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.

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.

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.

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.

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.

The dynamic nature of the market with constant technological developments continuously changes expectations from users over time. This drives businesses to alter their product and service offerings to maintain a competitive advantage, stable growth and, high customer satisfaction. Since economic goals drive businesses, it is often assumed that abundance of choice options is better, and will eventually result in increased profitability for the business. However, this is not necessarily the case. Consumers may experience a subjective state of mind termed as “choice overload” when presented with a plenitude of choice options. Consequently, consumers may fall victim to indecision, reduced customer satisfaction, and increased regret, to name a few. Previous research has formulated a cohesive understanding of choice overload in consumer decision making. Extant research in the field of consumer behaviour has identified several antecedents and concomitants of choice overload experienced by consumers. A vast bulk of research has also discovered repugnant effects of choice overload, due to context-dependency and other intrinsic and extrinsic factors influencing the choice overload effect. As a result, the questions of when and whether large assortments are detrimental to consumers remains open. This, offers an opportunity to extend the literature in this field by considering different contextual factors and variables that were thus far overlooked. The present study specifically aims to reduce the research gap that exists between Human-Computer Interaction (HCI) by understanding the choice overload effect, within the domain of e-tourism. Apprehension of the choice environment and consumer purchase behaviour is essential to close this existing gap and increase customer satisfaction. In this study, choice complexity is considered as an antecedent of choice overload. Choice complexity encapsulates two structural factors of the choice set – number of alternatives and number of attributes/levels. These factors allow for the construction of a measurement variable for choice complexity (entropy) where high entropy translates to high choice complexity. Moreover, individual differences in maximizing behavioural tendency (in terms of strategy and goal) are investigated. When consumers score high on maximizing tendency strategy, they optimize choice through employing a strategy of extensive information search. Similarly, when consumers score high on maximizing tendency goals, they strive to obtain the best possible choice from the available alternatives. Post-choice satisfaction is defined as the post-decision evaluation of the choice selected by the consumer. Specifically measured on two constructs - general satisfaction and outcome satisfaction. General satisfaction measures satisfaction of the consumer related to the process of arriving at a decision. Whereas, outcome satisfaction measures satisfaction related to the certainty in the choice decision. A choice experiment practically assessed the relationship between choice complexity and post-choice satisfaction, moderated by consumer purchase behaviour. The experimental design consisted of a Low Complex (LC) choice set and a High Complex (HC) choice set (distinguished based on entropy measurements) that allowed for the measurement and comparison of perceived complexity in a complicated choice environment. Respondents conducted a post-choice questionnaire designed to assess post-choice satisfaction and perceived choice complexity. Consumer purchase behaviour was assessed in a different section of the survey based on two different scales - maximizing tendency strategy and maximizing tendency goal. Statistical analysis was conducted on the obtained data to find the relationship between the variables under study. The experiment established the existence of choice complexity in e-tourism. Results showed an inverse relationship between choice complexity and post-choice satisfaction indicating that respondents were less satisfied with their choice when presented with a choice set of high choice complexity. Moreover, maximizing tendency strategy negatively influenced this relationship. Maximizers (i.e., respondents who scored high on the scale assessing maximizing behavioural tendency for strategy), extensively search through alternatives, eventually to formulate trade-offs and comparisons between the alternatives presented. Such maximizers were less satisfied with their choice having gone through a choice set of high complexity as compared to a choice set of low complexity. No such effects were found for the scale, maximizing tendency goals. The detriments of offering too much choice are real. Businesses within service industries such as e-tourism are therefore recommended to improve the quality/quantity of content due to intrinsic (i.e., intangibility, high monetary value, less purchase frequency) and extrinsic (i.e., a high number of alternatives and number of attributes/levels) factors. Each of these factors may make the service offering more complex for consumers to choose from. Managerial implications of the present study include the perspective (technology-centered view and human-centered view) that businesses can adopt. This perspective acknowledges the existence of choice complexity and maximizing tendencies, thereby optimizing the digitized environment towards better personalization. Doing this correctly would result in increased customer satisfaction due to better adaptation of digital environments by businesses to the needs and behaviours of consumers. The inclusion of entropy accurately provides the amount of information in bits; this measurement variable could be used by businesses to improve their algorithms. Finally, some companies in e-tourism have already begun to implement similar strategies, and reported in a significant increase in customer satisfaction, reservations, and overall sales. This gives evidence towards the practical importance of this study, and further emphasizes that businesses can indeed optimize their approach specifically towards the quality/quantity of content provided to consumers. In conclusion, the present study shows a negative relationship between choice complexity and post-choice satisfaction, with the inclusion of maximizing tendencies within the domain of e-tourism. Business may derive implications from this research to optimize their digital environments through increase in content personalization and reduction in choice complexity.

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.

With tackling the problems of resource depletion and waste, the currently booming topic of a circular economy (CE) comes up. This concept goes beyond the processes of reusing and recycling products and looks at the entire material and energy loops of products. The current economy, or linear economy, is still the status quo where products are used in a take-make-use-dispose manner. It is time for businesses to become part of the CE by designing circular business models (CBMs). This thesis studied circular business model patterns (CBMPs) in scientific literature and in business practice. CBMPs are building blocks of CBMs and considered powerful and useful tools for business model innovation (BMI). The case survey methodology analyzed 34 companies and two semi-structured interviews were conducted. Hierarchical clustering analysis and chi-square tests were used for data analysis. The result of this thesis is a more comprehensive list of CBMPs (13 in total). Recommendations are given to academia, policy makers and companies. For CE to happen, many changes are needed and currently the principles of CE are not widely adopted. With the arrival of CE companies and their business models need to change. CBMPs can help the BMI process by giving the process the right direction.

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.

Today’s market environment is characterized by increasing competition. This competition forces incumbents to innovate if they want to sustain their competitive advantage. Large enterprises start to adopt methodologies pioneered within the startup world, like the lean startup methodology, the innovate. The adoption of this methodology has proven to be difficult, and incumbents face a lack of control with regards to these ventures. The practical objective of this thesis is to enable large enterprises to control and steer internal lean ventures (ventures following the lean startup methodology) effectively. The theoretical objective is to bridge the knowledge gap concerning the understanding of internal venturing and the application of the lean startup methodology in large enterprises. To reach the objective, a case study within a large enterprise was conducted to answer the research question and reach the research objective. 16 in-depth interviews with relevant stakeholders were conducted to collect data. Finally, the results were validated through relevant literature and by comparison to the results of six additional interviews, conducted with two other large enterprises. The study found that two elements influence the success of the venturing process: First, the implementation of the venturing process itself (Section 5.1). Especially the alignment and coordination of different stakeholders as well as the quality of the employed metrics. Secondly, a set of additional factors arising from the context in which the process is embedded (Section 5.2). This context is made up of the mindset and culture of the company, the organizational environment as well as the quality of its adoption of the lean startup methodology. The analysis of resulted in the identification of the five recommendations to improve internal lean venturing within large enterprises. Details can be found in Section 5.4. In conclusion, implementing the lean startup methodology can help navigate this uncertainty surrounding innovation. This implementation requires reliable metrics, company-wide alignment, and the commitment to the methodology. Additional to the implementation, company and industry specific factors mediate the success of a venturing initiative. Large enterprises are advised to take an honest self-assessment and follow a searching and learning approach to iteratively and successfully optimize their process for maximal venturing success.

Information systems (IS) in Dutch first-line healthcare are organized complex and inefficiently. This limits innovation and interoperability of IS in this domain. Innovation is considered useful to make first-line healthcare more efficient. Digital platform literature shows a how a digital platform provides a more flexible approach to IS and greater rate of innovation. There is little ex-ante design knowledge on how to develop a digital platform. Also, transforming IS in healthcare is complex and often fails. Moreover, openness of a platform can enhance innovation but also introduces risks such as loss of control. This research focused on examining what architectural configurations can facilitate a desired level of platform openness for the Dutch primary healthcare domain. A design science research approach was used to answer this question. Design choices for architectural openness were distilled from literature. A concept artefact on design choices was developed and evaluated with domain experts. The study shows that openness is desired from the perspective of IS suppliers but that openness can never come at the cost of loss of quality or security. Respondents are reluctant to open up critical functionality embedded in the platform. Future researchers are suggested to study the design of boundary resources to manage control over complements and the relationship with complementors.

This report set out to provide an answer on the question of whether the standard format of a startup accelerator program would transfer well to an internal corporate accelerator program, and, when this was proven to not be the case, it was shown how this format could be adapted for internal corporate purposes by answering the three empirical research questions of the study. The first question was concerned with whether the components of this standard startup accelerator format would reduce the difficulty in reaching the goals of an internal corporate accelerator program, and it was shown that only half of the components in fact did so. The second question was what the actual levels of difficulty in reaching the goals of such a program were, and it was found that there was a strong distribution in the experienced levels of difficulty with reaching the goals by the population that was analyzed in the current study. The third and final question concerned whether this observed distribution in the levels of experienced difficulty could be explained by the characteristics that characterize internal corporate New Product Development projects, and it was found that this was indeed possible for some of the goals. The results of these analyses were combined to formulate a set of design principles, which could be used to determine which components to include in a personalized internal corporate accelerator program, depending on the characteristics of a specific project in the program. Furthermore, it was shown how the design principles could be used to determine the proper division of time and resources over these components in the program. Finally, it was argued that the principles could be used for selection purposes, to select only those projects to partake in an internal corporate accelerator program that would stand to gain the most benefit from such a program. After formulating the design principles for such programs, it was argued that these internal corporate accelerator programs could be used as a vehicle for introducing improvements to the regular New Product Development processes of corporate organizations.

This master thesis explores the boundary resources in Dutch first line healthcare that are required in order to increase the intent of Information Systems suppliers in to join a digital platform. This is researched, as information systems suppliers lack an ability to innovate. Innovation can be increased by an open digital platform for information system suppliers as boundary resources can affect the degree of openness in a platform, which in return affects the willingness to join the platform. When more IS suppliers join a platform, the platform becomes more innovative. This is important, as in Dutch healthcare, IS needs to innovate in order to deal with tomorrows challenges. Currently, unfortunately, this is not happing. The boundary resources are evaluated by industry experts by means of a Multi Criteria Decision Analysis, using the Best-Worst Method.

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.

Today’s energy system has originally been developed based on central energy production and a passive consumer, whose interests have to be represented by the energy suppliers and distribution system operators. However, the industry is quickly changing. The share of renewable energy is growing, and consumers are increasingly involved in the production of energy. As a result, the share of intermittent energy sources increases and so does the need for flexibility in the energy industry. Therefore, it is questioned whether the current system in the energy industry still fits with today’s developments. To allow for flexible demand and supply of energy, digital technology for communication between computers or devices and electricity providers and consumers will become necessary. This will help to balance production and consumption at each time resolution, without high costs and unnecessary bothering of consumers. Blockchain is a technology that could potentially serve as a solution for a new energy industry system. Blockchain enables direct and reliable transactions of assets, between every party willing to do so, without the need for an intermediary or central party in control...

Incorporating big data into decision-making provides a substantial competitive advantage, leading organisations to increasingly adopt a data-driven strategy. However, the adoption by organisations often remains unsuccessful due to limitations associated with monolithic data architectures, such as data lakes and data warehouses. Data mesh is introduced as a decentralised socio-technical approach to alternatively manage data, aiming to overcome the limitations and gain the benefits of embracing a data-driven strategy. However, there is a lack of guidance on how to implement data mesh. The availability of generic and concrete data mesh implementation steps, including a maturity assessment, would be helpful for organisations. Consequently, this research proposes the design of a Data Mesh Maturity Assessment Model (DMMAM). In response to the main research question: ”How to assess the maturity of a data mesh implementation within an organisation?”, enabling the assessment of how mature a data mesh implementation is, by means of the DMMAM, would provide the guidance that is currently lacking for organisations. The qualitative Design Science Research Methodology is employed to structure the design process. Literature research, interviews, and cases are conducted to explore the contribution of, as well as design, demonstrate, and evaluate the DMMAM. This research shows that the developed DMMAM evaluates data mesh based on four maturity levels, classified as Level 0: Non-Initiated, Level 1: Conceptual, Level 2: Defined, and Level 3: Achieved, and that data mesh is represented by five dimensions: A. Data Foundation & Organisational Change, B. Domain Oriented Decentralised Data Ownership & Architecture, C. Data as a Product, D. Self-Serve Data Infrastructure as a Platform, and E. Federated Computational Governance. These five dimensions are collectively represented by 54 characteristics. For each characteristic, labels for the People, Process, Technology (PPT) perspectives are assigned. Additionally, questions are formulated, and criteria and requirements are provided for all characteristics at each maturity level. This enables participants to self-assess their organisation’s maturity by individually rating 54 questions based on the current and target levels. Conducting the self-assessment yields various outcomes, including an overall data mesh maturity score, individual dimensional maturity scores, and maturity scores from PPT-perspectives. Moreover, the assessment helps to identify maturity gaps and allows benchmarking to compare results across organisations, providing organisations with guidance for improvement. The demonstration and evaluation of the DMMAM through maturity assessments for three organisations have demonstrated its applicability and usefulness. However, it is important to acknowledge that this research represents the first attempt to provide a comprehensive framework for assessing data mesh maturity in organisations and is not without limitations. Future research is proposed to further refine and improve the DMMAM, supported by data mesh SME’s and practitioners, to ensure that the model remains up-to-date with the latest available research on data mesh. In addition, including additional guidance as an outcome of the maturity assessment would make the assessment more actionable and pragmatic. Furthermore, examining the optimal assessment structure will enhance the model’s reliability and validity. Moreover, expanding the benchmark functionality will enable statistical generalisations and comparisons for organisations within and across industries. At last, it is suggested to do further research about examining the overall contribution of data mesh as a strategy element towards becoming data-driven.

Practical problem: In today's digitally transformed and connected world, data has become a critical strategic corporate resource. In this context, data marketplaces are becoming more popular since they enable wider accessibility and more efficient interaction among companies. Despite this, there are several barriers in sharing data through this type of platforms, for instance, lack of trust, security, privacy and transparency. The introduction of privacy-enhancing technologies, such as secure Multi-Party Computation (MPC) could offer a significant contribution to overcoming these barriers. However, it is still unclear if secure MPC could be implemented in the data marketplace domain, especially as an instrument for controlling the platform, and what are the affordances that it could offer to data marketplace providers. For this reason, this research study aims to investigate the potential adoption of secure MPC by a data marketplace provider for realising platform control. Methodology: the exploratory nature of this research required to conduct a qualitative study to address the problem and achieve the aforementioned target. In particular, given the specific characteristics of this research, a survey research was undertaken. Regarding the data collection method, semi-structured interviews were conducted among data marketplace providers operating in the mobility domain, data marketplace experts and MPC developers and experts. Results: the adoption of MPC could generate three main affordances for a data marketplace provider in terms of platform control: (1) preserving the data, (2) enabling data ownership and (3) preserving the result of the computation. These affordances are generated by the relationship between the data marketplace provider’s goals in terms of platform control and the features of the MPC technology. Regarding the former, the following goals were identified: (1) ensure the security and the privacy of the data; (2) guarantee that a data provider has complete control over its data; (3) ensure the correct execution of the computation. Concerning the latter, three key features offered by the MPC technology could enable platform control: (1) information-theoretic security or computational security, (2) agreement protocols before starting the computation and identification mechanisms if someone deviates from it, (3) and correct execution of the computation. The realisation of the affordances could be influenced by three factors: (1) perception of the technology, (2) need for the technology, and (3) degree of effort required. The results showed that secure MPC could satisfy several different needs of a data marketplace provider. However, some constraints could influence the adoption of MPC among data marketplace providers. Firstly, a data marketplace provider may perceive the MPC as unsafe because of the difficulty to understand the technology. Secondly, a data marketplace provider could consider that secure MPC does not currently present an adequate maturity level to adopt the technology in its platform. Finally, a data marketplace provider could prefer to maintain its current situation in order to avoid a radical change. The adoption of MPC technology by a data marketplace provider could cause several impacts on its platform. If the platform has a centralised structure, the data will not be stored in the platform anymore, but they will remain with the data provider. Moreover, if a data marketplace focuses only on data exchange offerings, it would be able to offer a new type of product in its platform (e.g. insights). Finally, the adoption of the MPC in a data marketplace could cause additional overhead in the functioning of the platform. Theoretical and practical contributions: regarding the former, this study contributes to the literature of data markets, platform control, MPC, and affordance theory. Concerning the latter, this research provides practical contributions to the business actors involved in data-sharing domains and to MPC developers. Limitations: firstly, it was not possible to focus the research on data marketplace involved exclusively in the mobility domain and to reach theoretical saturation also for the fourth sub-research question of the study because of the difficulty of reaching informants. Secondly, two of the data marketplace providers interviewed worked for a data marketplace, which is not currently operating anymore. Moreover, in some cases, it was necessary to interview a person with a different role in the company compared to the one initially selected. Thirdly, since some of the interviewees did not have the time to study the MPC description document before the interview, it was necessary to verbally explain it, thus possibly affecting both the validation of the document and the understanding of the technology. Future research: Firstly, by conducting more interviews, it could be possible to identify more factors. Secondly, future research could be undertaken to validate the model of this research through quantitative studies. Thirdly, further researches could be pursued to update the taxonomy of data marketplaces. Fourthly, future research could be carried out to explore the introduction of secure MPC in other settings. Finally, it could be interesting to explore the different perspectives of the actors involved in a data marketplace (e.g. data providers and buyers).

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.