technoliii
ogy”. The strategies the firms used to deal with these barriers were also sorted into four different categories: “Senior management vision/championing”, “Technological tools/skills”, “Stakeholder involvement/consensus” and the “Business case” strategy. Examples of these strategies are: “developing a global vision”, “standardization of technology”, “stakeholder involvement” and “building a broad business case”. A complete overview of the barriers and their corresponding strategies that were found in this thesis can be found in figure 4.1...
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As the data economy is growing, businesses increasingly utilize data marketplaces to share data with external parties. Due to the specialized nature of data products, data marketplaces exhibit a high degree of heterogeneity, often focusing on specific countries and industries. This specialization results in high transaction costs when sharing data in data marketplaces. Additionally, multi-homing costs are high, making it difficult for users to participate in multiple data marketplaces to expand their reach. The existing literature recognizes meta-platforms as a potential measure to reconcile highly heterogeneous digital platforms, thereby reducing transaction and multi-homing costs. Nevertheless, while sharing business data on a data marketplace is already difficult due to data sovereignty concerns, these concerns will likely intensify in a meta-platform setting because data may flow from one data marketplace to another. Control mechanisms can enhance data sovereignty; however, due to the novel and intricate nature of meta-platforms, existing knowledge on designing such mechanisms may not be directly transferable to this complex setting. Therefore, this research aims to create design knowledge for developing and evaluating control mechanisms for data sharing through meta-platforms for data marketplaces, focusing on investigating their efficacy in enhancing data sovereignty in the societal context of the data economy. To achieve the objective, this study employs the Design Science Research (DSR) approach. This study is structured based on the three DSR domains: the problem space, the solution space, and the evaluation space.

We find that data providers have different views on the efficacy of control mechanisms (i.e., smart contracts and certifications) to enhance data sovereignty facets in the context of business data sharing via data marketplace constellations federated by a meta-platform. Our research finds no significant differences in data providers’ perception of their ability to retain ownership and maintain control over shared data products in meta-platforms, regardless of the presence of smart contracts. In addition, our findings suggest that data providers using meta-platforms with certifications feel more confident in meeting data sharing compliance requirements compared to those using meta-platforms without certifications. Additionally, these data providers perceive a clearer division of responsibility between meta-platform and data marketplace operators. When combined with smart contracts, the responsibility divisions become even clearer. Contrary to our expectations, however, we find no significant difference in the perceived security of data providers when sharing data on meta-platforms with certifications compared to those without.

Considering the impacts of data sovereignty on the broader societal context of the data economy, we find that when data providers feel sovereign over their data products, they are more likely to trust both a) meta-platform operators facilitating data sharing and b) data consumers with whom they share data. Surprisingly, we do not identify a correlation between the trust and their willingness to share data. This suggests that when data providers possess data sovereignty, trust in platform operators and data consumers becomes a less important factor for data sharing. In addition, we discover that data providers, feeling sovereign over their data products, perceive lower risks in sharing their data. The reduced perceived risks subsequently increase their willingness to share data through meta-platforms. Therefore, our study emphasizes the significance of data sovereignty in the growth of the data economy by a) promoting trust toward meta-platform operators and data consumers, b) reducing perceived risks, and c) increasing the willingness to share business data through meta-platforms.

Our study contributes to the Information Systems literature, particularly in the intersection between data sharing and digital platform literature. We contribute by being among the first to create design knowledge to develop and evaluate control mechanisms for business data sharing through meta-platforms for data marketplaces, focusing on investigating their efficacy in enhancing data sovereignty in the societal context of the data economy. Specifically, our primary contributions are four-fold: 1) theorizing the potential impact of control mechanisms on data sovereignty, 2) outlining design options and principles as prescriptive knowledge, 3) defining goodness criteria to enhance data sovereignty, and 4) advancing context understanding of a meta-platform as a business data sharing setting. In addition, our secondary contributions are 1) providing evidence on the potential impact of data sovereignty on the broader data economy and 2) extending the applicability of theories employed in this research in the market-based data sharing context.

In conclusion, this study resolves the tensions in the European policy-making agendas that promote a single market for data and interoperable data sharing (e.g., in EU Data strategy, Data Act) while, at the same time, pushing sector-specific data marketplaces to exist (e.g., the eight verticals in the Digital Europe program). Furthermore, policy agendas also emphasize adherence to data sovereignty principles. As data sovereignty is vital for data providers to share their data via meta-platforms, addressing this concern may increase meta-platform adoptions. Hence, we hope a meta-platform can realize its potential to be one distinguished instrument to fulfill what we hope (and are optimistic) for in the data economy: a single European Data Market in 2030.
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Introduction: The global shift towards a circular economy aims to optimize resource use, minimize waste, and promote sustainable growth. Monitoring this transition is vital, and open data presents itself as a pivotal tool. This research explores the potential of open data in monitoring circular economy objectives, focusing on electric vehicle batteries.
Research Objective and Methodology: The study's primary goal was to uncover the potential of open datasets for monitoring circular economy goals. A framework was developed, drawing from existing literature and expert insights. This framework was then applied to the context of Electric Vehicle (EV) batteries, utilizing three distinct datasets. Validation interviews further refined the framework and the insights from the EV battery case study.
Conceptual Framework: The research introduced a comprehensive framework designed to evaluate the potential of open datasets in monitoring circular economy objectives. This framework was meticulously crafted by integrating insights from existing literature and expert opinions. Structured around the pivotal dimensions of open data attributes and circular economy principles, the framework delves into aspects such as data accessibility, quality, usability, material flows, resource evaluation, and stakeholder engagement. Serving as a robust tool, the framework offers a systematic approach to assess the compatibility, depth, and versatility of open datasets in the context of the circular economy, ensuring a holistic analysis that bridges the gap between data transparency and sustainable practices.
Case of Electric Vehicle Batteries: The case study on electric vehicle batteries provided a practical lens to test the framework. Three datasets from different sources, namely RDW, Eurostat, and the BatteryPass, were analyzed. The datasets revealed insights into material flows, resource consumption, and environmental impacts associated with the EV battery ecosystem. The RDW dataset, for instance, highlighted the importance of tracking at the vehicle level, while the BatteryPass project showcased potential in monitoring battery lifespans and end-of-life scenarios. The case study illuminated the framework's applicability, revealing usability, opportunities and constraints in the datasets.
Discussion: The research employed mixed methods tailored to each phase. A literature review identified key attributes for analysis, while expert interviews filled gaps overlooked in the literature. The framework was structured around the key dimensions of open data and circular economy principles. The open data division examined data accessibility, quality, and usability. The circular economy division delved into material flows, resource evaluation, product lifespan, end-of-life considerations, and stakeholder engagement.
Conclusion: The research culminated in a comprehensive framework for evaluating open data's potential in circular economy monitoring. The framework's elements spanned both open data attributes and circular economy dimensions. The methodology integrated these elements, refined through expert interviews, and validated using the electric vehicle battery case study. Practical contributions included guidance for governments and policymakers, insights for industries, and a focus on stakeholder engagement. Future research directions include enhancing the framework's comprehensiveness, creating an interactive catalog platform for open datasets, and broadening its scope.
The research journey unveiled the intricate relationship between open data and circular economy monitoring. The developed framework, validated through the electric vehicle battery case study, stands as a testament to the synergy between academic rigor and practical applicability. However, the journey is ongoing, with the identified limitations paving the way for future exploration. The potential of open data, when effectively harnessed, can revolutionize sustainability approaches, driving the world towards a more circular future. This research serves as a foundational step, illuminating the path for future endeavors in open data and circular economy monitoring. 
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Global warming is one of the biggest societal problems in 2023. The share of IT usage within this problem has been growing rapidly in the last decades, and this is likely to continue. To stop the increasing footprint of IT (usage) a significant change is required. However, due to the pervasive nature of IT integration in our daily lives, this is a very complex undertaking. Greening the IT sector would require multidimensional changes in technologies, behaviors, regulations and so on. Apart from the need of IT in society, most people within society are not aware of the consequences of IT usage... ... Read more

Effective data sharing plays a pivotal role in optimizing supply chain management and driving the operational excellence of businesses. However, certain barriers and challenges exist among the supply chain partners in communication and data exchange. This thesis investigates these barriers and proposes potential solutions, referred to as instruments, for data sharing in supply chains. The objective is to develop and validate a framework that addresses feasible barriers in high-tech supply chain organizations. Single case study interviews in a high-tech supply chain firm assess the framework's applicability, followed by a validation phase to test its generalizability. Infrastructural instruments include ICT, Blockchain, AI, ML, data standardization, and data security measures. Institutional instruments encompass cultural factors, trust-building techniques, contractual agreements, education and training programs, leadership practices, and ethical data sharing. The research contribution extends to a strategic deployment plan for these instruments, offering valuable insights to supply chain professionals. Additionally, the research emphasizes the significance of viewing risk as a comprehensive concept in relation to trust, technology, privacy, and governance-related barriers.
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The use and value of Open Government Data (OGD) are gaining increasing attention from governments, corporations, and individuals. Governments have developed portals and started initiatives to share, promote and facilitate OGD interactions. The key objectives of the release of OGD by public entities are public responsibility, economic expansion, and operational and technical values. However, the potential of OGD is limited by the ability to reuse public data. Although OGD is becoming more available, the value realisation of OGD is hindered by its limited use. Within the OGD ecosystem, data intermediaries, especially Non-Profit Organizations (NPOs), have been playing a role within the OGD ecosystem, perhaps making OGD more usable for users. We assume that the barriers that are most likely to hinder the potential of OGD are more linked to the barriers to using OGD other than structural or provisioning barriers. Furthermore, the barriers to using OGD might be more influenced by the activities provided by the non-profit data intermediaries (NPDIs). Given the anticipated transformation potential of OGD and the current barriers to reusing OGD, it is necessary to investigate how the existence and activities of the NPDIs affected the barriers to using OGD. This study explores the impact of the NPDIs’ activities, particularly their impact on barriers to OGD use. We introduced the following research question:

How do European non-profit data intermediaries reduce the barriers to using OGD?

Adopting an explorative case study approach, we first identified the barriers to using OGD through the use of a literate review method. We introduced three categories of barriers in which these barriers originated. These categories are OGD portals, OGD datasets and OGD users’ abilities. Furthermore, we conducted a narrative literature review to analyse and identify the roles and activities of the open data intermediates. We identified seven roles that summarise the set of functions of responsibility that open data intermediaries assume and fourteen activities by specifying certain actions or tasks that open data intermediaries fulfil. Based on the literature review, we could not link the activities to the barriers. However, we conceptualised the barriers to using OGD and the activities of the open data intermediaries separately.

Following this, we conducted desk research by analysing NPDIs’ websites, complemented by interviews with the selected NPDIs to analyse their activities and roles in the OGD ecosystem toward reducing the barriers to using OGD. Our findings of the activities of the NPDIs showed that NPDIs have a different scope and objectives, often a social goal, compared to open data intermediaries who might be, for instance, interested in profit. However, the roles and activities of NPDIs, are similar to open data intermediaries in terms of their operations. NPDIs do not necessarily focus on specific roles or activities but rather provide a wide range of services and conduct various activities. This may be driven by the overarching social goal where they try to offer a complete solution that does not lack in some areas. However, some of the activities we identified might be specific to NPDIs, such as promoting the use of OGD, allowing OGD users to disseminate their OGD-driven insights and improving and facilitating the process of using OGD.

We conducted nine semi-structured interviews with individuals from NPDIs and their users to learn how they perceive NPDIs are reducing the barriers to using OGD. Through coding the interview transcripts, we applied a thematic analysis approach to the data obtained from the interview. We have identified six themes that represent how NPDIs reduce the barriers to using OGD. These themes are; building OGD capacity and expertise, improving OGD accessibility quality and usability, empowering OGD users, OGD process optimisation, promoting and advocating OGD-related activities and policies, and facilitating and improving stakeholders’ collaboration and engagement. The findings showed that NPDIs mainly emphasised improving OGD capacity, accessibility, availability, and findability. Then, we conducted a focus group session to triangulate our case study data. Three participants participated in our session, two represented the NPDIs perspective, and one represented the user perspective. We concluded that NPDIs’ activities reduce the barriers to using OGD, such as OGD users’ ability, OGD accessibility, and quality of OGD datasets and portals. However, quantifying their impact or linking their activities to some of the barriers they reduce is challenging due to the multiple impacts of the NPDIs’ activities.

Our study attempted to address the gap in the literature regarding the NPDIs’ impact in reducing the barriers to using OGD. Also, our study provided insight into how NPDIs reduce the barriers to using OGD; we identified their characteristics and strategies, which contribute to setting the groundwork for future research exploring the link between NPDIs activities and barriers to using OGD. Our results underline the value of NPDIs to the OGD ecosystem. Policymakers or key NPDIs persons can leverage the results of this study to capitalise on the identified opportunities, such as trying to make NPDIs efforts more proactive in anticipating the barriers of OGD to contribute to better use of OGD ultimately.

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In today’s increasingly interconnected and digitized world, the logistics industry plays a critical role in global trade, enabling the seamless movement of goods across essential supply chains (Reinsel et al., 2020). The rapid evolution of digital technologies has led to an unprecedented proliferation of data generated from various aspects of logistics operations. However, despite the potential of this data to drive efficiency, cost reduction, and supply chain optimization, the logistics industry has been slow to embrace large-scale data sharing practices, resulting in a fragmented data ecosystem (PwC, 2019). Large-scale data sharing in the context of the logistics industry refers to the efficient exchange of information across various stakeholders, including shippers, carriers, freight forwarders, and other logistics service providers. By enabling access to a vast pool of data, logistics service providers can gain a more comprehensive understanding of the overall supply chain, leading to numerous benefits for the industry. Multi-sided data marketplaces (MSDM) are a promising concept that can play an important role in addressing this lack of data sharing on a large scale among businesses for any domain (Koutroumpis et al. (2020b)). They take the role of the neutral middleman by providing a platform to facilitate the data transactions, the ability to search for data sets and provide complementary services. Despite their perceived benefits, however, many initiatives remain in early stages (Spiekermann, 2019) and little research is published that demonstrates the usability of the concept for the logistics industry. As no open data sharing platform currently exists for the logistics industry (Bastiaansen et al., 2020), conceptualizing a multi-sided data marketplace architecture which meets the domain-specific multi-stakeholder context may contribute with novel insights... ... Read more

The labour market is constantly subject to economic, socio-demographic and technological developments. As both a policymaker and employer, the Dutch government must respond to these developments. As a policymaker to promote the functioning of the labour market, and as an employer to attract sufficient qualified personnel to guarantee the provision of public services to Dutch society. Both goals are under considerable pressure due to an unprecedentedly tight labour market, leading to labour shortages in parts of the public sector. These shortages have far-reaching consequences for the functioning of the Dutch labour market and the ability to provide public services, such as the provision of care and the renewal of crucial IT systems.

The Dutch government is pursuing a labour market policy to combat these developments and mitigate negative effects. The Dutch public wage policy, determining an adequate level of public sector wages relative to private sector wages, constitutes an important aspect. The Dutch public wage policy intends to offer competitive wages in the public sector to attract sufficiently qualified personnel to provide public services. The government has the reference model in place for this, but there is limited knowledge of to what extent the reference model leads to competitive wages in reality. This study addresses this knowledge gap and, as the first quantitative evaluation of the Dutch reference model, looks at the research question:

How does the Dutch public wage policy translate into public-private wage differentials and sectoral shifts?

An answer to this research question is sought by means of empirical econometric analysis, coupled with a thorough theoretical foundation. Oaxaca-Blinder decomposition methods, adapted to the specifics of the Dutch labour market, have been applied to analyse the public-private wage differentials to assess the competitiveness of public sector wages. Correlation analysis is applied to analyse the relationship between these wage gap estimates and sectoral job mobility to assess the influence of public-private wage differentials on the ability to attract qualified personnel in the public sector. Together, this forms an evaluation of the reference model, and thus the current public wage policy, addressing the questions of whether public sector wages are competitive with private sector wages and whether this (non)competitiveness can be a cause of shortages in the public sector.

The findings indicate that the current public wage policy does not lead to competitive public wages. While wages are reasonably competitive for the weighted average public servant, this is not the case when looking at specific personal human capital characteristics. Depending on one's capacities, one earns relatively more or less in the public sector than in the private sector. The reference model is too generic to be competitive for individuals and is only reasonably competitive when looking at the average. Application of compensation policies, as was done in 2015 and 2016, leads to a serious deterioration of public sector competitiveness. It is also established that these considerable wage differences can be a cause of shortages of qualified personnel in the public sector, including healthcare and ICT personnel.

The Dutch government should ask itself whether it wants to offer these non-competitive wages. There are egalitarian reasons for this approach, but there is a risk that there will be shortages of qualified personnel in the public sector. To respond to this, the reference model should be examined more closely, other options for attracting sufficient personnel in the public sector should be investigated, and options for increasing productivity in the public sector should be investigated. Academic points for future research are to apply other methods to the rich data set used in this study and to conduct further research into the causality between wages and sectoral job mobility, also in relation to other Public Service Motivation (PSM) and intrinsic motives. ... Read more

The intellectual disability population increased from 65.545 people in 2011 to 116.200 people in 2020. In other words, the population almost doubled in less than ten years' time. Interventions are required to be able to make sure everyone receives the needed care. Therefore, the causes for the growth should be further examined before any decision on intervention can be taken. This thesis contributed to understanding the factors influencing the increase in the intellectual disability population and evaluating the behaviour of the sector. To improve the understanding, a System Dynamics model was designed analysing the influence of self-reliance on the intellectual disability population.

The question of why there is an increase in the intellectual disability population has been attempted to be answered within this study. After evaluating multiple factors as a cause for the growth in the intellectual disability population, self-reliance was selected as being an important factor. In the conceptual model, three reinforcing feedback loops were found, indicating that when there is no intervention, the self-reliance of the intellectual disability population continuously decreases, resulting in more people applying for care from the Wlz. In the qualitative model, this proposition could not be rejected, indicating the importance of the role of self-reliance on the growth of the intellectual disability population. Especially for the population with an IQ score between 70 and 85, more attention should be paid to the increase or maintaining of self-reliance. In that way, the intellectual disability sector is always able to care for the ones who cannot live without.

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This study investigates the effects of governmental algorithm registers on citizens’ trust in the Netherlands. Algorithms are increasingly used by governments to support decision-making, but their opaque nature can negatively affect public trust. While transparency is often promoted as a solution, the literature presents mixed evidence: some studies suggest transparency improves trust, whereas others indicate it may reduce it or even create confusion. This research addresses the knowledge gap concerning how governments can best explain their algorithmic decisions to citizens and how the design of algorithm registers influences trust.

The research adopts an exploratory, empirical approach, combining quantitative and qualitative methods. Conjoint analysis is employed to examine how different attributes of algorithm registers influence citizens’ trust. Attributes are identified from grey literature and grouped into three categories: intention (legal basis, impact, proportionality), operation (human interference, risks, detailed description), and technology (methods and models, source data, source code). A survey with 131 respondents was conducted via Qualtrics, including nine conjoint questions, a holdout question, and demographic inquiries. The sample shows an overrepresentation of men and highly educated respondents, and an underrepresentation of older age groups, which is considered when interpreting the results.

Regression analysis of the survey data reveals that not all attributes positively affect trust. The attribute “risks” has a negative coefficient, suggesting that highlighting risks may reduce citizens’ trust. Other attributes, such as legal basis, methods and models, and source data, show smaller coefficients with relatively high p-values, indicating limited influence. Overall, the explained variance of the model is low, implying that trust depends on factors beyond the register attributes, such as respondents’ general trust in government or the expertise of the algorithm controllers. Trust in the central government has a stronger effect than trust in local authorities. The results also suggest that understandability plays a crucial role: registers that are unclear or overly complex can confuse citizens and reduce trust.

To complement the quantitative findings, a focus group of digital transition consultants was consulted. Experts emphasized that registers should use clear, simple language, visualizations, and uniform formatting. They also highlighted the importance of involving citizens in the iterative design process to ensure that information is comprehensible and relevant. These insights align with the literature warning against information overload and misinterpretation.

The study concludes that governmental algorithm registers can both positively and negatively affect citizens’ trust. Transparent registers do not automatically result in higher trust; the content, clarity, and contextual relevance are critical. Moreover, registers are only one component of building trust—general trust in government and effective communication also play significant roles. Policymakers should carefully design registers, balance transparency with comprehensibility, and engage citizens in dialogue about algorithmic decision-making. Future research should include larger, more representative samples, explore additional attributes and algorithm types, and consider alternative analytical methods beyond linear regression to capture the full complexity of trust dynamics.

In summary, the study highlights that an appropriate design of governmental algorithm registers is essential for supporting trust, but transparency alone is insufficient. Effective registers must be clear, relevant, and citizen-centered, while governments must also foster broader trust and communication to ensure algorithmic decisions are understood, accepted, and perceived as legitimate. ... Read more

In recent years, the medical world has seen rapid digitalisation. Digitalisation of healthcare and the opportunities of remote monitoring can help to keep the healthcare system affordable and accessible. A promising application of remote monitoring is the use of consumer-grade wearables for clinical care. However, vulnerabilities leave consumer-grade wearables susceptible to cybersecurity and privacy risks. When the wearables gain clinical care functionality, increased risks are problematic for the success of the technology. This research examined this problem by analysing the role of users in the cybersecurity and privacy environment and the impact of human factors on the cybersecurity and privacy system of medical wearables. Vulnerabilities and associated risks were identified with the help of notions from human-centric cybersecurity. To establish a user-centred approach to tackle these risks, the user needs and limitations were examined by performing a quantitative survey research. Based on the results of the survey and the human-centric components of user, usage, and usability, guidelines for the user-centred approach were formulated. These guidelines were subsequently linked with the challenges and attacker-oriented and user-oriented risks to establish recommendations for medical wearable providers. Providers can use these to steer the design of the cybersecurity and privacy system and the structuring of the system environment. The research showed that by taking into account the needs and limitations of users, the cybersecurity and privacy system design can be more effective in tackling user-oriented risks. The research concludes that a user-centred approach to cybersecurity and privacy can contribute to the successful use of consumer-grade wearables for clinical care purposes. ... Read more

Governmental organisations worldwide are working on their open data strategies and are openly sharing their data with the public. Recently also open data communities have been set up by several national governments. The effects of setting up these data communities on open data benefits and barriers and whether an effect exists at all is not yet clear, because there has been little scientific research into data communities. This thesis therefore focuses on the effects of setting up open data communities. 
A single-unit case study was conducted to gather qualitive information of the effects of data communities. The case study focussed on the Dutch open data community and consisted of two parts, a document analysis and in-depth interviews. The results of the analysis show how the Dutch community contributes to enhancing open data benefits, including (indirectly) creating more informed citizens, increasing the access to capacity and resources outside of the data publishing organisation and a higher problem-solving capacity. Furthermore, the interview participants agree to a large extent that communities contribute to intragovernmental collaboration and the use of collective intelligence to solve public problems. According to the interviewees, the community also (potentially) mitigates open data barriers such as the lack of interest in using open data (by governmental organisations). The interviewees stated that the community managers made sure every question that was posted got a sufficient answer within a reasonable amount of time and therefore the barrier stating that the data provider ignores requests and suggestions of data users could also be mitigated, as well as difficulties in the interaction with the data provider. Both researchers and most community users and managers argued that the community could also contribute to mitigating low engagement of public managers with open data and increasing the knowledge and skills of employees to use the open data. Lastly, according to the interview participants, the community can decrease difficulty in discovering/locating data and not being able to combine and connect datasets. The interviewees were also questioned about how institutional instruments could increase the value of the open data community. Although the participants concluded that the contribution of formal instruments (such as rules) is limited, they indicated informal rules (such as norms) and enforcing instruments (such as rewards) can contribute to the value that is created by an open data community. 
The community-specific challenges identified in the analysis can contribute to the process of designing an open data community, because policy makers can compare scope and design choices with the empirical experiences. This thesis also provides an exploratory scientific contribution: this research provides insight into how open data communities can contribute to enhancing open data benefits and the mitigation of open data barriers. Although the results are promising, certain limitations are applicable to the research. The thesis only studied one open data community and the full list of open data benefits and barriers was reduced based upon an assessment of the author of the thesis. Last of all, the interviews focused on qualitative data. Combining the results of this thesis with other case studies can improve generalisability.
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