Robin P. G. Tech

Financing High-Tech StartupsUsing Productive Signaling to E� ciently Overcome the Liability of Complexity

Financing High-Tech Startups

Robin P. G. Tech

Financing High-TechStartupsUsing Productive Signaling to EfficientlyOvercome the Liability of Complexity

Robin P. G. TechAlexander von Humboldt Institute forInternet and SocietyBerlin, Germany

ISBN 978-3-319-66154-4 ISBN 978-3-319-66155-1 (eBook)https://doi.org/10.1007/978-3-319-66155-1

Library of Congress Control Number: 2017963866

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Dedication

To my grandparents who taught me themeaning of this word.Anna and Heinz Sch€afer:You made this happen.

Foreword

The role high-tech startups play for the advancement of developed economies is ofmajor importance. Where would we be today if not for innovative startups such asBraun (that revolutionized home appliances), ABB (that defined modern generatorsand robots), and Apple (that shaped much of the way we use computers)? A frequentbottleneck for high-tech startups, however, is a lack of financing due to theircomplexity.

In this book, Robin Tech examines such complexity and shows how it leads toinformation asymmetries and transaction costs between the investor and the startup.He depicts how this in turn leads to uncertainty on the part of investors andultimately to frequent negative financing decisions. Since early-stage high-techstartups are particularly complex, they bear a peculiar liability that Mr. Tech callsthe liability of complexity. To examine this effect, Mr. Tech embeds the analysis ofhigh-tech startups into the greater discourse of complex products and systemsanalysis and synthesizes theories of new institutional and behavioral economics.He then uses quantitative and qualitative data to derive mitigation strategies toovercome the liability of complexity—particularly through signaling. Hiscomplexity-signal framework will greatly support entrepreneurs to pinpoint com-plexity factors that affect investors’ decision making the most and to identify signalsets to militate adverse effects.

The question of how to finance innovative and complex startups in particular andcomplex systems in general is highly relevant in practice and has received too littleattention. Mr. Tech’s work helps to better understand the underlying concepts ofhigh-tech startup financing and develops hands-on strategies to overcome the liabil-ity of complexity.

We hope that the startup entrepreneurs as well as investors will also find this workvaluable in their daily work of attracting and allocating financial assets. Startups areimportant drivers of the economy’s future competitive advantage. May the force bewith them!

Berlin, Germany Thomas SchildhauerSt. Gallen, Switzerland Oliver Gassmann

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Acknowledgment

I am also deeply grateful to everyone who inspired and helped me and everythingthat motivated me. Among many others, which includes Nike, Karina, Luisa, Chris,Konstanze, Larissa, Sascha, Steffen, Bene, Jan-Peter, Martin, Caro, Andreas, Tim,Bill, Thomas A., Thomas H., Thomas S., Oliver, and the wonderful and wickedcartoon series Adventure Time of course (make sure to check it out).

Thank you—it’s been quite a ride.

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Contents

1 Introduction: High-Tech Startup Financing . . . . . . . . . . . . . . . . . . . 11.1 Analyzing High-Tech Startup Financing . . . . . . . . . . . . . . . . . . . . 11.2 Startups and Entrepreneurs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.3 Investors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161.4 Chapter Summary: Contextualizing High-Tech Startup Financing . . . 21Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2 Theory: The Liability of Complexity . . . . . . . . . . . . . . . . . . . . . . . . . 292.1 Complexity Lies at the Heart of High-Tech Startups . . . . . . . . . . . 292.2 New Institutional Economics as a Venture Funding Framework . . . 33

2.2.1 Transaction Costs Are Complexities in Disguise . . . . . . . . 382.2.2 Signals Mark Distinction . . . . . . . . . . . . . . . . . . . . . . . . . 412.2.3 Agency and Property Rights Theory . . . . . . . . . . . . . . . . . 50

2.3 Behavioral Economics and Finance . . . . . . . . . . . . . . . . . . . . . . . 562.4 An Institutional and Behavioral Research Strategy . . . . . . . . . . . . 632.5 Excursus: Similar Approaches in Digital and Media Economics . . . 652.6 High-Tech Startups Face an Institutional and Behavioral Dilemma . . . 662.7 Chapter Summary: Theorizing Complexity and Signaling . . . . . . . 68Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

3 Methodology: Mixed Methods Approach . . . . . . . . . . . . . . . . . . . . . 793.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 793.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823.3 Mixed Methods Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . 833.4 Chapter Summary: Pragmatist and Explanatory Sequence . . . . . . . 85Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

4 Study I: Survey of German Startups . . . . . . . . . . . . . . . . . . . . . . . . . 894.1 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 894.2 Data Sources and Sample Selection . . . . . . . . . . . . . . . . . . . . . . . 904.3 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

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4.4 Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 924.5 Chapter Summary: High-Tech Startup Financing Patterns . . . . . . . 96Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

5 Study II: Interviews with Entrepreneurs and Investors . . . . . . . . . . . 995.1 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 995.2 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005.3 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1035.4 Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

5.4.1 Internal Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1075.4.2 Product-Related Complexity . . . . . . . . . . . . . . . . . . . . . . . 1125.4.3 External Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165.4.4 Investor Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1255.4.5 Validation Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

5.5 Chapter Summary: Complexity-Induced Uncertainty and Signals . . . 132Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

6 Framework: Matching Signals with Complexities of High-TechStartups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1356.1 Complexity Factor Framework . . . . . . . . . . . . . . . . . . . . . . . . . . 1366.2 Startups’ Locus of Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

6.2.1 Internal Locus of Control . . . . . . . . . . . . . . . . . . . . . . . . . 1386.2.2 Intermediate Locus of Control . . . . . . . . . . . . . . . . . . . . . 1416.2.3 External Locus of Control . . . . . . . . . . . . . . . . . . . . . . . . 153

6.3 Investor Specificities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1566.3.1 Differences Between the US and Germany . . . . . . . . . . . . 1566.3.2 Investor Risk Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

6.4 Chapter Summary: The Complexity Signal Framework . . . . . . . . . 162Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

7 Discussion: Why Signals Can Help to Overcome the Liabilityof Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1697.1 Theoretical Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

7.1.1 Behavioral Economics . . . . . . . . . . . . . . . . . . . . . . . . . . . 1697.1.2 Agency and Property Rights Theory . . . . . . . . . . . . . . . . . 1717.1.3 Signaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1757.1.4 Transaction Cost Theory . . . . . . . . . . . . . . . . . . . . . . . . . 1797.1.5 High-Tech Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

7.2 Practical Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1827.2.1 Investors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1827.2.2 Startups and Entrepreneurs . . . . . . . . . . . . . . . . . . . . . . . . 184

7.3 Limitations and Future Research . . . . . . . . . . . . . . . . . . . . . . . . . 1887.3.1 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1887.3.2 Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

7.4 Chapter Summary: Taking Stock and Looking Ahead . . . . . . . . . . 193Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

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8 Conclusion: Taming Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

9 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2019.1 Investor Interviews Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2019.2 Startup Interviews Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2029.3 Themes from Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204

Contents xiii

List of Abbreviations

BA Business angelBE Behavioral economicsBVDS Bundesverband Deutsche Startups/German Startups AssociationCAPM Capital asset pricing modelCPT Cumulative prospect theoryCVC Corporate venture capital(ist)DSM Deutscher Startup Monitor/German Startup MonitorEMH Efficient market hypothesisFFF Friends, family, foolsGE German entrepreneurGHM Grossman–Hart–Moore model of property rightsGI German investorHIIG Alexander von Humboldt Institute for Internet and SocietyIPO Initial public offeringIoT Internet of thingsIIoT Industrial Internet of thingsKPI Key performance indicatorMVP Minimum viable productNIE New institutional economicsNVCA National Venture Capital AssociationOECD Organisation for Economic Co-operation and DevelopmentPA Principal agentPWC PricewaterhouseCoopersTCE Transaction cost economicsUSE US entrepreneurUSI US investorVC Venture capital(ist)WACC Weighted average cost of capital

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List of Figures

Fig. 1.1 Logic of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Fig. 1.2 Locus of entrepreneurial control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Fig. 1.3 Annual growth rate of seed and series investments. From 2010

to 2016. Based on Thomson Reuters data and PWC Money Treeanalyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Fig. 2.1 Theoretical framework—new institutional economics . . . . . . . . . . . . . 36Fig. 2.2 Economics of institutions framework .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Fig. 2.3 Theoretical framework—transaction cost economics . . . . . . . . . . . . . . 39Fig. 2.4 Information asymmetries decrease over time and increase

with complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40Fig. 2.5 Theoretical framework—signaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Fig. 2.6 Theoretical framework—property rights and agency theory . . . . . . 51Fig. 2.7 Theoretical framework—behavioral economics and affect

heuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56Fig. 2.8 Parametric and structural uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Fig. 2.9 Theoretical framework—NIE and behavioral economics

combined . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64Fig. 2.10 Media and digital markets assessment. Adapted from Kiefer

(2001, p. 88) and Aberle (1992, p. 32). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Fig. 3.1 Research design—Mixed methods application . . . . . . . . . . . . . . . . . . . . . 85

Fig. 4.1 Business model orientations of DSM and high-tech startups . . . . . 92Fig. 4.2 Technology and business model innovativeness of surveyed

startups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Fig. 4.3 Financing sources of surveyed 12 months startups . . . . . . . . . . . . . . . . 94Fig. 4.4 Financing sources of surveyed startups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95Fig. 4.5 Financing sources of surveyed early stage high-tech startups . . . . . 95Fig. 4.6 Different kinds of VC financing of surveyed startups . . . . . . . . . . . . . 96

Fig. 6.1 Locus of control and complexity factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137Fig. 6.2 Complexity factor matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138Fig. 6.3 Complexity factor—team’s backgrounds . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

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Fig. 6.4 Complexity factor—controlling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140Fig. 6.5 Complexity factor—business model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141Fig. 6.6 Complexity factor—existing financing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142Fig. 6.7 Complexity factor—new financing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Fig. 6.8 Complexity factor—ascriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146Fig. 6.9 Complexity factor—timing and maturity . . . . . . . . . . . . . . . . . . . . . . . . . . . 147Fig. 6.10 Complexity factor—intellectual property . . . . . . . . . . . . . . . . . . . . . . . . . . . 148Fig. 6.11 Complexity factor—licenses and certificates . . . . . . . . . . . . . . . . . . . . . . . 149Fig. 6.12 Complexity factor—prototyping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150Fig. 6.13 Complexity factor—manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151Fig. 6.14 Complexity factor—logistics and distribution . . . . . . . . . . . . . . . . . . . . . . 152Fig. 6.15 Complexity factor—partnerships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152Fig. 6.16 Complexity factor—location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153Fig. 6.17 Complexity factor—political and legal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154Fig. 6.18 Complexity factor—market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155Fig. 6.19 Complexity signal framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

Fig. 7.1 Inefficiency at an early transaction stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180

xviii List of Figures

List of Tables

Table 1.1 Theoretical approaches to entrepreneurship . . . . . . . . . . . . . . . . . . . . . . . . 9Table 1.2 OECD high-tech industry categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Table 1.3 Seed stage characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Table 1.4 Startup stage characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Table 1.5 Growth stage characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Table 1.6 Later stage characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Table 1.7 Startup stages for growth-oriented ventures . . . . . . . . . . . . . . . . . . . . . . . . 15

Table 2.1 Product, internal, and external complexity factors . . . . . . . . . . . . . . . . . 31Table 2.2 Ex ante and ex post transaction costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Table 2.3 Signaling literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Table 2.4 Summary of transaction cost, agency, and property rights theory . . . 55Table 2.5 Resident risk dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Table 2.6 Examples of behavioral risk heuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Table 5.1 Interviews in Germany and the US . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102Table 5.2 Expert panel at Humboldt University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102Table 5.3 List of interviewees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104Table 5.4 Data codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105Table 5.5 Data categories and themes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Table 5.6 Expert panel ranking scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107Table 5.7 Validation panel complexity factor ranking . . . . . . . . . . . . . . . . . . . . . . . . 132

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Chapter 1

Introduction: High-Tech Startup

Financing

‘How can I invest my money to make it pay a fair interest, andat the same time insure its safety?’ is a question daily askedby thousands.

The Art of Investing—Hume (1888, p. 9)

1.1 Analyzing High-Tech Startup Financing

In 2015, US venture capital firms alone had a total of $165.3 billion under

management and invested $59.1 billion into startup companies—according to the

National Venture Capital Association (2016), that is the highest amount since 2000

and the second highest on record. That means that last year, startups in the US

received roughly the equivalent of Croatia’s GDP, a sovereign nation with a

population of 4.2 million. Some fear that “the next phase of investors’ irrationalexuberance may have already started” (Block and Sandner 2011, p. 161). Right

now, however, this money is fueling an unprecedented wave of innovation and

technological advancement. Healthcare startups like Theranos promise comprehen-

sive and almost universal medical analyses from a single drop of blood. Electric

super cars named after a Serbian-American inventor are roaming the streets—some

of them even autonomously. And spaceflight is on the brink of being within Joe

Public’s grasp, a development made possible by startups such as Virgin Galactic,Space X, or Blue Origin.

High-Tech Financing Is Constrained, But We Do Not Know Why

But these anecdotes are not at all representative of the norm. “The boom in Silicon

Valley gives an impression of a golden age of dynamism [. . .]. Overall, however,American capitalism is more sluggish than it was. Small firms are being started at

the slowest rate since the 1970s. [. . .] Giant tech firms with high market shares are

making huge profits” (Economist 2015, December 4, p. 13). With regard to the

financing of innovation at young and newly created companies, it is mostly soft-

ware and internet startups that receive external funding. In fact, startup seed

investments are dominated by e-commerce ventures, and more venture capital

dollars flow into marketing, software development, or social networking ventures

© Springer International Publishing AG 2018

R. P. G. Tech, Financing High-Tech Startups,https://doi.org/10.1007/978-3-319-66155-1_1

1

than into biotechnology, for example (Tunguz 2015). Except for the few examples

mentioned above, it seems as if high-tech and hardware innovation is the preserve

of incumbent corporations that are endowed with sufficiently deep pockets. PWC’s2015 Moneytree report also indicates that newly created high-tech startups partic-

ularly struggle to raise capital when compared to other early stage ventures.

Hardware and high-tech startups that are more mature, however, receive substantial

shares of the later stage venture capital funding and yield exits twice as large as

most other startup sectors (CB Insights 2015).

Hardware and high-tech startups are also beginning to tread some of the same

paths software startups embarked upon decades ago in terms of modularization and

standardization (Tech et al. 2016; Bonarini et al. 2014; Kratochvıl and Carson

2005). Over the past decades, software and digital development has become more

accessible and easier to learn due to more modular system architectures and the

setting of industry standards that ensure compatibility. Though digital systems have

become increasingly complex, architectural decisions have promoted common

standards that have spurred acceptance, dissemination, and building block con-

cepts. Just take DARPAnet’s evolution into the omnipresent and global network we

today call the internet. What is peculiar about purely digital products is their

capacity to be replicated at almost zero marginal costs. Such conditions do not

exist for any physical product. But more modular approaches, standardization, and

open source hardware communities create an environment that makes hardware and

technology development ever faster, cheaper, and more accessible. Given these

prospects, it is all the more unclear why early stage funding for high-tech ventures

remains so strikingly low (PWC 2015).

Thus far, we can only make assumptions. First, and more generally speaking,

there is replicable and systematic evidence that all small and new firms struggle

with financing constraints (Hottenrott et al. 2015; CB Insights 2015; Kaplan and

Zingales 2000; Rajan 1992; Fazzari et al. 1988). Second, financing constraints

increase when the novelty and innovativeness of the firm increases (Hottenrott

et al. 2015; Audretsch et al. 2012). Third, and in consequence, early stage high-tech

startups with complex products are most affected (Carpenter and Petersen 2002).

But why is that? Neoclassical economics—which informs most of contemporary

finance—stipulates that financing ought to happen when interest rates are in

equilibrium with the associated risk (Barberis et al. 2015; Sharpe 1964). In the

case of startup investing, interest rates can either be dividends from profits or yield

in the event of an exit. Efficient markets are assumed to always arrive at an

equilibrium of supply and demand, i.e., of expected risk and return. Yet, even at

equilibrium market interest rates, new, small, and innovative ventures struggle with

financing constraints. I argue that neoclassical economics fails to properly explain

this phenomenon because it assumes that perfect and efficient markets need to be in

place to allow for an instantaneous proliferation and computation of information by

the market participants.

What This Thesis Adds to the Academic Discourse

This work analyzes high-tech startup financing from a new institutional and behav-ioral economics perspective. These schools of thought assume that human beings

2 1 Introduction: High-Tech Startup Financing

are bounded in their rationality and ability to retrieve and process information. This

leads to information asymmetries and uncertainties—which are presumably the

decisive factors behind constrained financing (Stiglitz and Weiss 1981). In his

seminal work on venture capital firms, Sahlman (1990) found that information

asymmetries are in fact the key uncertainty driver for startup investors in terms of

investment identification, decision making, and governance. Furthermore, Leland

and Pyle (1977) found that “where substantial information asymmetries exist and

where the supply of poor projects is large relative to the supply of good projects,

venture capital markets may fail to exist” (p. 371). On a more granular level,

transaction cost, agency, and property rights theory support the description of

patterns of information asymmetries with regard to the investor-entrepreneur rela-

tionship. This is also where complexity comes into play, as it is “a proxy for

transaction costs” (Novak and Eppinger 2001, p. 190) and a key influencer of

transactional relationships and costs (Grover and Saeed 2007). The concept of

affect heuristics found in behavioral economics supports the mapping of investors’cognitive decisions, which ultimately cause the financing constraints and ineffi-

ciencies (Baron 2007). An instrument to mitigate the adverse effects of such

information asymmetries is signaling (Spence 1973). Signaling theory has already

received a fair share of scholarly interest with respect to venture financing and is

thus suited for the case at hand (Conti et al. 2013a, b; Davila et al. 2003; Elitzur and

Gavious 2003). In general, it can be assumed that information asymmetries

and uncertainties are amplified by young high-tech startups’ newness, complexity,

and limits of appropriation (Audretsch et al. 2012; Carpenter and Petersen 2002;

Harabi 1995), and that these startups would benefit most from efficient mechanisms

for mitigation such as productive signaling.

Within this nexus of institutional and behavioral economic theories, there are

four major research gaps concerning high-tech startup funding: First, little is

known about the institutional setup that entrepreneurs and investors are in. Their

relationship is anything but a traditional principal-agent setup and advances in

describing this ambiguous relationship and what it implies have only recently

been made (Ollier and Thomas 2013; Webb et al. 2013; Mylovanov and Tr€oger2012; Mondello 2012). Second, though the individual factors that affect informa-

tion asymmetries and behavioral uncertainties in startup settings have been inves-

tigated, we lack a comprehensive framework focusing on (a) the investor and

(b) early stage startups (Gregoire et al. 2011; Casson et al. 2008; Baron 2007;

Jensen 2005; Jeng and Wells 2000; Van Osnabrugge 2000; Black and Gilson 1998;

Maskin and Tirole 1990). Third, though there is previous research on signals in

connection with startups—ranging from patents (Haussler et al. 2009) to alliances

(Baum et al. 2000) and funding sources (Busenitz et al. 2005)—to my knowledge,

there is little theoretical and no practical analysis of the signal sets that high-tech

startups can employ (Conti et al. 2013a, b; Connelly et al. 2011; Elitzur and

Gavious 2003). And, fourth, while signaling is believed to increase welfare if

employed efficiently through a matching process (Audretsch et al. 2012; Hoppe

et al. 2009; Spence 1974), different investor classes have thus far been viewed “as a

homogeneous category” (Conti et al. 2013b, p. 619)—a tradition that this thesis

breaks with.

1.1 Analyzing High-Tech Startup Financing 3

Structure of This Thesis

These research gaps are addresses in multiple ways. In this first chapter, the generalsetup of the two main actors, startups and investors, is described. In the following

subchapters, I present the thesis’s 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.

The second chapter is devoted to this thesis’ theoretical foundation and logic

(Fig. 1.1). 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. If the transaction

costs for gathering relevant information are too high and no substitute information

is offered, investors face different kinds of uncertainties. These are the determinants

of (a) the pool of available funding decisions, and (b) the way investors come to and

make decisions. New institutional economics—i.e., transaction cost economics,

signaling theory, and agency and property rights theory—assist in analyzing the

available decision options and the relationship between investors and entrepre-

neurs. The affect heuristics propounded by behavioral economics allow for an

COMPLEXITY

INFORMATIONASYMMETRIES

TRANSACTIONCOSTS

UNCERTAINTY SIGNALIN

G

DECISIONMAKING

FINANCING

Fig. 1.1 Logic of this

thesis

4 1 Introduction: High-Tech Startup Financing

analysis of the ways uncertainty influences investor decision making. The mitiga-

tion strategy that I put in the center of my investigation builds on productive

signaling. It targets information asymmetries, transaction costs, investor uncer-

tainties, and decision making processes. My approach views the startup as the

signal sender and the investor as the signal receiver.

The third, fourth, and fifth chapter constitute the methodological body of the

thesis as well as the quantitative and qualitative investigation. Study I is a quanti-

tative 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). Study II informs the discus-

sion 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?

The sixth 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 the seventh 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. Chapter 7 also

describes the limitations of the studies and the thesis as a whole. I then synthesize

potential follow-up research and give an outlook.

1.1 Analyzing High-Tech Startup Financing 5

1.2 Startups and Entrepreneurs

What Is Entrepreneurship Research?

Research on entrepreneurship is an examination of “an activity that involves the

discovery, evaluation, and exploitation of opportunities to introduce new goods and

services, ways of organizing, markets, processes, and raw materials through orga-

nizing efforts that previously had not existed.” (Shane 2007, p. 4) Historically,

entrepreneurship research has evolved with respect to its focus—e.g., the entrepre-

neur, the organization she creates, or the surrounding systems that foster or hinder

entrepreneurship (Bull and Willard 1995). The scientific journey of entrepreneur-

ship research began with the entrepreneur herself, defining her as someone who

exercises business judgment in the face of uncertainty (Cantillon 1775 as cited in

Murphy 1986) and as “a person who carries out new combinations, causing

discontinuity” (Schumpeter 1912, p. 48). Inquiry on venture formation, entrepre-

neurial strategy, and economic interaction followed (Hayek 1948; von Mises 1940;

Schumpeter 1912). These laid the basis for often simplistic entrepreneurial business

strategy research (Stevenson and Jarillo 1990; Porter 1980).

Compared to these studies, investigations into psychological traits that affect

actions and the locus of control paradigm took a back seat (Wijbenga and van

Witteloostuijn 2007; Boone et al. 1996; Eisenhardt 1989; Brockhaus 1982; Rotter

1966). The traits and behaviors of entrepreneurs and investors, however, are

enjoying growing interest in the entrepreneurship research again (Meyer et al.

2014). More recent additions to this pool of entrepreneurship research focus on

opportunity recognition and exploitation (Baron and Ensley 2006; Zahra et al.

2005; Timmons et al. 1987), corporate entrepreneurship (Shane 2007), institutional

theory (Webb et al. 2013), and national innovation systems (Acs et al. 2014; Van

Praag and Versloot 2007; Scott 2006).

In sum, entrepreneurship has found its way into diverse academic research fields

such as psychology, political science, business studies, gender studies, economics,

and sociology (Meyer et al. 2014). The topic’s breadth allows “researchers to

investigate entrepreneurship in a manner that fits their interests” (Leitch et al.

2009). Though attempts to frame entrepreneurship research have been made

(Shane and Venkataraman 2000), the field today remains fragmented in terms of

research methods and theoretical approaches. This necessitates a concrete definition

of terms and concepts, ranging from actors and actions to organizations and

theoretical frameworks. In the following, I will therefore discuss and define entre-

preneurs, startups, and investors.

Who Is an Entrepreneur?

In the research field of entrepreneurship, the question of who is the entrepreneur is

the most commonly asked one (Kuratko et al. 2015; Grant and Perren 2002; Gartner

1989). This is hardly surprising as the term “entrepreneur” is about as vague and

indistinct a job description as “manager” or “artist”—everyone is an entrepreneur in

one way or the other. Upon hearing the term, most people probably have the image

of Richard Branson, Werner von Siemens, or Elon Musk in mind. ‘Entrepreneur’

6 1 Introduction: High-Tech Startup Financing

originates from the thirteenth century Old French word ‘entreprende’ (Godefroy1965, p. 296), i.e., to ‘undertake’ or ‘to begin something’. Most likely, the word

made its academic debut through the economist Richard Cantillon, who described

the entrepreneur as a risk-taker who “searches out market signals” (Murphy 1986,

p. 255) and makes investments accordingly.

This development marked an important shift in economic decision making

power from landlords to entrepreneurial merchants (Casson and Casson 2013).

The academic discourse on entrepreneurship was further developed in the nine-

teenth century by Jean-Baptiste Say (1823) and John Stuart Mill in the ‘Principlesof Political Economy’ (1870). Mill highlighted the risk affinity of the entrepre-

neur—or as he still called him: undertaker—by stating that “if he embarks in

business of his own account, he always exposes his capital to some, and in many

cases to very great, danger of partial or total loss” (1870, p. 496). Both Mill and Say

made a clear distinction between investors, managers, workers, and entrepreneurs,

as only the latter combined personal financial risk with “the trouble of business”

(1870, p. 497).

A major leap in the study of entrepreneurship followed in the twentieth century,

primarily through Joseph Alois Schumpeter (1912). Similar to Karl Marx, whom he

admired, Schumpeter was a master of capitalist research, combining historical,

social, and economic analyses. Rather than viewing entrepreneurs as mere

exploiters of given market opportunities, Schumpeter viewed them as destroyersof existing markets and creators of entirely new ones (Casson 2003). This is closely

connected to his notion of monopoly-seeking strategies, i.e., entrepreneurs creating

new markets and owning these for as long as possible through patents and other

market entry barriers with the aim of maximizing rewards (Schumpeter 1912). At

that time, the entrepreneurial process was recognized as an ephemeral founding act

followed by some mode of growth management (Casson 2003).

In contrast to Schumpeter’s image of the disruptive entrepreneur, Israel Mayer

Kirzner (1973) proposed another perspective on entrepreneurial opportunities and

their exploitation. Building on the works of the Austrian School of Economics (e.g.,

von Mises 1940; Hayek 1948), Kirzner suggested that market imperfections werethe main source of such opportunities. In his view, entrepreneurship is primarily

made possible by imperfections in market information. An entrepreneur who is

particularly “alert” (Kirzner 1973, p. 10)—i.e., good at discovering differences in

knowledge levels—can exploit these imperfections through arbitrage. The assump-

tion underlying this notion is, of course, that there is such a thing as objective,

perfect information that exists independently of the perception of individuals—a

classic positivist and realist mindset (Alvarez and Barney 2007). The entrepreneur-ial action of moving market actors closer to such a presumed reality would

eventually bring markets back to an equilibrium state. One could argue that this

vision is fundamentally different and almost diametrically opposed to Schumpeter’snotion of disruption. In my opinion, however, Schumpeter and Kirzner both

describe elements that constitute an entrepreneur: knowledge and information

arbitrage as well as technological and processual disruption, sometimes integrated,

other times discrete.

1.2 Startups and Entrepreneurs 7

Another addition to the science of the entrepreneur is rooted in the construc-tionist approach. In particular, the constructionist view on entrepreneurial oppor-

tunity is marked by an effectuation logic (Sarasvathy 2001) or so-called bricolage

(Baker and Nelson 2005). Alvarez et al. note that “[in] a constructionist view any

resource—information and knowledge—are subject to interpretation” (2010, p. 27).

Similar to Kirzner’s notion, the constructionist view of entrepreneur conceives of

her as someone who interprets her reality and thereby discovers opportunities.

Constructionists, however, do not assume an objective reality, but individual

realities. An entrepreneur is an individual who is particularly good at interpreting

her perceived environment, identifying what she assumes are entrepreneurial

opportunities, and transforming available resources to tackle these opportunities.

“Entrepreneurial knowledge” is what Bresnahan (2010) calls the ability to recom-

bine inventions and technologies to cater to existing markets or create entirely new

ones. In a way, the constructionist entrepreneur is working towards self-fulfilling

prophecies (Ford 1999), and “the formation of an opportunity and the entrepreneur

cannot be separated” (Alvarez et al. 2010, p. 27).

In response to this eminently entrepreneur-focused perspective, the evolutionaryrealist approach emerged (Grant and Perren 2002; Campbell et al. 1987). Table 1.1

summarizes the various lenses on entrepreneurship that developed over the centu-

ries and ultimately lead to the evolutionary realist view of the entrepreneur and her

surrounding. This notion expands the realist view by adding constructionist ele-

ments that assume an objective reality that imposes certain limitations on the

entrepreneur. The entrepreneur then constructs opportunities within the boundaries

of her resources and her environment, which then act as an external validator of the

entrepreneurial actions she takes (Alvarez et al. 2010).

Following this logic, opportunities for entrepreneurial actions stem from envi-

ronmental changes—e.g., of a societal, demographic, or technological nature.

These lead to information asymmetries that can be exploited by the watchful and

aware entrepreneur. The spectrum of entrepreneurs who exploit opportunities

ranges from those who start their own businesses from scratch to those who pursue

entrepreneurial opportunities within the boundaries of corporate firms (Rogers and

Makonnen 2014; Wolcott and Lippitz 2010; Grant and Perren 2002).

The class of entrepreneurs I focus on are founders of new ventures who seek to

develop opportunities, gather resources, and build a value-creating business by

controlling and manipulating their environment, at least to a certain degree. Build-

ing on the notion of an environment that is surrounding and influencing the

entrepreneur, one arrives at a classification of the entrepreneur that is, in one way

or the other, widely used today and that I will use throughout this book: an

entrepreneur is someone who “thinks, reasons, and acts to convert ideas into

commercial opportunities and to create value” (Leach and Melicher 2011, p. 7;

also cf. Spinelli and Adams 2012).

Entrepreneurs’ Locus of ControlThis is closely connected to the concept of locus of control (Wijbenga and van

Witteloostuijn 2007; Korunka et al. 2003; Boone et al. 1996; Eisenhardt 1989;

8 1 Introduction: High-Tech Startup Financing