Development Finance Institutions have been established to provide finance for long-term economic development in low and middle-income countries. However, these investments are exposed to climate-related risks. This means that Development Finance Institutions need to explore climate adaptation interventions, particularly in view of the fact that developing countries themselves have few resources for climate adaptation. This thesis aims to establish how, using an agent-based model, Development Finance Institutions can achieve long term investments by financing climate adaptation, while creating societal benefits and financial returns. This implies positively contributing to society, for example by creating jobs, generating a stable income, and supporting the resilience of households, while securing profitability for development banks. An investment by the Dutch Development Bank FMO in the cocoa sector in Ghana serves as a case study. Three main steps are followed in the study: (1) translating global climate scenarios at the local level; (2) capturing local behavior in a bottom-up model because understanding the specificity of the local context and individuals’ response is key to really make an impact; and (3) testing climate adaptation interventions. Downscaled climate scenarios are used as input in an agent-based model capturing the behavior of Ghanaian cocoa smallholders. Experimenting with climate adaptation shows that Development Finance Institutions have to make trade-offs in terms of priority, timing, and activity. This step-by-step approach could encourage DFIs to capture the impact of climate change and the dynamics at the local level, and to design tailored interventions. It is also a way to take a holistic perspective and look at the broader picture of economic and societal impacts of climate change, and the response across the whole value chain.

Modelling and simulation is at the heart of Digital Twin technology, which is revolutionising many industries. Organisations have access to legacy models that are too complex to maintain, preventing them from operationalising complex systems like Digital Twin. By creating an automated tool for model decomposition, it is possible to breathe life into complex models by extracting their embedded functionality as managable components. This thesis presents NewMODE as a network theory approach to model decomposition; a novel methodology that aims to automate the tedious task of model decomposition. Important contributions include the network theory metamodel specification, the decomposition criteria and the adaption of the Girvan Newman algorithm to identify components of the model. NewMODE has been implemented for models in developed in LSAT (Logistics Specification and Analysis Tool). In partnership with TNO and their Embedded Systems Innovation (ESI) group, NewMODE has been evaluated quantitatively and qualitatively with promising results to aid model developers in model decomposition.

The continuous innovation within the telecommunications industry is compounded by changing risks for the consumer. This is the case within the Netherlands, where the use of the internet has a pervasive influence on society, necessitating robust telecommunication networks and services. The telecommunications industry is regulated by the Radiocommunication Agency Netherlands, which currently use a method of risk-based regulation to monitor relevant organizations. This is characterised by allocating resources based on the estimated level of risk that is posed by a monitored organization. While these risk analyses have developed over the years, there is no standardised system heightening the possibility for cross-departmental inconsistencies. An overview of academic literature reveals a lack of research on the creation and implementation of data-driven model for risk-based regulation. Existing studies lean towards the merits of risk-based regulation, in addition to the risks associated with data-driven risk-based regulations. When this literature gap is compounded with the rising pressure for the regulator to perform quality supervision and ensure the uninterrupted continuity of the telecommunication networks and services in the Netherlands, it provides a suitable opportunity to explore the applicability of a data-driven solution. The use of data-driven risk detection models have the potential to identify companies that pose higher social risks to create a more efficient process for the allocation of resources. It can illuminate the relevant risk criteria and characterise the risks that certain organizations may pose. This information supports the decision-making process of regulation, which could lead to a more cost-effective process of supervision. Furthermore, given the inconsistencies that exist between the various supervisory divisions of the Radiocommunication Agency Netherlands, a data-driven model could increase internal cohesiveness. As a result, there is significant interest in the possibility of developing a risk detection model that can be adapted to the current organizational structure and makes use of the readily available data within the organization. The research objective of this study is to create and implement an exploratory data-driven model for risk-based regulation, specifically on the subject the continuity of the telecommunication networks and services within the Supervisory Field of Information Security of the Radiocommunication Agency Netherlands. Thus, the primary research question that will be explored is: How could a data-driven risk analysis for risk-based regulation support the process of risk-based regulation executed by experts? There are four primary challenges to the creation and implementation of data-driven models: the ability to turn data into useful information, the ability to assess the quality of the data, the availability of the data within the organization and the ability to create a process with a high cost-effectiveness ratio. Additionally, there are specific issues associated with the use of data-driven models in regulatory bodies. Firstly, the use of algorithms and data metrics can be distorted when applied incorrectly. In addition, this who create data-driven models or use the outcomes of the model are prone to interpret the data so that it will fit the pre-existing narratives The second issue is implementing the data-driven model so that it generates acceptance and can be integrated into a well-functioning working culture within the organization. The research utilizes data that is sourced from two different departments of the Radiocommunication Agency Netherlands; survey responses from supervised organization and self-reported data regarding network incidents. The final data source consists of social media mentions naming network failures of monitored organizations. The development of an Exploratory Model based on the available data within the organization highlighted several modelling challenges. The process of developing the Exploratory Model is preceded by an Exploratory Data Analysis which determines the quality and significance of the available data. This allows for a summary of the main characteristics of the data and supports the development of the Exploratory Model. The Exploratory Model involves clustering the data, and developing correlations between the various datasets. The execution of a K-means algorithm segments the organizations into groups that provide similar responses to the surveys. The correlations can be used for facilitating the initiation of risk profiles. The data from network incidents and failures are highly important when creating a model for data-driven risk-based regulation as they are key in defining the different risks posed by supervised organizations. The results are validated through interviews with experts of the relevant divisions in the Radiocommunication Agency Netherlands. Two primary challenges that exist for the implementation of the model are identified; ensuring all necessary data is available for the creation of the model, and defining the owner of the model. The existence of an institutional boundary between the Supervision Division Information Security and the Spectrum Division Continuity, which hinders data sharing regarding the reports of failures that caused discontinuities of the telecommunication services and networks. This poses challenges for the implementation of a data-driven model, as it is crucial for the relevant teams to have access to all necessary data within the organization. There are two possible solutions to overcome this challenge; either dismantling the institutional boundary, or relocating the data management of reports of failures that caused discontinuities of the telecommunication services and networks from the Spectrum Division Continuity to the Supervision Division on Information Security. The second challenge is defining the most suitable owner of the model. As the model is intended to support regulation for the Supervision Division on Information Security, it is necessary for the owner to have expertise in this area. Moreover, as this will be a data-based model, knowledge on quantitative analyses is preferable. Currently, the Monitoring & Analysis Centre of Radiocommunication Agency is most suitable, due to their expertise in analysing data and working on data-driven models. Moreover, they already have access to all necessary data, which means no organizational restructuring is needed. However, there will be challenges stemming from their lack of knowledge regarding the process of supervision on the continuity of the telecommunication networks and services. Two directions for future research are identified based on the outcomes of this research. The first step would be to further develop the Exploratory Model, which requires a higher volume of information about failures that caused discontinuities. In addition, the data currently within the databases must be frequently updated. Whilst this is certainly viable, it would be a time intensive and a bureaucratic process. In the meantime, it is possible to execute the clustering algorithm on the whole dataset of both of the surveys with the responses from around 1000 organizations and to compound this with qualitative data from experts and inspectors within the Supervision Division Information Security, to aid in the determination of the risk profiles.

Increasingly companies are facing tough challenges due to fast paced technological developments, shortening product life cycles and new forms of business models. Therefore innovating is crucial for companies to stay competitive and enter new markets. Large corporations have difficulty in breaking silos and developing breakthrough innovations due to pressures in the form of financial performance and resistance to change. It seems that due to volatility of markets and technological developments, companies need to “join the race” in order to survive. For the purpose of benefitting the innovation space network companies can interact with external parties. Research shows that collaboration with external actors lead to sources of higher innovation performance. Nevertheless, the innovation process is very intricate as it involves many complex interactions and challenges. Innovation depends on people´s expertise, capabilities and creative capacities. Companies need to recognize this and provide an environment that can foster innovation.

Infineon Technologies AG Corporate Supply Chain Innovation department recognizes the need to innovate in a peripheries of its core technological developments. The department is involved in several co-funded European projects which aim towards digitalization improvements.

The main company´s objective is to explore ways to better coordinate relationships between partners, to find ways to innovate and to foster knowledge management practices. Since the firm wants to improve its innovation performance, certain processes need to be develop to stimulate innovation. Infineon Technologies AG does not have processes for managing disruptive innovations therefore is in need to design a model that would enable the company to manage them, the main objective of this master thesis is to design:
A model which prescribes processes for management of disruptive innovations between firms, on a case study of Infineon Technologies AG Corporate Supply Chain Innovation department.
In order to reach objective of this master thesis, a following main research question is formulated:
How to facilitate inter-firm innovation management processes of disruptive innovations in the context of Infineon Technologies AG Corporate Supply Chain Innovation Department?
The thesis project takes a Design Science Project approach and is divided into five phases: 1) research motivation 2) requirements definition through exploration of theoretical foundation 3) design and development of the artefact 4) demonstration of the artefact through validation and 5) evaluation and conclusion.
The overall conclusion for this thesis is: in order to facilitate inter-firm innovation management processes of disruptive innovations Infineon Technologies Corporate Supply Chain Innovation department should implement co-innovation management practices. The co – innovation model has a network structure composed of co-creation, collaboration, innovation platform and convergence affordances. By expanding these elements to its practical implications, the company is able to implement a comprehensive and implementable solution. Scenario with actionable steps was developed as a recommendation for the company.

Agent-based modelling (ABM) is a widespread and commonly used technique for the understanding of social system behaviour. Despite ABM being a commonly used method for the analysis of complex social systems, simulated agents still can best be described as autistic before a specific set of social rules is ascribed to them. This discrepancy leads to model outcomes that can differ from reality. For improving agent-based models on this aspect, a social practice theory-based approach can be used as a theoretical framework. The main question to be researched is: How does enabling agents to reason about others' beliefs on social practices affect dynamics of belief and behaviour formation as opposed to action adaption models, with respect to transport mode choices? Two agent-based models have been created within this study to provide an answer to this research question. Through including a case study within the transport domain, model results can be hold against macro-behaviour within the state of the art of models on transport mode choices. The case study depicts a group of students making a decision for a transport mode to move from their university faculty to a compulsory seminar. The students influence each other, leading to belief adaption. Within the transport domain, usually this social influence is translated to compared and adapted actions instead. This study differs itself by letting beliefs be the reference point for social comparison. Therefore, belief dynamics and agent behaviour for the models with belief adaption are compared to the state of the art action adaption models. Lastly, the effect of limitations towards having insight to other agents' personal characteristics is analysed. The analysis provides the following conclusions. Within the field of transport mode choices, enabling agents to reason about others' beliefs on social practices results in the same opinion dynamics and behaviour as is presented by action adaption models. However, an agent-based model that allows agents to reason about others' beliefs on social practices with limited insight in others' private characteristics through a theory of mind capacity, does provide a different reaction to an increase in the social capital of agents. More specifically, enhancing social network connectivity through increasing the group size or similarity threshold does not lead to more consensus within the theory of mind model, which is the case in the other models presented within this study. This stresses the importance of considering the extent to which are capable of forming a just conjection on others' private characteristics when designing an agent-based model with social interaction.

More than 200 million Indians are without electricity, and the majority lies in the rural areas, where the central grid fails to reach due to unreliable distribution and inconsistent terrain. The solutions that reach include majorly, decentralized energy resources via solar micro-grids, or solar home systems. New projects of energy sharing are coming up which allows a household to become a consumer, as well as a producer of energy in case of excess and sell it to make profit. But these projects fail within few years due to number of social, technical, cultural, political, and environmental dynamic factors playing role in the community. Thus, the research question is raised that “Given the dynamics of rural communities of India, how can socio-technical systems of solar electrification be sustained?" This project develops an institutional innovation framework that helps answer this question. The framework is conceptualized and operationalized to understand that how the institutions around these factors can be changed by the community and individual actions in such socio-technical innovations and infrastructure systems. The study follows empirical analysis and develops simulation model (agent based model) for the case study of rural solar electrification in India, which helps in developing the institutions. The case study and the framework together emphasizes the importance of the institutional innovation approach, where institutions need to be adapted and diffused within the community to make the sustenance of the project possible in various domains. The final results show that new type of projects, labelled as hybrid projects, would be most sustained. These would be sharing projects and would only use the existing micro grids, when there is higher demand. Also, it emphasizes on the need to look at institutions development, not just as a collective perspective which happens with interactions of actions, but also a perspective of majority or collection of actions. The policies generated at two levels of usage (prioritization of resource usage, constraints on resource usage, etc.), and for acceptance of innovations (co-operative shops, in-house manufacturing, etc.) prove to help the sustenance of these electrification systems further.