The pitch-deck evaluation is a crucial first step in the Venture Capital (VC) investment process, where investors assess a startup’s potential in the first screening of the startup. Traditionally, this relies solely on manual human judgement, making the process time-consuming, subjective and prone to bias. With the rise of Artificial Intelligence (AI), new opportunities emerge to support the decision-making with data-driven insights. However, due to the qualitative nature of early-stage startup evaluations, implementing AI in this context remains challenging due to its quantitative and data-driven nature.

This research therefore explores the potential of hybrid human-AI models (combining human judgement with AI support) in early-stage VC pitch-deck evaluations. A case study was conducted at InnovationQuarter using the AI tool 'Deckmatch'. Through a combination of interviews, a pilot experiment and a follow-up workshop, this study investigates how hybrid models can impact the effectiveness and efficiency of pitch-deck evaluations. This aims to answer the following research question:

How can hybrid models, combining artificial intelligence and human judgement, improve the effectiveness and efficiency of pitch-deck evaluations in early-stage venture capital?

Throughout the research, data was obtained with guidance from the Technology Acceptance Model (TAM) and Behavioral Decision Theory (BDT). The interview dataset consists of six interviews with VC professionals and their perceptions prior-usage of hybrid models within pitch-deck analysis. This was followed by a pilot experiment and workshop including both seven participants. During the pilot experiment the efficiency and efficacy was measured, in addition to the user experience. Afterwards, perceptions on hybrid models after experience with hybrid models was gathered.

It was expected that the hybrid models would enhance both the efficiency and efficacy of the evaluations, leading to a positive attitude towards use. The actual findings suggest that hybrid models show potential to increase the effectiveness or efficiency of pitch-deck analysis depending on the hybrid model used. Here, the sequential and interactive search models show the potential for improved effectiveness, at a slight decrease of time efficiency. On the contrary, the autonomous search model shows the potential for an improved time efficiency at a lower effectiveness. For the usage and preference of hybrid model type, the trust and transparency in the AI output is highlighted to be of importance. These findings contribute to the real-world understanding of human-AI collaboration in VC decision-making and offers further practical insights for VC firms, developers and entrepreneurs.

The semiconductor industry, which has evolved over around 65 years, witnessed the emergence of the Integrated Device Manufacturer (IDM) business model, followed by the introduction of the fabless and foundry models. These three models continue to coexist and interact today. Characterised by cyclicality, capital and technology intensity, and high economic volatility, the semiconductor industry is shaped by the complicated interactions among these business models, which form the competitive dynamics of the industry. Companies must adopt strategic
adaptations in response to these dynamics, further influencing their market performance. The relationship among competitive dynamics, strategic adaptations, and market performance is not a linear causal chain but a complex, mutually influential process. This study focuses on examining and comparing these concepts’ relationships within the IDM, fabless, and foundry business models...

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

In the current environmental conditions, with the rise of sustainable finance concepts like Environmental, Societal and Governance (ESG), firms in the financial sector have found new ways to drive sustainable change. Banks in particular play a key role in promoting sustainable practices both within their organization and also have a significant effect on the global economy. However, banks have had difficulties integrating ESG practices in their sustainable credit distribution products to corporate clients.
Due to the qualitative nature of ESG data, even with the involvement of regulatory bodies, there are no established standardized processes for ESG integration. Literature exhibits a clear knowledge gap on the utilization of ESG data in the credit risk assessment process. Furthermore, the primary barriers faced during this process have been attributed to comparability, materiality, accuracy, and reliability of ESG data quality with the lack of comparability having the most significant impact. Hence, based on the knowledge gap identified, the following main research questions were formed:
“How can banks in the EU effectively utilize corporate ESG data during the sustainable corporate financing process?”
By fusing a thorough literature review and 11 interviews of professionals from the banking industry, consultancies, and asset management companies, a process flow diagram was mapped highlighting the different phases of the sustainable corporate financing process. The main phases were comprised of: Opportunity, Due diligence, Determining need for ESG data, Sourcing ESG data, Corporate Sustainability Assessment, Setting KPI’s and Decision. Furthermore, during this process the primary barriers were classified as Data Quality Barriers (Lack of Materiality, Lack of Accuracy, Lack of Comparability and Lack of Reliability) and Integration Challenges (Lack of Data, Sourcing Data, Quantification of Data). Given the relevance of the comparability barrier, its effects were studied in more detail and was followed by proposing a conceptual framework bolstered by stakeholder theory, legitimacy theory and institutional theory to solve the barrier of Lack of Comparability. The relevant dependent variables in this framework were identified as: Data Harmonization Initiatives, Standardization Efforts, Industry Collaboration, Regulatory Interventions/Compliance and Client Engagements. As directed by the conceptual framework, banks were recommended to adopt the dependent variables Data Harmonization Initiatives, Standardization Efforts and Industry Collaboration and were further advised to help clients adhere to compliance. Furthermore, stakeholders' motivation and regulatory incentives were highlighted as key determiners in addressing the barrier. A processual validity approach was adopted to ensure validity throughout the research process which additionally consisted of validating the generated process flow and conceptual framework by relevant experts.
The outcomes of the explorative research showcased the intricacies in the sustainability credit risk assessment and the stakeholders involved while highlighting the interrelation between the barriers identified. Though the lack of a comparability conceptual framework was proposed to be a barrier with significant effects, during the interview analysis it was revealed that the advent of the Corporate Sustainability Reporting Directive (CSRD) would have a significant effect on this barrier and that challenges like sourcing ESG data and lack of ESG data would be more relevant in a few years. Although the conceptual framework accounted for this, some of the limitations in this research study were the inability to recruit more interviewees and also the inability to gather interview data from an external data provider to retrieve a true holistic approach. For future research, studies are recommended to research the effects of the implementation of CSRD and to study the state of other pressing barriers.