The European Union is exploring the possibilities of blockchain for their services and processes as a bottom-up approach to the coordination of citizens and economic administrators. However, the multi-actor complexity and the systems complexity of blockchain technology makes decision-making in this subject a difficult task, resulting in a proliferation of blockchain experiments that do not provide significant value. To capture the benefits of blockchain in processes where this technology is applicable in the EU, a blockchain assessment tool is needed that allows for the structural assessment of the fit with blockchain for an information exchange or registration process. This thesis uses a Design Science to design this blockchain assessment tool. Six elements were found to be of importance for blockchain experimentation in EU Institutions and Bodies: Complexities, Process Factors, Ripple Effects, Organizational Factors, Decision-making Process and Design Features. It is critical to take the complexities involved in implementing blockchain in public administrations into account. Also, the factors that define the fit between the process and blockchain technology need to be considered as insight in this fit can enhance decision-making regarding blockchain experimentation in EU Institutions and Bodies. In addition, there are a number of organizational factors that determine the ability of a governmental organization to adopt blockchain technology. Given the institutional change that blockchains might present, it is critical to take into account the ripple effects of blockchains when deciding to experiment with blockchain technology as an EU Institution or Body. As there is not merely one decision-making process for IT innovation adoption, the process of how a decision on blockchain experimentation is reached needs to be considered as well. Lastly, as blockchain technology compromises of various types and design features, it is important to reflect on the impact of these features on the process criteria. Using explorative expert interviews combined with Qualitative Data and Matrix Prioritization Analysis, the elements are concretized and translated into requirements for the blockchain assessment tool. Based on these requirements, the blockchain assessment tool is designed using a Morphological Chart to structure the design process. The blockchain assessment tool consists of three steps that allow a user to assess the blockchain fit, create a high-level blockchain design and to map the ripple effects. The blockchain assessment tool was demonstrated in two case studies exploring the possibilities of blockchain for an EU registration or information exchange system, for which the tool proved to provide key insights. Also, experts were interviewed to evaluate the tool, which demonstrated the need for this blockchain assessment tool for the public sector. Future research is suggested to focus on the trade-offs between the design features to provide a better view on the possible blockchain architectures. Also, more research into the openness and interoperability of blockchain systems could create more depth in the debate of permissionless versus permissioned blockchains in governments. Lastly, research into applying Value Sensitive Design for blockchains could enable the design of permissionless blockchain systems where authorities can be supervisors to protect public values in permissionless blockchains.

Over the course of the past centuries, humanity’s influence on the Earth’s atmosphere and climate system has become so significant as to induce geophysical feedback effects, specifically in the form of a self-reinforcing process of global warming. In order to contain the global warming and prevent the expected impacts of climate change from happening, it has become imperative to undertake drastic steps directed towards reducing emissions of greenhouse gases (including CO2). Action needs to be taken soon, as with the growing population, the demand for carbon-intensive (conventional) energy sources is growing and thus greenhouse gas emissions (the main cause for global warming) are increasing. To slow down global warming, energy conservation, energy efficiency improvement and renewable energy generation are the only practical solutions available to mankind...

The dietary habits changed drastically since 1980 and the chemical composition of a lot of processed foods was altered to meet new requirements. This nutrition shift along with the industrialisation and the rise of the sedentary lifestyle, led to the spread of the obesity epidemic which still manifests in many countries, including European. Children are also affected from this epidemic, with the childhood overweight and obesity trends to grow. The OECD children average for 2017 reached a 15.5%. However, in each country different factors contribute to the manifestation of the phenomenon, composing its unique obesogenic environment. In the current thesis proposal, exploratory statistical modeling is employed, to identify the non-biological factors explaining children's weight for the Netherlands. The analysis is conducted with the use of a custom framework to capture factors which are linked with societal inequities. The outcome of this analysis is a group of factors which explain the weight in preschool children. Also, significant results for preschool children are derived by comparing different screening definitions for overweight and obesity, showing the clusters of risk-prone children. By using the insights from the analysis, data informed policies are proposed to aid the creation of a healthier urban environment for future population to thrive in. Data informed policies based on governmental data for the Netherlands can facilitate the decision making and make prevention and mitigation of the epidemic more effective, by targeting appropriately different population segments which are in need. Future research could focus on prediction models of obesity based on the significant factors identified in this study. Also, the expansion of the current models with more variables from the urban environment is needed to show more specific associations with urban features.

Over the course of the last decade the role of data in society, businesses and governments has changed tremendously. New technologies continue to influence the way data is collected, managed and used in organisations public and private alike. For public organisations and governments, the promise of data lies in data-enhanced decision-making. Data-enhanced decision-making gives governments and governmental agencies the possibility to improve by increasing their efficiency and effectiveness. The Dutch Authority for Nuclear Safety and Radiation Protection (ANVS: Autoriteit Nucleaire Veiligheid en Stralingsbescherming) is the regulatory agency responsible for the regulation of nuclear safety and security, and the regulation of all uses of ionising radiation in the Netherlands. In order to perform their primary task, regulation, the ANVS executes the proactive inspection process by utilising risk-based regulation. The ANVS is a future oriented organisation and subsequently employs a strategy of continuous improvement. The ANVS have noticed the increasing use of data science in the public domain, and subsequently want to improve their regulatory framework via data-driven tools. These data-driven tools could supplement the existing regulatory framework by creating a data-driven risk-based regulation (DDRBR) framework.

Wind Power Producers (WPP) incur a negative result through mandatory participation on the balancing market. This study analyzes the performance of multiple improved offering strategies for a WPP offering both on the day-ahead market and the balancing market. It does so on revenue, as well as its impact on the grid and electricity market as a whole. It tests two different stochastic optimization models, one under the price-taker assumption, one under the price-maker assumption and one analytic model, which is a closed-form reformulation of the latter. It also introduces a framework for modeling the stochastic processes, which form the input to the stochastic optimization models. This framework contains an innovation in the form of an extended Gaussian Copula model to directly generate the entire scenario tree, which is tested against a conventionally reduced symmetric tree. It also introduces a framework for evaluating each step of the stochastic process modeling, with the aim of enabling structured improvement. The study shows that when taking the influence a WPP has on the imbalance price into account in the analysis of revenues, proper dependency modeling between the processes as well as endogenizing the impact a strategy has on the imbalance price in the stochastic optimization model are strict requirements for positive real-world performance. Furthermore, it shows that successful strategic offering may prove valuable for the grid and market as a whole.

The limitation of natural resources forces humanity to rethink its current habits of material use. Plastic product packaging, fossil fuels for transportation and energy generation are just a few examples of highly resource intensive processes in modern society. Urgency of this matter increases due to the fast-growing world population and big economies as India, China and Brazil. Understanding the current material flows and the system they flow through might generate knew knowledge of material use dynamics. This knowledge can help policy makers to stimulate material use efficiency. To generate the necessary knowledge this research takes an Urban Metabolism (UM) approach. In contrast to established material flow focused and analytical aggregated UM approaches, this research proposes a more comprehensive approach. Defining the UM not only as material flows and processes, but giving more weight to the Social, Economic, and Institutional system aspects as well as the role of actors. On top of the different system perspective the metabolism analysis is conducted on different time and spatial levels. The addition of disaggregated analytical levels enables the use of statistical analysis to discover material use patterns over time and identification of actor group related material-use behaviour. The statistical methods used are Time Series, Correlation and Geospatial Analysis in addition to the common Material Flow Analysis and a Life Cycle Assessment. The proposed UM approach is applied to the case of household waste management in Amsterdam. This research showes the potential of a deeper and holistic metabolism analysis enabled by increasing amount of available data. Opening up and troughing light into the up to this point Urban Black-Box.

The theory of the Habermasian public sphere is applied to Twitter. To do so, the traditional literature on public opinion is combined with more recent literature on social media. After the literature research the main knowledge gap is identified. This gap is the lacking relation between the philosophical investigation on public opinion on Twitter and an empirical investigation on actual tweets. This thesis combines the fields of philosophy and data science to contribute to the knowledge gap. Palm oil is used as a case study. The main research question is: How can public opinion on Twitter be characterized, on the case study of palm oil? The findings of this research suggest how Twitter, in its current form, does not create the optimal environment for forming something that is approaching public opinion due to five characteristics: the exclusion of more than half the world population (a), the domination by a few users (b1) (often with strategic interest (b2)) , the pollution through spam (c), Twitter’s role (d) and the characteristics of tweets (e). a. The exclusion of more than half the world population from social media is at least based on the lack of internet access, new media literacy and cultural capital. b.1. The dominating key players are users who get the most attention from other users, policymakers and journalists. This can either be on a specific topic, or transcending topics. The findings from this research suggest that organizations like Greenpeace, BBC Earth and the Roundtable on Sustainable Palm Oil have an above average role in guiding the thinking of the public and therewith steering public opinion. It is difficult to find a user that is saying something completely different than what the defined influencers are saying. These few key players play an above average role in defining public opinion on Twitter, and the influence of this role is not limited to Twitter. b.2. The tweets analyzed show how the dominant key players often act with a strategic interest in mind. The public sphere should, according to Habermas, be free of power and ideology and the only common interest should be the interest in good opinion formation. c. The pollution through spam is argued to play a serious role in influencing public opinion on Twitter. The exact effects of fake accounts and hyperactive automated users is unclear but it can be argued, as can be seen from the examples, that they cause a stream of misinformation and therewith influence public opinion. d. It cannot be proved what Twitter is doing, and if and how they have influenced the debate on palm oil, since the algorithm is nontransparent. Examples like Morozov’s accusation of censorship in Twitter’s ‘trending algorithm’ shows the influence they can have e. tweets are not just 280 characters. They are often carefully considered combinations of hashtags, calls for action, snippets of information, interpreted data and images or videos. These characteristics all help in convincing the reader of the point the user is trying to make.

The use of machine learning systems has great potential to better predict probabilities of default for credit underwriting. Despite this advantage, herewith there exists the substantial risk of discrimination. Moreover, machine learning models with the highest prediction-accuracy are often the least explicable (i.e. explainable). Nonetheless, explicability is needed to create accountability of automated credit decisions by machine learning systems. Furthermore, there exists a regulatory need for explicability of machine learning systems in the General Data Protection Regulation (GDPR) and the Consumer Credit Directive (CCD). Besides that, an ethical- and societal need exists for explicability. Within the exploration of literature, it becomes clear that research lacks on how to move from a high-level principle like explicability, towards a prospective assessment of a machine learning use case on this principle, it lacks a multi-disciplinary perspective, and it misses an assessment framework that can guide decision-makers within machine learning use cases, aligned with a multi-organizational development lifecycle. This research aims to design a prospective pragmatic assessment framework that can guide decision-makers, within machine learning applications in European credit underwriting cases from the point of view of explicability. To accomplish this, the Design Science Research Methodology (DSRM), complemented with the Value Sensitive Design (VSD) approach, is utilized. To this end, the Explicability Assessment Framework (EAF) was developed. This framework is adapted to the context- and explanation characteristics of the case, and aligns with the CRISP-DM development lifecycle. It was found in two case studies that the framework helps with the decision-making whether a machine learning system is sufficiently explicable or not. Lastly, a wide range of future research areas is identified that needs attention: empirical validation and expansion of the framework, the relevance for automated explanation creation, the scalability to other context and a large amount of explanations, and the practical perspective regarding adoption in the industry.

Enhancing supply chain resilience is of vital importance in today’s business to manage and mitigate the risks, especially in the semiconductor industry challenged with long cycle times and short product life-cycles. Transferring production from a primary site to an alternative site after a disaster is one of the strategies to ensure resilience of the supply network. In this study, different types of alternative sites with various levels of preparedness are investigated. A discrete-event simulation is used to evaluate their operational and financial impacts under four different disruption scenarios. The simulation outcomes demonstrate unexpected positive benefits of various alternative sites in terms of fast recovery and resilience building.

Congestion and emissions have lately been termed as one of the major challenges for the urban traffic network through various governmental bodies. In this work, a potential solution to this, namely a functional combination between platooning and traffic- adaptive intersection control is first developed and then evaluated. The evaluation is concerned with the expected congestion and emission savings on a 6-km- long provincial road over a period of 60 minutes. For the purpose of evaluation a VISSIM simulation is em- ployed, calibrated and executed. The simulation results show that the proposed combination of platooning and JUNO, a traffic-adaptive intersection controller, allows for emission savings of 7.85 g/km and an increase in average velocity of 1.04 km/h when compared to conventional cars and model-predictive control. The work is embedded in a high-level context and concluded with a policy-development framework that can help to promote the proposed technical design.

Agility is increasingly being used in the public sector as its adaptive approach through short-cycled development could allow public services to keep up with developments in technology and the complex problems contemporary societies face. There are, however, some dilemmas at play when it comes to agility and the public sector’s transparency and stability for accountability. Furthermore, agile projects appear to be less predictable and more likely to go off track. The main paradox of agility is that even though it appears to be at odds with control, it is required for agility to be effective. Besides, public sector bureaucracies come with their own control mechanisms which theoretically conflict with agility. The research explores these phenomena by considering how control over agile processes in the public sector can be enhanced through the use of control mechanisms. By using an explorative and single case study approach with validation sessions the research combines qualitative data from theoretical and practical findings which are analyzed through a Revised Levers of Control framework to provide recommendations for controlling public sector agility. It indicates that the agile process itself is the main means of control for which an extensive list of mechanisms is provided of both strategic and operational nature. Besides, the vast presence of interfaces in the public sector increases complexity while external deadlines and tight budgets provide a clear desire for planning as control. A main recommendation therefore consists of mechanisms to be used to perform planning within agility. The research also showcases that types of bureaucratic control can facilitate or are even required for the agile process to function when it comes to centralized strategic visions on priorities and an organizational vision on implementation of agility. Overall, this implies control over agility in the public sector to be a balancing act between mechanisms associated with ‘pure agility’ and with public sector bureaucracies. Further empirical research into the use and implications of proposed control mechanisms and presence of interfaces in practice would help in enriching the findings and provide more insight into the requirements of different types of public sector organizations to control their agile processes.

The last decade saw the rise of e-commerce trade and the shift of the manufacturing industry to the emerging economies, China first of all. In this context, the European Customs Authorities experienced an explosion of small parcels coming from e-commerce websites, often from China, and faced difficulties to detect fiscal frauds and security threats using their conventional risk management systems. To address this problem, the European project PROFILE brings together the customs administrations of Netherlands, Belgium, Sweden, Norway, and Estonia, aiming to provide the EU with a shared platform for: (1) accurately assessing customs risks; (2) optimizing operation and logistics by integrating multiple sources of information; (3) developing a shared data platform to share customs risk management (CRM) practices. As part of this project, the Dutch Customs Administration (DCA) and International Business Machines (IBM) Corporation are collaborating to deploy the cutting-edge technologies of artificial intelligence to automatically cross-check the customs declarations coming from Chinese e- commerce against online information. Through a Design Science approach, I carried out this research for the Delft University of Technology, written in collaboration with IBM Netherlands, aiming to deliver a preparatory study for the developing team before the PROFILE project begins. This includes knowledge brokering between the Dutch Customs Administration and IBM Netherlands so that a more precise problem scope can be defined, and the requirements elicited. In particular, this research focuses on the first part of the project: the development of an adaptive web-crawler for e-commerce, able to compare the declarations documents against online information. According to the Dutch Customs Administration, the web-crawling system should gather the description of the goods from declarations, search the product on the web, find its price of sale on the e-commerce platforms, compare it with the value declared in the declaration, and return a risk indicator of green/red flag to the targeting officer. The design process of this system follows approaches coming from the systems engineering discipline, starting with the requirement analysis, addressing them with the state-of-the-art big data analytics, and finally deriving the logical components of the system, whose design is presented through a logical architecture. First, the application domain is investigated. When goods entry the Netherlands need an entry declaration. These goods arrive at the harbor of Rotterdam or airport of Schiphol, where some of these are imported into the country and become import/export, and others stop temporarily as transit waiting to be shipped somewhere else. The Dutch Customs Administration monitors these processes through risk management systems aiming to stop non-compliant goods. This research describes these practices, with a higher focus on the e-commerce risk targeting. About the e- commerce world, a study of the e-commerce processes behind an online purchase is also carried out through a real purchase on Chinese e-commerce. This was used to observe how the Chinese sender described the item, and how the Dutch Customs assessed the risk and decided on the duties to be paid. This led to reflect on the possible frauds scenarios and how to address them. Finally, the Dutch Customs also reported that the products descriptions are often vague and ambiguous, and a more accurate formulation of the problem is described. Secondly, an in-depth literature on the fields of web-crawling and big data analytics techniques is carried out. The possible technologies that could be useful to address the requirements and the problem formulation are investigated. Starting with an analysis of the existing literature on the field of big data analytics, this research also covers the recent trends of machine learning and artificial intelligence. To avoid reporting a too big literature, the topics reported have been accurately chosen, for instance describing only the techniques for web analytics and text analytics. This literature on big data analytics is further broken in two sub-topics, one more theoretical, which classifies the types of analytics methods and defines the technology of machine learning and natural language processing, including the last paradigms of deep learning and reinforcement learning, and one more practical, where guidelines for the design, development, and implementation of machine learning techniques are proposed. It is here that a theoretical framework to systematically reflect on the challenges of the field of big data analytics has been identified. This framework is then used to systematically collect the main technological challenges of the use case under analysis and translate them into non-functional requirements. Finally, the last part of the literature describes what a web-crawler is and what web- crawling/web-craping means. This later extends to the concepts of focused web-crawling and smart, intelligent, adaptive web-crawling, where machine learning techniques are deployed to improve performance. The literature concludes by providing related works of machine learning techniques implemented in smart web-crawling of the e-commerce websites and stating the knowledge gap that needs to be bridged to address the use case under analysis. After the application domain and the literature review, the knowledge from these previous phases combines in a continuous iterative process according to the design science methodology (Hevner, 2014). Through unstructured interviews with the DCA and IBM experts, the requirements elicitation is carried out. The approach by Armstrong and Sage (2000) deriving from the field of systems engineering is used. The main objective of the system to be developed is broken down into a series of sub-activities that must be carefully structured to formulate the requirements. About the non-functional requirements, instead of reflecting on the different domains – technological, environment, law compliance, etc. – as it is proposed by the same systems engineering approach mentioned earlier, this research uses the framework identified in the literature review about the main challenges of big data project (Sivarajah, 2016). To derive the components of the architecture from the requirements and customer needs, the methodology proposed by Suh (1998) called Axiomatic Design has been used, mapping the requirements into architectural components in a rigorous manner. In this way, the design domains proposed by this methodology – customer, functional, physical and process domains – are taken as the reference point for the design process: first, the business needs are identified, then these are translated into requirements, which are mapped into design features. The process domain is left out of this research and will be addressed by the IBM development team in Ireland. The design cycle leads to the design of a web-crawling system represented through a service- oriented architecture (SOA). Its block diagram and black-box description of each application service are provided. Furthermore, the architecture functionality is described with an architecture walk-through and a sequence diagram in the unified modeling language (UML). The result is an innovative real-time web-crawling system to identify the value of a given product on the e-commerce websites. It deploys natural language process models to filter the non-relevant search results, and other machine learning models to best matching the remaining relevant results with a given item description. The design and architecture description of this innovative web-crawling system is the main artifact of this research, while the mixed methodology of systems engineering methodologies and big data frameworks is another important scientific contribution.

Research shows that CIOs of both public and private organizations are searching for new ways to support better outcomes of their data-driven decision-making. As linking external data with internal company data can result in interesting new insights and value, CIOs are increasingly developing IT strategies that involve the real-time use of external data. One of the innovations that is being adopted in real-time to enable better informed decision-making is external Big and Open Linked Data (BOLD). Combining internal company data silos with external BOLD in real-time helps in the creation of these real-time insights, such as new relationships, patterns, or more context to existing data that was not yet useful. These new insights can be used for better decision-making on a variety of problems and opportunities, such as strengthening customer relationship, better pricing, building a better product market fit, expanding products and services, and managing risk. However, the adoption of external BOLD addresses several adoption barriers that hinder the real-time integration of external BOLD with internal company data. The presence of adoption barriers show that there is a lack of alignment across the different levels of an organization and between the organizations concerned with the data integration. A common practice to align a new strategy and its implementation across the organization is Enterprise Architecture (EA). Countless Enterprise Architecture Frameworks (EAFs) are in existence, but none of them seems to focus on aligning an organization with its business ecosystem. This study shows that the foundation of EA is not capable of satisfying the external mindset of organizations and that an extended approach is required to align a new strategy between organizations. Therefore, this thesis answered the following main research question: How can organizations enable the real-time integration of external Big and Open Linked Data with internal company data for decision-making? This thesis used a Design Science Approach to develop an extended EAF that enables the real-time integration of BOLD for decision-making. Throughout this thesis five sub-research questions are answered to derive to the answer of the main research question. First, two systematic literature reviews are conducted to explicate the research problem by identifying the adoption barriers of BOLD and the limitations of the foundation of EA. Secondly, explorative expert interviews and qualitative data analysis are conducted to define the requirements for an extended EAF that deals with the adoption barriers and enables the real-time integration of external BOLD with internal company data. Thirdly, several systems engineering techniques (Morphological chart, Best-of-class chart, and brainstorm sessions with creative methods) are used to transform the requirements into an extended EAF. Lastly, the extended EAF is evaluated on quality and suitability through a ‘Plan Evaluation’, which resulted in potential improvements of the design. The findings are synthesized for answering the main research question. To explicate the misalignment across organizations and between organizations, the different barriers that hinder the real-time adoption of BOLD are identified. This resulted in a conceptual model of adoption barriers of BOLD related to information quality (data absence, data quality, a lack of meta-data), task complexity (task handling issues, lack of standards and different sources), technology (Lack of supporting infrastructure, legacy systems, data fragmentation), use & participation (Lack of knowledge, implementation issues, constraints, lack of acceptance), legislations (privacy, security issues, and permits and license issues) and governance (Policy issues, Scalability Issues and a Lack of Data Governance). If organizations want to use external BOLD for decision-making in real-time, no adoption barriers can be present. This study shows that the foundation of EA can align a new IT strategy and its implementation across the organization, but not of aligning a new strategy between organizations and its ecosystem. This means the practice can mitigate the adoption barriers that arise across an organization can be overcome, but not the adoption barriers that arise between organizations. This thesis shows that a general transformation from a system of record to a system of engagement is required to adapt to this external mindset, which is key part of the paradigm shift that comes with an organization’s digital transformation. Modern organizations should embrace the system of engagement, including the company’s ecosystem as part of their Enterprise Architecture Framework. Hence, this thesis presents the ‘Ecosystem Architecture Framework’ that facilitates this transformation and has incorporated the required capabilities to mitigate the adoption barriers that arise between an organization and its ecosystem. The Ecosystem Architecture Framework provides guidance for organizations to implement an IT strategy that involves the real-time integration of external BOLD on an institutional, technological and transactional level. First, the framework provides a proactive approach that initially deals with the potential adoption barriers. Secondly, the framework enables collaboration between the data users and BOLD providers by establishing strategic alliances with mutual incentives to mitigate the adoption barriers. Thirdly, the framework allows organizations to involve all stakeholders concerned with the BOLD integration in the design of the system. By developing a shared architecture vision and architectural requirements alignment between the organization and ecosystem is created. Fourthly, the framework enables organizations to initially link their architectures through a direct real-time connection. This study demonstrates that organizations need to invest in a hybrid integration platform (e.g. an API Management Platform) that facilitates that organizations can easily integrate and share integration services (e.g. APIs), different applications can be linked together, collaboration between applications is possible, and orchestration of the applications by a single interface. Fifthly, the framework looks beyond the technical implementation and ensures that an inter-organizational model developed to make the architecture working. The inter-organizational model consists of shared artifacts that make sure the same way of working is implemented within the business ecosystem. This can be achieved by developing uniform standards for data, meta-data, technical information about integration services; A common data model that ensures one data model, unified schema and semantics are used within the business ecosystem; An independent API policy that ensures that each external data provider can be internally linked to the architecture by conforming to this policy; and institutions between the organizations that enforce the same way of working to enable sustainability of the data. Lastly, the framework ensures that the architecture keeps running by adapting to changes of the environment. Therefore, it is important that organizations develop a governance mechanism that ensures the system is scalable, maintainable and sustainable. It is important that the inter-organizational model and shared artifacts of the ecosystem are managed and adapt to changes of the environment to keep the architecture running. The outcomes of this research have both scientific and societal relevance. The former is achieved by adding the following contributions to science: new adoption barriers of BOLD; a critical assessment of the foundation of EA and EAFs; the required capabilities to mitigate the adoption barriers; and an approach that can be used to enable the real-time integration of BOLD with internal company data. The latter is achieved by providing a validated approach that can help to increase the success rate of data-driven strategies, which is relevant for national governments and organizations that want the data-driven strategies to succeed. The thesis also has some limitations. Firstly, the Ecosystem Architecture Framework is only validated trough a plan evaluation, which is a ‘design on paper’. Secondly, the framework is dependent on the TOGAF-ADM and can only be used as design principles without TOGAF-ADM. Also, the report has become very lengthy, as the research covers many different research methods. For future research, it is recommended to also validate the design through a ‘product- and process evaluation’ that investigate the implementation issues and effects in a real-world context. Secondly, it is recommended to do future research on additional capabilities to create an EAF that can be used independently. Thirdly, an in-depth study on the specific capabilities is recommended to investigate the different design patterns for incorporating the capabilities. Fourthly, it would be interesting to do research on engaging the Ecosystem Architecture Framework in different industries with different organizations part of different ecosystems and evaluate the differences.

To address complex societal issues, cross-sector partnerships are needed that specifically aim to create value to address such challenges. Data collaboratives are initiatives that encourage and promote such partnerships, by the collection, sharing, or processing of data. Data collaboratives are faced with barriers that complicate collaboration between data contributors and data users. As a result, the potential of public value creation is not being reached. To overcome these barriers, decision-makers in data collaboratives need a better understanding of the Trusted Data Intermediary as a coordination mechanism. Trusted Data Intermediaries are entities entrusted with enabling data transactions between data contributors and data users across sectors, thereby generating value for collaborators, and enabling the creation of public value. Currently, academic knowledge on how these intermediary entities is absent. By a qualitative, exploratory multiple-case study, descriptive business models of six cases are developed and analyzed. From the analysis, it was found that Trusted Data Intermediaries implement different business models, depending on their characteristics in specialization to specific segments of data contributors and users. Further, the implementation may depend on the profit motive and chosen centrality of data storage. Based on these dependencies, two Trusted Data Intermediaries' business model archetypes are developed: the Generic archetype and the Specialized archetype. In addition, variations to these archetypes are discussed. As the two archetypes offer an initial theory on Trusted Data Intermediaries, next steps may include the testing of the archetypes on more cases, as well as extending the theory for Trusted Data Intermediaries with other characteristics.

Machine learning applications are increasingly being implemented in socio-technical safety-critical systems, but the safety of these applications is not well understood. This thesis used a mixed-method design and applied four methods to explore the field of machine learning and safety. With the results of these four methods, a framework was constructed for the risk analysis of machine learning applications. All findings were synthesised in two ways: by drawing conclusions about machine learning capabilities, and by drawing conclusions about organisational capabilities. This approach provided a way to think conceptually about how far we can take machine learning to increase safety in socio-technical safety-critical systems.

PROBLEM The business and marketing environments of today are rapidly changing due to several trends in society and in the market (Kotler & Keller, 2016). To maintain their position, companies must continually renew their knowledge management systems. The emergence of information technology (IT)-based intelligence systems suggests more possibilities for gathering knowledge than ever before. These possibilities can help to better meet the information needs of marketers in corporate setting. Sparked by implementation of such technologies in the business-to-consumer (B2C) marketing environment, business-to-business (B2B) marketers of the case firm (an IT consulting company) were interested in the possibilities that implementation of state-of-the-art IT based intelligence systems would offer for meeting their information needs. The objective of this research was, therefore, to explore the possibilities of intelligence systems that could add to the marketing information system of B2B marketers. This resulted in the following research question: How should an intelligence system look like to effectively add to the B2B marketing information system of an IT solution consulting company? The literature suggests that differences exist between B2B and B2C marketing. These differences point to different intelligence needs in B2B, which should be met with a different marketing information system. However, literature on the specific intelligence needs of B2B marketers and their marketing information system was not available. Furthermore, the existing literature provides enough knowledge on intelligence systems, but lacked on specific knowledge on IT based marketing intelligence systems, let alone marketing intelligence systems designed specifically for B2B marketing. Based on the available literature on overall intelligence systems, a framework was made that combined several methods and possibilities for intelligence systems. By adding challenges for implementation, this framework could help in determining how an intelligence system for specific purposes would look like. The identified gaps of the literature, combined with the challenges for implementation for intelligence system provide an overview of what information is needed about the B2B marketing environment to answer the research question. METHODOLOGY The information needed for answering the research question was extracted by means of an embedded case study on an IT consulting company. Several units of analysis were distinguished, based on general approaches to B2B marketing. In-depth semi structured interviews with the marketers within the case company were used to gather data. The topics were based on the information gaps in the literature, combined with knowledge from several resources about the marketing environment such as observation, available documentary and introductory interviews. The interviews were transcribed and coded using specialized software (Atlas TI). The analysis focused on finding relationships between key concepts found in the interviews.   FINDINGS By using this methodology, three phenomena were found. First, by linking the available marketing information system with the marketing needs that were suggested by the marketers, gaps could be identified, which could be used in the development of an intelligence system tailored to the needs of the case company. Converging evidence was found between the identified units of analysis suggesting the existence of an intelligence gap for knowledge on the people working at their customers. Specifically, the B2B marketing information system does provide information on customers, however does so on (too) high (read: general) an abstraction level. The B2B marketers indicated a need for information on a lower (read: more personal and precise) abstraction level, specifically: interest, hobbies, roles, activities, how they are linked together and whether they are part of a decision making unit. Second, the interviews also produced a list of potential data sources for an intelligence system. Scoping these sources towards the needed intelligence suggest that an intelligence system should make use of user-generated content (i.e. social media, blogs and forums), online published interviews and surveys. These sources might contain relevant information, but they are in textual unstructured format. Finally, the preferences for presenting the intelligence were extracted from the marketers. The marketers would prefer intelligence presentation to be implemented in other systems. They would like to have a search option, and they preferred easy to use display of the intelligence. PRACTICAL IMPLICATIONS Based on the found phenomena, choices could be made using the framework for intelligence system implementation based of the literature. The proposed intelligence system design was built from the following layers: (1) the intelligence system will use user-generated content (UGC) (i.e. social media, blogs and forums), online published interviews, and surveys as data source. (2), Data will be extracted from these sources using natural language processing methods such as named entity recognition, relationship extraction and sentiment analysis. (3) The extracted data will be stored in databases. (4) OLAP servers will be used for slicing and dicing of the data. (5) The data will be presented as intelligence by use of searchable relational graphs. This form of presentation could be implemented in dashboards of other systems such as the marketers’ CRM system. The intelligence system can add to the marketing information system by providing valuable insight on customers on a lower (read: more personal and precise) abstraction level. This form of intelligence is useful to the marketers, but is not currently provided decently by existing B2B marketing information systems.

Facing the age of digitalisation, inspectorates are changing their way of working using a risk-oriented and data-driven approach. Social Network Analysis (SNA) seems to be promising in detecting fraud and simultaneously contributing to increasing the effectiveness and efficiency of inspection work. SNA emphasises the structural aspects of networks to detect and interpret patterns of social entities, both graphically and mathematically. Although the emergence of big data opens great opportunities in the public domain and the benefits of using SNA in fraud detection seem to be clear cut, a more institutional view challenges the assumption that simply working data-driven leads to better deployment of enforcement assets. In practice, the way in which business value is created from big data often remains unclear. There seems to be a gap between the promises of big data and its practical realisations, in particular in the public domain. Therefore, this research considered big data in the context of SNA from an institutional perspective. This means it is assumed that actors that shape the process from data as a raw material to the final deployment of inspection capacity based on the outcome of the analysis. This research underlined a decision-making perspective which states that the way in which the alternatives are framed impacts the alternative chosen by people and in turn the subsequent decision. By using a qualitative research approach consisting of a multiple-case study design combined with action research this research contributed to two main purposes. First of all, the study had a functional purpose aimed to explore the use of SNA in fraud detection, more specifically fraud in the context of food and consumer products. Following from the analysis of two large real-world data sets, it turned out that network visualisation offers a powerful solution to make information hidden in networks easy to interpret and understand. With one glance at the network one could identify who does business with whom, which entities act as bridges between two clusters, trace suspicious patterns, and gain insight into the overall structure of the networks. Applying network metrics helped to quickly identify the important players in the networks and could be used to evaluate or predict the possible consequences of removing specific actors from the networks to destabilise the networks. Secondly, the research had an institutional purpose aimed to get insight into big data value creation in the public sector. During the research, it became evident that important assumptions and decisions have been which appeared to be fundamental for the outcome of the analysis. This prevented the creation of options or led to options that were sub-optimal. Neglecting them would be at the detriment of any SNA-ambition an organisation may hold. As the research into both areas is still in a very young state, the findings from this thesis form a starting block for other studies to expand on. Future research will be addressed to widen the empirical evidence on how big data affects public decision-making.

The incidence of natural disasters worldwide is increasing. As a result, there is a growing number of people in need, whereas limited resources to help these people are available. It is therefore important to effectively and efficiently prioritize the most vulnerable people in the preparedness phase, and the most affected people in the response phase of humanitarian action. Data-driven models have the potential to help in doing so. However, to be able to apply these models in a country, a certain level of data preparedness is required. Therefore, there is a need to understand how to facilitate, stimulate and coordinate data-sharing between humanitarian actors. To realize this, a generic governance structure is needed that can facilitate the achievement of this level of data preparedness on a large scale. This thesis aims to develop this generic governance structure using a data ecosystem perspective, by providing insight in the success criteria for establishing a ‘humanitarian data ecosystem’. As no scientific literature exists on this topic, as a first step, a general framework with data ecosystem governance success criteria is developed by means of a systematic literature review. Subsequently, the applicability of this framework in the humanitarian sector is assessed through a case study on the ‘Community Risk Assessment and Prioritization toolbox developed by 510 Global, the data team of the Netherlands Red Cross. It is found that the data ecosystem approach provides a suitable framework for assessing the criteria to be addressed when aiming to establish a successful humanitarian data ecosystem. Moreover, the empirical findings resulting from the case study led to the creation of a generic governance structure to be used when determining on the approach to adopt when rolling out the Community Risk Assessment and Prioritization toolbox in a new country.

During the last decade, two developments on the Dutch housing market have received a lot of attention. One of these developments concerns the recovering house prices and specifically the strong price rises in cities. The other development is related to the growth of the private rental sector and particularly the buy-to-let market. This market consists of the private rental dwellings owned by reasonably small private landlords. These landlords predominantly buy owner-occupied dwellings to rent them out, hence the name buy-to-let. The growing buy-to-let market and the recent price rises have often been linked to each other. Some people claim that the buy-to-let landlords are driving up the house prices. Nevertheless, the relation between these two developments has not been properly studied yet for the Netherlands. Consequently, the objective of this study is to gain more insight in the Dutch buy-to-let market in general and specifically the relation between this market and the regional house price development. Mostly quantitative research methods are applied to answer this research question since especially quantitative information on the Dutch buy-to-let market is missing. House price models have for instance been made for Groningen, Breda and Zoetermeer. The research shows that the Dutch buy-to-let market is still quite small as the buy-to-let dwellings only comprise 5% of the total Dutch housing stock. By comparing the house price development of different municipalities with a varying share of buy-to-let, it can be seen that the recent house price increases are on average stronger for the municipalities with a high share of buy-to-let dwellings. It is however possible that these stronger increases are caused by other factors than buy-to-let. When comparing the results of the three regional house price models no big differences in the drivers are found. The results show that the determinants for the house price development are, despite the varying share of buy-to-let dwellings, in general the same for the three municipalities. Furthermore, a buy-to-let indicator could not explain some of the remaining unexplained variance. Based on these results, it can be concluded that none of the results actually point at an inflationary effect of buy-to-let investments. However, because only three municipalities were studied, no general conclusions can be drawn and the possible inflationary effect of buy-to-let landlords cannot be completely excluded.

A nation’s main port is a crucial component that contributes to the economic development of a country. Therefore, the role of the port becomes more critical nowadays. However, due to the existing hub-and-spoke structure of the European multimodal transportation network, polarisation and regionalisation are visible in the network. The increasing regionalisation and polarisation that happens in the European region widen the inequality in the network. Moreover, the inequality in the network will impact the sensitivity and resistance of the whole network to targeted disruptions, such as infrastructure failures, natural disasters, or potential terrorist attack. Then, on the real aspect, the widening of inequality in the network also inhibits the well-distributed economic development in the region. In this study, various methods will be employed to have multiple points of view of the network itself, such as the multilayer network analysis, community detection, and Network-based Hub Port Assessment (NHPA). Furthermore, the study will be structured into three building blocks that shapes as a foundation to perform the final analysis and provide a multifaceted overview of the overall network, which is versatility analysis, community structure analysis, and quantification of collaboration-connectivity of the container hubs. Afterwards, three determinants as measurements of network performance are defined, to perform the criticality analysis. This study concludes that the development of connections between different communities can help to decrease the existing inequality in the network. Then, the appearance of new community structure in the network can help to decrease the inequality in the network, which lead to more well-distributed economic development in the region.