Signaling Effects of Crowdfunding on Venture Investors‘ Decision Making [1st ed.] 9783658315894, 9783658315900

Table of contents :
Front Matter ....Pages i-xix
Introduction (Michael Mödl)....Pages 1-10
New Venture Financing Research (Michael Mödl)....Pages 11-34
Theory and Hypotheses (Michael Mödl)....Pages 35-48
Methods (Michael Mödl)....Pages 49-79
Data and Sample Description (Michael Mödl)....Pages 81-100
Venture Investors’ Perceptions of Entrepreneurs’ Financing Hierarchies (Michael Mödl)....Pages 101-123
Effects of Crowdfinancing on Subsequent Venture Capital Selection (Michael Mödl)....Pages 125-145
Summary, Discussion and Outlook (Michael Mödl)....Pages 147-152
Back Matter ....Pages 153-192

Citation preview

Innovation und Entrepreneurship

Michael Mödl

Signaling Effects of Crowdfunding on Venture Investors‘ Decision Making

Innovation und Entrepreneurship Series Editors Nikolaus Franke, Abt. Entrepreneurship, Wirtschaftsuniversität Wien Abt. Entrepreneurship und, Wien, Austria Dietmar Harhoff, Max-Planck-Institut, München, Germany Joachim Henkel, Dr. Theo Schöller-Stiftungslehrstuhl, TU München, München, Germany Carolin Häussler, Universität Passau, Passau, Germany

Innovative Konzepte und unternehmerische Leistungen sind für Wohlstand und Fortschritt von entscheidender Bedeutung. Diese Schriftenreihe vereint wissenschaftliche Arbeiten zu diesem Themenbereich. Sie beschreiben substanzielle Erkenntnisse auf hohem methodischen Niveau. Innovative concepts and entrepreneurial performance are crucial for prosperity and progress. This publication series brings together scientific contributions on these topics. They describe substantial findings at a high methodological level.

More information about this series at http://www.springer.com/series/12264

Michael Mödl

Signaling Effects of Crowdfunding on Venture Investors’ Decision Making

Michael Mödl Max-Planck-Institut für Innovation und Wettbewerb München, Bayern, Germany Dissertation, Ludwig-Maximilians-Universität, 2018 D 19

ISSN 2627-1168 ISSN 2627-1184  (electronic) Innovation und Entrepreneurship ISBN 978-3-658-31589-4 ISBN 978-3-658-31590-0  (eBook) https://doi.org/10.1007/978-3-658-31590-0 © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Responsible Editor: Carina Reibold This Springer Gabler imprint is published by the registered company Springer Fachmedien ­Wiesbaden GmbH part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

Foreword

The financing of young firms is a central topic in innovation and entrepreneurship research. Crowd-funding has emerged as a novel way for entrepreneurs to secure scarce early-stage financing. While crowd-funding is a research topic in its own right, there are also many questions regarding its interplay with classic forms of new venture financing, such as equity funding by business angels or venture capitalists. Michael Mödl’s dissertation sheds light on this highly topical nexus and examines how professional venture investors react to crowd involvement in early funding rounds. Michael Mödl applies advanced econometric methods to a unique data set specifically assembled for the purpose of this thesis. In two extensive ­choice-experimental surveys, more than one hundred business angels and venture capitalists made over 5000 decisions and responded to a questionnaire regarding their personal attributes and the process of investment evaluations. The thesis proceeds in three steps. First, the author conducts a comprehensive review of the largely segmented literature on entrepreneurial equity finance and on the growing number of contributions on crowd-based funding mechanisms in order to broaden the understanding of venture financing in its entirety. Second, the author analyzes whether venture investors interpret the choice of a specific form of early-stage financing (crowd vs. professional investor) as an indicator of the quality of the respective start-up firm. Finally, he examines the impact of such prior funding on the selection decisions of subsequent venture investors.

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Foreword

This doctoral thesis delivers intriguing new research insights promoting our understanding of the interconnectedness of entrepreneurial finance channels. The results presented by Michael Mödl are an important contribution to the fields of innovation and entrepreneurship research. I am sure that the insights presented here will find the attention of researchers and practitioners alike. Prof. Dietmar Harhoff, Ph.D. Director Max Planck Institute for Innovation and Competition Honorary Professor at Ludwig-Maximilians-Universität München

Acknowledgments

They say it takes a village to raise a child. After five years as a research fellow I can safely say it also takes a village to write a dissertation. This thesis would not have been possible without the support of many people to whom I am grateful. First and foremost, I would like to thank my advisor Dietmar Harhoff for all his guidance throughout my dissertation. He granted me a remarkable degree of freedom in choosing and developing my topic, steadily encouraged me and constantly gave very constructive comments. His enthusiasm and passion for research have been a true inspiration to me. Moreover, I am thankful to Tobias Kretschmer, my second advisor, for his excellent advice and the fruitful discussions, commencing during the Master of Business Research program and continuing during my dissertation. I am also deeply indebted to Karin Hoisl for her constant support throughout all stages of this endeavor. She provided me with valuable input for my dissertation, numerous times I benefitted from her research experience, and our discussions have been a steady source of inspiration and motivation. The past few years would not have been such an enjoyable experience without my peers and the staff at the Max Planck Institute for Innovation and Competition, at Ludwig-Maximilians-Universität and at the LMU Entrepreneurship Center. A huge thanks goes to my past and present colleagues for creating a truly intellectually inspiring environment, maintaining an atmosphere of mutual support and many priceless fun moments. Representative for the excellent ecosystem for innovation and entrepreneurship research in Munich across institutional borders, I would like to thank Nicola Breugst for her valuable feedback on my questionnaire, Joachim Henkel for our insightful discussions on econometric discrete choice models, and Oliver Alexy for his constructive comments on the draft of my first paper. I am also grateful to

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Acknowledgments

Christian Scheiner for the hints on vignette analysis and his moral support during the ups and downs of academic life ever since my diploma thesis. My research critically depended on access to data on the decision making of venture investors. I owe sincere gratitude to numerous venture capitalists, business angels and entrepreneurs for their willingness to participate in my surveys and interviews. For supporting my quest for information I also wish to thank the Bundesverband Deutscher Kapitalbeteiligungsgesellschaften, BayStartUP, the Venture Capital Magazin as well as Robert Redweik and the Munich Angels Network. I was often able to fall back on the support of student research assistants, who relieved me of a lot of work. I wish to express my gratitude to Julia Sendtner, Nathalie Römer, Lisa Knauer, Moritz Miersch, Lucy Ivanova, and Stephanie Riedl. Furthermore, I thank the directors of the LMU Entrepreneurship Center, Andy Goldstein and Matthias Notz, for their confidence in my capabilities and ideas. Working with start-up teams at a top-notch accelerator has not only left me with the good feeling of not being trapped in the ivory tower, but also inspired the research of this dissertation. Finally, I would like to extend my thanks to friends and my (wider) family for both being a source of affirmation and distraction. In particular, I thank my parents for their love, support and faith in me throughout my life. This thesis is dedicated to them. Michael Maximilian Mödl

Contents

1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2  Context and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Definitions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.4 Structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2  New Venture Financing Research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1 Economic Impact of New Venture Financing . . . . . . . . . . . . . . . . . . 11 2.2 The Equity Funding Landscape. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3 Crowdfinancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.4 Interactions Across Funding Sources. . . . . . . . . . . . . . . . . . . . . . . . . 30 2.5 Research Gap. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3  Theory and Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.1 Decision Making of Venture Investors. . . . . . . . . . . . . . . . . . . . . . . 35 3.2 Asymmetric Information and Signaling in New Venture Financing. 38 3.3 Pre-Funding as a Signal—Hypotheses Development . . . . . . . . . . . 42 4 Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.1  Overview of Data Gathering Methods. . . . . . . . . . . . . . . . . . . . . . . 49 4.1.1  Qualitative Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.1.2  Web Questionnaire. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.2  Stated Choice Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.2.1  Methods of Preference Measurement. . . . . . . . . . . . . . . . . . . 53 4.2.2 Choice-based Conjoint Analysis . . . . . . . . . . . . . . . . . . . . . . 54 4.2.3 Vignette Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.2.4 Discussion of Choice-Experimental Approach . . . . . . . . . . . 60

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4.3  Discrete Choice Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.3.1 Utility Concept. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3.2 Behavioral Models and Derivation of Choice Probabilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.3.2.1  Standard Logit. . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.3.2.2 Rank-ordered Logit. . . . . . . . . . . . . . . . . . . . . . . . 69 4.3.2.3 Mixed Logit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.3.3 Estimation Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.3.3.1 Maximum Likelihood . . . . . . . . . . . . . . . . . . . . . . 74 4.3.3.2 Hierarchical Bayes. . . . . . . . . . . . . . . . . . . . . . . . . 75 5  Data and Sample Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.1 Sample. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.2  Descriptive Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.2.1 Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.2.2 Investment Experience, Routines and Focus. . . . . . . . . . . . . 86 5.2.3 Stated Relative Importance of Evaluation Criteria. . . . . . . . . 90 5.2.4 Risk Attitude. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.2.5 Digital Social Networks Usage . . . . . . . . . . . . . . . . . . . . . . . 93 5.2.6 Crowdinvesting Knowledge and Experience. . . . . . . . . . . . . 95 5.3  Controls for Sample Bias. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 6 Venture Investors’ Perceptions of Entrepreneurs’ Financing Hierarchies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 6.1  Theoretical Considerations and Objectives. . . . . . . . . . . . . . . . . . . . 101 6.2  Experimental Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 6.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.3.1 Descriptive Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.3.2 Multivariate Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 6.3.3 Robustness Test: Bivariate Logistic Regression. . . . . . . . . . . 118 6.4  Interpretation and Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 7 Effects of Crowdfinancing on Subsequent Venture Capital Selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 7.1 Experimental Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 7.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 7.3 Interpretation and Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 8  Summary, Discussion and Outlook. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 8.1 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

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8.2 Implications for Academic Research. . . . . . . . . . . . . . . . . . . . . . . . . 148 8.3 Practical Implications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 8.4 Limitations and Future Research. . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Appendix A: Questionnaire. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Appendix B: List of Conducted Interviews . . . . . . . . . . . . . . . . . . . . . . . . . 169 Appendix C: Correlations of Control Variables. . . . . . . . . . . . . . . . . . . . . . 171 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

Abbreviations

B2B business-to-business B2C business-to-consumer BA(s) business angel(s) BAND Business Angel Netzwerk Deutschland e. V. BNL binomial logit BVK Bundesverband Deutscher Kapitalbeteiligungsgesellschaften e. V. (German Private Equity and Venture Capital Association) CBC(A)  choice-based conjoint (analysis) CEO chief executive officer CF crowdfunding CI crowdinvesting CL conditional logit CVC corporate venture capital dF degrees of freedom DSN digital social network HB Hierarchical Bayes ICT information and communications technology IIA independence from irrelevant alternatives i. i. d. independent and identically distributed IPO initial public offering KPI key performance indicator LL log-likelihood function MCMC Monte Carlo Markov Chain ML(E) maximum likelihood (estimation) MNL multinomial logit P2P peer-to-peer

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PVC RMSE RRR R&D SD SE SSI VC VCs

Abbreviations

public venture capital root mean square error relative risk ratio (multinomial odds ratio) research and development standard deviation standard error Sawtooth Software Incorporated venture capital venture capitalists

Symbols

a ratio of posterior probabilities b mean of a distribution β part-worth coefficient β ′ vector containing the respondents’ part-worths (β1,…,βn) βˆ part-worth estimation d probability of simulated part-worths occurring with the probability of the higher-level distribution of part-worths (in Metropolis-Hasting algorithm) ε random/stochastic error term f density g random derivation vector drawn from a normal distribution with mean 0 i choice alternative j other choice alternative(s) L likelihood LL log-likelihood l number of an individual part-worth m segment in a latent class model n respondent/decision-maker η systematic error term describing the deviation of an individual’s utility from the population mean P choice probability for one choice alternative ⌣ average probability P p likelihood of observed choices with simulated part-worths r number of a part-worth draw sm probability that β has value of segment m in latent class model SLL  simulated log-likelihood

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Symbols

U utility V observable utility portion of the utility function/part-worth equation W covariance of a distribution x vector containing all properties of one choice alternative y dummy variable indicating if a choice alternative is chosen (1) or not chosen (0) vector of covariates of one respondent z θ parameter summarizing distributional parameters of the density of β in the mixed logit model ϕ ′ matrix of regression parameters of the covariate regression of the Hierarchical Bayes method

List of Figures

Fig. 2.1 Entrepreneurial equity funding landscape. . . . . . . . . . . . . . . . . . . 18 Fig. 5.1 Respondents’ investment focus with regard to phase. . . . . . . . . . 88 Fig. 5.2 Respondents’ investment focus with regard to business model.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Fig. 5.3 Respondents’ risk attitude. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 Fig. 5.4 Digital social networks used for professional purposes . . . . . . . . 93 Fig. 5.5 Respondents’ self-reported state of knowledge of crowdinvesting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Fig. 5.6 Respondents’ experience with crowdinvesting. . . . . . . . . . . . . . . 97 Fig. 6.1 Exemplary vignettes as presented to participants. . . . . . . . . . . . . 107 Fig. 6.2 First order choice frequencies for 2 × 2 scenarios. . . . . . . . . . . . 110 Fig. 7.1 Reference setting as presented to participants. . . . . . . . . . . . . . . . 126 Fig. 7.2 Exemplary choice set as presented to participants . . . . . . . . . . . . 127 Fig. 7.3 Interaction effects (plus main effects) between a start-up’s business model and its type of pre-funding. . . . . . . . . . 138 Fig. 7.4 Mean part-worth comparison of pre-funding types between VCs and angels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

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List of Tables

Table 2.1 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table 6.1 Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 7.5

Crowdfinancing research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Selected demographics of respondents. . . . . . . . . . . . . . . . . . . . 86 Investment experience and routines of respondents. . . . . . . . . . 87 Stated relative importance of respondents’ evaluation criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 Digital social network usage patterns. . . . . . . . . . . . . . . . . . . . . 94 Extrapolation tests for non-response bias. . . . . . . . . . . . . . . . . . 100 Tests of difference between vignette survey participants and ­drop-outs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Results of multinomial logistic regression. . . . . . . . . . . . . . . . . 112 Average and conditional marginal effects. . . . . . . . . . . . . . . . . . 116 Wald tests for combining outcome categories. . . . . . . . . . . . . . . 119 Results of bivariate logistic regression. . . . . . . . . . . . . . . . . . . . 121 Start-up attributes and levels. . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 Estimated part-worth utilities from Maximum Likelihood and Hierarchical Bayes estimation. . . . . . . . . . . . . . . . . . . . . . . 134 Relative mean importance scores of attributes. . . . . . . . . . . . . . 135 Part-worth utilities from HB estimation for sample split. . . . . . 140 HB estimation results for design specification with split attribute No. of crowd contributors. . . . . . . . . . . . . . . . . . . . . . . 144

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Introduction

1.1 Motivation In 2012, Munich-based start-up Smarchive (a developer of semantic software allowing users to store, organize, and finish their paperwork) was in need of capital for its expansion plans. Since the founders were not able to identify professional venture investors and conduct negotiations in the short term, they decided to run a crowdinvesting (equity-crowdfunding) campaign on Germany’s then leading crowdinvesting platform Seedmatch. They raised 100,000 Euros through 144 crowd backers in exchange for an aggregated 6 percent stake in equity. Three months later, Smarchive received an offer of a seven-digit funding round by a renowned venture capital organization—however, this offer was conditional on termination of the crowd-investment. Smarchive then offered up to a 25 percent premium payback on the investment sum if the crowd-investors retracted from their holding. If only one of the 144 investors had not agreed, the deal would have been nil and void and would have brought Smarchive to the brink of bankruptcy.1 Around the same time, S ­ilicon-Valley-based start-up Pebble Watch (the first major smartwatch project) faced a similar problem: After having graduated from start-up incubator Y Combinator, the company was searching for follow-up funding but was rejected by venture capital firms. Admittedly as a last resort, founder Eric Migicovsky turned to crowdfunding in April 2012. The original campaign to raise 100,000 US dollars through (reward-based) crowdfunding platform

1Source:

Interviews with Steffen Reitz, CEO and founder of Smarchive/Gini (5 February, 2014), and Jakob Carstens, Head of Marketing, Seedmatch (14 February, 2014). Also see Weverbergh (2013).

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 M. Mödl, Signaling Effects of Crowdfunding on Venture Investors’ Decision Making, Innovation und Entrepreneurship, https://doi.org/10.1007/978-3-658-31590-0_1

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1 Introduction

Kickstarter skyrocketed into more than 10 million USD by more than 68,000 contributors (in contrast to the platform Seedmatch, Kickstarter did not have a fixed funding limit). The biggest problems Pebble Watch now faced were to build production facilities to meet the demand of so many pre-ordered watches, and to decide between the VC companies now queuing to invest (Dingman, 2013). These cases illustrate two aspects which are central to the research presented in this dissertation: First, the seed-funding gap is a major obstacle for the development of new ventures. In both cases considered here, the founders were facing serious financing constraints. They had to raise external capital within a short time period and turned to an attractive new source of capital: the crowd. Second, the presented cases also show that venture investors react differently towards pre-investments by the crowd. In one case, the crowd’s assessment led the way and convinced VCs to invest in the young company. In the other, the prior crowdfinancing was an obstacle to the prospective VC financing round. Crowdfinancing, representing a market-based option for partly bridging the seed-funding gap, might thus come with an extra benefit if it attracts further resource partners or with a trade-off if it obstructs chances of later-stage investments. This dissertation seeks to cast light on this intriguing nexus and on the reactions of venture investors to crowd involvement in early financing rounds.

1.2 Context and Objectives The creation of new ventures is of pivotal importance for innovation-based economies (Schumpeter, 1942; Wennekers & Thurik, 1999). One of the biggest difficulties for founders of innovative start-ups is to raise capital for their entrepreneurial endeavors. (e.g., Ferrary, 2010; Hochberg et al., 2007; Cassar, 2004). Since start-up companies usually do not generate their own profits and cannot offer high-quality collateral to credit lenders, they depend on external sources of equity funding. The high-uncertainty environment characterizing young innovative firms makes investment decisions a challenging mission. Venture capitalists specialize in financing new ventures and are one of the most important external providers of equity for growth-oriented start-ups (Gompers & Lerner, 2004). Their judgments are highly consequential to the survival of young innovative firms, as VCs not only provide start-ups with capital but also with monitoring and governance (Gompers & Lerner, 2004); they facilitate access to additional resources for the firm (Baum & Silverman, 2004; Ferrary & Granovetter, 2009), and are a door opener for networks of influence (Ferrary & Granovetter, 2009). Venture capitalists thus fulfill a critical role in the innovation process of high

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technology ventures (Ferrary & Granovetter, 2009) and are hence considered to serve as a catalyst for innovation within an economy (Shepherd et al., 2000). The question of how these key gate-keepers make decisions therefore plays a central role in the entrepreneurship literature—with first studies dating back to the 1970s, the topic has remained of constantly high interest until today (Franke et al., 2008: 459). Knowledge on venture capitalists’ decision policies not only helps start-ups seeking funding. Their selection criteria are also interpreted as success factors for new ventures, since they generally are thought to be experts in predicting emerging firms’ performance (Riquelme & Rickards, 1992). However, venture capitalists are increasingly specializing in financing ­later-stage projects, aggravating seed capital constraints as a major obstacle for the initiation of new ventures (EFI, 2012; Elitzur & Gavious, 2003b; Hellmann & Thiele, 2015; NVCA, 2017; OECD, 2016). As a consequence, early-stage ­start-ups initially have to turn to other sources: in seed finance wealthy individuals, known as business angels, have taken their place (Elitzur & Gavious, 2003b), and recently, the digitalization has enabled the emergence of a new form of ­early-stage funding, crowdfunding. The provision of finance by many small contributors through an online network has received much attention as a promising mechanism to fostering entrepreneurship and innovation. Yet, for innovative growth-oriented ventures these early-stage financing forms appear to be complements to rather than substitutes for venture capital: Goldfarb et al. (2013) emphasize that when larger investments are needed, VC participation is generally necessary; even sophisticated angels are insufficient. The same holds true for crowdfunding: considering the volume of funding involved in most efforts, “VCs can also typically invest larger amounts of money than a [crowdfunding] campaign could raise”, as Sorenson et al. (2016: 1527) point out. Besides capital, VCs also provide important connections to other resources (Baum & Silverman, 2004; Ferrary & Granovetter, 2009), which is why often even start-ups without funding obstacles aim to attract venture capital. As venture capital remains an important resource for the development of innovative ventures but is increasingly scarce in early stages, sequential financing from a mix of equity sources becomes more frequent and an understanding of their interplay ever more important. The cases presented above illustrate that venture investors react (differently) towards pre-investments—an issue not yet fully addressed by academic literature. While prior VC funding has been shown to positively affect the probability of getting more money (e.g., Lerner, 1994; Davila et al., 2003), a subject of great controversy is, whether angel or crowd money also represents a positive informational value to subsequent financiers and therefore increases the likelihood of getting follow-up funding. There are two potential views of the crowd: Supporters

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1 Introduction

of crowdfunding say it can be interpreted as a positive signal of market acceptance in that it reflects early adopter consumers’ reactions (e.g., Strausz, 2017).2 Whereas critical voices argue that turning to crowdfinancing indicates a failed “litmus test” of being able to convince a reputable professional investor, and—in case of a securities-based crowdfunding—an increased complexity in the governance of a start-up (which was the case in the Smarchive example).3 Initial scholarly attention on crowdfunding has focused on identifying the factors that predict greater campaign success (e.g., Mollick, 2014), while less attention has been paid to potential interactions with traditional financing entities (Bellavitis et al., 2017; Drover et al., 2017a; McKenny et al., 2017). Correlational evidence suggests that crowdfunded start-ups can attract subsequent VC financing (e.g., Kaminski et al., 2016; Sorenson et al., 2016). Large-scale empirical evidence on the causal effects of the pre-funding situation of a new venture on subsequent financing rounds, however, is lacking—which is in sharp contrast to the extensive literature on other start-up characteristics influencing the decision making of venture investors (e.g., Baum & Silverman, 2004; Certo, 2003; Conti et al., 2013a; Franke et al., 2008; Zhang & Wiersema, 2009). It appears important to address this research gap, since VC money becomes increasingly scarce in seed phases, and start-ups without knowledge of the signaling effects of alternative funding options might obstruct their chances for VC funding at an early stage (especially since signals can not only be sent on purpose, but also unintentionally; see, e.g., Janney & Folta, 2003). This dissertation seeks to identify the impact and signaling effects that crowdfunding has on subsequent venture investors’ funding decisions. More specifically, we are interested in shedding light on three questions about how crowd-based financing interacts with traditional later-stage venture capital: A first objective is to explore whether venture investors perceive a negative selection bias for crowdfunding, i.e., that low-quality start-ups have a higher probability than high-quality start-ups of approaching crowd-based financing.

2In

the Pebble Watch case a managing partner of one of the VC funds that rejected the initial approach by the founder admitted that “venture capital always wants (…) to get validation, and with Kickstarter, he could prove there was a market” (Dingman, 2013). 3In fact, how earlier-stage funding mechanisms communicate information to other prospective investors is an ongoing conversation (Drover et al., 2017a). Regarding the direction of the informational value a prior business angel funding has for subsequent investors the academic literature comes to ambiguous findings and has not yet agreed upon whether VCs and angel investors are “friends or foes” (Hellmann and Thiele, 2015).

1.2  Context and Objectives

5

Second, we aim to provide a robust analysis of how an initial crowdfunding, relative to financing from business angels or no funding, influences the likelihood of professional investors to consider a start-up for an investment. A third related question we seek to understand is whether the conditions of a prior funding (i.e., the actions and identities of pre-funders) communicate information to prospective venture investors and moderate their preferences and thus determine selection decisions. In this thesis, we focus on a signaling perspective (Spence, 1973, 2002) and challenge the view that prospective venture investors treat successful prior investments by the crowd as a monolithic positive signal. Instead, we argue that previous crowd-based financing yields a smaller signaling cost differential than prior funding from professional investors and should accordingly reduce the likelihood of follow-up funding by VCs. We extend the classic signaling framework in the entrepreneurial setting and reason that the founders’ choice of a p­ re-funding channel as well as endorsements via the actions and identities of pre-investors can serve as uncertainty-reducing signals about a start-up’s latent quality, and that these signaling effects can have ambiguous directions in venture investors’ decision making processes. In order to address these questions and the research gap in empirical evidence, we collect data on 5,280 investment decisions nested within 120 venture investors in a choice-experimental survey approach called choice-based conjoint analysis (CBCA). To back up our considerations on underlying quality signaling mechanisms, we conduct an additional vignette experiment among respondents and supplement this quantitative evidence from survey experiments with qualitative information from 20 interviews with venture investors. Several features of our methodological approach aim to address the challenges associated with the research context. Being a real-time data collection technique, stated choice experiments overcome the shortcomings of traditional post hoc methods (e.g., questionnaires and interviews), which may suffer from people being poor at introspection, having difficulties to recall crucial aspects or being inclined to retrospectively rationalize decisions (Shepherd & Zacharakis, 1999). Furthermore, CBCA is particularly suitable to mimic the real choice situation of venture investors, namely the screening of business plans. Above all, due to their experimental design, stated choice experiments make causal inference a realistic goal, whereas real-market-data approaches might suffer from an endogeneity problem, e.g., because high-quality start-ups may find it easier to attract both early crowdfunding and subsequent venture capital. The thesis has three main findings. Our first set of results includes evidence that venture investors perceive that entrepreneurs have a hierarchy in approach-

6

1 Introduction

ing equity funding channels and that it is affected by venture quality, i.e., they assume that start-ups of lower quality have a higher relative probability than high quality start-ups of turning to crowd-based financing. This is important because it suggests that the choice of pre-funding might communicate a signal of latent quality to prospective investors. Second, we find causal evidence that on average previous crowdfinancing reduces the likelihood of young innovative ventures to be selected for investment by professional venture investors, crowdinvesting (securities-based crowdfunding) even more so than the reward-based variant. Our results also suggest that traditional forms of pre-funding, i.e., prior business angel investments, by contrast significantly increase the likelihood of subsequent VC financing rounds. Yet, a third finding is that interactions with a B2C business model and a relatively fast achievement of the funding goal, for example, increase the perceived utility of venture investors for a crowdfunded start-up, indicating that while the crowd itself is a negative signal, it can generate positive signals to which professional investors react in their decision processes. The contributions of this dissertation are of both theoretical and practical relevance. This work may contribute to several streams of academic literature: First, it can make an important contribution to the nascent literature on crowdfunding by shedding light on its compatibility with traditional venture capital— an open research question that has specifically been identified by, e.g., Bellavitis et al. (2017) and Drover et al. (2017a). Second, the research adds to the literature on venture capital selection criteria (Hall & Hofer, 1993; MacMillan et al., 1985; Petty & Gruber, 2011; Shepherd, 1999; Tyebjee & Bruno, 1984) by providing evidence that prior financing history is a relevant evaluation criterion in venture investors’ screening decisions. Third, it extends the stream of research on the role of observable characteristics that signal a new venture’s quality in innovation financing (Amit et al., 1990; Baum & Silverman, 2004; Haeussler et al., 2012; Hoenig & Henkel, 2015; Hsu & Ziedonis, 2008). Finally, with crowds increasingly gaining the attention of management scholars as a tool for organizational problem solving (e.g., Afuah & Tucci, 2012; Boudreau et al., 2011; Mollick & Nanda, 2016), the results may also contribute to a broader understanding of general organizational practices by shedding light on how experts react to the collective evaluations of crowds. Furthermore, the findings can have important implications for practitioners and public policy-makers. They may (1) contribute to the knowledge of entrepreneurs on venture capital selection criteria, (2) shed further light on critical success factors of start-up companies, (3) provide an empirical basis for the investors’ community for comparing their own judgment to that of their peers, and (4) help public policy, which has a high demand for scientific evidence on the new phenomenon of crowdfinancing with regard to

1.3 Definitions

7

governmental support for entrepreneurial activities and regulatory perspectives. All in all, a better understanding of the role of pre-funding as a decision policy of venture investors is desirable from a theoretical as well as from a practical perspective.

1.3 Definitions Entrepreneurs pursuing innovative start-ups usually secure financial capital as a mixture of debt and equity to help fuel their ventures. The literature on high-growth entrepreneurship has largely focused on external equity finance ­ (Drover et al., 2017a), which is also the centerpiece of this work.4 This section provides definitions of the four focal private external equity (or equity-like) funding sources as they are used in this thesis (for a more detailed description see Chapter 2). We use the term venture investors for the two most widely recognized “traditional” forms of entrepreneurial equity financing, namely venture capital and business angels: (1) Venture capital (VC): In congruence with Gompers and Lerner (2013: 183) and Da Rin et al. (2013: 574) we define venture capital as independent, professionally managed, dedicated pools of capital—raised from institutional investors or wealthy individuals—that focus on equity or equity-linked investments in promising new ventures with a high growth potential. By venture capitalists (VCs) we here mean managers of the VC firm, who are involved in the screening and selection of potential portfolio companies. Our analyses focus on private independent venture capital. We acknowledge the existence of “captive VCs” (i.e., corporate, bank-owned, governmental or public VCs), but will not consider them in this research, as their alternative ownership structures might affect their funding and strategic decisions (Da Rin et al., 2013). (2) Business angels (BA): These are high net-worth individuals, who finance young, innovative ventures directly by purchasing equity stakes or equity-like securities, putting in their own capital (and thus do not take on the

4For

a distinction between private equity and debt markets for new venture financing, see, e.g., Berger and Udell (1998); for a comparison of challenges of R&D financing in new and established corporations, see, e.g., Hall and Lerner (2010).

8

1 Introduction

i­ntermediary role of VCs). Business angels are often referred to as informal venture capital in comparison to venture capitalists being the formal venture capital (see, e.g., Freear et al., 1994). An emerging financing mechanism for start-up companies is crowdfunding. The term is used by both the nascent academic literature and popular media as a collective basin for various categories, including debt-based variants ­(“peer-to-peer lending” or “crowdlending”) and for non-enterprise endeavors such as artistic or philanthropic projects. For the purpose of this study we introduce the term crowdfinancing as an umbrella term for two distinct variants of new venture financing: (3) Crowdfunding: Similar to Belleflamme et al. (2010) and Mollick (2014) we describe crowdfunding as an open call through the internet for the provision of financial resources either in the form of a donation or in exchange for some form of reward or pre-purchase of a product or service, where the many small contributors do not receive any securities. This definition subsumes the categories reward-based, donation-based, and pre-purchase-based crowdfunding, which are oftentimes intermingled (e.g., a crowd contributor has to “donate” a certain threshold amount of money in order to receive a reward or the to-be-developed product—which is also often referred to as the “reward”). (4) Crowdinvesting: Following Ahlers et al. (2015) and Hornuf and Schwienbacher (2014) we characterize crowdinvesting as the funding of a company by selling equity or hybrid financing instruments to a large number of small investors, essentially through an online platform, whereby investors obtain a residual claim on future cash-flows. While crowdinvesting is an established notion in the German-speaking world, synonymous terms more commonly used in Anglo-Saxon countries are equity crowdfunding, securities-based crowdfunding or investment-crowdfunding.

1.4 Structure This thesis contains eight chapters. Following the introduction, Chapter 2 offers a systematic review of the prior academic literature on new venture financing through private external equity channels. It begins with the assessment of the societal relevance and the economic impact of new venture financing (2.1). After describing the (changing) entrepreneurial equity funding landscape (2.2), it outlines the nascent literature on the emerging funding alternative crowdfinancing (2.3). As the entrepreneurial finance literature is largely segmented, Section 2.4 summarizes scarce existing studies on the interconnectedness of different equity

1.4 Structure

9

funding sources for new ventures. The chapter concludes with a summary and the identification of a research gap (2.5). Chapter 3 is dedicated to the development of the theoretical and conceptual context of this thesis. Section 3.1 outlines our understanding of venture investors’ decision making. To examine the effects of prior (crowd-)financing on subsequent venture capital selection we draw on information economics. Section 3.2 reviews the basic concept of information asymmetry, explains its existence in the market of new venture financing, and applies signaling theory—a framework fundamentally concerned with reducing information gaps—to the context of venture investors’ selection decisions. The third section, finally, develops how prior funding can serve as an effective signal in subsequent financing rounds and derives hypotheses. Chapter 4 explains the research design. The first sub-section provides an overview of our research methods, namely qualitative pre-interviews and a subsequent quantitative online survey. The core part of the latter consists of two stated preference experiments, a choice-based conjoint analysis and a vignette analysis. Section 4.2 explains the methods’ fundamentals and discusses their validity and suitability for our research goals. Their technical foundations of advanced discrete choice modeling are described thereafter (4.3). Chapter 5 is concerned with describing the primary data of this work. After outlining the sampling strategy (5.1), it provides descriptive statistics of our extensive dataset of 120 practicing venture investors with regard to their demographics, investment preferences, risk attitude, affinity for digital social networks, as well as their knowledge of and experience with crowdinvesting (5.2). The chapter closes with a discussion of potential biases of our sample (5.3). In Chapter 6 we examine whether signal receivers assume a negative selection bias for crowd-based financing. More specifically, we study whether venture investors perceive a hierarchy in approaching equity investors by founders and how it is influenced by their start-up’s quality. After providing theoretical considerations and the objectives of this section (6.1), we explain the design of our research instrument vignette analysis (6.2) and present results of the descriptive and multivariate statistical analyses (6.3). The chapter closes by interpreting and discussing the findings (6.4). Our findings on the effects of prior funding on subsequent venture investors’ selection decisions are presented in Chapter 7. It begins with the operationalization of key variables and a detailed derivation of the choice-based conjoint design (7.1). Thereafter, it outlines the causal empirical analysis (7.2), and interprets and critically discusses the results of the hypotheses tests (7.3).

10

1 Introduction

Chapter 8 contains discussion, conclusion and outlook. The results are summarized with regard to raised research questions (8.1). Implications for academic research (8.2) as well as for public policy and managerial practice (8.3) are discussed. A discussion of limitations and avenues for future research follows (8.4). The dissertation closes with concluding remarks (8.5). The appendix depicts the questionnaire (A), an overview of conducted interviews (B), and the correlation table of control variables (C).

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New Venture Financing Research

2.1 Economic Impact of New Venture Financing “The fundamental impulse that sets and keeps the capitalist engine in motion comes from the new consumers’ goods, the new methods of production or transportation, the new markets, the new forms of industrial organization that capitalist enterprise creates. (…) [The process] incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating the new one. This process of Creative Destruction is the essential fact about capitalism.” – Joseph A. Schumpeter (1942), in Capitalism, Socialism and Democracy.1

Innovation is a key source of economic growth (Solow, 1957). While in his famous statement on the Process of Creative Destruction, selfsame can be set in motion by every entity of the economy, including within large organizations, Schumpeter becomes more explicit in his Theory of Economic Development. He draws attention to “the individuals whose function it is to carry out [new combinations, whom] we call ‘entrepreneurs’” (Schumpeter, 1934: 74). In modern management literature, too, particularly new entrepreneurial ventures are seen as the primary driver of economic development and employment (e.g., Wennekers & Thurik, 1999). Reasons for this view are, first, that new and small firms grow, on average, systematically larger than big and old firms (Carree & Thurik, 2013), and, second, that established corporations are considered to suffer from

1Cited

from Schumpeter (2010: 72 f.).

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 M. Mödl, Signaling Effects of Crowdfunding on Venture Investors’ Decision Making, Innovation und Entrepreneurship, https://doi.org/10.1007/978-3-658-31590-0_2

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“incumbent inertia” and tend not to engage in radical innovation (Lieberman & Montgomery, 1988). As new competitors, young firms thereby also force incumbents to advance their products, services and processes. In times when economies around the world face challenges of low growth, weak trade and rising inequalities, the OECD sees in entrepreneurs “a driving force for open and prosperous societies” (OECD, 2017: 3).2 One of the most foundational issues of entrepreneurship is access to financial resources (Cassar, 2004; Drover et al., 2017a). Higher levels of capitalization empower entrepreneurs to undertake more ambitious strategies, enable them to scale their business to meet the demands of rapid growth, and to quickly adapt to changing environments (Cooper et al., 1994; Plummer et al., 2016). However, financing is also one of the biggest obstacles for the creation of new ventures (Blanchflower & Oswald, 1998; Cassar, 2004; EFI, 2012; Hubbard, 1998). Since start-up companies usually do not generate stable internal cash flows, and are characterized by a lack of tangible assets as collaterals and severe informational opacity (Hall & Lerner, 2010)—are, thus, initially unlikely to receive bank loans or other debt financing—they are reliant on external sources of equity funding (Gompers & Lerner, 2001). Among the most important providers of external equity for start-ups with high growth potential—the focal venture type of this work—are venture capitalists, who specialize in financing these high-risk, potentially high-reward ventures (Amit et al., 1998; Gompers & Lerner, 2004). Venture capital has fueled many of the most successful start-ups of the last decades. The world’s five largest companies in terms of market capitalization3—Apple, Alphabet/Google, Microsoft, Amazon and Facebook—have been funded in part by VC within the last 30 years.4 The belief that innovation across the world, especially in high-tech, information, and bio-technology areas, is vitally dependent on a flourishing entrepreneurial finance sector, has led to a constantly high interest by policy-makers and researchers in the nature and behavior of financial markets that fund new

2For

an overview of theoretical and empirical studies on the impact of entrepreneurship on economic growth, see, e.g., Carree and Thurik (2013). 3Source: Thomson Reuters EIKON database; accessed 31 July 2017. 4On a broader level, Kaplan and Lerner (2010) also show that from 1999 through 2009, over 60% of US initial public offerings (IPOs) have had VC financing, and that in only two of these eleven years fewer than half of IPOs have been VC-backed, concluding that VC funding very significantly increases the likelihood that a start-up will eventually go public.

2.1  Economic Impact of New Venture Financing

13

businesses (Berger & Udell, 1998). But is the large amount of research attention new venture financing receives justified from a societal perspective, given the fact that only a small fraction—empirical evidence suggests less than one percent5— of all newly founded firms receive external equity funding by venture investors? The answer lies in the disproportionate positive impact that venture capital is considered to have on the promotion of innovation and employment growth at macro level, as well as for the development and professionalization of new businesses at micro level. On an aggregate level, several studies have examined the question whether venture capital is particularly effective in stimulating innovation and job creation. A systematic assessment of the claim that venture capital promotes innovation is provided by Kortum and Lerner (2000) who examine the influence of venture capital on patenting in the United States manufacturing industries over three decades. They find that increases in VC activity within a sector are associated with significantly higher patenting rates, and that the effect of VC funding on patenting is larger than the effect of corporate R&D funding. Causality concerns are addressed by using a regulatory change for the funding of VCs firms and various ways of instrumenting for R&D, and main results survive these endogeneity tests. Furthermore, a recent study by Schnitzer and Watzinger (2017) finds that spillovers from venture capital-backed start-ups on patenting activity of other companies are nine times larger than those generated by R&D investments of established firms, again indicating that VC fosters the commercialization of new technologies. With regard to employment generation, studies show a positive link to VC, too. Puri and Zarutskie (2012), for instance, use an extensive

5In

a recent comparison from 2009 to 2013 the US National Venture Capital Association (NVCA) reports that an average of less than 1,200 companies received venture capital for the first time annually in the United States, which is roughly 0.2%—or one out of 500—of the 600,000 firms started each year according to the US Small Business Association (Kaplan and Lerner, 2016). Of all US businesses created within the 25-year sample period between 1975 and 2000 listed in the Longitudinal Business Database (LBD), only 0.11% accounted for VC-backed firms; the fraction increases to 0.22% between 1996 and 2000 (Puri and Zarutskie, 2012). In the 2004 Kauffmann Firm Survey, which tracks 4,928 US firms founded in the same year, 110 (2.2%) new businesses attracted financing from angels, and only 26 (0.5%) from venture capitalists (Robb and Robinson, 2014). The 1993 US National Survey of Small Business Finance (NSSBF) data has shown that VC funding accounted for an estimated 1.85% and angel financing 3.59% of small business finance (Berger and Udell, 1998). All these numbers apply to the United States, which is by far the largest VC market in the world.

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US panel data set and find that while only 0.22 percent of all new firm fundings between 1996 and 2000 received venture capital, the amount of jobs created by VC-backed firms in the same time-span accounts for up to 6.8 percent of total employment in the United States, justifying the perception that venture capital is an engine for employment growth. Looking at the individual firm’s level, studies find a positive impact of venture capital on the development and professionalization of new businesses. For instance Hellmann and Puri (2000) examine a hand-collected sample of 170 recently formed Silicon Valley start-ups and show that the presence of a VC is related to a significant reduction in a venture firm’s time needed to bringing a product to market, especially for companies pursuing an innovator strategy. In another study they also find that VCs play an important role in the professionalization of the human capital force of a start-up (Hellmann & Puri, 2002). Cole et al. (2016), finally, compare the impact of VC and bank financing on firm growth based on US data spanning the years 1995 to 2001, and find (regardless of controls for endogeneity) a significantly stronger impact on growth by VC funding than by bank financing. The effects of business angel funding on the success and growth of new ventures are less well studied, but evidence suggests similar positive outcomes. Kerr et al. (2014), for instance, provide empirical evidence by comparing the outcome of ventures within a narrow quality range that received funding by angel groups to those that were rejected. The study is based on 130 firms with similar ex-ante characteristics which were scored around a cutoff level by the angel investors and that fall just above and below the investment threshold. The authors find that funded firms are, among others, 20–25 percent more likely to survive for at least four years, are 9–11 percent more likely to undergo a successful exit (IPO or acquisition), have 16–20 more employees after four years, and are 16–18 percent more likely to have a granted patent. Overall the literature consistently finds a positive relationship between funding by venture investors and measures of economic value creation, justifying the central role the decision making of venture investors plays in innovation and entrepreneurship research. The next section characterizes these key players in the equity funding landscape, including the emergence of new funding mechanisms.

2.2  The Equity Funding Landscape

15

2.2 The Equity Funding Landscape Access to financial resources is one of the main factors determining which new ventures thrive or languish (Gilbert et al., 2006). In particular innovative and growth-oriented start-ups often need considerable amounts of capital for research and development and the scaling of their business. As such new firms usually do not generate (enough) stable internal cash flows to finance high growth (Gompers, 1995), they are reliant on external sources of capital.6 Debt financing is typically only available to a limited extent, since new ventures—especially in early stages—are characterized by a high level of uncertainty and a lack of collaterals (Gompers & Lerner, 2001; Hall & Lerner, 2010).7 As a consequence, equity (or equity-like) financing is critical to the survival and flourishing of young, innovative firms. Initial funding may be sourced from the founders, friends, or family (Berger & Udell, 1998), but the capital requirements of high-growth start-ups regularly outstrip the abilities of this group (Cassar, 2004; Plummer et al., 2016). For these reasons, governments often provide assistance for start-ups in many countries, e.g., in form of grants (Howell, 2017) or public venture capital funds (Guerini & Quas, 2016). In this work we focus on market-based financing, i.e., private sources of external equity financing.8 Over the last decades the entrepreneurship literature has emphasized the importance of venture capital and business angels (Bellavitis et al., 2017; Berger & Udell, 1998; Drover et al., 2017a). In particular VC tends to be the most widely recognized form of equity financing (Drover et al., 2017a). Gompers and Lerner (2013: 183) define venture capital as “independent, professionally managed, dedicated pools of capital that focus on equity or equity-linked investments in privately held, high growth companies.” The prevailing organizational form in the VC industry is the limited partnership, where venture capitalists (acting as “general partners”) raise funds from a set of large institutional investors (e.g., pension

6Handling

the need for resources using other means than external finance is referred to as “financial bootstrapping” (see, e.g., Winborg and Landström, 2001). 7New research, however, indicates that the market for venture lending is more active than thought under certain circumstances: e.g., Hochberg et  al. (2014) show that, when secondary patent markets are liquid, redeployable (less firm-specific) patents can be used as a collateral for venture debt. 8For a distinction between private equity and debt markets for new venture financing, see, e.g., Berger and Udell (1998). For a more general comparison of challenges of R&D financing in new and established corporations, see, e.g., Hall and Lerner (2010).

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2  New Venture Financing Research

funds) or wealthy individuals (“limited partners”) (Sahlman, 1990). These funds are selectively invested in young, innovative companies with high-growth potential, usually in exchange for preferred equity stock with various privileges (Hall & Lerner, 2010). VCs often work closely with their portfolio firms and provide value beyond capital to foster their development. Because VCs provide returns to their limited partners within around ten years, they try to guide entrepreneurs to timely exits with positive returns usually by taking the firms public in an initial public offering (IPO) or in the form of a trade sale to corporate acquirers (Drover et al., 2017a). For a detailed discussion of the VC industry and its history refer to, e.g., Gompers and Lerner (2001). VC funds typically provide finance in a range of about 1 to 10 million per portfolio company (e.g., Clercq et al., 2006; Morrissette, 2007). While total venture capital investments exhibit cyclical ebbs and flows, in 2016 they totaled 934 million Euro in Germany (BVK, 2017), 4.3 billion Euro in Europe (Invest Europe, 2017), and 69.1 billion US Dollar in the USA (NVCA, 2017). Over the last five years 2012 to 2016 annual investments averaged 745 million Euro in Germany, 3.8 billion Euro in Europe, and about 60 billion USD in the USA (ibid.). VC-like investment organizations have developed as divisions of financial institutions (bank VCs), as subsidiaries of incumbent companies, so called corporate VCs (CVCs) (Chesbrough, 2002), or as governmental-funded public VCs (PVCs) (Guerini & Quas, 2016). Studies indicate that such captive VCs (Da Rin et al., 2013) have different goals, different abilities and different organizational forms impacting their selection, value adding and exit decisions (e.g., Bottazzi et al., 2008; Da Rin et al., 2013; Pahnke et al., 2015). For instance, the key idea of Hellmann’s (2002) model of CVC investing is that unlike private VCs who only seek financial gains, CVCs have additional strategic investment goals, e.g., arising from synergies with their core business. Besides, Gompers and Lerner (2004: 95) argue that most corporate VCs are structured as corporate subsidiaries with much lower incentive-based compensation and that this might distort their process of selecting (and overseeing) portfolio start-ups. Yang et al. (2009) point out that the ability to select high-quality ventures increases when corporate investors syndicate with independent VCs. With regard to public VCs, it is likely, too, that they consider not only financial returns, but also policy objectives, such as creating jobs or investing in the regional economy (see, e.g., Lerner, 2009). For a detailed description of the differing institutional logics of private, corporate and

2.2  The Equity Funding Landscape

17

governmental VCs, also see Pahnke et al. (2015). As in this thesis we aim to investigate the signaling effects of pre-funding, i.e., the decision policies of venture investors with regard to their assessment of inherent start-up quality—and consequently primarily with regard to the start-ups’ inherent potential for high financial returns —, we limit our scope to independent private venture investors. While VCs are considered to be the formal, business angels are referred to as the informal venture capital market (e.g., Freear et al., 1994; Wetzel, 1983). Angel investors are characterized as affluent individuals, who directly invest their own personal capital into new ventures. They appear to prefer investing in earlier development stages of start-ups (Freear et al., 1994). Business angels are often former entrepreneurs themselves, and seek to adopt a hands-on role in the firms in which they invest and to provide them with advice as well as network contacts (Freear et al., 1994; Harrison & Mason, 2000; Kerr et al., 2014; Wetzel, 1983). However, the extent might differ largely depending on expertise and engagement of individual angels; e.g., Barry (1994) argue that business angels do not generally take on the consulting role of VCs. While they are said to take a less formal approach to investing than VCs (Berger & Udell, 1998; Elitzur & Gavious, 2003a; Maxwell et al., 2011)—in particular with regard to contract formality and control rights—angels also invest in the hope of achieving a financial return. Yet, they are said to be more patient than VCs, because they are not forced to exit in predetermined periods (Mason & Harrison, 2002). A recent phenomenon is that angels increasingly form semi-formal networks and syndicate investments in groups as a consortium of angel investors (Kerr et al., 2014). Business angels usually provide funding in a range of 50,000 to 1 million per start-up, thus below typical VC investments (e.g., Berger & Udell, 1998; Clercq et al., 2006; Morrissette, 2007). As the market for angel finance is an informal market and due to the fact that business angels often act as discreet investors, it is hard to quantify aggregated market volumes. Sohl (2015) estimates total business angel investments in the USA in 2015 were 24.6 billion US Dollar. For Germany, the ZEW research institute takes the approach to ask a representative sample of n­ ewly-founded companies about their funding. Estimates predict an average investment volume of business angels in Germany over the years 2009 to 2012 of 650 million Euro annually (Egeln & Gottschalk, 2014). Business angels thus fill an important position in the market for venture financing.

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IPO

Venture Capital Level of funding

Business Angels

Crowdfinancing FFF Pre-Seed

Seed

Start-up

Later stage

Expansion

Stage of Development Fig. 2.1   Entrepreneurial equity funding landscape (Own illustration; based on Drover and Zacharakis (2013) and Moritz and Block (2014))

Figure 2.1 sketches the (private) equity funding landscape. Until recently, a usual funding cycle started with the three ‘Fs’, representing founders, family and friends (sometimes one of the ‘Fs’ is also facetious referred to as “fools”), followed by business angels, VCs (and at a later stage Private Equity), and capital markets, e.g., an initial public offering (IPO) (see, e.g., Bellavitis et al., 2017). Young growth-oriented firms typically need various rounds of financing to become profitable (Colombo & Grilli, 2005), since the market for venture financing is staged. Staged capital infusions have two reasons: First, venture investors use staging as a control mechanism (Gompers, 1995). Investors typically do not provide all capital a start-up requires in a single financing round, but tie further

2.2  The Equity Funding Landscape

19

infusions to the accomplishment of milestones, and might abandon investments whose prospects no longer appear promising. The funding process is thereby used both as an ex-post monitoring device for the uncertain outcomes of new ventures and as an incentive for entrepreneurs to deliver. Second, there is a “stage specialization” in terms of funding sum and development stage. E.g., Wong (2002) provides evidence that business angel investments are typically smaller than VC investments and take place in an earlier stage of a start-up’s life cycle (also see Freear et al., 1994). By way of comparison, business angels usually provide funding at an early stage in a range of about 30,000 to 1 million Euro (see, e.g., Berger & Udell, 1998; Denis, 2004; Morrissette, 2007; Wetzel, 1983), whereas VCs invest substantially higher amounts in later stages. Their first-time fundings, i.e., the first round of financing by a formal venture investor, averages more than 1 million Euro in Germany (Egeln & Gottschalk, 2014) and around three million Dollar in the US (NVCA, 2017). In fact, venture capitalists are increasingly specializing in financing later-stage projects at the expense of smaller early-stage investments (Elitzur & Gavious, 2003b; Hellmann & Thiele, 2015). The stage drift has been well-documented in both the USA and Europe (EFI, 2012; NVCA, 2017; OECD, 2016) and aggravates the “seed-funding gap” in early development stages, where capital is hardest to acquire and capital constraints have a substantial influence on firm survival and performance (e.g., van Praag et al., 2005). As a consequence, early-stage ventures initially have to turn to other sources in order to become “VC-ready”. Business angels have in part taken their place (Elitzur & Gavious, 2003b), and recently a new equity funding mechanism is emerging: crowdfinancing, where a large number of private individuals contribute smaller amounts of funding through an online platform.9 The digital era is enabling entrepreneurs to directly collect funds from laypeople and potential consumers and has thereby substantially expanded the start-up financing landscape. Skirnevskiy et al. (2017: 211) see crowdfinancing “as an additional source of entrepreneurial financing that fills the gap between professional capital providers and ‘family and friends’”, whereas Drover et al.

9Entrepreneurial finance is rapidly evolving and other forms of early-stage support are emerging, too, e.g., so-called accelerators or incubators. They are described as fixed-term, cohort-based programs providing a service structure of mentorship, educational programs, network opportunities and access to funding; the latter through introduction to an investor network or in form of own equity investments ranging from “pizza money” to serious seed investments over 100,000 (see, e.g., Pauwels et  al., 2016). As their focus usually is not on funding but on training, Plummer et al. (2016) call them venture development organizations.

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(2017b) emphasize that crowd-based means of funding can also parallel the investment stage of business angels. While crowdfinancing increasingly appears to be a viable market-based option for filling the “seed-funding gap” (e.g., Klöhn & Hornuf, 2012; Mollick, 2016; Schwienbacher & Larralde, 2012; Vulkan et al., 2016), it is not likely that this funding mechanism is a perfect substitute for professional venture investors. On the one hand, considering the volume of funding involved in most efforts, “VCs can (…) typically invest larger amounts of money than a (…) campaign could raise” (Sorenson et al., 2016: 1527); the same holds true for business angels— when larger investments are needed, VC participation is generally necessary, even sophisticated angels are insufficient (Goldfarb et al., 2013). On the other hand, besides their deep pockets, professional venture investors provide important connections to other resources (Baum & Silverman, 2004; Ferrary & Granovetter, 2009) and foster the professionalization of young firms (Hellmann & Puri, 2002; Sapienza et al., 1996), which is why sometimes even start-ups without funding obstacles aim to attract venture capital or participation of an experienced business angel. Growth-oriented young firms are thus likely to use the crowd to bridge the seed phase, but still seek subsequent investments by professional venture investors. Crowdfinancing’s entering of the arena has changed the entrepreneurial funding landscape. We discuss the new phenomenon and existing literature in the following section. Since the increasing move of venture capitalists to finance start-ups at later stages makes co-investments across funding types more frequent, we also review studies on the interplay of start-up financing sources in the section thereafter.

2.3 Crowdfinancing “For the first time, ordinary Americans will be able to go online and invest in entrepreneurs that they believe in.” – US President Barrack Obama, in April 2012, when signing the JOBS Act.10

10Quoted

from The White House Office of the Press Secretary (2012). The “Jumpstart Our Business Startups Act” (JOBS Act), passed in April 2012, includes under Title III the “Capital Raising Onling While Deterring Fraud and Unethical Non-Disclosure Act” (CROWDFUND Act), which authorizes securities-based crowdfunding to non-accredited investors in the USA. The Securities and Exchange Commission (SEC) adopted final concretizations in October 2015, set to become effective in May 2016.

2.3 Crowdfinancing

21

In recent years, crowdfinancing has become a valid alternative source of seed-funding for entrepreneurs seeking external financing (Belleflamme et al., 2014). The concept of crowdfinancing, which embraces a wide range of potential funding needs, is rooted in the concept of crowdsourcing. The latter refers to the collection and commercial utilization of feedback, ideas and solutions from a large, dispersed crowd of consumers by corporates (Bayus, 2013). In case of crowdfinancing, the primary objective11 is the collection of financial resources from many small contributors. The idea is therefore also related to the concept of microfinance (Morduch, 1999). In fact, the notion is not new. A famous example of an early crowdfunding is the construction of the Statue of Liberty’s pedestal. In 1885, Joseph Pulitzer started a fundraising from the readership of his New York World newspaper, where citizens contributed on average $1 each, raising over $100,000 to fund the pedestal’s completion; in return, their names were printed in an issue of the newspaper. What is new is the emergence of online platforms, which dramatically lower the costs to facilitating campaigns by “leveraging the geographic and social reach of the internet to connect fundraisers to millions of potential backers” (Fleming & Sorenson, 2016).12 One of the early online platforms which helped in making the modern concept worldwide famous was Sellaband.com (Agrawal et al., 2015b; Schwienbacher & Larralde, 2012). Launched in 2006, it acted as intermediary between new music bands and their supporters who can invest in the production of the artists’ first album in exchange for rewards, such as a mentioning on the cover, a free copy of the CD, or participation in future sales.13 Among the first significant crowdfunding platforms that also included entrepreneurial ventures were Indiegogo (2008), Kickstarter (2009), and in Germany Startnext (2010) and Seedmatch (2011).

11Side

benefits such as marketing value and consumer involvement have been argued; see, e.g., Ordanini et  al. (2011).

12Block

et  al. (2017) discuss as additional factors contributing to the emergence of crowdfinancing: on the supply-side these are capital constraints due to the 2008/2009 financial crisis, increased regulation on traditional stock markets, and a favorable legislation due to increased policy awareness for supporting entrepreneurs. On the demand-side the authors mention an increased importance of network externalities due to the internet and social media ­(“winner-takes-it-all markets”), and a trend to financial disintermediation. 13Sellaband.com, incorporated in Bochholt, Germany, and located in Amsterdam, later Munich, operated from 2006–2015 (bankruptcy). Interestingly, Sellaband raised venture capital funding in 2008 (Butcher, 2008; van Buskirk, 2010).

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The literature distinguishes up to four different categories of crowdfunding14 (e.g., Bradford, 2012; Block et al., 2017), depending on what backers expect in return for their contribution: (1) reward-based crowdfunding, where backers receive some sort of compensation in return for their financing, or oftentimes the product the entrepreneur intends to develop; the latter resembling a pre-purchase, which is why this type is also referred to as pre-purchase crowdfunding; (2) in donation-based or charitable crowdfunding, backers give money with no direct reward, usually to support individuals or n­on-governmental organizations for a cause (type 1 and 2 are oftentimes subsumed and platforms frequently adopt them simultaneously; Dushnitsky et al., 2016: 65); (3) equity crowdfunding, also referred to as securities-based crowdfunding, ­investment-based crowdfunding, or crowdinvesting, where backers act as investors and obtain a share in the future cash-flows of a firm (not merely a project); and (4) lending-based crowdfunding, where backers provide money to fund a consumer (peer-to-peer lending) or business loan with expectation of repayment (with or without interest). In terms of total capital raised, crowd-lending is the largest market segment (Block et al., 2017; Dorfleitner & Hornuf, 2016; Hornuf & Schmitt, 2016b) (25.1 bn. USD in 2015 according to Massolution, 2015).15 Since, firstly, a large part of this segment is peer-to-peer (P2P) lending, thus consumer credits and not venture debt,16 and, as secondly, our analysis concentrates on equity financing (see above), we follow Belleflamme et al. (2014) and narrow our focus to the funding types where entrepreneurs collect equity or equity-like funds from the crowd, thus reward- and donation-based crowdfunding, as well as securities-based crowdfunding. The estimated total global fundraising volume for these categories neared 10 billion US dollars in 2015 (5.5 bn. USD for rewardand donation-based crowdfunding, and 3.8 bn. USD for securities-based crowdfunding; Massolution, 2015). This market size indicates that crowd-based financing already plays a significant role in entrepreneurial finance. High growth rates and an “increasing appetite to ‘cut out the middleman’” (Vulkan et al., 2016: 37) predict that crowd-based financing might have staying power as a fundraising mechanism for new ventures and not just to be just a fad (cf. McKenny et al., 2017).

14The

term crowdfunding is often used as a collective basin for all its sub-categories. academic studies on crowd-lending, see, e.g., Lin et  al. (2013); Zhang and Liu (2012); Lin and Viswanathan (2016). 16Most P2P lending platforms would not even refer to themselves as crowdfunding. 15For

2.3 Crowdfinancing

23

For the purpose of this study, we use crowdfinancing as an umbrella term to distinguish between crowdfunding for the funding of start-up firms without backers receiving any securities, and crowdinvesting for the securities-based variant. We define crowdfunding as an open call, essentially through the internet, for the provision of financial resources either in the form of a donation or in exchange for some form of reward and/or pre-purchase of a product or service, where the many small contributors do not receive any securities.17 Prominent examples of crowdfunding platforms are Kickstarter or Indiegogo, which are US-based but attract projects and funders from around the globe. The largest German-based crowdfunding platform is Startnext. Campaigns are generally offered in one of two schemes: “keep-it-all”, where the entire amount raised is kept; and “all-ornothing”, which involves the start-up setting a funding target and reimbursement unless the goal is achieved (Cumming et al., 2015). The most typical reward to crowd contributors is the delivery of the funded start-up’s product or service offering, which makes it similar to a pre-purchase (however, with the uncertainty of the entrepreneurs’ failure to develop or deliver the product or service). Other rewards include personalized items, such as fan T-Shirts, “ego-boosting” rewards, such as honorable mentions, or “community” rewards, such as invitations to social events (Block et al., 2017). The average amount of funding attracted in successful campaigns is rather limited (around 40,000 US dollars), while notable exceptions are possible, such as the aforementioned Pebble Watch campaign. Studies indicate that the success of crowdfunding campaigns depends on the fundraisers ability to mobilize their social capital within (Colombo et al., 2015) and outside (Agrawal et al., 2010; Mollick, 2014) the platform. Distinct from that, we define crowdinvesting as the funding of a company (not only a project) by selling equity or hybrid financing instruments to many small investors, usually through an online platform, whereby investors obtain a right in future cash-flows. The variant has been similarly defined by, e.g., Ahlers et al.

17Similar, frequently cited descriptions come from, e.g., Belleflamme et  al. (2010: 5), who define crowdfunding as “an open call, essentially through the Internet, for the provision of financial resources either in form of donation or in exchange for some form of reward and/ or voting rights”; and Mollick (2014: 1), who defines crowdfunding as “a novel method for funding a variety of new ventures, allowing individual founders of for-profit, cultural, or social projects to request funding from many individuals, often in return for future products or equity” (the last two words indicating the umbrella term function, which we exclude in our narrower defintion).

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(2015: 955) as “a form of financing in which entrepreneurs make an open call to sell a specified amount of equity or bond-like shares in a company on the Internet, hoping to attract a large group of investors.”; or by Hornuf and Schwienbacher (2014: 2), who introduce „crowdinvesting as new source of entrepreneurial finance[,which] denotes the internet-based investment in start[-]up companies by the crowd with the intention to obtain some residual claim on future cash flows of the firm.“ We avoid the more commonly used term ­equity-based crowdfunding, since it is strictly spoken imprecise. As the definition by Ahlers et al. (2015) underlines, equity-like or mezzanine financial instruments are frequently in use. In Germany, for instance, crowdinvesting platforms typically broker silent partnerships (Stille Beteiligungen), non-securitized participation rights (Genussrechte), and nowadays predominantly subordinated p­rofit-participating loans18 (partiarische Nachrangdarlehen), which fall under the definition of equity crowdfunding or crowdinvesting, respectively (Klöhn et al., 2016). The worldwide largest crowdinvesting portals are Crowdcube and Seedrs, both based in the United Kingdom. In Germany, three platforms—Companisto, Innovestment,19 and Seedmatch—make up for over three quarters of the market, indicating a strong market concentration (Fuer-gruender.de, 2016); all run under the above described “all-or-nothing” model. For detailed descriptions of functioning and markets, see, e.g., Klöhn and Hornuf (2012) and Hornuf and Schmitt (2017). Typically, single crowdinvesting campaigns in Germany raise up to 2.5 million Euro, as until this threshold funds can be raised without issuing a (costly) securities prospectus.20 Crowdinvesting could emerge in Europe, precisely because existing securities regulation has been benevolent towards the issuance of small offering or suffered from loopholes in prospectus requirements (Hornuf & Schwienbacher, 2017). Looking at market data, in countries where both variants are possible, such as

18Note

that even though they are called “loans”, they represent a mezzanine instrument, replicating the uncertain future cash flows of a start-up firm (cf. Hornuf and Schwienbacher, 2015b). 19Innovestment went off the market in November 2017 (Penke, 2017). 20This exemption from the securities prospectus requirement falls under the Kleinanlegerschutzgesetz (KASG) from July 10, 2015. Before, raising funds without prospectus was possible up to 100,000 Euro when issuing silent partnerships, and unlimited for subordinated loans.

2.3 Crowdfinancing

25

Germany or the UK, crowdinvesting is of higher significance for start-up finance; e.g., in Germany the total investment volume until the end of 2015 was 50.9 million Euro, almost double compared to 26.4 million Euro for crowdfunding (Fuer-gruender.de, 2016). In the United States securities-based crowdfunding was prohibited for non-accredited investors21—thus the majority of the “crowd”— until mid-May 2016 due to a delayed concretization and promulgation of the JOBS Act regulation by the Securities and Exchange Commission (SEC), de facto limiting the crowdfinance market to crowdfunding.22 It is conceivable that the US market might experience a similar shift towards securities-based crowdfunding now that regulation allows it to a wider public.23 Evidence suggests that our distinction between the two categories for the crowd-based funding of entrepreneurial ventures is appropriate. Vulkan et al. (2016) have shown that securities-based crowdfunding is a substantially different fundraising phenomenon than reward-based crowdfunding in the view of backers. Our research will, among others, also answer the question whether professional venture investors as potential follow-up funders regard the two variants as fundamentally different, too. The academic literature on crowdfinancing is still nascent but emerging rapidly. Table 2.1 summarizes existing studies on the topic which are either the most cited articles in the field (McKenny et al., 2017), and/or published in a leading management or entrepreneurship journal (Short et al., 2017). Scholarly interest is, however, not limited to these fields. Crowdfinancing is increasingly being studied in neighboring disciplines, too, such as economics (e.g., Strausz, 2017).

21To

become accredited, non-professional investors need to meet certain wealth and/or experience requirements. 22See e.g., U.S. Securities and ExchangeCommission/Investor.gov (2016). Under effective rules, start-up companies may now raise a maximum aggregate amount of 5 million US Dollar through crowdinvesting offerings in a 12-month period. 23To put it with the words of Stephen Fleming, Chief Commercialization Officer at GeorgiaTech, Atlanta: “Once the SEC is done it’ll be a big hit—not like Kickstarter, where you get a mousepad, but when you’ll get real equity.” Quote from personal interview in February, 2013.

Observational data

Butticè et al. Sample of 34,217 cam(2017) paigns

Chan and Parhankangas (2017)

Sample of 334 Kickstarter campaigns

Observational data

Observational data Burtch et al. Dataset with contribution (2013) events and web traffic statistics for approximately 100 story pitches

Conceptual

Belleflamme Conceptual model et al. (2014)

Entrepreneurs should prefer pre-ordering when capital requirements are small and profit sharing as capital needs increase.

(continued)

Creativity and Inno- Crowdfunders are more likely to support vation incremental rather than radical innovations.

Information systems Evidence of a substitution effect in peoples’ contribution to this marketplace for crowdfunded online journalism, which suggests that altruism is a key incentive to contribute in this marketplace.

Social capital theory Having social capital and an established community of backers provides serial crowdfunders with a significant advantage in comparison to novice peers.

Price theory in economics

Highlight to which extent economic theory can explain the rise of non-equity crowdfunding and offer a framework for speculating on how equity-based crowdfunding may unfold.

Conceptual

Agrawal Conceptual model et al. (2014)

Transaction costs, reputation and market design

Signaling and social Retaining equity and providing risk informacapital theories tion serve as effective signals while social and intellectual capital have minimal impact on funding success.

Observational data

Ahlers et al. 104 equity crowdfunding (2015) campaigns from Australian ASSOB platform

Key findings

Literature/theory base

Crowdfunding context

Method

Name

Table 2.1   Crowdfinancing research

26 2  New Venture Financing Research

Crowdfunding context

Drover et al. Results from two experi(2017b) ments utilizing 104 VCs making 1,036 screening decisions.

Traditional conjoint Certification experiment

Affective events theory

Experiment

Davis et al. (2017)

102 student participants’ reactions to 10 Kickstarter. com funding pitches

Research on social capital

Observational data

Colombo 669 projects from Kicket al. (2015) starter.com

Signaling theory

Observational data

Research on motivations to engage in reward-based crowdfunding

Literature/theory base

Method

Survey Cholakova 155 surveys from Sympid and Clarysse investors (the largest equity crowdfunding platform in (2015) the Netherlands)

Courtney Comprehensive dataset et al. (2017) of crowdfunding projects on the Kickstarter website during 2009–2015.

Name

Table 2.1   (continued) Key findings

(continued)

Both angels and crowdfunding organizations can certify nascent firms, while certification from the collective is a function of crowdfunding platform type.

Perceptions of product creativity positively influence crowdfunding performance through positive affective responses among potential backers.

The positive impacts of internal social capital on the campaign success are fully mediated by capital and backers collected in the early days of the campaign.

Equity funding motivation is financial/utilitarian with no significant role of nonfinancial motives.

Start-up actions and founder characteristics reduce information asymmetry between potential backers and crowdfunding entrepreneurs, making crowdfunding success more likely. Third party endorsement signals serve to validate other types of signals.

2.3 Crowdfinancing 27

Research on the role There is significant agreement between the of experts in decision funding decisions of crowds and experts with crowds being more likely to fund campaigns. making

The analysis offers important insights about investor behavior in crowdfunding service models, the potential determinants of such behavior, and variations in behavior and determinants across different service models.

Stratified random sample of Observational data theater projects attempting to raise at least $10,000 on the Kickstarter platform between May 2009 and June 2012

Grounded Theory Ordanini Three cases involving approach/Case et al. (2011) crowdfunding initiatives: Sellaband in the music busi- Study ness, Trampoline in financial services, and Kapipal in non-profit services

Investor behavior in crowdfunding service models

(continued)

Crowdfunding success is driven by personal networks, project quality, and geography where project success is tied to goods and services common to the area of funding. Most crowdfunding campaigns deliver on their project goals although over 75% do so later than expected.

Mollick and Nanda (2016)

Exploratory examination of determinants of crowdfunding success

Kickstarter data set of over 48,500 projects

Mollick (2014)

Observational data

Goal Gradients/Per- Backer support for a crowdfunded project will ceived Impact increase as the project nears its target goal.

Observational data

300,000 project-day observations from 10,000 randomly selected Kickstarter projects

Kuppuswamy and Bayus (2017)

Key findings

Literature/theory base

Method

Crowdfunding context

Name

Table 2.1   (continued)

28 2  New Venture Financing Research

Loyal backers are especially influential to crowdfunding performance in the early stages of the campaign and strong track record encourages funding from loyal backers.

Observational data, Social Capital Survey data Theory

In part derived from Short et al. (2017) and McKenny et al. (2017).

Skirnevskiy Sample of 19,351 Kicket al. (2017) starter campaigns in conjunction with survey data

Key findings Provide a theoretical discussion of the concept of crowdfunding. Conclude with recommendations for entrepreneurs seeking to make use of crowdfunding, based on the study and analysis.

Case Study

Media No Mad (a French start-up)

Schwienbacher and Larralde (2010)

Literature/theory base Crowdfunding practices

Method

Crowdfunding context

Name

Table 2.1   (continued)

2.3 Crowdfinancing 29

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Previous literature on crowdfinancing has focused on describing the phenomena crowdfunding (e.g., Schwienbacher & Larralde, 2010; Agrawal et al., 2014; Belleflamme et al., 2014; Mollick, 2014) and to a lesser extent securities-based crowdfunding (Ahlers et al., 2015; Hornuf & Schwienbacher, 2015a; Vulkan et al., 2016). Several studies have examined how project or firm characteristics, timing, and campaign dynamics influence backer contribution (e.g., Ahlers et al., 2015; Block et al., 2016; Cholakova & Clarysse, 2015 Courtney et al., 2017; Hornuf & Schmitt, 2016a; Kuppuswamy & Bayus, 2017; Mollick, 2014). A stream of literature has argued that crowdfinancing has the potential to democratize access to capital and provides indications that it can be a less biased funding channel in terms of, e.g., gender (Fleming & Sorenson, 2016; Greenberg & Mollick, 2017; Mohammadi & Shafi, 2017; Mollick & Robb, 2016) and geography (Agrawal et al., 2015b; Hornuf & Schmitt, 2016a; Sorenson et al., 2016). The majority of research on crowdfinancing has focused on what happens during a crowdfunding campaign, leaving fairly little academic knowledge on what happens afterwards (Macht & Weatherston, 2015). Few studies have looked at post-campaign outcomes for firms (Mollick & Kuppuswamy, 2014), returns to crowd-investors (Signori & Vismara, 2016), or fraud after successful campaigns (Cumming et al., 2017). Research on the interplay between crowdfinancing and traditional sources of venture capital is scarce. The next section discusses initial attempts together with earlier work on the general role of pre-funding in new venture financing.

2.4 Interactions Across Funding Sources The staged and heterogeneous equity funding landscape as sketched in Section 2.2 implies two critical aspects: First, since growth-oriented ventures usually require various rounds of financing to become profitable (Colombo & Grilli, 2005), it is important to know whether and how previous rounds of financing affect the likelihood of securing an additional round. Second, the increasingly heterogeneous equity financing landscape—owing to the emergence of new funding mechanisms—and the fact that in practice entrepreneurs increasingly raise equity from a multitude of sources—due not least to the shift of VCs to invest in later stages—makes insights on the interactions across different funding sources ever more important. Most studies, however, consider financing types in isolation, thus, largely segmented streams of literature have emerged (Bellavitis et al., 2017; Block et al., 2017; Drover et al., 2017a). This section provides an overview of scarce existing studies on the various ways how different sources of

2.4  Interactions Across Funding Sources

31

p­ re-funding—in particular business angels and crowdfinancing—can affect subsequent venture capital selection decisions24 and discusses their limitations. While prior VC funding has been shown to positively affect the probability of getting more VC money (Hochberg et al., 2007; Lerner, 1994; Stuart et al., 1999),25 the literature has not yet agreed upon the direction of the informational value a prior angel investment has on subsequent venture capital selection decisions. On the one hand, arguing in favor of a positive effect, Elitzur and Gavious (2003b) design a game-theoretical model according to which a prior business angel investment ought to be a positive signal for potential VC investors. The moral hazard model reveals that if an “entrepreneur incurs some cost in dealing with the angel, this action, in itself, signals that the entrepreneur has chosen to exert a positive level of effort and, thus, (…) that angel-backed firms could be seen as firms whose founders opted for a viable firm, rather than choosing to ‘take the money and run’” (Elitzur & Gavious, 2003b: 722). In order to provide empirical evidence, Kerr et al. (2014) analyze a sample of 130 US ventures with similar ex-ante qualities which are close around a threshold in the evaluation by professional angel consortia of either getting funded or rejected. The authors find that being funded by an angel consortium is associated with superior follow-on funding in the base data. However, when applying a regression discontinuity approach to consider unobserved differences between angel-funded and rejected firms, the effect vanishes. This example demonstrates a common limitation when working with real-life data, that is a difficulty to disentangle causal effects. On the contrary, Hellmann et al. (2015), using a sample of 469 Canadian start-up

24Harrison

and Mason (2000) find four types of complementarities between venture capital and business angels: (1) sequential investing in start-ups at different stages; (2) co-investing in deals at the same time; (3) provision of finance to VC funds by business angels; and (4) deal referring. These types of complementarities may also be considered to exist between crowdfinancing and venture investors. For instance, on platforms like AngelList angel investors operate as syndicate “lead” and invite the crowd to back their curated ventures, see, e.g., Agrawal et  al. (2015a); Also, business angels, and to a lesser extent VCs, sometimes place investments in crowdfinancing campaigns. In this work we focus on the first type of interaction, sequential investing. 25Guerini and Quas (2016) find that funding by government-managed venture capital firms, too, increases the likelihood that companies will receive private venture capital. For mere governmental grants or R&D awards, respectively, Howell (2017) shows that an increased probability of receiving subsequent venture capital is not due to a positive certification effect about firm quality, but because of the usefulness of the grant resources for the development of a venture.

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firms over the period 1995–2009, discover that prior business angel funding reduces the likelihood of obtaining subsequent VC funding. In order to empirically separate selection and treatment effects, the authors exploit exogenous variation in a tax credit program that affected the relative availability of VC and angel financing. Their findings are driven by two distinct types of angels: inexperienced (“casual”, one-time) angel investors and angel consortia. The negative interaction pattern does not apply to “serial angels”, who are likely to be more experienced and committed to venture financing. Ambiguous findings, whether venture capitalists and business angels are “friends or foes” are also theorized in a dynamic model by Hellmann and Thiele (2015). The emergence of crowdfinancing also raises the question about interactions between new players and established instruments of equity financing. First empirical attempts of understanding the link between crowdfinancing and subsequent VC investments predominantly propose a positive interaction. Kaminski et al. (2016) hypothesize two competing views: on the one hand, that successful crowdfunding campaigns may validate the feasibility and viability of new technologies and therefore enhance the probability follow-up financing; on the other, that the crowd and VC investors compete for a limited number deals and that thus crowdfunding would crowd out VC investments. The authors draw on 54,943 successfully crowdfunded projects on the US reward-based crowdfunding platform Kickstarter and 3,313 venture capital investments from the CrunchBase database throughout the period 2012–2015 and find no evidence for a crowding-out effect. They conclude that crowdfunding and venture capital are rather complements than substitutes. Babich et al. (2017) supplement their empirical work by suggesting a theoretical framework. Shafi and Colombo (2017) look at which conditions of a crowdfunding campaign influence the hazard of a start-up firm of obtaining later VC financing. The empirical tests of their theoretical model with data from reward-based crowdfunding campaigns from Kickstarter and Indiegogo reveal that the hazard of subsequent external financing increases with both a higher fundraising target and a higher pledged amount of money in excess of the fundraising goal. This implies that larger collected sums through a reward-based crowdfunding are correlated with a relatively faster follow-up funding by VCs than lower sums. Sorenson et al. (2016) find that an increase in successful crowdfunding campaigns within a geographic region corresponds to an increase in VC investments in the same area. More specifically they employ regional data at US county-year level and a matched sample of 55,005 Kickstarter campaigns and 17,493 VC investments from 2009 to 2015 and find that a 1 percent increase in the annual number of crowdfunding campaigns in one year predicts a statistically significant

2.4  Interactions Across Funding Sources

33

0.097 percent increase in the annual number of VC investments in the following year (and a 0.092 percent increase in the second year, 0.067 percent in the third year). The authors conclude that “[s]uccessful campaigns may attract the attention of VCs to innovators in the region or to the specific people running these successful campaigns.” While this study offers interesting insights, we cannot derive how prior crowdfunding influences the chances of a specific entrepreneur to attract VC financing for her endeavor. Even if firm-level data were available, approaches with observational data face limitations with regard to deriving causal conclusions, e.g., since high-quality start-ups will find it easier to attract both crowdfunding and venture capital. An attempt promising causal empirical evidence on the interconnectedness of crowdfinancing and venture capital (the only so far, as known to the author), is taken by Drover et al. (2017b) who conduct two separate traditional conjoint analysis experiments26 with practicing venture capitalists in the United States. They examine how variation of attributes of an angel investor who invested in a start-up (experiment 1:8 start-up profiles, each evaluated by 53 VCs) and variation of attributes of a crowdfinancing (experiment 2:12 start-up profiles, each evaluated by 51 VCs, different to the ones who have taken part in the first experiment) influence the willingness of venture capitalists to consider the screened start-up for formal due diligence (measured using a three-item Likert-type rating scale). Decomposing 1,036 screening decisions by Hierarchical Linear Modeling (HLM) analysis the authors find that angel experience and an angel’s membership in a reputable angel group27 both have a positive and statistically significant effect on venture capitalists’ willingness to consider a start-up for formal due diligence. With regard to crowdfinancing they likewise find a positive and significant relationship between track record of a crowdfinancing site (serving as a proxy for the platform’s reputation) and the willingness to conduct due diligence. Moreover the conjoint analysis shows that the type of crowdfinancing matters: the overall willingness to conduct due diligence on reward-crowdfunded start-ups is higher than on ventures that have been funded by a crowdinvesting or a crowd-lending. In line with that, results reveal a significant positive interaction between platform type reward-based crowdfunding and the number of crowdfunders, while

26For

further information on this method see Chapter 4. that the scenario in the study by Drover et  al. (2017b) was that the to be evaluated start-up has been invested in by one angel investor who is a member of an angel investing group (or not), and not by a group of angels (also referred to as “consortium”). The effect of the latter is tested in this thesis. 27Note

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2  New Venture Financing Research

the main effect of a high number of crowd contributors backing the venture (thus over all platform types) on VCs’ screening decisions is nonsignificant. Overall, Drover et al. (2017b: 341) provide important evidence that various characteristics of seed fund investments can serve as an influential certification to VCs as they make screening decisions. However, a central question remains unanswered: since the trade-off between different seed-funding types had not been addressed within one experiment, we still lack an understanding of the causal effects of a prior crowdfinancing relative to other, competing sources of funding (or no pre-funding) on subsequent venture investors’ decisions.

2.5 Research Gap This chapter has provided an overview of a vast body of literature in the field of entrepreneurial equity finance with an emphasis on the emerging literature on crowdfinancing. The review has brought attention to the largely understudied perspective of the interconnectedness of financing sources. Considering the importance of entrepreneurial firms for the overall economic system, and the fact that access to financial resources is one of the biggest obstacles for new ventures, there is a need for understanding how different sources of financing impact s­tart-ups. The emergence of the new start-up funding mechanism crowdfinancing has opened new perspectives in that area with so far limited scholarly knowledge (Short et al., 2017). Thus far, largely separate streams of literature on new venture financing have emerged, and the nascent research on crowdfinancing has, too, focused on explaining its own dynamics (Drover et al., 2017a). However, in practice, entrepreneurs raise funding from a multitude of sources and the increasing tendency of VCs to limit their engagement to later stages makes co-investments even more frequent. Consequently, we need a better understanding on how “traditional” and “new” funding sources interact (Bellavitis et al., 2017; Drover et al., 2017a; McKenny et al., 2017). Initial studies have found that crowdfinancing and venture capital are rather complements than substitutes and that certain conditions correlate with increased VC investments in crowdfunded start-ups. We aim to complement this research by shedding light on the causal effects of earlier-stage funding mechanisms—both crowd-driven and in comparison with expert-driven funding (angel investments)—on subsequent venture capital selection. More specifically, we seek to answer the questions whether and how a prior crowdfinancing and its conditions communicate signals and thereby affect a s­ tart-up’s likelihood of obtaining a positive evaluation in prospective venture investors’ screening decisions.

3

Theory and Hypotheses

3.1 Decision Making of Venture Investors Venture capitalists solve an important problem in market economies: they connect founders with good ideas (but no money) with people who have money (but no ideas) (Gompers et al., 2016a). VCs are thus considered to be key actors in the entrepreneurial ecosystem, and to serve as a catalyst for innovation within an economy (Shepherd et al., 2000). Venture capital has held this critical position over the last decades1 and—even though the VC industry faced considerable booms and busts—the nature of venture capital itself has been pretty stable (Gompers & Lerner, 2001, 2004). Because of their importance, the question of how venture investors make decisions plays a central role in the entrepreneurship literature—with first studies dating back to the 1970s (for an overview, see Zopounidis, 1994)—and is of constantly high interest until today (Franke et al., 2008: 459). The “venture capital cycle” (Gompers & Lerner, 2004) consists of three phases, namely fundraising, venture investing, and exiting investments, with core activities during the investing phase (Gompers et al., 2016a). The investment process (also see Tyebjee & Bruno, 1984) begins with VCs sourcing new ventures of interest. While they generally wait for deals to come to them (Fried & Hisrich,

1The

first true venture capital company, American Research and Development (ARD), was founded in Cambridge, Massachusetts, in 1946. The industry started to increase dramatically—promoted by beneficial changes in legislation—in the US in the late 1970s, and gained momentum in Europe in the 1990s (Gompers and Lerner, 2001).

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 M. Mödl, Signaling Effects of Crowdfunding on Venture Investors’ Decision Making, Innovation und Entrepreneurship, https://doi.org/10.1007/978-3-658-31590-0_3

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3  Theory and Hypotheses

1994: 31), VCs make themselves known in the industry and develop a network of referrers (Sahlman, 1990). Out of this pool of potential investments (“deal flow”) venture capitalists screen and select start-up firms for financial investment based on certain criteria and decision policies (see below). Next, VCs engage in sophisticated contracting (e.g., Kaplan & Strömberg, 2003) and structuring (e.g., Hochberg et al., 2007) of their investments. After the initial investment round, venture investors play an active role in adding value to portfolio companies by monitoring and governance (Gompers & Lerner, 2004), assisting with strategic advice, and facilitating access to additional resources (Baum & Silverman, 2004; Ferrary & Granovetter, 2009). Of these many roles, one of the most critical is new venture selection (Mollick, 2013). For instance, Sorensen (2007) finds that the higher likelihood of going public of start-ups that got funded by an experienced VC than by an inexperienced VC is attributable to both VCs’ value adding activities (experienced investors add more value than inexperienced) and selection or sorting, respectively (experienced VCs make better selection decisions and high-quality founders choose experienced VCs), but with a greater influence of selection/sorting. When venture capitalists are asked directly which of their activities helped generating their returns, as done by Gompers et al. (2016a), who surveyed more than 800 US VCs, deal selection again appears to be the most important activity. Apart from new venture selection being a central driver for their financial success, by choosing which start-up firms to provide with funding, venture capitalists ultimately also determine which innovations will succeed (Mollick, 2013). This explains the particular strong research interest in how venture investors make their selection decisions. The literature investigates venture capital selection along two lines: evaluation criteria (MacMillan et al., 1985; MacMillan et al., 1987; Tyebjee & Bruno, 1984; Hall & Hofer, 1993; Muzyka et al., 1996; Shepherd, 1999; Zacharakis & Shepherd, 2001; Franke et al., 2008; Petty & Gruber, 2011) and the decision-making process (Tyebjee & Bruno, 1984; Riquelme & Rickards, 1992; Fried & Hisrich, 1994; Zacharakis & Meyer, 1998).2 With regard to criteria venture investors use to evaluate potential deals the literature indicates that, overall, VCs emphasize four broad categories of start-up characteristics when making investment decisions: the founder team, the product or service, the market attractiveness,

2For

an overview of studies, see, e.g., Petty and Gruber (2011: 174). For a comparison of business angel and VC investment procedures, see, e.g., van Osnabrugge (2000).

3.1  Decision Making of Venture Investors

37

and the venture’s financial prospects (Petty & Gruber, 2011). With regard to founder team, prior research suggests that VCs tend to favor teams of members with mixed educational backgrounds (in particular management and engineering expertise) and with relevant industry experience (Dixon, 1991; Franke et al., 2008; Hall & Hofer, 1993; Shepherd, 1999).3 In terms of a start-up’s offering, the literature indicates that VCs consider the innovativeness of a product or service, the value proposition for potential customers, its competitive advantage, and proprietary protection, such as intellectual property protection (Khan, 1987). Concerning a new firm’s target market, studies point to VCs prefering large-sized market opportunities with high growth rates and easy accessibility of customers (Tyebjee & Bruno, 1984). Customer adoption plays a crucial role as investors often reward founders that secure a threshold number of paying customers (Kaplan & Strömberg, 2004). Finally, with regard to financial prospects VCs look for favorable expected rate of return to expected rate of risk ratios. As a rule of thumb, venture capitalists expect a ten times return of capital over five years to engage in an investment opportunity (Zider, 1998: 135). The core selection decision-making process can be broadly described in two stages: an initial generic screening of received deals and, for those proposals considered interesting, a more thorough evaluation, eventually leading to an extensive due diligence. While the latter consumes more time in the decision process, the screening stage—which usually only involves a cursory glance on the business plan (Kirsch et al., 2009)—is highly consequential for both start-ups and investors. Approximately 80 percent of all applications are rejected (Dixon, 1991; Petty & Gruber, 2011), suggesting the importance of research on this stage of venture investors’ decision making. Evidence suggests, that venture capitalists are quite efficient at selecting promising new ventures relative to other entities (e.g., Denis, 2004; Kortum & Lerner, 2000; Lerner, 2002). However, their decision-making is also plagued by high levels of uncertainty and informational opacity. In most cases, the selection criteria described above, or their latent “value”, respectively, are not readily

3The

founder team has often been attributed to be the most important investment criterion, however, Zider (1998: 133) argues that “[o]ne myth is that venture capitalists invest in good people and good ideas. The reality is that they invest in good industries—that is, industries that are more competitively forgiving than the market as a whole.” That the management team is not necessarily the dominant evaluation criteria has also been shown by, e.g., Hall and Hofer (1993), Muzyka et al. (1996), and Petty and Gruber (2011).

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3  Theory and Hypotheses

observable when the decision is made. Problems arising from this and strategies how venture investors deal with information challenges are discussed in the following section.

3.2 Asymmetric Information and Signaling in New Venture Financing To examine the effects of prior (crowd-)financing on subsequent venture capital selection we draw on information economics.4 Risk capital investments are characterized—by definition—by a high degree of uncertainty. New organizations are confronted with numerous obstacles, such as a lack of experience, knowledge of their environment, employee commitment, working relationships with customers and suppliers, and of size to withstand periods of poor performance (Baum & Silverman, 2004). They are thus highly vulnerable, a phenomenon that Stinchcombe (1965) portrayed as liabilities of newness. Moreover, with their product offering still under development—and with new technology being, by its very nature, vastly uncertain—, young firms face a high technical and commercial failure risk (Aldrich & Fiol, 1994). In addition to these hazards, start-ups lack long-term track records by which outsiders could assess their quality (Hoenig & Henkel, 2015; Shane & Stuart, 2002). Besides this general opaqueness, prior research has established that a salient factor which makes evaluations of the promise of young ventures a challenging task is the presence of asymmetric information (Drover et al., 2017b). Venture investing is characterized by a large difference between the knowledge of capital-seeking entrepreneurs and capital-providing investors: naturally, founders are assumed to have better knowledge about the true quality of their venture than external investors (Amit et al., 1998; Backes-Gellner & Werner, 2007; Busenitz et al., 2005; Cassar, 2004; Gompers & Lerner, 2001). The market for venture financing therefore faces severe ex-ante5 information asymmetries.

4We

thereby acknowledge the existence of market imperfections. Under the assumption of perfect financial markets, companies will always find suitable funding for value-creating investments (Modigliani and Miller, 1958). 5Asymmetric information also occurs ex-post in entrepreneurial finance, as investors (principals) cannot perfectly observe the intent and actions of entrepreneurs (agents), leading to a moral hazard problems (e.g., Jensen and Meckling, 1976). Kaplan and Strömberg (2001) provide an overview of how venture capitalists attempt to mitigate principal-agent conflicts, namely through pre-investment screening, sophisticated contracting, and post-investment monitoring. In this study, we focus on investors’ screening decisions and therefore on ex ante hidden characteristics.

3.2  Asymmetric Information and Signaling in New Venture Financing

39

Akerlof (1970) was the first to provide a formal model, illustrating how dramatically asymmetric information can affect market equilibria. He uses the example of a market for used cars in which potential buyers only know the distribution of car quality but cannot observe the quality of a single car traded. When above-average quality car sellers are unable to communicate the true value of their vehicles, the market prize of all used cars will reflect the average quality car, causing high-quality cars to withdraw from the market. This incentive of high-quality types to withdraw leads to adverse selection, a market equilibrium in which only cars of worst quality (“lemons”) are traded. Several studies have shown that markets of external equity financing6 may also be associated with a lemons problem (e.g., Chan, 1983; Greenwald et al., 1984; Myers & Majluf, 1984).7 A theory which is fundamentally concerned with reducing the information gap between two parties is signaling theory (Spence, 1973, 2002). The adverse selection problem can be mitigated if high-quality types use observable “signals” to communicate their above-average quality by taking some costly action, which cannot easily be imitated by low-quality types.8 In his seminal article on labor markets Spence (1973) illustrates that high-quality job applicants distinguish themselves from low-quality prospective employees via the costly signal of rigorous higher education. Higher education can serve as separating equilibrium for the unobservable characteristic of an applicant’s productivity, since the “cost” of acquiring this signal is relatively lower for high-skilled persons than for low-skilled (also referred to as “signaling cost differential”, Connelly et al., 2011). Education in this example can therefore reduce information asymmetry and hamper the selection ability of employers. While Spence’s initial 1973 article stresses the function of a signal to mirror higher ex-ante productivity, this does not mean that acquiring a signal cannot

6In fact, problems arising from asymmetric information have also been demonstrated to exist in debt markets, see, e.g., Stiglitz and Weiss (1981). 7A ­well-known example for the effects of asymmetric information on capital structure is the pecking order theory (Myers, 1984); Myers and Majluf (1984), which suggests the existence of a corporate financing hierarchy (see Chapter 6 for further details). Another example is the model by Leland and Pyle (1977), who argue that the entrepreneur’s willingness to invest in his own project can communicate commitment and confidence in the venture’s quality. 8Another possibility on the other side of the market is that the uninformed party “screens” the market by providing an additional offer which is only attractive to high-quality subjects, as demonstrated for the insurance market by Rothschild and Stiglitz (1976). Hence, in screening theory the less-informed party bears the cost for the signal, while in signaling theory it is the better-informed party for whom the signal is costly. Riley (2001: 474) therefore calls screening and signaling “twin theories”.

40

3  Theory and Hypotheses

c­oincide with becoming (even) more productive ex-post (for instance, in the original example prospective employees acquire additional skills by studying; in our example companies acquire funds as productive resources). Spence becomes explicit in this regard in his 1974 article and argues that there are “(…) three distinct senses in which a signal can be productive. It can be privately productive for the individual by distinguishing him from others who may be less productive. On this level, signals have a distributive role to play. Second, the signal can be directly productive as embodied human capital of some kind. Both education and job experience probably fit this description. Thirdly, the information carried by the signal may be productive by permitting more efficient allocation of human resources by employers.” (Spence, 1974: 301 f.).

Michael Spence’s work, for which he has been award the Nobel Memorial Prize in Economic Sciences together with George Akerlof and Joseph Stiglitz in 2001, triggered a rich literature applying signaling theory to selection scenarios in economics as well as in a wide range of other disciplines (Connelly et al., 2011). The signaling framework holds a prominent position in the entrepreneurship literature, too, since the market for entrepreneurial financing is plagued by a high degree of asymmetric information, as outlined beforehand. It has been demonstrated that venture investors tend to address this problem by relying on signals, which are observable at the time of the investment decision and for which the probability is high that they correlate with the non-observable determinants of the quality of a start-up (Hoenig & Henkel, 2015; Kirsch et al., 2009; Stuart et al., 1999). In fact, VCs invest significant time and energy in the screening process and the evaluation of quality signals (Amit et al., 1990; Hall & Hofer, 1993). Conversely, entrepreneurs invest in observable attributes which signal the quality of their start-up (Zott & Huy, 2007). Hence, when explaining entrepreneurial selection, signaling theory is frequently applied. The innovation and entrepreneurship literature distinguishes between two types of information sources that can reduce uncertainty: those coming from the start-up, and those originating from third parties (Courtney et al., 2017). Firstly, new ventures can signal their latent potential to the outside world through their attributes and actions (Plummer et al., 2016). Regarding attributes9 Stuart et al.

9In

his retrospect on signaling theory Spence (2002: 434) defines signals as “things on does that are visible and that are in part designed to communicate.” He explicitly distinguishes between unalterable attributes, which he calls “indices”, and over which one has no control, such as gender or race; and attributes that are alterable, thus require previous actions (such as human capital acquisition or patenting). Only the latter can be signals.

3.2  Asymmetric Information and Signaling in New Venture Financing

41

(1999: 317) note: “Because the quality of young companies often cannot be observed directly, evaluators must appraise the company based on observable attributes that are thought to co[-]vary with its underlying but unknown quality.” Coming close to the original example of Spence (1973)—and being a critical decision criteria of venture investors as shown in the section before –, the background and experience of the firm’s management team has been demonstrated to be an important signaling device (Baum & Silverman, 2004; Beckman et al., 2007; Gompers et al., 2010; Hsu, 2007). Patents are another factor which has been repeatedly shown to be a credible signal of a technology start-up’s quality of innovation that facilitates access to financing (Conti et al., 2013a; Conti et al., 2013b; Haeussler et al., 2014; Hsu & Ziedonis, 2008, 2013).10 There are also a number of actions a start-up can take to alleviate the information gap for investors: For instance, designing compensation plans in a way that they signal credible commitment of the founders to suffer the negative consequences if they do not deliver (Arthurs et al., 2009). Another example for a signaling action that is both costly and hard to imitate is developing and introducing new products onto the market; e.g., Deeds et al. (1997) argue that new product development is a signal of technological competence and find that it is positively associated with the amount of capital a start-up can raise. With regard to the capital structure of a venture, the traditional entrepreneurial finance literature has emphasized the percentage of equity retained by the founder as a signaling mechanism (Janney & Folta, 2003; Leland & Pyle, 1977). Secondly, quality signals can originate not solely from start-up s themselves, but are also based on network connections. New ventures can communicate their value by aligning with reliable third parties, who bear the cost of signaling (Courtney et al., 2017; Gulati & Higgins, 2003; Plummer et al., 2016). Studies have demonstrated that endorsements by third parties11 can assist new ventures

10Hoenig

and Henkel (2015), too, find that VCs value patents highly when evaluating new ventures, however, rather in their productive function as a property right than as a signal of the venture’s technology. Hsu and Ziedonis (2013) provide evidence that patents confer dual advantages as a resource and a signal. 11This process may also be seen in the area of certification literature (e.g., Biglaiser, 1993; Booth and Smith II, 1986; Lizzeri, 1999; Viscusi, 1978). In line with Stahl and Strausz (2017: 1843), who state that certification can be used as a “signaling device” by ‘sellers’, we propose for our study that pre-funders generate the certification which is then used as a signaling device—intentionally or unintentionally (Janney and Folta 2003)—by entrepreneurs in later funding rounds. Correspondingly, e.g., Plummer et al. (2016: 1587) call endorsements “third-party signaling”.

42

3  Theory and Hypotheses

in gaining access to resources (Baum et al., 2000; Baum & Oliver, 1996; Stuart et al., 1999), and that VCs look for these endorsements when making funding decisions (Baum & Silverman, 2004). For instance, strategic alliances with well-known established companies (Baum et al., 2000; Stuart et al., 1999) and board compositions with prominent members (Certo, 2003; Sanders & Boivie, 2004) can serve as reputational signals, especially when high-status players (“with reputational capital at stake”, Megginson & Weiss, 1991: 881) back a venture (Baum et al., 2000; Shane & Cable, 2002; Stuart et al., 1999). Interestingly, endorsements by venture capitalists, too, have been shown to reduce information asymmetry, enhancing the ability of start-ups to attract subsequent financing and favorable valuations in an initial public offering (Gulati & Higgins, 2003; Megginson & Weiss, 1991).12 The next section discusses how earlier-stage funding, and the status and actions of pre-funders, could be a credible signal to venture investors.

3.3 Pre-Funding as a Signal—Hypotheses Development According to Spence’s (1973, 2002) definition, signals have to be observable, costly and discriminatory to be effective (Connelly et al., 2011). We propose that prior funding can be an effective signal, since, on one hand, ownership structure and financing resources are bluntly observable from the business plan. On the other, different types of pre-funding, being differently costly to obtain for ­start-ups of diverging quality, are discriminatory and can therefore achieve a separating equilibrium between ventures of different quality. A number of studies have reasoned that previous external financing can contain information about the quality of a venture and therefore reduce the uncertainty of subsequent investors (Drover et al., 2017b; Elitzur & Gavious, 2003b; Janney & Folta, 2003; Megginson & Weiss, 1991). With crowdfinancing emerging as a novel source of seed-funding, questions of the informational value it transports to venture investors at a later stage arise, also in comparison to established players in the ­early-stage funding game. There are indications that the signaling cost differential is arguably smaller for crowdfinancing than for funding from (semi-)professional venture investors,

12For

an overview of studies that discuss signals in the context of venture capital financing also see Hoenig and Henkel (2015: 1051).

3.3  Pre-Funding as a Signal—Hypotheses Development

43

such as business angels. First, while a business angel funding may be regarded as an observable proxy for communicating, e.g., a high-quality management team’s positive effort and ability in identifying, approaching, negotiating and dealing with investment managers (Elitzur & Gavious, 2003b), crowdfinancing usually involves standard terms, no search and negotiation costs and is thus almost equally approachable for low- and high-quality start-ups.13 In other words, the relative transaction costs for acquiring an investment by professional investors compared to via crowdfinancing should be relatively higher for firms of lower quality. Second, the crowd is expected to be less good at selecting promising ventures than professional investors, yielding to a higher probability that low-quality start-ups obtain funding. In favor of capable crowd decision processes it has been argued that a greater number of individuals reviewing a project than in traditional settings, weighing in a variety of perspectives, may enhance the chances to notice something amiss (Agrawal et al., 2014: 83 f.). At the same time, the crowd can be subject to many group decision-making fallacies (Mollick & Nanda, 2016: 1534), as Isenberg (2012) argues about crowdfinancing: “group irrationality is ­well-documented—crowds are ‘wise’ only in a very limited set of circumstances. As often as not, crowds bring us tulip crazes, subprime meltdowns, the Kitty Genovese scandal, Salem witch trials, and other tragedies. Crowdfunding advocates claim that social media will self-correct the madness of crowds, but this seems to me highly suspect.” At large, due diligence processes by crowd contributors are likely to be less thorough than by professional investors, owing to the facts that they are on average less experienced, cannot rely on face-to-face interactions, and typically invest small(er) sums per individual, reducing the incentives for deep investigations (Agrawal et al., 2014). The latter may potentially yield a free-rider problem: Vismara (2015) documents that crowdfunders free-ride on the investment decision and back the projects with the greatest number of investments in an information cascade fashion. The weak individual-level possibilities and incentives to perform due diligence might lead the crowd to make less informed selection decisions than professional venture investors. Less informed decisions may in turn increase the relative likelihood of low-quality ventures to successfully close a funding round with the crowd,

13Standard

terms may also lead to a tendency of average valuations. These are favorable for low-quality firms, thus increasing their incentives of opting for crowdinvesting. On the contrary, the “costs” of receiving funds, e.g., giving away a share of equity, could be relatively lower for high-quality start-ups when turning to individual contract negotiations.

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c­ompared to acquiring funds from professional investors (put differently, the “cost” of obtaining funding from business angels is seemingly relatively lower for high-quality ventures than for start-ups of lower quality). And indeed, Mollick and Nanda (2016) find that the crowd is willing to fund projects that experts may not.14 On the other side of the market this view is backed by a large-scale survey among British fundraisers where 64 percent of those using donation-based crowdfunding and 53 percent of those using reward-based crowdfunding state that it is “unlikely” or “very unlikely” they would have been able to get funding elsewhere (Zhang et al., 2014). Prospective venture investors might, hence, assume that crowdfinanced start-ups have turned to this means of funding because they considered the chance of obtaining funding from traditional sources to be low, or because they had previously been rejected. Above’s reasoning for the smaller signaling cost differential for crowdfinancing makes a compelling argument to propose that venture investors perceive that one dominant rationale for a start-up “choosing” to get funded by the crowd (or being forced to turn to the crowd as a last resort) is its inferior quality. For all these reasons, an initial crowdfinancing could communicate a signal of negative quality to prospective professional investors and should hence reduce their probability to consider a such-funded venture for investment. Our first hypothesis is thus: Hypothesis 1:  A  prior crowdfinancing decreases the likelihood of a start-up’s selection by venture investors This signal originates from the entrepreneur by choosing a certain source of early-stage funding. It thereby deserves mentioning that signals cannot only be sent on purpose but also unintentionally (e.g., Janney & Folta, 2003; Daily et al., 2005). While signaling theory emphasizes that signals need not (but can) be productive (see above), founders might—by primarily seeking the productive function, namely financial resources—involuntarily send a quality signal to subsequent potential investors. Such unintended signals may as well communicate negative information to receivers (Ryan et al., 2000). Besides, entrepreneurs might not even be aware of their quality. Evidence suggests that founders tend to

14The

judgments of crowds may not necessarily be bad decisions, and Mollick and Nanda (2016) discover a remarkable congruence between experts and the crowd. Yet, providing evidence from online peer-to-business lending platforms, Mohammadi and Shafi (2016) show that crowds perform inferior to institutions when screening the creditworthiness of small and medium sized enterprises. At large, crowds appear to have a higher likelihood of funding projects than professionals.

3.3  Pre-Funding as a Signal—Hypotheses Development

45

be overly optimistic about their venture (e.g., Cooper et al., 1988). In this case, the signal has a decision-theoretic function, providing information to both the investor and the entrepreneur (Hoenig & Henkel, 2015: 1050). The initial impulse for the signal thus comes from the start-up firms themselves by self-selection into a certain seed-funding channel. Such a signal is per se informative but may additionally initiate a process of signaling at the early funders’ level. Early investors might serve as intermediaries who actively certificate their perceived quality of a start-up to follow-up investors. In their review of signaling theory, Connelly et al. (2011) underline that different types of signals can work together to reduce information asymmetry. Bapna (2017) has shown that signals in early-stage equity investment decisions can be complementary. More specifically, recent research from, e.g., Courtney et al. (2017) and Plummer et al. (2016) suggests that signals originating from the start-up and endorsements originating from third parties interact to affect the likelihood of obtaining external capital. In line with this argumentation, we propose that subsequent to the initial quality signal by seed-funding selection, these seed investors can create credible information serving as complementary signals to subsequent investors. As outlined in the section before, endorsements by third parties can assist new ventures in gaining access to resources (Baum et al., 2000; Baum & Oliver, 1996; Stuart et al., 1999), and VCs look for these endorsements when making selection decisions (Baum & Silverman, 2004). Bruton et al. (2009) and Drover et al. (2017b) have demonstrated that ventures can in part communicate their value by both the identity and the actions of their previous investors. In terms of identity, to what extent an endorsement qualifies as a signal depends on the reputation of the third party backing a venture (Stuart et al., 1999). This is because prominent players are perceived as reliable and well-informed evaluators who are able to discern quality (Shane & Cable, 2002; Stuart et al., 1999; Stuart, 2000). In contrast to the crowd backers, who are typically an anonymous mass to subsequent potential investors,15 individual angel investors can bear a reputation. For instance, recent years have seen the rise of “celebrity angels” (such as Peter Thiel or Paul Buchheit), and business angels are even featured in own TV shows (such as “Shark Tank” in the USA, “Dragon’s Den” in the UK, or “Die Höhle der Löwen” in Germany). In general, there is quite a variety in what calls themselves business angels. Investment amounts and frequencies vary considerably across

15Drover

et al. (2017b) argue that crowdfinancing platforms might take on the role of a reputation bearer.

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3  Theory and Hypotheses

investors, ranging from one or two investments to professional portfolios of serial angels (van Osnabrugge, 2000). In that context, Hellmann et al. (2015) have found evidence that previous investments by inexperienced angels reduce the likelihood of a start-up to receive subsequent VC funding (see Section 2.4). In some cases angels are former entrepreneurs themselves, possess considerable industry knowledge, and a network within a branch (Freear et al., 1994) which others do not. Certification by an acknowledged “industry expert” might then add legitimacy to a start-up supporting its ability to attract venture capital. On a personal scale, some VCs rely on the proposals of their “business angel in residence” to enhance their deal flow (Braun, 2013; Harrison & Mason, 2000). We thus argue that pre-investors known to venture investors and considered as reputable create a more reliable signal with their investment than initial investors not known to subsequent deciders. As the crowd typically consists of unknown backers,16 this logic mainly applies to angel investors, and suggests the following hypothesis: Hypothesis 2:  A  prior funding by a known and reputable business angel increases the likelihood of a start-up’s selection by venture investors, relative to a funding by an unknown angel In contrast to potential connections between angels and VCs, as stated before, the crowd is usually anonymous to prospective investors. Rather than by reputation, a more credible signal is thus likely to be generated by the actions of the crowd. As Kaminski et al. (2016) argue, the behavior of the crowd might reduce uncertainty of prospective venture investors. For instance, when investors observe that a certain new technology received widespread community support, they may interpret this as an indicator of the true venture viability and technological feasibility in this field (Kaminski et al., 2016: 5). Crowdfinancing processes are less opaque than traditional equity fundraising and the behaviors of funders thus become more visible. While an entrepreneur’s effort and time that was necessary to convince a business angel—not to mention how many investors had to be approached before one was willing to invest—is typically not readily observable for follow-up investors, a detailed record of a crowdfinancing campaign’s conditions is usually just a click away, making this process of acquiring funds as

16It

is conceivable that “star investors” emerge within a crowdfinancing platform’s community. It is, however, unlikely that prospective professional investors take the trouble to go through the list of individual crowd backers (if available at all), especially when making initial screening decisions on business plans.

3.3  Pre-Funding as a Signal—Hypotheses Development

47

transparent as never before. For instance, spectacular funding records in terms of speed, such as German hardware start-up Protonet’s collection of 200,000 Euro in just 48 min or cracking the threshold of 1 million Euro after 3.5 h (converted 1 million US Dollar after 89 min) in another crowdinvesting campaign, even spur international media interest.17 Conversely, calmer crowd sentiment reflected in prolonged campaign periods also becomes public. Additionally, an utterly successful course of campaign might also reveal information about the founding team’s project management and sales skills. While cases of extreme speed are certainly affected by some sort of herding behavior (Agrawal et al., 2014),18 we reason that a faster collection of the target funding sum reduces uncertainty about the public’s sentiment towards a new technology, as well as about the entrepreneurs’ ability to arouse interest and hence increases the odds that positive inferences will be drawn by VCs: Hypothesis 3:  A  faster collection of the target funding in a case of crowdfinancing increases the likelihood of a start-up’s selection by venture investors An action that both the crowd and business angels can undertake, and that is by the very definition “costly” and may therefore serve as an endorsing signal, is to certificate their perceived quality of a new start-up by investing their own money. The crowd as a collective may have an overall higher likelihood of funding projects than business angels, as described beforehand, thus low-quality s­tart-ups will find it easier to attract at least some money from the crowd than from traditional equity sources. However, crowdfinanced ventures vary considerably in the level of funding volumes. Attracting significant amounts certifies at least the crowd’s higher level of positive appraisal of a start-ups offering and—in case of a crowdinvesting—the funders’ perception of a start-up firms underlying business opportunity and financial potential (also see below). The view that larger

17See,

e.g., http://www.whiteboardmag.com/crowdfunding-seedmatch-raises-200-000-e-inequity-based-funding-in-48-minutes/; http://www.faz.net/aktuell/wirtschaft/netzwirtschaft/ crowdfunding-stellt-neuen-rekord-auf-13038525.html; https://www.rt.com/news/163968nsa-proof-server-crowdfunding/; https://www.mittelstand-nachrichten.de/unternehmen/ wegen-grosser-nachfrage-protonet-oeffnet-crowdfunding-erneut/ (all accessed 19 February 2017). 18Despite the fact that firm information is usually provided before the start of the actual funding period, so backers could pursue their due diligences in advance without being pressed for time.

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crowdfinancing sums can help new ventures getting attention from other investors is shared by Kaminski et al. (2016) and Shafi and Colombo (2017). Their empirical observations with data from the crowdfunding platform Kickstarter suggest, that larger collected crowdfunding sums correlate with higher and faster follow-up funding by VCs (see Section 2.4). In the same fashion, higher angel investment sums might signal an angel investor’s higher faith in the venture and bigger ­commitment to the company. We consequently formulate our fourth hypothesis as follows: Hypothesis 4:  A  higher pre-funding sum increases the likelihood of a start-up’s selection by venture investors Finally, we consider that characteristics of the signal sender might influence the receiver’s interpretation of a signal. More specifically, we argue that there exists an interaction between characteristics of a venture and investors’ perception of pre-funding sources. We narrow our focus to the business model or, respectively, the target customers of a firm. Crowd backers are assumed to be primarily consumers, and therefore their positive appraisal of business-to-consumer (B2C) companies might be seen as a proof of market acceptance for a start-up’s offering. An illustration is provided in the introductory example: despite the initial negative reaction Pebble Watch received from traditional venture investors, the information conveyed from the crowdfunding community’s strong response to their product idea likely served as a positive signal of future consumer adoption of smartwatches. Strausz (2017) discusses a theoretical model in which ­reward-based crowdfunding can be used as a screening mechanism to uncover information about future demand for a venture’s offering. Since—as we have pointed out in Section 3.1—market demand and customer adoption are important evaluation criteria in the decision making of venture investors, successful crowdfinancing campaigns of B2C ventures could serve as informative and relevant signals to subsequent investors. This certification pertains to a start-ups offering. In case of securities-based crowdfunding, i.e., crowdinvesting, the crowd’s investment might additionally certify its perception of the value of the firm more generally, e.g., its underlying financial potential. Since it is assumed that laypeople are better in assessing B2C than B2B business models, we also expect a positive interaction effect between crowdinvesting and B2C ventures. Our final hypothesis is thus: Hypothesis 5:  T  he interaction of a B2C business with crowdfinancing increases the likelihood of a start-up’s selection by venture investors, relative to a B2B business

4

Methods

4.1 Overview of Data Gathering Methods To test our hypotheses on the effects of crowdfinancing on subsequent venture investors’ decision making, we combine a variety of research instruments. For our core analyses, we conduct two online stated choice experiments, namely a choice-based conjoint analysis and a factorial survey, also known as vignette analysis. As Hoenig and Henkel (2015: 1055) point out, “[t]he use of controlled experiments involving the manipulation of available information is a ­well-established approach used across all kinds of scientific research fields to answer questions that otherwise might go unanswered.” The survey experiments are embedded in an online questionnaire, which allows the collection of further data on control variables. These quantitative research instruments are preceded by qualitative interviews with practicing venture investors.1 We provide a brief description of performed pre-interviews and the web questionnaire in the following. Given the complexity of the method, the fundamentals of stated choice experiments (4.2) as well as their technical foundations of discrete choice analysis (4.3) are explained in detail in the remainder of this chapter.

1Our

research approach may therefore also be seen in the light of a Mixed Methods Research (e.g., Creswell and Plano Clark, 2007; Onwuegbuzie and Leech, 2005, 2006).

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 M. Mödl, Signaling Effects of Crowdfunding on Venture Investors’ Decision Making, Innovation und Entrepreneurship, https://doi.org/10.1007/978-3-658-31590-0_4

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4.1.1 Qualitative Interviews Before rolling out the online survey we conducted qualitative interviews with 20 practicing venture capitalists and business angels, who are actively involved in start-up selection decisions. More specifically, we performed ten pre-interviews— thereof seven in-depth semi-structured guide-based interviews and three short interviews—, and ten pre-tests of our questionnaire design, each followed by an interview, with another ten venture investors. A tabular overview of conducted interviews can be found in Appendix B. The objectives of the qualitative interviews were threefold: First, we attempted to gain initial insights into the sentiment of venture investors towards crowdfinancing, and, more generally, into their selection decision processes and the role they assign to a start-up’s prior financing structure. Second, the pre-interviews ought to help us designing a realistic and understandable choice experiment. The pre-tests aimed at refining the survey design and reassuring external validity. Finally, we also sought to benefit from the interviews by supplementing prospective quantitative evidence with more nuanced qualitative information, which can only be gathered by direct discussion (this can be seen as a “pin factory” approach; see, e.g., Borenstein et al., 1998). We were interested in getting the views of different types of venture investors and selected our interview partners accordingly (Schnell et al., 2013: 290 ff.). We interviewed investment managers of large international VC companies as well as of smaller funds, and single angel investors as well as angel group members and investment managers of family offices. We also tried to account for a mix of investors with regard to their focal industries, investment stage and sums. We began with a convenience sample by conducting interviews with venture investors who were introduced by the author’s network. We identified further investors through the technique of snowball sampling, in which interview partners are asked to recommend additional interviewees (Schnell et al., 2013: 292). These recommendations lead to an increased variation in our data and also triggered interviews with particularly interesting cases, such as VCs who were experienced with portfolio companies that had collected funds via crowdfinancing. Additionally, we actively searched interview partners through the participation of conferences and events from the start-up eco-system. Apart from arranging in-depth interviews, this also gave us the chance to conduct shorter pre-interviews spontaneously with three venture capitalists. All interview partners were asked a series of open-ended questions, which were augmented by follow-up questions from the researcher that served to probe and clarify answers. In order to ensure comprehensiveness and structuring of

4.1  Overview of Data Gathering Methods

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the course of conversation, and hence the comparability of interviews, we constructed a guide (Meuser & Nagel, 1991). Wording and order of posed questions were, however, handled situational. We paid attention to the revision of the guide as the research progressed (Gioia et al., 2013). Questions in the pre-interviews addressed the following broad issues: (1) general impression of crowdfinancing and perceived role in the entrepreneurial funding landscape; (2) potential motives of entrepreneurs to pursue crowdfinancing; (3) effects of a start-up’s prior crowdfinancing and/or its conditions on interviewees’ selection decision making; (4) legal structure of crowdinvesting contracts; and (5) respondents’ demographics and general investment preferences and routines. The pre-test interviews also stressed questions on feedback and suggestions for improvement with regard to the web survey experience and the comprehensibility and realism of the online choice experiments. At the conclusion of all interviews, we asked interviewees to comment on issues that had not yet been brought up. The pre-interviews were carried out mostly in summer 2013, face-to-face or by telephone2 and lasted on average 45–60 minutes. Pre-tests were conducted in fall 2015 in presence of the researcher followed by face-to-face interviews3 and lasted on average 1.5 hours in total. Best practices for the conduct of ­semi-structured guideline-based interviews (Diekmann, 2012; Gioia et al., 2013; Meuser & Nagel, 1991; Schnell et al., 2013), as well as for coding and analyzing qualitative data (Mayring, 2008) were closely followed. Audio recordings and transcripts for in-depth pre-interviews, as well as written protocols of all interviews, are available from the researcher upon request.

4.1.2 Web Questionnaire We collect quantitative data through an online questionnaire. Our web-based survey has two objectives: In the first place, it provides a favorable data collection environment for the conduction of our stated choice experiments. Firstly, online surveys enable a flexible and location-independent collection of data. Secondly, the computer-assisted procedure and graphical stimulus presentation is supposed to both make it easier and more enjoyable for interviewees to carry out a

2We

paid attention to the special requirements for conducting telephone interviews (e.g., Schnell et al., 2013: 355 ff.). 3We chose this sequence in order to ensure that conversation topics did not influence the survey experience.

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d­ eliberate preference evaluation than with paper-based questionnaires (Gustafsson et al., 2007a; Shepherd & Zacharakis, 1999: 209). Thirdly, a specialized software package (Sawtooth Software Inc.) provided advanced features to design and analyze the choice-based conjoint experiment. A second goal of the web questionnaire is to collect detailed background information of our respondents. These ­individual-level data on venture investors’ characteristics and routines cannot only be used as control variables in our analyses, but may also be per se informative: while rich data on entrepreneurs’ characteristics, motivations and ambitions is available (e.g., through the Global Entrepreneurship Monitor4), we know comparably little about the key gate-keepers of entrepreneurial financing. The web questionnaire was implemented with the Sawtooth Software package (version 8).5 Participants accessed the survey via an online link which logged them on to a secure server hosted by the author’s research institution. After a welcome screen with background information on the research project and usual disclaimers, participants had to answer an initial “gate-keeper” and “ice-breaker” question, i.e., which type of venture investor they refer to themselves. The questionnaire is divided into five sections (thereof the latter two are optional): Section A contains the choice-based conjoint experiment. Section B covers data on investment preferences and knowledge on and experience with crowdinvesting. Section C is concerned with demographics, including measures for risk attitude and usage patterns of digital social networks (further information on utilized measures and the operationalization of control variables is presented in Chapter 5). Section D contains the vignette experiment and Section E deals with legal clauses in crowdinvesting contracts.6 Appendix A documents the original wording of the entire questionnaire. Best practices for designing and conducting online surveys were closely followed (e.g., Cooper & Schindler, 2008; Schnell et al., 2013). For example, we paid extraordinary attention to the dramaturgy of the questionnaire in order to prevent “Halo effects”, i.e., the systematic influencing of the answers to the following questions by previous questions (Cooper & Schindler, 2008: 307). Since venture investors have a busy schedule and are said to be overwhelmed by research requests, we tried to design our survey to be as convenient and interesting as possible by making it available online, also optimized for mobile devices,

4http://www.gemconsortium.org/. 5http://www.sawtoothsoftware.com/; 6Not

Newer versions are called Lighthouse. all data collected are used for the analyses in this dissertation.

4.2 Stated Choice Experiments

53

and providing straightforward but entertaining choice experiments. On average, it took respondents 25 minutes to complete the main survey, thereof 15 minutes for the choice-based conjoint experiment, plus an additional 5 minutes each for the two optional parts, the vignette study and rating scales on legal clauses in crowdinvesting contracts.

4.2 Stated Choice Experiments 4.2.1 Methods of Preference Measurement A general premise of all methods of preference measurement is that an object (e.g., a product or a service) is composed of several different components (attributes) and that the total utility of the object can be derived from the partial utilities from those individual attributes (the structure can be composed in, e.g., an additive or multiplicative manner). The literature proposes several approaches of preference measurement which can be divided into compositional, decompositional and hybrid (Green & Srinivasan, 1990). Compositional approaches—such as expectancy-value models—, also referred to as self-explicated methods, directly ask for separate utility judgments of each single attribute of an object (such as: “How important is the color of a car to you”), which are then composed to a total utility (Baier & Brusch, 2009: 3). Advantages are the relatively quick and easy data collection and analysis. In contrast, decompositional methods—such as stated preference experiments—ask for evaluations of complete profiles of, e.g., products, people, or social situations, which are the basis of a deduction (decomposition) of relative importances and preference scores that respondents might have assigned to the individual components (that would have resulted in those overall evaluations) (Hainmueller et al., 2015: 2395; Orme, 2010: 29). The approach of asking for judgments of complete concepts is considered to be more realistic since respondents cannot always reliably assess and express how they weight separate features of an object (Green, 1984: 156). A drawback is an increased complexity in data collection designs.7

7In

order to combine the simplicity of self-explicated approaches with the greater validity of decompositional methods (and also their greater generality to develop multi-attribute utility functions that retain individual differences (Green, 1984), hybrid methods have been

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4.2.2 Choice-based Conjoint Analysis A well-established (decompositional) measure of preference measurement is conjoint analysis, which Green and Srinivasan (1990)8 define as follows: “[C]onjoint analysis is any decompositional method that estimates the structure of a consumer’s preferences (i.e., estimates preference parameters such as part-worths, importance weights, ideal points), given his or her overall evaluations of a set of alternatives that are prespecified in terms of levels of different attributes.”

The technique requires respondents to make a series of judgments or preferences choices based on ‘complete’ profiles of objects (e.g., products or services). A profile (from a psychological perspective called stimuli) is a combination of multiple attributes (e.g., product features)9, where each attribute is described by one of its levels (a level is an assigned value for an attribute). Due to collecting the data at the moment the decision is made, conjoint analysis represents a concurrent method, rather than a retrospective or self-reported (Shepherd & Zacharakis, 1998: 207 f.). By “breaking down” (decomposing) the decisions’ into its underlying preference or utility structure, respectively, the objective of conjoint analysis is to answer two general questions: (1) What is the partial utility contribution (part-worth) of individual attribute levels to the total utility of an object (e.g., when deciding for a smartphone, which partial utility offers an internal memory capacity of 16 GB, 64 GB or 128 GB)?; and (2) what is the (relative) importance of the different attributes in the judgment process (utility valuation) (e.g., how important is internal memory capacity compared to camera resolution or data rate)? Since the to be rated stimuli are substitutive in nature, conjoint analysis is

developed, such as hybrid conjoint analysis (Green, 1984) and adaptive conjoint analysis (Johnson, 1987). They combine two phases: initially, respondents are asked to directly state their preference for attribute levels, followed by a regular conjoint task, usually containing a limited number of stimulus profiles (in adaptive conjoint analysis the self-explicated information is used interactively for designing the a succeeding conjoint task (Orme, 2010: 42). Various studies and meta-analyses have found that hybrid methods are not generally superior over standard conjoint models (Green, 1984); Sattler and Hensel-Börner (2007). A major drawback is a usually increased survey time. 8also see Green and Srinivasan (1978: 104). 9The word conjoint analysis is often thought to refer to respondents evaluating features of products “CONsidered JOINTly”—however, the adjective “conjoint” is derived from the verb “to conjoin”, meaning “joined together” (Orme, 2010: 29).

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also referred to as trade-off analysis. Lohrke et al. (2010: 19) summarize the most important relative benefits when studying decision-making issues: “[conjoint analysis] is specifically designed to assess respondents’ ‘theory in use’ by capturing respondents’ preferences as they make decisions. In contrast, studies using compositional research designs have often used retrospective accounts to study critical decision-making issues. (…) Although retrospective accounts are often necessary in organizational research, they may produce biased results if respondents are unwilling or unable to recall their decision processes.” For a comprehensive overview of conjoint reliability and validity studies see Green and Srinivasan (1990: 11–13). Development and applications Conjoint analysis is one of the most widely used methods for preference measurement in a wide range of disciplines (Günthel et al., 2009). It has originally been developed by mathematical psychologists Luce and Tukey (1964) and can be used in almost any scientific field where measuring people’s perceptions and judgments is important (Riquelme & Rickards, 1992: 508). In a management research context it was first introduced into the marketing literature by Green and Rao (1971) and has since been applied in various other fields, including strategic decisions of managers (Fischer & Henkel, 2013) and new product planning (Kohli & Sukumar, 1990).10 The pilot study of Riquelme and Rickards (1992) was the first to demonstrate that the ­decision-making of VCs can be modelled through conjoint analysis with a high degree of consistency. (Shepherd & Zacharakis, 1998) further proposed the method for entrepreneurship research and particularly recommended it for researching the decision policies of venture capitalists (Shepherd & Zacharakis, 1999). Numerous conjoint studies have since been undertaken to analyze, among others, venture managers’ assessments of a new venture’s performance (Muzyka et al., 1996), VCs’ evaluations of founding teams (Franke et al., 2008), the influence of VCs’ experience on the performance of their decisions (Shepherd et al., 2003), their decision to seek a new strategic alliance (Patzelt et al., 2008), and the role of patents and alliances in the investment decision process (Hoenig & Henkel, 2015). These and other studies have proven that “[c]onjoint analysis represents a robust technique for decision modeling research providing structured insight into VCs’ decision policies” (Shepherd et al., 2003: 388).

10For an exemplary overview of studies using conjoint analysis sorted by field see Gustafsson et al. (2007b: 3 f.).

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Types of conjoint analyses The number of variants of the conjoint method is considerable. Green and Srinivasan (1978) developed a flow diagram11 of steps involved in conjoint analysis which all variants have in common and thus can be used as a basis for categorization (for updates see Green & Srinivasan, 1990; and Gustafsson et al., 2007a).12 The most common criteria of distinction are regarding (a) data collection method, and (b) procedure of preference disclosure13. There are two basic methods of data collection: full-profile and two-factor method. The full-profile method utilizes the complete set of factors, which means each stimulus is compiled of one value for every attribute. The main drawback of this approach is that it can lead to an information overload for the respondent. Thus, it was considered to be confined to research settings with a limited number of attributes (Wittink et al. 1989) see a maximum number of approximately eight attributes). However, newer forms of stimulus presentation (such as computerized surveys) make it much easier for interviewees to carry out a deliberate preference evaluation (Gustafsson et al., 2007a) and newer studies have found that respondents can deal with well-organized grids of information better than early researchers had supposed (Orme, 2010: 51). Further, only full-profile approaches allow the estimation of interaction effects between attributes. The major argument in favor of the full-profile description is that it more realistically resembles the real choice situation (Green & Srinivasan, 1978: 108). Originally, all conjoint studies were full-profile settings until (Johnson, 1974) developed the two-factor-at-a-time method, also referred to as ‘trade-off procedure’ (Orme, 2010: 31). Rather than asking for evaluations of all attributes at the same time, only two attributes are used at a time. They are set up in a matrix and respondents are asked to rank the various combinations of each pair of attribute

11The

defined steps are: selection of (1) preference model, (2) data collection method, (3) stimulus set construction, (4) stimulus presentation, (5) measurement scale for the dependent variable, (6) estimation method. 12A detailed taxonomy of conjoint methods can also be found in Carroll and Green (1995). 13Originally, Green and Srinivasan (1978) derived their framework for the traditional conjoint analysis, where the dependent variable (step 5) is always measured directly by requesting preference statements (through, e.g., rating scales, rank order decisions, paired comparisons, constant-sum paired comparisons, category assignment). However, the dependent variable can also be measured indirectly by choice decisions among alternative stimuli (choice-based conjoint)—generalizing step 5 to ‘selection of preference disclosure procedures’.

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levels. While the two-factor method is easier to apply and reduces the information overload problem, its limitations are that it typically entails more judgments to be made by the respondent and that it sacrifices realism (Green & Srinivasan, 1978: 107)—which is why it is hardly ever used (Riquelme & Rickards, 1992: 508).14 Given the fact that venture investors usually are time-scarce and are used to making complex decisions, a full-profile approach imposes upon the underlying research context. The disclosure of preferences of people regarding the shown stimuli can be measured by direct or indirect procedures. The traditional way is to ask for preference statements directly. Respondents either rank (e.g., “Please order the presented products according to your preference!”) or rate (e.g., “How many points do you assign product A?”) the contemplated stimuli. This procedure is known as traditional conjoint analysis or preference-based conjoint analysis. In ­Choice-based conjoint analysis (CBCA) people’s preferences are measured indirectly by asking them to make choice decisions (e.g., “Which one of these products would you buy?”), thereof preferences are deducted. Preference statements of traditional conjoint analysis usually contain richer information, since they are scaled ordinal (rankings) or metric (ratings), while CBC data is usually scaled nominal (1 for the chosen stimulus and 0 for all other alternatives).15 Choice-based Conjoint Analysis Choice-based conjoint analysis is the method of choice for our analyses presented in Chapter 7. As stated above, CBCA uses the basic ideas of the experimental data collection of conjoint analysis, but instead of asking for ranking or rating each profile separately, it asks respondents to make discrete choices among multiple alternative full profiles (sets of profile alternatives are called choice sets).16 For analyzing these responses it uses a particular type of statistical analysis called discrete choice models, for which the groundwork has been laid by McFadden

14A

further frequent criticism used to be that it enjoys less flexibility in scaling, i.e., is limited to ranking data. However, it can in fact be generalized for other evaluation scales (Gustafsson et al., 2007a: 10). 15By changing choice tasks a higher information content can be achieved, e.g., rank-censored data by b­ est-worst choices (Keener and Waldman, 1985); also see below) or metric data by allocating a limited number of chips (‘chip allocation’; Sawtooth Software, 1993: 10 f.). 16For a theoretical description of CBCA also see Keener and Waldman (1985).

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(1974) (explained in 4.3). As a consequence, CBCA requires more complex iterative estimation methods, such as maximum likelihood (ML) or Hierarchical Bayes (HB) building on logit or probit models, while for traditional conjoint analysis ordinary least square regressions are usually sufficient. Technically, CBCA was designed to estimate part-worths on an aggregate but not on the individual respondent’s level (Louviere & Woodworth, 1983). Thereby it is assumed that the same utility function holds true for the whole population (Balderjahn et al., 2009: 135). However, with the availability of Hierarchical Bayes (Allenby et al., 1995) and Latent Class Models (Ramaswamy & Cohen, 2007), individual level analyses are possible. The breakthrough for CBCA came with an article by Louviere and Woodworth (1983) in which they integrated conjoint and discrete choice approaches (Haaijer & Wedel, 2007: 199). Today it is one of if not the most widely used conjoint studies in the world (Orme, 2010)—not least because of the following benefits: Above all, in CBCA data collection is more realistic: the choosing of an alternative mimics real market decisions (further enhanced by the possibility of including a ‘none-option’, that can be selected if none of the displayed stimuli appeal to the respondent; Haaijer et al., 2001). Moreover, stating choices requires less cognitive effort of the respondent than ratings or rankings in a traditional conjoint approach (Balderjahn et al., 2009: 134). From a data analytical standpoint another advantage of CBCA is that it allows the estimation of interaction effects between attributes, unlike traditional or adaptive conjoint approaches, which use a main-effects model (Orme, 2010: 41–43; Patzelt et al., 2008: 473). Besides, no assumptions have to be made on the scale respondents use when they state choices instead of ratings or rankings (Fischer & Henkel, 2013: 329; Haaijer & Wedel, 2007: 203). In the end, additional to part-worths, CBCA also derives choice probabilities which can be used for prognosis (DeSarbo et al., 1995) (that is because it already includes a choice model, while for traditional conjoint analysis a separate choice model needs to be applied). On the downside, choice-based conjoint studies are typically more difficult to design and analyze than traditional or adaptive conjoint approaches (Orme, 2010: 34). This is why Section 4.3 puts emphasis on the statistical foundations of CBC data analysis.

4.2.3 Vignette Analysis For the study presented in Chapter 6 we apply another, slightly different form of survey experiment which we will refer to as vignette analysis, also known

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as ­factorial survey17 in sociology (Rossi & Anderson, 1982; Wallander, 2009). Vignettes are defined as “short descriptions of a person or a social situation which contain precise references to what are thought to be the most important factors in the decision-making or judgment-making processes of respondents” (Alexander & Becker, 1978: 94). In vignette analyses, several versions of systematically varying vignette descriptions are shown to respondents within surveys in order to elicit their judgments about the scenarios’ characteristics (Atzmüller & Steiner, 2010). It is therefore, too, a decompositional method of preference measurement that combines the advantages of survey research with the advantages of experimental designs (Auspurg et al., 2009; Auspurg et al., 2017).18 In particular, if characteristics of scenarios are manipulated within and between respondents, vignette experiments enable the analysis of causal effects. When decision policies of respondents are the focus (as in our research), vignette analysis and conjoint analysis basically describe the same form of stated preference experiment.19 However, there are three notable differences that separate the research design of Chapter 6 from the archetypical definitions of conjoint analysis variants: First, in contrast to a CBCA, in Chapter 6’s choice task design, only one stimulus (vignette with varying conditions) is shown at a time and answer alternatives remain the same across all scenarios (the independent variables are alternative-invariant). In the CBCA design in Chapter 7, respondents choose one out of three stimuli at a time with varying attributes (the independent variables are alternative-varying).20 Second, while this methodological approach is similar to a traditional conjoint analysis, it involves a categorical measurement scale (whereas ordinal or metric scales are typical for traditional conjoints). Third, as Hainmueller et  al. (2015: 2396) point out, in conjoint analysis’ choice sets, information on varying attribute levels is presented in a tabular form, whereas in vignettes it is usually presented as a narrative to respondents

17The

term factorial survey has been associated with randomly selecting subpopulations or vignette sets (Atzmüller and Steiner, 2010: 130). As in our design all respondents are shown the total vignette population, problems of subset selection do not occur. 18For a discussion of disadvantages see Section 4.2.4. 19In fact, conjoint analysis is a special type of vignette study; see, e.g., Aguinis and Bradley (2014). 20For a detailed description of research designs and the operationalization of variables see the respective chapters.

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(also see Auspurg et  al., 2009: 3). For all these reasons, we use the broader term vignette analysis for the research design of Chapter 6, and distinct from that the more specific term choice-based conjoint analysis for the method used in ­Chapter  7.

4.2.4 Discussion of Choice-Experimental Approach We have chosen to conduct survey experiments in order to shed light on the role of pre-funding in venture financing mainly for three reasons: To begin with, stated choice experiments represent a concurrent method, rather than a retrospective or self-reported, thus potential weaknesses of post-hoc methodologies are avoided (Shepherd & Zacharakis, 1999: 205 f.). In contrast to, e.g., rating-scale surveys, there are no inconsistencies in how respondents interpret scales. Since trade-off decisions are made in CBCA when selecting start-ups from the presented alternatives, there is no lack of discrimination among attributes, also referred to as inflation of importance (e.g., “every attribute is important”). Moreover investors are asked to make actual judgments, thus do not think of particular recent start-ups or rate according to social desirability. Further, VC decisions are often subject to cognitive biases (Baum & Silverman, 2004; Ferrary & Granovetter, 2009). In fact, it is questionable whether VCs really understand their decision policies (Zacharakis & Meyer, 1998). In the setting of a choice experiment there is no problem with respondents lacking insights in their assessment process (e.g. “gut feeling”), since they make actual decisions; and, as data is collected at the same time when the decision is made, people cannot forget important aspects of a former decision-process or retrospectively rationalize their decisions. Second, in particular choice-based conjoint experiments come close to the real-life situation of venture investors selecting start-ups and represent a convenient survey method for subjects. When taking investment decisions, VCs perform an initial screening—which usually only involves a cursory glance on the business plan (see, e.g., Kirsch et al., 2009)—where they select start-ups for further consideration (Dixon, 1991: 339; Fried & Hisrich, 1994: 34). The variable descriptions in the choice experiment are of the same nature as the object itself, namely its description in a business plan. A frequent critique, that the choice alternatives are unrealistic “paper ventures” (Shepherd & Zacharakis, 1999: 210) is thereby overcome. In addition, the fact that venture investors usually form decisions when pressed for time is another argument for the realism of the intended approach. In general, choice tasks of selecting alternatives require low cognitive

4.2  Stated Choice Experiments

61

effort (Balderjahn et al., 2009: 134). All that helps to increase both validity and response rate of the study.21 Third, and probably the most essential feature of conjoint and vignette analysis is their experimental setting which allows causal inference. With a ­choice-experimental approach an omitted variable bias is excluded by construction, the desired signaling effect of a pre-funding structure can be isolated experimentally, and ceteris paribus analyses are possible. In contrast, real market data approaches might suffer from an endogeneity problem, e.g., because high-quality start-ups may find it easier to both attract early funding and subsequent venture capital. Besides, conditions can be controlled for, making survey experiments a quite cost-efficient alternative: “[t]he environment of a hypothetical choice can be precisely specified, with a design which allows straightforward identification of effects” (McFadden, 2001: 373), and a large number of causal hypotheses can be tested in a single study (Hainmueller et  al., 2014: 3).22 This efficiency makes choice experiments particularly suitable for studying the behavior of hard to obtain and time-scarce subjects, such as venture investors. In fact, in particular choice-based conjoint analysis is one of the best methods when it comes to reaching statistically significant results with a relatively small sample size. As with every research instrument survey experiments do not come without limitations. While controlled experiments offer control and precision in variable measurement (i.e., internal validity), a concern is that they lack external validity owing to the choice situation not being realistic. This is in part overcome in our research context due to the above mentioned reasons that the choice task in the experiment mimics the initial stage of real-life funding decision-making situation quite well. External validity was also reassured by extensive pre-testing which confirmed that the experimental settings are realistic, understandable and manageable. Further, one needs to consider that survey experiments are just simplified models of real-life decision-making. For instance, only a limited number of attributes can be included in the choice design (Mitchell & Shepherd, 2010: 151), whereas in real-life other criteria than pre-funding certainly play a role, too.

21For

a comprehensive overview of conjoint reliability and validity studies see Green and Srinivasan (1990: 11–13). 22In addition, interaction effects can be better modeled in CBCA than in traditional conjoint analysis Balderjahn et al. (2009: 135).

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For instance, an aspect relevant to the decision policies of venture investors is that VCs often make judgments about a new start-up in relation to their existing firm portfolio composition (Petty & Gruber, 2011), while CBCA requests isolated choices (Shepherd & Zacharakis, 1999: 211). However, this aspect is likely to be more relevant for start-up characteristics concerning, e.g., industry and market; how they got funded appears to be less relevant with regard to, for instance, portfolio diversification. In general, as mentioned beforehand, CBCA does not suffer from an omitted variable bias since all the variables not included are set equal in a reference scenario (Hoenig & Henkel, 2015: 1062). Finally, it deserves mentioning that decisions made in a stated choice experiment are never actual investment choices (revealed preferences), but only stated preferences (see, e.g., Train, 2009: 152 f.). We acknowledge the fact that hypothetical investment decisions are not incentive-compatible, as respondents do not have to bear financial consequences. Online experiments can never have the emotional attachment or immediacy of the “real world” (McKelvie et al., 2011). While this is justified criticism, scholars have shown that conjoint analysis significantly reflects decision policies employed by individuals (Hammond & Adelman, 1976), and in particular by VCs (Shepherd et al., 2003; Shepherd & Zacharakis, 1998). In sum, despite the boundaries choice experiments appear to be particularly suitable to expose which pre-financing conditions determine the investment decisions of investors.

4.3 Discrete Choice Modeling In both the choice-based conjoint analysis and in the vignette analysis surveyed venture investors face a set of choices from a limited number of options. The outcome (dependent) variable is thus not continuous but discrete. Hence, linear models cannot be used and discrete choice models23 have to be applied. Discrete choice analysis consists of two interrelated tasks (Train, 2009: 7): (1) specification of the behavioral model (as discussed in Section 4.3.2) and estimation of the parameters of that model (presented in Section 4.3.3). General properties of the underlying utility concept are described in the next section.

23Discrete

choice analysis has its origins in quantitative psychology; usage in economics largely builds upon the work of Daniel McFadden (1974), for which he received the “Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel” in 2000.

4.3  Discrete Choice Modeling

63

4.3.1 Utility Concept The goal of discrete choice models is to understand the behavioral process that leads to the agent’s choice, thus a causal perspective is taken (Train, 2009: 3). A fundamental assumption of discrete choice analysis is that from the observed choices the underlying utility function of respondents can be derived (the stimuli are interpreted as utility (Train, 2009: 14)). Moreover it is presumed that respondents always choose the alternative with the highest utility value for them (utility maximization hypothesis, Ben-Akiva & Boccara, 1995). Utility functions are derived as Random Utility Models (RUM)24, according to which the utility of an alternative is composed of an observable (deterministic) and an unobservable (random) component (Train, 2009: 14 f.):

Uni = Vni + εni

(4.1)

with

Uni :  u tility that decision-maker n obtains from alternative i; Vni :  observable utility component determining an decision-maker n’s choice; εni :  unobservable component that affects n’s utility but are not included in Vni The observable utility component Vni, also called deterministic (Balderjahn et al., 2009: 130) or representative utility (Train, 2009: 15), can further be decomposed into an equation formulating the model of preference. For choice-based conjoint analysis an additive part-worth function preference model25 is the most common form (Hair et al., 2010: 365–367):

Vni = β ′ × xni ,

(4.2)

with

24In

contrast to, e.g., the Concept of Constant Utility, which “decomposes” the utility of an alternative only into an observable deterministic component (Balderjahn et al. 2009: 130). 25There exist different preference models, such as the vector or ideal point model (Green and Srinivasan, 1978; Bichler and Trommsdorff, 2009). However, for experimental designs with attributes that have discrete levels (as usually the case with CBCA; and so in our research setting, where almost all attributes are modeled as binary variables), only the partworth function preference model is possible (Bichler and Trommsdorff, 2009). It is, hence, also the standard preference model of the deployed Sawtooth Software (Orme, 2010).

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β ′ :  a vector containing the respondents’ part-worths (which are to be estimated statistically); xi :  a vector containing all attribute levels of one choice alternative;26 these factors are assumed to be the observable determinants of the decision makers’ choices. In general, εni is simply defined as the difference between the true utility Uni of a decision-maker and the part of his or her utility that is captured in Vni. Hence, the characteristics of εni depend solely on the specification of Vni—in particular it is not defined by the real choice situation, but by the experimental design’s model of that choice situation (Train, 2009: 15). This is important for the selection of a specific discrete choice model (e.g., standard logit models assume that εni is independent and identically distributed (i.i.d.)27 extreme value for all i; probit models assume that all εn′ are distributed jointly normal; mixed logit (see 4.3.2.3) allows the unobserved term to follow any distribution28, thus is fully general (Train, 2009: 18 f.)). Behavioral models potentially relevant for choice-based conjoint analysis, and especially for our research, are presented in the next section. Derivation of choice probabilities The probability that agent n chooses alternative i (and not another alternative j) is:

Pni = Prob(Uni > Unj ∀i � = j)   = Prob Vni + εni > Vnj + εnj ∀i �= j

(4.3)

= Prob(εnj − εni < Vni − Vnj ∀i �= j)

As stated above, different discrete choice models are obtained from different assumptions about the distribution of the error term. Likewise it determines whether the cumulative probabilities can be written in the closed form or not (as for mixed logit, see below) (Train, 2009: 15 f.).

26Indices

depending on method of coding (dummy coding or effect-based coding); see 4.3.3 (specification of utility function). 27All random variables have the same probability distribution and all are mutually independent. 28by splitting the unobserved term up into one part that is i.i.d. extreme value and one part that contains all correlations and heteroscedasticity. The second part can follow any distribution.

4.3  Discrete Choice Modeling

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Properties of choice models Discrete choice models have two general properties (Train, 2009: 19–29): • Only differences in utility matter, the levels of utility are irrelevant. This can easily  be seen from the  equation for the choice probability (4.3): Pni = Prob Uni > Unj ∀i � = j = Prob(Uni − Unj > 0 ∀i �= j). If a constant is added to the utility of all alternatives, the alternative with the highest value does not change. • The overall scale of utility is irrelevant; multiplying each alternative’s utility by a constant likewise does not change the agent’s choice. In order to consider these facts, behavioral models need to be normalized. In logit models with distributional assumptions of iid extreme value of the error terms (such as the ones presented in this paper, except for mixed logit), the normalization for scale and level occurs automatically (Train, 2009: 29).

4.3.2 Behavioral Models and Derivation of Choice Probabilities This section presents relevant behavioral models describing the choice processes in this work, briefly explaining their theoretical base as well as discussing their advantages and shortcomings. It focuses on logit models (and its extensions), which are by far the most widely used discrete choice models (Train, 2009: 18). Another class of conceivable choice models are probits (Train, 2009: 97 ff.). As stated above, the main conceptual difference is the distribution of the error term, which is assumed to be normally distributed for probits and iid extreme value for logits. Since the normal distribution and the extreme value distribution are very similar,29 logit and probit models predict about the same proportions (Chambers & Cox, 1967: 576).30 In the end, “[t]he choice between the logit and probit models is largely one of convenience and convention, since the substantive results

29The

logit curve has “fatter tails” at extremely high or low scores which makes it more appropriate if you assume that decision-makers do not switch so fast decisions. √ π  30Multiplying the logit estimates by (≈0.627) makes the logit estimates comparable 8 to probit estimates.

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are generally indistinguishable.” (Long, 1997: 83).31 However, when it comes to more complex econometric approaches which require a multinomial probit or mixed logit model, respectively, the latter is generally preferred (Cameron & Trivedi, 2005: 527). The overview starts with the basic binary logit model, which can easily be extended to a multinomial logit, suitable for situations in which among more than two options can be chosen (4.3.2.1). A variation that builds upon this concept, namely rank-ordered logit, is presented in Section 4.3.2.3. Finally, a more generalized and hence more complex model, the mixed logit, is described in Section 4.3.2.3.

4.3.2.1 Standard Logit The following derivations are based on the explanations by Train (2009: 34–75). When other sources are used, it is indicated. The central assumption of the (standard) logit model is that the unobserved terms ε are independent and identically distributed extreme value. Independent means that the unobserved portion of utility for one choice option is unrelated to the unobserved utility portion for another choice option.32 Recall equation (4.3) stating the choice probabilities of the basic utility model: Pni = Prob(εnj − εni < Vni − Vnj ∀i � = j) = Prob(εnj < Vni + εni − Vnj ∀i �= j). Mathematical transformation under consideration of the properties of the extreme value distribution leads to the equation for the choice probability of the binomial logit:

Pni =

1 1 = , ′ ′ 1 + eVnj −Vni 1 + eβ xnj −β xni

(4.4)

where xnj is a vector containing the observed properties of choice alternative j, and β ′ is a vector containing the respondents’ part-worths (β1 , . . . , βn ).

31Concerning

convenience it is often argued that logits have the advantage that normalization for scale and level occurs automatically, while probit models require normalization (Train, 2009: 100)—however, that does not apply for, e.g., mixed logits, which also require normalization (Train, 2009: 29). 32This is a quite restrictive assumption; however, it basically says that the observed term V is specified so well, that the unobserved portion of utility is essentially “white noise”— which is the ultimate goal of a model. Yet, a drawback is that taste patterns outside the model cannot vary systematically (limitations see below).

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67

The curve of the relation of the logit probability Pni to the observed portion of the utility Vni is sigmoid-shaped (which is shared by most discrete choice models). This means that an increase in Vni leads to a small increase in probability of it being chosen if Vni is very small or very big, and to a large increase if Vni is at a medium level. This distribution is intuitively attractive and corresponds to respondents’ behavior in most real choice situations: when an option is completely unattractive (or very attractive), a small positive shift in its utility does not have a big impact on the choice decision; however when a respondent is indifferent between two alternatives, a small utility shift is more likely to alter the choice (Hoetker, 2007: 332 f.). The binominal logit is appropriate for binary choice situations, i.e., for discrete outcome variables that can take one of two possible values. For more than two available choice options a multinomial model has to be applied. These models are often summed up under the label “multinomial logit”, however, an important distinction has to be made, depending on whether regressors vary across alternatives or are alternative-invariant.33 When regressors do vary across alternatives, i.e., they take different values for different alternatives, the so-called conditional logit (CL) is used. This is the case for our CBCA design discussed in Chapter 7.34 Equation (4.4) can easily be expanded to the conditional logit (CL) choice probability (McFadden, 1974) by including an extra term for every additional choice option in the denominator (with option i versus a number of other options j = i): ′

Pni =

eβ ′ xni

eβ xni  ′ + j eβ xnj

(4.5)

When instead all independent variables (regressors) are alternative-invariant, i.e., that they do not vary across choice options (such as, for example, socioeconomic characteristics of the respondent), the multinomial logit (MNL) is used. This model applies to the choice situation in the vignette analysis in Chapter 6. The multinomial logit choice probability for alternative-invariant independent variables is (e.g., Cameron & Trivedi, 2005: 500):

33For

a derivation see, e.g., Maddala (2008: 41–46) or Cameron and Trivedi (2005: 491– 500); Cameron and Trivedi (2010: 491–511) for respective Stata commands. 34The CL is the correct specification for the underlying research purpose in Chapter 7, since we are mainly interested in alternative-varying independent variables (which are, e.g., characteristics of pre-funding of the presented startup alternatives).

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Pni =

′

eβi xn

eβi xn  ′ + j eβj xn

(4.6)

The two models can be combined into what is sometimes called “mixed logit”— not to be confused with the mixed logit model presented in Section 4.3.2.3 (Cameron & Trivedi, 2005: 500). Bowen and Wiersema (2004: 91) graphically sum up the “choice problem of the researcher” of selecting the correct statistical technique for discrete dependent variables. For the remainder of this chapter we continue with extensions of the conditional logit specification, as we apply these models to the choice situation in the CBCA. Limitations Standard logit models have some limitations in light of the presumption that the error terms of each respondent’s choice are independently and identically distributed. Firstly, coefficients are fixed for all decision-makers, hence all respondents are assumed to have homogenous preferences, random taste variation is not possible. A second consequence is the so-called Independence from Irrelevant Alternatives (IIA) property.35 It implies that the utility Ui of an alternative i is not influenced by whatever other alternatives are or are not in the choice set (Louviere & Woodworth, 1983: 352). In other words: adding or subtracting choice options to/from the existing set, or changing characteristics from a third option, should not affect the odds36 between any two options. Practically, when alternatives are close substitutes the IIA assumption may be inappropriate. A third—and particular problem for stated choice experiments with several choice tasks—is that the IIA property is quite unrealistic with repeated choice data owing to preference heterogeneity (Fischer & Henkel, 2013: 331): “a person who puts greater value than the average respondent on a specific alternative in the first choice, will also put greater value on a similar alternative in subsequent choices (Hausman & Wise, 1978), leading to a correlation among the error terms”, consequently violating the IIA assumption. Extensions of standard logit models, such as generalized extreme value models (e.g., nested logit) and the mixed logit explained in Section 4.3.2.3, relax the undesirable IIA assumption by estimating individual partworth vectors, thereby accommodating preference heterogeneity.

35This

problem is often referred to as the red ­bus-blue bus example (e.g., Long and Freese (2006: 243)). 36Odds represent the ratio of the probability that an event occurs and the probability that it does not occur.

4.3  Discrete Choice Modeling

69

4.3.2.2 Rank-ordered Logit37 Before turning to logit models enabling coefficient estimations on the ­individual-level, it is worth looking at another variation particularly relevant for the choice situation in our questionnaire. Since venture investment decision-makers are extremely time-scarce, only a limited number of choice sets can be polled. It is therefore important to “squeeze” the most information out of the choice sets, which can be done by asking for more than one decision. It is obvious that making several choices within one more or less complex situation deserves less time and cognitive effort than understanding a new choice set. Even more information can be gained, when all choice alternatives are ranked by the respondents. Such data can be handled in a so-called rank-ordered logit model, first introduced by Beggs et al. (1981) and Chapman and Staelin (1982). A ranking can be asked in a variety of ways: e.g., participants are requested to simply rank all choice options from best to worst. Or—as set up in our research design—by asking to identify the best and the worst out of three alternatives, one also obtains a complete ranking of the choice set, since the not chosen alternative can be interpreted as the middle-rank. Such a ranking can be treated as if one best and one second best option was chosen (Allison & Christakis, 1994: 203 f.). Following Train (2009: 156  f.) the rank-ordered logit model decomposes (“explodes”) each ranking of all alternatives into several independent ­pseudo-choices: it assumes that the first alternative is chosen from the whole choice set, reducing the choice set for the next choice by one. In the next round, another choice is made, eliminating another alternative. This is continued until one alternative remains. Using this procedure not all possible pairwise combinations need to be considered. To illustrate, consider a decision-maker who is presented a choice set of three alternatives A, B, C, where he/she identifies A as the best and C as the worst option (leaving the second rank for B). The probability of this ranking can be expressed as the product of logit formulas: the logit probability of choosing A from set A, B, C, times the logit probability of choosing B from the remaining alternatives B, C. The mathematical expression of the probability of the ranked alternatives is: ′

Pn ranking(A,B,C) =

37Also

e

β ′ xnA

′

eβ xnA eβ xnB × ′ ′ ′ + eβ xnB + eβ xnC eβ xnB + eβ ′ xnC

(4.7)

referred to as exploded logit, since each observation is exploded into several pseudo-observations for estimation purposes (Train, 2009: 157).

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where again xnj is a vector containing the observed properties of choice alternative j, and β ′ is a vector containing the respondents’ part-worths (β1 , . . . , βn ). In choice-based conjoint experiments participants are typically asked to answer several choice tasks. The equation needs, thus, to be generalized to allow for repeated choice. In standard logit models, preferences of respondents are treated as homogenous, thus every respondent ought to have the same choice probability. The probability to rank alternatives in order A, B, C in choice situation t over a sequence of T choices is:

Pranking(A,B,C) =

T   t=1

′

′

e

β ′ xAt

eβ xBt t eβ xAt t × ′ ′ ′ + eβ xBt + eβ xCt eβ xnBt + eβ ′ xCt



(4.8)

4.3.2.3 Mixed Logit38 As indicated above, the mixed logit relaxes some limitations of the standard logit by accommodating preference heterogeneity. The following derivation is based on the respective chapter in Train (2009: 134–144), a detailed explanation can also be found in McFadden and Train (2000). Mixed logit probabilities are the integrals of standard (conditional) logit probabilities—as derived in (4.5)—over the density of part-worth parameters f (β):

Pni = ∫



′

eβ ′ xni

eβ xni  ′ + j eβ xnj



f (β)dβ

(4.9)

Hence, the choice probability is a weighted average of the logit equation (called “mixed function”) evaluated at different values of the part-worths β, with the weights given by the density of the part-worths f (β) (with f (β) being called “mixing distribution”). Assuming different betas The standard logit is a special case of that function (4.9), when the mixing distribution (density function) is degenerate at fixed parameters, i.e., when it is set to 1 if β assumes a certain value b, and zero otherwise ( f (β) = 1 for β = b, and f (β) = 0 for β = b). Thus, standard logit assumes that all respondents have the

38Also

referred to as random parameter logit (RPL) (as in Cameron and Trivedi, 2005: 512–516), mixed multinomial logit (MMNL), kernel logit, hybrid logit and error components logit (Hensher and Greene, 2003: 3).

4.3  Discrete Choice Modeling

71

same β (“aggregate approach”), while mixed logit allows the βs to vary across respondents (“disaggregate/individual approach”). When β takes a finite set of values, the mixing distribution f (β) is discrete. This specification is a so-called latent class model (Train, 2009: 135 f.), which divides the respondent population into segments, each with its distinct preferences or choice behavior. Suppose there are M segments, thus M possible values that β can take, and let sm be the probability that β takes on the value for segment m, then the choice probability is M 

Pni =

sm

m=1



′

′

eβm xni

eβm xni  ′ + j eβm xnj



(4.10)

In most applications f (β) is specified to be continuous. Part-worth densities can follow any distribution, presented as a function of parameters θ, that represent, e.g., the mean and the covariance of the βs in the population. The choice probability can thus be written as

Pni = ∫



′

eβ ′ xni

eβ xni  ′ + j eβ xnj



f (β|θ )dβ

(4.11)

For this study, βs are assumed to follow a normal distribution (in congruence with Fischer and Henkel (2013) and Hoenig and Henkel (2015)). The rationale is— besides the practical usability—that most professional investors have ­part-worths that are close to their peer group’s mean, yet outliers far from the mean (like investors with exotic preferences) are possible. The choice probability in case of a normal density φ(β|b, W ) with mean b and covariance W is:

Pni = ∫



′

eβ ′ xni

eβ xni  ′ + j eβ xnj



φ(β|b, W )dβ

(4.12)

As can be seen from Eq. (4.11), there are two sets of parameters: (1) parameters b and W , describing the density f (β), and (2) parameters β which enter the logit formula and represent each respondent’s individual part-worths.39

39The

first set of parameters can be estimated by most estimation procedures for mixed logit, such as maximum simulated likelihood. Bayesian procedures can estimate both sets.

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Overcoming of limitations of standard logit Independence of the error terms εni, as presumed for the standard logit, is no longer needed for the mixed logit. Its utility function can be expressed with an additional parameter ηni, which is a systematic error describing the deviation of the individual’s β from the mean of the population distribution (also denoted as random effects model):

Uni = β ′ xni + [ηni + εni ]

(4.13)

Hence, despite the fact that εni is independently distributed, the two error terms ηni + εni together are not random (Hensher & Greene, 2003: 135). This lays the foundation for the mixed logit overcoming the three major limitations of the standard logit (also see above): • Random taste variation: mixed logit allows the coefficients to vary across individuals; • Unrestricted substitution patterns: mixed logit can represent general substitution patterns because it does not exhibit the IIA assumption; • Repeated choice: mixed logit takes into account that unobserved factors affecting a person’s choice can persist over time for a given decision-maker, and are not new every time the person makes a choice. However, while mixed effects models reduce bias, they tend to yield more unstable prediction results due to the additional variance component (Fahrmeir & Tutz, 2001). Nevertheless they allow more flexibility and different perspectives for different individuals. Sequence of choices As stated beforehand, the relaxation of the IIA property enables mixed logit models to be specified in such a way that the choice situations can be correlated across each individual. Equations can easily be generalized to allow for repeated choices by each respondent: Conditional on coefficients βn (which vary across respondents but are constant across choice situations for each person), the probability of respondent n to choose alternative i  in choice situation t is ′

Pnit (βn ) =

′

eβn xnit

eβn xnit  ′ + j eβn xnjt

(4.14)

73

4.3  Discrete Choice Modeling

The probability that the person makes a sequence of T choices is then the product of formulas (4.14):

Pn (βn ) =

T  t=1



′

eβn xnit t  ′ ′ eβn xnit t + j eβn xnjt



(4.15)

Integrating the conditional probability of this product over all values of β leads to the unconditional probability of the sequence of choices made:40   ˆ  ′ T eβn xnit t  ′ f (β|θ)dβ Pn (θ ) = (4.16) ′ eβn xnit + j eβn xnjt t=1

where f (β|θ ) again describes the density of β, with θ specifying the distribution. As one can see, the only difference between the mixed logit with only one choice per respondent (4.10) and with repeated choice (4.15) is that the integrand involves a product, with one factor for each choice. Generalizability The mixed logit is a generalization of the standard logit model, hence every standard logit model can be approximated by a mixed logit (McFadden & Train, 2000). As a consequence, ranked data—as presented in Section 4.3.2.2—can also be estimated by a mixed logit model. Conditional on β, the probability of a respondent’s ranking is a product of logits, as given in formula (4.7). The unconditional probability then simply is the integral of this product over f (β):  ˆ  ′ ′ eβ xnB eβ xnA Pn ranking(A,B,C) = × f (β|θ)dβ eβ ′ xnA + eβ ′ xnB + eβ ′ xnC eβ ′ xnB + eβ ′ xnC (4.17) Such a model is also called rank-ordered mixed logit (Hoenig & Henkel, 2015). In accordance with (4.16), the equation can also be extended for repeated choice (as proposed by Revelt & Train, 1998). The probability of observing a certain sequence of rankings is the product of all rankings T:

40The

index it indicates the alternative i chosen in choice t .

74

Pn ranking(A,B,C) =

4 Methods

ˆ  T  t=1

eβ ′ xnAt

 ′ ′ eβ xnBt t eβ xnAt t × f (β|θ)dβ + eβ ′ xnBt + eβ ′ xnCt eβ ′ xnBt + eβ ′ xnCt (4.18)

For all the reasons given above the mixed logit is not only currently the ­state-of-the-art behavioral model (Cameron & Trivedi, 2005: 527), but also particularly suitable for estimating choice-based conjoint data. A drawback41 of mixed logit models concerns its estimation, which is more complex than that of standard logit models: since the log-likelihood function to be maximized does not have a closed form solution, simulation procedures have to be used for approaching a solution. The next section deals with estimation methods for discrete choice models.

4.3.3 Estimation Methods This chapter describes the estimation methods of discrete choice models used in this work. The maximum likelihood procedure, the numerical maximization method most commonly used for standard logit models, is briefly covered in the following section. Mixed logit models require simulation-based procedures for taking draws from various different densities. The technique which has proven to perform best in the research setting of our stated choice experiment, Hierarchical Bayes estimation, is presented thereafter.

4.3.3.1 Maximum Likelihood The goal of analysis of standard logit models is to estimate the vector of ­part-worth coefficients β ′ for all respondents. Since only nominal data from discrete choices is available, linear regressions are not possible. Instead, another estimation method called maximum likelihood estimation has to be applied. Its principle is to pick parameter estimates that imply the highest (maximum) probability (likelihood) of having obtained the observed sample (Aldrich & Nelson, 1989: 48 ff.).42

41Another

downside is that for mixed logit normalization is not automatic as for standard logit, and consequently has to be accounted for (Train, 2009: 29). 42In contrast, ordinary least square method is concerned with picking parameter estimates that yield the smallest sum of squared errors in the fit between model and data.

4.3  Discrete Choice Modeling

75

Technically, the choice probabilities of all respondents N over all choices i are combined with the actual choices made to the so-called likelihood function. This function simply multiplies the probabilities of all choices that were actually observed for all respondents (indicated by the dummy variable yni, which assumes value 1 if the alternative i was chosen, and 0 otherwise):

L(β) =

N   n=1

(Pni )yni

(4.19)

i

Because it is easier to deal with sums than with products, function (4.19) is logarithmized to the so-called log-likelihood function (LL):

LL(β) =

N   n=1

yni ln(Pni )

(4.20)

i

Since this function is not linear, an algebraic solution is not possible. However, McFadden (1974) shows that the log-likelihood is globally concave, making it possible to maximize it through iterative approximations (Aldrich & Nelson, 1989: 53), usually by statistical software packages. Owing to the fact that the standard logit model assumes respondent homogeneity, the maximum likelihood estimation just delivers one uniform part-worth parameter for each attribute level.

4.3.3.2 Hierarchical Bayes As indicated above, mixed logit models do not have a closed form solution, thus simulation-assisted techniques are necessary, which allow estimation of otherwise intractable models. Two methods of estimating disaggregate-level as well as aggregate-level betas from mixed logit models are conceivable for our purposes: maximum simulated likelihood estimation (MSLE)43 and Hierarchical Bayes estimation (HB). The latter is seen to be more powerful than classic MSLE since it can estimate accurate and stable individual-level part-worths even with relatively little data from each respondent by “‘borrowing’ information from population information (means and covariances) describing the preferences of other respondents in the same dataset.” (Sawtooth Software, 2009: 1).44 For conjoint data, HB was introduced by Allenby et al. (1995).

43For

a detailed derivation and properties of the estimator, see Train (2009: 237–258). also ran a simulation study with synthetically generated data which confirmed the superior performance of HB for our experimental design.

44We

76

4 Methods

Basic idea of Bayesian analysis HB estimation is based on Bayes’ probability rule (Bayes & Price, 1763):

P(X|Y ) =

P(Y |X) × P(X) P(Y )

Where P(X) stands for the probability of an event X and P(X|Y) for the probability of an event X given Y. Modern Bayesian statistics assumes that the researcher has some prior idea about the distribution of a parameter θ. These assumptions (usually about the mean b and covariance W) are represented by function P(θ ), called prior probability of the distribution parameters. The likelihood of data Y occurring given the distribution parameters θ can be written as conditional probability P(Y |θ ), in the following denoted as L(Y |θ ). Applying the Bayes’ theorem leads to P(θ |Y ), which is the posterior probability of distribution parameters given the data:

P(θ |Y ) =

P(Y |θ ) × P(θ ) P(Y )

(4.21)

The denominator P(Y ) is not dependent on θ and simply a normalizing constant which is often omitted and replaced by a proportionality statement (denoted with ∝). (4.21) can therefore also be written as:

P(θ |Y ) ∝ L(Y |θ ) × P(θ )

(4.22)

The key takeaway is that the posterior probability P(θ |Y ) contains all information about θ, both from the researcher’s assumption about its distribution, and from the information from the data (Baumgartner & Steiner, 2009: 148 f.). This means that with a prior statement about the likelihood of a hypothesis, a posterior estimation can be achieved that incorporates information from the data (Sawtooth Software, 2009: 5). Hierarchical Bayes estimation procedure The parameter sets to be estimated by HB are an individual’s part-worth vector βn and its distributional parameter θ, which can in case of an assumed normal distribution such as in Eq. (4.11) further be split up into a vector of means b and a matrix of covariances W. Hierarchical Bayes estimation is called hierarchical because there is a hierarchy of these three parameter sets to be estimated (Train, 2009: 305): • the βns lie on the lower (individual) level, describing the tastes of person n; • the distribution parameters b and W lie on the upper (population) level.

4.3  Discrete Choice Modeling

77

HB estimations in this work are carried out by statistical software package Sawtooth Software Incorporated (SSI) (version 9, “Lighthouse”), which is one of few programs supporting HB estimation of mixed logit models (Temme, 2009: 313). Hence, in the following the HB procedure as implemented by the SSI CBC/HB module is described. If no other sources are stated, the description is based on Sawtooth Software (2009) (with notation continuing as beforehand). For a more formal description of the method refer to Train (2009: 299–305) or ­Frühwirth-Schnatter et al. (2004). The estimation of the parameters is an iterative Monte Carlo Markov Chain (MCMC) procedure (Train, 2009: 293). That means that estimates in each iteration are determined from those of the previous iteration by constant probabilistic transition rules, namely Metropolis-Hastings algorithm and Gibbs sampling algorithm45 (a description of these algorithms is provided below). These MCMC methods assure that the process converges. Each iteration consists of the following three steps: 1. Given initial values of the covariance matrix W and individuals’ betas (βn),46 a new value for b is drawn with help of the Gibbs sampling procedure from a normal distribution with mean equal to the average of the betas, and covariance matrix equal to W divided by number of respondents N. 2. Using the present values of the betas and the new b drawn in step 1, a new estimate of W is drawn with the Gibbs sampling procedure from an inverse Wishart distribution. 3. Using the newly drawn values of b and W, new estimations of the betas are generated by the Metropolis-Hastings algorithm procedure by drawing random variables from the distribution. In an iterative process successive draws of the betas provide an increasing better fit of the model to the data—until when increases are no longer possible, the process has converged. These three steps are repeated for a large number of iterations. After a ­“burn-in”-phase (Orme & Baker, 2000: 7) of usually some ten thousands of

45In

fact, Gibbs sampling is a special, simpler type of the M-H algorithm. Usually, the term Metropolis-Hasting algorithm is reserved for the more complex versions (Train 2009: 293). 46As indicated above, prior β s and their distribution θ [b, W] are specified by the n researcher. In this work we follow the default of SSI by taking a conservative approach with flat priors setting the elements to zero. Yet, the process is quite robust and results barely depend on starting values.

78

4 Methods

i­terations, when convergence is assumed, the procedure continues with a large number of iterations for each respondent. Of the resulting parameters, we save only every 10th parameter (“sinning”) to account for still possible outliers. Averaging the saved draws finally delivers point estimates of individual part-worths.47 Gibbs sampling algorithm In each of the steps of the above described process, two distributions ‘X’ and ‘Y’ serve as a base for drawing another distribution. To be able to draw from it, their joint distribution should be known, but it is—unfortunately—unknown. This is why a special procedure is needed to obtain draws: the Gibbs sampler. It uses a random value x from the distribution X as a starting value, then draws a value y from Y conditional on x. Given the drawn value y, it draws a new value x conditional on y. When this procedure is repeated many times, the scatterplot obtained from x and the corresponding draws y is approaching the real joint distribution (Train, 2009: 212). Metropolis-Hastings algorithm The Metropolis-Hastings algorithm (Train, 2009: 213 f.) used in step three is a more general form of the Gibbs sampler.48 As a starting point, the old individual beta estimates are changed by a random vector g which is drawn from a distribution with mean 0 and covariance matrix proportional to W: βnew = βold + g. Then the likelihood of the observed choices is calculated with the new and the old betas and labeled pold and pnew. The parameters b and W, which are considered fixed in this step, describe the distribution of individual betas. Hence, the probability of the new and old betas, based on this higher-level distribution, can be calculated: dold and dnew. Finally, the following ratio is calculated:

a=

dnew × pnew . dold × pold

(4.23)

As dnew and dold play the role of the “prior probability” from Eq. (4.22), and pnew and pold are the likelihood of the data, a is the ratio of posterior probabilities. If a

47By

averaging respondent draws after convergence to create a single vector of point estimates for each respondent we strictly spoken leave pure Bayesian statistics in favor of frequentist statistics. 48Gibbs sampling is a special case of the MH algorithm with acceptance probability always equal to 1, i.e., each new draw is accepted.

4.3  Discrete Choice Modeling

79

is greater or equal to 1, the new draws are outperforming the old betas in terms of posterior probability and thus are used in the next iteration round, otherwise the old betas are kept for one more iteration. Since the aggregate-level beta distribution as well as the likelihood of the new draws are considered, the algorithm tries to fit individual betas to the observed choices (like a maximum likelihood estimator). Simultaneously, it considers the overall distribution of betas. Hence, HB uses an efficient ‘data borrowing’ technique that works with distributional data also for the individual-level part-worth estimation, stabilizing the l­ower-level results (Chib & Greenberg, 1995; Sawtooth Software, 2009) Discussion of Hierarchical Bayes Hierarchical Bayes is assumed advantageous over other simulation-based estimation techniques first and foremost because of its ability to provide estimates of individual part-worths given only a few choices by each individual. It can do so, as explained above, by “borrowing” information from population information describing the preferences of other respondents in the dataset. Other strong points in favor of Bayesian procedures include: they avoid common difficulties associated with classical estimation methods. For instance, since HB does not maximize any underlying function, convergence problems—e.g., due to misspecified starting values or the issue of global versus local maxima—do not occur (Train, 2009: 283). Furthermore, HB performs well in various robustness tests: even with a strongly decreased number of tasks Johnson (1999: 15) finds robust hit rates for holdout tasks.49 When few individual-level information is available and lower-level parameters are estimated with large errors, upper-level measures—like mean and standard deviation (SD)—found by HB are still fairly accurate (Lenk et al., 1996: 178 f.). Finally, HB usually outperforms classical individual-level, aggregate-level, and l­atent-class models in part-worth recovery and predictive accuracy (even with modest respondent heterogeneity, Orme (2010: 48); Allenby et al. (1998: 387); Andrews et al. (2002: 97)), making a compelling argument for Hierarchical Bayes estimation. Prior to the rollout of our online survey we tested various behavioral model specifications in combination with several estimation techniques in a simulation study with synthetically generated data and have found that Hierarchical Bayes estimation of a rank-ordered mixed logit model performs best in correctly measuring part-worth utilities in our choice-experimental setting, endorsing its superior conceptual attractiveness.

49Choice

tasks included in the experiment not used to estimate utilities, but to assess the quality of estimated part-worths.

5

Data and Sample Description

The following chapter is concerned with describing our primary dataset of 120 practicing venture investors, thereof 73 independent venture capitalists and 47 business angels. Section 5.1 explains our sampling strategy and extensive data collection efforts, owing to the difficulty of obtaining a large sample in surveys among venture investors (e.g., Muzyka et al., 1996). Thereafter, we provide detailed information on our respondents’ demographics, investment preferences, risk attitude, their usage patterns of digital social networks, as well as on their knowledge of and experience with crowdinvesting. Whereas rich data on entrepreneurs’ characteristics, motivations and ambitions is available (e.g., through the Global Entrepreneurship Monitor1), we know comparably little about venture investors, the “catalysts for innovation” (Shepherd et al., 2000). Section 5.2 might thus be per se informative with regard to limited availability of data on formal and informal venture investors. The gathered data therefore constitutes a contribution in itself. In addition to these descriptive statistics, a table of variable correlations can be found in Appendix C. The chapter closes with a discussion of potential sample biases (5.3). Several presented controls find no evidence to suggest our respondents are systematically different from non-participating venture investors in any observable way that may threaten the validity of this thesis’ ­findings.

1http://www.gemconsortium.org/.

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 M. Mödl, Signaling Effects of Crowdfunding on Venture Investors’ Decision Making, Innovation und Entrepreneurship, https://doi.org/10.1007/978-3-658-31590-0_5

81

82

5  Data and Sample Description

5.1 Sample Target Population The target population of our online survey is venture capital managers who work at a German private independent venture capital company2 and practicing business angels located in Germany. Data from the German market for venture financing is appropriate for researching the impact of crowdfinancing on subsequent funding, since it is a sophisticated market for both reward-based crowdfunding and the securities-based variant. The first crowdinvesting campaigns in Germany started relatively early, in mid-2011, and projects have continuously broken worldwide funding records in terms of collected sums and speed. Start-up companies funded in this way have experienced successful follow-on VC funding rounds as well as significant complications and rejections solely based on their previously collected crowdinvesting.3 Hence, the German venture capital market was able to gain considerable early experience with this new form of start-up financing. This in opposite to, for instance, the United States, where ­securities-based crowdfunding was not open to non-accredited investors—thus the majority of the “crowd”— until Mid-May 2016 due to a delay in the concretization and promulgation of the JOBS Act regulation by the Securities and Exchange Commission (SEC), a hold-up which de facto limited the crowdfinance market to crowdfunding.4 While this is a strong rationale for focusing on venture investors located in Germany, the focus might be considered as a potential threat to the generalizability of results. In this context, however, it deserves mentioning that the venture investing industry— which emerged in the USA and has since been a role model across the world—is described as quite homogeneous internationally, in particular with regard to decision policies and screening (e.g., Brettel, 2002; Wright et al., 2005). Sample Frame As it is considered to be difficult to obtain a large sample in surveys among venture investors (e.g., Muzyka et al., 1996), and to ensure that a large number of active venture investors had the same probability of being invited to our survey, we solicited participants through multiple channels:

2For

rationales of excluding captive VCs from our analyses refer to Section 2.2. examples than the beforehand described case of Smarchive include companies BloomyDays, Neuronation, Sugar Shape, Blue Patent, Protonet or Doxter. 4See, e.g., U.S. Securities and Exchange Commission/Investor.gov (2016). 3Other

5.1 Sample

83

• Direct mailing: Personalized post letters with an invitation note to participate in our online survey were sent to 125 venture capitalists, each heading a different German private VC company, compiled from the membership roster of the Bundesverband Deutscher Kapitalbeteiligungsgesellschaften e. V. (BVK—German Private Equity and Venture Capital Association),5 the Financial Yearbook Germany (FYB; www.fyb.de), and the gruenderszene.de register. A reminder was sent out per post after six weeks and an additional per e-mail after another five weeks. Furthermore we mailed a personal invitation letter to the 15 business angels who have been awarded the “Business Angel of the Year”-Award by the Business Angel Netzwerk Deutschland e. V. (BAND— Association of German Business Angels and their networks) within the last 15 years. Additionally, a personalized e-mail was sent to 178 German-based private venture investors derived from the PitchBook database. For the latter two, we e-mailed one reminder after two weeks. • Third party e-mailing lists: Several network organizations supported us by forwarding our survey invitation via e-mail to venture investors in their databases and recommending participation, namely the BVK, BAND, BayStartUP GmbH (a Bavarian network association for the promotion of innovative entrepreneurship which is supported by the Bavarian Ministry of Economic Affairs and which has a large network of business angels), Munich Angels (an informal network of roughly 40 business angels based in the Munich area), and the Venture Capital Magazin (a leading German VC industry journal). Moreover the Technical University Munich sent an e-mail invitation to current participants and alumni of their Certified Private Equity Analyst (CPEA) program (executive education training for private equity professionals). • Canvassing at industry meetings: The author promoted the survey at distinguished venture investor events, including Deutscher Eigenkapitaltag 2016 (annual meeting of the German Private Equity and Venture Capital Association) in Berlin, Deutscher Business Angel Tag 2016 (biennial meeting of the Association of German Business Angels and their networks) in Nuremberg, and Cashwalk 2016 (bi-annual pitch day of the LMU Entrepreneurship Center, the start-up incubator of Ludwig Maximilians University Munich) in Munich. • Personal contacts: Finally, an invitation to participate in the survey was sent to practicing VCs and business angels who were personal contacts.

5According

to its own statement, the BVK is covering about two thirds of all German VC companies and 90% of VC investment volume in Germany.

84

5  Data and Sample Description

Since we cannot rule out that subjects received multiple invitations, we accounted for duplicate entries.6 Accordingly, we controlled for respondents actually being active as venture capital manager or business angel through check-up questions. Venture investors participated in our online survey from 02/2016 to 02/2017. Despite this relatively long sampling period—reflecting the challenge of obtaining a large sample of venture investors—an analysis of variance between late and early respondents revealed no significant difference (see below). Sample Size Our sample consists of completed choice experiments from 120 private ­German-based venture investors, of whom 73 (61%) are venture capitalists and 47 (39%) business angels.7 This sample size more than doubles the recommended minimum for a conjoint study (e.g., Shepherd & Zacharakis, 1999: 209) and well exceeds the average sample size of empirical studies with venture capitalists (despite the fact that the German venture capital market is considerably smaller than, e.g., the US market). Missing values for successive questionnaire parts, e.g., due to item-nonresponse, are not filled by imputation methods. The two optional parts at the end of the questionnaire, the vignette survey and the rating of legal clauses in crowdinvesting contracts, were completed by 94 and 92 venture investors, respectively. The willingness of almost three of four respondents to also participate in optional parts can be seen as an indication of a convenient questionnaire design as well as of the content being of interest for venture investors. In light of the non-specifiable sample frame due to our multifaceted sampling procedure we cannot compute an overall response rate. Exemplary, we do calculate the response rates for our postal invitations and the e-mailing by the BVK. From the 125 letters8 we sent out to private venture investors, four were returned as undeliverable. From that sample frame we received usable data from a total of

6In

addition, we extensively screened our dataset for answer patterns, consistency and processing time and dropped suspicious entries. For data cleansing also see Schnell et al. (2013: 425 ff.). 7We also obtained complete data from 24 corporate VCs and 29 public VCs, which we excluded from our sample for the reasons mentioned above. 8In fact, we randomly sent out 89 letters (three of them undeliverable) with a gift enclosed (USB stick), and 35 letters (one undeliverable) without a gift. The response rate among gift recipients (33.7%; 29) was significantly higher than for the treatment group (23.5%; 8), which is in line with related literature on survey incentives (e.g., Diekmann and Jann, 2001).

5.2  Descriptive Statistics

85

37 respondents, resulting in a response rate of 30.6 percent.9 The BVK listed 75 member organizations with VC activities, of which 43 fall under the definition of a private VC company. Their mailing list thus contained 43 venture capital managers as contact persons, each from a different private VC company. With 14 participants joining through that channel, the response rate here (32.6%) is about the same level as that of our postal mailing.

5.2 Descriptive Statistics 5.2.1 Demographics Table 5.1 provides details on investors’ demographics. Most respondents in our sample are mid-aged, well-educated, male German investors. In accordance with the male-dominated venture investor population our sample is predominantly male (94%). The overwhelming majority is German (96%) and almost two thirds are located in the start-up hubs Munich or Berlin. The investors in our sample are relatively well-educated with three out of four respondents holding at least a Master’s degree, and more than a quarter holding a doctoral degree. A majority holds their highest degree in the field of Management or Economics, yet, a considerable proportion of over 30 percent have a science or engineering background. There are no statistically significant differences between VCs and business angels concerning the distribution of demographic variables,10 except with respect to age (|t| = 2.974; p  0.1), VCs have on average invested in more companies (40) than angels (18) (|t| = 3.12; p