The electrification of the production processes in the chemical industry is seen as a promising option to reduce its greenhouse gas emissions. This would result in a significant increase in the demand for electrical energy. The supply of electricity is becoming more fluctuating due to the variable nature of solar and wind energy. To ensure the stability of the grid, the consumption of electricity needs to equal the supply. Additionally, many countries are dealing with grid congestion, which means that there is insufficient transmission capacity for all consumers who want to receive power. One potential solution to these challenges is industrial demand response whereby companies adjust their electricity consumption to the available supply. The chemical industry might be able to provide this demand response through flexible operations of its production processes.
The goal of this research was to investigate whether companies have concrete plans to engage in energy flexibility with their production processes, and to determine the priority level they assign to it. This included examining how widely energy flexibility is incorporated into companies’ ten-year roadmaps, identifying the obstacles to electrification in the chemical industry, and understanding the challenges of operating chemical production flexibly.
To answer these questions, interviews were conducted with participants who work at chemical companies in the Netherlands. The participants were recruited by compiling a list of all chemical companies in the Netherlands and using LinkedIn to approach suitable candidates within each company with a request for an interview. Those participants who responded positively were interviewed using a semi-structured interview methodology in interviews lasting 30-45 minutes. The interviews were processed into anonymous summaries which were then used to answer the research questions.
Participants from twelve different companies were interviewed, representing approximately a quarter of all companies active in the Dutch chemical industry. These companies are active in a mix of sub-sectors of the chemical industry. A possible response bias should be noted, as companies already engaged in electrification and energy flexibility may be over-represented among these twelve companies compared to the chemical industry as a whole. Moreover, as only one participants was interviewed per company, there is a significant chance of personal bias affecting the results.
The results showed that flexible energy use was included in half of the twelve interviewed companies. However, it was a priority for only two companies. For the remaining four companies, flexible energy use was a side benefit of using both natural gas as well as electricity as a source for process heat in the transition to fully electrified process heat generation. Although four more companies had tentative plans for flexibility, they did not expect these to be feasible within the next ten years.
The results also showed that for eight out of twelve companies, lack of sufficient grid capacity was a crucial obstacle to electrification. However, only two of those companies indicated that they would be willing to consider operating their process flexibly in return for accelerated access to the desired grid capacity. The benefits of quicker access to the grid do not outweigh the obstacles to operating flexibly for most companies. The most important obstacles were related to the high investment cost of chemical plants in the chemical industry. At this juncture, the financial benefits of demand response do not outweigh the increased investments costs needed to make flexibility possible for most companies.

This thesis goes into the challenging process of selecting Enterprise Resource Planning (ERP) strategies, highlighting the complexities involved in deciding between different approaches. The study uses a qualitative research methodology, combining literature and interviews, to gain a better understanding of ERP strategy selection. The study sheds light on the intricate dynamics of several ERP strategies, such as the Two-Tier strategy and Best of Breed solutions, and provides insights into their benefits, downsides, and implementation implications.

The study creates a decision tool using an iterative approach driven by the double diamond technique with the goal of offering a systematic framework for selecting ERP strategies. This tool incorporates essential criteria determined through a literature research and interviews, including functionality, organisational complexity, IT expertise, and business alignment. The decision tool is intended to help organisations navigate the intricacies of ERP strategy selection, allowing them to make educated decisions that are aligned with their strategic goals and operational requirements.

The study's theoretical implications add to the existing body of knowledge by clarifying the nuances of the Two-Tier ERP strategy, highlighting the complexities of ERP strategy selection, identifying key criteria and constraints, and emphasising the importance of IT-business alignment in decision-making.

A transition from a linear to a Circular Economy is called for. Futures markets in secondary raw materials can support the circular economy through allowing circular businesses to hedge price risk. A blockchain-based architecture is designed using Design Science Research. The architecture can be implemented to facilitate the underlying infrastructure for futures markets: spot markets.

The Metaverse, although not a new concept, has had many developments recently. One of the core contributions for this is the current developments around blockchain technology and non-fungible tokens. Although it might be possible to create a metaverse on the blockchain, it would also be possible to create it on traditional technology, namely cloud computing. Regardless of the technology it will be built on, the space is expected to grow rapidly within the next few years, but still does not have any regulations attached to it within the EU. This thesis therefore looked into both technologies and compared them on 7 metrics: energy usage, security, scalability, governance, democratization, costs, and additional features. It was found that cloud computers are much more scalable and cost effective, while blockchain has more benefits when it comes to governance, democratization and additional features. Interviews were conducted with experts on blockchain, cloud computing, and policy advisors/strategist to investigate the most critical parts that must be regulated. The interviewee results showed that data privacy, IP rights, and governance & legal documentation were most important. Regulations and policies were then formulated with the help of the qualitative data obtained from the interviewees. These interviews yielded valuable solutions for the potential regulations, which the EU can use and

There is a need for a more comprehensive sociotechnical systems view on ML. Such a view looks at the development and use of an ML system in practice as being a sociotechnical ML system: "a system consisting of technical artefacts, human agents and institutions, in which a machine-based subsystem influences its real or virtual environment by automating, supporting or augmenting decision-making". This research takes on this view to design a sociotechnical guide for ML practice, centring the specication of sociotechnical ML systems. Taking on the guidelines contributes to a safe and effective development and use of ML systems.

This thesis combines a high-tech, capital product, with servitization methods in order to create a product-service system. Servitization has allowed companies to align their interests and create a co-creation environment. The overall objective of this thesis was to find an improvement over the current service design of Beaver vessels. This new design is specifically tuned towards an increase in customer retention and needs to have a tier-based approach.
The methodology used to create this service design had a research and a design component. The research component is twofold, research is done on the available literature to gain insights in relevant state of the art literature. The next part of research is done on the internal processes of the company, in order to determine where opportunities lie regarding best practices as seen in the literature.
Following from this research, requirements have been determined on which the eventual service design has been created. Requirements follow from either literature, analysis of internal processes, or company instructions.
This project has shown that there is an opportunity to be captured by offering a tiered-based agreement with customers. Advantageous financial effects are a better grip on expenses on the customers end, making it a strong option compared to maintaining the product themselves.

"Imagineering is letting your imagination soar, and then engineering it down to earth." (Alcoa Aluminum, 1942)

The fourth industrial revolution is focused on integrating technologies into the production environment that can increase revenue and optimize production. As the lines between the physical and digital worlds are blurring with the introduction of new technologies, companies have to be open to digital transformations and employ new management approaches. Industry 4.0 has also shown to be a great opportunity for the progress of the food sector. Augmented reality smart glasses, a technology that is worn like normal glasses but overlays the user’s view with virtual information is one such technology within Industry 4.0 that can transform the internal processes of a business. However, in literature, opportunities for digitalization in the food sector seemed under-researched and had not covered the possibilities of digitalizing food safety and quality business processes using smart glasses.

Cargill is a multinational corporation operating in the areas of food, agriculture, financial, and industrial products, that started exploring the usage of augmented reality smart glasses in the early stages of the COVID-19 pandemic. During these dire times, Cargill explored the solution of AR smart glasses for conducting remote food audits. However, this device was limited to this use case only Cargill was keen on expanding the applications of the novel technology. Additionally, they were looking for an implementation plan to adopt smart glasses in different use cases. The working, principles, and components required for the smooth functioning of augmented reality technologies were explored in great detail. Value drivers and barriers to adoption that may affect the future of the technology were studied. The domain of food safety, quality, and regulatory within the edible oils supply chain of Cargill was chosen to identify potential applications. Resources, competencies, and capabilities required to embrace the technology were recognized. Finally, an implementation plan was drafted for organizations that would like to adopt augmented reality smart glasses to digitalize relevant business processes.

The Dutch transition towards carbon-neutral cities is in full swing and the share of local solar and wind energy is growing rapidly, as well as the electrification of heating, cooling and mobility. Amsterdam is getting the energy transition started and is a forerunner when it comes to developments within the smart energy domain. As a result, multiple smart energy projects are carried out and will be carried out in Amsterdam in order to make Amsterdam a smart energy city. However, it is still unclear in what way these technological developments influence local communities and businesses and how we make sure vulnerable groups are not pushed out, isolated, and marginalized from important urban services, amenities, commercial flows en jobs. This brings us to the concept of inclusion and the need to build inclusive cities. Inclusion exists in many forms but in the smart energy domain mainly ensuring equal rights and equal participation of all citizens including the most vulnerable groups, remains a challenge. Moreover, enabling the disadvantaged and typically low-income people to share in rising prosperity as a result of technology development and eliminating inequities by providing information and financial support, is often not a main concern in smart energy projects which results in smart energy cities being not inclusive. Therefore, the main research question in this thesis is as follows: "How can inclusion be achieved within the smart energy domain of the city of Amsterdam in order become an inclusive smart energy city?". This thesis consists of a theoretical chapter in which a theoretical framework is used that aims at mapping the pillars of an inclusive smart energy city. This framework is used in the empirical part of the thesis in order to found out to what extent inclusion is present within smart energy projects in Amsterdam and how inclusion can be achieved. This empirical part of the thesis consists of an embedded case study that involves two smart energy projects in Amsterdam that are chosen as sub-units of analysis. The first sub-unit of analysis is the Local Inclusive Future Energy (LIFE) project and the second sub-unit of analysis is the Lighthouse City Amsterdam Project. Data is collected through documents and interviews. The interviews are conducted as part of qualitative research and analysed using ATLAS.ti. The results of the case study showed that despite the fact that participation and inclusion are often part of smart energy project plans, effectively achieving it remains a challenge. Challenges related to inclusion identified in this research are the gap between technology and society, limitations as a result of regulations, the rise of inequality, and the existence of different perspectives. Nevertheless, these challenges can be faced by using the theoretical framework as a guideline and in this way meet the objectives of an inclusive smart energy city.

Digital transformation in procurement is important for Purchasing and Supply Management (PSM) organisations to develop global organisational competitiveness. Generally, procurement performance can be determined and improved through the use of a maturity model. Existing models do not cover the latest (e-)procurement processes (E-Sourcing, Procure-to-Pay, and Supplier Relationship Management) resulting from digitalisation. Therefore, an extension to existing maturity models was required to assess company performance. A literature study was conducted to describe and explain meaningful concepts and several maturity models were selected to review and compare the frameworks most suitable for an extension. Experiences of a major PSM organisation called the Corporate Group strengthened the research study by determining key topics and industry best practices, providing a foundation for the extension. A company analysis resulted in an understanding of (digital) procurement and the selection of Schiele (2007) due to its comprehensiveness. Semi-structured interviews were conducted with procurement experts to review Schiele's model and develop an extension. The extended model contains an additional spend management domain and a new dimension covering e-procurement. Multiple purchasing functions and stage descriptions were developed, whereby the model consists of 56 original and 22 new assessment questions. The new dimension includes Spend & Data Management, Contract Management, Market Intelligence, E-Sourcing, Transactional Procurement, and Supplier Relationship Management. Entity A is part of The Corporate Group and provided the ideal opportunity to test the extended model through a multiple-case study. Maturity assessments were conducted of several operating companies (OpCos) to determine maturity levels and whether the extended model actually measured purchasing maturity. The extended model was able to quickly provide a performance overview and guide the OpCos towards improvement. Self-assessments as an alternative for third-party audits were provided, including a digitalised approach to process data easily. However, comparing results among OpCos outside of their sub-unit delivered an unexpected outcome. It was questionable whether the model measured purchasing maturity or rather the perception of it. Although the results were unexpected, the extension appeared to be more accurate. This was most likely due to the elaborate stage descriptions, enabling assessors to identify stage levels more easily. This research study provided a foundation for assessing e-procurement using an extended maturity model but limited itself to the Corporate Group. Thereby, it confirmed the criticism regarding the four stages being too rigid. Potential future research is recommended to improve both the extended model and the assessment approach. First, it must be shown to what extent e-procurement is covered in the new dimension and whether the extended model can be sufficiently generalised. Interviews should be conducted with procurement experts from different companies or industries to validate the extension through statistical analysis. Secondly, the assessment approach could be improved by including a control person and more experienced people, ensuring critical thinking and more realistic maturity levels. Self-assessments are not necessarily less reliable compared to external audits, but they rather need support to guide the process through a standard protocol. The subsequent step should be to formalise a proper action plan to initiate improvement.

The European Commission recently published a proposal for an Artificial Intelligence (AI) act that requires the development of trustworthy AI systems for European Union markets. The proposal clearly mentions that AI systems should make use of Explainable AI tools to increase transparency and interpretability.Financial institutions in the Netherlands are required by law to detect and monitor fraud in their infrastructure. Fraud detection in Financial Services (FS) or the FinTech industry is increasingly performed by Machine Learning (ML) and Artificial Neural Network models that provide high classificationperformance. ML/ANN-based fraud detection systems that are necessary for maintaining trust in the Dutch financial system are classified as high-risk applications by the proposal for the EU AI act. The EU AI act will directly impact high-risk AI applications used within the EU markets, Therefore,the Dutch financial institution sponsoring this research wants to future-proof their ML-based fraud detection to improve transparency and trust by solving the model interpretability problem. Explainable Artificial Intelligence (XAI) is a domain of AI research that seeks to solve model interpretabilityproblems of black-box ML models. In this thesis research, proofs of concepts are demonstrated for the investigation of two XAI approaches - TreeSHAP & Diverse Counterfactuals to improve the model explainabilityor interpretability of ML/ANN-based fraud detection systems. This research pioneers the investigation of Diverse Counterfactuals to improve model interpretability in ML/ANN-based frauddetection systems. Based on the existing literature, this is the first instance of research investigating Diverse Counterfactuals for generating explanations to ML/ANN-based fraud detection models trained using synthetic transaction datasets. Before demonstrating the proofs-of-concept, an extensive literature survey has been conducted to map the XAI research landscape, formulate an XAI taxonomy, and conduct a comparative analysis of XAI approaches to select and describe in detail, the relevant approaches for the use-case at hand. Subsequently, several ML and ANN models have been trained and tested using the PaySim synthetic transaction datasets. To overcome model performance challenges due to data quality issues and high class imbalancein the datasets, several experimentation scenarios involving hyperparameter optimization, SMOTE Oversampling and class-weighting have been investigated. Subsequently, two high-performing models (XGBoost & MLP) from these experiments have been used to demonstrate the proofs of conceptsby investigating TreeSHAP and Diverse Counterfactual algorithms. TreeSHAP algorithm greatly improved the interpretability of the global and local model behavior of the XGBoost-based fraud detection models. Diverse Counterfactuals algorithm-generated diverse but unfeasible counterfactuals. Counterfactual algorithms suffer from computational inefficiency and therefore, further research has to be conducted to generate feasible counterfactuals. Future work on model explainability should also conduct a human-grounded evaluation of the explanations to evaluate the quality or goodness of the explanations. Finally, real-world transaction datasets should be used instead of synthetic datasets so that the research is generalizable to the real world

The international trade sector is projected to undergo a significant shift in the next years, with transparency and trust being the most pressing concerns to be resolved as part of the Industry 4.0 revolution (PwC, 2019). International trade consists of several complex process involving several intermediaries (actors). Due to the complexity in international trade, the amount of errors in manual paperwork, delayed order deliveries and the deterioration in quality of products throughout its shipment may arise. As the industry tries to deliver precise and realtime information that can be transmitted to many parties, the volume of data increases.

This master thesis research has the aim to investigate how Data & Analytics enhance monitoring, reporting and control in project portfolio management practices to improve portfolio decision-making in the Architecture, Engineering and Construction (AEC) industry. Adequately and regularly reconsidering the project portfolio, with interdependencies towards the internal and external complex and rapidly changing environment, has shown to be a determinant for being successful in creating competitive advantage and securing the future of organisations that are project-based. Especially in the information technology (IT) field, rapid technological changes aim for dynamic organisational capabilities. This is reflected by the demand for digitized project portfolio management (PPM) using Data & Analytics. Although literature in the academic research field of project and project portfolio management describe different frameworks for PPM, there is a lack of context and practice. Information regarding comprehensive PPM frameworks that incorporate organisational, technological, and environmental factors found through empirical research are missing. Moreover, industry specific factors are missing as frameworks are generic and standardized, existing PPM practices miss adequacy and regularity and are often based on intuition, power, opinion, and leadership. To enhance competitive advantage within the AEC industry, a comprehensive approach in PPM is investigated with a focus on digitization and Data & Analytics. The conceptual framework that is developed is built on the three pillars, people, processes, and technology. Adherent to the conceptual framework, key success factors (KSFs) for portfolio performance and project success in the AEC industry are found that in the end drive business success. Findings indicate that the whole range from descriptive analytics to prescriptive analytics apply to PPM practices. Most value can be derived through starting with improved descriptive analytics through visualization and dashboarding. The developed conceptual framework is theoretic and implementation of the framework in practice is subject to influencing factors. Findings indicate different categories of influencing factors, under which digitalization in the AEC industry, general barriers to PPM, project management and data management maturity, and barriers to Data & Analytics. Some factors are covered through the framework, other factors are refuted with resolutions.

Autonomous vehicles have advanced rapidly in the last decades. The automotive industry is making valiant efforts to strengthen autonomous vehicle technologies and improve overall road safety. Besides actual safety, feeling safe is equally important. In other words, there is also a socio-technical perspective of self-driving vehicles that should not be ignored. Currently, when pedestrians wish to cross the road, they tend to rely on informal communication with drivers. However, this will not be the case with fully autonomous vehicles as the driver or passenger will most probably execute various tasks and will not pay attention to the road. Consequently, this master thesis investigates the loosely coupled relationship between pedestrians and fully autonomous vehicles in crosswalk use cases with no traffic lights. The main research question that is investigated is "How should the interaction between humans and fully autonomous vehicles be established in order to influence road safety and road awareness?". To address this research question, this master thesis espouses notions used in the scientific field of Interaction Design and applies the goal-directed design approach.

Adaptive website content is an increasingly popular method for e-commerce platforms to personalize the content shown to its visitors in an attempt to increase the conversion rates of their platforms. However, in order to select the content relevant for a specific customer, the online platform should be able to rapidly interpret the explicit choices, as well as the implicitly sensed context, in which the choices are made. Creating a so-called context-aware e-commerce platform thus requires not only the understanding of users’ content-preferences, but also of the users’ context influencing this decision-making and the opportunities of the e-commerce platform to actively respond to this, in addition to analysis of customer behaviour that goes beyond traditional A/B testing used by websites and online services to quickly evaluate hypothesis with real users. Statistical choice modelling and machine learning are both methods with the ability of doing such advanced analysis of choice behaviour. By inferring preferences and trade-offs from people’s choices observed in real life, or hypothetically stated choice experiments, choice models can be estimated with which future choices can be predicted. As statistical choice models provide insight into the most important parameters that influence decisionmaking, conversion rates of e-commerce platforms can be maximized by optimizing those parameters. Moreover, it has long been acknowledged in discrete choice literature that the context in which a decision is made affects one’s decision-making. Machine learning on the other hand, takes an algorithmic approach and bases its prediction on patterns found in the data. The characteristic of machine learning to allow for high-order interactions that are not pre-specified, could be beneficial as the context in which decisions are made, is often fuzzy and highdimensional. However, as this is a black box method, the method will not explicitly give insight into the parameters influencing behaviour, but instead, train itself to maximize the outcome. Although a substantial amount of research is conducted on the difference between statistical choice models and machine learning, and A/B testing and machine learning are both widely used methods for the dynamic content selection on e-commerce platforms, and thus for conversion optimization, less research however, has been done on the complementary use of the two methods within the boundaries of a simple context-aware system-experiment. Nowadays, it is often stated that machine learning has an accuracy far beyond statistical methods and that it is the recommended method for future data analytics and prediction, as machine learning enables determination of outcomes in which large number of variables with complex relationships are involved. However, should machine learning always be the recommended method for an e-commerce platform trying to maximize its conversion rate with the use of dynamic content, or is more traditional statistical choice modelling sometimes still a better solution for smaller organizations less experienced in data analytics? The scientific objective of this study was to investigate the applicability of both statistical discrete response modelling and machine learning methods to maximize, separately or complementary, conversion rate on ecommerce platforms. By using the e-commerce platform Fatboy.com as test environment, in which both methods were required to handle the context-aware opportunities of contemporary online platforms, the potential in applicability of both methods could be examined. The objective within the case study Fatboy® was to understand how the conversion rates of e-commerce platforms can be maximized using statistical discrete response modelling, machine learning, or complementary to each other.

This paper presents a new approach combining dynamic capabilities (DCs) theory and stages of growth model theory determining that growth leaders in the insurance sector surpass pre-digital organisations operating in traditional markets due to high embedded learning capacity. I conducted nine in-depth structured interviews with senior and director-level experts working in the Dutch insurance sector. This allows for generalisability within the Financial Services sector, being very diverse: varying between pre-digital organisations and digital enterprises. This setting provides a useful context to explore how Dutch insurance businesses individually build DCs that bring about change by exploring past, current, future transformations, and reflect on what is desired from a normative perspective. Moreover, by focusing on the underlying processes and routines, I further investigated questions related to the methods, successes, risks, and failures identified at Dutch insurance companies that trigger or hinder the development of DCs for digital transformation.

Startup arguably becomes one of the key drivers in the country economy. The growth of startup depends on how well the ecosystem provides the support needed by the startup. Like other young companies, startups have higher failure rates due to lack of resources, networks, reputations, credibility, innovation, or marketing knowledge for developing and selling the products. To survive, most startups collaborate and join business incubation to sustain and improve their performance. Several factors have been identified on influencing startup performance growth in business incubation. However, the impact of business incubation to startup performance has yet to be validated quantitatively by including all possible factors known from previous research. Thus, the research focuses on measuring the impact of business incubation to startup performance and whether business incubation is beneficial to startup performance based on quantitative approach. The performance measurement criteria was selected based on previous business incubation research and adjusted based on Indonesia startup ecosystem. The academic and practical gap on business incubation and its implications for startup performance are also addressed by doing survey research to startups community in Indonesia. The expected result will give an insight of business incubation impact to startup performance, and which factor most influenced startup performance in business incubation. Moreover, advice to help incubators improve and increase their impact on startup performance will also be presented in this research.

This thesis discusses the way individuals in their role as citizens make trade-offs between open education data attributes. The Dutch government agencies lack of insight in the citizen preferences for open data policy attributes lead them to evaluate and develop their open data policy only from the data provider perspective. In order to address the problem, this research aims to identify the citizens preferences for the open education data policy using the citizens stated choice experiments. The experiment is based on Random Utility Maximization theory, the study infers the citizen preferences of open education data attributes based on their choices for several hypothetical open education data policy. The study combines 4 attributes with 3 attribute levels to create the hypothetical open education data policy: mode of information presentation, number of free engaging hackathon events, number of free citizen data skill training events, and risk of your personal data exposed to the public. The citizens significantly value risk of your personal data exposed to the public and mode of information presentation. Government agencies have limited option if it wants to extend the open education data implementation because citizens reluctance to compromise the data protection attribute. This research is a first attempt to extend citizen stated choice experiment approach for the valuation of citizen preferences in the context of open education data policy. It provides an alternative method for governments to evaluate and develop their open data policy alongside the commonly used government/data provider perspective