Entrepreneurial exit can be considered one of the most important moments in the entrepreneurial life cycle. However, within the literature, this importance was only recognized recently. Research regarding entrepreneurial exit went through a few stages; Firstly, the focus was on re-defining entrepreneurial exit and giving it a place in the life cycle. Entrepreneurial exit used to be an event that was associated with failure. Right now, not perse. It’s an event with many choices and can be studied on many levels. Within this study, exit will be looked at the level of the entrepreneur. Secondly, the role of the entrepreneur was considered; this includes variables such as intentions and personality traits. Lastly, state-of-the-art research seems to be looking at team dynamics. Currently, the main focus points were on homogeneity within the team. This study is an exploratory research which tries to link team dynamics and entrepreneurial exit. The found connections can be used for further studies.
To achieve this, the main research question for this study is formulated as follows:
How do team-dynamics in technical firms affect the entrepreneur's intention to exit the start-up and the chosen exit strategy?
To answer the research question, a qualitative method was used, namely the comparative case study by using contrasting cases. The case studies are created by conducting interviews with management team members of technological start-ups/scale-ups. This contrast between cases is created by using a quadrant that divides the cases into “entrepreneurial exit incidents” and starting and more developed start-ups/scale-ups. Furthermore, the semi-structured interview is created by doing a literature review. Out of this literature review, multiple propositions were derived, and a conceptual model was formed. Afterwards, the propositions and conceptual model were the guidelines for the interviews. Lastly, the literature review was based on the sub-research questions.
12 case studies were formed, which were analyzed first individually and second cross-case. For the individual analyses, an open code method was used. For the cross-case, axial coding. Lastly, to find something coherent, selective coding was used.
The conceptual model created with the literature cannot be supported by the case studies. However, out of the cross-case analysis, new interesting hypothesis have been formed. Such as 1. The friendship of the founding team and how they met are directly correlated with the homogeneity of the team. 2. Conflict in the early stages most happens because of a lack of entrepreneurial experience and an unclear separation of tasks. Furthermore, homogenous teams are more likely to have an unclear separation of tasks. 3. Conflict in the later stages of the companies is mostly related to different core values between management team members.
Each of these relationships could be interesting for future research, such as how fast certain milestones are reached within a homogenous founding team and a heterogenous founding team. How could this early unclear separation of tasks be improved? This could be a bigger issue for homogenous teams where people have similar skills and interests. Lastly, how could management team members be selected for long-term value and minimal conflict?

Explainable AI is the field concerned with trying to make AI understandable to humans. While efforts have resulted in significant improvement in research and practical methods of Explainable AI, there is an urgent need for additional research and empirical studies. The academic research gaps identified in this thesis show that Explainable AI is still in its infancy and is mostly approached with a technocentric perspective while not being focused on the audience the explainability is actually intended for. Next to this, there is no structured approach to defining and establishing explainability in dynamic complex systems that involve people, institutional, and organizational elements. Lastly, there are limited empirical studies that investigate the needs, usage, and risk of explainability in complex systems.

The research tries to define and address explainability in the socio-technical context within the Machine Learning decision support systems of Transaction Monitoring. It does so by performing an extensive literature review and by conducting semi-structured that try to collect empirical knowledge within local practice. The goal of this research is to expand the definitions and view on explainability by incorporating the social, organizational, and institutional elements that influence explainability. Next, the goal is to develop a method that can help practitioners approach explainability in a structured manner taking the audience into account while applying this socio-technical perspective.

The user-centered method for operationalizing explainability takes on a socio-technical perspective and can provide requirements for design choices, in addition to this the method shows how these requirements can be satisfied and controlled by instantiating control structures. The method has been demonstrated and evaluated within the bank by providing a workshop using a Toy Case during a focus group. Practitioners experienced the method as useful and actionable, also the method provides broader perspectives and insights on explainability and invites the discussion of dilemmas and questions. The practitioners added that the method could be further refined by focusing on additional guidance on the control structure because the method assumes prerequisite knowledge of systems theory and system safety theory.

Emerging technologies are revolutionizing organizational growth, productivity and in- vestments more than ever before. One such technology that has come into the limelight in the past few years is the Digital Twin. The so-called ’digital twin’ is a real-time virtual replica (representation) of any given physical asset/object. The full-potential of a digital twin lies in its ability to not only communicate with the physical asset, but also control it remotely. Although the concept of digital twins is more than a decade old, digital twin initiatives are now been deployed in the manufacturing, automotive and healthcare industries among others. One such industry, experimenting with digital twins is the Energy sector. The objective of this research was to perform an exploratory investigation into the adoption of Digital Twins in the Dutch Energy sector predominately by Trans- mission System Operators (TSOs) and Distribution System Operators (DSOs). The investigation comprised of four elements: establishing the most fitting technology adoption model when it comes to digital twins, determining the relevant adoption variables, exploring the perception of digital twins in the industry and investigating the relationship between absorptive capacity and organizational characteristics. The research method deployed for the former two elements was desk research, whereas the latter elements were probed by conducting (semi-structured) interviews and targeted questionnaires respectively. There were a total of nine participants involved in this research which included technology adoption decision-makers having a wide range of work experience (1-5 years to 20+ years) from the five of the eight TSO/DSOs of the Netherlands.
The research found that the most fitting technology adoption model when it comes to digital twins in the Dutch energy sector was the Technology-Organization-Environment (TOE) Framework with the following relevant (non-exhaustive) adoption variables: Complexity, Compatibility, Perception, Technological Characteristics, Availability, Organizational culture, Organizational size, Budget size, Incentives, Management support, Ab- sorptive capacity, (decision-maker’s) Demographics, Attitude towards technology, Regulations, Competitive pressure and Network effects. In addition, the overall perception of digital twins was found to be positive across the Dutch Energy sector, however, there was no consistent relationship established between organizational characteristics and the levels of digital twin perception. Similarly, the research suggested that organizational characteristics and absorptive capacity were not correlated. Nonetheless given the limitations of having a low number of study participants and the potential of bias amongst respondents towards their employer, the strength (significance) of these discovered relationships are indicative and should be further investigated in future research prior to making any additional claims that are conclusive.