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.

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.

Monitoring organisations are currently in a transition phase towards risk-based inspections using data models and algorithms in order to increase their efficiency and transparancy. The use of data by monitoring organisations has several benefits, but can at the same time have negative effects in the medium- to long-term. This study explores these effects when monitoring organisations choose to perform their inspections based on data collected during their own inspections.

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.

Malicious software such as botnets are a threat to society and increasingly so through Internet of Things (IoT) devices. The large volume, pervasiveness and high vulnerability of IoT devices make them low hanging fruit for malicious actors. Currently, the biggest threat for insecure IoT devices is Mirai, a botnet which is deployed for DDoS attacks. Home users often fail to detect and resolve Mirai on their IoT devices. For this reason, Internet Service Providers (ISP) increasingly take efforts to increase remediation. Sending their infected customers a notifications containing cleanup instructions is currently the most feasible measure on a large scale. However, previous studies point out that it is not clear how people process these notifications, if they comply with it and how this effects the remediation rate and speed. The central research question of this study is ‘What is the role of IoT device end users in Mirailike bot remediation?’. We have conducted an eight-week experiment at the KPN Abuse Desk that notifies customers about abuse incidents. 177 Mirai-infected consumers have been randomly assigned to a walled garden notification (i.e., a quarantined environment), an e-mail notification, or control group. All subjects within the experiment have been tracked for two weeks to estimate the infection time and are contacted afterward for interview purposes. Male consumers and consumers younger than 54 years possess relatively more often a Miraiinfected device compared to other consumers. Both e-mail and walled garden notifications are effective in reaching consumers, informing them and encouraging them to take action. The majority of consumers do not follow the recommendations provided by the notification. In contrast, the number of actions that are performed while not mentioned in the notifications is remarkably high. Since many consumers asked for additional help, we conclude that consumers appear don’t have a full understanding of how to tackle the problem. In the control group, several consumers remediated Mirai unintentionally. However, these cases do not explain all observed remediation. Using two survival analysis modeling techniques, we find that consumers placed in a walled garden have a 29% to 85% shorter infection time than other consumers. We conclude that there is a discrepancy between stated behavior and the actual behavior of consumers. Although we cannot observe all cleanup efforts of consumers, we observed that awareness of the Mirai-infection and the intention to comply with the recommended actions influence that unobserved behavior. Gender also influences the unobserved behavior. Women clean up their device quicker than men while their statements during the interviews contradict this. One explanation is that women may unintentionally clean up their device. We conclude that age, consumer market, device type and customer satisfaction have no significant influence on remediation. We believe that it is unlikely that all unexplained remediation can be attributed to the unobserved behavior. We thus cannot explain all observed remediation from the user perspective. Therefore, we argue that future work must also focus on the attacker perspective. Since we only observed Mirai-infections, we cannot exclude the possibility that competing malware confiscated infected devices within our experiment. In addition, novel Mirai variants may have evolved scanning behavior which obstructed proper detection of infected bots.

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.

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.

This thesis deals with the current debt crisis is Sri Lanka, which was brought about by rapid development following the end of the civil war, and how best to prevent future debt crises by providing recommendations to policy makers. This thesis makes use of two models, the Balance-of-Payments (BOP) Constrained growth model, and the Flying Goose Model (FGM) of development which was observed in East Asian countries during their fast-paced growth period. The BOP model is used to identify industries within Sri Lanka’s economy which have a high income elasticity of demand by the rest of the world. The results of the data analysis find that Sri Lanka has a high income elasticity of demand in the machinery and chemical sectors with in the manufacturing industry. The Sri Lankan economy is also diagnosed in the context of the FGM to understand in what stage of the FGM Sri Lanka is in, by means of carrying out non-parametric analysis on the FDI per capita, the trade dependence ratio, the revealed comparative advantage index and other economic parameters. From the evidence presented by the two models, policy recommendations go in the direction of promoting selective industrial policy, in order to enhance the growth of these targeted industries identified by the BOP model through means of accelerating the Flying Goose Model of development.

The extensive amount of knowledge possessed by an organization represents one of its most valuable resources. Nonetheless, it is not always easy to pinpoint, arrange and reuse such knowledge in an effective and productive manner. Moreover, in the last two decades companies have been increasingly using serious games and simulations for the most diverse scopes. While a consequent interest has been growing in the field of knowledge management regarding how to codify and reuse the information generated by these new innovative tools, there is still no clear understanding of what are the games' requirements which are responsible for the generation of knowledge. In addition to this, many theories can be identified in the available literature which developed methods and models for knowledge conversion and sharing; however, none of this models has yet been applied to the context of serious games and simulations.
As a consequence of this recognized gap in the available literature, as well as of the need for organizations to develop a series of knowledge management systems and practices for the sharing and reusing of knowledge generate through games, this thesis research aims at understanding what are the necessary requirements for games to foster the development, as well as the reuse, of tacit knowledge.

Primary scope of this thesis project has thus been to determine what games' elements represent a fundamental requirement for the generation of both explicit knowledge and tacit skills. In addition to this, the research has taken into consideration different groups of actors (game designers, participants/players, project team members, managers), and attempted to verify whether any dissimilarities exist among these groups in the way of perceiving games' requirements, and their impact on knowledge creation and sharing. Finally, in the light of the obtained results, suggestions have been proposed on the most appropriate knowledge management systems and procedure to use in the context of serious games and simulations.

The research has been carried out in the form of an embedded, single-case study: the main unit of analysis - and client - was ProRail, the government organization which manages the maintenance and extensions of the national railway network infrastructure, the allocation of rail capacity, and the traffic control. The embedded units of analysis were instead represented by different games employed within the organization (OV-SAAL, Ketensimulatie, ERTMS). Once the games have been identified, other research methods have been applied within the case study framework, in particular: semi-structured interviews and Q-sorting.

Concluding this dissertation report, recommendations for ProRail - the customer of this thesis project - have been outlined, by combining the obtained insights with theories from available literature. The scope has been to define suggestions for a knowledge management framework for serious games and simulations. The suggested framework, result of the joint efforts of different TU Delft researchers and ProRail's employees, is presented in the second-last chapter of this dissertation in the form of a set of recommendations for ProRail.

The last decades, the role of data in society has increased rapidly. The Dutch regulation agencies have to embrace this age of digitization. It provides challenges, as the inspection work becomes more complex. It also provides opportunities, because data analysis could make the inspections more efficient. One of these inspection agencies is the NVWA, the Netherlands Food and Safety Authority. The NVWA strives to work risk-oriented, and expects data analysis to be an important tool to achieve this goal. However, most inspectors have little knowledge on data analysis. Thus, a new type of employee needs to be contracted: the data analyst. The NVWA wants the data analysts and inspectors to work together and share their knowledge. Together, they should create a risk model, which predicts where inspections should take place. These kinds of risk models are expected to change the inspection landscape. However, most of the knowledge of data analysts and inspectors is tacit, which means that their knowledge is rooted into intuition. This makes it hard to transfer to others. To solve this problem, the following research question has been used during the research: How can synergy between multiple sources of knowledge be created and utilized to achieve risk based regulation?

Venture capital (VC) and startups are a natural duo. While startups create disruptive and incremental innovations with their technical and creative expertise, many lack the financial resources to grow their company. VC funds provide this needed finance – in addition to business acumen – in exchange for an equity share of their company, with the hope that this risky investment will produce a significant return. Though the importance of adequate VC stimulation in the startup community is well discussed and agreed upon by many studies, as a theoretical concept, ecosystems of VC remain underdeveloped, making it difficult to comprehend how a VC ecosystem works, and more importantly, how to improve one. Nations hope to improve their VC activity and therefore their global competitiveness, by consulting the current VC literature and other comparative VC publications (scoreboards, case studies and regional benchmarks), but these do not approach VC from an ecosystem perspective, which might explain the discrepancy in their results and conclusions. The absence of an ecosystem perspective in VC literature, results in a lack of a holistic understanding of the mechanisms of a venture capital ecosystem (VCE). Consequently, this shortage of comprehension might explain why current studies do not provide the reasons for the shortcoming of the Dutch VCE. This is the first study to take the ecosystem perspective of the entrepreneurial ecosystem (EE) theory in the field of VC and seeks to answer the following main research question: How can the Dutch venture capital be improved through a conceptual model, which explains the determinants influencing the development of a venture capital ecosystem?

A distribution network is the pathway that all products and services go through before reaching the end consumer. It consists of interdependent institutions which perform tasks such as inventory carrying, generation of demand and physical distribution. The physical distribution has direct impact on both supply chain costs and customer service and is therefore a key driver when it comes to overall profitability of a firm. The design and management of a distribution network is crucial for companies in order to maintain competitive advantages.
Grocery retailers often have complex distribution networks with many distribution centres and different temperature requirements depending on the goods. The format of the stores varies as well in terms of size, range of products and sales volume. Therefore, the way grocery retailers operate, their retail structure, distribution standards and service requirements differ greatly. Making a choice about appropriate ordering and delivery schemes for grocery retailers is not an easy task. Many factors should be considered such as shelf life of products, their sales patterns and their limited shelf space. Moreover, all possible distribution contributions should be considered. This is, however, not considered to full extent in the literature on the subject so far.

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.

Battery electric and fuel cell electric vehicles have the potential to cover the shortage in renewable power generation by engaging in vehicle-to-grid. However, the vehicle-to-grid service cannot completely make up for the intermittent nature of renewables. Deterministic models were used to compute the extent to which the vehicles can engage in the vehicle-to-grid service in a smart city domain using the ‘Car as Power Plant’ model. The extent to which the vehicles can provide grid support in terms of energy valley filling is dependent on the method of selecting the vehicles for vehicle-to-grid and the nature of the load demand. Constraining algorithms limiting the extent of refuelling and recharging of the vehicles can help curtail import of hydrogen and power and spread the demand more evenly across the timeline, but also increase the waiting times during the same. An aggregator while coordinating vehicles for the vehicle-to-grid service may encounter some conflicts of interests with respect to ensuring equal vehicle-to-grid participation amongst its customers and investing in the supporting energy infrastructure. The setting of a minimum threshold fuel requirement for participating in vehicle-to-grid strongly relates to the effectiveness of the vehicle-to-grid service. There are some barriers for the adoption of vehicle-to-grid adoption such as competition from stationary batteries and its unreliability that is limiting its uptake. Additionally, the lack of mass uptake of battery electric and fuel cell electric vehicles has not yet got the market participants interested to invest in the vehicle-to-grid technology. Optimal smart charging strategies must address a variety of variables such as the solar hours, hourly grid prices, peak hours surcharge, charging infrastructure and congestion management. Many of the variables associated with smart charging are conflicting in nature and it sheds light on the multi-actor optimisation role of an energy aggregator.

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...