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.

Circular Economy (CE) aims to achieve sustainability through recycling, refurbishing, reusing, and other similar activities. Even though the concept of CE has gained considerable interest throughout the previous years and yielded significant research contributions, many critics stress the lack of incorporating ethical and social considerations into CE. This thesis researches how to facilitate such embedding of socio-ethical aspects into CE through the support of the Responsible Research and Innovation (RRI) methodology. Thus, this thesis addresses the main research question “How can the RRI methodology complement the CE framework to assess ethical, legal, and social aspects (ELSA)?”. Within this study, a conceptual framework was developed that allows for embedding socio-ethical aspects in terms of assessing ELSA (aspects) that are seen as design points contributing to a socio-ethical responsible and just CE. This framework contains substantial elements of RRI, including its four dimensions (Inclusion, Anticipation, Reflexivity, Responsiveness) suggested by Stilgoe et al. (2013). Furthermore, the conceptual framework is inspired by a draft framework of Purvis et al. (2023) for a responsible CE, claiming their framework as a starting point and suggesting future recommendations for its further refinement. The conceptual framework developed in this thesis allows for an inclusive assessment procedure of (bounded) CE systems by addressing its various components and all involved stakeholders by actively communicating with them. The system’s components, such as of technical or organizational nature, must be thoroughly assessed to recognize potential ELSA risks/impacts that can harm a responsible CE. Defining appropriate design points serve to mitigate such risks.

In order to test and evaluate the conceptualized framework, it got applied to a case study. The Horizon Europe granted project ALICIA represents the case study environment. ALICIA intends to establish a CE for industrial automotive manufacturing equipment (machinery and robots) within Europe. Since circularity for industrial automotive manufacturing equipment constitutes, especially on this scale, an under-researched field, this research also investigates such an automotive CE approach. Through the framework’s demonstration, significant findings could be obtained. First, even though the framework manifested as effective due to the achieved insights, which are subsequently further expressed, future recommendations for further development regarding the framework's recognized limitations are suggested. Another crucial insight the case study provided emphasizes the major challenges that exist in realizing a CE for automotive production equipment. Those challenges were identified by assessing the ELSA risks of ALICIA and the design points to mitigate such. To realize equipment circularity, detailed data must be shared by companies that often contain sensitive corporational information. Originating from the automotive industry’s competitiveness, such data exchange is hard to realize as it can jeopardize companies’ privacy. To overcome this, it requires collaboration between the companies and clearly defined data-sharing policies to enable such data exchange. Such ideal data exchange should ensure that still, sufficient data is shared to realize equipment circularity but simultaneously does not infringe a corporation’s privacy which could harm their market position by disclosing it to competitors.

Additionally, since the automotive industry is a profit-driven one, socio-ethical dimensions must also be incorporated into such equipment CE from the beginning. Compared to the electric mobility transition, which is another sustainability endeavor of the automotive industry, socio-ethical dilemmas that refer to unethical origins of certain parts (e.g., lithium batteries), such as child labor or farm desiccation, are prevalent. To prevent such or similar dilemmas within the sustainability agenda of an automotive CE, it requires the embedding of socio-ethical considerations that were detected throughout the case study. These considerations contain to ensure, inter alia, an ethical origin of parts used for equipment refurbishment. Otherwise, if these socio-ethical issues are neglected from the start, potential drawbacks might be difficult to remedy during an already ongoing CE implementation. These both outlined challenges with their socio-ethical considerations must be more precisely addressed in the future by relevant experts.

Thus, this research's key findings cannot only be seen as beneficial for the methodology of assessing socio-ethical considerations in CE from the context of RRI but also for the circularity of automotive production equipment. Both results constitute fundamental groundwork for further recommended research in these lacking research fields.

Open banking is an evolution driven by new regulations, advance in technology, competition, and change in customer expectations. Open banking is about enabling consumers to share their financial data with authorized third parties in return for more personalized products and services. However, the adoption of open banking products and services is still very limited. Consumers are also not familiar with the open banking environment and are not well informed about the new regulations such as PSD2.
Previous studies have shown that the success of open banking depends on: the safe transfer of the financial data and on the control consumers have over their data. Moreover, literature around the disclosing of financial data has demonstrated that consumers are unwilling to share their data, with privacy concerns being the dominant factor. Further, the new data economy brings a lot of around who actually owns the data and what data ownership leads to.
To explore the adoption of open banking among consumers, this study combined the technology acceptance model with external factors related to consumer personality attributes. Through intensive literature review, three dimensions are identified as crucial: psychological ownership, user’s privacy concerns, and technology readiness. A conceptual framework was developed along with nine hypotheses. The data was collected via an online survey. The reliability and validity of all the constructs were assessed. The hypotheses were tested through structural equation modeling using the SmartPLS software.
The results of the analysis indicated that the conceptual model was a good fit. The analysis indicated that there is a strong and significant effect on the intention to use open banking products and services. Specifically, perceived usefulness and perceived ease of use were important predictors for intention to use. Although psychological ownership had a negative effect on the intention to use, it showed a significant total effect through the mediating effects of perceived usefulness and ease of use. While the results of privacy concerns were insignificant, the technology readiness construct registered a total positive effect through the mediating role of the technology acceptance model on the intention to use. These findings allow the understanding of what affects the consumer’s intention to use open banking products.
The study supports the literature by being the first study to include the construct of psychological ownership to the technology acceptance model. Although the results are quite encouraging, the generalization should not be based on one single study. Future studies could use these findings to explore more the effect of psychological ownership on the disclosing of personal and financial data. Furthermore, by being a pioneer in the topic of open banking, the study provides banks and financial organizations with new insights on the consumers’ adoption of open banking products and services.

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.

The emergence of the Data Marketplaces is the latest iteration in the phenomenon of data-driven transformation of the world. Data marketplaces have emerged as a new form of data-driven business models which enable trading of data between the data owners/providers and data consumers by providing the necessary technological and non-technological infrastructure. These features present an alternative to the cumbersome logistics currently involved in searching, buying and selling data; thus, simplify the data supply chains between the data-driven business entities. However, they suffer to take off into mainstream success because of a myriad of reasons. Of all the reasons, 2 of them are focused in this thesis. Firstly, the difficulty involved in architecturally enabling a data marketplace platform as the prospective enabling technologies are still immature. Secondly, the uncertainty associated with the commodification of data which comprises of the intellectual property enforcement of data (data ownership), privacy and confidentiality breach (threats), regulatory ignorance (implication of GDPR), reluctance of businesses from participating because of the previous reasons et cetera. This reason is collectively referred as due to the uncertainty around the threat landscape of the data marketplaces. Multi-Party Computation (MPC) technology provide a solution to these problems. Through its capabilities to preserve the confidentiality of data architecturally and thereby securing the interests of the data actors with respect to the uncertainty of the threat landscape around data, MPC can enable safe and secure data sharing between data actors. This characteristic of MPC can help data marketplaces to overcome their challenges and foster their realisation. However, since MPC cannot handle the scale of real-life application, it is not mature enough yet to be incorporated into real-life data marketplaces. An EU funded project called SafeDEED: Safe Data-Enabled Economic Development, proposes to overcome the scalability issue and intends to achieve the maturation of MPC for real-life application. Building upon this forecast, a research was conducted to investigate the implication of the maturation of MPC technology towards the 2 problems faced by data marketplaces, architectural and threat landscape; and the same is documented in this thesis.

Cascading effects are high-impact, low-probability phenomena that have caused catastrophic impacts in various chemical and process plants around the world. With the increasing trend of cyberattacks targeting critical infrastructures, there is a concern that accidents caused by cyberattacks may trigger cascading effects in these facilities. In this study, we have demonstrated that the implementation of network segmentation to ICS networks to improve its robustness against the risk of cyberattack-related cascading effects. A risk-based methodology is developed to investigate and evaluate the robustness of design alternatives. The risk-based methodology also incorporates a risk assessment method based on Bayesian networks for cascading effects modeling. Further, this thesis also presents some design guidelines for developing robust networks segmentation designs, which includes the application of a graph-theoretic approach that enables early identification of the severity of cascading effects in network design alternatives. The risk-based methodology and the design guidelines are applied to a case study in which the efficacy of the approaches are demonstrated. The outcome of the risk-based methodology offers an alternative risk mitigation technique that can be considered in the risk management process.

The Internet-of-Things has grown much in popularity over the past years, leading to the gathering of large amounts of pervasive and detailed data. This same trend is seen in the agriculture industry, where a lot of data can be collected on the cultivation methods. The agriculture industry is considered to be a strategically interesting industry for the Internet-of-Things by KPN, the company that commissioned this research. Despite the rapid data collection, businesses rarely exploit the full potential value of their IoT data. One of the methods of value creation with IoT data is to share the data with other parties. Data platform ecosystems make the sharing of data possible, but require the participation from all sides of the platform, especially the data providers, to be successful. Platform sustainability is an important research topic within Information Systems research. However, it remains unsure how the participation of data providers can be ensured. Furthermore, Information Systems researchers have often stressed data governance to have an important role in the success or effectiveness of data platforms. This research aimed to explore how important ecosystem data governance is relative to other relevant factors for the participation willingness of IoT data providers. This knowledge can consequently be used to develop data platforms which IoT data providers are more prone to participate in. Due to the limited time to complete this research, it was decided to focus on the Dutch horticulture sub-sector within agriculture. This sub-sector is considered to be the most advanced Dutch agricultural sub-sector in terms of digitalisation and Internet-of-Things adoption. Relevant factors to data platform participation were identified by means of a literature review. The Best-Worst Method was used in a survey amongst 30 Dutch horticulturists to determine the relative importance of each of the relevant factors. Additionally, six face-to-face semi-structured interviews were conducted to identify concerns related to the relevant factors. The survey results show a low spread of the factors in terms of weight, indicating that Dutch horticulturists do not base their participation decision on just a few decisive factors. Ecosystem data governance is considered around half as important as the most important factor, the benefits of participation, and around three times as important as the least important factor, the external pressure. Although the benefits, human readiness and technological readiness are weighted higher, KPN is recommended to focus on the factors that can be directly influenced by them to direct development time and resources more efficiently. It is therefore recommended to focus on developing a data platform with a high level of security and a low level of complexity. Furthermore, the ecosystem data governance should be another focal factor as appropriate ecosystem data governance can likely eliminate concerns related to several other factors, such as the benefits of participation, safety of data, and the trust in and readiness of other participants. The concerns regarding the misuse of data, unfair distribution of generated value, and the loss of ownership and control over own data, can all be addressed in the ecosystem data governance.