This thesis investigates the impact of increased value capture by a dominant platform sponsor on user behavior and platform ecosystem stability, focusing on YouTube in a single case experiment. This research aims to address the existing gap in understanding how users respond to this increase in ecosystems characterized by high switching costs. The literature review identified key concepts to build a theoretical framework. The experiment tested the direct effects of increased ad load on 79 participants by simulating a real-world YouTube experience and evaluating behavioral responses. The findings revealed that a higher ad load significantly increased user disengagement. However, the introduction of a premium subscription mitigated these effects to some extent, serving as a stabilizing mechanism. Notably, users willing to pay for premium exhibited aversion to ads independent of increased ad load. This study highlights the trade-offs platforms face between value capture and ecosystem sustainability. While ad monetization can drive short-term profits, excessive strategies risk destabilizing the ecosystem. Practical implications suggest sponsors must balance value capture with reinvestment strategies, focusing on user-centric improvements to maintain ecosystem health. Policymakers are urged to consider regulatory measures ensuring transparency and long-term user welfare in platform governance. The theoretical framework presented in this thesis can serve as a basis for future research in this field of platform research.

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 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.