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.

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.

This master's thesis addresses a common problem of the adoption of IT automation; the nonoptimal use of the technology due to ineffective managerial communication. It searches for a solution by using the Design Science Research methodology to create a design artifact in the form of a managerial communication framework. This framework provides values to all stakeholders in the IT automation adoption process; the managerial level, the employees, and the technology provider. It improves managerial communication during the adoption process and identifies actionable guidelines for managers.

Narrowing down the scope of the thesis to the adoption of Robotic Process Automation (RPA) within the recruitment industry allows the research to be manageable within the constraints of a master's thesis. A literature review is used to investigate the knowledge base of the topic of the thesis. Additionally, semi-structured interviews with managers and employees are used as a means of collecting data from the environment. Combining these inputs, this research designs a model that explains the role of managerial communication in the effective adoption of IT automation. The model consists of four main components: managerial communication, motivation & vision, impact awareness, and the effective adoption of IT automation. It explains the constructs that were found relevant in this process and how they relate to each other. Several root constructs are used as a basis to create actionable guidelines to facilitate an effective adoption process. Together with the explanatory model, these guidelines form the managerial communication framework. The main goal of the framework is to bridge the gap between the manager's and the employee's views on the nature of the change. This study finds that managers often view the change as a process optimization, whereas employees view it as an organizational change. The managerial communication framework allows to identify these differences and act upon them.

This study not only designs the communication framework, but also evaluates its defined objectives with several experts. The results of these evaluation interviews demonstrate the validity and usability of the research conducted and the design artifact. Future research can extend the framework to other domains & technologies and tailor it to specific problem cases. Furthermore, the framework can be researched and evaluated in practice by conducting a case study.

Yearly, 20.000 fatalities are recorded in road accidents on European roads alone. This situation is far more severe (1.3 million humans) when the global scope of road accidents is considered. Thus, Policymakers are initiating policy programs to overcome this challenge by enhancing safety and maximizing the associated positive economic impact. The EU committed to minimizing fatalities by 50% by 2030 and to be net zero long-term by 2050.

To achieve this, vehicular communication networks (VCN) can play a crucial role. It is anticipated that the connectivity and communication of vehicles with their environment contribute to these goals by enabling safety functionalities or improving the traffic flow. Hereby, it is crucial to understand that this communication requires an infrastructure that assists the vehicles and ensures data transmission and operation. However, due to the European size and heterogeneity of the member states, realizing a vehicular communication network on a large scale requires increased coordination and collaboration efforts. Currently, no vehicular communication network has been introduced working EU-wide. Hence, this resembles a potential for future road improvement. To realize this, decision-makers must understand the complex properties and system interrelationships of such a large-scale infrastructure project so that a common VCN can be designed to ensure interoperability and robust functionalities across the EU. This work contributes to the aforementioned challenge by addressing the following research question:

What are fundamental socio-technical factors to consider in a future European vehicular communication network design?

A mixed approach that combines Peffer´s design science research framework and system engineering methodology is used to synthesize the contributions. By this, a vehicular communication network mission, stakeholder, and system analysis are presented in this work. Further novel and scientific sound requirements and stakeholder insights are synthesized by systematically reviewing 57 articles and interviewing 15 experts from the institutional, scientific, and industry domains. In this context, a stakeholder classification for vehicular communication networks, a 4-layer stakeholder complexity model, and a system requirement structure from a system perspective are proposed to contribute to the VCN understanding and future design attempts. Further, the reflection of the socio-technical interrelations between the technical and social VCN subsystems are subjects of this work.

The conclusion is that the reflection on socio-technical system properties plays a critical role in vehicular communication network design. Further, a future vehicular communication network consists of a magnitude of stakeholders with high interest and power; thus, designers must understand the characteristic of their co-evolutionary cooperative development. Hence, a multidisciplinary understanding and approach are critical for designers. Furthermore, the geographical segregation of designing and decision-making in a future vehicular communication network is identified, and certain goals/issues should be addressed in the respective layer. Another major conclusion is that the VCN discussion is determined more by social and socio-technical conditions, such as stakeholder cooperation/coordination and interoperability, than by technical feasibility.
These findings result in three main research contributions, which are summarized as follows:

- A 4-layer stakeholder complexity model contributing to the understanding of VCN development is contributed.
- An approach of integrating the socio-technical system perspective on the complex, large-scale infrastructure VCN project is contributed. This focuses on the processes and requirements between social and technical subsystems, addressing the integration of heterogeneous stakeholder interests.
- A comparison is made between scientific focus, stakeholder needs and objectives, and expert insights, highlighting the mismatch and alignment of requirements. This contributes valuable insights for adjustment and further research in the VCN community.

Further methodological contributions can be concluded:
- The design science research approach is aligned with systems engineering iso standards and methodology.
- The design science research framework is used as an approach to address the complexity of VCN systems. This novel perspective helps VCN stakeholders design solutions for their field problems.

The results have implications for designing stakeholders in a VCN. Based on the analysis, it is recommended that policymakers identify and extend common objectives with the industry to establish public-private business cases. Further, vehicle manufacturers should participate and embrace the transition to a vehicular communication network by cooperating strongly with stakeholders. In addition, implementing the relevant technologies to communicate with the heterogeneous infrastructure is suggested to shape future infrastructure connectivity development. Lastly, the lack of socio-technical reflection in scientific literature is identified. Hence, scholars should elaborate on the interactions and interrelationships between the social and technical subsystems in future work.

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.

With the advent of a smart society and an era of connectivity, there remain a numerous amount of challenges yet to be solved. One of the key IoT innovations analysed in this research is known as Unmanned Aerial Systems (UAS). UAS, or drone technologies, allow carrying out repetitive and dangerous tasks with almost no human intervention or supervision (Fernández-Caramés, Blanco-Novoa, Froiz-Míguez & Fraga-Lamas, 2019). The advent of drone utilisation in both public and civil domains has led to application areas such as realtimemonitoring and surveillance, parcel delivery, search and rescue missions (e.g. ER), remote sensing in agriculture and multiple other application domains (Valavanis & Vachtsevanos, 2015). However, the fast pacing development of interconnected devices and systems has outrun the human understanding and experience of usage, imposing challenges with regards to technical security, trust and of course privacy (Coetzee & Eksteen, 2011). Subsequently, most drones are limited to computing, storing and sharing data, making them vulnerable to adversarialattacks. A need for a more responsible adoption of drone-use exists and must be fulfilled with care. To this, a blockchain-based solution is proposed.The focus of this research is on a Dutch independent agency part of the Ministry of Agriculture, Nature and Food Quality, called the Netherlands Food and Consumer Product Safety Authority (NVWA). They have been actively using drones since 2017 and their main tasks consist of supervision, risk assessment and risk communication with the goal to protect the human and animal welfare (NVWA,n.d.). An explorative research approach was taken whereby the current bottlenecks experienced by the NVWA were identified and visualised, by means of desk research and semi-structured interviews. Subsequently, the sociotechnical effects of the use of drone detecting services by the NVWA were identified to obtain the key values for a responsible redesign. In this way, the role of the values, an understanding of the roles of institutions realizing these values, and the stakeholder engagement is understood. After identifying the open issues and conflicting values, a redesign phase began with the trade off between three mitigating security techniques.Blockchain technology is found to be the most suitable security technique in this research and fills in the ’lack of transparency’ gap which is crucial for a responsible redesign. Although it hasn’t reached full maturity and could induce undesired delays, it increases the security level of the organisation significantlyand takes into account the key values security and privacy. Moreover, the data managing method allows users to validate, maintain and synchronize the content of a transaction ledger which is replicated across the other users in the network (Tapscott & Tapscott, 2017).The solution has proven to work in existing literature and automotive contexts and is therefore generalisable in multiple on-going projects of the NVWA. Subsequently, the novel data managing method could be used as for revenue models such as digitised inspections. This indicates future growth options andpotential competitive advantages. All in all, the benefits outweigh the uncertainties of this technologyand it is believed that a blockchain based solution will be the next big step to achieve more responsible implementation of drones.

Burnouts are a severe, negative consequence of lacking performance management within firms and a result of a disbalance between energy input (e.g: sleep, social activities, physical activity) and output (e.g: workload, anxiety on the job, caring for a kid) Burnouts lead to employment challenges for firms, resulting in a potential threat for the strategic competitive advantage for the firm. Moreover, on an individual level, high risks of burnout lead to severe consequences for the individual health of knowledge workers (e.g: cardiac diseases, impact on personal life, auto-immune-diseases) and major costs for both the society and firms itself. Dutch Journalists of the NRC concluded that an average burnout accounts for €60.000,- per burnout, while the total societal burden account for €1.8 billion each year as a result of direct and indirect effects of burnouts. Despite the competitive significance of vital employees for firms, we see an increasing number of burn-out related cases within the Netherlands. In 2019, 14,6 people per thousand inhabitants were officially diagnosed with neurasthenia/surmenage (=burnout) by their general practitioner. Moreover, in 2018, Approximately 16,4% of the male employees and 18,1% of the female employees experienced “multiple times or more” burn-out related symptoms during the month. Generally, this is 1 out of 6 employees of the Dutch workforce. This account for a 41% increase of burn-out related symptoms with men and an increase of 66% of burn-out related symptoms with women. The Quantified-Self (QS) philosophy prescribes the approach of the human being as a machine and, following the QS fanatics, has the potential to optimize performance. For example, the QS-approach is widely adopted within athletes sporters to optimize performance based on oxygen-levels and heartrate measurements. However, measuring different elements of the human body (such as nutrition, sleep or physical activity) has not yet been able to decrease the development of burn-out related symptoms. This research addresses the question on how the quantified-self and life-logging can contribute to the optimization of energy management within firms, and thus decrease the risk on burn-out related amongst knowledge workers and implicated side effects as stated above. First, an integrative, holistic framework is presented (the Vitality Map) which incorporates all relevant determinants (21 in total) influencing the knowledge worker battery, and thus, determine sustainable performance of knowledge workers. These determinants are categorized on four levels, which consist of two levels of non-adjustable determinants and two levels of adjustable determinants. An extensive literature is conducted, integrating five theoretical domains which lay around the solution, including health & vitality, artificial intelligence, knowledge workers, performance management and human resource management systems. Based on the literature research, societal and competitive challenges are analyzed and translated into 9 Design Objectives. The Design Objectives are generic objectives which human resource management systems should incorporate in order to facilitate optimal performance management of individual knowledge workers within firms. The following Design Objectives are formulated:Thirdly, the Performance Management System (PFMS) Framework is developed, which builds forward on the Vitality Map and which presents how the data collection, aggregation, analysis and presentation should be implemented within firms. The PFMS is presented in Figure 2. This framework provides a fundamental description of the performance delivery process (energy charge to performance) and shows how the data can contribute to informed consent in performance management and policy developments, which leads to better strategic decision making in firms in relation to the human resources available within the organization and preventive interventions which decrease the costs which are associated by curation of burmouts. Moreover, the PFMS Framework suggests how the positing and collaboration of health, vitality and performance consultants and internal stakeholders within the firm can cooperate in order to optimize human resources within the company. At last, the research contributes to the scientific development of cross-disciplinary approaches in human resource practices. Five theoretical domains are integrated and incorporated in a single artefact which is supported by the interviewees. Further research is suggested to explore the practical implementation of the artefact prototype as developed in this This Thesis Research. However, human resource managers and IT managers within firms can adopt the practical tools as provided in this Research Paper.

The sustainability of the Dutch healthcare system is under severe pressure with increasing expenditure, more demand for care and a shortage of healthcare professionals. In the current healthcare system, providers are rewarded for increasing volume, but not for adding value, which only exacerbates the situation. Value-based healthcare (VBHC) is a candidate framework to reform the current healthcare system into an outcome-based system. Information systems are essential for the implementation of VBHC to execute outcome and cost measurements and collaborate across the care cycle. A literature review shows that there is a knowledge gap in the scientific literature to the architectures and components for such value-enabling information systems. This thesis aims to fill that gap by identifying and designing the essential components for a value-enabling IT architecture for the Dutch healthcare system.
The Information Systems (IS) research framework of Hevner et al. (2004) is adopted to guide the research. The environmental analysis shows several deficiencies in the as-is architecture for VBHC including a lack of patient-centredness, inaccessible or unavailable data, and complex to extract and integrate data. Preliminary semi-structured interviews reveal that, besides IT barriers, there is a deeply rooted trust issue among stakeholders which hampers successful implementation of VBHC. It is found that trust is often related to transparency and can be managed. Increasing transparency in the healthcare architecture would allow for (i) continuous quality improvements, (ii) improved decision-making, (iii) positive financial stimulus and (iv) patient empowerment. The design activities aim to facilitate VBHC through integrating components that reduce or eliminate the IT barriers and increase transparency. Another round of semi-structured interviews is carried out to find the principles, requirements and components for the design. The overall design developed uses three types of environment: a Healthcare Information System (HIS) used by caregivers, a Personal Healthcare Environment (PHE) used by patients and a Quality Registration System, available to all stakeholders. The design and components are evaluated with an expert panel. The most essential components are: PHE, HIS, Quality Registry system, Clinical building blocks (Zibs), terminology standards, a data integration centre, logging and monitoring services, measurements and evaluations, auditing IT and care processes and an intermediary that stimulates improvement and collaboration. The expert panel reached a consensus that transparency in healthcare would contribute to solving some of the problems, but it will not solve the core problems that lie within the healthcare structure. There are several areas recommended for future research. First, the components should be evaluated with a broader expert panel to increase the validity. Second, the PHE is an essential component, but still in its infancy. It is recommended to further evaluate the utility for VBHC. Third, blockchain technology might enhance the design due to its inherent characteristics that offer transparency. Fourth, further research on the privacy-utility trade-off in the context of VBHC is recommended. Finally, interoperable systems rely on a shared and uniform language. It is recommended to research which standards have the highest potential to facilitate the architecture components.

Information technology innovation has given worldwide access to each other, to information, and to entirely new digital platforms forming new markets over the last decade. Multi-sided digital platforms are platforms that mediate between different groups of users, both buyers and sellers.
With IT the multi-sided platforms are exploiting the economic phenomenon known as network externalities, increase the users, increase the value of the platform.
However, the cultural differences between countries can make or break a successful entry into another national market. Simply copying the monolithic algorithms has not been the answer, as recently admitted by Amazon's departure from China.
This project looked at seemingly similar cultures (the Netherlands and Germany) to identify if the macro-level cultural dimensions influenced the acceptance and use of multi-sided platforms. This was examined through a survey that assessed the acceptance and use of online food delivery platforms, a prime example of multi-sided platforms.
The cultures do have significant differences, but their impact on the acceptance and use of online food delivery platforms was non-significant. The acceptance of online food delivery platforms still followed a 10-year different diffusion, but the significant differences on macro-level cultural dimensions did not relate to this acceptance.
An interesting conclusion to this project is that the countries, expecting to have similar cultures, are significantly different and the data gathered proves to be more substantial than this project could investigate. The UTAUT2-model did
show a significant acceptance and use of online food delivery platforms, which shows that the online food delivery platform has successfully expanded cross-culturally in the case of the Netherlands and Germany.

Predictive maintenance (PdM) is one of the promising technologies coming along with the fourth industrial revolution being pushed by disruptive technologies like Internet of Things (IoT), Artificial Intelligence (AI), robotics and Augmented and Virtual Reality (AR/VR). Adopting PdM potentially allows companies to reduce equipment downtime, increase the safety of their processes, increase revenue and develop additional business models. Although the promises of the technology are extensive, the successful adoption rate of this technology is still relatively slow. This is stemming from PdM’s multi-disciplinary nature and “hype” that over-promised its ease of implementation. Organizations are now starting to understand what is needed for efficient implementation, and this helps to manage the expectations about this technology. The fundamental problems highlighted in this research are the complexity, unclear vision, lack of knowledge and know-how in adopting AI predictive maintenance technologies inside an organization. According to Bain & Company’s survey companies in the industrial sector indicated that implementing IoT inside their organization proved to be more complicated than anticipated (Schallehn, Schorling, Bowen, & Straehle, 2019). There is a knowledge gap in the scientific literature, where a lack of best practice methods in terms of predictive maintenance implementation can be identified. Based on the problem highlighted and knowledge gap, the main research question was formulated: “How to facilitate the adoption of Artificial Intelligence-based predictive maintenance technology in the manufacturing industry?“. This study follows a phase-wise approach to obtain the research results. In the first phase, a literature study is conducted to identify the current situation about PdM, what information is available about the factors affecting this technology’s adoption and where is the knowledge gap to be filled. Selected factors to focus on with this research are discussed and agreed upon with the researcher and supervisors. In the second phase, the development of the best practices checklist is commenced. The centrepiece of this phase and the research project overall is the set of semi-structured interviews with 11 industry experts with extensive domain knowledge about predictive maintenance to collect best practices in PdM implementation. The insights gathered from the interviews are analysed in-detail in multiple iterations and then that filtered, aggregated information is used to develop the predictive maintenance project reference checklist. In the third phase, expert panel evaluates the practical applicability, generalizability and the validity of the constructed PdM checklist. Efficient implementation of PdM inside the organization could face numerous barriers and difficulties. Most of these barriers related to technologies using big data could be divided into three categories: technical, organizational and people related (S. Li, Peng, & Xing, 2019). Addressing all of these barriers in those 3 major categories would be unwise since that would not provide sufficient depth of analysis for each one of them. Selection of barriers is based on 3 criteria: the barriers must be relevant and applicable to the adoption of the PdM technologies; there should be a noticeable knowledge gap about how to overcome the barriers; the barriers must be complex enough (affecting multiple layers and stakeholders of the organizations) to fit with the Management of Technology multidisciplinary problem-solving perspective. Based on the information from scientific literature and consultancy reports on PdM, 3 relevant barriers to be focused on are chosen: business case building for PdM; trust in AI-based PdM (lack of trust in big data analytical results) and data management for PdM (the challenge of collecting the data, utilizing it and making sense of it). The interviews with the industry experts revealed valuable insights about predictive maintenance adoption, factors affecting the implementation and best practices that other companies have followed during the process of PdM realization. The most notable best practice that all the interviewees mentioned was involving all the relevant stakeholders early on. In addition, taking small steps, maintaining PdM platforms, celebrating small successes, showing a broad picture and providing a range for PdM business case were outlined. Furthermore, key factors that emerged from the conducted interviews influencing PdM adoption are delineated and summarized in this research project. These are useful for both practitioners and academic personnel who have an interest in this domain and want to gain further understanding of the dynamics surrounding predictive maintenance projects. This research project developed best practices reference checklist for predictive maintenance project implementation that supports organizations on high-level in adopting this novel technology by illustrating and bringing awareness to best practices that other organizations have been following during PdM implementation. This reference checklist is constructed to be a holistic, high-level PdM project support tool for the stakeholders proceeding with predictive maintenance implementation for the first time. This means that a detailed analysis of separate nuances is not sought after since that would misalign with the goal of being a wholesome, comprehendible overview of PdM project implementation checklist. Having a clear, structured and holistic perspective allows stakeholders to conveniently follow this checklist commencing and during predictive maintenance projects without being overwhelmed by excessively detailed information. This best practice checklist based on empirical study comprises a five-phase approach where the enablers and barriers in each phase are mentioned and suggestions on how to deal with them are outlined. These 5 phases are as follows: concept, feasibility, data, PdM algorithm development and operation phase. Furthermore, high-level, structured steps in each phase are laid out to support and offer recommendations to organizations with their PdM activities. In the end of each phase, an overview of best practices and barriers is delineated to recapitulate. In the concluding section of this best practices checklist, a compact, five-page adaptation of this reference checklist is devised for a quick overview of this constructed PdM project support medium and it is advisable to resort back to phases in the checklist itself if the more detailed explanation is needed. This compact version is meant for practitioners in the industry who have strict time limitations and wish to receive information quickly in a condensed format. To the best of our knowledge, such kind of high-level compact overview to assess PdM projects was not existing in the scientific literature. This research project directly investigates and provides a best practices checklist to fill this gap. In addition, this research provided design improvement ideas for different stakeholders to incorporate in their processes/products to facilitate better adoption of PdM. Trust factors affecting the implementation process of predictive maintenance are also outlined, helping companies to better communicate with their clients and internal organization about the benefits and usefulness of PdM. The developed research output has been preliminarily validated and evaluated by the expert panel that concluded that this best practice checklist indeed supports organizations in adopting predictive maintenance technologies. Furthermore, it was agreed that the output is clear and understandable with a well-structured approach. Coming from the high-level nature of this research, experts agreed that this research is generalizable to other industries. Main recommendations (for future research) include validating the best practice checklist in practice with multiple organizations inside the industry to correlate usage of this approach and success factor of implementing PdM. Furthermore, the development of additional support tools and frameworks to facilitate efficient implementation of predictive maintenance technologies would yield increased adoption rates of the technology. This research highlighted important factors contributing to the adoption of predictive maintenance technologies from organizational, people and technology perspectives. This helps to create more awareness about what is needed to consider for better adoption of this technology. Furthermore, a high-level structured overview of best practices checklist supporting PdM implementation is contributed to the scientific and practical domain, filling the previously outlined gap in the literature. In addition, coming from the analysed literature, this research complements the scientific literature on the topic of predictive maintenance by providing original content and additional awareness to the overall academic context regarding the dynamics of this technology’s adoption.

Agriculture is a fundamental element of every economy. However, global issues such as climate change, land deterioration, and a continuously growing population are strongly impacting the sector. It is estimated that by 2050 the population will increase by two billion, reaching 9 billion people to be fed. In the past 34 years, researchers investigated the use of potential technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), robotics, etc. The readiness of these technologies and the positive impact that they have on the sector is proven by numerous studies. However, these technologies are still not diffused enough within common farming practices. Adoption rates are very low, as well as full understanding of the technologies from the farmers’ perspective. Without a shift towards the digitalization of farming practices, the agriculture sector could be damaged, impacting both the future of society and general economy. This is why a change in the actual regime of production is needed: from traditional agriculture to AgTech. In this work, through interviews with winegrowers and analyses of technologies proposed by vineyards start-ups, an understanding of future market development is derived. The concept of sustainability is investigated as one of the major drivers towards the change of regime. More specifically, the study has been conducted on the viticulture domain, tackling in a very context-specific manner the problems concerning this branch of the field. A set of to-be-used criteria for start-ups business model creation is derived by the main needs of the growers and the degree of technology adoption observed. The criteria, or archetypes, have been developed on the base of the Sustainable Business Model Archetypes. A new model is proposed, which is a starting point towards the shift of regime and the digitalization of the agriculture field.

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.

Recent developments in technology innovations show that huge efficiency improvements can be made in the manufacturing industry. Moreover, companies that adopt the new innovations, associated with Industry 4.0, can create a huge competitive advantage. However, these rather conservative businesses are slow adopters and usually wait for proof-of-concept before actual implementation. Since this industrial revolution, Industry 4.0, is still in its infancy, more research is required to get to full adoption. Although the industry still awaits proof-of-concepts, many different case studies have been performed, and with success! These clearly exhibit the versatility of the Industry 4.0-philosophy, makingwidespread adoption just a matter of time. One of the identified reasons for this lag in adoption is the lack of clear implementation guides despite the thorough research and redundancy of technology. Due to the holistic Industry 4.0-concept, many practitioners lose sight on how and what to implement. Various researches proposed the creation of a widely applicable implementation model, but this is yet to be developed. One of the prominent issues related to creation such model is the all-encompassing nature of Industry 4.0; it includes novel innovation in supply chains, in factories, and even in the products manufactured. Since there are clear differences between these ’applications’, a generic overarching model seems unreasonable considering the immense amount of variables to consider. This thesis depicts the first ever-made implementation model specifically aimed at improving raw material supply & planning in complex manufacturing companies.Supply & planning processes are the closest connections between a manufacturers’ own operations and its closest neighbours in the supply chain; i.e. suppliers and customers. Tapping into this specific field of operations enhances the utilization of Industry 4.0 both on the supply chain and manufacturing aspects. Through answering the main question, and several subquestions, relevant information is gained that enable the construction of an implementation model. The design of such implementation model includes a step-by-step approach for practitioners of manufacturing companies, and a clear description on what to consider at each step. Creation of the artefact (i.e. implementation model) happens by explaining the research question: How can Industry 4.0 be implemented into supply and planning departments of complex manufacturing companies using an implementation framework?By means of a design science research methodology (DSRM) the Industry 4.0 supply & planning implementation framework is designed. Through 6 pre-determined steps; (i) problem identification, (ii) objective definition, (iii) design & development, (iv) demonstration, (v) evaluation, and (vii) communication, it ensured that all relevant stages are included to construct a scientific substantiate artefact. Three of these elements in particular were considered to be main constructs of the thesis report. Through the objective definition stage, qualitative research in the form of interviews and literature review imposed what had to be included in the implementation model. In the design & development stage this information was casted into a mold, thereby being the first result to the thesis’ ultimate goal. The demonstration phase was assigned to check the applicability and effectiveness of the model by putting it into practice. Altogether a significant base of information was collected, obtaining the firstconceptual implementation model for Industry 4.0. In the existing tight markets in which various manufacturers operate, the utilization of improvement technologies is high. Techniques derived from methods like Lean, Agile and Six Sigma are used on a daily basis. Because companies are familiar with the use of these models, the adoption of newer versions becomes straightforward. Consequently, the implementation model is a derivative of such method, namely the DMAIC (Define, Measure, Analyze, Improve, and Control). Since these overarching steps do not provide sufficient information for actual implementation, extra delineation is applied through a combination of the Continuous Quality Improvement model and practitioners’ experiences. Via combination of the two, a first model consisting of 11 steps (i.e. within the 5 DMAIC stages) was constructed.The implementation model starts with goal identification, in which the companies’ digital transformation (i.e. Industry 4.0-adoption) strategies are adapted to local needs. Subsequently, the business processes are investigated thoroughly. By clever modification of an existing model called RAMI (ReferenceArchitecture Model Industrie 4.0), a standardized approach for identifying the key aspects of the business processes was obtained. Using the results of this business process modelling allows to diagnose the so-called key variables that have a considerable effect on the performance of operations. The top five of these key variables provide the focus for the execution of the consecutive steps. Data and information about these 5 variables is gathered through a process of replacing paper forms by digital forms and through connection of existing Operational Technology (OT) systems with Information Technology (IT) systems. Once all the relevant data for the five variables is obtained, a data analysis follows. Examining the inconsistencies in this data pinpoint the location where data enhancement (i.e. Industry 4.0-adoption) will significantly improve the process. Defining the performance indicators then help to know the business’ existing performance and allow comparison with future results, but also help users to monitor real-time process-efficiency by means of a dashboard. According to the Key Performance Indicators (KPI’s) chosen, technology introduction can finally happen. Thirteen different enabling technologies were identified during the literature research, providing practitioners a wide portfolioof options in their Industry 4.0-implementation. Shortly after implementation follows continuous monitoring according to the aforementioned KPI’s. By carefully assessing the business process’ performance, improvement studies can be performed and actual improvement of the system can take place. In the final stage it is evaluated whether the implementation was effective and what lessons-learned should be brought to the next technology-implementation. To test whether the implementation model indeed fulfil its vows, a test run is performed at an agriculture fertilizer manufacturing facility that definitely classifies as a complex factory according to the definition of this thesis (i.e. large portfolio of products and raw materials). The first few stages were quite obvious in their execution, mainly because of the clear instructions given. Especially the modified RAMI model gave useful insights and abandoned the requirement of complete Business Process Mapping which is very time consuming. Various key variables were obtained using a quality team. Since the majority of data -for these key variables- was already available, it was only a minor effort to obtain the rest using either OT-IT merger or digital reporting. In the case study, the data analysis stage was the most demanding task in both time and extra investigation. After describing the KPI’s related to the data analysis and describing the technology introduction stage, the real version of the case studyhad come to an end due to time and resource limitations. Continuous monitoring, improvement, and evaluation were further concluded through the sense of ’modelling’, where providing examples and describing the expected outcomes served as enclosure of the first trial. Although the model was designed with extra care and the input from both the literature review and the interviews were significant, some limitations still apply. It was observed that some of the stages were not definitive enough, making the actual goal of each step rather vague. As a result, some stages could take considerably more time than necessary, diminishing the model’s effectiveness. Moreover, the power of the data analysis, as described before, was truly reliant on my experience in statistical and data analytics. Therefore, the current data analysis-description requires more attention to advance the usefulness of this stage regardless of the users’ experience. Finally, the effectiveness and generalizability of the model were only touched upon briefly and require more in-depth investigation before claiming its novelty.

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.

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.

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.

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.

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.

Edge computing can deliver substantial value to the general idea of the Internet of Things (IoT). However, there is a myriad of potential IoT applications for edge computing. Stakeholders are left with uncertainty about how the business potential of edge computing for these IoT applications can be identified. This research contributes in solving this, by designing a business model tool that can be used to identify the business model potential of edge computing for distinct IoT application areas, based on business model viability and feasibility. Through the Design Science Research Methodology (DSRM), the tool has been designed, demonstrated, and evaluated. Based on the STOF ontology, and supplemented by the theoretical domains of business ecosystems and platform theory, nine generic variables have been identified to explain business model viability and feasibility. These generic variables have in turn been contextualized towards the edge computing domain, in terms of 45 contextual input variables. This is the first research that unfolds these business model variables for edge computing.

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.