The increasing dependence on technology has made society vulnerable to cyber threats, with ransomware becoming a major concern. The complex nature of the Ransomware-as-a-Service (RaaS) model makes identifying the attackers challenging. Traditional methods often rely on direct indicators, but these may not be readily available or reliable. This research explores the potential of utilizing indirect indicators to improve correlating ransomware attacks to specific threat actors. This results in the formulation of the following research question:

"If and to what extent can the analysis of indirect indicator be utilized to improve correlating ransomware attacks with cyber-threat actors?"

The research employs a mixed research approach. A literature review examines the techniques, indicators, and taxonomies used for attributing cyber-threat actors in general. Additionally, expert interviews explore differences in the attribution of ransomware threat actors compared to general cyber-threat actors. Furthermore, it highlights the need to use indirect indicators in the attribution process of ransomware threat actors. Therefore, a cybersecurity company's 2023 ransomware incident reports are analysed to understand how the ransomware attacks are investigated and how the conclusions are drawn.

This study identifies differences in attributing ransomware and general cyber-threat actors. While indirect indicators are crucial for attributing general cyber-threat actors, ransomware attackers often directly identify themselves through ransomware notes. These notes often provide access to communication channels and leak sites, offering substantial evidence for attribution.
The study also finds that Tactics, Techniques, and Procedures (TTPs) tend to be generic in ransomware attacks, offering limited value for differentiating between different actors. However, based on the interviews, there is a need for a central database of observed indirect indicators to facilitate future research and attribution efforts. Consequently, the research finds some promising results for using indirect indicators in ransomware threat actor attribution. The first finding is that the TTPs are less generic as initially thought as 32\% of the techniques and 47\% of the sub-techniques were unique. In addition, analysing the specific tools and techniques used by different actors, such as Blackcat's use of "nltest" for domain trust discovery, can help identify and differentiate them. Furthermore, threat actors observed only once in ransomware attacks of 2023 often used unique techniques, potentially allowing for differentiation based on this factor.

In conclusion, this study demonstrates that analysing indirect indicators can be a valuable tool in correlating ransomware attacks to specific threat actors. While certain limitations exist, continued research and development of this approach have the potential to significantly improve our ability to identify and track ransomware attackers.

The rapid rise of short-format video platforms such as TikTok and YouTube shorts in the last 5 years has fundamentally transformed the way that users consume content. These platforms rely more than any other on recommendation systems to provide content to users. These systems analyze user behaviors on certain types of content to learn their interests and deliver content to users directly, limiting direct user inputs. While this innovative approach to content delivery simplifies the user experience and generally increases user satisfaction, there are concerns that they can lead to an increase in rabbit holes. These rabbit holes are defined as situations where users are recommended increasingly narrow content which can reinforce biases and limit diverse perspectives and content.

The scientific body on recommendation systems has proven time and time again that they generate rabbit holes. However, a gap exists in the understanding how exactly these rabbit holes form specifically on Short-Format Video (SFV) Platforms, such as YouTube Shorts or TikTok. While many studies have attempted to audit recommendation systems there is a lack of methodology to effectively prove the importance of different users actions in both influencing the output of the algorithm as well as the generation of rabbit holes. To address this knowledge gap, the main research question of this study is:

"How can the feature importance of user actions in algorithmic recommendation systems on Short-Form Video Platforms, such as YouTube Shorts, be analyzed to understand their role in leading users down a rabbit hole?"

This research question can be divided into two sub-questions, which when answered will provide a comprehensive understanding to the main research question. These sub-questions are as follows:

1. How can a tool be built to collect a large dataset of algorithmic recommendations based on user actions?
2. How can this dataset be analyzed to understand the key user actions that lead to falling into a rabbit hole on YouTube Shorts?

As seen by the sub questions, this research will focus on distinct phases and objectives. The first will be to develop a tool that is able to fully simulate a human user of YouTube Shorts, the SFV platform of interest in the study, with full user interests, actions and decision making. This will permit the study to then focus on the second phase which will be to run this automation tool with varying user personas each interacting with content in different ways and recording all the output of the algorithm. This tool is novel in that it utilizes Large Language Models (LLMs) prompts in order to understand the content of the videos it is watching and decide how to react (positively or negatively) based on the video classification and the user interests. This will allow the researchers to understand how different user actions influence the algorithm and permit it to learn the user interests. It will also permit the researchers to investigate if rabbit holes form on YouTube Shorts as well as the user actions that intensify them.
The tool is built to collect a time series of video recommendations with two key data variables for each point: the video category and the reaction of the user (positive or negative).

A time series of 550 videos is collected for the watch, like, share, dislike and skip reactions. The runs for the watch and like are ran at varying probabilities of the reaction happening to moderate their influence. The research then analyses these time series by plotting the running average of videos that positively aligned with the user interests as well as the different video categories recommended to the user. These plots reveal which actions are most important in influencing the algorithm and pertaining to user interests as well as how rabbit holes form. These results are presented below:

- Au automated tool to fully simulate users on YouTube Shorts was successfully developed and can now be used perform algorithmic audits. A full explanation of the development and architecture of the bot/tool is provided in the methodology section in order. The tool itself is also provided to the scientific community on GitHub in order for other researchers to perform more audits.
- Among user interactions, watching videos is identified as the most influential in guiding the YouTube Shorts algorithm. Liking also has a significant impact, though less than watching. In contrast, sharing and disliking videos have minimal influence on the algorithm’s output.
- The results also confirms that the YouTube Shorts algorithm has a strong tendency to create rabbit holes by progressively narrowing down content recommendations based on user engagement, particularly through actions like watching and liking videos. It is also shown that the consistency in user actions is key in the intensity of rabbit holes and that to avoid rabbit hole.

From these results, this study aims to advance the understanding of how YouTube Shorts' recommendation algorithms foster algorithmic rabbit holes. The study introduces a novel automated bot for simulating user behavior, offering a new tool for auditing algorithmic processes on short-form video platforms. By providing insights into the mechanics of content personalization, this work contributes to ongoing discussions about algorithm transparency and ethical considerations in digital content curation.

The research on auditing SFV platforms for the rabbit hole effect aligns seamlessly with the overarching theme of the CoSEM program within the Technology Policy Management faculty. By delving into the intricacies of algorithmic recommendation systems on SFV platforms, the study intersects policy and technology within the context of a complex system. The objective is not only to understand the dynamics of these algorithms comprehensively but also to provide a tool for policy makers to analyse and understand the algorithms shaping our world. The results of this thesis can help platform developers optimize algorithms to reduce rabbit holes, guide policymakers in crafting regulations to promote transparency, and inform users on how to engage more critically with content.

Integrating artificial intelligence (AI) into Japan's life insurance sector marks a significant move towards data-centric precision, reflecting the nation's shift towards Society 5.0. In this domain, AI is revolutionizing decision-making processes and enhancing operational efficiency, with applications ranging from fraud detection in credit card systems to predictive underwriting. While AI offers notable benefits, it also introduces risks, including privacy breaches, algorithmic biases, and inadequate human supervision. To tackle these risks, the Japanese government has initiated guidelines for societal protection, but gaps remain in the insurance industry's implementation, especially in translating social norms into the industry's context to protect stakeholders' values.

The industry needs a guide for safely designing, developing, and deploying AI systems, considering stakeholders' perspectives. This guide fills two knowledge gaps: a framework for translating high-level values into Japanese life insurance industry requirements and an initial process for converting these high-level values into organizational guidelines.

An empirical study on predictive underwriting informed the research, identifying 13 values and four informal social institutions for the AI design process. It involved eight experts who defined 54 norms, which were later refined and categorized into process and assessment norms focusing on data and AI.

The result is ten design guidelines for AI system developers, which are validated by experts, addressing the full AI lifecycle. These guidelines contribute scientifically by introducing an initial process combining design for values with system safety concepts, reporting standardization, and AI governance frameworks.
Future research should replicate this process in various contexts, reevaluate the value framework with broader stakeholder inputs, investigate the dynamics between Japanese society and AI in more detail, and delve deeper into system theoretic hazards analysis. This approach promises to strengthen the value framework and process applicability in different organizational settings.

The 21st century has seen a significant increase in the global prevalence of mental health problems, affecting almost a billion people. These conditions not only reduce the quality of life for individuals but also lead to stigmatization, discrimination, and social isolation. The COVID-19 pandemic has further exacerbated mental health issues, with depression and anxiety becoming the most common mental disorders among young workers. However, encouraging young adults to seek help for mental health is challenging.

To address this issue, the concept of digitalizing psychotherapies through mental health chatbot services is proposed as a potential solution. Giving the chatbot a voice to communicate is suggested to increase perceived trust and intention to use among users. A research question is posed: "How does vocal communication and its personalization in chatbots affect the intention to use of young adults with depression?"

The study conducted an online survey using Qualtrics with three groups of participants: the control group (text-based communication) and two treatment groups (vocal communication, and personalized vocal communication). The chatbot followed a rigid script based on a psychotherapy protocol called behavioral activation. Data analysis involved Mann-Whitney U tests, ordinal logistic regression, and one-way ANOVA for randomization check.

Findings from the study indicated that chatbots communicating through speech did not significantly influence trust compared to text-based chatbots. Personalization of the voice did not increase trust based on emotions and even negatively affected cognitive trust. Both cognitive and affective trust were positively associated with the intention to use chatbots. Cognitive trust indirectly mediated the relationship between personalized vocal communication and intention to use.

The study contributed to existing literature by exploring the adoption of conversational agents for mental health support, focusing on young adults with depression. It also provided insights into the use of speech in chatbots and the impact of personalization on trust and chatbot adoption. Additionally, the study shed light on the adoption of mental health chatbots in developing countries, which lack access to healthcare.

From a managerial and societal standpoint, the study's findings have implications for companies developing chatbots for mental healthcare. The insights gained can improve the design of mental health chatbots, potentially easing the burden on mental health professionals and contributing to long-term societal well-being. Moreover, the study highlights the potential benefits of chatbot accessibility for users in developing countries with limited access to healthcare.

Overall, the research provides valuable knowledge about the adoption of chatbots for mental health support, voice communication, personalization, and its implications for various demographics and regions. It also raises ethical considerations, particularly regarding gender-based biases when using gender-specific voices for chatbots.

Public participation plays a crucial role in democratic governance, as it allows citizens to contribute their opinions and influence decision-making. However, traditional methods of public participation often face challenges such as lack of transparency, limited accessibility, and low citizen engagement. This master thesis explores the potential of blockchain technologies to increase citizens' involvement in public participation processes. The thesis employs a mixed-methods approach, combining qualitative and quantitative research methods to provide a comprehensive understanding of the topic. Through multiple interviews and surveys, it analyses the current pain points of the public participation methods and finds the missing pieces of the blockchain technology that still has to be developed. The research presents multiple future research areas for further development, providing starting points and ideas that could be further evaluated.

Industry 4.0 and the Industrial Internet of Things (IIoT) growth will result in an explosion of data generated by connected devices. Adapting 5G and 6G technology could be the leading enabler of the broad possibilities of connecting IIoT devices in masses. However, the edge solution has some disadvantages, such as the loss of resource elasticity compared to cloud solutions. The research questions of this thesis are whether Deep Neural Networks (DNN)-porting can solve the accuracy-performance trade-off of edge computing solutions and how to implement an edge computing system based on open-source container-orchestrated DNN model inference platforms to enable vertical model autoscaling capabilities.
The thesis shows how porting techniques like structured pruning on DNN enable the accuracy performance trade-off in hardware-constrained settings. It generates models with reduced complexity and size while minimally degrading the accuracy. By using these ported models in the proposed inference platform, the thesis demonstrates how an edge computing system can achieve vertical model autoscaling capabilities, enabling efficient use of computational resources. This research focuses on CPU hardware and Real-Time (RT) request scenarios, where the latency Service Level Objective (SLO) combined with current demand are crucial factors. When the resources in an inference system deplete, the latency of individual requests can increase significantly due to queuing. The results show how an orchestrator can make live model version selections based on the model versions and demand. The proposed system increases the maximum possible throughput compared to the state-of-the-art while avoiding creating a queue in the RT scenario and improving system accuracy when CPU resources are available. Additionally, this work proposes a design to implement these benefits in industry-adopted open-source DNN inference platforms.

Recent technological advancements have enabled the development of increasingly impactful and complex Artificial Intelligence (AI) systems. This complexity is paired with a trade-off in terms of system opacity. The resulting lack of understanding combined with reported algorithm scandals have decreased public trust in AI systems. Meanwhile, the AI risk mitigation field is maturing. One of the proposed mechanisms to incentivize the verifiable development of trustworthy AI systems is the AI audit: the external assessment of AI systems.

The AI audit is an emerging subdomain of the Information Technology (IT) audit, a standardized practice carried out by accountants. Contrary to the IT audit, there are currently no AI-specific defined rules and regulations to adhere to. At the same time, some organizations are already seeking external assurance from accountancy firms on their AI systems. AI auditors have indicated that this has lead to challenges in their current audit approach, mainly due to a lack of structure. Therefore, this thesis proposes an AI audit workflow comprised of a general AI auditing framework combined with a structured scoping approach.

Interviews with AI auditors at one accountancy firm in the Netherlands revealed that the demand for AI audits is increasing and expected to keep growing. Clients mainly seek assurance for management of stakeholders and reputation. Furthermore, the challenges the auditors currently experience stem from having to aggregate auditing questions from a range of auditing frameworks, causing issues in their recombination and in determining question relevancy. Subsequently, design criteria for a general auditing framework as well as feedback on a proposed scoping approach were obtained.

Fourteen AI auditing frameworks were identified through a literature search. Following their typology, these could be subdivided into three source categories: academic, industry, and auditing/regulatory. Academic frameworks typically focused on specific aspects of trustworthy AI, while industry frameworks emphasized the need for public trust to drive AI progress. Frameworks developed by auditing and regulatory organizations tended to be most extensive.

Comparison to four common IT audit frameworks and standards showed that AI audit frameworks need to cover a broader range of topics than the traditional IT audit themes. This is a result of the complex socio-technological context involving multiple stakeholders in which AI systems operate. Additionally, it was shown that AI performance monitoring dashboards could cover technical parts of the audit, but that they fall short when it comes to context-dependent topics such as human oversight or societal well-being.

Following analysis of the similarities between the corporate Environmental, Social and Governance (ESG) reporting materiality assessment and the AI audit scoping problem, an ESG materiality assessment approach was translated to a scoping approach for the AI audit. In this translation, feedback from the AI auditors was incorporated. Combined with a general auditing framework, which was built through combination of the fourteen identified frameworks along the obtained design criteria, this formed the basis for the proposed AI audit workflow. The proposed workflow was demonstrated to be executable through a mock case study. Investigation from the data subject perspective for the Public Eye crowd monitoring AI system of the Municipality of Amsterdam resulted in a scoped list of auditing questions relating to privacy, transparency and fairness.

Recommendations for future AI audit workflow designs include exploring the option of incorporating subthemes in the general framework, closer co-development with AI auditors, obtaining insights from auditors at multiple accountancy firms, and automating parts of the audit.

In today's business landscape, software has become an integral part of operations for all companies, with a growing reliance on third-party components. This increasing complexity in software supply chains has led to a significant reduction in transparency and visibility, posing challenges for effective management and security. Software Bill of Materials (SBOMs) emerges as a promising concept to address this issue by providing detailed information about software components and their supply chain relationships, ultimately enhancing transparency within these supply chains. However, despite its potential benefits, SBOM adoption remains limited in practice.

This research examines the perspectives of four key business stakeholders involved in the software supply chain to understand their incentives and disincentives surrounding SBOM adoption. Through a series of in-depth interviews with representatives from each stakeholder group, we aimed to identify stakeholder-specific risks, benefits, concerns, and incentives related to SBOM adoption. The analysis reveals that SBOM adoption potential is notably higher among system integrators and software vendors. These stakeholders perceive the benefits of enhanced transparency and supply chain risk mitigation, which align with their strategic objectives. On the contrary, B2B customers and Individual Developers exhibit the least motivation for SBOM adoption. Their limited interest stems from a perception that SBOMs may impose additional complexities without commensurate benefits. Given that B2B customers and individual developers are the primary consumers and suppliers of SBOMs, respectively, the findings suggest that the overall adoption potential of this technology remains restricted.

Mobile phones are playing an increasingly significant role. The surge of services and tasks performed on mobile phones is accompanied by an ever-increasing amount of personal data about the owner. This has made mobile phones ideal targets for cyber criminals and it has translated into an increase in malware targeting mobile phones. Social engineering threat actors have very effectively adopted SMS texts, as these are universally trusted by phone users, for Flubot, a new and very dangerous malware. The malware spreads through SMS texts and secretly harvests personal and financial information. Because of the novelty of the malware and its tactics, academic and industrial knowledge is very scarce on how to remediate such infections and how to best involve victims.
This research is focused on a better understanding of how the remediation has influenced the impact Flubot has had on victims and smartphone users in general. A quantitative research approach, based on a survey of victims within a large Dutch telecom provider’s client database, is used to gain this understanding. This is aided by desk research, an interview with an active case of Flubot and expert input (employed by telecom providers and governmental bodies). The results from these research methods are put into context by making use of the Fogg Behavior Model, to better understand what might trigger certain target groups to or not to remediate the infection. The larger environment Flubot functioned in, is analysed too, as it was developed over time and by June of 2022 it had been taken down.
This research has found that the detection methods used against Flubot, before it was taken down, were ineffective in detecting and stopping the spread of the malware. This is a result of a misunderstanding of the more recent workings of Flubot and a larger incorrect presumption that there was no urgency to do much about the malware. Furthermore, in the remediation process some important issues are unclear or unaddressed for victims, leading to a situation where it is often not clear what might have caused the infection or what can be done to prevent a future infection. It is important to prevent further infections, as similar malware does exist, functioning on similar principles, and there is a chance that Flubot might reappear.
The research is based on victims and there was no target group reached that had not been victimised. This makes for a possibly skewed understanding of the situation which should be researched. The data has been gathered through one of the largest telecom providers of the Netherlands, which is not necessarily representative for the whole Dutch industry. Researching other telecom providers in and outside the Netherlands could provide a more comprehensive understanding. The research has led recommending an adaptive notification systems and improvements to the notifications currently used.

In 2050, it is expected that 70% of the world’s population will live in cities (Jin et al., 2014), leading to increasing congestion in and surrounding cities. This will raise new challenges, requiring more efficient and interactive cities. A novel paradigm contributing to these so-called smart cities is participatory sensing. Also known as mobile crowdsourcing, this solution enables both public and professional users to actively gather, analyse, and share local data about the urban environment using built-in sensors in smart devices (Truong et al., 2019). Considering that over 94% of the population has access to a mobile network, obtaining real-time data from these already existing sensors can be a low-cost solution for acquiring a huge amount of information (International Telecommunication Union, 2016). These real-time data can be used to analyse and predict mobility flows, and make public and private transport more efficient, safe, and sustainable.

However, a clear benefit is required to motivate smart device users to share data about their activities and their environment. Sharing data comes with the risk of disclosing private information, as location data can lead to the identification of living and work locations, as well as individual habits. Research on motivations of
smart device users to engage in participatory sensing tasks is required in order to be able to design valuesensitive participatory sensing applications. This study aims to identify factors related to incentives and privacy that explain choice behaviour of users in participatory sensing applications. The main research
question being addressed is as follows: “How do factors relating to incentives and privacy affect the willingness among smart device users to contribute to participatory sensing systems for smart mobility?”

A choice modelling approach was taken in order to identify the trade-offs made by users between potential benefits and costs of sharing data. This is an approach not often used before in the field of participatory sensing and provides novel insights in user behaviour in these systems. First, a literature review was
conducted identifying possible factors relating to incentives and privacy, influencing the willingness of people to share data. Five factors were selected: monetary reward, effort, risk of re-identification, types of data, and
data use. These factors were incorporated in a stated choice experiment distributed among smart device users through an online survey. In total, 125 valid responses were collected. The required effort of participating was regarded the most important factor influencing the willingness to share data in sensing
applications for smart mobility. This provides new insights, as previous studies do not include effort in choice experiments regarding data sharing. As expected, the perceived ease of use declines if more inputs by the user are required. Moreover, respondents are reluctant to the collection of contextual and multimedia in
addition to location and motion data, a finding which is confirmed by recent studies. Almost half of the respondents indicated to be highly concerned about their privacy. Therefore, a surprising finding is that the risk of re-identification was regarded the least important factor influencing the willingness to share data.
However, when taking a deeper look at the data, it appears there is a group of people having extreme preferences regarding privacy and trust, who assign a higher importance to privacy related factors (risk of re-identification, types of data, data use).

The identified trade-offs were used to evaluate the implications for different use cases in the field of smart mobility. Three interviews were conducted, which each led to the definition of a use case, being crowd management in a city, safety research using car accident information, and real-time travel information in
public transport. By aggregating the quantitative and qualitative parts of the research, it can be concluded that the accuracy of collected information can be improved by collecting more types of data in addition to location data. However, this will lead to a decline in the acceptance rate. A proposed solution is to provide
tailor-made sensing applications, giving the user control to indicate which data they agree to share.

Furthermore, the communication of the purpose of the data collection is important to users. Moreover, being transparent about the risks related to the data collection can help users to make a well-informed decision and
will ensure an ethical design of sensing applications. Finally, increasing the attractiveness of the application is recommended to reduce the perceived effort, which could be done by gamification of sensing tasks.

Besides societal implications, this study provides several recommendations for future research. First, it is recommended to repeat the experiment without the provision of a financial compensation, in order to see if this leads to different choice behaviour and preferences of users. Furthermore, research on the understanding of privacy risks among users is recommended. Finally, this study can be used outside the smart mobility field, in order to analyse the willingness to share data in a broader sense.

Modern supply chain and procurement departments are faced with a lot of challenges. Supply chains have become global, resulting in complex networks. With access to more information customers have access and have become more demanding on issues such as quality or sustainability. This has led to an increased interest among different companies in improving their traceability capabilities. As this requires companies to collect data across their entire supply chain, new technologies are being explored to make this change happen. One of these new technologies is blockchain. It rose to fame in the financial industry, but it is certainly a topic of current interest in supply chain management as well. Blockchain is a way to store and share data in an immutable ledger by decentralizing consensus, without a central record-keeping authority. This thesis is about the use of blockchain in supply chain traceability use cases and the comparison between blockchain-enabled traceability systems and traditional, centralized systems. Through literature review and a case study, which involved expert interviews and a survey, the main research questions from this thesis was answered: Which food supply chain traceability use cases are suitable for the application of blockchain-enabled systems? In order to answer the main question, the following sub-questions were addressed: • What drives the demand for traceability in food supply chains? • How do traceability systems based on blockchain compare to centralized systems? • Which criteria make a use case suitable for blockchain technology? • Which challenges exist for successful traceability system implementation? Based on the literature review, many studies suggests that food safety and quality are the primary drivers for an increased attention to traceability. However these suggestions were not found in this study. The needs for traceability at Heineken are mainly related to consumer demands and operational efficiency. Sustainable or local sourcing and decreasing costs related to mismatched information between Heineken and its suppliers or customers are dominant elements in the case study. In these situations, trust in data integrity is critical, which leads to certain managers looking to increase their traceability capabilities. Next, blockchain traceability systems were compared against traditional, centralized systems to answer research the second research question. Blockchain systems outperform centralized systems on Transparency and Integrity, Trust and Data Quality. Although technologically the two implementations are distinct, it was found that in its usage the two are not that different. It was never mentioned that blockchain systems could fulfill use cases that traditional systems could not handle with regards to supply chain traceability. This study found that blockchain use cases in supply chains should adhere to the following criteria: • The network is decentralized, meaning it involves multi-tier suppliers and/or distributors. • A high degree of proof of integrity is required. • There is a desire to obtain a single view / version of the state of the system. • Data sovereignty is essential. These criteria were found to be most applicable to Product Provenance, Sustainable Sourcing or Product Recall use cases. This research focused on understanding the differences between centralized and blockchain-based traceability systems. It has shown that blockchain can provide value to modern supply chains, and that in its usage, it is not radically different from centralized systems. The research presents criteria to decision makers, which provide support in judging the need for blockchain in their applications and reviews the areas where these needs could be and which challenges this presents.

According to a report published by the Dutch Safety Investigation Board in early September 2019, the safety of remotely controlled bridges is not su_cient (Onderzoeksraad voor de Veiligheid, 2019, pg.58). This report was published after the occurrence of two severe accidents in Zaandam, on the Den Uylbrug and the Prins Bernhardbrug. On both occasions, the victims were standing on the movable part of the bridge deck during the opening of the bridge, and despite being visible for over a minute on the camera screens, were not observed by the operators, making the accidents human factor-based. The Dutch Safety Investigation Board concluded that part of the problem was safety mainly being considered a technical problem, instead of an integral one. In this research, the goal was to analyse how object detection could provide decision support for mitigating human factors for operating remotely controlled bridges. This was done by identifying the problems through literature studies, interviews and observations, and by building a proof of concept to mitigate these problems. Finally, this model was evaluated to gain experimental insights into the possibilities of object detection as a decision support tool.

An increase in shipping quantities has put its strain on incumbent integration mechanisms in third-party outbound logistics. Therefore, Application Programming Interfaces (APIs) have recently witnessed increasing attention from practitioners in the logistics industry. However, implementing so-called Shipping APIs has proven to be difficult. With the goal of overcoming barriers to API adoption in outbound logistics, factors influencing logistics buyers’ adoption motivations are determined by adopting the case study methodology. It involved nine interviews with logistics buyers, fulfilment companies and integrators, after which findings were validated by means of a focus group with industry experts. Key challenges were found to be mainly technological; a lack of relative advantage when comparing it to the status quo and the risk of potential downtime were indicated by participants numerous times. Furthermore, among organizational factors, available resources were identified to play a moderate role and regarding environmental factors, the lack of marketing efforts surrounding Shipping APIs was argued to be of marginal impact. From this unique view of different stakeholders in the outbound logistics chain, this study hopes to promote API adoption and provide insights into the new emerging API paradigm within the supply chain network. The insights gained from this research can help to better formulate implementation strategies concerning integration technology, in an industry that is adapting to a new composition of consumer buying behaviour.

The main problem hampering innovation in the Internet of Things (IoT) is the fragmentation and lack of interoperability between IoT platforms. A possible solution for IoT platform sponsors to overcome this problem is to open up towards other platforms. To better understand how the IoT platform market is evolving and to inform future decisions regarding the desired degree of openness between IoT platforms, this thesis aimed to develop a theory on the openness between IoT platforms by identifying, prioritizing and theorizing the interrelations between factors driving the decisions from IoT platform owners related to the openness of their platform towards other IoT platforms. To this end, a preliminary theoretical framework was developed which was used as input for 13 semi-structured interviews with decision makers and field experts. It was found that openness between platforms is mostly driven by complementarities. Due to the cyber-physical nature of the IoT, the domain is characterised by a high need for specialisation and platforms are often developed from a product-centric, bottom-up approach. This results in a fragmented market in which there are strong complementarities between IoT platforms. It is found that these complementarities are the main factor driving the openness between IoT platforms.