This thesis explores the strategies used by European cleantech startups to navigate the Valley of Death. This critical stage in the development of startups is characterised by a funding gap that hinders commercialisation efforts. Cleantech startups, however, with their focus on sustainable innovations addressing global issues such as climate change and energy transition, often experience a Valley of Death longer and deeper than other startups due to significant additional barriers in this phase. The additional barriers are high capital intensity, long development time, high technological risk, and ecosystem dependence. Thus, understanding how these startups overcome these barriers is crucial, given their potential to drive environmental and energy solutions. Therefore, this study seeks to uncover the strategies that allow cleantech startups to successfully navigate this challenging period. Through a multi-case study involving semi-structured interviews with eight cleantech startups and four investment stakeholders, the research seeks to identify the strategies used to navigate the Valley of Death. To ensure the strategies are grounded in a shared understanding, the timeline and barriers encountered by cleantech startups are first compared to the literature. This comparison is important because the timeline and barriers strongly influence the strategies that startups adopt. After establishing this foundation, the study then identifies the specific strategies used by cleantech startups to overcome these barriers.

The findings reveal that cleantech startups generally experience the onset of the Valley of Death around Technology Readiness Levels (TRL) 4-5. However, the exact timeline is highly dependent on the sector, market conditions, and type of technology being developed. Compared to other deeptech sectors, cleantech startups experience a delayed onset of the Valley of Death, largely due to the greater availability of government grants and strong public support that help mitigate early financial constraints. The Valley of Death typically ends around TRL 8, when startups begin to generate revenue and achieve financial independence.

In terms of barriers, the findings are strongly aligned with the existing literature, confirming the multifaceted nature of the challenges cleantech startups face. However, the interviews provide additional practical nuances that are less emphasised in the literature. For instance, while the literature touches on the limitations of grants, interviewees pointed out specific challenges such as grant application biases and restrictive conditions, the scarcity of skilled personnel was highlighted as a significant barrier to team-building, adding a practical layer to the more general discussions in the literature.

Another key difference is the strong emphasis placed on product-market fit by both startups and stakeholders during the interviews, which is underrepresented in the literature. Once startups progressed into the Valley of Death, tech risk appeared to diminish in importance, with startups shifting their focus to commercialisation and market challenges. These practical insights provide a deeper understanding of how cleantech startups navigate the VoD and highlight areas where existing research may need further exploration.

To overcome these barriers, European cleantech startups implement a combination of strategies that address both immediate financial constraints and the inherent risks of their ventures. The key strategies identified include non-dilutive funding, blended financing, alternative revenue streams, bootstrapping, investor engagement, deployment-led innovation, customer discovery, market demand signals, strategic partnerships, consortia-building, semi-commercial deployment, and early commercialisation.

Non-dilutive funding, such as grants, plays a crucial role, particularly in the early and intermediate stages, allowing startups to retain ownership while advancing development. The reliance on non-dilutive funding is one of the distinguishing features of cleantech startups, as they often use grants extensively, even until late stages, due to high capital intensity and the regulatory support available for climate and sustainability solutions. This reliance sets them apart from startups in other sectors, which may not leverage grants to the same extent.

In terms of managing inherent risks, cleantech startups employ strategies such as deployment-led innovation, customer discovery, and strategic partnerships to validate technology, establish credibility, and demonstrate market fit. By engaging with investors and pursuing blended financing, cleantech ventures can reduce perceived risks and secure the capital needed for scaling. These strategies are most effective when integrated, providing both short-term solutions for bridging funding gaps and establishing a foundation for long-term stability.

While the strategies employed by cleantech startups to overcome the Valley of Death align broadly with general entrepreneurial practices, the reliance on government support and the nature of addressing urgent global issues such as climate change and sustainability distinguish cleantech ventures from other sectors.

The study seeks to develop a better understanding of the unique drivers and barriers encountered by Dutch Deep-Tech Startups (DTS) in the Sustainable Energy (SE) sector during their pre-founding to founding stages. The aim is to identify effective strategies employed by entrepreneurs to navigate these challenges and to leverage the drivers. Despite their potential to address major societal challenges, these startups often face high failure rates due to inherent complexities and dependence on heavy R&D. To gain a better understanding of the drivers, barriers, and strategies, the study utilizes literature review and semi-structured interviews. Key findings highlight supportive academic culture, regulatory environment (RE), business networks, entrepreneurial factors, and personal motivations as drivers, while identifying barriers such as academic-commercial divergence, regulatory complexity, investor discrepancies, entrepreneurial factors, market research (MR) challenges and personal barriers. Strategies for success include forming cross-disciplinary teams, leveraging academic resources, strategic funding opportunities, adapting to market needs, and maintaining resilience and motivation. A conceptual framework is proposed to guide future entrepreneurs.
The study aims to benefit future DTS entrepreneurs, by offering practical strategies and insights for overcoming barriers and leveraging drivers. Besides DTS entrepreneurs, the study also provides valuable insights for universities and policymakers. The research offers policymakers insights on optimizing the regulatory environment to better support DTS. It suggests the need for streamlined regulations and targeted funding initiatives. For universities, the findings highlight the importance of embracing an entrepreneurial culture and providing support systems for DTS, including access to specialized resources and mentorship programs. Overall, the study contributes to a deeper understanding of the factors that influence the success and failure of DTS, offering practical recommendations for enhancing their viability and impact.

In this research, a literature review is done to create a framework on all current known criteria other countries' VC firms set when selecting a Tech start-up. The framework also includes possible governmental and economic influences that could affect the decision-making of Chinese VC firms. Subsequently, nine in-depth interviews have been held with three different perspectives: Chinese VC firms, European start-ups, and Specialists with an external view on Chinese VC investments in Europe. By conducting interviews, it was possible to get in-depth information on Chinese investments in the past. With this, valuable information on essential criteria for Chinese VC firms when investing in Europe is obtained. After data gathering, transcripts were made, and data were grouped into themes using open coding. Finally, a within-case- and cross-case analysis have been executed to trace possible causal relationships regarding selection criteria of Chinese VC firms on one side and governmental and economic aspects on the other side. Based on the findings of this research, European start-ups can better assess the risks and understand the decision-making of Chinese VC firms investing in Europe. It appears that mainly governmental decisions from China and Europe influence the decision making of Chinese VC firms investing in Europe. Especially Chinese governmental capital requirements need to be considered before investments in European start-ups; however, VC firms try to circumvent the Chinese regulations as far as possible. The influence of the Chinese government comes forward in the fact that: 1. The investment needs to contribute to the Chinese economy 2. The European start-up should not compete with investments in start-ups funded by the Chinese government. In addition, European rules restrict Chinese investors from investing in sensitive technologies, making it increasingly challenging for Chinese investors to invest in specific European sectors. This happens mainly due to the negative coverage of US politics on China. Furthermore, this study has identified criteria Chinese VC firms particularly use when investing in European Tech start-ups. It was found that Chinese VC firms mainly focus on the following categories: 1. experience, personality, and capabilities of entrepreneur and team 2. characteristics of the market 3. characteristics of product 4. financial considerations. More specifically, based on the interviews, a list of 21 selection criteria is found that European start-ups could consider when attracting Chinese VC investments. To conclude, since every investment is unique, and the strategy of VC firms differs, it is good to consider all criteria found in this study, including the criteria found in previous literature of other countries' VC firms to get a better overview on the requirements Chinese VC firms set when investing in European High-Tech start-ups.

This study aimed to analyze the decision-making process and criteria of angel group members. These factors form the basis of the final decision of a member when they decide whether or not to invest in a start-up. To investigate this topic, the factors that affect member decision-making are investigated when these members receive a pre-validated set of investment options from an investor club. To collect data, eight interviews with members of one investor club in the digital health sector have been conducted. These members are of diverse member type and nationality to analyze differences between members. During these interviews, different factors that influence the investors, like regulatory approval or a strategic fit, have been indicated by members. Throughout these findings, differences of focus between different types of members were observed. In some cases, the service provided by the investor club does not line up with the strategy of members, posing a question for their future collaboration. This dissimilarity in strategy shows as well in the factors of importance for members. Where business angel members have many commonalities with traditional business angels and usually fit well with the opportunities provided by the club, especially corporate members focus on other factors. Because of these differences in focus between members, providing a satisfying service to all their members is complicated for the investor club. Creating a more synonymous member portfolio will lower the variance in member demands, allowing them to provide a better service. Although the results show many similarities between the decision-making of business angel members and individual business angels, this will need to be researched further to definitively come to this conclusion.

The benefit of secondary use of data leads to the belief that data can be monetized through exchanging and trading between businesses. Many new data marketplaces emerge from this, resulting in the heterogeneity of the data marketplace that leads to market fragmentation in the industry. The aggregator business model can offer various values to users in a fragmented market. Therefore, the idea of designing aggregator business models for data marketplaces could be worthwhile investigating. Design Science Research (DSR) methodology is used in this research. Business model requirements to design aggregator business models were defined in the literature review. The business models of various aggregators are analyzed with case studies. From the requirements and case studies, four new aggregator business archetypes are derived: search engine, advanced search engine, comparison sites, and one-stop shop. These four aggregator business models are distinguished by their service offerings and their degree of the network. It is also discovered from case studies that there is a connection between the degree of network and aggregators' embedded technology. The high degree of network aggregators tend to use API, while the low degree of network aggregators tend to use information crawlers. The aggregator business model archetypes are then demonstrated to the data marketplace cases by translating the business models into business activities and services that can be offered to the users. Then, we evaluate the data marketplace aggregator business model through semi-structured interviews with experts. From the evaluation, it is discovered that data marketplace aggregators could offer value to the users, e.g., navigating users to find data sales and providing a single access point to collect data. Different technology aspects that could help to realize the aggregation services have also been developed by various projects. Additionally, data providers are willing to join the aggregation services due to the economic benefit. However, although the benefit and possibilities, multiple challenges need to be addressed by the data marketplace aggregator. There are issues with technical standardizations and interoperability that could be problematic in integrating various platforms for aggregation. Maintaining collaboration is difficult due to the need to provide data assurance, which could be expensive and exhausting. Additionally, although it is beneficial to navigate users in finding sales, data marketplace aggregators might offer little value to data buyers with market knowledge because they will directly buy the data from the sellers.