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 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.

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.

Since the start of the 21st century, the amount of captured data has been continuously increasing in this digital age. With almost 2.5 quintillion bytes of data being generated and captured every day (Liang et al., 2018), researchers and companies have a strong interest in exploring the value that can be created with this data, called big data analysis. Also, since companies strive to be market leaders, they constantly evaluate methods/approaches to discover hidden trends/ potential opportunities. One method of finding hidden trends is through market segmentation, a process which can be defined as a division of a heterogeneous market into several smaller homogeneous markets to precisely understand the desires of consumers. Identifying and targeting the right consumers through market segmentation is highly dependent on the collected data. Due to the usage of obsolete data collection methods and privacy regulations, most often, companies only possess siloed data. If siloed data is used, then companies might not be effective with their segmentation strategies.
One way to ensure that data is complete and consistent might be through data sharing in a ‘data market’ between players to holistically understand the consumers. With this thought in mind, this thesis considers the telecom industry as an example and explores the effectiveness of market segmentation using shared data. The main research question of this thesis study is Before going deeply into the aim of the thesis, let’s first understand the current problems of the telecom industry. Traditionally, telecom firms have generated revenue via three streams i.e. voice, messaging and data. However, over the past decade, the market has witnessed an emergence of Over the top content players such as Netflix, YouTube, and Amazon Prime. These players do not need any association with the telecom firms to provide their services and thereby have impacted traditional telecom companies’ voice and messaging revenue streams. In addition to this with new content frequently being updated in these OTT services, customer preferences are constantly changing, and telecom firms are finding it hard to predict these varying needs with the siloed data present in their databases. This has therefore resulted in low average revenue per user (ARPU) levels for these telecom firms. As the first step in the thesis, we performed a literature review and identified four common segmentation techniques used by the telecom industry. The four techniques are customer value segmentation, customer behavior segmentation, customer lifecycle segmentation, and customer migration segmentation. These techniques are customer-centric and are heavily reliant on data for their effectiveness. To observe if these identified techniques are employed at the industry level and to contemplate the viewpoints of experts on data sharing in market segmentation, we interviewed market segmentation experts from the industry. The following subsections provide an overview of the interviews, questions asked in the interview and findings from the interviews.

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.

Societal and political attention to the effects of climate change and possible mitigation and adaptationpolicies has increased sharply in the last decades, resulting partly from increasing awareness about therole of humanity and partly from the ever more noticeable changes in our world caused by climatechange. This societal interest has highlighted a lack of knowledge about the effects that a changingclimate will have on many aspects of our lifes, one of which is the transport section. To understand theimpact climate change will have on our transport system we need to know how travel behaviours areaffected by weather circumstances, which is the main topic of our research.We focus on four aspects of the relationship between weather and travel behaviour: (1) how weatheris taken into account in the decision-making process (2) if the influence of singular weather variables(such as temperature) depend on the value of other parameters (3) if the influence of weather is differentfor urban and rural areas and (4) whether there are groups of people whose response to travelbehaviour are distinctly different from one-another. This knowledge can be used for climate changeadaptation measures, such as ensuring that our supply of travel infrastructure will be able to copewith changes in travel demand resulting from a changed climate, and mitigation measures, such asincreasing the number of people that use more sustainable travel options like the bicycle.For our analyses we use travel data provided by the KiM Netherlands Institute for Transport PolicyAnalysis, which is the result from a travel diary survey held in autumn. We use weather data asmeasured by weather stations, provided by the Royal Netherlands Meteorological Institute (KNMI).This data is used to estimate the influence of weather on travel demand and mode choice in the Netherlands,using regression and choice models respectively. Within our analyses we try to find factorsthat moderate the relationship between weather and travel behaviour, such as urban density and sociodemographics.With respect to the four aspects identified above, we report the following findings:(1) that people use a general perception of the weather during the whole day for their mode choicetravel decisions, which contrasts with the most common practice of using the weather at the trips’departure time.(2) by accounting for the fact that meteorological variables always co-occur in our models we areable to more accurately capture its effect on travel behaviour. The difference with the current practiceof estimating separate effects for each weather variable is particularly stark for days at the extreme endof the observed range of weather variables.(3) The influence of weather on travel behaviour differs more qualitatively between rural and urbanareas: the total effect size of the weather similar, but they are brought upon by different weathervariables. The difference is also very specific to travel modes. For bicyclists the effects of wind speedseem to be more sizeable in urban environments, whilst temperature, rain, and sunshine have smallereffects in urban environments.(4)We find multiple groups of travellers whose responses to weather variations are different from oneanother. These differences seem to be caused by the set of travel modes that are used during averageweather conditions. People that only use the car during average conditions are not very affected, withonly enjoyable weather conditions prompting increased bicycle use. If the car and the bicycle are usedoften people swap between the modes, although use of the bicycle during inclement conditions isrelatively much higher than for the other two groups. The last group has a more multi-modal travelpattern, which results in the largest variations caused by weather. Inclement conditions favour bothpublic transport and the car, with car use increasing quite sharply during wet weather with high windspeeds.
Additionally we find that weather variations account for differences in travel behaviour across boththe spatial and temporal dimensions. A particularly surprising finding is that the smaller number ofbike trips in the western provinces of the Netherlands can be fully explained by the fact that there arehigher average wind speeds and lower temperatures in this part of the country.
Our results have several implications for the research community and policy makers. We advice researchersto account for the fact that the weather is perceived as a whole and thus that the effect ofone single variable (such as temperature) will depend on the values of other variables. We also foundinteresting subgroups with different reactions with regards to weather. We advice researchers to moreclosely investigate the effects of weather for the separate subgroups. Finally we find sizeable differencesin the effect of weather between different regions, even within the relatively small country of theNetherlands. Researchers studying a relatively large study area would do well to estimate separateeffects for regions within their study area, for example based on population density and geographicallocation.
For policy makers our findings imply that there is a sizeable effect of weather that could be usedto improve the forecasts of future travel demand, both in the short- and long terms. Whilst policymakers obviously can’t control the weather, we have found that changing travel patterns or attitudesto travel modes will have repercussions for the effect weather has on travel behaviour. We think thatpolicies aimed at allowing commuters to gain experience with using the bicycle for their daily commuteduring summer, coupled with temporary financial incentives when weather conditions becomeless favourable, could be one way of achieving more cyclists during inclement conditions. Policy makerscould even target younger professionals specifically, as they are much more likely to have alreadydeveloped such habits during their education.

The increasing availability of data, computing power & advances in the algorithms has really driven the development of Artificial Intelligence (AI) in recent years. However, many industries & societies despite realizing the value of AI are still skeptical in accepting AI, especially when several controversial incidents have come into our spotlights and the challenges that AI have been posing in recent years. This has increasingly raised the concern over the trust in AI and has become a major impediment while adopting AI. Almost every stakeholder, potential users put their concerns upfront to the developers of the technology & management and all these concerns address to one main question – How can I trust AI or Whether AI can be trusted. Addressing the concerns posed by the clients & ensuring that AI solutions developed are trustworthy and responsible has now become one of the top priority and challenges for several technology-based companies. From the stands of scientific literature, there hasn’t been substantial research done on the factors influencing the trust in AI despite the growing attention paid over the importance of trust in AI in recent times. At least, there hasn’t been enough study done on the concepts of trust in the field of AI from the management and socio-technical aspects. This research will focus mainly on improving the trust in AI by identifying the essential trust factors of data in terms of data quality dimensions (DQ) & AI model and the prime objective is to develop a trusted AI model incorporating such trust factors that can help the management & developers to assess the trust factors and improve the trust in AI. The research would mainly be employed with a qualitative study using an inductive approach in order to generate valuable theories as it is mainly supported by literature review, desktop research, interviews, and use of a case study. To be more precise, the research was divided into two phases where the first phase involves the identification of potential factors that influences the trust in data and AI model and they were primarily derived from the extensive study done on the literature review & desktop research, and the second phase involves the identification of important trust factors from the perspective of actors involved in the development of AI. Based on the findings from the interview combined with the initial analysis done on the literature review, an initial version of the model was developed. Since the model was relatively new & comprehensive, it required further evaluation with the experts and based on those reflections combined with the previous analysis (literature review & findings from the initial interviews), a final version of the model was developed. To improve the utility of the proposed model & overall research, the model was compared with some of the core themes laid by AI-based research institutions and leading tech firms to ensure that the model has considered those themes and distinguish the major value of this model. The final version of trusted AI model thus contains nine main phases involved in AI development and in each of the phases, trust factors that were crucial to be considered were tagged along with the detailed indicators for each of the phases. The trusted AI model at the end would mainly help the management and developers ( Technology creators) to establish a robust trust over the AI model or the solutions created & provide a seal of trust to the investors, clients and other stakeholders involved. From this study, identification of essential trust factors of resulting AI model and essential trust factors of data in the form of DQ dimensions were considered to be one of the prime handouts to the scientific research apart from the trusted AI model proposed.

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 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.

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...