The focus of this work is the creation and application of an experimental framework that tests the effectiveness and the impacts of micro-targeted ads. Micro-targeted ads have been used extensively by both political and retail carriers to sway peoples’ opinion, but there haven’t been any academic studies (in an environmentwhich is not controlled) that explore the impacts and the effects of micro-targeted ads. To this end the idea is to use a measurable variable (in this case the misplaced trash in The Hague) and throw generalized (non micro-targeted) ads along with micro-targeted ads, both of them aiming to reduce misplaced trash-bags. The ads will be sent to specific neighborhoods in The Hague through Facebook. The ad itself is considered a "nudge"(an intervention that hints towards behavioral change without forcing it).

The consumer lending domain has increasingly leveraged Artificial Intelligence (AI) to make loan approval processes more efficient and to make use of larger amount of information to predict their applicants’ repayment ability. Over time, however, valid concerns have been raised about whether decisions made about individuals using these data-driven technologies can lead to bias against women. In an attempt to assess the fairness of an algorithm, 21 prominent definitions of fairness have been proposed by the computer science community over the years. However, what remains absent is consensus on which definitions are suitable for assessing gender equality in consumer lending. There is also a lack of knowledge on how to appropriately implement these metrics in practice. To tackle the problems mentioned above, this research has investigated how automated loan approval processes can be assessed for gender equality. Two essential elements for assessing predictive tools were identified and investigated through a separate research question: What fairness metrics are suitable for assessing gender equality in consumer lending? How can the metrics be applied to observe gender bias in lending history data? Based on the questions above, the research was conducted in two stages: Stage 1 focused on analyzing the prominent definitions of group fairness, but before doing so, it conceptualizes gender equality in consumer lending by conducting an extensive literature review encompassing domains of philosophy, economics, gender studies, and history. In investigating the first research question, it is found that group fairness metrics are a measure of distributive justice. These metrics are based on three different statistical criteria commonly known as independence, sufficiency, and separation and each underlie different moral assumptions which should be verified based on the application scenario at hand. In Stage 2 of this work, the second research question was investigated by conducting an exploratory case study in which a logistic regression model is built to classify a sample of loan applicants in an open source dataset. Both the dataset and the model are then tested for bias using IBM’s open source Python AIF36 toolkit. After applying the group fairness metrics, it was found that the choice of separation and sufficiency can have different repercussions for each demographic group in the dataset. When false distribution of utility is under inspection, sufficiency advantaged male applicants more than the female applicants while separation advantages males more than females. Such inconsistency highlights the importance of realizing how relevant distribution of harm/benefit depends on the choice of fairness criteria made by decision makers. Lastly, the research provides an extensive discussion on possible root causes of bias and some recommendations to managers and data stewards on how to tackle bias issues that stakeholders may face in the context of consumer lending.

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.

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.

Tracking is a pervasive mechanism across the web which is capable to trace and collect users’ online data. One of the most widely used mechanisms for tracking are cookies, and they are used to deliver online behavioral advertisement. Since these mechanisms pose a threat to privacy, the E-Privacy Directive was created as the regional legal instrument to pay special attention to this issue in the European Union. Now, we are at a crucial moment, there is a discussion to replace the E-Privacy Directive for the called E-Privacy Regulation. However, the nature of privacy policies is complex, and there is an ongoing debate about how the E-Privacy Regulation needs to be implemented or what elements should entail. This master thesis project sought to provide empirical evidence for this debate. Our findings emphasize the need to promote harmonization of the provisions of the E-Privacy Directive in member states, especially on consent and guidance. Also, our work revealed that businesses’ incentives play an important role in explaining the variability of tracking presence. Hence, this suggests that understanding the “parsimonious” factor of businesses’ incentives can be considered by policy-makers to commence an important debate and re-solve the privacy problem.

Although fully-automated vehicles (FAVs) are not yet allowed on public roads and are barely present in people’s daily lives, according to experts their wide implementation will happen within the next few decades. The technology is expected to bring major benefits in terms of safety, traffic flow efficiency and environmental impact. Furthermore, by eliminating the necessity of actively steering the vehicle, automated vehicles (AVs) are expected to bring extra free time during the day to perform activities, as well as increase mobility of some excluded societal groups (e.g. elderly). However, taking away the burden of driving might also bring negative results. The convenience of the technology might encourage people to travel further and more often. Moreover, AVs users might transfer some of the activities from the traditional environments into the vehicle (e.g. work). The associated rearrangement of daily activity plans might have an impact on travel and residential location choices, possibly leading to further increase in overall travel distance. In such way, the expected positive impact of the technology could be compromised.
For that reason, it is necessary to examine behavioural response to AVs, namely the way travel time will be used in the vehicle and its consequences for daily activity plans, travel patterns and residential location choices. In order to fill in this scientific gap five focus groups were conducted. The participants were confronted with a simplified scenario of the future after broad AVs implementation and were asked a set of questions regarding the aforementioned topics. It was found that, in contrary to the current modes, time inside AVs will be perceived not in terms of burden or opportunity, but rather in terms of pure opportunity. The participants perceive AV time as an extension of either work or private time. In context of daily activity plans it brings about a number of possible behavioral adaptations. In case of regular commuters, a willingness to use the in-vehicle time to release pressure from daily life was often identified, whereas people with less stationary jobs claim that the AV would allow them to bring more flexibility into their daily plans, or move their actual office or even the entire business into the vehicle.
However, the findings regarding daily activity plans do not provide a clear answer on their impact on travel and location decisions. At the same time, a number of advantages of AVs for irregular, long travels was identified, and many participants explicitly expressed their willingness to increase the amount of such travels. The impact of AVs on residential location choices appears to be limited: although people are not willing to move because of the AV, they claim it would still play a significant role in their relocation decisions if it was necessary for other reasons. Moreover, some AV-specific phenomena were discovered, which might have an important significance on the actual AV use: namely increased pressure to perform onboard activities and feeling of “being trapped” in the vehicle. Furthermore, the participants pointed at the striking easiness of making travel decisions in case of AVs, compared to other modes. Although the results bring a valuable indication of possible behavioral adaptation to the technology, one must also take into account limitations of the study. The most important of them is the speculative, imaginary character of the focus groups discussions, simplified future scenario and subjective character of qualitative analysis. Therefore, in the future studies on behavioural adaptation to AVs it is suggested to take a more experimental path, aiming at emulating the actual AV experience as far as possible. In that way the credibility of the findings from the present study could be further validated and extended.

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 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.