Poland represents an emergent venture capital (VC) market and it faces several critical challenges that impede its growth and dynamism. Key issues identified include a lack of growth capital, diminishing market activity, dependency on public funds, and a scarcity of foreign investment. To address these challenges, this thesis proposes the internationalisation of Poland's VC market by attracting foreign capital as a viable solution. Given that emergent VC markets similar to Poland are understudied in existing literature, this study provides a qualitative analysis framework to better understand these markets through the perspectives of industry participants. Focal to the research are the determinants of the foreign investor's decision to enter the Polish market with different degrees of commitment. The key results reveal ecosystem dynamics, incorporating complex network of variables and processes, which ultimately have their effect on the perception of risk and return in such ecosystem. This comprehensive analysis aims to offer new insights into the unique dynamics of emergent VC markets and propose actionable strategies to address the ecosystem issues and enhance their development, by integrating into the global venture capital landscape.

This thesis explores how electronic human resource management (e-HRM) enhances employee performance in technology startups. During their transition phase, technology startups face challenges, such as maintaining effective communication, sustaining organizational culture, and managing growth. These challenges reduce employee performance, threatening the survival and growth of startups. Despite the potential of e-HRM to address these issues, there is limited research on its specific impact within startups. Existing e-HRM studies focus on large corporations or only broadly discuss startups, leaving a significant gap in understanding its effect in the entrepreneurial context. This gap is important because startups, unlike established firms, face unique challenges such as rapid growth, limited resources, and high failure rates. To close this literature gap, the thesis employs a qualitative research design, using semi-structured interviews with HR managers from 32 EU-based technology startups. The selected startups, founded between 2016 and 2022, employ between 35 and 249 people.
This research shows that e-HRM practices in technology startups primarily focus on administrative tasks (used by 94% of companies) and performance management (84%). However, practices related to employee relations, safety, and strategic HR are not frequently adopted.
The findings show that HR managers have mixed opinions on the impact of e-HRM on employee performance. Some argue that e-HRM systems primarily save administrative time and do not directly affect core performance metrics such as sales or primary business outcomes. Conversely, other HR managers report that e-HRM systems can enhance employee performance through employee engagement and feedback/ education. These systems should be user-friendly and reduce complexity in HR processes, and target employee engagement through addressing motivation, offering benefits, minimizing disruptions, and reducing confusion. To improve employee performance, performance management practices like employee evaluations and feedback mechanisms are indicated to be most suitable. Task tracking tools, while potentially increasing performance, often do not enhance engagement and may reduce it, thus their use should be carefully considered.
The study also identifies challenges that startups encounter when implementing e-HRM tools. Predominantly, people-related issues include employee reluctance to use e-HRM, the time-consuming nature, manager hesitancy, incorrect or incomplete software usage, and managing an too many tools. Despite the expectation that these startups, with their technological and innovative focus, would face minimal technological challenges, issues like data integration, system reliability, and user interface problems were also frequently reported.
In conclusion, e-HRM systems can have a positive effect on technology startups if implemented with a focus on time savings, simplicity, and employee engagement. Successful e-HRM implementation requires strategic planning that considers the values of all stakeholders, including employees, HR, line managers, and the founder. Communicating the change in advance helps mitigate challenges and ensures a smoother transition. With these findings, entrepreneurs in the field of HRTech learn which e-HRM characteristics are most important to HR managers (time savings) and which are most relevant to enhancing employee performance (employee engagement and feedback/education). At the same time, founders of technology startups and HR managers of those startups get to know which e-HRM practices are most relevant to improving employee performance (Performance Management tools) and that they need to communicate an e-HRM tool implementation early, including company stakeholders and explaining how the new tool helps the different internal stakeholders in their daily work.
The findings guide the reader on how to effectively use technology to improve HRM, enhance employee performance, prepare a technology startup for scaling, and increase the probability of startup success.

While many researchers have researched the factors affecting standard consortium success, the size and diversity of a consortium were given the most importance. However, investigating the combined effect of these two factors on standard consortium success using specific indicators of success was limited. Past research required action on applying a new approach to determine the extent to which these variables work together toward the success of standard consortia. It was also important to investigate the network configurations of consortia given the multiple factors that come into play in the process of standardization over the years. This required a configurational approach with huge consortium-level data to test. The objective of this research is to find the configurations of size and diversity that lead to standard consortium success, in the ICT and telecommunications industry, success being represented by specific indicators. The data was collected from the websites ‘consortiuminfo.org’ and LinkedIn using Python packages and was analyzed using Crisp-set Qualitative Comparative Analysis (csQCA). This analysis was done considering two indicators of success individually and combined as a single variable, summing up to three analyses. The analysis revealed that the size and diversity of consortia are necessary conditions, but only diversity is found to be a sufficient condition for standard consortium success in this sample dataset of ICT consortia. The findings suggest that researchers should continue to explore the involvement of other factors along with the size and diversity as combinations instead of only analyzing the variables’ effects individually. Furthermore, by doing this the scope of the research broadens by breadth and depth as more data and more insights about factors of standard consortium success can be gathered.

The European Union has implemented Sustainable Aviation Fuel (SAF) blend mandates in its member states, starting for bio-SAF in 2025 and for synthetic SAF (e-SAF) in 2030. Various e-kerosene plant projects have taken off in the last years in Europe. However, due to the high investment costs and dependency on feedstock availability, no plant has reached final investment decision yet. In the Netherlands, two full scale e-kerosene plants have been announced to be build in the next decade. This paper estimates the net present value of one of these in the current market conditions of the Dutch aviation fuel market. For this, a real option tree model is created to represent the current risks, investment costs and market state in the Dutch geographical context. In addition, the impact of various policy measures are added to the model to create different policy scenarios. The objective of this paper is to find which policy scenario's yields a positive net present value for the analyzed PtL plant in the Netherlands.

The real option decision tree was composed in various steps. The model is based on an e-jet fuel plant based in the Netherlands with annual jet-fuel production capacity of 50,000 tonnes. The e-fuel mix contains 75% jet fuel, 12.5% diesel and 12.5% naphtha. This plant sources green hydrogen and CO2 externally, therefore does not require investment in direct air capture systems or an electrolyzer. First, the project stages and options were defined as in other energy projects. The length of each is approximated based on the status and expected deployment of current PtL e-kerosene plants. Next, the project investment and value was determined following the findings of previous works. The CAPEX was split up over the investment stages determined previously, and adjusted for inflation. Likewise, the OPEX found in various literature sources was inflation adjusted and averaged. The selling price is modeled to decrease at the same rate as the projected electrolyzer costs because of technology maturity. Market conditions were based on both fuel demand projections in the Netherlands and the European blend mandates for synthetic fuels until 2050. From this, two market condition scenarios were modeled. These were based on whether the modeled plant or its smaller competitor reaches market first. This makes a difference, as the fuel demand in the first years of operation is limited because of lower blend-mandates. Next, the abandon options were modeled by determining the salvage value. The salvage value was defined as the current replacement costs minus the depreciation. For this, the depreciation rates for each investment during both testing and operation were determined. After finding the values, the probability distribution for the options in the different project stages were determined. This was done using the probability ranges as defined in the classical risk matrix. The current and forecasted status of most prominent project and market risks were described, where after the risks were allocated to a probability range. The main value of each was used in the probability distribution. Lastly, the different policy incentives and scenarios were defined.

Results indicate that the blend-mandates imposed by the European Union are insufficient for an large scale e-kerosene plant to yield a positive NPV in the market landscape within the Netherlands. Optimizing market conditions for these plants increases the NPV, however, not sufficiently. Instead, the risks of green hydrogen, grid electricity availability and the high CAPEX investment costs early in the project significantly impact the NPV, resulting in an unfeasible business case. Therefore, measures to reduce CAPEX and ensure feedstock supply will be most beneficial to increase the project value. Scenario's imposing combinations of certain policy incentives resulted in a positive NPV, hence, financial viability. Those with the most effect combined CAPEX lowering with either optimizing market conditions or increasing cash flow through a tax cut. In addition, a scenario was modeled that included a waiting period before the construction phase to increase the probability of hydrogen and green grid electricity availability. As the markets of each are projected to grow annually, there is a trade-off between risk reduction during construction and increased cost of capital and asset depreciation by waiting. It was found that a waiting period of 3 years before starting the construction phase resulted in the highest NPV.

This study found that more is required than currently incentivized by the European Union for PtL projects to become operational and successful long-term within the Netherlands. The focus of the Dutch and European policy makers needs to shift towards lowering capital expenditures for PtL plant construction and reducing feedstock supply risk in the following years. Only then can the first two full scale plant in the Netherlands continue to the next phases of their projects and eventually cover the market demand of the first years following 2030. The real option tree model composed in this study combined with the policy scenario's contribute to the economic analysis performed on PtL e-fuel plants. The decision tree model incorporates managerial flexibility and both project and external risk within the valuation within the Dutch context specifically. As the presence of both risk and options significantly affects the project NPV, this method adds to results found in previous techno-economic analysis works. The model provides an adjustable framework for policy makers, to test under what market conditions and within which policy scenarios investing in these plants would be profitable. The challenge lies in finding the right combination of incentives that allows the financial viability within the right time frame, while avoiding to put the entire financial burden on one group of stakeholders. Large scale deployment of (e-)SAF is the best pathway to meet European climate goals for aviation in 2030 and beyond. A trade-off needs to be made between sustainability and economic prosperity to enable this. Achieving this balance will be necessary to ensure the both environmental and economic objectives are met, enabling a sustainable and prosperous future for the Dutch aviation industry.

The European Union has implemented Sustainable Aviation Fuel (SAF) blend mandates in its member states, starting for bio-SAF in 2025 and for synthetic SAF (e-SAF) in 2030. Various e-kerosene plant projects have taken off in the last years in Europe. However, due to the high investment costs and dependency on feedstock availability, no plant has reached final investment decision yet. In the Netherlands, two full scale e-kerosene plants have been announced to be build in the next decade. This paper estimates the net present value of one of these in the current market conditions of the Dutch aviation fuel market. For this, a real option tree model is created to represent the current risks, investment costs and market state in the Dutch geographical context. In addition, the impact of various policy measures are added to the model to create different policy scenarios. The objective of this paper is to find which policy scenario's yields a positive net present value for the analyzed PtL plant in the Netherlands.

The real option decision tree was composed in various steps. The model is based on an e-jet fuel plant based in the Netherlands with annual jet-fuel production capacity of 50,000 tonnes. The e-fuel mix contains 75% jet fuel, 12.5% diesel and 12.5% naphtha. This plant sources green hydrogen and CO2 externally, therefore does not require investment in direct air capture systems or an electrolyzer. First, the project stages and options were defined as in other energy projects. The length of each is approximated based on the status and expected deployment of current PtL e-kerosene plants. Next, the project investment and value was determined following the findings of previous works. The CAPEX was split up over the investment stages determined previously, and adjusted for inflation. Likewise, the OPEX found in various literature sources was inflation adjusted and averaged. The selling price is modeled to decrease at the same rate as the projected electrolyzer costs because of technology maturity. Market conditions were based on both fuel demand projections in the Netherlands and the European blend mandates for synthetic fuels until 2050. From this, two market condition scenarios were modeled. These were based on whether the modeled plant or its smaller competitor reaches market first. This makes a difference, as the fuel demand in the first years of operation is limited because of lower blend-mandates. Next, the abandon options were modeled by determining the salvage value. The salvage value was defined as the current replacement costs minus the depreciation. For this, the depreciation rates for each investment during both testing and operation were determined. After finding the values, the probability distribution for the options in the different project stages were determined. This was done using the probability ranges as defined in the classical risk matrix. The current and forecasted status of most prominent project and market risks were described, where after the risks were allocated to a probability range. The main value of each was used in the probability distribution. Lastly, the different policy incentives and scenarios were defined...

This thesis explored the contrast between the overarching category of entrepreneurship (from here on, “mainstream entrepreneurship” or ME) and the subset of technology entrepreneurship (TE) through a comparison of The Netherlands (low TE/high ME) and Japan (high TE/low ME), addressing the problem that currently no dedicated frameworks for TE exist. Five influencing variables were explored at the country level of analysis, namely economic development, technological development, institutions, education, and culture. In contrast to ME, TE benefits from better technological development and education. These influencing variables showed high values, whereas for ME they showed low values. With analysis of 44 additional countries, these results were confirmed, and economic development was also deemed important to TE. A culture high on long-term orientation and individualism (Hofstede dimensions) appeared to benefit TE. Institutions showed little difference between ME and TE. The additional analysis firmly showed that ME and TE are different and unrelated. which was confirmed by a low correlation coefficient and corresponding p-value.

This thesis shows that TE and ME are different, should be treated as such, and has identified several influencing variables that affect TE differently than ME. Thus, TE and ME can be independently stimulated by increasing the levels of the influencing variables. For governments seeking to increase their levels of TE through relevant policies, despite a traditionally non-entrepreneurial environment, it means that all is not lost, and that the levels of TE can be raised by focusing on increasing levels of education, economic environment, and technological environment. Managerial recommendations include the geographical placement of technology start-ups and the diversification of the team to improve success.

This master thesis seeks to better understand the investment valuation procedure followed by software venture capitalists (VC) in the European context. I explain how VCs perform fair value estimations of software start-ups with the emerging comparable analysis technique. Furthermore, this study examines the relative importance of start-up characteristics in determining the multiple and how these factors influence the VC’s valuation behaviour. Additionally, I explore whether this behaviour and the multiples paid can be explained by differences in VC firm experience at a time of historically low interest rates and record-breaking fund inflows. Based on 36 interviews with European VCs, primarily from the Benelux region, I find that all start-up characteristics matter in the determination of the multiple, but the management team a little more. As a result, software VCs are willing to pay higher multiples for stellar management teams than for exceptional business characteristics. In contrast with the other characteristics, poor traction does not necessarily kill the deal, but VCs might rather use it to enforce a lower valuation. Overall, VC firm experience is not a strong predictor of the valuation behaviour and ARR multiples paid for deals. However, I do find that more experienced VCs are willing to pay higher premiums for benchmark-exceeding traction than their less experienced counterparts.