Traditional financial companies play a central role in ensuring financial stability and reliability. However, these companies face institutional problems due to inefficiencies, lack of transparency and limited innovation. For this reason, the use of distributed ledger technology, or blockchain, has been explored as an alternative and its integration into traditional finance towards a 'hybrid' approach. The Dutch mortgage market was chosen as the focus of this research and is further analyzed due to its high transaction costs, including the high cost of trust. This led to the research objective of designing a Hybrid Mortgage Lending System (HMLS) that reduces the cost of trust.

A design science research methodology was used for this research where two interwoven artefacts were developed. The first artefact was a contextualized trust model based on three trustworthiness factors: integrity, ability and benevolence. The second artefact was a demonstration for the HMLS using a technological model.

The trust model was validated through expert interviews and enriched with additional information. Based on the feedback from the expert interviews, nine meta-requirements for the technological model were developed. The proposed solution for the design of the HMLS is to automatically distribute fractional ownership of mortgages via Non-Fungible Tokens (NFTs) through an ecosystem with the traditional finance company as originator and public investors as lenders. The ownership of the NFTs is transparently tracked via the blockchain to monitor the fractional ownership of a home. These NFTs exist in mixed token pools and are controlled by the traditional finance companies. These pools are supplemented with other digital assets to create different risk profiles to suit different types of investors. A demonstration of these token pools is shown, with an overview of the architecture explaining the mortgage application and transaction processes through the different layers. Finally, a stakeholder map is shown and their roles within the ecosystem are explained. The research provided new insights by making the second model dependent on the first. The findings highlight new challenges for further research, such as prototyping or improving the versatility of the model.

Data-driven health research, specifically the development of AI models, is hampered by poor data availability and associated administrative burdens, caused complex and fragmented data protection regulation. To reap the benefits of using high quality health data, while safeguarding data protection of patients, the synthetic data generation is seen as a promising privacy-enhancing technology to avoid the need for personal data sharing. Although synthetic data is widely discussed, research primarily focuses on a technical development and evaluation of data protection, leaving substantive matters on the use of synthetic data within its institutional context open. By combining legal and technical knowledge, this thesis aimed to bridge this gap, by analysing how synthetic data generation could enable health data sharing for research in a privacy-enhancing manner. Specifically, a design science research approach is followed to combine the requirements from the institutional environment, focusing on a use case with a Dutch healthcare provider and research institute, with scientific knowledge on synthetic data generation and data protection evaluations. The research objective was to design a framework that structures the data protection-related factors that influence the extent to which synthetic health data enables secondary use of health data for research. The identified barriers and drivers of synthetic data generation focus on the health data sharing process, the interplay between the legal definition of anonymisation and technical data protection evaluations, as well as data protection principles. Synthetic data can enable secondary use of health data for research, but, measures should be implemented in the various phases of synthetic data sharing to safeguard patients’ data protection. As policy opportunity, this thesis argues for narrower definition of personal data to support privacy-enhancing technologies such as synthetic data generation.

The prevalence of mental health issues among adolescents and young adults is increasing, with social media usage identified as a potential contributing factor. As technology becomes more integral to daily life, the design decisions of social media platforms emerge as significant determinants of users' mental well-being.
Despite awareness of social media's general adverse effects, there is a gap in understanding the specific impact of different design features. Academic literature identifies features like likes and direct messaging as having nuanced effects on mental well-being, but lacks a comprehensive overview for developers to reference. This thesis seeks to bridge this gap, focusing on design features and their psychological impacts to propose design interventions for enhancing mental well-being.
Employing Value Sensitive Design (VSD) as an analytical framework, this study is the first to apply VSD to social media in the context of mental well-being. It involves a tripartite methodology — conceptual, empirical, and technical investigations — to identify significant design features and values related to mental well-being on social media platforms. A novel social media platform, Dime, designed with mental well-being in mind, serves as a case study for evaluating these features and proposing design requirements.
The study identifies 8 key design features affecting mental well-being and outlines 13 values divided into higher and lower levels, facilitating focus on critical values for desired outcomes. It explores value interrelationships and conflicts, particularly noting indirect tensions between values like authenticity and connection, and between personalization, control, and autonomy.
Proposed design interventions aim to resolve identified value conflicts, enhancing authenticity to support connection and enhancing personalization and control to support autonomy.

This research explores the role and challenges of openness between digital healthcare platforms through the use of boundary resources in the Dutch healthcare system, focusing on factors influencing openness decisions. Traditionally, boundary resources facilitate arm’s-length relationships between platforms and their periphery. This study examines their specific contribution to platform-to-platform openness in digital healthcare. To properly define openness between platforms, a distinction was made between interoperability and platform-to-platform openness: interoperability focuses on the compatibility of platform resources, while openness emphasizes the provision or accessibility of these resources. Boundary resources are further divided into technical and social aspects, shaped by laws and regulations. A document analysis of key policy documents maps how these relevant boundary resources function. The importance of APIs is emphasized as a means to progress toward an interconnected digital healthcare system, however, the practical implications and impact of openness decisions leading to the formulation of API’s, particularly between platforms, remain somewhat unclear. Interviews with the most relevant stakeholders in the field provide further insight into the drivers and barriers affecting boundary resources adoption. Most emphasis is placed data exchange and transactional based platforms by the stakeholders. The distinction between interoperability and platform-to-platform openness in healthcare can help avoid conflating compatibility requirements with broader governance and strategic considerations, enabling more focused discussions on factors essential for platform-to-platform openness. Boundary resources, while holding significant potential to facilitate openness, are not clearly distinguished by platform owners in their application for inter-platform openness compared to openness toward complementors, while these two forms of openness can serve a distinct purpose. Openness concerns are further composed of involved technical costs, security and data liability issues, as well as competitive pressures, with the potential loss of operating models and market position adding additional complexity to these openness decisions. Regulatory direction stimulates the development of boundary resources to promote inter-platform openness by embedding platform connections within the broader market ecosystem. Boundary resources could be further utilized further to enable platforms to xtend their periphery and for addressing deeper infrastructural and collaborative challenges within the healthcare sector. Platform-to-platform openness is influenced not only by decisions of the platform owners themselves, but also by broader market dynamics, regulatory frameworks, and the platform environment. This research highlights the potential of boundary resources in tackling infrastructure, innovation, and data-sharing challenges in healthcare.

As the data economy is growing, businesses increasingly utilize data marketplaces to share data with external parties. Due to the specialized nature of data products, data marketplaces exhibit a high degree of heterogeneity, often focusing on specific countries and industries. This specialization results in high transaction costs when sharing data in data marketplaces. Additionally, multi-homing costs are high, making it difficult for users to participate in multiple data marketplaces to expand their reach. The existing literature recognizes meta-platforms as a potential measure to reconcile highly heterogeneous digital platforms, thereby reducing transaction and multi-homing costs. Nevertheless, while sharing business data on a data marketplace is already difficult due to data sovereignty concerns, these concerns will likely intensify in a meta-platform setting because data may flow from one data marketplace to another. Control mechanisms can enhance data sovereignty; however, due to the novel and intricate nature of meta-platforms, existing knowledge on designing such mechanisms may not be directly transferable to this complex setting. Therefore, this research aims to create design knowledge for developing and evaluating control mechanisms for data sharing through meta-platforms for data marketplaces, focusing on investigating their efficacy in enhancing data sovereignty in the societal context of the data economy. To achieve the objective, this study employs the Design Science Research (DSR) approach. This study is structured based on the three DSR domains: the problem space, the solution space, and the evaluation space.

We find that data providers have different views on the efficacy of control mechanisms (i.e., smart contracts and certifications) to enhance data sovereignty facets in the context of business data sharing via data marketplace constellations federated by a meta-platform. Our research finds no significant differences in data providers’ perception of their ability to retain ownership and maintain control over shared data products in meta-platforms, regardless of the presence of smart contracts. In addition, our findings suggest that data providers using meta-platforms with certifications feel more confident in meeting data sharing compliance requirements compared to those using meta-platforms without certifications. Additionally, these data providers perceive a clearer division of responsibility between meta-platform and data marketplace operators. When combined with smart contracts, the responsibility divisions become even clearer. Contrary to our expectations, however, we find no significant difference in the perceived security of data providers when sharing data on meta-platforms with certifications compared to those without.

Considering the impacts of data sovereignty on the broader societal context of the data economy, we find that when data providers feel sovereign over their data products, they are more likely to trust both a) meta-platform operators facilitating data sharing and b) data consumers with whom they share data. Surprisingly, we do not identify a correlation between the trust and their willingness to share data. This suggests that when data providers possess data sovereignty, trust in platform operators and data consumers becomes a less important factor for data sharing. In addition, we discover that data providers, feeling sovereign over their data products, perceive lower risks in sharing their data. The reduced perceived risks subsequently increase their willingness to share data through meta-platforms. Therefore, our study emphasizes the significance of data sovereignty in the growth of the data economy by a) promoting trust toward meta-platform operators and data consumers, b) reducing perceived risks, and c) increasing the willingness to share business data through meta-platforms.

Our study contributes to the Information Systems literature, particularly in the intersection between data sharing and digital platform literature. We contribute by being among the first to create design knowledge to develop and evaluate control mechanisms for business data sharing through meta-platforms for data marketplaces, focusing on investigating their efficacy in enhancing data sovereignty in the societal context of the data economy. Specifically, our primary contributions are four-fold: 1) theorizing the potential impact of control mechanisms on data sovereignty, 2) outlining design options and principles as prescriptive knowledge, 3) defining goodness criteria to enhance data sovereignty, and 4) advancing context understanding of a meta-platform as a business data sharing setting. In addition, our secondary contributions are 1) providing evidence on the potential impact of data sovereignty on the broader data economy and 2) extending the applicability of theories employed in this research in the market-based data sharing context.

In conclusion, this study resolves the tensions in the European policy-making agendas that promote a single market for data and interoperable data sharing (e.g., in EU Data strategy, Data Act) while, at the same time, pushing sector-specific data marketplaces to exist (e.g., the eight verticals in the Digital Europe program). Furthermore, policy agendas also emphasize adherence to data sovereignty principles. As data sovereignty is vital for data providers to share their data via meta-platforms, addressing this concern may increase meta-platform adoptions. Hence, we hope a meta-platform can realize its potential to be one distinguished instrument to fulfill what we hope (and are optimistic) for in the data economy: a single European Data Market in 2030.
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The Dutch first-line healthcare domain faces challenges arising from an ageing population and resource limitations, accentuating the need for an effective IT domain. Inadequate interoperability and data portability between information systems used by healthcare organisations in daily processes hinder patient treatment and innovation. This study explores a platform-based information system as a solution to enhance innovation and interoperability. Previous research focused on the IS supplier perspectives, but this research aimed to examine patients and healthcare professionals' needs considering the development of a platform-based information system.

The study is conducted within a socio-technical context, guided by the Design Science Research Methodology (DSRM). This study centers around five sub-research questions distributed across design cycles within DSRM. The preliminary research (including a focus group, semi-structured interviews and expert talks) revealed inefficiencies in communication regarding care around the medication process. The statements gathered during this research phase were validated on a bigger scale by conducting a survey. The statements resulted in a number of requirements, which were formalised and categorised using a System Requirement Structure (SRS) design. The SRS resulted in five main areas: supporting healthcare professional participation, regulating access, enabling clear and concise content, supporting agreements, and supporting patient participation. The low-level requirements of each of these areas were used to construct the platform modules and platform architecture, using a layered approach (showing the business, information and technology layer). The platform architecture was developed through iterative evaluations with domain experts, enhancing quality and detail. The evaluations also resulted in a new perspective on the platform-based information system, rather seeing it as a platform-based ecosystem solution.

The practical contribution of this research lies in two areas. Firstly, the designed platform addresses the need to improve communication within the healthcare domain, offering a communication service that can significantly enhance efficiency and collaboration among healthcare providers and patients. The insights and requirements from various perspectives can also be applied to enhance communication practices in other healthcare lines and countries. Secondly, the platform-based ecosystem design provides valuable insights into the roles, responsibilities, and interactions among different stakeholders, serving as a blueprint for developing similar ecosystems in other domains. The design also aligns with the Communication Platform as a Service (CPaaS) concept, offering the potential for further exploration in other industries.

Additionally, the research contributes to the scientific literature by addressing the research agenda on digital platforms by providing a comprehensive overview of how platform-based ecosystems can be conceptualised and designed in the healthcare sector. The findings can guide further research on feasibility, financial implications, and governance in platform-based ecosystems. The study’s value also extends to other domains, such as education and government, where ISs play a crucial role. Moreover, this study found that the interoperability layers can be applied and adapted within the healthcare context and potentially in other IS ecosystems, providing strong guidance and support for designing platforms and services. The research design demonstrates the relevance and applicability of interoperability layers, and with further experimentation, these layers may have broader applicability across different domains. Overall, the study’s positioning in Design Science Research (DSR) places it as an improvement for communication and an exaptation for the platform-based ecosystem, with potential for reuse and further research in various domains and contexts. The academic contribution also entails identifying knowledge gaps that must be explored in the future: the next steps.

Several areas need further research to enhance the development of the platform-based ecosystem and communication service. Firstly, understanding the patient’s perspective through focus groups or interviews can provide valuable insights into communication preferences and platforms. Secondly, detailed research on communication variety in healthcare and specifying platform content and user details will ensure successful implementation. Thirdly, following the Architecture Development Method (ADM) cycle can optimise the business, information, and technology architecture, cost-sharing decisions and stakeholder governance. Additionally, exploring governance frameworks like the Institutional Analysis and Development (IAD) framework will facilitate feasible implementation. Lastly, developing new design principles and guidelines for digital platform (ecosystem) design will better support the platform-based ecosystem’s growth and refinement.

The adoption of data marketplaces is lacking. Data marketplaces work differently from other information sharing platforms (such as supply chain platforms), because they are more open. This openness allows data to be shared with many more companies, bringing more potential benefits, but also increasing the risk. On data marketplaces there typically is less trust between users, however this lack of risk can be compensated by adding more control mechanisms. For these reasons I want to address the research question:

How does the interplay between control, trust and risk affect a company’s decision to share data in a data marketplace?

The objective of this research is to define and validate the relationships between control, trust, risk and willingness to share data. This will be done through a literature review and a qualitative study using structural equation modelling.

The literature review resulted in a conceptual model, which was subsequently tested using an online survey. The demographic of the survey was managers in the manufacturing industry and they were given two scenarios in which they could share data. In both scenarios their trust in other users, trust in the platform, perceived risk, perceived sensitivity of data and willingness to share data was measured.

Using structural equation modelling we found 5 significant relationships, out of the 6 that were hypothesized. Answering the research question, this study has shown that increasing trust in other users and perceived control can really help decrease the risk of sharing data, which in turn is an important predictor of willingness to share data. However, the sensitivity of data can also have an influence over risk. This insight is new in the field of data marketplaces. We now have a more clear picture of how control and trust can be used to lower the risk and in turn increase the willingness to share data. This study also provided some data that confirms these relationships in the context of the manufacturing industry. This data shows that companies are willing to share their data, if preconditions such as a fair price are met. These results contribute to the overall literature on willingness to share data in a marketplace and on the literature on control mechanisms, trust and risk. The insights from this study can be useful for people designing data marketplaces and regulators that want to encourage the sharing of data.

In Dutch primary care, there is a dilemma that needs to be solved: caregivers using different digital platform in information system (DPIS) have difficulties in transferring necessary medical information. Although DPIS, as an emerging technology, assists in the decision making, coordination and control that occurs in the organizational environment. Its lack of openness leads to difficulties in exchanging information data. Therefore, this study focuses on the architectural requirements that can facilitate the openness of DPIS for primary care in the Netherlands. A design science approach was used to answer this question. The study first clarifies the current situation using document analysis and literature review, then defines the factors influencing openness and the requirements of stakeholders regarding openness and identifies architectural requirements. Finally, a conceptual artifact was developed and the requirements were validated in discussion with domain experts. The results of the study suggest that a clear distinction should be made between open to share or open to use when defining the openness of an architecture, and in addition, we should focus on the development of standardized data exchange regulations.

This thesis explores the motivation of developers to initiate and sustain participation in a Platform-as-a-Service Software Ecosystem (PaaS SECO). PaaS, integral to SECOs, is gaining prominence for its cost advantages and scalability. The study focuses on understanding what drives developers in diverse roles to engage with PaaS SECOs and examines potential differences in motivation among developer groups.

The research adopts an exploratory case study approach, with the Mendix SECO, a low-code PaaS for enterprise application development, as the subject. Semi-structured interviews with developers from Independent Software Vendors (ISVs), service providers, and customers inform the study. Self-Determination Theory (SDT) is applied to structure and comprehend developers' motivators, considering intrinsic and extrinsic motivations.

The findings underscore the significance of intrinsic motivators, such as enjoyment, intellectual stimulation, and skill enhancement, in attracting developers to PaaS SECOs. Competence-related factors, like challenge and creativity, play a pivotal role. Extrinsic motivators, like knowledge exchange and community size, also contribute to initial participation, with SECO support being crucial.

In continued participation, intrinsic motivators remain vital, with skill maturation and the evolving nature of fun being emphasized. Learning new skills aligns with staying abreast of platform innovations, sometimes driven by external pressures. Developers become more aware of the SECO's offerings, with social events acting as supplementary motivators for connection and inspiration.

Extrinsic motivators, particularly in autonomous form, gain importance in sustained participation. Developers align with the platform's strategic direction, emphasizing satisfaction with the SECO and its innovative efforts. PaaS providers' reputation and engagement in social events further enhance developers' motivation.

The study reveals overarching similarities and differences between low-code and other SECO types, questioning assumptions about developer motivations. The dominance of intrinsic motivators aligns with previous research in proprietary SECOs, but nuances, such as the desire for fast and agile development, emerge as unique to PaaS SECOs.

Theoretical contributions include enriching understanding of developer motivation in PaaS SECOs, reevaluating the intrinsic-extrinsic binary scale, and offering insights into nuanced motivators through SDT. Practical implications suggest strategies for PaaS providers, emphasizing the importance of competence-related motivators and the ongoing need for intrinsic satisfaction.

In conclusion, this thesis contributes to both theoretical and practical aspects of developer motivation in PaaS SECOs, paving the way for future research and strategies to attract and retain developers in evolving software ecosystems.

The benefit of secondary use of data leads to the belief that data can be monetized through exchanging and trading between businesses. Many new data marketplaces emerge from this, resulting in the heterogeneity of the data marketplace that leads to market fragmentation in the industry. The aggregator business model can offer various values to users in a fragmented market. Therefore, the idea of designing aggregator business models for data marketplaces could be worthwhile investigating. Design Science Research (DSR) methodology is used in this research. Business model requirements to design aggregator business models were defined in the literature review. The business models of various aggregators are analyzed with case studies. From the requirements and case studies, four new aggregator business archetypes are derived: search engine, advanced search engine, comparison sites, and one-stop shop. These four aggregator business models are distinguished by their service offerings and their degree of the network. It is also discovered from case studies that there is a connection between the degree of network and aggregators' embedded technology. The high degree of network aggregators tend to use API, while the low degree of network aggregators tend to use information crawlers. The aggregator business model archetypes are then demonstrated to the data marketplace cases by translating the business models into business activities and services that can be offered to the users. Then, we evaluate the data marketplace aggregator business model through semi-structured interviews with experts. From the evaluation, it is discovered that data marketplace aggregators could offer value to the users, e.g., navigating users to find data sales and providing a single access point to collect data. Different technology aspects that could help to realize the aggregation services have also been developed by various projects. Additionally, data providers are willing to join the aggregation services due to the economic benefit. However, although the benefit and possibilities, multiple challenges need to be addressed by the data marketplace aggregator. There are issues with technical standardizations and interoperability that could be problematic in integrating various platforms for aggregation. Maintaining collaboration is difficult due to the need to provide data assurance, which could be expensive and exhausting. Additionally, although it is beneficial to navigate users in finding sales, data marketplace aggregators might offer little value to data buyers with market knowledge because they will directly buy the data from the sellers.

The term "digital transformation" (DT) has gained popularity, sparking interest across many academic fields and inspiring new lines of inquiry(Hausberg et al., 2019). With the adoption of new technologies helping deliver secure high-quality patient care and drive greater business efficiency, healthcare sector has gained significant benefits from digital transformation. Digital technologies, driven by the exponential rise in computing power, have successfully altered the majority of economic sectors, from finance to entertainment, with one notable exception: healthcare. The increasing advancements in medical equipment and imaging technology serve as evident illustrations of how high-tech medicine is in many aspects. Nevertheless, healthtech companies are still on a learning curve on how to transform their companies digitally, despite billions of dollars being invested in digital health solutions by both startup and established technology companies(Walsh & Rumsfeld, 2017).
This study advances the body of knowledge in the field of digital transformation by identifying the barriers to and enablers of business transformation as well as the role of IT technology as an enabler of business transformation. By creating a theoretical framework for barriers and enablers of business transformation with IT technology as an enabler, it empirically studies Philips to undertake a case study and offers a basis for theory building. On the subject of business transformation, a literature review utilizing the five-step grounded theory methodology was carried out. According to the literature study, this research highlights four primary barriers facing business transformation: organizational, knowledge, financial, and technical. Strategy, people, process, data, and technology are the different categories for business transformation enablers. The theoretical framework for this study is developed using the results of this literature review. A case study is performed on the digital transformation journey of Philips by identifying the IT technologies of the acquired firms that are part of Philip’s healthcare transformation portfolio. This research captures the goal of business transformation while identifying the barriers and enablers of business transformation at Philips by conducting inductive qualitative research by interviewing. Analysis of data is carried out by following the steps stated by Miles & Huberman (1994) and using content analysis to analyze the interview data. The results of this study show that At Philips, the organizational barrier has emerged as the most pervasive barrier to business transformation. This study demonstrates that Philips' organizational structure, or the way they are set up, is a significant impediment to business transformation. The matrix organizational structure of Philips makes organizational change challenging. According to researchers, business transformation begins with establishing a strategy and achieving the organization's aim. According to the research, this strategy is misleading, and that any business transformation strategy should be developed by first comprehending customer demands before being in line with the intended outcome of the business transformation. According to this study, people with proper experience and knowledge in the field of business transformation are the key enabler of business transformation. This study supports the resource-based hypothesis, which contends that an organization has a great chance to outperform its rivals when it possesses strategic resources that can provide it an advantage in the market and could even help it generate sizable profits. This research examines three important IT technologies provided by the acquired companies of Philips, namely Tomtec, RDT, and Capsule technologies as resources that can enable business transformation. According to this study, Philips' business transformation was made possible by the IT technologies' significant contributions to the company's sales and revenues following the acquisition of these companies. By integrating these IT resources into other products in their portfolio to facilitate business transformation, Philips can further develop these technologies and achieve their objective of becoming a fully solutions-based firm. However, the findings of this study also suggest that IT technologies have not yet reached their peak value since Philips lacks the competencies and innovation culture to fully utilize these technologies and add value as a result of preexisting limitations.

The financial domain is losing ground to rapid-developing fraud schemes. It puts intense pressure on organizations such as banks to find new approaches to tackle financial misconduct. This financial crime has always existed and is present in the financial industry. However, the rise of technology and the use of online transactions has enhanced the presence of the impact of fraud in this industry. The increase in financial fraud cases in a technological era result from a lack of inter-organizational synergy and the privacy concerns that entail by making data available. On top of the increasing fraud cases, organizations are exposed to increasing regulatory, financial, reputational, and legal risks. Hence, the financial crime industry and fraud prevention organizations must act on this threat. Therefore, these actors need to improve their current workflow continuously to keep up with the new developments. Different studies propose that it is a potential opportunity to take the chance and bundle data together to learn from the existing environment and improve their workflows and prediction models. However, the main concern is that parties are reluctant to share data as it involves confidential and sensitive data, which malicious parties can leverage and abuse. Also, the increasing focus on privacy protection regulations makes it complex and challenging to exchange data easily. The dataset that actors are providing will contain personally identifiable information, which in fact cannot be shared and proposed without any legitimate reason and is subjected to the data privacy regulations.
The existing set of techniques for sharing and analysing data securely, such as differential privacy, homomorphic encryption and federated learning have been proposed in studies and use cases are built in the real world. However, these techniques are insufficient and capable enough to facilitate multi-actor (data owners) data exchange and analysis. Secure Multiparty Computation, however, is capable of having multiple data owners securely perform a joint analysis. For this reason, this study has been focused on SMPC. In the case of the financial crime industry, it requires the involvement of multiple stakeholders sharing data simultaneously. Especially in the case of banks, it is essential to have bundled data for all transactions as these are connected.
To understand the purpose and the concept of the study, it is essential to have a basic understanding of SMPC. Secure Multiparty Computation is a cryptographic method for parties to mutually compute a function over all parties' acquired inputs while keeping the intake private throughout the entire process. Independent computation nodes will perform and provide analysis outcomes to designated parties. The concept of Secure Multiparty Computation has been studied by academia since the 1980s (Yao, 1982). However, the applications and introduction of SMPC are relativity novel to organizations and will not be immediately accepted. A technique such as SMPC will require participating parties to share a mutual interest and willingness to contribute continuously. It is uncertain if organizations will accept and adopt SMPC as mentioned before. Therefore, the study will also incorporate the concept and theory of collective action to understand the motives and the common goal for stakeholders in the anti-fraud industry to accept the technique and collaborate. A common goal, also known as a collective goal or interest, would create acceptance among the group. In this case, it will help to identify the factors and interests that influence an organization's decision to engage in collective action for developing MPC for fraud detection in the financial industry...

The growing aviation industry asks for innovative solutions to be able to handle the increasing amount of baggage. An example of such an innovative solution is Computer Vision Technology (CVT), which uses cameras to identify bags using data and artificial intelligence. The value of CVT for the baggage handling ecosystem is currently unknown. Besides this practical knowledge gap, a scientific gap is found as well. The majority of the literature on digital transformations has an organizational viewpoint and does not incorporate established ecosystem perspectives. It is unknown how a value proposition needs to be designed for a data driven technology to lead to a digital transformation of an established ecosystem.

A DSR approach is executed in a situated setting at Schiphol Airport to capture the value proposition of CVT for the baggage handling ecosystem by the identification of use cases. . The results show that the implementation of CVT provides value for Schiphol Airport, the baggage handling system provider, airlines, handlers, passengers, and society. The value proposition of CVT is the automated identification of bags based on visual images that provides thirteen use cases applicable throughout the whole baggage handling process, which leads to more autonomous processes, process improvement, the generation of more (types of) valuable data compared to the current identification techniques and can contribute to the achievement of sustainable goals if it replaces the current identification techniques.

The results not only contribute to the aviation industry, but the insights gained during the research are also valuable for future digital transformations within other established ecosystems. During the research, a lack of ecosystems’ support for the digital transformation was identified, caused by two factors. It was found that certain process choices had a positive influence on these two factors, which inspired the formulation of process guidelines. These guidelines contribute to the digital transformation knowledge base as they provide insights into how to enhance ecosystems support for digital transformations. In this way, it guides future digital transformation processes within established ecosystems. Furthermore, the research provides an approach to get a grip on a complex established ecosystem and a tool to specify data-driven use cases in combination with its implications for the established ecosystem. No tool existed to accommodate that. Therefore, a tool was constructed and used, which provided guidance on the use cases’ specification and could be valuable within future ideation processes of data-driven use cases for established ecosystems.

The revision of the eIDAS Regulation proposes the implementation of a European digital identity wallet for citizens to authenticate themselves across the EU. The process to decide if a person already has an account at the service where they seek to authenticate themselves is called identity matching. Regulated and public Dutch Relying Parties and the Dutch government are not always able to assess whether a citizen authenticating themselves has a pre-existing record at the Relying Party or in the Dutch national registry. Moreover, the current dependency on the Dutch central identity matching service creates a Single Point of Failure. Besides these reliability problems, privacy issues such as profiling data minimization must be accounted for. To this end, this research proposes three possible solution directions for solving these problems: a government-centric, wallet provider-centric, and a hybrid solution direction. The design of the solution directions follows a design science research methodology. The possible solution directions are evaluated by experts in focus groups to elicit the benefits and the barriers which these experts identify as relevant factors for accepting a solution direction. These factors are categorized using the TOE model, which is adapted to suit the current context. Expert evaluations during the focus groups have resulted in factors which relate to the organizational, technological, and external environment of the solution directions. The evaluation uncovered tradeoffs which the Dutch Ministry of the Interior must make to choose between one of the proposed solution directions: reuse of infrastructure at the cost of citizen privacy, or more privacy for citizens at the cost of additional logic requirements needed for the identity wallet. Based on the privacy and reliability requirements and the objective of the Dutch government to give citizens more control over their personal data, the wallet provider-centric solution direction is the most fitting choice of the three proposed solution directions. The explication of the problem, requirements, and solution directions can be used as a starting point in the exploration of new solution directions for the identity matching problems. More research is needed on other possible solution directions than are proposed in this research and their viability to meet the objectives of the Dutch government and deadlines set by the European Commission.

Over the last several years, the volume of data generated by the Internet of Things (IoT) has expanded at a rapid pace around the world. These often very detailed sensor data could be utilized by many different parties in order to improve services. This same tendency is seen in the automotive sector, where lots of data are gathered by newer and smarter cars. However, despite the fast data collection, car businesses seldom utilize this potential value. One way to make better use of automotive data is to share it with others via data marketplaces. Furthermore, data gathered by increasingly smarter cars is often very sensitive data. By aggregating and analyzing these car data, other parties can learn a lot about individual car users which could be experienced as unpleasant. Therefore, Multiparty Computation (MPC), a fairly new technology that facilitates encrypted and anonymized data sharing, tries to overcome this problem in order to increase worldwide data sharing. Academics and businesses realize that these data marketplaces have huge potential but most of the research done on MPC is very technical. As Multiparty Computation is a fairly unknown and not yet massively adopted application and also not specifically on data marketplaces, it is unclear to what extent car users are willing to provide their car data. The scientific problem herein is to find the consumers' preferences in the main measurable data sharing factors which influence consumers' data sharing behavior on MPC-enabled data marketplaces. To do so, we will employ a explorative approach by the Discrete Choice Modeling (DCM) method. Conclusions are drawn on how to improve these data marketplaces by revealing the relative importance of general data sharing factors. This paper aims to explore the consumers' preferences of MPC-enabled data marketplace factors to reveal the consumers' willingness to share automotive GPS data on data marketplaces in order to enhance data sharing and increase (road suggestion) products and thereby overall innovation. The findings revealed that the factors risk of data disclosure and benefit stay far more important to consumers in data sharing on MPC-enabled data marketplaces. Data control and trust (in terms of a herding effect) were least important to consumers, although consumers feel they are losing control over their data in this online era. This knowledge can then be utilized to create a data marketplace in which data providers are more likely to join.

Data has often been stated to be “the new oil”. This is said because both oil and data can be very valuable but only after they have been pre-processed. Among other things, a challenge for creating value from data lies in realizing data exchange. This is what data marketplaces aim to do. The number of shutdowns of data marketplaces during the past years implies that their business models are not viable and that there are obstacles that should be overcome to create a data marketplace that is robust in the long run. In this research, we explore a new research context: the relationships between business models of data marketplaces and data marketplace obstacles. To do this, by the means of explorative theorizing, we first provide an overview of the obstacle categories for data marketplaces: trust, lack of governance mechanisms, privacy and control, product quality and product description, matching data providers and data buyers, data pricing mechanism, and ‘other’ that do not fall within a specific category. We then followed a multiple-case studying approach in which we subject three different real-life data marketplaces to a Business Model (BM) Stress Test on the identified obstacles. The cases were selected by theoretical sampling of the types of data marketplaces: personal data marketplaces, Internet-Of-Things (IoT) data marketplaces, and Business-to-Business (B2B) data marketplaces. By reflecting upon the results, we conclude that the assessment of the true viability of a data marketplace business model goes way beyond just the relationships between business model choices and obstacles. It is concerned with the business model strategy and dynamic capabilities of a data marketplace. We add to the literature by presenting a first explanatory model on the relationships between data marketplace business models and data marketplace obstacles. This model leads us to the practical implications of our results. These include the considerations that a data marketplace owner can take into account when making business model choices. Based on the results, we are also able to give recommendations that go beyond certain business model choices.

In the past decade, the world has experienced numerous severe and impactful data breaches, without indications of this development slowing down. Even worse, research has shown data breaches are still waiting to happen. The occurrence of a data breach has consequences for several involved parties and for society in general. It is therefore only natural that there exists a pursuit to prevent data breaches from happening. Research claims that data breaches happen because of simple and preventable errors made by human, also known as security misconfigurations. This study aims to investigate whether the root causes of severe data breaches are frequently related to security misconfigurations, which would make most data breaches preventable. No such structured research had been done before. We conducted a multiple case study, wherein a number of data breaches was analysed based on publicly available case literature. Assessing the data breaches with the help of our developed framework was part of that analysis, resulting in a systematic characterization of each data breach. The results indicate that in breaches the data are mostly subject to unauthorized access by outsiders, which frequently is made possible by poor security. The organizations directly responsible for that data are large organizations which get breached especially in their storage facilities. Next to the organization which got breached, these sizeable data breaches always affect individuals since at least part of the compromised data is about them or linkable to them. Usually this is not even discovered by the breached organization itself and sometimes only after a long period of time. Ultimately, it can be concluded that data are frequently caused by security misconfigurations and therefore are mostly preventable. On this basis, it is recommended that organizations responsible for sensitive data should be more incentivized to thoroughly combat security misconfigurations, instead of treating IT security as only a technical endeavor.