This thesis examines the competitiveness of renewable energy technology compared to conventional power generation methods by examining the relative cost of solar PV, wind, and coal. The findings of this analysis seek to answer the primary hypothesis: The unique application of Levelized Cost of Electricity (LCOE), entailing the manipulation of CAPEX and CO2 variables, reveals the points in time and the exact LCOE values where it becomes cost competitiveness to switch from a coal asset to a renewable asset within the context of the German electricity market. The results illustrate that cost competitiveness between new renewables (solar PV, onshore wind, and offshore wind) and coal in the German electricity market does cross at certain points. The research seeks to determine the precise scenarios under which new renewable plants are more cost competitive than existing coal plants. The LCOE calculation has been chosen as the methodology to estimate the relative cost competitiveness of these differing power generation methods, under predefined scenarios for both CAPEX-reduction for renewables and CO2-price increase for coal. New assets are compared to existing assets; therefore, LCOE has been chosen in order to compare operational costs (OPEX) plus the capital cost (CAPEX) of new assets, with only the operational cost (OPEX) for existing assets. i.e. the investment cost for coal-fired assets is denoted as a sunk cost. This allowed for the development of a simplified framework to analyze an electricity-producing firm involved in an investment decision, whether a new solar or wind project or the continuation of an existing coal power plant...

This master thesis explores to what degree information about a company's commitment to privacy can be extracted from a privacy statement. To do this, firstly a new database of more than 1500 privacy statements is created using Amazon Mechanical Turk. Next, 72 different aspects related to privacy of the statements are enumerated by using natural language processing. Multiple analyses are run on this data to look for signals which can indicate commitment to privacy. Ultimately, the analyses result in a number of ways which the quality and completeness can be assessed, and a method to find out the degree to which a privacy statement has likely copied other statements. This can be useful for privacy regulators in identifying companies which are less likely to comply with existing privacy regulations.

The EU General Data Protection Regulation (EU GDPR) is about to come in force in May 2018. It poses new queries for both policymakers and businesses. Policymakers want o know how effective the new EU GDPR will be while the businesses would like to know how the EU GDPR should be implemented. To answer that question, empirical studies on how businesses/organizations implement privacy and data protection as well as their perception towards the EU GDPR are needed. This thesis aims to answer the fill this gap by mixed methods. Literature review and interview are exercised to get the current practices, while survey and statistic analyses are done to investigate the organizations' plans to change related to the EU GDPR. The findings are interesting, which include but not limited to the existence of DPO and organizations' high dependency would not limit the organizations' plan to change. Based on the findings, a number of recommendations are formulated for both policymakers and the practitioners, such as the encouragement to designate a DPO, to specify different approach of enforcement by policymaker for different industrial sectors and to be open to having menu of contracts by organizations for a balanced flexibility.

Privacy and security are perceived as of great importance. However, readily available Privacy-Enhancing Technologies (PETs) and their development, innovation and standardization is not very forthcoming. Indeed, the dominant business model is privacy invasive: gathering and using private data as part of the business model to safeguard continuity. Meanwhile, users want their digital privacy and security safeguarded. The research strategy consists of desk research (analysing transcripts and the literature) followed by a survey to quantitatively analyse and find drivers and barriers of Privacy-Enhancing Technology adoption and diffusion. The research objective is to discover what managerial and policy recommendations can be stated in order to help PET producing organizations to increase the adoption and diffusion of PETs in general.

One of the key challenges of adopting cloud computing is related to trust. Trust is necessary for effective collaboration between customers and providers and reducing transaction costs, however, in practice this trust is often lacking. From the scientific literature, it becomes clear that there is still a lot of ambiguity about trust in cloud services and that it is unclear which factors influence this trust. Hence, the research question is defined as: which factors influence the trust of an organisation in a cloud service? In order to answer this, first, an analysis of the concepts of cloud computing and trust, and the integration of those concepts in scientific literature is performed. From this analysis, a conceptual model is derived. This conceptual model is then evaluated with a Q-method study on the potential factors influencing trust in cloud services. In general, security, privacy, transparency and the willingness to change the organisation are found to be important factors. Besides the general viewpoints, there are also viewpoints specific to the three perspectives. Techno-optimist perceive technological advantage and interoperability as important, responsibility-shifters perceive contracting and accountability as important and the operational-conservatives perceive technological advantage and reliability as important. The OPF Framework as proposed in this thesis uses these perspectives in combination with the different parts of the organisation: strategic apex, cloud service management, operational IT department and cloud experts. By addressing the main concerns of the specific part of the organisation, in combination with the relevant perspective, it is possible to improve the trust of an organisation in a cloud service.

The Netherlands is becoming increasingly diverse, amongst other things, due to ethnicity, religion, and urbanity. These diverse societies cluster in districts all across the Netherlands. There is an increase in tension between social groups, especially between people with and without migration background. Data analysis is used to measure the public opinion of each district in the Netherlands based on election results. System dynamics is used to model the public opinion over time and the polarizing behavior within districts. Polarization is a thought construct and does not need physical evidence to occur so the public debate plays a large role because it creates fuel for polarization. Even though there are national trends regarding public opinion, it is advised to look at a district in specific to understand why polarization may occur there.

Organizations share data for collective purposes: new opportunities are created to allow business enhancement. While new businesses contribute to economic development, valid reasons exist to inhibit data sharing (e.g. citizen privacy and sensitive information). Multi Party computation (MPC) provides a solution to these risks. However, MPC implementation remains limited, and we lack knowledge about the willingness to use MPC-enabled applications in organizational settings. The objective of this study is to investigate the effect of MPC on organizational willingness to contribute protected data for collective purposes. We ask: "To what extent does MPC affect organizational perception of the contribution of protected data?" From the quantitative assessment, MPC enhances organizational perceptions of data contribution and therefore significantly increases perceived trustworthiness and perceived security. Both of these aspects are found to be important and of approximately equal importance when considering contribution of protected data. That is, both are considered as the locus of willingness to contribute protected data through a web-based application. From the qualitative assessment, it is assumed that the positive contribution of MPC herein is because it allows data contribution independently from conventional data processors, which typically have access to raw data. The extent to which MPC increases perceptions depends on the extent to which an organization is able to assert the trustworthiness of the application and the security measure used by the application. MPC also affects perceived relative advantage. A weak correlation is reported between perceived relative advantage and willingness to contribute protected data, suggesting that relative importance is not perceived to be important as perceived trustworthiness and perceived security with respect to willingness to contribute protected data. Nevertheless, MPC also seems to enhance perceived relative advantage. Finally, although the relative advantage of MPC was not perceived as necessary, several findings are reported to further enhance the utility provided by an MPC application.

This study aims to provide insight into how factors relating to privacy and incentives influence people's willingness to participate in sharing their vehicle based sensing data with governmental parties for the purposes of improved dynamic traffic management in the Netherlands. Through the use of a stated preference experiment data is gathered in order to estimate a discrete choice model using binary logistic regression. Respondents are most likely willing to share their data when trip registration is not personally identifiable and this data is not shared with third parties. Sharing of data with emergency services and for research purposes actually increases the odds of participation. Furthermore, potential users who have not been exposed alternatives which offer monetary reward are more likely to participate for free. Clear communication of the purpose and the social benefits of participation is important for obtaining sufficient levels of participation without offering monetary reward. Being parsimonious in data collection will result in the least amount of privacy harm and avoid the perception of a system as unfair and inefficient.

A portion of the digital fraud occurring on the dark web comprises the illegal exchange of vouchers, coupons, and stolen accounts, defined in this research as service fraud. Despite its existence, this type of fraud had not been previously explored. This thesis employs a quantitative approach to examine which company characteristics influence the target selection process, and the financial impact of service fraud, conducted on eight prominent underground markets, from 2011 to 2017. Initial understanding of the matter is provided by mapping out the digital fraud landscape; exploring and classifying into four categories the different types of service fraud. The direct costs of such fraud for the analyzed companies are quantified, showing that the sustained losses are relatively low compared to the figures reported in various resources. Regression analysis is used to model which characteristics make companies more attractive to cyber criminals, and how they influence losses suffered by businesses. Reputation and domain popularity are able to explain to an extent the frequency of being targeted. Furthermore, companies operating locally, as well as smaller businesses seem to experience higher financial losses. The implications of the results for businesses and society are discussed. Expanding the current model with other factors or additional data, such as employed security controls and strategies, as well as using different research methods could enhance this topic and provide more insights.

What happens when we treat our policy messages more like mythology and less like rhetoric? How can we even begin to structure policy as a mythology? What is a myth anyway? This thesis endeavors to explore the mysterious world of stories, through an extensive literature review of literary analysis, and a exemplary case study about the impact of global plastics production on the future. This study leads to an experiment, where four different forms of communication: the best of rhetoric and stories, are tested against each other for effectiveness in shifting the mood, transferring information, and effecting the audience's experience. The results show that even with a small sample size the impact of stories as effective tools of policy communication are real. They result in a drastically different message than the other forms tested, hinting at the possibility of a future where stories are central to shaping policy.

This research aims to identify what the customers’ acceptance is regarding various technological alternatives designed to prevent unnecessary apparel returns within the context of apparel e-commerce. This is done by applying a more qualitative approach and operationalization of the Technology Acceptance Model (TAM), whereby less data is required to produce reliable results. As such, a Multi-Criteria Decision-Analysis (MCDA) approach is used, wherein the novel Bayesian Group Best-Worst Method (BWM) is applied to infer the optimal group weights of the indicators (i.e. criteria) that influence customers’(users’) technology acceptance (TA). This is done within the context of apparel e-commerce and with the application of qualitative tools such as an online BWM survey and expert interviews. This research contributes to the empirical application of the novel Bayesian BWM, in the specific field of apparel e-commerce and proves that users’ technology acceptance can be predicted by applying the aforementioned MCDA approach as well.

In a period where digitization rapidly influences the corporate world, Cloud Computing (CC) has emerged over the past years as well. CC refers to offering hardware, software, and data by a provider over a network and can benefit organizations enormously regarding cost efficiency, operational excellence, and innovation. Software-as-a-Service (SaaS), a delivery model of CC, allows organizations to deploy and use complete applications over the internet, which are managed by an external provider. The use of SaaS can bring firms several benefits, such as scalability, transparency of costs, access to high-end applications, and avoidance of up-front costs. However, it is unclear how the migration to SaaS impacts the IT-department on an organization level, and how IT-departments should adapt to perform in a SaaS-environment. This research contributes to this knowledge gap by investigating which resources and capabilities of the IT-department should change for the IT-department to perform. Propositions were formulated based on a literature research and a questionnaire, and empirical data was collected by means of a case study research. The results show that financial assets, technological tools, organizational structure, management systems, skills, knowledge, organizational culture, contractual governance, and relational governance are important resources of the IT- department, that should change in order to perform. The insights gained from this study can support organizations’ decision- makers improving the organization of the IT-department when migrating to a SaaS-environment.