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...

Executive Summary
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. 

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...

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.

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.

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...

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.

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.

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.

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.

Modern organisations are increasingly dependent on data to develop new products, services and to improve their operations. However, they are increasingly dependent on external data resources which has led to the rise of data marketplaces where data are traded. This rapid rise of data marketplaces has led to a fragmented data sharing landscape resulting in data discovery issues for data consumers and a lack of trust by data providers. Recently, the concept of platforms to integrate different data marketplaces to reduce fragmentation has arised: data marketplace meta-platforms. However, these platforms still need to make sure that data providers stay in control over their data, as data providers will otherwise not be willing to share data at all. Staying in control over data for data providers is about data sovereignty. As data marketplace meta-platforms can reduce fragmentation in the data marketplace landscape, but relatively little is known regarding how data sovereignty can be achieved in that context, this research project explores governance mechanisms to enhance data sovereignty for data providers in the context of data marketplace meta-platforms.
Using existing literature, four antecedents of data sovereignty were identified: data ownership, data access, data usage and data storage. Next, the current state of existing data marketplaces and data sharing initiatives was analysed using industry literature and insights from expert interviews. This analysis indicated that the use of a common legal basis among participants of data sharing ecosystems is often the first step. Additionally, processes were data providers arrange a data sharing agreement with data consumers and certification of entrants and participants are adopted in practice.
Analysis of the data marketplace meta-platform context identified three areas of challenges for data sovereignty in the meta-context, namely DMMP-ecosystem where data providers fear DMMP-dominance. Secondly, at the level of the data provider, there is a lack of capabilities to estimate required control and to estimate the value of data assets. Thirdly, data access and usage is difficult to arrange at the level of a data transaction between data providers and consumers. The use of DMMPs to exchange very different types of data, across different industry sectors further complicates the process of granting access and arranging data usage.
Using findings from the expert interviews, three categories of solutions were identified: architectural, trust-related and technology. Architectural solutions are about the structure of the DMMP and how decisions rights and ownership are distributed across the platform and its users. Trust-related solutions are both about building and sustaining trust, for example by use of certifications, and by increasing autonomy for data providers. Lastly, technology-related solutions such as data tagging and labelling can help to enhance visibility over the data chain and to further help data providers to stay in control over their data.

The “Wet open overheid” or Woo is a law that facilitates access to government information for Dutch citizens and states that “everyone has the right to access government information without having to show an interest in it, subject to the limitations provided for by this law.” This law is the Dutch embodiment of a Freedom of Information (FOI) Act. Facilitating citizens with access to government information is an essential promotor of an open and democratic society. Furthermore, it allows citizens to hold their governments accountable for their actions and make informed political decisions. The proper execution of this right to government information is essential, however, it was found that processing the information requests directed to Ministries of the Dutch Government takes an average of 161 days, where the law states a maximum handling term of 42 days. This delay impedes the citizens’ access to government information and should be addressed. While previous research has been performed towards improving “Woo-request processing,” there is little known about the process itself, nor what the direct causes of delay are. Information management is suggested as a possible culprit. Yet, there is little known about its effects, which are likely to be noticeable in the information search process (ISP) that is part of Woo-request processing. Therefore, the objective of this research is to identify how the existing Woo-request ISP is executed at Ministries of the Dutch National Government and what influences it in order to define improvements for said process in relation to its facilitation of an open and transparent government.

This research concludes that the Woo-request ISP is a complex process that is influenced by many factors. Whereas the ISP described by academic literature portrays multiple iterative steps, the Woo-request ISP hardly portrays any iterative steps in relation to an improved definition and understanding of the information problem. As a result, the requestor cannot learn from the search itself and the information found throughout it. Combined with a possible lack of prerequisite knowledge of the requestor, this means that requests are likely to remain broadly defined and vague due to a fear of missing out on information caused by the requestor’s distrust. Therefore, this research suggests that through increased informative interaction between the requestor and the handler, supported by search and content experts, specification of the requests should be facilitated, which is expected to improve the search process. This also builds a relationship of trust between the parties involved, which is expected to increase the chance of a requestor’s cooperation. Furthermore, the Woo-request ISP as it is does not guarantee completeness nor traceability of the search performed. Therefore it is suggested that the information locating phase makes use of a metadata-based centralized search tool that is operated by experienced and trained personnel who are able to access all information applicable to the Woo.

Whilst the suggested improvements are likely to have a positive influence on the existing process; there are large hurdles to be overcome. It must be noted that some problems or dependencies might in fact never be overcome. For example, the realization of a centrally accessible archive might never see the day of light, as it conflicts with government tasks and values other than openness and transparency. Additionally, it must be noted that the existing “document definition” used by the Dutch government is incredibly broad. This broad definition encases that if the government is to adhere to its laws, almost all currently produced information must be searchable by Woo-request handlers. Especially in a government where a policy rarely stands on its own, it can be challenging to define the end of relevance to a request. Lastly, it must be noted that some requestors might never be pleased with the outcomes of their Woo-request.

Governmental organizations are increasingly making non-privacy-restricted and non-confidential data publicly available on the internet that anyone can freely use, reuse, and distribute without any restrictions. The opening up the government data enables citizens to engage with OGD, i.e., to collaboratively convert OGD into valuable artifacts that benefit society. However, citizen engagement with OGD is contingent upon many factors, and researchers often overlook these factors, and insight is needed to stimulate engagement. This study develops the OGD Citizen Engagement Model (OGD-CEM) that can be used to understand factors contributing to citizen engagement with OGD. The OGD-CEM model hypothesizes that (both extrinsic and intrinsic) motivations toward the engagement and perceived political factors toward OGD and government determine citizens’ intention to engage with OGD. Notably, the more citizens perceive that engaging with OGD will give them an advantage and provide the opportunity to broaden their social networks, the more inclined they will be to engage with OGD. Furthermore, the more citizens perceive that they can engage with OGD easily, that engaging with OGD is enjoyable, and that OGD engagement challenges them intellectually, the more likely they will engage with it. Finally, the more citizens perceive that their engagement with OGD will influence public policy, and the higher citizens’ trust in OGD and the governmental organizations that provide it, the more they will be inclined to engage with OGD. This study is among the first to provide an integrated overview of the profiles of citizens who engage with OGD and the drivers and inhibitors of citizen engagement with OGD. This dissertation also contributes to science by integrating political participation and trust theories into the model because engaging with OGD can yield outcomes with high political values. Moreover, this study is among the first to provide empirical evidence about the citizen-led OGD engagement. Finally, this study is also among the first to empirically test assumptions held in the open data research regarding the effects of OGD quality, political participation, and trust on the intention to engage with OGD.@en

The use of Open Data (OD) in an Engineering Asset Management (EAM) context is largely unexplored. Even though research into OD is moving forward, this knowledge is not fully usable in EAM because of the complexity and the specific characteristics of EAM. The research is centred around the case study that has taken place. As input for the case study, a literature review has been conducted to summarize and analyse the existing knowledge of OD and EAM. This created a starting point for the case study. A case about the traffic situation around the IJsselbruggen and the VIA15 project (A12) has been selected to research the possibilities to use OD with the goal to evaluate the current traffic measures and investigate the possibilities for Data-Driven Decision Making (DDDM). The combined results of the literature review and the case study have been reviewed by a group of experts in an expert workshop session. Besides there critical opinion about the research, the experts where also asked about their view on the use of OD in the EAM sector.

The national government of the Netherlands has mandated heat transition to the municipality as part of the global execution of climate agreement. However, despite many guides has been offered, municipal heat-transition policy is a tricky matter that the municipal policy-makers struggle to grasp. To support the systematic municipal heat transition policy-making process, we propose the usage of a Decision Support System (DSS) that serves the whole cycle of policy-making. However, there is a knowledge gap of an integrated DSS framework with both policy target profiling and policy system (policy cycle) that is a context-specific for the municipal heat transition policy-making in the Netherlands. Therefore, this thesis study was conducted to answer the research question: "what decision support system framework can systematically assist municipalities in the heat transition policy-making process?" To answer this question, a refined Heat Transition Policy Making (HeTPoM) DSS framework is made based on a functional visual design method to cater to the needs of DSS creation in the context of municipal heat transition policy-making in the Netherlands. The refined HeTPoM DSS framework is designed to support policy-makers to create a systematic municipal heat transition policy by supporting the process of policy design, assessment, and evaluation. Furthermore, the refined HeTPoM DSS framework can potentially be used as a base to create a computerised municipal heat transition policy-making DSS. This then leads into two types of future research recommendations that need to be done in series. First is the HeTPoM DSS framework components mechanism study (i.e. the interaction between municipal heat transition influencing factors and the policy-making process). Which then can be followed by digital architecture of the DSS system to support the development of municipal heat transition DSS.