Audit analytics (AA) enables the improvement of the effectiveness and efficiency of the internal audit function (IAF). In this research, we follow Lambrecht et al.’s (2011, p.3) and define AA as “the process of identifying, gathering, validating, analyzing, and interpreting data using information and communication technology to further the purpose and mission of internal auditing”. AA is an umbrella term encompassing various forms of technology-based audit approaches, from continuous auditing to advanced machine learning. Due to the increased effectiveness and efficiency, AA enables the IAF to provide proactive and ongoing assurance services instead of periodic assessment, expand the (internal) audit service areas with the same resources, enable larger samples or even the whole population for analysis, and can provide insight and foresight for its stakeholders, in addition to the hindsight of the (possibly) already occurred risks...

Despite the recent expansion of digital lending platforms in developing countries, marginalized segments still face challenges in accessing credit. Many borrowers are excluded due to the absence of formal financial histories or insufficient profiles. Existing research primarily focuses on improving model accuracy and ensuring profitability rather than addressing inclusion. Hence, more inclusive digital lending systems are needed. Inclusion in this study refers to “the equitable access, distribution, and utilization of financial resources, ensuring that all societal segments, particularly underserved populations, can participate meaningfully in lending systems.” This definition emphasizes removing systemic challenges and fostering empowerment by enabling individuals to make informed decisions....

System developers and automotive manufacturers have proposed and used advanced driver assistance systems to enable the deployment of next-generation applications in connected vehicles, such as cooperative perception and vehicle-to-everything communication, while addressing autonomy-related challenges. The example deployment of these applications and systems, including proof-of-concept levels, generally depends on vehicle sensor suites, communication units, large-scale memory systems, and high-performance computing (HPC) units, which are together responsible for sensing the vehicle's environment, efficient data processing, and application deployment using machine learning models or rule-based algorithms. These models and algorithms are generally computationally complex as they process sensed and transformed data using statistical algorithms and deep learning models, such as those using convolutional operations and attention mechanisms, for tasks including decision-making, actuation, system analysis, environment sensing, monitoring, and infotainment applications. The computational complexity of these algorithms and models further increases upon scaling, primarily due to high data volumes, which also requires deep convolutions or similar operations for data processing. These operations require high-performance computing units to meet the application's operational and performance requirements, including latency, throughput, and accuracy.

Generally, the AI models used in connected vehicle applications are designed with a prime focus on model performance metrics; however, their high energy consumption and resulting carbon footprints are often overlooked. Recent studies have shown that the computing requirements for autonomous and connected vehicles can themselves become a significant component of overall energy consumption. For example, large-scale deployment of on-board AI across a global vehicle fleet could generate carbon emissions comparable to those of today's entire data center infrastructure. The computing hardware inside autonomous and connected vehicles can consume hundreds to over a thousand watts when running multiple perception and decision models simultaneously. Since these vehicles are battery-powered, this computing energy directly reduces driving range and increases operational cost. At fleet scale, this translates into a substantial carbon footprint, even when vehicles are electric. Therefore, energy efficiency is a primary design requirement, not only for sustainability but also for maintaining vehicle usability, battery longevity, and cost-efficiency. This thesis addresses the disparity between the strong research focus on model accuracy and the limited focus on energy usage by developing and evaluating an energy-aware adaptive framework for AI-driven vehicular services, such as non-safety-critical perception and high-definition mapping applications. The framework achieves energy and runtime improvements through energy-aware training, resource allocation, and adaptive deployment of computationally intensive models.

Previous research has proposed developing energy-efficient solutions in the hardware and software domains. For example, hardware-related energy-efficient solutions include transitioning from high-end graphical processing units to specialized AI accelerators and integrated circuits, which can process neural networks and related operations more efficiently. Similarly, architectural shifts and software-level solutions include transitioning from the centralized computing approach to dedicated edge computing and tiny machine learning solutions. However, the research scope remains within hardware-software co-design and optimization. By specifically targeting software-level optimizations, this thesis explores approximate computing (AxC) as a mechanism to utilize the error resilience of AI models in the perception and latency-tolerant applications of the vehicle-edge computing ecosystem. By balancing a trade-off between quality of experience and energy efficiency, AxC provides opportunities to reduce on-board energy demands and resulting carbon footprints of vehicle and edge devices while maintaining acceptable application performance levels. To explore and optimize the trade-off between model performance and energy consumption for connected autonomous vehicle applications, the following research questions are addressed:

1) What are the requirements for enabling energy efficiency in data-intensive vehicular services?

2) Which components can enable task deployments energy-efficiently and collaboratively in vehicle-edge-cloud computing?

3) How can energy-efficient components be integrated into an energy-aware adaptive software framework?

4) Can the framework effectively balance the trade-off between energy efficiency and performance in vehicle-edge-cloud computing scenarios?

Building upon existing research and knowledge on energy-efficient computing, this thesis addresses the above-mentioned questions. By addressing (RQ1), this research identifies the technical and operational requirements to enable and integrate energy efficiency into data-intensive vehicular services. These functional requirements include performing high-level computations with minimal energy use and efficiently processing large data streams on edge devices. Secondly, to design and develop energy-saving components (RQ2), the thesis proposes software-level approximation schemes combined with variational inference for both training-time and post-training model optimization and acceleration. Third, contributing to (RQ2) and (RQ3), the research explores ML model partitioning and computing resource allocation mechanisms to utilize the distributed and heterogeneous nature of the vehicle-edge-cloud environment for distributed training and inference. These explorations aim to meet service-level objective deployment using lookup table-based mechanisms. Addressing (RQ3) and (RQ4), the thesis integrates these components into an energy-aware adaptive software framework. This framework provides optimized model training and deployment strategies for distributed training and inference on heterogeneous computing resources, while effectively balancing the trade-off between energy efficiency and on-device application performance.

This thesis utilizes the design science methodology, adapting principles from the Information Systems Research Framework (design-as-a-search-process). This approach ensures research rigor to develop artefacts based on the application domain and existing theoretical knowledge. Further within the process, it adds design knowledge to the existing knowledge base of the application domain. As the development cycle of the research methodology includes tests and experiments, the effectiveness of the developed artefacts can be seen through test and experimental evaluation. Applying the proposed software approximation schemes, model partitioning, resource allocation, and adaptive deployment strategies on the state-of-the-art models shows up to 40% improvements in energy saving for less than 7% quality or model performance degradation when compared to the full precision and central computing methods. Software approximation schemes include the design of approximate multipliers, probabilistic approximation mechanisms, approximating convolutional, and fully connected layers for CNNs/DNNs. For the next-generation and memory/compute-intensive vision transformer models, this work proposes software-level approximation schemes based on variational inference, combined with post-training quantization and quantization-aware training, which show up to 35% improvements in energy efficiency for 6-8% quality loss. As the backbone of these next-generation vision models also includes multi-precision operands such as 8-bit, 16-bit, and 32-bit in layers and channels, the research also explores the advantage of mixed-precision operation to facilitate a balanced trade-off between models' energy usage and accuracy.

This research is among the first to investigate energy-aware requirements for application deployment beyond the traditional approach that generally focuses on cloud-based offloading mechanisms and model compression in the context of connected vehicle services and systems. The evaluation of the energy-aware framework on the popular edge devices shows the contribution of the thesis within the scope of distributed model computing using edge AI and sustainable computing practices. The research is set within the area of tiny machine learning and green AI principles. Future research can further develop adaptive algorithms that dynamically optimize energy use in real-time and investigate predictive models under varying conditions. Additionally, exploring the integration of Approximate Computing with emerging technologies like neuromorphic computing can improve processing efficiency in vehicular systems.

Open data intermediaries are crucial for the sustainability (i.e., long-term durability) of the open data ecosystem (ODE). They enhance the access to and the (re-)use of open data and connect other open data actors. Additionally, open data intermediaries play a role in mitigating information asymmetry between actors. However, despite the importance of open data intermediaries in the ODE having been widely acknowledged in research and practice, studies on open data intermediation business models are limited. This knowledge is essential to better understand the role of open data intermediaries within the ODE and provide recommendations to develop their business models in such a way that they support the overall sustainability of the ODE.

Through various methods, including case studies of Esri and OpenStreetMap, this dissertation advances the understanding of open data intermediaries, their position within the ODE, and their business models. In particular, this dissertation theoretically contributed to the definition of open data intermediaries, potential contributions of open data intermediaries, and the archetypes of open data intermediation business models. It also identified practical aspects to consider in developing open data intermediation business models that contribute to a sustainable ODE, ultimately enhancing the generation of open data value. This value, in turn, can be leveraged to foster innovation, promote economic well-being, and address pressing social and environmental challenges.

In the Dutch energy market, the facilitation of digital communication and information exchange is essential. From monitoring real-time data of energy supply to ensuring secure energy distribution, varying applications operate together to communicate information in the energy sector. With the advancement of quantum computing technology, security threats to cryptographic algorithms may no longer be safe. While there is a need to shift towards quantum-safe solutions, there is little knowledge about quantum-safe technology and the trajectories of the Dutch energy market in the quantum era. This research aims to explore potential trajectories in the energy sector to allow better preparation for the quantum era. The results provide insights into the stakeholder and institutional layers that may require interventions to develop quantum-safe applications in the Dutch energy market. Viewing the system as a complex system and determining target areas using a Technological Innovation System (TIS) framework, data is abstracted from an extensive literature study and interviews with experts. The functions of the TIS are used as codes to analyse the interviews. The results of the desk research, literature review and expert interviews are triangulated. These results show potential for three scenarios of the Future Energy System (FES), varying in interconnectedness and decentralisation. Scenario 1 presents a simultaneous transition across the target system, scenario 2 shows a top-to-bottom approach with priority at the highest level of Internet of Energy (IoE) and scenario 3 focuses on the security of localised energy communities with a bottom-to-top approach.

This thesis investigates the socio-technical factors influencing the use of Agile methods within project teams in local government organizations in the Netherlands, aiming to develop a practical framework to enhance Agile practices. The primary research question is: How can project teams in local governments stimulate the use of Agile methods? To answer this, the study addresses three sub-questions: (1) What team archetypes represent the typical use of Agile methods in local governments? (2) WWhat barriers and drivers towards the use of Agile methods are experienced by the teams? (3) How do different types of (Agile) team management impact the perceived performance by team members? Data was collected through qualitative methods, including interviews with team representatives of 15 teams across three local government organizations, and surveys with team members from one of these government organizations. The study categorizes teams into archetypes based on their Agile methodologies, reason to change, experience levels, and management support. Key barriers include lack of management support, lack of focus, lack of knowledge, unclear
roles, lack of alignment and organizational resistance to change. Important drivers for Agile adoption include Agile mindset, Agile coaching, dedicated key roles, dedicated management and freedom to experiment and structure. No significant difference in perceived performance was observed between team archetypes, indicating that perceived performance may be influenced by a broader range of factors beyond the scope of this study. The theoretical contribution extends the understanding of Agile adoption in public sector contexts, while the practical framework provides actionable steps for local government teams to navigate barriers and leverage drivers. Future research should broaden the scope to include diverse local government contexts, integrate quantitative data, and explore the long-term impacts of Agile practices and the application of the framework in a government, providing valuable data to inform ongoing Agile transformations in government settings.

Background: A shift in the organisation of oncology care towards collaboration between hospitals in oncology networks (CCNs) is observed to allow high-quality, accessible, and affordable care in the future. The collaborating hospitals align their care processes and design a joined regional care pathway (RCP) to standardise and structure their care. Within an RCP the patient is transferred between hospitals in the CCN, often for specialised and complex care like surgery.

Problem statement: The various disciplines and institutions involved in care delivery ask for coordination of care, clear agreements, and proper handovers of tasks. Inter-organisational collaborations within a CCN introduce new challenges (e.g. in information exchange) regarding coordination compared to multidisciplinary intra-hospital collaboration, because organisational and geographic boundaries are crossed. Unfortunately, little is known about how coordination between collaborating hospitals in a regional oncology care pathway can or should be achieved. Therefore, this thesis aims to contribute by developing a framework guiding improvements in coordination for RCPs.

Methods: A mixed-method approach was used which included a literature search and semi-structured interviews with healthcare professionals in oncology networks, policymakers, and patients. A selected conceptual framework of care coordination was adapted based on literature and each aspect within the framework was defined. Additionally, evaluation measures and improvement actions for each aspect were identified. The interviews were used to elaborate the findings from literature and incorporate the right context for RCPs. Moreover, the interviews gave insights into the experienced challenges and best practices. Lastly, the developed framework was applied in a case study.

Results: The framework for Care Coordination in Chronic and Complex Disease Management was opted as an initial conceptual framework and was extended by three additional aspects identified by other studies. The framework categorises the aspects of care coordination in domains creating an overview of the relation of each aspect in the coordination processes. The interviews resulted in two extensions of the original framework: 1) Alignment of resources and processes besides cultural alignment; and 2) leadership/coordination at the network level besides the boundary-spanning role of the case manager at the patient level. All aspects of the framework fit the RCP context, although some overlap between the aspects was identified.

Conclusion: The framework supports hospitals in improving coordination between hospitals within the RCP by offering an overview of aspects, evaluation measures and improvement possibilities. Information exchange, communication, and clear RCP protocols with defined roles and responsibilities turn out to be important aspects that facilitate coordination. These should be supported by boundary spanning actions and emergent conditions, like personal relationships and trust. Especially organising digital exchange of patient information is important for CCNs, but is still challenging to establish. A well-designed monitoring system including reminders and alerts will support coordination. However, regional dashboard development is challenged by non-structured data and differences in data structures between the hospitals. To further scale up initiatives for larger challenges and more resourceful improvement initiatives (e.g. digital exchange of EMR data), close collaboration within and between CCNs, and exchange of knowledge, tools, and best practices should be established.

Decentralized Autonomous Organizations (DAOs) are a fast-growing group of blockchain and smart contract-based applications. DAOs aim to serve new decentralized governance and organizational needs for various business purposes, ranging from decentralized exchanges such as UniSwap and theDAO, to R&D like VitADAO, managing infrastructures like Arbitrum, to charity funds like PactDAO. DAO projects have experienced an exponential growth over the past years. Although strongly rising in number, there is no clear consensus on what a DAO exactly entails. Additionally, despite this exponential growth, various DAO projects tend to pivot away from this form of governance or organization. They either experience a mismatch in the governance setup or the business objective, threatening their viability in the long-term. Also, as DAOs are built on blockchain infrastructure, this poses new governance challenges.
The goal of this thesis is to analyze how governance elements, business objectives and infrastructural choices can influence the long-term viability of DAOs. The main research objective of this PhD thesis is to:

“Develop a theory to explain how governance and infrastructure elements influence the long-term viability of decentralized autonomous organizations on public permissionless blockchains.”

In the Netherlands, there has been a growing exposure of discriminatory practices across various domains. Notable instances such as the child benefit allowance scandal (referred to as the "toeslagen affaire") and concerns surrounding student allowance (DUO) have captured public attention. These problems are not confined to public domains; instances of discrimination are also being found within the financial lending markets.

While existing research offers critical discussions, practical analyses, and technical solutions regarding discrimination, there is a lack of well-defined design principles specifically addressing discrimination challenges in financial lending in the Netherlands. This study aims to bridge the gap by providing a concrete set of design principles that architects can utilize to effectively design non-discriminatory financial lending systems, while also recognizing the potential for policymakers to draw inspiration from these principles and develop universal guidelines or other policies to address this issue more broadly.

This study combined the Design Science Research approach and Principle Based Design to derive design principles for a socio-technical topic like non-discrimination. The study started with identifying the factors contributing to financial discrimination in the lending system through desk research and 13 semi-structured interviews with a wide range of scientific experts and industry professionals. To make the concept of discrimination more applicable in the context of financial lending, the study defined specific dimensions which are differential treatment, differential outcome, unequal access, and lack of transparency. This study then identified and categorized various factors that can contribute to one or more dimensions of discrimination into six domains: regulatory, human interaction, user experience, business, data, and technical. These domains help identify the specific areas in which discrimination can arise within the lending context and how different factors may overlap across multiple domains. This also highlighted the complexity of discrimination, demonstrating that discrimination is not limited to one domain, and addressing a single factor can have cascading effects, either reducing or exacerbating discrimination across other domains.

The study proceeded to identify the challenges encountered in realizing a non-discriminatory financial lending system in the Netherlands. This was accomplished through a combination of semi-structured interviews and literature review, which led to the identification of 13
challenges. Design principles were then developed that could address those challenges in collaboration with seven business and IT architects. These principles were also evaluated in two workshops involving another group of architects. Based on the feedback received, efforts were made to improve the structure and consistency of the principles, resulting in a final set of 11 principles. To enhance clarity, the principles were rearranged and grouped into four categories: culture, governance, process, and models.

The study acknowledges that while the identified design principles can contribute to reducing discrimination in the financial lending system, they do not fully resolve the problem. This is because many discriminatory issues are deeply rooted in society. However, the principles are valuable additions to existing practices and tools, providing a framework for organizations to improve their lending practices. In addition, the involvement of regulatory authorities is essential to address discrimination in the lending system. Without regulatory incentives or oversight, organizations are not motivated to proactively implement such principles or other initiatives aimed at promoting non-discriminatory lending practices. Moreover, the design principles have the potential to be applied in other areas, such as governmental financial allowances, extending their impact beyond the financial lending system.

Technological systems and infrastructures form the bedrock of modern society and it is system administrators (sysadmins) who configure, maintain and operate these infrastructures. More often than not, they do so behind the scenes. The work of system administration tends to be unseen and, consequently, not well known. After all, do you think of your IT help-desk when everything is working just fine? Usually, people reach out for help when something is not working as expected or when they need something. A lot of work and effort goes into ensuring that systems are working as expected most of the time and, paradoxically, this smooth functioning results in the invisibilization of the work and effort that went into it.
This PhD research focuses on system administration work and what that entails in day-to-day tasks. Instead of proposing technical and social solutions, we try to better understand the “problem” that these proposed solutions are meant to solve. Drawing from safety science research and feminist research approaches, we perform a qualitative exploration of sysadmins’ work. We center their experiences via an in-depth interview investigation and a focus group study. We identify and describe the coordination mechanisms and gender considerations embedded in their work. We shed light on care work as part of sysadmin work and the phenomenon of double invisibility that is experienced by sysadmins who are not cis men. The thesis wraps up with a set of recommendations for moving toward safe and equitable work environments for sysadmins.

From smart phones to speakers and watches, Edge Al is deployed on billions of devices to process large volumes of personal data efficiently, privately and in real-time. While Edge Al applications are promising, many recent incidents of bias in Al systems caution that Edge Al too, may systematically discriminate against groups of people based on their gender, race, age, accent, nationality and other personal attributes. More so, as the physical restrictions of Edge Al, together with the complexity of its heterogeneous and decentralised operating environment pose trade-offs when deploying Al to the edge.

This thesis is motivated by the societal demand for trustworthy Al, by the propensity of Al systems to be biased, and consequently by the need to detect and mitigate bias in diverse Edge Al applications. To address this need, this thesis develops design patterns for detecting and mitigating bias in the development of Edge Al systems. The design patterns present a generalisable approach for capturing established practices to detect and mitigate bias in machine learning. They make this knowledge readily accessible to researchers and practitioners that develop Edge Al, but who have limited prior experience with detecting and mitigating bias.

This thesis applies design science to the monetary and financial system as a whole. The application of this novel methodology offers new possibilities to examine this complex system. The contribution of this thesis is threefold. First, different theories on money, banking and systemic financial crises have been researched through an extensive literature review and balance sheets. Second, those theories have been used to develop design requirements and guidelines. Finally, the consensus and pivotal dissensions about the systemic problem(s) of the current monetary and financial system, requirements and guidelines among experts have been identified through semi-structured interviews. This research process results in widely supported requirements that demarcate the design space and widely supported guidelines that aim to give direction within the design space, that is, to the future development of the monetary and financial system.

The main artifact of this research are these three guidelines:
GDG 1: Develop and gradually introduce public digital money.
GDG 2: Move the financial system towards funding based on securities offering market liquidity.
GDG 3: Move financial regulation towards transparency.

In the current digitalised society keeping assets secure is one of the most prominent challenges organisations face. In the ongoing arms race between attackers and defenders, software security patching is a well-recognised and effective strategy to mitigate vulnerabilities in software products. However, organisations struggle with the best practice to “patch early and often”, resulting in vulnerabilities in software being exposed for much longer than desired. Prior research indicates the socio-technical nature of this practice forms the core of delays in software patch management. Developing a deeper understanding of the decision-making of IT practitioners and what socio-technical factors play a role in this process allows organisations to address the ineffectiveness of their security patch process. The main research question in this explorative research is: What socio-technical factors influence the effectiveness and timeliness of the security patching process in organisations? This Mixed Methods research combines qualitative data from interviews with IT practitioners, with a quantitative data exploration of the meaningfulness of organisational measurements. Findings show that IT practitioners go through a funnel of decision-making that influences the decision of what to patch, and when to patch. The presence and interplay of different socio-technical factors related to four main aspects of this decision (i.e., security, applicability, operability, and availability) result in tensions and trade-offs influencing the decision space of IT. Furthermore, this study indicates the interrelations between the significance of socio-technical factors, which is reduced by certain coping strategies applied by IT practitioners. This research reveals that having some measurement in place helps to understand the existence of challenges and the working of coping strategies, therefore contributing to an understanding of socio-technical challenges. However, it also reveals several limitations to the quality of existing data and difficulties in coming to measurements that provide meaningful information, due to socio-technical factors. The main contribution of this research is a better understanding of how socio-technical factors influence the decision-making process of IT practitioners. This research is limited in the way it uses quantitative data to understand patching activity. Future research is recommended to compare the potential discrepancy between what IT practitioners state influences the effectiveness of their security patch process and what the actual patching activity of IT practitioners reveals about the effectiveness of patching. This research furthermore hypothesises that not all socio-technical factors have the same level of significance. It is recommended to investigate the possibilities of quantification of the importance of each of the socio-technical challenges identified in this explorative study.

The modern world is under great pressure to fight climate change and limit global temperature rises to a sustainable level. As part of new mitigation strategies, the Voluntary Carbon Market (VCM) emerged after the compliance markets materialized. Via the voluntary carbon market companies can offset their carbon emissions. The captured carbon is transformed into ’carbon credits’ representing one tonne of Greenhouse gas (GHG) emissions captured or reduced. One of the leading quality and integrity concerns is the potential double counting of credits. In a nutshell, the voluntary carbon market is based on voluntary carbon offset projects, compliance schemes require governments to report their national mitigation activities in Nationally Determined Contributions (NDC)s, and the private sector needs to report on their environment, governance, and sustainability responsibilities. Double counting occurs among these three activities when captured carbon is allocated to more than one actor. This thesis consists of an exploratory, single case study based on the blockchain solution of Acorn Rabobank. Acorn is both a project developer of carbon credits and the enabler of its own digital marketplace. The data for the case study is comprised of in-house research provided by Acorn, interview transcripts, reports, and news articles. This data is used to answer the research question: “What framework can be developed to evaluate the benefits and limitations of blockchain-based platforms to prevent double counting in the voluntary carbon market?

To answer this research question several research activities were performed. The research activities consisted of selecting and defining the case, data collection, data interpretation, and theory building. Three forms of double counting were established. To further enrich the knowledge base the stakeholder dimension was mapped based on secondary sources and empirical knowledge gained through preliminary interviews. Several mapping techniques were employed to map the interrelations between the market mechanisms and double counting.

The MLGBE framework allows carbon market actors to evaluate and, if applicable, reason about the various available blockchain-based infrastructures as tokenizing carbon credits and the digitized trading of these credits. By doing so, they can identify the stakeholder dynamics with all policies, interrelated treaties, and community plans in place, to develop or evaluate sustainable blockchain-based platforms tailored to the market to prevent double counting.

Scientifically, new theory was built through the development of the MLGBE framework. By integrating multi-level governance theory and business & governance information to the particularities of asset tokenization in blockchain applications, a new approach to evaluate blockchain designs is presented. Secondly, the research delineates the issue of double counting in the voluntary carbon market, Lastly, the research corroborates earlier findings in the logistics domain of the importance of reliability of data elements of blockchain-based applications used for audit trails.

Concerning double counting further research is recommended on the potential of blockchain solutions to link schemes together to facilitate a global carbon market, how these link to the eventual implementation of Article 6, and the potential nesting within countries’ NDCs. For
the MLGBE framework, future research should look into the interaction between the standardization/fragmentation dependent on policy layers and the different rights in the governance
requirements.

With the rise of the internet, mobile technologies, and, most crucially, e-commerce, the retail industry has changed dramatically in recent decades. Emerging digital technologies, such as information and automation, have recently made it feasible for even more types of retail business to exist. One of the key strategic themes for retail businesses in Europe, and indeed the entire world, is the digital transformation of the supply chain. However, many retail representatives have only a vague idea of the exact steps that need to be taken in the digital arena. The retail supply chain does not seem to know how to measure the actual level of its digitalisation, and it is not clear whether the current digital development of the supply chain is on a good track, thus lacking guidelines for further strategy development and investment. Previous studies have shown that Maturity Model (MM) helps enable retail companies assess their level of digitization. On the one hand, MM can help companies assess the gap between themselves and competitive benchmarks. On the other hand, it can help managers to have a more comprehensive and scientific understanding of the current digital performance of their companies. MM is a great tool for both comparison with competitors and internal optimization of companies. All in all, using MM not only helps to position an existing relationship on the maturity scale but also helps to identify areas where improvement is needed to achieve a higher maturity.

This research focuses on the effects of the socio-technical boundary conditions on the success of chatbot implementation in organizations. To do so, different actor perspectives on "successful implementation" are explored and 33 boundary conditions are proposed. The findings suggest that satisfying the boundary conditions has a positive effect on the success of the chatbot implementation. This indicates that the suggested boundary conditions can be used as a tool to guide future chatbot implementations. By doing so, the social and technical requirements can be ensured.

Recent advances in the field of ‘Zero Trust’ security strategies have revealed that there is still much novelty regarding the concept of Zero Trust architecture (ZTA). Zero Trust has recently gained attention as the traditional approach, based on network perimeter security, is being outplayed by sophisticated cyber attacks. This research contributes significantly to the scientific knowledge base, as ZTA is hardly investigated. Moreover, recent developments are causing the perimeter to disappear, such as increasing collaboration between companies, ecosystem connections, and working from home due to Covid-19. As a result, public and private organizations need to rethink how to protect their IT infrastructure, assets and data better.
Several organizations are willing to opt for a Zero Trust approach because of its benefits. These benefits include improved security, reduced complexity, and lower overhead and operational costs. Additionally, innovation in enterprise architecture security is urgently needed as it can reduce data breaches, decrease lateral movement, and avoid ransom payments and a company freeze.

Even though Zero Trust brings many advantages, it has not yet replaced existing perimeter-based security approaches. The complication is that many organizations struggle with the implementation of ZTA due to a lack of knowledge and clarity on how to implement the Zero Trust security strategy. Additionally, “Zero Trust” is one of the most frequently used buzzwords in cybersecurity, making it hard to distinguish an actual ZTA. Complexity and misunderstandings of Zero Trust lead to failed projects and implementations. Furthermore, ZTA implementations are complex, and a predefined one-size-fits-all approach does not exist.

Moreover, organizations willing to transform their traditional architecture to a more advanced ZTA lack guidance in their transformation. However, Zero Trust solutions are marketed by multiple vendors, including Zscalar, IBM, Microsoft, and Palo Alto. There is no clear guidance for Enterprise Architects to support organizations in the transformation to a ZTA. Thus, research is needed to investigate 1) what Zero Trust architectures are, 2) what the challenges are, and 3) what the design principles for a successful ZTA transformation are.