Financing High-Tech Startups

In this chapter, the general setup of the two main actors, startups and investors, is described. In the following subchapters, I present the thesis’ academic grounding in terms of entrepreneurship research, the definition of high-tech startups as a subset of all startups, and the development stages that startups usually go through to distinguish early stages from later ones. I then briefly discuss the current state of venture financing and highlight the specificities of the relevant investor groups.

This chapter is devoted to this thesis’ theoretical foundation and logic. It integrates the theoretical bodies of knowledge on (entrepreneurial) finance, new institutional economics, and behavioral economics. The underlying assumption is that high-tech startups are characterized by a complexity that leads to information asymmetries between entrepreneurs and investors and ultimately prompts the investor’s decision not to finance the startup if her uncertainty is too high. I thus create a theoretical system to capture the effects of startup complexity on information asymmetries between both parties. This allows for an examination of the various transaction cost drivers that investors experience.

This chapter is a discussion on methodological consideration. I acknowledged that most financial decision making research is based on mathematical models. This study, however, departs from this objectivistic approach and instead regards its subject as a multi-layered system that requires a pragmatist approach. This approach allows for the flexible combination of methods that best suits an examination of high-tech startup financing patterns, complexity and uncertainty factors, and matching signals. That is also why an explanatory sequential mixed methods design was chosen. It features a quantitative survey-based Study I with 903 German startups, and a qualitative Study II that is composed of 34 interviews with German and US investors and entrepreneurs as well as an international validation panel of experts.

This chapter covers Study I. Study I is a quantitative survey-based assessment of 903 German startups that seeks to highlight factors that distinguish high-tech ventures from other startups—primarily on a business model and financing level. This is necessary to test three hypotheses: (1) Whether the neoclassical notion of interest rate equilibrium and the diminishing effect of information asymmetries is valid (H1: Startups require more time to acquire external venture funding when their product is highly complex). (2) If there are differences in financing structures pertaining to investor classes (H2: When high-tech startups raise capital, they raise it from different investors than non-high-tech startups). And (3) what differences exist between the financing of startups by venture capital firms (H3: High-tech startups receive less traditional venture capital financing than non-high-tech startups).

This chapter covers Study II. Study II informs the discussion about the reasons for these differences. It follows a qualitative approach and summarizes the findings from 34 interviews that were conducted with entrepreneurs and investors in Germany and the US, as well as a validation panel with eight international experts. The objective of Study II is to inform three research questions with regard to the complexity of high-tech startups, the effect of the complexity on investors’ risk perception, uncertainty, and decision making, and possible signals that startups can send: (1) What early stage high-tech startup complexities induce investor uncertainty? (2) How do these complexity factors and uncertainties relate to investors’ decision making? (3) What (productive) signals can entrepreneurs and startups send to purposefully mitigate the adverse effects of these complexities and uncertainties?

This chapter identifies and examines the most important complexity factors from Study II, i.e., the ones that affect early stage high-tech startups and their investors the most. It also describes matching signals that startups can send to counter the adverse effects of complexity and newness. This makes it possible to validate key dimensions of investor uncertainties and classes of appropriate signals, which ultimately enables the development of a comprehensive ‘complexity signal framework’ that is specific to early stage high-tech startups.

In this chapter, the theoretical and practical implications of the findings and the framework are discussed. This concluding discussion mirrors the theoretical body of the thesis and the subchapters following this introduction. This chapter also describes the limitations of the studies and the thesis as a whole. I then synthesize potential follow-up research and give an outlook.

This chapter covers the concluding remarks on this thesis.

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