Founder Turnover in Venture Capital Backed Start-Up Companies

Martin Heibel Founder Turnover in Venture Capital Backed Start-Up Companies

GABLER EDITION WISSENSCHAFT Innovation und Entrepreneurship Herausgegeben von Professor Dr. Nikolaus Franke, Wirtschaftsuniversität Wien, Professor Dietmar Harhoff, Ph.D., Universität München, und Professor Dr. Joachim Henkel, Technische Universität München

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.

Martin Heibel

Founder Turnover in Venture Capital Backed Start-Up Companies With a foreword by Prof. Dietmar Harhoff, Ph.D.

GABLER EDITION WISSENSCHAFT

Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at http://dnb.d-nb.de.

Dissertation Universität München, 2008

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Foreword The economic importance of entrepreneurs as innovators, drivers of societal change, and contributors to wealth creation is uncontested. Researchers from various disciplines have investigated the process of new firm creation as well as the role of entrepreneurs in that process. In the same fashion, the role of venture capital investors as supporters of entrepreneurs has been widely studied and is reasonably well understood by entrepreneurship scholars today. Rapid firm growth fueled by venture capital investments triggers tremendous dynamics in the development of young firms. However, entrepreneurship research frequently ascribes a rather static role to the entrepreneur in his start-up company. The phenomenon of founder turnover – a situation in which entrepreneurs decide to leave their initially taken job position – has mostly been neglected in the literature. Moreover, little is known about the frequency, causes and the impact of founder turnover. With the present book, Martin Heibel seeks to fill this gap by analyzing founder turnover in German venture capital backed start-up companies. Martin Heibel develops two unique data sets specifically assembled through an experiment and an online survey. His in-depth analyses cover antecedents and performance implications of founder turnover. They combine venture capitalists’ as well as entrepreneurs’ perspectives on founder turnover, and yield detailed insights into the interaction between financiers and founders. This book is the result of three years of intensive research at the Ludwig Maximilian University Munich which earned the author a doctoral degree. The results presented by Martin Heibel are a remarkable contribution to the field of founder turnover research. I strongly recommend this book to practitioners – especially entrepreneurs and venture capitalists – and entrepreneurship researchers alike. Prof. Dietmar Harhoff, Ph.D.

Acknowledgement First and foremost, I wish to thank Dietmar Harhoff, my doctoral advisor, for his support and guidance throughout the past three years. He has been a knowledgeable and reliable source of encouragement and help to me throughout my dissertation project. The strong degree of freedom he granted to me made working in his team an instructive and rewarding endeavor. In particular, I wish to thank him for supporting my stay at Harvard Business School in Cambridge during my dissertation project. Moreover, I would like to thank Arnold Picot, my thesis referee and scientific advisor, for his outstanding support and valuable advice. I am also grateful to Werner Kirsch and Rainer Leidl, who have been my teachers and advisors during my doctoral Master of Business Research studies. The support by my colleagues at the Institute for Innovation Research, Technology Management and Entrepreneurship (INNO-tec) and the LMU Entrepreneurship Center (former ODEON Center for Entrepreneurship) has been of tremendous value to me. I wish to thank Thomas von Eggelkraut-Gottanka, Georg von Graevenitz, Marc Gruber, Carolin Häussler, Joachim Henkel, Stephan Herrlich, Karin Hoisl, Ulrich Lossen, Philipp Sandner, Celine Schulz, Maria Soltau, Jeannine Sütterlin, Hortense Tarrade, Christian Tausend, Felix Treptow, Lars Ullerich, Stefan Wagner and Richard Weber for their helpful comments to my research project during seminars and in personal discussions. I am indebted to Noam Wasserman, Professor in the Entrepreneurial Management Unit at HBS, for his hospitality and helpful academic support he provided to me during my stay at HBS during the summer of 2007. With particular gratefulness I would like to thank Rolf Christof Dienst, cofounder of the LMU Entrepreneurship Center, for his generous and enthusiastic support of my dissertation project and his ardent patronage of entrepreneurship research and founder education at the LMU.

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Acknowledgement

The financial and ideal support by the German National Academic Foundation (Studienstiftung des Deutschen Volkes) and the German Academic Exchange Service (Deutscher Akademischer Austauschdienst) is gratefully acknowledged. Finally, I wish to express my deepest gratitude to my parents Elisabeth and Rudolf for all the love and educational support they have given me throughout my life. This book is dedicated to them. Martin Heibel

Table of Contents Foreword.............................................................................................................. V Acknowledgement .............................................................................................VII Table of Contents................................................................................................ IX List of Figures.................................................................................................. XIII List of Tables .................................................................................................. XVII List of Appendices........................................................................................... XIX List of Abbreviations ....................................................................................... XXI 1 Introduction................................................................................................... 1 1.1 Topic and Motivation............................................................................. 1 1.2 Objective of Research ............................................................................ 3 1.3 Definition of Terms................................................................................ 5 1.4 Conceptual Framework .......................................................................... 6 1.5 Structure of Thesis ................................................................................. 8 2 Prior Research and Theoretical Background ........................................... 11 2.1 Literature Review................................................................................. 11 2.2 Research Contribution.......................................................................... 17 2.3 Theoretical Framework of Founder Turnover...................................... 19 2.3.1 Job Matching Theory ................................................................ 23 2.3.1.1 Start-Up Growth through the Company Life-Cycle .... 23 2.3.1.2 Job Matching under Dynamic Job Designs ................. 25 2.3.1.3 Job Matches and Founders’ Human Capital................ 28 2.3.2 Corporate Governance Theory.................................................. 30 2.3.2.1 Corporate Governance Structures in Venture Capital backed Start-Ups............................................. 31 2.3.2.2 Corporate Governance through Voice and Exit........... 32 2.3.3 Organizational Psychology ....................................................... 33 2.3.3.1 Procedural Justice in Exchange Relationships ............ 34 2.3.3.2 Job Dissatisfaction as Cause of Withdrawal................ 36

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2.3.4 Synopsis of Theoretical Considerations.................................... 38 3 Hypotheses and Combined Theoretical Model......................................... 41 3.1 Selection-Related Hypotheses.............................................................. 41 3.2 Outcome-Related Hypotheses .............................................................. 48 3.3 Performance-Related Hypotheses ........................................................ 52 3.4 Combined Theoretical Model............................................................... 54 4 Analysis of VCs’ Founder Turnover Decisions......................................... 57 4.1 Research Design................................................................................... 58 4.1.1 Model of Preference.................................................................. 60 4.1.2 Data Collection Method............................................................ 61 4.1.3 Scenario Set Construction......................................................... 61 4.1.4 Scenario Presentation................................................................ 63 4.1.5 Questionnaire ............................................................................ 66 4.2 Dataset and Variable Description......................................................... 66 4.2.1 Dataset ...................................................................................... 66 4.2.2 Variable Description ................................................................. 68 4.2.2.1 Dependent Variable..................................................... 68 4.2.2.2 Independent Variables................................................. 68 4.3 Descriptive Statistics............................................................................ 71 4.3.1 Respondents’ Characteristics .................................................... 71 4.3.2 Respondents’ Experience.......................................................... 77 4.3.3 Respondents’ Assessment of Founder Turnover....................... 81 4.4 Conjoint Analysis................................................................................. 88 4.4.1 Estimation Method.................................................................... 88 4.4.2 Estimation Equation.................................................................. 89 4.4.3 Multivariate Analysis................................................................ 90 4.5 Implications for Subsequent Course of Analysis ................................. 95 5 Determinants and Impact of Founder Turnover...................................... 97 5.1 Research Design................................................................................... 97 5.2 Dataset.................................................................................................. 98 5.2.1 Sample Selection....................................................................... 98

Table of Contents

5.3

5.4

XI

5.2.2 Data Collection ....................................................................... 102 5.2.3 Selection Bias and Response Bias .......................................... 106 Variable Description .......................................................................... 113 5.3.1 Dependent Variables............................................................... 113 5.3.1.1 Turnover Decision..................................................... 114 5.3.1.2 Turnover Type........................................................... 114 5.3.1.3 Company Performance .............................................. 115 5.3.2 Independent Variables ............................................................ 116 5.3.2.1 VC Influence ............................................................. 117 5.3.2.2 VC Type .................................................................... 117 5.3.2.3 Company Growth ...................................................... 118 5.3.2.4 Founder Job Match.................................................... 118 5.3.2.5 Founder Performance ................................................ 120 5.3.2.6 Founder Professional Experience .............................. 120 5.3.2.7 Founder Firm-Specific Know-How........................... 121 5.3.2.8 Founder CEO............................................................. 122 5.3.2.9 VC Procedural Justice ............................................... 122 5.3.2.10 Turnover Decision and Turnover Type ..................... 122 5.3.3 Control Variables.................................................................... 123 5.3.3.1 Founder-Related Control Variables........................... 123 5.3.3.2 Company-Related Control Variables......................... 124 5.3.3.3 Environment-Related Control Variables ................... 126 Descriptive Statistics.......................................................................... 127 5.4.1 Descriptive Statistics Regarding Entrepreneurs...................... 127 5.4.1.1 Job Matching ............................................................. 127 5.4.1.2 Personal Performance................................................ 133 5.4.1.3 Professional Experience ............................................ 134 5.4.1.4 Firm-Specific Know-How ......................................... 138 5.4.2 Descriptive Statistics Regarding Start-Up Companies............ 139 5.4.2.1 Team Size.................................................................. 139 5.4.2.2 Industry Affiliation.................................................... 142 5.4.2.3 Company Growth ...................................................... 143 5.4.3 Descriptive Statistics Regarding VCs ..................................... 145 5.4.3.1 VC Types................................................................... 145

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5.5

5.6

5.7

5.4.3.2 VC Influence ............................................................. 148 5.4.3.3 VC Procedural Justice ............................................... 152 5.4.4 Descriptive Statistics Regarding Founder Turnover ............... 153 5.4.5 Descriptive Statistics Regarding Performance........................ 159 5.4.6 Summary of Variables Used in Subsequent Empirical Analysis.................................................................. 162 Empirical Analysis of Determinants of Founder Turnover ................ 164 5.5.1 Estimation Method.................................................................. 166 5.5.2 Estimation Equation................................................................ 168 5.5.3 Multivariate Analysis.............................................................. 169 5.5.3.1 Correlation Matrix..................................................... 169 5.5.3.2 Estimation Results..................................................... 173 5.5.3.3 Review of Hypotheses............................................... 180 Empirical Analysis of Performance Impact of Founder Turnover ..... 183 5.6.1 Estimation Method.................................................................. 184 5.6.2 Estimation Equation................................................................ 186 5.6.3 Multivariate Analysis.............................................................. 188 5.6.3.1 Estimation Results..................................................... 189 5.6.3.2 Review of Hypotheses............................................... 193 Results in the Light of the Theoretical Model.................................... 194

6 Conclusion ................................................................................................. 197 6.1 Results................................................................................................ 197 6.2 Recommendations.............................................................................. 199 6.3 Caveat ................................................................................................ 201 6.4 Future Research.................................................................................. 202 7 Appendix.................................................................................................... 205 List of References............................................................................................ 241

List of Figures Figure 1.1: Figure 2.1: Figure 3.1: Figure 4.1: Figure 4.2: Figure 4.3: Figure 4.4: Figure 5.5: Figure 4.6: Figure 4.7: Figure 4.8: Figure 4.9: Figure 4.10: Figure 4.11: Figure 4.12: Figure 4.13: Figure 4.14: Figure 4.15: Figure 4.16: Figure 4.17: Figure 4.18: Figure 4.19: Figure 5.1: Figure 5.2: Figure 5.3: Figure 5.4: Figure 5.5: Figure 5.6:

Conceptual framework of founder turnover.................................... 7 Theories supporting the conception of founder turnover .............. 22 Combined theoretical model illustrating hypothesized effects ..... 54 Determinants of founder turnover discussed from the VC perspective .................................................................................... 58 VC survey: breakdown of participants per VC company.............. 67 VC survey: age of respondents ..................................................... 73 VC survey: respondents’ positions in VC companies ................... 74 VC survey: respondents’ educational background........................ 75 VC survey: respondents’ industry specialization .......................... 76 VC survey: respondents’ investment phase specialization............ 77 VC survey: respondent’s work experience in VC industry........... 78 VC survey: number of companies founded by respondents.......... 79 VC survey: number of companies coached by respondents.......... 80 VC survey: number of board seats held by respondents ............... 81 VC survey: time needed for turnover implementation.................. 82 VC survey: time loss from founder turnover ................................ 83 VC survey: assessment of turnover influence on performance ..... 84 VC survey: assessment of founders’ reluctance to leave .............. 84 VC survey: assessment of founders’ ability to grow..................... 85 VC survey: assessment of risk inherent in founder turnover ........ 86 VC survey: assessment of transparency in decision making......... 87 Contributions of conjoint experiment to the theoretical model..... 96 Deviation of commercial register against CREDITREFORM.... 101 Flowchart illustrating invitation mailing process........................ 103 Answering behavior of survey participants................................. 105 Founder survey: respondents’ initial position at company.......... 128 Founder survey: respondents’ educational background .............. 130 Founder survey: founders’ functional background ..................... 130

XIV

Figure 5.7: Figure 5.8: Figure 5.9: Figure 5.10: Figure 5.11: Figure 5.12: Figure 5.13: Figure 5.14: Figure 5.15: Figure 5.16: Figure 5.17: Figure 5.18: Figure 5.19: Figure 5.20: Figure 5.21: Figure 5.22: Figure 5.23: Figure 5.24: Figure 5.25: Figure 5.26: Figure 5.27: Figure 5.28: Figure 5.29: Figure 5.30: Figure 5.31:

List of Figures

Founder survey: respondents’ industry background ................... 131 Founder survey: types and quality of job matches...................... 132 Founder survey: respondents’ personal performance.................. 134 Founder survey: respondents’ professional experience............... 135 Founder survey: respondents’ experience in project management ................................................................................ 136 Founder survey: respondents’ experience in people management ................................................................................ 137 Founder survey: respondents’ experience in process design....... 137 Founder survey: patent holdings among respondents ................. 139 Founder survey: size of founding teams ..................................... 140 Founder survey: dates of inception, death and turnover ............. 141 Founder survey: industry affiliations of companies.................... 142 Founder survey: slowly and strongly growing companies.......... 143 Founder survey: number of employees today ............................. 144 Founder survey: type of VC dominating the investor group....... 146 Founder survey: number of financing rounds ............................. 147 Founder survey: number of VCs involved in companies over time ..................................................................................... 149 Founder survey: average number of VCs over first 3 years........ 150 Founder survey: VCs’ voting rights in companies over time...... 151 Founder survey: VCs’ fairness towards founder......................... 153 Founder survey: turnover, rotation, and departure among founders ...................................................................................... 154 Founder survey: VCs’ share of voting rights at effected rotation........................................................................................ 155 Founder survey: VCs’ share of voting rights at effected departure ..................................................................................... 156 Founder survey: VCs’ share of voting rights at considered rotation........................................................................................ 157 Founder survey: VCs’ share of voting rights at considered departure ..................................................................................... 158 Founder survey: number of years between foundation and turnover ................................................................................ 159

List of Figures

XV

Figure 5.32: Founder survey: post-turnover employee CAGRs of companies ................................................................................... 160 Figure 5.33: Founder survey: survival of companies ...................................... 161 Figure 5.34: Antecedents of turnover in the conceptual framework ............... 165 Figure 5.35: Performance impact of turnover in the conceptual framework................................................................................... 184 Figure 5.36: Support of the theoretical model from online survey.................. 195

List of Tables Table 2.1: Table 2.2: Table 4.1: Table 4.2: Table 4.3: Table 4.4: Table 4.5: Table 5.1: Table 5.2: Table 5.3: Table 5.4: Table 5.5: Table 5.6: Table 5.7: Table 5.8: Table 5.9: Table 5.10: Table 5.11: Table 5.12: Table 5.13: Table 5.14: Table 6.1:

Relevant studies on management turnover in start-up companies I ................................................................................... 15 Relevant studies on management turnover in start-up companies II.................................................................................. 16 Reduced factorial conjoint design including holdouts .................. 65 Description of scenario cards handed out to VCs ......................... 69 Description of questionnaire dataset ............................................. 72 Rank ordered logistic regression................................................... 91 Rank ordered logistic regression including interaction variables.. 94 Variables and results of non-response analysis I ........................ 111 Variables and results of non-response analysis II ....................... 112 Number of financing rounds and turnover .................................. 147 Post-turnover growth by turnover, rotation, and departure ......... 161 Description of variables included in selection and outcome estimation I ................................................................................. 162 Description of variables included in selection and outcome estimation II ................................................................................ 163 Correlation matrix of variables used in binary probit selection regressions .................................................................................. 171 Regression results of bivariate probit selection models .............. 175 Regression results of selection model and alternative models .... 176 Test of exclusion restrictions ...................................................... 179 Marginal effects of restricted binary probit selection model ...... 180 Description of variables included in OLS estimations................ 188 Results of OLS regressions including turnover as regressor....... 191 Results of OLS regressions including rotation and departure as regressors................................................................................ 192 Comparison of results from conjoint experiment and online survey............................................................................... 198

List of Appendices Appendix 1: Task description handed out to participants in the conjoint experiment .................................................................................. 205 Appendix 2: Sample scenario card used in the conjoint experiment ............... 206 Appendix 3: Questionnaire used in the conjoint experiment ........................... 207 Appendix 4: Sample letter used in first and second wave of online survey invitations ................................................................................... 210 Appendix 5: Sample letter used in third wave of online survey invitations .... 211 Appendix 6: Sample letter used in fourth wave of online survey invitations .. 212 Appendix 7: Transcript of questionnaire used in online survey among entrepreneurs............................................................................... 213 Appendix 8: Sample screenshots of online survey – excerpt of entry screen..... 239 Appendix 9: Sample screenshots of online survey – excerpt of section C.1.... 240

List of Abbreviations AG Avg. BLUE BMWi BVK CAGR CEO CFO CSO CLT C-level Conf. COO Cp. CTO CVC DB EBIT e.g. ERP et al. EUR EVCA GmbH H HBS HR HRM ICT

Aktiengesellschaft average best linear unbiased estimator Bundesministerium für Wirtschaft und Technologie Bundesverband Deutscher Kapitalbeteiligungsgesellschaften compound annual growth rate chief executive officer chief financial officer chief sales officer central limit theorem chief-level confidence chief operating officer compare chief technology officer corporate venture capitalist database earnings before interest and tax exempli gratia (for example) European Recovery Program et alii (and others) Euro European Private Equity and Venture Capital Association Gesellschaft mit beschränkter Haftung hypothesis Harvard Business School human resources human resource management information and communications technology

XXII

i.e. IIA IPO IRR IT L.M. LMU LR M MBA Min Max n.a. Num. NVCA OLS p. PC ROI SEC Std/std US USA VC VCF WW II Yrs./yrs. ZEW

List of Abbreviations

id est (this means) independence from irrelevant alternatives initial public offering internal rate of return information technology magister legum Ludwig-Maximilians-Universität likelihood ratio million master of business administration minimum maximum not available / not applicable number National Venture Capital Association ordinary least squares page portfolio company return on investment Securities and Exchange Commission standard deviation United States United States of America venture capitalist venture capital firm Second World War years Zentrum für Europäische Wirtschaftsforschung

1 Introduction In section 1.1 of this introductory chapter I present the topic and the motivation of my research. In section 1.2, I derive the objectives to follow throughout this book. Subsequently, in section 1.3, I define important terms frequently used in this thesis. In section 1.4, I introduce a conceptual framework to which I frequently refer in the following chapters and finally I give an overview of the structure of this thesis in section 1.5. Thus, the following pages aim at introducing the reader to the issues covered by this thesis and at making him familiar with the structural approach used herein.

1.1 Topic and Motivation It is a frequently observed phenomenon in venture capital financed start-ups that the founders do not keep their originally chosen job positions but at some point in time take a different position inside or outside their companies. In many of those cases externally hired managers are chosen to replace the entrepreneur.1 Such situations of founder turnover in which entrepreneurs leave their job position during the time when VCs are invested in their companies are in the focus of this thesis. With this book I aim at contributing to a better understanding of the antecedents and consequences of founder turnover in venture capital financed firms. In the following paragraphs I describe the implications of founder turnover for both, entrepreneurs and investors from which I derive my motivation to more deeply analyze the topic.

1

Very recent examples of founder turnover in venture capital backed start-ups include Technorati’s CEO David Sifry in the US on August 16, 2007 (cp. Mr. Sifry’s posting on the company’s weblog at http://technorati.com/weblog/2007/08/366.html – last visit: September 9, 2007) and Spreadshirt’s CEO Lukasz Gadowski in Germany on July 19, 2007 (cp. press coverage by business magazine “Manager Magazin”, at http:// www.manager-magazin.de/koepfe/personalien/0,2828,495450,00.html – last visit: September 9, 2007).

2

1 Introduction

I first consider the entrepreneur’s perspective. Generally speaking, entrepreneurs have to take a very fundamental decision when starting up their business. They need to decide whether or not to accept external investors among their shareholders, thereby jeopardizing total control over their company. However, the decision to stay independent and to not hand over voting and decision rights to venture capitalists would very often come at the high cost of a permanent lack of financial resources.2 Therefore, profit-oriented entrepreneurs often do accept VCs among their shareholders though knowing that the investors’ exclusive interest lies in achieving the company’s full financial potential. As a consequence, founders at some point might be asked by their VCs to leave their management position and to hand over their tasks to more professional or more experienced managers who from the investors’ perspective might be more potent to increase firm value. In this type of situations, entrepreneurs in general have the choice between staying inside the firm and collaborate with the new manager or leaving the company and follow their career elsewhere. However, as the founders of their companies, having invested much from a financial, personal and also psychological perspective, entrepreneurs often are surprised by such developments and tend to not want to let go. Having said this, part of the motivation of my research on founder turnover is to help start-up entrepreneurs build very realistic expectations on the scope of their professional career inside their startup, especially if they accept active, value maximizing investors like VCs among their shareholders. In fact, founding a company and financing its growth through venture capital might have often unforeseen career implications for the entrepreneur. As I will explore in the course of this book, the common belief of the entrepreneur being fully autonomous in his choice of occupation might have to be questioned for entrepreneurs in venture capital funded start-ups.3 Adding to the entrepreneur’s perspective I shed some light on the VCs view on founder turnover decisions in portfolio companies. For VCs who are eager to 2

3

Wasserman (2006) refers to this as the “rich versus king” trade-off. Refer to paragraph 2.4.1.1 for a detailed discussion of Wasserman’s proposition. For instance, Hannan et al. (1996) find that in the first 20 months of a high-tech startup’s existence the probability of a non-founder being appointed CEO is around 10%. This likelihood linearly increases to around 40% after 40 months of company life and goes up to around 80% after 80 months.

1.2 Objective of Research

3

obtain attractive financial returns through investing in superior teams with superb business ideas it is not only important to actually find and select those. In addition, as time goes by, VCs inevitably come to the point where they need to consider whether the original team composition is still appropriate or if some management change would possibly enhance company performance. Therefore, right at the outset of any investment, it is highly relevant for VCs to understand the potentially necessary management changes to be expected over time. In fact, the issue of founder turnover is to the VC’s concern in times of bad as well as in times of good company performance. This is due to the fact that independent of the status quo a possible increase in performance is always in the interest of financial investors. It must be accepted as a matter of fact that entrepreneurs are not per se the best managers to run their business in any phase of its development. Supposed investors do consider the founder to be an inferior manager, VCs in their role as active investors can therefore be expected to raise this issue and push for change. Moreover, VCs not only are in a constant need of management team assessment but they also have to consider and anticipate the consequences of founder turnover in specific corporate setups. In other words, they need to the degree of trouble and uncertainty which come along with founder turnover and thus decide based on a trade-off between the potential gains in performance obtainable with a different manager and the potential losses in performance caused by troubling a given corporate setup in which the founder remains in his position. As a part of this thesis, it is my objective to deliver results that may allow VCs to more systematically anticipate founder turnover situations and the performance implications of turnover decisions in their portfolio companies.

1.2 Objective of Research While it has been argued in the entrepreneurship and venture capital literature that VCs’ investment decisions are to a major extent based on the qualities and the complementarities in the skills of founding team members (Tyebjee and Bruno 1984; MacMillan and Siegel 1985; Fried and Hisrich 1994; Franke et al. 2004; Franke et al. 2006) little emphasis has been put on analyzing actual start-

4

1 Introduction

up management team changes in VC portfolio firms. Consequently, despite its importance for entrepreneurs as well as investors, little knowledge exists about the drivers of founder turnover, nor about its impact on subsequent company performance. As a matter of fact, a consistent theory of founder turnover is absent from the entrepreneurship literature. This thesis aims at filling this gap by proposing and testing a comprehensive theoretical framework, adequate to lay the cornerstone of a future theory of founder turnover. To this end, I analyze two unique datasets collected among German investors and entrepreneurs and find strong empirical evidence for a number of propositions. The thesis has three objectives which are closely intertwined. My first objective is to develop a theoretical framework of founder turnover based on testable hypotheses. In order to achieve this goal I draw on disciplines ranging from neoclassical economics to organizational psychology. I derive a comprehensive theoretical model which very realistically captures the dynamics of turnover incidents in start-up companies. Second, being especially interested in the role VCs play in situations where founders leave their initial positions, I intend to integrate VC decision making into the analysis. To this end, I isolate VCs’ most important decision factors by conducting a real time decision making experiment with participants from VC firms. Third, being interested in the antecedents and consequences of founder turnover, I aim at empirically scrutinizing the theoretical model in order to identify the most important factors leading to founder turnover. I also aim at deriving performance implications of founder turnover in VC portfolio companies from the empirical test. The analyses related to the latter objective are based on a unique set of field data collected through an online survey. It must be noted that both datasets used in this thesis are especially appropriate to study the subject at hand and to reach the mentioned objectives. By using a real time experimental setup I am able to realistically measure VCs’ decision making. Consequently, the data can be expected to be highly reliable in terms of identifying the most important decision criteria used by VCs. Due to the fact that the group of participating investors is largely identical to the group of VCs hav-

1.3 Definition of Terms

5

ing funded the surveyed entrepreneurs4, results from the experiment are especially useful to integrate the VC perspective into the full picture of founder turnover. The second data set was collected among German entrepreneurs and is a valuable, large-scale source of field data to study founder turnover. Founders can be expected to provide idiosyncratic insights allowing for a very detailed understanding of founder turnover. Thus, the chosen survey approach is especially suited to address the issue at hand. With VCs being active in Germany for about one decade both the number of venture capital funded start-ups as well as the covered time period are reasonably large for a precise study of the phenomenon.

1.3 Definition of Terms I briefly define the core terms used throughout this book. At this point I restrict myself to the terms “founder”, “founder turnover”, “venture capital funded” as well as “start-up company”, adding the definitions of other terms in their respective context at a later point in time. The term “founder” is used for founding directors only, which implies two conditions. First, a founding director necessarily is part of the founding team. He is not an externally hired manager. Second, only founders active in managing the start-up at the time of the first venture capital investment are taken into account. Thus, a founder in the context of this book is simultaneously characterized as a shareholder and an executive of his company. In the following, I differentiate between internal turnover – which I denote as rotation – and external turnover – which I refer to as departure. Internal turnover, i.e., rotation, includes a founder’s switch from one executive position to another (e.g. the former CEO takes the position of CTO after a while) as well as his leav4

Note that the German venture capital industry is rather small with only 20 to 30 relevant investors on the market (including CVCs and governmental VCs, cp. detailed descriptions of BVK members at http://www.bvk-ev.de/privateequity.php/cat/81/title/ ordentliche_Mitglieder_%28Investoren%29 – last visit: September 10, 2007). I collected data from individuals working at 22 different VC firms. When additionally accounting for syndication effects, the probability of having any of the interviewed investors involved in at least one German start-up having received funding over the past decade is reasonably high.

6

1 Introduction

ing from an executive position in favor of a non-executive board position (e.g. a supervisory board seat). External turnover, i.e., departure, not only describes situations in which the entrepreneur breaks all ties with the company but also comprises scenarios in which a founder after having left the company still works closely together with its management (as a consultant for instance). Besides the two types of turnover – rotation and departure – there is the case of survival. Founders not leaving their initially taken executive position while being venture capital financed will therefore be referred to as survivors. I consider companies to be “venture capital funded” if they have received external equity financing from a professional VC firm. I neither consider private or business angel investments, nor private equity investments in established companies here. Thus, the study is exclusively focused on venture capital funded “start-up companies” being newly created ventures obtaining financing to develop a new business idea (seed phase investments), to roll out a proven product or service (start-up investments), or to expand and internationalize their quickly growing business (expansion investments).

1.4 Conceptual Framework I introduce a three-tier framework as a structural reference for important parts of the remainder of this book. This framework, which is illustrated in Figure 1.1 clarifies the basic understanding of founder turnover in venture capital financed start-ups. It has three consecutive tiers which are briefly discussed here and referred to throughout the course of my analyses. At tier 1, a general decision whether a founder leaves his initial position or not is taken. Technically speaking, the first step separates survivor from turnover candidates, which implies a selection into two groups. It needs to be understood that the hazard of turnover rises over time. This is due to the fact that both, the number and the importance of potential problems increase as companies mature. However, thinking of the general turnover decision as a mainly economically driven process is reasonable, acknowledging that both, the VC and the entrepreneur find themselves in a professional relationship aiming at value creation.

Performance

Outcome

Selection

1.4 Conceptual Framework

Figure 1.1:

7

Tier 1

Survival

Turnover

Tier 2 Rotation

Departure

Tier 3 Performance

Conceptual framework of founder turnover

Should either the VC want the founder not to stay in his position or should the entrepreneur prefer to leave his position, a respective decision at tier 1 selects entrepreneurs experiencing founder turnover from those surviving in their initial positions. At tier 2 of the model a decision as to internal or external turnover is taken. In general, both alternatives are possible outcomes for an individual entrepreneur after experiencing turnover. Founders can either opt to take a different job internally or to leave the company continuing their professional career elsewhere. Unlike in the general mostly economically motivated turnover decision at tier 1, at tier 2 psychological aspects of the relationship between the VC and the founder come into play. VCs typically prefer to keep founders inside the company. However, the final decision for rotation or departure mainly lies with the entrepreneur himself. Therefore, once a turnover decision has been made, it is reasonable to assume that the psychological quality of the relationship between the investor and the founder – besides the latter’s job perspectives inside his own

8

1 Introduction

firm – to an important extent determine whether the entrepreneur stays with the company or not. Tier 3 of the framework covers the performance impact of the two types of founder turnover. My proposition is that turnover decisions as described at tier 1 and tier 2 of the framework have different consequences for corporate performance.5 Not only being interested in discovering the drivers of founder turnover in VC backed start-ups but also in determining the impact of founder turnover on subsequent company development, I include tier 3 in the framework. This three-tier framework serves as the basic guideline of my reasoning and thinking about founder turnover laid out in this book. At several points throughout this thesis I will therefore come back to it and elaborate specific issues related to any of the three tiers presented above.

1.5 Structure of Thesis Subsequent to those introductory pages, the remainder of this thesis is divided into 5 chapters. The book is structured as follows. In chapter 2, previous research on founder turnover and a comprehensive literature overview are presented. With respect to this summary, the contribution of my research to entrepreneurship literature and theory is formulated. Subsequently, I introduce three main fields of theory – corporate governance theory, job matching theory, and organizational psychology – as the underlying theoretical foundations of founder turnover in start-up firms. In chapter 3, I derive hypotheses based on the proposed theories, differentiating between selection-related, outcome-related and performance-related hypotheses according to the conceptual framework presented in Figure 1.1. I sum up my propositions in a combined theoretical model to be tested in subsequent empirical analyses. Chapter 4 presents a first study I conducted among VCs in order to explore their decision behavior with regards to founder turnover in their portfolio com-

5

Refer to section 2.3 of this book for broad theoretical support of this supposition.

1.5 Structure of Thesis

9

panies. I describe the experiment performed to collect data, the dataset, as well as results and their implications for the subsequent course of analysis. Chapter 5 presents the analysis of founder turnover in start-up companies. I describe the data collection process as well as the variables used in subsequent modelling. Based on the conceptual framework, I introduce a two-stage selection and outcome estimation which at the first stage – the selection stage – tests hypotheses regarding the antecedents of turnover. At the second stage – the outcome stage – propositions related to rotation and departure are analyzed. In the second part of chapter 5, I study the performance impact of founder turnover, rotation, and departure on company performance and empirically test related hypotheses. I conclude this book with a summary of my results in chapter 6. From my key findings I derive recommendations for entrepreneurs and VCs and discuss important future research questions directly resulting from my work.

2 Prior Research and Theoretical Background At the outset of this chapter, in section 2.1, I give an overview of the existing literature on management turnover in start-ups. In section 2.2, I describe existing research gaps and formulate the intended contributions of this thesis to entrepreneurship theory and literature. Subsequently, in section 2.3, three fields of theory and their relevance in the context of founder turnover are presented. First, I look at corporate governance in venture capital financed start-ups. Second, I introduce job matching theory as an important reference. Third, organizational psychology is discussed as a field of theory adding an important relational perspective to the understanding of founder turnover in start-up companies. Chapter 2 lays the foundation for the subsequent derivation of hypotheses to follow in chapter 3.

2.1 Literature Review This section provides an overview of the most important publications related to founder turnover. I briefly discuss each single contribution before I sum up the results at the end of this section. Broad research on management turnover in large corporations has been carried out since the early 1960s6 and has remained at the heart of corporate governance research and organization theory until recently (Fee and Hadlock 2004; Lucier et al. 2004; Bresser et al. 2007). However, it is not in the focus of this thesis to present the broad literature on management turnover in large companies, but rather to highlight the particularities of founder turnover in venture capital backed start-ups. While managers in large corporations typically have the status of employees, new venture founders are entrepreneurs and hold major shares in their companies. This leads to important differences between large corporations and rela-

6

For an overview of early studies and results refer to Allen and Panian (1982).

12

2 Prior Research and Theoretical Background

tively small start-up firms regarding the inherent dynamics affecting management turnover. Wasserman (2003) names three main differences between large company CEO turnover and founder CEO turnover. First, he argues that the separation of ownership and control is much less emphasized in start-up firms than in large corporations. Second, he points out that compared to mature big corporations the identity of organizational founders is tightly linked to that of the organization. Third, with regards to inside versus outside succession, in start-ups successors are quasi always taken from outside of the company – compared to large corporations, where the opposite is observed more often (Borokhovich et al. 1996). Researchers’ interest in management turnover in small venture capital financed start-up companies can be traced back to the mid 1990s (Lerner 1995). Lerner shows that VCs’ representation on the board of their portfolio companies increases around the time of CEO turnover. According to the common understanding of VCs being active investors he argues that investors increase their monitoring effort around the time of corporate crisis and take action by replacing the CEO. Bruton et al. (1997) look at CEO dismissals by boards of directors on which venture capitalists serve. They conclude that a CEO’s failure on the strategic rather than on the operational dimension leads to his dismissal by the board. Moreover, they find that replacing a CEO typically has a strong positive effect on subsequent performance. Fiet at al. (1997) support the view that dismissal occurs when firms perform poorly. Additionally, the authors analyze the structure of supervisory boards of new ventures. They point out that the larger the board the more difficult it will be to make changes unless the majority of board seats is occupied by VCs. Moreover, they stress the importance of fair procedures and conclude that there is a need to demonstrate that founder dismissals are carried out in a fair manner and in the best interest of the company. There is broad agreement among entrepreneurship researchers on the importance of human resources acquired by founders as a key determinant of new venture success. Schefczyk and Gerpott (2001) look at portfolio companies of German venture capital firms and find that portfolio managers’ functional and industry-specific experience significantly correlate with portfolio company per-

2.1 Literature Review

13

formance. Moreover, they find that a manager’s chance of being dismissed increases as company performance falls behind VC expectations. Acknowledging that new ventures may outgrow the managerial capabilities of their founding teams Boeker and Karichalil (2002) find that founder departure increases with firm size, decreases with founder ownership and board membership, and has a U-shaped relationship with firm growth. They also find that founders who work in research and development have a lower probability to leave their positions. Hellmann and Puri (2002) add two other important aspects to the picture of founder turnover by looking at CEO replacements. First, similar to Forbes et al. (2006) they find that VCs tend to not replace a founder by bringing in somebody else and taking out the entrepreneur, but that they rather try to add a new manager – often the new CEO – to the team. Based on this observation, the authors argue that there is a hard and a soft side to VCs’ involvement in their portfolio companies. There might be cases in which founders agree with the VC on bringing in an outside CEO, because they want to focus on different aspects of the business, e.g. be more involved in research and technical development. In such cases, in which the founder retains some position in the company after having left the CEO position, Hellmann and Puri speak of accommodating turnover. On the other hand, founders might be reluctant to accept an outsider as CEO while they would have to step down themselves. In this case they will typically disagree with the VC. This eventually forces the investor to take a hard position and fire the founder. As a consequence, the founder typically leaves all positions in the company. In this case, Hellmann and Puri speak of separating turnover. Moreover, Hellmann and Puri (2002) find that the average start-up firm in their sample was more than 2.3 times more likely to experience a CEO turnover event once it had a VC among its shareholders. Wasserman (2003) outlines the paradox of entrepreneurial success by studying more than 200 US internet start-up firms. He argues that a founder not only runs the risk of being replaced when performing poorly but that achieving critical milestones causes the chance of founder CEO succession to rise, too. This suggests that the sign of the correlation between firm performance and founder turnover is ambiguous. Wasserman’s findings are in line with the results of other studies indicating that founders’ education and experience are paramount to

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2 Prior Research and Theoretical Background

company success (Reuber and Fischer 1994). Assuming that an individual’s level of education and experience is constant in the short run, Wasserman’s results suggest that there is an increasing probability for successful founders to be replaced at the point where their profile does not fit the company’s rapidly changing management requirements any more. Ucbasaran et al. (2003) base their analyses of entries and exits in entrepreneurial founding teams on ninety owner-managed ventures monitored between 1990 and 2000. However, they do not indicate how those companies are financed, thus no information about venture capital participation is included in their dataset. With regards to turnover in the founding team they find a significant positive relation between heterogeneity of prior entrepreneurial experience in the team and member exit from the team. Moreover, they state that family firms are significantly negatively associated with team member exit. Chandler et al. (2005c) analyze turnover effects in start-up teams by comparing nascent entrepreneurs in newly founded and yet established start-ups. To this end, the authors separately analyze emerging ventures and 5-year-old start-ups. They find that the greater the number of team members in emerging ventures, the more likely they are to add members while founding team size has no effect on team member departures. Conversely, looking at 5-year-old ventures, the greater the number of initial members in the team, the more likely they are to drop members, while team size has no effect on team additions. Additionally, like Ucbasaran et al. (2003), Chandler et al. (2005b) analyze the effect of team heterogeneity on turnover. They find that higher heterogeneity in the major field of education, the number of years of industry experience, and the diversity in functional expertise positively relate to turnover in the team, which the authors speculate to be explainable by increasing levels of conflict. In recent works, Beckman et al. (2007) find that entrants to and founder exits from the top management team of a start-up increase the likelihood that a firm achieves an IPO. Their results also show that prior job experience is consistently associated with positive firm outcomes. These findings suggest that founder and team experiences, team composition and management turnover are important for bringing new insights to the firm and are associated with the success of entrepreneurial firms.

2.1 Literature Review Table 2.1:

Relevant studies on management turnover in start-up companies I

15

16 Table 2.2:

2 Prior Research and Theoretical Background Relevant studies on management turnover in start-up companies II

2.2 Research Contribution

17

Tables 2.1 and 2.2 provide a comprehensive overview of the mentioned studies on founder and new venture team member turnover that have been conducted over the past 10 to 15 years. At this point it becomes clear that research on the dynamic nature of new venture team membership in general and founder turnover in particular is in its infancy which calls for a deeper theoretical and empirical analysis. I will elaborate on both in the remainder of this book.

2.2 Research Contribution In this section I describe the contributions of this thesis to a theory of founder turnover and thus to the broader field of entrepreneurship theory. Additionally, I introduce three research questions to be addressed and answered by this thesis. Even though there are several studies related to personnel changes in venture capital financed start-up management teams, the existing literature shows substantial gaps in the analysis of founder turnover. There are several issues which have not been addressed in sufficient depth so far. My research contributes to the founder turnover literature along the following three dimensions. First, this thesis hypothesizes and empirically tests important differences in the antecedents of internal and external turnover. While most studies do not consider internal turnover at all, Hellmann and Puri (2002) suggest that founders might in some situations prefer to take a different position inside the company instead of leaving it. As an extension to their findings, my analysis seeks to provide a deeper understanding of drivers of rotation and departure. Investigating internal and external turnover is at the forefront of research, since similar issues are currently being looked at by several social scientists in the US (Kaplan and Minton 2006; Jenter and Kanaan 2006). However, these two working papers focus on large corporations and do not take into account the multitude of start-up particularities associated with internal and external management turnover. Second, in this thesis the entire founding team is considered. This is a remarkable difference in comparison to most other studies where only CEOs of start-up companies are in the focus of interest. This research design allows me to arrive at differentiated results especially on the determinants of turnover for founders in different C-level positions.

18

2 Prior Research and Theoretical Background

Third, the chosen research design allows for a tight integration of both the VC and the entrepreneur view on founder turnover. My analyses are based on two major data sources. First, I conduct a conjoint experiment among VCs which provides a deep understanding of criteria important to VCs in founder turnover decisions. Second, I perform a survey among founders of venture capital funded start-ups who were active as managing directors in companies registered in Germany between 1996 and 2006. To the best of my knowledge, this is the first survey among German founders investigating founder turnover. The only German study on founder turnover (Schefczyk and Gerpott 2001) is based on no more than 10 questionnaires received from VCs, who provided information on 103 of their portfolio companies. Another important difference is that these authors not only include founders but also key employees, successors of founders, and individuals added to the management team subsequent to founding. However, there is an important disadvantage associated with this diverse group of individuals. In fact, the resulting heterogeneity in the group of managers renders the interpretation of the results very difficult. Based on the described data retrieved from German VCs and entrepreneurs I intend to contribute to the understanding of the antecedents and the performance effects of founder turnover. In order to do so, I address four principal research questions: –

Which criteria are important in VCs’ decision making on founder turnover?

–

What drives founder turnover in venture capital financed start-up companies?

–

Under which conditions will internal turnover prevail over external turnover and vice versa?

–

Which impact on performance do turnover, rotation, and departure have in start-up companies?

I will relate to those questions when deriving my hypotheses in the following chapter, as well as when specifying estimation equations and interpreting empirical results in chapter 5.

2.3 Theoretical Framework of Founder Turnover

19

2.3 Theoretical Framework of Founder Turnover In this section I first look at the decision makers involved in founder turnover as well as their incentives prevalent in those decisions. Subsequently, I introduce three fields of theory explaining the effects and the dynamics of founder turnover. I chose corporate governance theory, job matching theory, and organizational psychology as theoretical pillars which I link to the conceptual framework of founder turnover as introduced in section 1.4. A number of researchers have tried to scrutinize the issue of management turnover more deeply by better qualifying and classifying observable turnover cases with regards to the decision makers involved. Schrader and Lüthje (1995) distinguish inevitable turnover due to exogenously given events such as illness or death, voluntary turnover where the manager freely decides to leave his position and involuntary turnover where the manager has to leave his position due to somebody else’s decision to do so (in most cases the supervisory board). They find that 49% of the turnover cases in their sample of 57 turnovers in 32 corporations were inevitable, 26% voluntary, and 25% involuntary. Since this study looks at large German and US American corporations, the results do certainly not apply to venture capital backed start-up companies. Leker and Salomo (2000) point out, that inconsistent results from previous research on pre- and post-succession performance relationships could be explained by a lack of differentiation between inevitable, voluntary, and involuntary turnover. This also calls for caution when studying founder turnover in young start-up firms. Nonetheless, the three types of turnover proposed by Schrader and Lüthje (1995) do not quite fit the picture of young start-up firms. Not surprisingly, the authors’ study finds that smaller and owner-controlled companies are more likely to change their CEO for reasons other than retirement, i.e., inevitable turnover. In fact, in venture capital backed start-ups I expect the distribution across the three types to be fundamentally different. With founders being rather young than old (Westhead and Wright 1998), inevitable turnovers will hardly occur in venture capital backed start-ups. With founders having high-powered incentives to stay with the company, the same holds most likely for what Schrader and Lüthje (1995) call positively motivated voluntary turnovers – cases in which an execu-

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2 Prior Research and Theoretical Background

tive actively opts for an outside job option. Negatively motivated voluntary turnovers – cases in which an executive for personal reasons does not want to remain in his position any longer – can be expected to be similarly rare as in large corporations.7 In contrast to the authors’ findings, involuntary turnover will probably be most important in venture capital funded start-ups. I suggest that a more suitable typology for the research carried out here needs to be developed. Accounting for the weaknesses sof the typology as proposed by Schrader and Lüthje (1995) in the context of venture capital financed start-ups, I postulate that it is more suitable to distinguish two types of turnover – undesirable and desirable turnover cases. The desirability expressed in this typology is related to the person of the founder, i.e., from his perspective turnover may be undesirable or desirable. For three main reasons I assume that in general turnover is undesirable for founders. First, VCs’ decisions to provide funding to entrepreneurs are primarily based on the qualifications and the skills combined in the entrepreneurial team (Franke et al. 2004; Franke et al. 2006). In order to keep the founding team with the company, VCs initially provide high incentives to founders to stay in their jobs. Such incentives include long-lasting vesting schemes and adverse pay-off schemes in case of voluntary retirement (Hellmann 1998; Kaplan and Strömberg 2003).8 Second, venture capital funded founders often start companies after having left lucrative prior jobs or by foregoing attractive job offers. Consequently, their financial upside almost exclusively lies in the success of their venture. Therefore, in most cases they do not have any reason to quit their position as long as the company has a realistic chance to become successful. Third, even if the company performs badly and eventually tends to go into insolvency, founders in their positions as directors are legally obliged to orderly close down the business. Thus, even if the venture turns out to be a failure, the founders will typically be among the last to abandon the “sinking ship”.

7

8

Schrader and Lüthje (1995) identify negatively motivated voluntary turnover in as little as 10% of all cases they study. In most cases investment terms make voluntary job termination extremely unattractive for founders. For instance, founders may be forced to sell their equity share to investors at a pre-defined company valuation substantially below fair equity market value.

2.3 Theoretical Framework of Founder Turnover

21

Acknowledging those facts, I consider desirable turnover as an exception from the general rule of undesirable turnover in VC backed start-ups. Nonetheless, this type of turnover is worth mentioning given the fact that I differentiate between internal and external turnover. In situation in which the founder wishes to rotate to another position internally he may be able to communicate the resulting advantages for the company to VCs. Under the condition that such rotation goes without punishment through adverse incentives or a loss of financial upside, founders may find it desirable to leave their initial job position.9 At this point it becomes clear, that an important discussion immediately linked to the question of undesirable and desirable turnover is concerned with the party that triggers and ultimately takes the turnover decision. In fact, since the VC and the entrepreneur are closely associated with one another, any turnover decision requires some input by both parties and always includes some negotiation between the investor and the entrepreneur. As a matter of fact, both parties may raise the issue of a potential turnover. In cases of desirable turnover the founder may even be the initiator of such discussions, while in cases of undesirable turnover the investor is most likely to be the initiator. Provided that VCs are sufficiently strong in terms of voting rights, the actual final turnover decision may either be taken by the founder or by the supervisory board.10 For the remainder of this thesis I assume turnover to be generally undesirable for the founder. However, in some cases, founders may anticipate potential management difficulties and might therefore voluntarily opt for turnover. Discrepancies from this logic of turnover decisions in venture capital funded start-ups may occur but can be regarded as rare exceptions from the rule.11 9

10

11

In fact, such desirable internal turnover can often be understood as the founder’s anticipation of future difficulties in his initially taken position. In order to enhance firm performance in the longer term, in many cases investors are likely to agree to such desirable turnover decisions. Anecdotal evidence suggests that VCs often do not force a turnover decision but prefer the founder to conclude for himself that he should take the decision proactively. Therefore, from a founder’s perspective it is reasonable to differentiate “undesirable” from “desirable” turnover, while a mere differentiation between “involuntary” and “voluntary” turnover does not capture the full picture. Such exceptions might be unexpected illness or death, as well as personal decisions exempt from any economic reasoning but entirely privately motivated.

2 Prior Research and Theoretical Background

Performance

Figure 2.1:

• Job matching theory • Corporate governance theory

Turnover

Survival

Outcome

Selection

22

Rotation

Performance

Departure

• Job matching theory • Organizational psychology

• Job matching theory

Theories supporting the conception of founder turnover

In what follows I expand the conceptual framework presented in section 1.4. I propose three strands of theory adding to the understanding of the dynamics of selection, outcome, and performance as conceptualized in the framework. I argue that corporate governance theory, job matching theory, and organizational psychology provide explanations for the effects leading to founder turnover and for the impact of turnover, rotation, and departure on firm performance. Figure 2.1 shows which of the mentioned theories help predict selection, outcome, and performance effects. In the following paragraphs I summarize those theories in the light of the present research and lay out how they add to the theoretical conception of founder turnover. First, I look at the selection stage. It has been argued that founders may not be the ideal choice for specific top management positions, because they might not possess the required skill set. Job matching theory looks at how well the personal skills of an employee and the skills required in his job correspond to each other. Though this theory is often used in a more macro-economic context it also provides explanations for the selection of survivors and turnover entrepreneurs. As I have mentioned earlier, the given power distribution between the VC and the entrepreneur crucially determines whether or not turnover decisions are

2.3 Theoretical Framework of Founder Turnover

23

effected. Although both parties are always involved in this decision to some extent, the ultimate decision will mostly be determined through the corporate governance mechanisms in place. It can especially be assumed that if the VC wants the founder to leave his position and has sufficient contractual power to enforce this decision, he will in most cases do so. This is why corporate governance theory besides job matching theory helps explain main dynamics in step 1 of the framework. With respect to outcomes, two theories need to be considered. Again, job matching plays an important role because the founder’s skill set can be expected to determine whether or not he stays inside the company. Additionally, his decision to do so or to leave the start-up is also driven by psychological and relational aspects, which I will describe in paragraph 2.3.3. This is why organizational psychology, besides job matching theory, adds to the understanding of main dynamics in step 2 of the conceptual framework. My theoretical reasoning on the performance impact of founder turnover in the last step of the framework will exclusively be based on job matching theory. I assume both, the replacement of the founder with a different manager, as well as the founder taking on a different job internally to be explainable by improved job matches and their respective consequences for firm performance. 2.3.1 Job Matching Theory This section introduces job matching theory as the first core concept to explain founder turnover and its impact on company performance. This is accomplished by comprehensively combining three strings of theory. First, implications of company growth on management tasks is discussed, second, the nucleus of job matching theory is introduced to explain the increasing hazard of management turnover as companies develop and grow. Third, relevant aspects from human capital research with regards to founders’ education, experience, and skills are included into the picture. 2.3.1.1

Start-Up Growth through the Company Life-Cycle

The life-cycle paradigm is well established in organizations research. Hanks et al. (1993) give an overview of some established life-cycle models and the related

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2 Prior Research and Theoretical Background

literature. As an important baseline model and reference they present the lifecycle model by Greiner (1972) who suggests that organizations grow through five evolutionary stages with relatively calm periods of growth, separated by brief periods of revolution entailing dramatic organizational change. He assumes the latter to be necessary for further growth in the following stages of the life– cycle. The model comprises phases of creativity, direction, delegation, coordination and collaboration. Greiner provides recommendations for appropriate managerial action in each of the five phases. Moreover, he provides insights on how to overcome the crises of growth which occur in the transition from one phase to another. Another important and well-know model is proposed by Churchill and Lewis (1983). They also focus on managerial implications of company growth and derive strategies to overcome growth problems at the different stages. The authors structure the company life cycle into five stages: existence, survival, success, take-off and resource maturity. They argue that most businesses go through those five stages as they mature. Each stage of the cycle poses a different set of challenges and requires the owners to apply a different set of skills and resources in order to ensure the continued success of their business. The majority of venture capital backed high-growth start-ups faces important changes in scope and structure within a short time span. Consistent with the Churchill and Lewis (1983) model it is therefore paramount that in different stages of growth different management skills can be made available to the firm in order to handle its well-managed, controlled and successful development. Terpstra and Olson (1993) empirically show that there are dramatic changes in the top management team’s tasks between start-up and growth phase. Based on their sample of 500 fast-growing venture firms they report that the percentage of companies that mention obtaining external financing as one of their top management priorities decreased from 17% in the start-up phase to 1% in the growth phase. While not a single firm cited organizational structure as a key problem in the start-up phase, 6% of respondents did so in the growth phase. Similarly, organizations describing human resource management as a critical issue increased from 5% to 17%, those naming marketing and sales among their top problems decreased from 38% to 22% and last, companies mentioning the regulatory environment as a key management issue, increased from 1% to 8%. Those results

2.3 Theoretical Framework of Founder Turnover

25

clearly suggest that in the early start-up phase top management is mainly concerned with financing the venture and getting the product out to the market, i.e., marketing and sales. In later phases of growth, structural changes in the company, human resource management and regulatory issues move into the focus of top management. Even though Willard et al. (1992) in their study of 155 mostly high-tech manufacturing firms taken from a list of the fastest-growing privately held companies in the US between 1985 and 1990 find no significant differences in the performance of founder-managed and professionally managed firms, their empirical results partially support the view that rapidly growing new firms quickly outgrow the founder’s managerial capacity. Their results expose that there is a fraction of about one third of all founders leaving their management positions in favor of an external manager. Ceteris paribus, this suggests that some founders are capable to develop their skills as the company matures while others get overwhelmed by the requirements of their management position. In line with this reasoning, Kaplan et al. (2004) come up with an interesting result. They study the evolution of firm characteristics of 49 venture capital financed companies over an average time span of 6 years. Among other results they find that non human capital aspects of a business are more stable over time than human capital aspects. In their study, while the level of differentiation from competition, alienable assets, customers, and competitors remained relatively constant over time, the human capital of the sample firms changed substantially. After just one year half of the CEOs had changed and only one quarter of the next four top executives remained. Even though those figures might be extraordinarily high due to sample selection, the results demonstrate that human capital is phase specific to a large extent and therefore subject to replacement in fast growing companies. 2.3.1.2

Job Matching under Dynamic Job Designs

Labor market economics suggest that in a world of imperfect information individuals are in quest for the job best suiting their skills and know-how. Jovanovic (1979) formally shows that for a given worker in the market, a non-degenerate distribution of productivities exists across different jobs. The same holds true for the employer, since different employees show different productivities in a given

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task that the employer needs to have performed. Hence, the problem – which is assumed to be solved in market equilibrium – is to optimally assign workers to jobs. In any given job, individuals are in a person-job match. The employee might find this job match inferior to a different match offered to him and quit the job. The employer on the other hand might consider the person currently employed in a given job not to be the best match and may replace him with a different, more suitable employee. While the labor economic literature mainly stresses the first case, i.e., the employee deliberately leaving his job due to a bad match, in the context of founder turnover the second case is more in the center of the discussion. This is due to the fact that in most cases of founder turnover the decision of staying or leaving is not at all solely taken by the founder himself but in accordance with – or even as a rather one-sided decision by – the supervisory board.12 In the sense of classical job matching theory and given a crucial amount of voting rights lying with the VC, the supervisory board can therefore be taken as the “employer” of the entrepreneur. Joos et al. (2003) argue that boards of directors actively select CEOs matching the specific requirements of the CEO job in a particular company. Looking at companies of different ages and sizes they find that younger CEOs who are less effort and risk averse and have longer time horizons tend to be hired by start-up and high-growth firms, i.e., by riskier firms and firms with large portfolios of growth options. The authors’ most important contribution with respect to founder turnover is that value-maximizing boards of directors on the market for CEOs actively bid for suitable candidates. This holds especially true for very active board members like venture capital investors. In the labor economics literature there are two fundamentally different notions of job matching. While some scholars assume a job to be an experience good (Johnson 1978; Wilde 1979; Viscusi 1979), others claim it to be a pure search good (Lucas and Prescott 1974; Burdett 1978). Hirshleifer (1973) introduces the notion of inspection good instead of search good. Inspection implies an

12

Note that this statement refers to the general turnover decision, not to the subsequent decision of the founder to stay or to leave the company (cp. the conceptual framework in Figure 1.1).

2.3 Theoretical Framework of Founder Turnover

27

evaluation of the job offered to a labor market participant that can take place prior to taking the job. Experience in contrast requires the employee to take the job first and evaluate it after having been employed for a while. In what follows, jobs and job matches will be regarded as experience goods, because founders indeed have to take their jobs and only over time realize the quality of the job match they are in. Following this understanding, whether or not a founder experiences a high match in his executive position cannot be distinguished ex ante, but only after a while of experience on the job. A strong assumption of given and stable job-characteristics is inherent in most labor economic models. Jobs are seen as a definite set of static tasks without any dynamics in the challenges the employee has to handle. While there is good reason for this assumption looking at short-term macro-economic equilibrium13, this understanding does not fit firm-level reality. Mainly in start-up companies job requirements change rapidly and dramatically. As a consequence, job mismatches more often occur due to changing managerial challenges than due to an ex ante existing, but not observable mismatch of static job requirements and individual skills. This holds especially true for venture capital funded entrepreneurs who from their side have actively decided to take the job they are in and who have been given money by VCs after a thorough evaluation of the entrepreneur and his initial match with the respective job. Beckmann (2004) looks at job matching in organizational and technological innovation processes. He argues that in dynamic managerial environments job matching is a continuous process. He points out that organizational innovations typically imply changes in job designs for employees. As striking examples of changing job designs he introduces team work and the delegation of decisions. He concludes that such changes in job design require the employee to change his focus from operations to management. Some are capable of doing so, others are not. Consequently, some employees might no longer be a match for the same but newly designed position even though they were a perfect match prior to job design modification. Given the different phases of growth that start-ups often rush through, one can argue that those companies are constantly exposed to a high level of organ13

This is what labor economics are typically concerned with.

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2 Prior Research and Theoretical Background

izational innovation. Consequently, start-up managers face permanent changes in job design. Therefore, the arguments as put forward by Beckmann (2004) are fully viable in the context at hand. Some founders are capable and willing to adapt their managerial actions according to job design changes as they occur throughout the different stages of company development. Others do not have the ability or the intention to do so and leave their position. The assumption that entrepreneurs in many cases will be unable to make it to effective managers is so prevalent that it has its own name: the founder’s disease (Willard et al. 1992; Utset 2002). Parsons (1972) finds that employees’ propensity to leave their positions decreases over time. The author argues that workers, as they accumulate human capital specific to the firm improve their job match and hence reduce the probability of finding better matches in other positions. In a theoretical model, Kräkel (1997) argues from an employer’s perspective and comes to the same result: due to a decreasing marginal reduction of information asymmetries about the quality of a match, the propensity of an employer to dissolve this job match decreases over time. However, Beckmann (2004) theoretically derives that under the regime of changing job designs, the exact opposite can occur in innovative environments. He reports that due to dynamics in job design, quit rates among employees may increase rather than decrease with tenure. Transferring Beckmann’s (2004) line of argumentation to fast-growing start-up companies, it is intuitive that for founders the hazard of turnover increases as their management jobs become more and more demanding. 2.3.1.3

Job Matches and Founders’ Human Capital

There is one question left open so far: What makes a good founder job match? To assess this matter, the human capital of an entrepreneur needs to be understood. It has been pointed out that a founder’s job is an experience good. Consequently, the quality of a given job match can ultimately only be judged from an entrepreneur’s performance in his job. Following Haber and Reichel (2007) three aspects are considered to determine whether or not a potentially high job match exists: education, prior experience (in entrepreneurship as well as industry and functional experience) and skills. Evidence for the importance of all three dimensions is presented in the following paragraphs.

2.3 Theoretical Framework of Founder Turnover

29

The entrepreneur’s education and its relationship to performance have been widely examined. Robinson and Saxton (1994) find that measures of general education have a strong positive influence on entrepreneurship in terms of becoming self-employed in the first place and being successful as an entrepreneur thereafter. Even though empirical research has yielded mixed findings (Reuber and Fischer 1994), in a meta-analysis Cooper and Gimeno-Gascon (1992) find a significantly positive relationship between entrepreneur education and start-up performance. Besides general education, the relevance of specific education such as business and technical education needs to be taken into account. Chandler and Jansen (1992) report that competence in the managerial role is enhanced by business education. Moreover, they argue that among others, business education lays the ground work for a successful entrepreneurial career – in the course of which one might assume a reduced likelihood of founder turnover. They empirically show that the most successful founders not only rate themselves as competent in the entrepreneurial and managerial dimension, but also in the technical-functional role. This suggests that technical education, too, helps to explain entrepreneurs’ ability to match top-management jobs in their companies. Furthermore, prior experience as an entrepreneur is a good predictor of successful re-venturing, i.e., serial entrepreneurship. Gompers et al. (2006) present results indicating that entrepreneurs who succeeded in a prior venture (which they define as exiting a company in an IPO) had a 30% change of succeeding in their next venture, while first-time entrepreneurs only had an 18% change of doing so. Interestingly, entrepreneurs who failed in their first venture also had an increased chance of success of 20% in a subsequent start-up. This clearly suggests that experience contributes to entrepreneurial success or, in other words, that entrepreneurially experienced founders show better job matches than their inexperienced counterparts. Besides entrepreneurial experience, prior industry experience also increases the likelihood of success. Chatterji (2005) assesses the impact of prior industry experience on entrepreneurial performance and innovation in medical device start-ups. He finds that ventures started by former employees of incumbent firms perform better than other new entrants, which suggests that entrepreneurs’ industry experience has a positive influence on start-up performance. Klepper and

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Sleeper (2005) study spin-offs in the laser technology industry and ask how they inherit knowledge from their parent companies. The authors find empirical evidence that spin-offs exploit knowledge their founders acquire from their former employers. Since in young industries like the laser technology industry such knowledge is more likely to be embodied in human than in physical capital, their results suggest that founders’ highly specific industry knowledge is conducive to market success. According to Kanungo and Misra (1992), managerial skills are behavior sequences specific to the task. Haber and Reichel (2007) construct an index covering all relevant skills important to the entrepreneur. In accordance with Hisrich and Brush (1984) their managerial skill index comprises the acquisition of financing, personnel management, product innovation, ongoing business operation, strategic management, marketing and selling. Consistent with prior studies in other industries (Bird 1989; Yammarino and Waldman 1993; Hood and Young 1993), their index was significantly correlated with most of their performance measures and it was found to be the strongest contributor to small venture performance from both short- and long-term perspectives. There is an obvious implication of the cited studies and their results with regards to founder turnover: founders with inferior job matches are more likely to experience turnover. However, while this first conjecture only covers the impact of job matching in step 1 and 2 of the conceptual framework of founder turnover (cp. Figure 1.1) there also are performance consequences as conceptualized in step 3 to be addressed here. Bringing in a manager who demonstrates an arguably better job match, as well as moving the founder to a more appropriate position inside the company should be beneficial to company performance. I will elaborate on this in further detail in section 3.3 of this book. 2.3.2 Corporate Governance Theory This paragraph introduces corporate governance theory as the second component of the theoretical basis explaining founder turnover. I introduce VCs as active investors and illustrate particularities in start-up corporate governance resulting from VCs’ involvement in their portfolio companies.

2.3 Theoretical Framework of Founder Turnover

2.3.2.1

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Corporate Governance Structures in Venture Capital backed Start-Ups

Taking VC money implies the entrepreneur’s ambition to catalyse growth and company performance. However, once having VCs participate in the company, corporate governance structures change dramatically, turning the start-up company into a highly objective driven and performance oriented enterprise. In other words, after having accepted VCs among their shareholders, entrepreneurs have made a fundamental choice, which Wasserman (2006) calls the “rich versus king” decision. Having VCs on board, allows the entrepreneur to become “rich” through fast growth and successful exit. However, on its way to success, he often loses control of the board and hence of the company. On the other hand, not taking VCs on board means that the entrepreneur can basically do whatever he likes. In Wasserman’s terms he remains “king” of his realm, which is his start-up company. The downside of the “king” option, however, is to not have the financial support and the pressure induced by tight corporate governance which both arguably contribute to value creation. While “kings” might be able to afford inertia and complacency, those on the track to richness – and even more so their investors – cannot. Thus, VCs are active investors interested in the value maximization of their shareholdings (Gorman and Sahlman 1989). However, they remain outside owners, so they are not involved in actively managing the company. As a consequence, VCs while being invested in a portfolio company, find themselves in a principal agent type of situation introduced by Jensen and Meckling (1976) as “a contract under which one or more persons (the principals) engage another person (the agent) to perform some service on their behalf which involves delegating some decision making authority to the agent”. Even though the core problem described by the authors, the separation of ownership and control, is not a predominant one in most start-up companies, VCs tend to be well represented on the supervisory boards of their portfolio companies (Rosenstein et al. 1993). This is due to the fact that hidden action and moral hazard remain potential problems of the post-contract relationship in venture capital financing, because incentives of insiders and outsiders do diverge on several dimensions (Picot et al. 1997). Entrepreneurs especially might not use

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venture capital money in the best sense of the investor but consume perks. Additionally, they might reduce effort and increase risk inherent in their investment decisions. VCs being value maximizers have an interest in avoiding such hidden action and moral hazard through monitoring the entrepreneur. As a consequence, the boards of venture capital backed firms are in a way unique, because both insiders (founders) and outsiders (investors) have significant ownership in the firm. This distribution of concentrated ownership makes venture capital backed start-ups and their governance structures different from other small firms, where ownership is rather concentrated among insiders, and large corporations, where ownership is often so dispersed that in general neither inside nor outside directors hold a significant ownership stake. Consequently, in venture capital backed firms the board of directors is often presumed to be key to firm governance (Pearce and Zahra 1991). 2.3.2.2

Corporate Governance through Voice and Exit

In most cases, as VCs do not acquire the majority of shares in a first or second round of investment but face the described principal agent problems as of day one of their engagement, they tend to increase their share of voting rights in the board of directors over their share of equity. This means that VCs acquire an over-proportionate share of board seats as measured by the amount of their shareholdings. As a consequence, in most cases the boards of venture capital backed firms are relatively balanced between insiders and outsiders, even if VCs hold substantially less equity than the founding team (Sapienza et al. 2000). This is realized through contractual clauses, mainly designed to protect the investor from mismanagement of the funds provided to the entrepreneur. Landström et al. (1998) find four dimensions in venture capital contracts related to corporate governance. First, venture capital contracts include terms on the management of the venture, such as the approval of a pre-defined set of business decisions taken by the entrepreneur. Second, they contain terms directed towards exit, third, terms regarding changes in ownership and management – e.g. exchanging founders for professional managers – and fourth, terms focusing on the monitoring of the venture. This description of contracts shows that VCs have a greater influence on

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contract provisions than entrepreneurs and that existing contractual clauses give strong voting rights to investors. Generally speaking, voice and exit are governance mechanisms available to the VC (Hirschman 1970). While voice can be thought of as an active intervention by the VC, exit means a discontinuity of the relationship. The latter case describes a termination of the relationship triggered by the VC. In accordance with Parhankangas and Landström (2006) exit may take the form of replacing the entrepreneur or even exiting, i.e., leaving the investment altogether, e.g. by writing off the investment or liquidating the company. Hellmann (1998) postulates that the importance of investor control can be explained by several factors. He argues that when only a small equity share remains with the entrepreneur, investors tend to be in control. Moreover, the more wealth constrained the founder, the less able the entrepreneur is as a manager and the higher the quality and the availability of external managers the more likely is investor control. 2.3.3 Organizational Psychology In this paragraph I look at the third theoretical pillar shown in Figure 2.1. As a matter of fact, the importance of organizational psychology is often forgotten in VC research, because the entrepreneur VC relationship is exclusively regarded as an economically motivated partnership. In what follows I will especially point out why implications from organizational psychology must not be left out when theorizing about founder turnover. The predominant model of the relationship between the VC and the entrepreneur looks at the VC as the principal monitoring the entrepreneur as his agent (Kaplan and Strömberg 2001). Since this one-sided perspective of the relationship does not capture the full picture, the construct has been expanded to a twosided principal agent model, considering opportunistic action by the VC, too (Gorman and Sahlman 1989; Amit et al. 1990). The authors show that in fact both parties are highly dependent on each other. Consequently, the cooperation between the entrepreneur and the VC can only be successful if the entrepreneur brings in a viable business idea, his skills and his execution effort while the VC adds cash, access to his network and professional management support.

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However, some entrepreneurship researchers have argued that principal agent theory overlooks interpersonal or relational dynamics that cannot be modelled by neo-classical assumptions and have called for a more integrative approach to the relationship between the VC and the entrepreneur (Perrow 1986; Fried and Hisrich 1995). Accordingly, Korsgaard and Sapienza (1996) argue that it is not only import to account for the economic dimension of the relationship between the VC and the entrepreneur – which is what most principal agent theorists do – but also to include the role of social bonds necessary to maintain the relationship. They suggest that particularly in relations where economic stakes are high, both perspectives need to be considered. Taking the reciprocal character of the relationship between the VC and the entrepreneur into account, I integrate additional important theoretical perspectives in the further discussion to explain antecedents of founder turnover in venture capital backed start-ups. As I have argued, the psychological dimension of the VC entrepreneur relationship is especially important with regards to turnover outcomes when the entrepreneur decides whether to stay with the company or to leave it after a turnover decision. Organizational psychology offers a wide range of theoretical points of reference as to the interaction of humans in organizational setups (Hoyos and Frey 1999; von Rosenstiel 2003). In the further discussion I refer to two selected fields of organizational psychology and adapt those to the context of venture capital funded start-ups. First, procedural justice as a relevant facet in the relationship between the investor and the entrepreneur will be included in the subsequent argumentation (Sapienza and Korsgaard 1996). Procedural justice theory is concerned with fair decision making and hence plays an important role in the analysis of founder turnover. Second, I refer to the theory of job satisfaction as one of the core concepts in organization theory. Job satisfaction is concerned with an employee’s level of positive affect towards his job or job situation (Locke 1976; Spector 1997). Both aspects of Organizational Psychology are introduced in the subsequent paragraphs. 2.3.3.1

Procedural Justice in Exchange Relationships

In the core of procedural justice theory (Cobb et al. 1995; Korsgaard and Sapienza 2002) is that the mutual perception of exchange partners’ fairness is critical to the continuity of these relationships. In this sense, founder turnover – espe-

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cially the founder’s departure from the company – would be a disruption of the relationship between the VC and the entrepreneur. Procedural justice concerns the fairness of procedures used to take decisions. It can be distinguished from distributive justice, which evaluates decision outcomes (Fehr and Schmidt 2000). Research has shown that factors such as the opportunity for voice in the procedure, judgment based on evidence, correctability of decisions and consistent application of procedures contribute to the perception of procedural justice (Thibaut and Walker 1975; Leventhal 1980; Kim and Mauborgne 1991; Kim and Mauborgne 1993). Research on the consequences of procedural justice in organizational relationships reveals two main results. First, procedural justice can have a positive effect on attitudes and behaviors (Lind 2001). People are more willing to accept and even positively support unfavorable decision outcomes when they perceive decision procedures as just. Korsgaard et al. (1995) examine how decision-making procedures can facilitate the positive attitudes necessary for cooperative relations in decision-making teams. They find that when processes are seen as fair, team members show greater commitment to the decisions taken, greater attachment to the team, and greater trust in the leader. Second, procedural justice interacts with distributive justice in a way that the impact of procedural justice is stronger when distributive justice is lower (Brockner and Wiesenfeld 1996). This is particularly important in the context of decisions – including turnover decisions – taken in phases of poor start-up performance, when distributive justice as perceived by the entrepreneur might be especially low while at the same time unfavorable decisions might be taken by the investor. An important difference between principal agent and procedural justice theory is the role of trust. Because agency theory implicitly assumes insiders will behave opportunistically whenever it is profitable, the supervisory board is seen as a provider of information, eliminating outside members’ need to trust. In contrast, procedural justice theory sees trust as varying with decision procedures, independent of exchange outcomes. As a consequence, perceptions of agency risks, i.e., opportunism, are directly influenced by the quality of decision-making processes (Sapienza et al. 2000). Tyler and Lind (1992) identify mutual trust as core belief directly shaped by procedural justice. Procedurally just treatment

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from the side of investors will hence increase entrepreneurs’ trust in the VC over time. Additionally, communication behavior and derived attitudes from both, VCs and entrepreneurs, are paramount to build and maintain the perception of mutual procedural justice. While VCs ask for timely feedback by the entrepreneur (Sapienza and Korsgaard 1996), founders from their side should listen to external advice, especially given by the VC (Barney et al. 1996; de Clercq and Fried 2005). Thus, open and timely communication between the VC and the entrepreneur can help to establish and to maintain procedurally just decision making. 2.3.3.2

Job Dissatisfaction as Cause of Withdrawal

According to Jex (2002), job satisfaction has a cognitive and a behavioural component which – taken together – social psychologists refer to as an employee’s – or in the present case founder’s – attitude towards his job (Zanna and Rempel 1988). While the cognitive aspect of job satisfaction represents an employee’s beliefs about his job situation – e.g. whether he finds it stimulating, interesting, demanding and the like – the behavioural component is concerned with an employee’s behaviour or his behavioural tendencies towards his job – e.g. how long and how hard he works or how hard he tries to stay with the organization or leave it. In the following, I especially focus on employees’ withdrawal behaviour as presented by Spector (1997) and transfer his findings to the second tier of the three-tier framework (cp. Figure 1.1). The author argues that turnover can be regarded as one form of withdrawal behavior resulting from job dissatisfaction. In fact, several studies have consistently found a negative correlation between job satisfaction and turnover (Hulin et al. 1985). Spector (1997) proposes a model where characteristics of the individual are combined with characteristics of the job environment to determine the employee’s level of job satisfaction. He argues that if the level of job satisfaction falls below a certain threshold, the person will develop a behavioural intention to leave the job. This behavioural intention might lead to job search activities eventually followed by turnover, i.e., the employee’s withdrawal from the firm (departure). There is good research support for employee and workplace factors to jointly contribute to job (dis)satisfaction (Shore et al. 1990; Blau 1993). More-

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over, job satisfaction has been found to correlate well with the intention of quitting the job (Tett and Meyer 1993).14 What are individual and job environment characteristics that determine job satisfaction? As start-up companies grow, the founder’s personal characteristics remain stable while his job environment changes dramatically. Acknowledging this, I only briefly look at personal characteristics here but present job environment characteristics in more detail. Personal antecedents of job satisfaction as described by Spector (1997) include locus of control and negative affectivity. Locus of control represents an individual’s generalized belief in his ability to control positive and negative reinforcements in life. “Internal control” is the term used to describe an individual’s belief that control of future outcomes resides primarily in himself while “external control” refers to a person’s expectancy that control is outside of himself, either in the hands of powerful other people or due to fate or chance. In essence, the more internal a person scores, the higher his job satisfaction (Moyle 1995). Negative affectivity reflects a person’s tendency to experience negative emotions, such as anxiety or depression, across a wide variety of situations. Research has consistently found that measures of negative emotions are negatively related to job satisfaction (Cropanzano et al. 1994). Much more important in the context of changing job designs are environmental antecedents of job satisfaction. A model introduced by Hackman and Oldham (1976) provides a framework of reference. The authors argue that job satisfaction is determined by three main psychological states: (i) experienced meaningfulness of the work, (ii) experienced responsibility for outcomes of the work, and (iii) knowledge of the work results. They state that employees respond with higher job satisfaction, the more pronounced their job in those three states is. I will briefly look at each of the states. Experienced Meaningfulness Experienced meaningfulness describes the extent to which a person believes a job is important, valuable and worthwhile. Hackman and Oldham (1976) suggest

14

Tett and Meyer (1993) carry out a meta analysis and find a statistically significant mean correlation between job satisfaction and the intention of quitting the job of -0.58.

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task variety, task identity and task significance as the three dimensions of job characteristics driving experienced meaningfulness. According to Howell and Dipboye (1986), task variety is the degree to which the job involves activities that require a wider range of abilities and skills. Task identity is the degree to which the job requires a whole and identifiable piece of work to be completed as opposed to an isolated operation. Task significance is the degree to which the job has substantial impact on the lives or work of other people. Experienced Responsibility Experienced responsibility describes the extent to which an employee feels responsible for the results of his work. In the understanding of Hackman and Oldham (1976) task autonomy leads to the employee’s experience of responsibility. Knowledge of Results Knowledge of results is the extent to which the employee has knowledge about how well he is doing. Feedback given to the employee increases his knowledge about the quality of his work. According to the model proposed by Hackman and Oldham (1976), employees who experience (i) task variety, (ii) task identity, (iii) task significance, (iv) task autonomy and (v) feedback are more likely to be satisfied with their job. Vice versa, a reduction in any of the five dimensions reduces job satisfaction and – provided job satisfaction becomes sufficiently low – can provoke the employee’s turnover intention. Based on this model I will derive propositions for founders’ turnover behaviour regarding rotation versus departure decisions in chapter 3.2. 2.3.4 Synopsis of Theoretical Considerations In the previous paragraphs, I have laid out theoretical foundations rooted in different grand theories. First, I referred to labor economics by introducing job matching, second, I discussed corporate governance as a facet of neoclassical theory, and third, I used organizational psychology as point of reference to discuss procedural justice and job satisfaction. I have argued that those theoretical components comprehensively integrate when linking the theories to the three levels of the conceptual framework (cp. Figure 2.1).

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While the initial decision of turnover versus survival is mostly economically motivated which calls for theories assuming perfectly rational agents in a world of imperfect information (job matching theory and corporate governance theory), the subsequent decision of rotation versus departure has main psychological aspects (procedural justice and job satisfaction), which are paramount to consider if one aims at a realistic theoretical derivation of founder turnover dynamics. With respect to tier 3 of the conceptual framework, I have argued that job matching theory alone is capable to deliver explanations for post-turnover performance variations. In the following chapter I will use the theoretical foundations introduced above in order to derive hypotheses for each step of the conceptual framework. In what follows, I will make propositions regarding selection, outcome, and performance.

3 Hypotheses and Combined Theoretical Model In this chapter, I derive hypotheses from the theoretical foundations of job matching theory, corporate governance theory pertaining to venture capital funded firms, as well as from organizational psychology as laid out in chapter 2. In accordance with the conceptual framework introduced in chapter 1, I present hypotheses for each step of the three-tier framework. Consistent with Figure 2.1, I use the presented theories to motivate my hypotheses, which I differentiate into selection-related, outcome-related, and performance-related propositions. In section 3.1, I derive selection-related hypotheses on the antecedents of turnover based on job matching and corporate governance theory. In this section, I only look at factors that explain founders’ leaving from their initial positions. In section 3.2, I consider turnover outcomes and discuss under which conditions rotation or departure are most likely to occur. Outcome-related hypotheses are derived from job matching theory as well as from organizational psychology. Section 3.3 covers hypotheses on the influence of turnover, rotation, and departure on start-up performance, which are all grounded in job matching theory. Finally, in section 3.4, I combine all derived hypotheses into one coherent and comprehensive theoretical framework which lays the foundation for subsequent empirical modelling.

3.1 Selection-Related Hypotheses The first hypothesis presented here is rooted in corporate governance theory as presented in chapter 2.3.2. Based on the observation that entrepreneurs entering venture capital contracts remain with rather little job protection, Hellmann (1998) asks why and under what circumstances entrepreneurs voluntarily relinquish the right to appoint the CEO of their own company. He formally shows that when VCs are in control, they strengthen their effort to find professional

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managers who are expected to increase company performance and thus shareholder value. In a study on venture capital in the US, Kaplan and Strömberg (2003) find that the contingent allocation of control rights – often being separated from VCs’ cash flow rights (Chan et al. 1990) – is one of the most important instruments in venture capital financing. In line with this argument, Gorman and Sahlman (1989) argue that in the extreme case of exercising control, VCs use their rights to replace the original founder from the position of the CEO. Given their performance dictum, VCs with major control rights will thus execute those rights in order to shape the management team according to their agenda. It can therefore be assumed that founder turnover occurs more frequently in such start-ups in which VCs are in control, i.e., where they have sufficient control rights to literally decide on their own whether or not a change in the management team should be effected. Consequently, the following prediction is made: H1:

The higher overall VC influence, the higher the probability of turnover.

Acknowledging that VCs implement founder turnover with the aim of enhancing value creation, it is helpful to understand their need for performance. In fact, there are differences in the types of VCs regarding performance objectives. Generally speaking, VC funds can be divided into independent and captive VCs (Van Osnabrugge and Robinson 2001). While independent VCs are required to collect their funds under management from other market participants and thus face fierce competition in the capital acquisition process, captive funds are typically created by one single parent organization, such as a big corporation (corporate venture capital fund), a bank, or a government. As Tausend (2006) and Lossen (2007) show, independent VCs’ past performance largely determines their success in the competition for new funds. Captive funds, however, are much less exposed to this type of competition because their shareholders typically follow goals different from pure value maximization. While corporate venture capitalists enter the business of investing in emerging companies to have a “window on technology” (Dushnitsky and Lenox 2004; Maula et al. 2005), banks do so to complement their debt product offerings and governments engage in the VC market to foster employment and innovation (BMWi 2005) Thus, for private VCs the need to prove value creation is much higher than for captive VCs. As a

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consequence, based on corporate governance theory as laid out above, I postulate: H2:

For founders with mainly independent VCs as their investors the probability of turnover is higher than for founders with mainly captive VCs as their investors.

Introducing dynamic job designs as presented in paragraph 2.3.1.2 into the discussion here, it must be acknowledged that job matches might deteriorate as companies grow and as job designs for top management positions change. Thought it might be bad news for the entrepreneur, the more successful his company, the less likely he might be at some point in time to suit his position any longer. Therefore, it must be accepted that if the founder does really well, he increases his chances of being replaced. Especially if companies grow extremely fast at an early time in their life-cycle, it can be expected that even very adaptive and fast-learning founders will not be able to keep pace with company development and job design dynamics. For the remainder of this book, I refer to such extreme growth start-ups as hyper growth companies.15 On the one hand, high company growth indicates that a founder is doing well in his job, which should strengthen his authority in his job position. On the other hand, hyper growth may lead to turnover due to an overly strong change in job requirements. Thus, I postulate two different effects of growth on founder turnover: H3a: The probability of turnover is lower for founders of strongly growing companies than for founders of slowly growing companies. H3b: The probability of turnover is higher for founders of hyper growth companies than for founder of non hyper growth companies.

15

In paragraph 4.2.2.2 I define start-ups as strongly growing companies if their compound annual growth rate in the number of employees over the first three years is in excess of 100%.

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The fourth hypothesis on the selection stage is based on job matching theory, too. I look at the job match founders find themselves in when taking on their initial management position. According to the basic dictum of job matching theory, the more similar a founder’s individual profile of education and professional experience and the requirements of his initially taken management job, the better the match should be. With the founder’s profile being stable in the short term, the quality of the job match can be regarded as a predictor of his initial chance to survive. The following hypothesizes relationships between the quality of a founder’s job match and his individual turnover hazard. Sub-hypotheses are formulated for four dimensions of job matching which were introduced in paragraph 2.3.1.3 and can be expected to positively contribute to the founder’s chances to survive in his initial job position. Besides education, those four dimensions are entrepreneurial, industry and functional experience. H4: H4a: H4b: H4c: H4d:

The better the founder’s job match, the lower the probability of turnover. The better the founder’s educational match, the lower the probability of turnover. The better the founder’s entrepreneurial match, the lower the probability of turnover. The better the founder’s industry match, the lower the probability of turnover. The better the founder’s functional match, the lower the probability of turnover.

The fifth hypothesis is based on both, job matching theory and corporate governance theory. With reference to the former, I come back to my previously mentioned reasoning that the founder’s job match – no matter how good it may be at company inception – is very likely to change over time due to a change in job design and associated job requirements. This will especially happen in phases of dynamic company growth. However, why would an evolving job mismatch then actually result in founder turnover?

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45

This is where corporate governance theory adds to the picture. VCs have been introduced as active portfolio investors (Gompers and Lerner 2004) who support and control the management of their portfolio companies (Sapienza et al. 1994; Barney et al. 1996; Brinkrolf 2002). According to Hellmann and Puri (2002), especially in times of bad company performance, VCs rather show their “hard side” through an increase of management control than their “soft side” through management support. In line with this argument, Gebhardt and Schmidt (2006) argue that VCs adapt their control rights contingent on the performance of the entrepreneur. The authors come to the conclusion that entrepreneurs are in control when the company performs well, while VCs take control when it performs poorly. In order to model the inherent dynamics, Gebhardt and Schmidt (2006) look at a convertible security issued to the VC. In their model, the VC is given a convertible debt contract including the control right to replace the entrepreneur. Only if the company performs well, the investor will exercise his conversion option and change debt for equity, thereby losing the right to replace the management. However, since I look at single founders here, those prior findings need to be translated to the level of the individual entrepreneur. To this end, it is helpful to understand the link between the individual founder and company performance. Previous empirical literature has studied the effect of founder-CEOs on firm performance. Several papers report a positive correlation, a negative effect, or no effect (Jayaraman et al. 2000). Authors such as Morck, Shleifer, and Vishny (1988), McConaughy et al. (1998), Fahlenbrach (2005), as well as Villalonga and Amit (2006) report a positive effect of founder-CEOs on firm performance. Thus, comparable to large corporations where CEOs retain their positions more frequently when the firm is doing well (Weisbach 1988; Warner et al. 1988; Jensen and Murphy 1990), it should also be true for founders that they are likely to remain in their positions as long as they meet their performance targets. However, not only founder-CEOs but every member of the top management team must be considered a direct influencer of company performance in start-ups (Wasserman 2003). In case of bad company performance, VCs need to figure out who within the management team can be held responsible for the company not meeting its goals. Therefore, when it comes to founder turnover, the VC will not remove a founder from his position, unless the investor has good reason to be-

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lieve that this particular person is causing inferior company performance. In other words, if the VC can attribute bad company performance to a single founder, he will strive to replace him in order to improve company performance. In essence, VCs will execute their control rights if founders do not deliver on their performance targets. In their role as principles in a principle agent relationship, VCs will observe inferior founder performance as bad company performance.16 From what has been said, the following can be derived: H5:

The lower the founder’s performance, the higher the probability of turnover.

At this point, I add insights from human capital theory presented earlier to job matching theory. I have argued that the quality of the initial job match determines turnover probability (hypothesis 4). Moreover, I have pointed out that founders’ performance on the job will influence VCs executing their control rights (hypothesis 5). Building on both lines of argumentation, I introduce professional experience as an important factor explaining founder turnover. Professional experience may especially allow the entrepreneur to adapt to changing job designs because he has been able to cope with different or changing job requirements or both before. In accordance with Bruton, Fried and Hisrich (1997), I not only consider the overall amount or length of the founder’s job experience but I also look at specific facets of experience acquired by a founder prior to initiating the company. I hypothesize that the founder’s general professional experience, his project management experience, his people management experience, as well as his process design experience influence his turnover probability: H6:

16

The higher the founder’s professional experience, the lower the probability of turnover.

In fact, in several interviews VCs commented that they do hardly care about individual founder performance as long as the company attains its goals. In other words, VCs do not strive to assemble the “perfect team” but a “functioning team”. This means, they do not try to implement management team changes to make high performing portfolio companies perform “even better” but that they only care about how to make low performing companies perform “according to plan”.

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H6a: The higher the founder’s project management experience, the lower the probability of turnover. H6b: The higher the founder’s people management experience, the lower the probability of turnover. H6c: The higher the founder’s process design experience, the lower the probability of turnover. Furthermore, a founder’s firm-specific know-how – the fraction of his know-how that is highly valuable inside the company but comparatively valueless outside of the company (Marshall 1964) – is important to judge his turnover propensity. Especially technically oriented founders are able to build important firm-specific know-how as part of their human capital, e.g. by inventing and developing the innovative, often very technical product, which they are the mastermind of. Given the fact that such firm-specific know-how tends to be idiosyncratic in start-ups, the founder might turn out to be of immense value to the firm. Therefore, even if the founder – after a certain time – might partially no longer fulfil the requirements associated with his position, VCs might still be reluctant to force founders with high firm-specific know-how to leave their initial management positions.17 Linking idiosyncratic know-how to job matching has three implications. First, as a matter of fact, on the firm level, jobs might be so specific, that there is exactly one person – the founder – who could match it building on his idiosyncratic know-how. Second, even if the founder’s profile may not entirely fit the requirements associated with his position any more, a certain aspect of his profile, especially his idiosyncratic know-how, might nevertheless be mission-critical to the firm. That’s why VCs face a trade-off between the danger of not finding anybody to match a specific job and accepting the founder as a somewhat suboptimal but badly needed match. Assuming prudent VC behavior, it is therefore reasonable to state that:

17

So solve this type of problem, VCs might still try to add a new manager to the team who they perceive as complementary to the founder. Team additions, however, are not in the focus of this book.

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H7:

3 Hypotheses and Combined Theoretical Model

The higher the founder’s firm-specific know-how, the lower the probability of turnover.

The seven hypotheses put forward above all make predictions on why and under which circumstance a founder would have to leave his initially taken management position. After analyzing this selection of survival from turnover cases, outcomes are regarded next.

3.2 Outcome-Related Hypotheses This section looks at the second step in the initially introduced tree-tier framework and develops propositions on why founders after having left their positions might rather stay with the company or leave it. First, I come back to the founder’s professional experience and firm-specific know-how which I already introduced in the discussion of two selection-related hypotheses (cp. hypotheses 6 and 7). Because professional experience is rather general, it can easily be transferred between companies. For instance, project and people management experience, as well as process design experience can be applied in virtually any other company and almost every possible business context. Contrarily, this does not hold true for firm-specific know-how. Such know-how is best used inside the start-up, where the founder can extract most value from it. Therefore, I speculate that for founders with general professional experience potential internal job matches deteriorate while they improve for founders with firm-specific know-how. I subsequently provide some reasoning supporting this view. If a founder with broad professional experience has to leave his management position, there is good reason to assume that his experience does not suffice to remain in this position any longer. At the same time, there is most probably no other internal position available in which the founder could make optimal use of his broad professional experience. Contrarily, the probability for the founder to find a better job match externally is high. Therefore, I hypothesize that founders with broad professional experience will most likely leave the start-up after turnover. Every facet of professional experience, i.e., project management experi-

3.2 Outcome-Related Hypotheses

49

ence, people management experience and process design experience should make externally available job match opportunities more attractive to the founder. H8

The higher the founder’s professional experience, the more likely he opts for departure instead of rotation. H8a: The higher the founder’s project management experience, the more likely he opts for departure instead of rotation. H8b: The higher the founder’s people management experience, the more likely he opts for departure instead of rotation. H8c: The higher the founder’s process design experience, the more likely he opts for departure instead of rotation. However, for founders with high firm-specific know-how the dynamics are different. As companies develop and management tasks become more specific, job designs ask for more specialization. As a consequence, for founders with high firm-specific know-how job matching opportunities within the start-up tend to improve. Therefore, when management specialization in growing companies increases rotation to a more specialized management position is likely for founders with firm-specific know-how. This allows the company to keep firmspecific know-how inside its resource pool. Based on what has been argued, the following relationship is hypothesized: H9:

The higher the founder’s firm-specific know-how, the more likely he opts for rotation instead of departure.

I have introduced job satisfaction as an important facet of organizational psychology. Building on this strand of theory, the following hypothesis is derived. Referring to Hackman’s and Oldham’s (1976) framework, I argue that the perceivable change in job environment after turnover is of different magnitude for the CEO and for other C-level founders. I postulate that changes in the characteristics of job environment are more severe for CEOs on several of the dimensions introduced by Hackman and Oldham (1976). A CEO, after having lost his position as the top executive of the company loses on all three dimensions associated with the experienced meaningfulness of

50

3 Hypotheses and Combined Theoretical Model

his work. While his loss on task variety and task identity might be comparable to the loss faced by any other C-level founder, his loss in task significance must be considered extreme. In fact, other than his C-level colleagues, the CEO will always be required to not only “step aside” but “step down”. This is associated with a tremendous loss of perceived task significance, i.e., the degree to which his potential new job inside the company has an impact on the lives or work of co-workers, customers, and other stakeholders. In the same manner, the CEO’s loss in experienced responsibility driven by a stark cut of decision and task authority must be regarded as much more important than for any other C-level executive. In fact, from the CEO’s perspective, the loss of authority can be compared to a switch from the role of the boss to the role of the subordinate. The types of feedback a CEO receives prior and post turnover are substantially different from each other. Being the top executive, the CEO receives feedback from every employee through more or less formalized reporting, thereby always being informed about the results of his work. After stepping down, he becomes integrated into the company’s hierarchical structure being at least one level below the new CEO. Consequently, the amount and the scope of feedback he receives after losing his CEO position are greatly reduced.18 Again, the loss of feedback for any other C-level founder, not having been on the top of the organization’s hierarchy, would most likely be less important. Hackman and Oldham (1976) argue that a loss of task variety, task identity, task significance, task autonomy or feedback reduces job satisfaction. I have shown that after turnover the cumulated losses across all those dimensions are more severe for CEOs than for other C-level founders. Acknowledging this, CEOs will experience a more important drop in their level of job satisfaction than other founders and are therefore more likely to develop the behavioral tendency to withdraw from the company. In other words: H10: Founder-CEOs are more likely to opt for departure instead of rotation than other C-level founders. 18

This same would hold true in cases in which the founder opts for a rotation to a nonexecutive position, for example by taking on a seat in the supervisory board.

3.2 Outcome-Related Hypotheses

51

Procedural justice has been introduced as an important theoretical perspective within organizational theory. In exchange relationships in which decisions such as founder turnover decisions are taken in a procedurally just manner, partners build trust and are willing to accept unfavorable outcomes – such as founder turnover – acknowledging that those have resulted from fair decision making. Fiet et al. (1997), who include procedural justice as one key driver in their analysis of the dismissal of new venture team members, find that procedural justice in the relationship between the VC and the new venture team has a negative effect on the probability of new venture team member dismissal. The authors argue that VCs can exercise power only in a fair way if they do not want to jeopardize the founders’ commitment and enthusiasm. In order to promote and maintain procedural justice, VCs have to argue why a certain “punishment” in terms of changes in the executive team, is favourable for both, the investor and the entrepreneur. It is important to point out that when differentiating between internal and external turnover – as undertaken in this thesis – procedural justice can be expected to not influence the general turnover decision (cp. the first step of the conceptual framework in section 1.4 above) but only the subsequent outcome. In chapter 2.3 I have argued that the general turnover decision is based on economic reasoning and the given control and power distribution between the VC and the entrepreneur. However, once a turnover decision is taken, procedural justice amongst others will determine whether a founder leaves the company or stays with it. Following this line of argumentation, founders accept a general turnover decision more easily if they perceive it to be the result of a just decision procedure. Following Lind (2001), in cases of what I call undesirable turnover19, founders accept and potentially even support the turnover decision by opting for a rotation solution rather than leaving the company. Vice versa, external turnover will occur if perceived procedural justice is low, i.e., if founders perceive the decision of losing their prior position as unfair. From this, the following hypothesis can be derived:

19

For a discussion of the terminology differentiating undesirable from desirable turnover refer to section 2.3.

52

3 Hypotheses and Combined Theoretical Model

H11: The lower the level of perceived procedural justice, the more the founder is likely to opt for departure instead of rotation.

3.3 Performance-Related Hypotheses This paragraph introduces hypotheses regarding the third step in the three-tier framework where I look at performance implications of turnover, rotation, and departure. First, I consider turnover in general. Second, I hypothesize on different turnover implications of rotation and departure. All performance-related hypotheses derived in this section are based on job matching theory with special emphasis on extensions to the life cycle paradigm and human capital theory as introduced in chapter 2.3.1. Considering turnover, it is reasonable to assume that VCs only make a founder leave his position if they can find somebody who they expect to be a better replacement for him. It must be stressed again that VCs act with the intent of value maximization when changing management team setups. Acknowledging this, it should also be true that founder turnover gives way to the hiring of a manager better matching the newly given job design. He can therefore be expected to enhance company performance through better management decisions. Chandler et al. (2005a) provide some complementary reasoning mainly considering low-performing founders. Reviewing the literature on personnel turnover, the authors note that there is substantial empirical evidence for the poorest performers to leave an organization (McEvoy and Cascio 1987). A rationale for this is that both employers and employees – in the given context VCs and founders – recognize when things are not working out well. The combination of not achieving and not being valued in the organization creates an environment in which poor performers are more likely to leave than good performers. The authors argue that if poor performers leave there are also considerable positive effects on overall team performance. In fact, there are transaction costs and risks involved with management turnover in start-ups. Transaction costs include the cost associated with recruiting and hiring a new manager and the company’s distraction with non-market oriented activities. Anecdotal evidence suggests that VCs indeed fear the replace-

3.3 Performance-Related Hypotheses

53

ment of founders, who tend to play central roles in their start-ups. Investors fear that a removal of central figures from small teams may reduce team and hence company performance. Thus, VCs are aware of the fact that changes in the team are associated with a certain risk of failure and a temporary slow down in company development. Nevertheless, I assume VCs on average to take management decisions which in the long run improve company performance. In fact, especially if there is risk involved in the decision of founder turnover, VCs will only act if their prospective benefit from a turnover decision is sufficiently high. Combining the presented perspectives on the performance impact of turnover leads me to the following hypothesis: H12: Founder turnover improves subsequent company performance. However, there are differences in the magnitude of expectable performance impact dependent on the type of turnover the founder chooses. I argue that rotation should generally be better for firm performance than departure. The main reason for this is that departing founders may take valuable idiosyncratic knowledge with them leaving the company with substantial problems to acquire substitutes over the labor market. Especially if founders were entrenched in the company, the cost associated with their departure can be assumed to be high. Bamford et al. (2005) argue that much of the social capital of the organization is embedded within the founder. Furthermore, anecdotal evidence suggests that founders rotating to a new job position internally can remain important as “evangelists” of their companies towards employees, as well as external stakeholders like suppliers and customers. In other words, a founder leaving the company may lastingly harm the start-up. Thus, given a positive impact from turnover as proposed in hypothesis 12, I expect the turnover type to determine an additional incremental improvement or deterioration of post-turnover performance. Based on this, I derive the following hypothesis: H13: Founder rotation and departure have opposite effects on post-turnover company performance. H13a: Founder rotation is beneficial to post-turnover company performance.

54

3 Hypotheses and Combined Theoretical Model

H13b: Founder departure is detrimental to post-turnover company performance.

3.4 Combined Theoretical Model Figure 3.1 combines the hypotheses related to all three tiers of the conceptual framework. Selection-related, outcome-related, as well as performance-related

VC influence

+ (H1)

+ (H2) Private VC

Company (hyper) growth

+ (H12)

Turnover

- (H3a) + (H3b) Control variables

+ (H8)

Company performance

Founder job match

- (H4)

Founder performance

- (H5)

Founder professional experience

- (H6)

+ (H10)

Founder firm-specific know-how

- (H7)

- (H11)

(H13)

Rotation/ Departure

Founder CEO

- (H9)

Figure 3.1:

Combined theoretical model illustrating hypothesized effects

VC procedural justice

3.4 Combined Theoretical Model

55

hypotheses are visualized and combined in one model. The model also included control variables which are discussed in detail in paragraph 5.3.3. I will refer to this model in the subsequent chapters when defining variables to operationalize measurements of the hypothesized effects and when testing the shown hypotheses in multivariate analyses.

4 Analysis of VCs’ Founder Turnover Decisions In this chapter, I look at the decision making of VCs who according to the reasoning laid out in chapter 2 and 3 are assumed to be driving turnover processes in their portfolio companies. In order to better understand founder turnover decisions at VC firms, I analyze a dataset collected in a conjoint experiment among Germany-based VCs. Evidence derived from the VC perspective on founder turnover decisions can serve as an important complement to the results obtainable through a survey among entrepreneurs. Since it is my goal to develop an as complete understanding of founder turnover as possible, I first consider the VC viewpoint before I turn to my second study covering the entrepreneur’s perspective (cp. chapter 5). The following analysis unveils first evidence supporting the combined theoretical model (cp. Figure 3.1). Since VCs are major drivers of founder turnover in their portfolio companies, their decision criteria importantly coincide with its empirically observable determinants. Acknowledging this, at the end of this chapter, I will interpret the results from the following analyses with respect to the theoretical model. Figure 4.1 shows the theoretical model and highlights six determinants which I will cover in my analyses throughout this chapter. This chapter is structured as follows. In section 4.1, I introduce the research design. In section 4.2, I describe the collected dataset and key variables used subsequently. Section 4.3 covers the descriptive analysis, while section 4.4 presents multivariate estimation results. Finally, I discuss implications of the obtained results for the remainder of this book.

58

4 Analysis of VCs’ Founder Turnover Decisions

VC influence

+ (H1)

+ (H2) Private VC

Company (hyper) growth

+ (H12)

Turnover

- (H3a) + (H3b) Control variables

+ (H8)

Founder job match

- (H4)

Founder performance

- (H5)

Founder professional experience

- (H6)

Founder firm-specific know-how

- (H7)

Company performance

(H13)

Rotation/ Departure

+ (H10) Founder CEO

- (H11)

VC procedural justice

- (H9) Determinants discussed from the VC perspective Determinants not discussed from the VC perspective

Figure 4.1:

Determinants of founder turnover discussed from the VC perspective

4.1 Research Design There is no doubt that the perfect data source for an empirical study of VC decision making on founder turnover would be the extensive and comprehensive data inventories kept at every VC company. Containing meeting notes, board reports, internal memos, and similar documentation, those inventories comprise concise

4.1 Research Design

59

information about VCs’ decision processes and their reasoning as to turning over a founder in real-life cases. However, after explorative talks with several partners at Munich-based VC firms, I realized that they would not grant access to those data. Decisions about founder turnover in portfolio companies and related business practices are regarded as strictly confidential by the VCs. Therefore, they do not allow any analyses of their data inventories. Accepting the reluctance of VCs to provide real-life data, I decided to overcome this obstacle by facing decision makers with hypothetic scenarios in which they had to decide about founder turnover. This setup allowed me to have the same decision makers taking hypothetical turnover decisions based on scenario information. The method used to this end is a conjoint analysis. As this method allows me to simulate respondents' decisions in real time, it has several advantages in comparison to commonly used post-hoc methods collecting data on VCs’ selfreported decision policies. Shepherd and Zacharakis (1999) point out that in a conjoint experiment VCs are not required to introspect about their thought processes. Thus, the method removes both, recall and post-hoc rationalization biases. Zacharakis and Meyer (1998) argue that those biases are especially relevant in the VC context. They show that there is only a low correlation between VCs’ reported decision policy with their actual decision policy, indicating important biases in post-hoc answers. Shepherd and Zacharakis (1999) note that conjoint analysis is an excellent technique for theory testing, i.e., for investigating hypothesized relationships between a number of decision criteria and a particular judgement. In the case of the research presented here this particular judgement is a founder turnover decision being influenced by a selected set of criteria. In the logic of the conceptual framework introduced in Figure 1.1 the analysis carried out here is concerned with step 1, i.e., the general decision of survival versus turnover. In the conjoint experiment, respondents were asked to judge a series of scenarios being combinations of parameter specifications for a set of attributes. From the preferences revealed in this way, conclusions can be drawn about the contribution of the various parameter specifications of each attribute to the overall valuation a certain scenario receives by each individual. In particular, tradeoffs between different parameter specifications of the attributes presented to the

60

4 Analysis of VCs’ Founder Turnover Decisions

participant are quantified, so that ultimately a utility score is obtained for each parameter specification. In the following paragraphs I present how this method is applied to the specific research context of this thesis. In doing so, I follow the considerations by Green and Srinivasan (1978) who suggest the following 6 steps approach to conjoint analysis: 1.

Selection of a Model of Preference

2.

Data Collection Method

3.

Stimulus Set Construction

4.

Stimulus Presentation

5.

Measurement Scale for the Dependent Variable

6.

Estimation Method

The remainder of this section follows this structure from step 1 to 4, supplemented by a paragraph on an additional questionnaire handed out to participants. Step 5 is discussed later in paragraph 4.2.2 in the context of variable description. I elaborate on step 6 in the course of the empirical analysis in section 4.4. Since in the context of my research a stimulus means a scenario card presented to participants, I subsequently use the term scenario instead of stimulus. 4.1.1 Model of Preference The model of preference captures the assumed preference function underlying participants’ optimization calculations in decision making. There are several models that can be used to approximate the participants’ preference function in conjoint analysis (Green and Srinivasan 1978).20 Even though each of those models is particularly suited for specific research objectives, the authors point out that the most commonly used model is the part-worth function model being compatible with any arbitrary shape for the preference function. In order to benefit from the flexibility of the part-worth model, I select this function as the model of preference in the following considerations. The part-worth function model posits the following equation: 20

For a detailed discussion of different conjoint estimators also refer to Tausend (2006).

4.1 Research Design

61

t

sj

¦f

p

( y jp ) ,

(4.1)

p 1

where an individual’s preference s for the j th scenario is the sum over all fp being the function denoting the part worth of different parameter specifications of yjp for the p th attribute. Thus, in this equation yjp denotes the parameter specification of the p th attribute for the j th scenario. This equation shows that the part-worth model assumes additive preference structures. 4.1.2 Data Collection Method I use the full profile approach here. This approach utilizes the complete set of attributes in every scenario presented to participants. Given the fact that I work with five attributes only, information overload – the commonly mentioned limitation of the full profile approach – can safely be disregarded.21 The full profile approach rather gives a very realistic description of scenarios by defining the parameter values of each of the attributes and possibly taking into account the potential environmental correlations between attributes in real-life situations. In fact, even though there is a necessary simplification associated with conjoint analysis, the full profile approach very well replicates the type of trade-off situations VCs find themselves in when taking founder turnover decisions. 4.1.3 Scenario Set Construction In the actual experiment, due to the full profile approach, all five attributes were identical across the scenario cards, while parameter specifications differed. In order to determine which attributes to include in the scenario cards, I followed a two-step approach. First, from an exploratory, interview-based study among 10 Munich-based VCs investigating criteria important to them in founder turnover decisions, I compiled an extensive set of possible attributes. This exploratory 21

For more than five or six attributes the possibility of information overload and the resulting temptation on the part of the respondent to simplify the experimental task by ignoring variations in the less important attributes exists. The conjoint results obtained under such conditions may not be representative of the real life decision making of the individual (Green and Srinivasan 1978).

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4 Analysis of VCs’ Founder Turnover Decisions

approach is in line with recommendations by Braun and Srinivasan (1975) regarding the identification of parameters to be included in the experimental design. Second, I mapped the extensive set of potential attributes against the determinants explaining the selection of turnover cases as described by the combined theoretical model (cp. Figure 3.1). Since it is my intention to study VCs’ decision criteria in the selection of turnover cases from survivors, I combined all those selection-specific determinants in the scenario description that were founder and company – but not VC – related. Ultimately, the following 5 attributes formed the scenario description:22

–

Founder’s performance over the last 12 months (FPE)

–

Founder’s professional experience in his function (FFE)

–

Founder’s firm-specific know-how (FSK)

–

Founder’s expected future job match (FJM)

–

Company’s expected growth (CGR)

In order to further keep scenarios easily understandable for participants, I decided to have no more than 2 parameter values per attribute, one for a high specification and one for a low specification. However, even this simple set of 5 attributes with only 2 parameter values each results in a total of 32 (25) scenario cards. This full set of combinations is called the full factorial plan. Due to the fact that each attribute has the same number of parameter values it is referred to as symmetrical in the literature.23 However, in practice, the set of scenarios resulting from the full factorial plan tends to become too extensive for respondents to rank in a meaningful way. Consequently, a fractional factorial design is used, which presents a suitable selection of scenarios from the full plan. Box, Hunter and Hunter (1978) show that in fact there is estimation redundancy in full factorials, stating that in terms of absolute magnitude main effects tend to be larger 22

23

For the sake of understandability of the task, Green and Srinivasan (1978) recommend to include not more than 5 to 7 attributes in a conjoint experiment. Contrarily to symmetrical factorial plans, asymmetrical designs include attributes with different numbers of parameter specifications across attributes (Addelman 1962b).

4.1 Research Design

63

than two-factor interactions, which again tend to be larger than three-factor interactions, and so on. Therefore, even if the reduction of experimental effort comes at the cost of interacting higher-order effects with main effects and lower-order interactions, the reduced design yields satisfactorily precise estimation results (Winer 1971).24 The most parsimonious fractional factorial design however is the orthogonal array, since it only captures the main effects for each parameter specification.25 By doing so, interactions between parameter specifications of one attribute with parameter specifications of another attribute are assumed to be fully negligible, i.e., two-factor and higher-order interactions are assumed to be irrelevant in participants’ decision making.26 The conjoint experiment I conducted for this research is based on a 25 factorial design as depicted in Table 4.1. The table illustrates the 32 scenarios resulting from the combination of 5 factors with either high or low parameter specifications. The scenarios tagged with asterisks were included in the fractional factorial design computed with the statistics package SPSS. Since the reduced design comprises 8 scenarios out of a total of 32 it is referred to as a one-fourth replication of the 25 factorial design or a 25-2 design. Two more scenarios were included in the set of scenarios presented to participants. Those carry an asterisk in brackets in Table 4.1 (scenario numbers 29 and 30) and are not used to estimate main effects but to check the validity of the model. 4.1.4 Scenario Presentation Participants were asked to rank the 10 scenario cards according to the probability with which a founder in a given scenario would face turnover. In order to notionally put participants in a realistic setting, I described a reality-like situation. I gave a description of this situation to every participant in written before they

24

25

26

For an in-depth discussion of the construction of fractional factorial designs refer to Cochran and Cox (1950), Winer (1971), and Box, Hunter and Hunter (1978). The set of scenario cards generated by SPSS is such an orthogonal array drawn from the full factorial design (SPSS Inc. 2005). Refer to Addelman (1962a) for a detailed description of symmetrical and asymmetrical orthogonal main effect plans.

64

4 Analysis of VCs’ Founder Turnover Decisions

received the cards. The description read as follows: “As one of the lead investors you are evaluating the management team of a portfolio company. You are thinking about the occupation of the CEO position. More precisely, you are evaluating whether the founder-CEO should be staying in his position or whether he should be leaving it within the next months (turnover). Given a turnover incident, it is not predictable if the founder will leave the company or stay with it in a different job position.” Since it is an important issue in conjoint analysis to keep the thought-experiments manageable for the interviewees, I chose to describe a situation in which the respondents were put in the position of being lead investors. This allowed them to think of a setting in which they were likely to be powerful enough to execute their decisions. Furthermore, I simplified the hypothetic situation by only considering CEOs facing turnover, excluding other C-level founders from the though-experiment. A third simplification made in the experiment was to not distinguish between rotation and departure outcomes but to focus the experiment on the initial turnover decision taken by the VC. Thus, with respect to the conceptual framework of founder turnover the experiment only investigates the first step of the three-tier framework, i.e., the selection of turnover cases. After the description of the reality-like, though hypothetical situation, I presented the following task to every participant: “You are now given 10 scenario cards. Imagine each of those scenarios. Please order the 10 scenario cards according to the probability at which you think a turnover will occur. Order the cards in a way that the scenario with the highest probability of turnover is first, while the scenario with the lowest probability of turnover is last.” Refer to Appendix 1 for the original German-language description given to the interviewees. This setup was thoroughly pre-tested with 4 VCs.27 Though some minor adaptations resulted from the pre-test, the interviewees confirmed that the task was clearly understandable and that the card ordering was easily manageable for

27

Given the explorative interviews with 10 VCs, the pre-tested setup proved to be widely adequate to study the problem at hand, so there was no need to pre-test with more than 4 individuals. Given the rather small number of VCs available for participation, it was my intention to keep the pre-test group small which left more participants for the final experiment on which my dataset (to be described in the following section) is based.

FFE

low high low low low high low low low high high high low low low high

FPE

high low low low low high high high high low low low low low low high

low low high low low low high low low high low low high high low high

FSK low low low high low low low high low low high low high low high low

FJM low low low low high low low low high low low high low high high low

CGR

Scenario Number 17 18 19* 20* 21* 22 23 24 25 26 27 28* 29(*) 30(*) 31* 32 high high high high high low low low low high high low high high high low

FPE high high low low low high high high low high low high high high high low

FFE low low high high low high high low high high high high low high high low

FSK high high high low high high low high high high high high high low high low

FJM low low low high high low high high high low high high high high high low

CGR

Table 4.1:

FPE = founder performance, FFE = founder functional experience, FSK = founder firm-specific know-how FJM = founder job match, CGR = company growth * Orthogonal array, computed with SPSS, included in conjoint experiment (*) Holdouts, computed with SPSS, included in conjoint experiment

Scenario Number 1 2 3 4 5 6 7 8 9 10 11* 12* 13 14 15 16*

4.1 Research Design 65

Reduced factorial conjoint design including holdouts

66

4 Analysis of VCs’ Founder Turnover Decisions

them. They explicitly stated that a total of 10 cards with 5 attributes each was an amount of information they felt comfortable to deal with. After incorporating the feedback from the pre-test, the conjoint experiments were conducted according to a fixed scheme. I was personally present during every experiment. None of the participants encountered any problems in ranking the conjoint cards. 4.1.5 Questionnaire Besides the card ordering, there was a second task presented to every participant. All interviewees were assigned to fill in a two-page questionnaire (see Appendix 3). I used this questionnaire to gather additional, mostly respondent-related data such as background information on the respondent (e.g. current position, education, industry and investment phase focus, professional experience, age, gender) and on the respondent’s experience as a VC (e.g. number of portfolio companies under supervision, number of board seats, experience with founder turnover decisions). In the last section of the questionnaire, I asked respondents to indicate their level of agreement to a set of statements regarding founder turnover. All questions in the questionnaire were directly related to the individual respondent, i.e., I was not interested in general company information but in information regarding the participant himself. For instance, I did not ask for an industry and investment focus of the VC firm in general but for the respondent’s industry and investment focus within his firm. During the pre-tests, as well as during the final experiment, the questionnaire was easily understood and quickly answered by respondents.

4.2 Dataset and Variable Description In this section I first describe the dataset I collected among German VCs. Subsequently, I give an overview of the variables used in the multivariate analyses. 4.2.1 Dataset My sample consists of 54 conjoint experiments which were conducted at 22 different VC firms located in the cities of Munich and Bonn in Germany. I had

4.2 Dataset and Variable Description

5%

67

5%

9% 35%

14%

32% Figure 4.2:

1 participant 2 participants 3 participants 4 participants 6 participants 9 participants

N=22, mean=2.45, std= 3.08

VC survey: breakdown of participants per VC company

contacted individuals at 32 companies, 22 agreed to participate in the experiment, 8 did not respond or were not interested, and 2 answered my request too late and could not be included in the interview round any more. Provided an individual agreed to participate, I asked him to invite as many of his colleagues as possible to also take part in the experiment. On average, 2.45 participants per company joined the experiment. Figure 4.2 shows the distribution of the number of participants per VC company. In two third of the cases only 1 or 2 individuals from a specific company took part in the experiment. In 23% of the companies 3 or 4 employees joined the experiment and in only 10% of the companies there were 6 or even 9 individuals available and willing to order the conjoint cards and fill in the questionnaire.28 This first description shows that I am using a convenience sample, which I cannot claim to be representative. However, a random sample of participants is hard to obtain given the time constraints in the VC industry and the time required 28

Cluster effects may result from the participation of several respondents employed with the same company. However, I avoid biased statistical results by accounting for clusters in the subsequent conjoint analysis.

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4 Analysis of VCs’ Founder Turnover Decisions

for interviews (Smart 1999). Nevertheless, as can be seen from Table 4.3 the sample contains a broad range of information collected among different types of decision makers in the German VC industry. In fact, with regards to the VC type, my sample includes respondents from private VCs (48%), company-related VCs (33%), and government-related VCs (19%). 4.2.2 Variable Description In this paragraph, I describe the dependent variable, as well as the independent variables which are used in the subsequent empirical analysis in section 4.4. I first describe the construction of the dependent variable followed by the description of independent variables compiled from the scenario cards. 4.2.2.1

Dependent Variable

The fifth out of the six relevant issues to be addressed in the implementation of a conjoint analysis is the measurement scale for the dependent variable.29 In order to identify the impact of founder and company characteristics (independent variables) on VCs’ turnover decision (dependent variable) I asked participants to provide an ordinal ranking of scenarios according to turnover probability. Following Franke et al. (2006), the model used interprets the 10 rankings assigned to the scenarios by each respondent as a rank ordering of choices from a given set. Consequently, the dependent variable is a non-metric variable. In conjoint experiments, those tend to be more reliable, since it is easier for respondents to say which scenario they prefer more as compared to expressing the magnitude of their preference which would be required in case of metric measurement. 4.2.2.2

Independent Variables

I use two types of independent variables. First, the attributes used in the scenario description form five independent variables. Second, in order to isolate effects attributable to the type of private VC as proposed in hypothesis 2, I construct five interaction variables by multiplying those original variables with a dummy indicating that the participant is a private VC.

29

Cp. Green and Srinivasan (1978) and section 4.1 above.

10

9

8

7

6

5

4

3

Skills needed in the founder 's position to further develop the business… … can be clearly observed with the founder. … can be clearly observed with the founder. … can be clearly observed with the founder. … cannot be clearly observed with the founder. … cannot be clearly observed with the founder. … can be clearly observed with the founder. … cannot be clearly observed with the founder. … cannot be clearly observed with the founder. … can be clearly observed with the founder. … cannot be clearly observed with the founder. ... is expected to grow very strongly . ... is expected to grow very slowly . ... is expected to grow very strongly . ... is expected to grow very slowly . ... is expected to grow very strongly . ... is expected to grow very slowly . ... is expected to grow very slowly . ... is expected to grow very strongly . ... is expected to grow very strongly . ... is expected to grow very strongly .

According to planning, the company …

Table 4.2:

2

… has gained extensive experience … has gained extensive experience … has gained little job experience … has gained little job experience … has gained little job experience … has gained little job experience … has gained extensive experience … has gained extensive experience … has gained extensive experience … has gained extensive experience

… mostly missed the goals of the business plan. … mostly missed the goals of the business plan. … mostly achieved the goals of the business plan. … mostly missed the goals of the business plan. … mostly achieved the goals of the business plan. … mostly achieved the goals of the business plan. … mostly achieved the goals of the business plan. … mostly missed the goals of the business plan. … mostly achieved the goals of the business plan. … mostly achieved the goals of the business plan.

1

Idiosyncratic know-how held by the founder …

job … is high and of high value to the company. job … is low and of low value to the company. … is low and of low value to the company. … is low and of low value to the company. … is high and of high value to the company. … is high and of high value to the company. job … is high and of high value to the company. job … is low and of low value to the company. job … is low and of low value to the company. job … is high and of high value to the company.

In his current function, the founder …

Over the last 12 months, the founder …

Scenario Card Number

4.2 Dataset and Variable Description 69

Description of scenario cards handed out to VCs

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4 Analysis of VCs’ Founder Turnover Decisions

Looking at the original dependent variables first, I introduce founder performance, founder functional job experience, founder firm-specific know-how, founder job match, and company growth. As I have mentioned above, in any given scenario, each attribute either takes on a high or a low parameter value. Founder performance was operationalized by describing a situation in which the founder had mostly achieved (high parameter specification) or mostly missed (low parameter specification) the goals of the business plan. Next, the founder was either assumed to have gained extensive (high parameter specification) or little (low parameter specification) prior functional job experience. The founder’s firm-specific know-how was either assumed to be at a high level associated with a high value for the start-up (high parameter specification) or at a low level associated with a low value for the start-up (low parameter specification). To describe the quality of the founder’s job match, I assumed the skills needed in the founder’s management position to further develop the business were either clearly observable (high parameter specification) or not clearly observable (low parameter specification). Finally, I described company growth as either very high (high parameter specification) or as very low (low parameter specification). Table 4.2 summarizes all scenarios used in the conjoint experiment. The dependent variables either occur in their high or their low states in each scenario. As an example, Appendix 2 illustrates the card presenting scenario number 1. In addition to the five dependent variables from the scenario cards I calculate five variables by interacting the original variables with a dummy composed from participants’ answers in the additional questionnaire. The dummy used for interaction takes on the value of 1 for all VCs who indicated they were working for a private VC firm and 0 for the remainder of VCs. The interaction variables thus measure the combined effects of a high or low specification of each attribute and the fact that the respondent is a private VC.

4.3 Descriptive Statistics

71

4.3 Descriptive Statistics In this section, I describe variables retrieved from the additional questionnaire. The sample compiled from the questionnaire can be divided into three groups of variables. The first group are individual characteristics of the respondents, the second group gathers variables on participants’ experience in the VC industry, and the third group are turnover-related statements for which every respondent was asked to express his personal assessment. Table 4.3 (see next page) gives an overview of the variables from all three groups. The following paragraphs discuss every group in detail by delivering comprehensive descriptive statistics. The presented descriptive statistics provide background information on the VCs who contributed their rankings in the experiment. Apart from describing the group of respondents, I will interpret the results with respect to founder turnover. By doing so, I intend to provide a first understanding of the role of VCs in founder turnover decisions. 4.3.1 Respondents’ Characteristics The first set of variables includes information on the VC type, respondents’ age and gender, their position at the VC firm, their educational background and industry specialization, as well as their specialization across investment phases.30 Figure 4.3 shows a summary of participants according to age groups. With VCs generally not hiring university graduates but professionals with relevant industry work experience, there was only a small fraction of participants aged under 30 (6%). The majority of participants was aged 30 to 39 (46%), the second largest group was represented by the 40 to 49 year olds (35%). 9% were between the age of 50 and 59, and 4% were over 60. Consequently, my sample includes both, rather young and potentially inexperienced investment professionals and older, i.e., rather experienced decision makers, with a minority of very senior VCs.

30

The classification of VCs into private, company-related and government-related is not based on the questionnaire but on separate Internet research.

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4 Analysis of VCs’ Founder Turnover Decisions

Turnover-related

Experience

Individual Characteristics

Table 4.3:

Description of questionnaire dataset

Variable Mean Age 39.815 Demographics Gender 0.963 General Partner 0.556 0.333 Position at VC Senior Associate Firm Junior Associate 0.093 Other position 0.019 Business 0.759 Engineering 0.352 Natural sciences 0.148 Educational Background Humanities 0.037 Law 0.056 Other specialization 0.037 Technology 0.685 Life sciences 0.130 Industry Services 0.093 Specialization Media 0.389 Other field 0.093 Seed phase 0.648 0.796 Investment Phase Early phase Specialization Later phase 0.537 Expansion phase 0.241 VC since (year) 2000.370 Num. of founded companies 1.596 Respondent's Former VC funded manager 0.296 Experience in VC Num. of coachings 5.426 Industry Num. of supervisory board seats 2.648 Involved in turnover decision 0.704 Time of implementation 4.868 Implementation Time loss 6.796 VC impact 4.000 Founder reluctance to leave 4.204 Attitudinal Founder development 3.778 Variables Turnover risky 3.019 Turnover process transparent 4.167 Turnover much too early 0.000 Turnover too early 0.000 Timing of Turnover Turnover at right time 0.037 Decisions Turnover too late 0.796 Turnover much too late 0.167

Std 8.460 4.596 2.004 4.833 2.468 3.830 3.890 0.583 0.730 5.309 0.934 1.102 -

Min 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1984 0 0 0 0 0 0 0 3 2 1 1 1 0 0 0 0 0

Max 67 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2007 10 1 20 10 1 18 15 5 5 42 5 5 0 0 1 1 1

N 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 52 54 54 54 54 53 49 53 54 54 54 54 54 54 54 54 54

4.3 Descriptive Statistics

9%

4%

73

6%

under 30 30 to 39 40 to 49 50 to 59 46%

35%

over 59

N=54, mean=39.81, std=8.46 Figure 4.3:

VC survey: age of respondents

Figure 4.4 gives a view on positions held by the respondents: More than half (54%) indicated they were General Partners and one third (33%) stated to be Senior Associates. A total of 9% were Junior Associates and 4% held other positions in the company. An interaction of the age group and position dummies shows that the sample very well replicates the classical hierarchy levels and roles in VC firms being related to work experience and therefore age. 100% of all interviewees under the age of 30 indicated they were Junior Associates in their company. 68% of all participants in the age group of 30 to 39 were Junior or Senior Associates. Even 100% of participants older than 49 years were General Partners. This finding is in line with Sahlman (1990) who finds that a fund with USD 200 M in committed capital is typically managed by a professional staff of between 6 and 12. According to the author, most venture-capital firms have several General Partners and a staff of Associates and administrative support personnel, where Associates function as apprentices to the General Partners and often become General Partners themselves in later funds.

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4 Analysis of VCs’ Founder Turnover Decisions

9%

4%

General Partner Senior Associate 54% 33%

Junior Associate Other Position

N=54 Figure 4.4:

VC survey: respondents’ positions in VC companies

It is worth mentioning that only 4% of all respondents, i.e., 2 individuals, were female. This shows that the German VC industry is strongly dominated by men. Even though my sample is not representative, anecdotal evidence suggests that the dominance by men in the VC industry is even more pronounced than their general dominance in entrepreneurship in Germany (Wagner 2007). As a consequence, I am not able to statistically analyze female versus male decision making in turnover situations. Looking at respondents’ education, 41 individuals (76%) had an educational background in business, 19 had been educated in the field of engineering (35%), 8 in natural sciences, 2 in law, 2 in humanities, and another 2 in a different field. This reflects the well-known observation that VCs combine business with technical know-how, thereby building competencies to evaluate new venture proposals and start-up management issues from a business model and a technical feasibility point of view. In this regard, the respondents included in the sample can be regarded as highly and broadly educated. Figure 4.5 depicts the respondents’ educational background.

Number of respondents_

4.3 Descriptive Statistics

75

45 40 35 30 25 20 15 10 5 0 Business Engineering

Natural Sciences

Law

Humanities

Field of education Figure 5.5:

Other

N=54

VC survey: respondents’ educational background

Participants were asked to indicate their industry specialization.31 Answers are outlined in Figure 4.6. 37 interviewees (66%) indicated they were specialized in the field of new technologies. 21 respondents were specialized in new media and entertainment (39%), 7 in life sciences (13%), and 5 in services (9%). From this, it can be seen that VCs tend to focus their activities and invest their funds in young, dynamic high-growth industries. Moreover, this result allows drawing the assumption that the sample widely covers the VC landscape.

31

Note that Figure 4.6 does not cover the fields of activity of the 22 venture firms included in the dataset but the respondents’ individual specializations.

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4 Analysis of VCs’ Founder Turnover Decisions

Number of respondents_

40 35 30 25 20 15 10 5 0 Technology Media/Entertainment Life Sciences Industry Specialization Figure 4.6:

Services N=54

VC survey: respondents’ industry specialization

Moreover, I asked participants in which investment phases they were currently managing portfolio companies. Figure 4.7 shows their answers. 35 VCs or 65% of all respondents indicated they were focussing on seed phase investments, even more, 43 individuals or 80% said they were involved in start-up phase investments. Still more than half (29 respondents, i.e., 54%) responded they were doing later phase investments. The minority of 24% (13 interviewees) was involved in expansion deals. The graph thus clearly shows VCs’ focus on early stage investments with a strong foothold in both, downstream seed and upstream expansion investments. In essence, the dataset covers a comprehensive range of different VC types. The dataset includes both, experienced and inexperienced VCs – most of them men. Participants have broad educational backgrounds and are specialized in typical VC industries and investment phases.

Number of respondents_

4.3 Descriptive Statistics

77

50 45 40 35 30 25 20 15 10 5 0 Seed Phase

Early Phase

Later Phase

Investment phase Figure 4.7:

Expansion N=54

VC survey: respondents’ investment phase specialization

4.3.2 Respondents’ Experience This paragraph discusses insights compiled from the second subset of variables obtained through the questionnaire. Including information on respondents’ exposure to the VC industry, their own entrepreneurial record, their roles in the support and supervision of portfolio companies, and their involvement in founder turnover decisions, those variables capture important additional information on the experience of respondents. Figure 4.8 gives an overview of the number of respondents’ total years in the VC industry. Several insights can be gained from this data. First, only 5 individuals equalling 9% of the sample have been in the industry for more than 10 years. This shows that the German VC industry still is very young compared to the established US VC industry. Second, the graph shows two cycles characterizing the German VC industry. There are VC “veterans” from the first VC boom phase between 1998 and 2001 who today are in the industry for 9 to 6 years. The downturn of the industry is well observable in Figure 4.8, too. There is hardly anybody who started his career in the industry between 2002 and 2004 when global stock and equity markets plunged. However, the chart also shows the

Number of respondents_

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4 Analysis of VCs’ Founder Turnover Decisions

10 9 8 7 6 5 4 3 2 1 0 0

1

2

3

4

5

6

Number of years Figure 4.8:

7

8

9

10

>10

N=54, mean=6.63, std=4.60

VC survey: respondent’s work experience in VC industry

recent upturn in the VC industry with more people entering VC businesses over the last three years. In essence, Figure 4.8 again supports the fact that the sample includes both, well experienced senior VCs and rather junior and potentially less experienced VCs. Own entrepreneurial experience and expertise helps VCs to better select promising investment ideas and to successfully support the development of portfolio companies, especially in their early phases of growth (Brinkrolf 2002; Tausend 2006). Moreover, own prior entrepreneurial activity allows VCs to better understand the perspective and the situation of entrepreneurs in their portfolio companies. I asked participants in the conjoint experiment to answer two questions, first, to which extent the participant had been involved in company foundations himself and second, whether the respondent had financed own startup activities with VC money. Figure 4.9 illustrates that the majority of participants had never been involved in new venture creation, 21% had co-founded one company, 23% two companies, 8% 3 companies, 6% 4 companies and another 6% more than 4. Interacting the variable with a dummy for the group of VCs aged 39 and younger shows that

4.3 Descriptive Statistics

6%

79

6%

8% 36%

23%

21% Figure 4.9:

0 companies 1 company 2 companies 3 companies 4 companies >4 companies

N=52, mean=1.60, std=2.00

VC survey: number of companies founded by respondents

more than 68% of those who had never founded a company were in this age bracket. This allows for the interpretation that most of the senior and upper-level VCs do have own entrepreneurial experience while the majority of the younger and more senior-level employees do not have such experience. 35 individuals indicated that they had co-founded at least one company. 37% of them (13 individuals) had received VC funding during their entrepreneurial career while the majority of 22 VCs (63%) had not. To further explore the experience of respondents, I asked them to indicate, how many companies from their portfolio they were currently coaching. Moreover, I was interested in the number of companies at which respondents held supervisory board seats. Additionally, I wanted to explore whether participants had ever been involved in founder turnover decisions. Figure 4.10 gives an overview of the number of companies coached by the individual VCs. In this context, coaching refers to a situation in which the VC is the designated investment manager or partner at the VC firm who is responsible for the interaction with the management team of a specific portfolio company.

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4 Analysis of VCs’ Founder Turnover Decisions

9% 22% 17%

0 companies 1 or 2 companies 3 or 4 companies 5 or 6 companies > 6 companies

28%

24%

N=54, mean=5.43, std=4.83 Figure 4.10: VC survey: number of companies coached by respondents

Coaching includes frequent communication providing support to the top management as well as corporate control – including potential founder turnover discussions. It can be seen that only very few VCs do not coach any of their portfolio companies at all. Those are mostly new-hires at the VC firms. 32 About 70% of all participants coach between one and six companies. With a value of 5 at the median, the variable shows a long tail distribution with one respondent even coaching 20 companies at a time (cp. Table 4.3). With respect to the number of supervisory board seats per participant, the distribution across interviewees looks different. 28% do not hold supervisory board seats. 11% of VCs hold 1 seat, 15% hold 2 seats, 9% hold 3 seats, 15% hold 4 seats, and 13% hold 5 seats. Holding more than 5 board seats is rather exceptional.

32

One might wonder whether those junior VCs are competent to comment on founder turnover issues at all. However, since newly hired employees can be expected to very quickly learn the functioning of the VC business from both interaction with senior colleagues and own project work their answers are also taken into account.

4.3 Descriptive Statistics

81

9%

0 seats 1 seat 2 seats 3 seats 4 seats 5 seats >5 seats

28%

13%

15% 11% 9%

15%

N=54, mean=2.65, std=2.47

Figure 4.11: VC survey: number of board seats held by respondents

Both results, the average number of coached as well as the average number of supervised companies, clearly indicate that VCs are highly involved in the management of their portfolio companies. In how far this involvement implies founder turnover decisions is discussed next. In fact, the vast majority of respondents had been involved in founder turnover decisions. 70% reported they had actively been involved in founder turnover decisions. This finding again stresses the relevance and the importance of founder turnover in venture capital funded start-up companies. To summarize, the sample includes mostly experienced VCs who are active in coaching and supervising their portfolio companies. More than two third of them had been involved in founder turnover decisions throughout their careers. 4.3.3 Respondents’ Assessment of Founder Turnover This paragraph summarizes variables related to founder turnover decisions. It gives insights into how VCs assess and judge founder turnover. In accordance with the results I obtained from my exploratory interviews with VCs, data from the questionnaire suggest that founder turnover tends to be a lengthy process which

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4 Analysis of VCs’ Founder Turnover Decisions

6%

4%

8%

8%

immediately 1 to 3 months 4 to 6 months 37%

7 to 9 months 10 to 12 months >12 months

37%

N=53, mean=4.87, std=3.83 Figure 4.12: VC survey: time needed for turnover implementation

often retards company development. The following diagrams show that the participants in my experiment mostly support both suppositions. I asked participants to give their estimate of the average time it takes to implement founder turnover. Implementation time was defined as the time between a definite positive turnover decision and the moment in which the founder actually left his position. Figure 4.12 shows that roughly two third of all respondents estimate a time below half a year to implement founder turnover. Only 4% believe that implementation time is more than one year. Most VCs estimated the overall amount of time a company loses in the course of a turnover event in a range between 4 and 6 months (40%). 12% thought the time loss was less and quoted values between 1 and 3 months. 18% said it was between 4 and 8 months, another 18% indicated it was rather 7 to 9 months. Only 4% felt that companies lose more than 12 months of time in their development due to founder turnover. 8% however did not think the company loses any time at all. Those results support the view that turnover importantly troubles the corporate setup and puts the company under organizational stress detrimental to its development. Refer to Figure 4.13 for a summarizing chart.

4.3 Descriptive Statistics

4% 18%

83

8% 12%

none 1 to 3 months 4 to 6 months 7 to 9 months 10 to 12 months

18%

>12 months 40%

N=49, mean=6.80, std=3.89 Figure 4.13: VC survey: time loss from founder turnover

Next, I presented a set of six statements to the interviewees asking them to indicate their level of agreement or disagreement on a 5-point rating scale. The following charts present VCs’ assessments of those statements. Figure 4.14 shows a very clear vote. 83% of the respondents agree or fully agree to the statement that founder turnover induced by VCs has a positive impact on company performance. This supports the view of VCs adding value through reshaping the top management team. However, the actual influence of turnover on firm performance yet has to be analyzed based on field data, which I will do in section 5.6. The positive assessment of founder turnover on company performance by VCs is likely to be highly biased. Therefore, it is recommendable to compare field data with VCs’ assessments before deriving results. VCs’ evaluation of founders’ behavior associated with management team changes is similarly clear. 85% agree or fully agree that founders – to the company's detriment – often hold on to their positions too long. Figure 4.15 depicts the results.

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4 Analysis of VCs’ Founder Turnover Decisions

Number of respondents_

Statement: "Turnover has a positive influence on company performance."

40 35 30 25 20 15 10 5 0 1 (fully disagree)

2 (disagree)

3 (neither, nor)

4 (agree)

5 (fully agree)

N=54, mean=4.00, std=0.58

Figure 4.14: VC survey: assessment of turnover influence on performance

Statement: "Founders hold on to their positions too long."

Number of respondents_

30 25 20 15 10 5 0 1 (fully disagree)

2 (disagree)

3 (neither, nor)

4 (agree)

5 (fully agree)

N=54, mean=4.20, std=0.73

Figure 4.15: VC survey: assessment of founders’ reluctance to leave

4.3 Descriptive Statistics

85

Statement: "Founders are mostly able to grow with their tasks."

Number of respondents_

35 30 25 20 15 10 5 0 1 (fully disagree)

2 (disagree)

3 (neither, nor)

4 (agree)

5 (fully agree)

N=54, mean=3.78, std=5.31

Figure 4.16: VC survey: assessment of founders’ ability to grow

Participants’ opinion on whether founders are able to grow with their tasks in a growing company is symmetrically distributed. While 19% agree or fully agree, 22% disagree or fully disagree. The majority of participants (59%) does neither agree, nor disagree. The result suggests that on average there is a group of founders who indeed are able to grow with their tasks, while others are not. According to the theory laid out in paragraph 2.3.1, the probability for the latter group to face turnover should be higher. I will refer to this conjecture in section 5.5 where I study the determinants of turnover in multivariate analyses. A similar distribution results from participants’ ratings on whether or not founder turnover threatens the positive future development of the start-up company (cp. Figure 4.17). While 30% agree or fully agree to the statement that founder turnover is detrimental to company development, another 30% disagree or fully disagree to it. 40% neither agree, nor disagree. Therefore, I speculate that there are both cases in real life. While some VCs may have made rather positive experiences, others might have experienced cases of bad company development subsequent to founder turnover. Presumably, those 40% not opting to agree or disagree may have seen both types of cases in the course of their careers or lack any substantial experience at all.

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4 Analysis of VCs’ Founder Turnover Decisions

Statement: "Founder turnover threatens company development."

Number of respondents_

25 20 15 10 5 0 1 (fully disagree)

2 (disagree)

3 (neither, nor)

4 (agree)

5 (fully agree)

N=54, mean=3.02, std=0.93

Figure 4.17: VC survey: assessment of risk inherent in founder turnover

In order to get a VC perspective on the importance of organizational psychology as described in paragraph 2.3.3, I surveyed participants about their appraisal of transparency in founder turnover decisions. 78% of respondents agreed or fully agreed to the statement that the turnover decision process among the investors should be made fully transparent to founders at any time. Only 13% disagree or fully disagree. This may be taken as a hint that VCs do not clandestinely decide to fire a founder but tend to involve him in the turnover decision process. It seems that VCs appreciate procedural justice and hence create decision process transparency for entrepreneurs. With respect to timing, a striking majority of 96% of participants stated that founder turnover decisions were always taken too late or even much too late. Only 4% thought they were taken at the right point in time. Even though this result is somewhat surprising, an explanation for VCs reluctance to push for founder turnover early on might be their fear of time losses and insecurity associated with management replacements (cp. Figure 4.12 and Figure 4.13). Moreover, this result provides further evidence for the fact that VCs rarely take a proactive role by exchanging founder-manager early enough but rather wait until

4.3 Descriptive Statistics

87

Statement: "Decision processes should be transparent for founders."

Number of respondents_

35 30 25 20 15 10 5 0 1 (fully disagree)

2 (disagree)

3 (neither, nor)

4 (agree)

5 (fully agree)

N=54, mean=4.17, std=1.10

Figure 4.18: VC survey: assessment of transparency in decision making

performance problems arise before they become active. While it is understandable that VCs need some evidence of a deteriorating job match in terms of performance problems before they are able to implement founder turnover, their tremendous fear to “rock the boat” associated with management team changes often seems to make them irrationally hesitant to promote turnover decisions. In essence, this paragraph has brought up some mixed results. While VCs on the on hand seem to appreciate the positive performance impact of turnovers, on the other hand they often are hesitant to implement such decisions. Even though many investors support the view that founders may not be able to grow with their tasks, an important number of respondents also fears time loss and other negative influences associated with founder turnover. This may be one reason for the observation that an astonishingly high percentage of VCs supports transparent decision processes leading to founder turnover.

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4 Analysis of VCs’ Founder Turnover Decisions

4.4 Conjoint Analysis This section discusses empirical analyses based on the data collected in the conjoint experiment. I first introduce the method used for estimation in paragraph 4.4.1. In section 4.4.2, I present the estimation equation I use to analyze the data collected in the experiment. Last, I give an overview of the estimation results and their interpretation with respect to the combined theoretical model in section 4.4.3. 4.4.1 Estimation Method There are two methodological issues to be addressed. First, I introduce the assumed preference function of participants. Based on this function, respondents are expected to order the scenario cards. Second, given the observed ordering of the cards, I look at a discrete choice model. The model estimates the benefit respondents derived from the presence of any of the 5 scenario attribute. I assume that VCs’ ranking of scenarios is based on their expected benefit b from turning over a founder CEO in a given scenario. I denote the individual VC’s assessment bik for the benefit that VC i would be able to draw from removing the founder from his CEO position in situation k. The ranking chosen by each venture capitalist results from an ordering of the K alternatives, that is the unrestricted set of scenarios, according to their bik values b ik

x ik ß  H ik ,

(4.2)

where xik is a row vector of the characteristics of alternative k and ß is a column vector of coefficients. xik ß forms the deterministic component of utility and İik is the stochastic component. I assume that choices are consistent with the independence from irrelevant alternatives (IIA) property, which states that for any individual, the ratio of choice probabilities of any two alternatives is unaffected by the systematic utilities of any other alternatives. In other words, the IIA implies that adding another alternative or changing the characteristics of a third alternative does not affect the relative odds between alternatives k and j (Wooldridge 2002). Additionally, assuming an independent identically distrib-

4.4 Conjoint Analysis

89

uted extreme value distribution for the error term İik, the probability that any alternative k is ranked as the superior one by respondent i is given by

prob{b ik ! max(b ij ) jz k } exp(x ik ß) /(¦ j exp(x ijß)) .33

(4.3)

In a second step I aim at estimating the absolute and relative importance of scenario attributes and their respective parameter values to the participants. A suitable estimator to analyze the data resulting from the conjoint design is what marketing scholars refer to as the choice-based conjoint analysis method (Dillon et al. 1993). The model is also referred to as the Plackett-Luce (Marden 1995) or as the rank ordered logit model (Koop and Poirier 1994). It presumes that the given alternatives are assessed by the VCs based on a function which reflects the probability of turnover as a linear additive function of founder and company characteristics. The rank ordered logit model is statistically efficient due to detailed information in the dependent variable and due to the fact that – compared to other methods of preference measurement – only few respondents are needed to obtain reliable estimates (Shikano et al. 2005).34 4.4.2 Estimation Equation

Based on the description of the independent variables and the estimation method presented above the full specification of the model can now be derived. The estimation equation of the benefit bik that rater i would expect to derive from turning over a founder CEO in scenario k can be written as follows: b ik

D  FPE ik ß1  FFE ik ß 2  FSK ik ß 3  FJM ik ß 4  CGR ik ß 5  H ik (4.4)

where FPE denotes founder performance, FFE founder functional experience, FSK founder firm-specific know-how, FJM founder job match and CGR company growth as described in a given scenario k.

33

34

For a detailled description and derivation of the likelihood function for this model refer to Hausmann and Ruud (1987). Calfee et al. (2001) point out that the model makes full use of all ranking information by repeatedly applying the multinomial logit model to an “exploded” data set.

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4 Analysis of VCs’ Founder Turnover Decisions

The following example illustrates the mechanics of the rank order model. An individual’s ranking of A-C-B-D in a set {A, B, C, D} is taken to represent an observation in which A is chosen as the most preferred alternative from the full set {A, B, C, D}, C is the preferred alternative from the restricted set {B, C, D}, and B is chosen as the preferred alternative from the remaining set {B, D}. As I have mentioned before, the presented estimation method is also referred to as rank ordered logit. It is implemented in STATA 9 through the rologit command which I used to run estimations. Estimation results are presented next. 4.4.3 Multivariate Analysis

Table 4.4 features the rank ordered logit estimation results including the five scenario attributes. The dataset includes 540 observations which I group with an identifier variable linking the 10 choice alternatives for each participant. Furthermore, in order to adjust standard errors for intragroup correlation, I cluster respondents from the same VCF. This procedure leads to 54 groups (number of participants) of 10 observations each and 22 clusters (number of participating venture capital firms) included in the estimation. The coefficients (cp. first column in Table 4.4) can be interpreted as partworths, i.e., utility values added by each attribute and its respective parameter value. It can be seen that all five attributes are significant at the 1% level in explaining the ranking provided by VCs. In the given setup positive coefficients indicate positive utilities which reduce founder turnover probability. In turn, negative coefficients would stand for negative utility, equalling an increase in founder turnover probability. With the dichotomous nature of parameter specification used in the conjoint experiment, each variable showing a positive coefficient can be interpreted as adding utility in the case of a high state of the scenario attribute relative to the case of a low state of the scenario attribute. For instance, high founder performance adds 1.612 as an absolute additional utility perceived by VCs relative to the case of low founder performance. While the part-worths allow determining exact utility values for each scenario of the full factorial design (cp. paragraph 4.1.3) they do not directly indicate the relative importance of each scenario attribute to VCs. The latter however is

4.4 Conjoint Analysis Table 4.4:

91

Rank ordered logistic regression

The sample consists of 54 rank-ordered subsets of 10 choices each. The dependent variable is the rankordering. Independent variables include the 5 scenario attributes, i.e., founder performance, founder functional experience, founder firm-specific know-how, founder job match, and company growth. Rankings are grouped, observations from identical VCFs are clustered. The table reports coefficients, as well as the relative importance of independent variables. The relative importance is the share of the maximim utility achievable. It is calculated as the value of any coefficient devided by the sum of all coefficients. Dependent variable: rank-ordering Coefficient/ [Standard Error] Founder performance Founder functional experience Founder firm-specific know-how Founder job match Company growth

1.612*** [0.181] 0.731*** [0.113] 0.794*** [0.093] 1.811*** [0.182] 0.565*** [0.150]

Log pseudolikelihood Wald Chi2 Prob > chi2 Observations Number of groups Number of clusters

Relative importance 0.292 0.133 0.144 0.328 0.102 -613.512 193.020 0.000 540 54 22

* significant at 10%; ** significant at 5%; *** significant at 1%

needed to understand preferences underlying VCs’ decision making. I therefore look at the importance of all five attributes next. In detail, the relative importance of each attribute is calculated as follows. The contribution of founder performance to the overall score of the scenario with the highest turnover probability, compared to that of the scenario with the lowest turnover probability, equals 1.612, that of the founder’s functional experience 0.731, that of the founder’s firm-specific know-how 0.794, that of the founder’s job match 1.811 and that of company growth 0.565 (cp. the coefficients in the first column of Table 4.4). The relative importance as shown in the second column of Table 4.4 is attained by dividing the coefficient of any variable by the sum of the coefficients of all variables included in the analysis. For instance, the

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4 Analysis of VCs’ Founder Turnover Decisions

part worth for founder performance in Table 4.4 is calculated as follows: 1.612/(1.612+0.731+0.794+1.811+0.565). Founder job match is the most important attribute (32.8%) followed by founder performance (29.2%). Lower partworths are attributed to the founder’s firm-specific know-how (14.4%), the founder’s functional experience (13.3%) and company growth (10.2%).35 With respect to the theoretical model presented in section 3.4 the results of the conjoint analysis provide first very strong support for several hypotheses. Table 4.4 shows that VCs’ preferences underlying their decision making in founder turnover situations in fact widely support the relations hypothesized in the theoretical model. In detail, company growth (hypothesis 3a), founder job match (hypothesis 4), founder performance (hypothesis 5), founder firm-specific know-how (hypothesis 6) and founder functional experience (hypothesis 7) all have a highly significant positive influence on VCs’ turnover decisions. In accordance with the theoretical model, those five variables are negatively related with founder turnover decisions taken by investors.36 This indicates that those factors are indeed important in founder turnover decisions. Moreover, from a VC perspective a ranking of those five factors exists which puts founder job match first, followed by founder performance, founder firmspecific know-how, functional experience, and company growth. Thus, VCs obviously put high emphasis on the job match as well as on founder performance. The founder’s firm-specific know-how, his functional experience, as well as company growth clearly fall behind in their importance. The aggregate estimation as shown in Table 4.4, however, ignores the possibility that groups within the population have distinctive preferences. Generally 35

36

Franke et al. (2006) point out that the importance of a characteristic depends on the available parameter values. The more similar these are, the lower the importance of a given attribute will turn out to be. Therefore, the importance of an attribute must be interpreted with the underlying parameter values in mind. However, with the simple differentiation of high versus low parameter specifications as used in the experiment, both parameter values are in fact clearly differentiated. Note that the positive signs of coefficients in Table 4.4 are explicable by the fact that participants in the conjoint experiment were asked to rank the scenario card with the lowest turnover probability first. This leads to the effect that high parameter values are interpreted as being positively related to survival, which in turn means they are negatively related to turnover.

4.4 Conjoint Analysis

93

speaking, the direct comparison of groups defined a priori can help to unveil such distinctive preferences for a subgroup from VCs. In line with my reasoning motivating the first hypothesis, I separate respondents at private VC firms from those working with captive VC funds expecting the former to be more performance-oriented in their decision making than the latter.37 Table 4.5 shows the results obtained by including interaction variables in the analysis. The interaction variable is based on a VC type dummy with value 1 for private VCs and value 0 for captive VCs.38 The table can be interpreted as follows. When taking founder turnover decisions, private VCs prioritize founder performance and firm-specific know-how higher than the average VC in the sample. On the other hand, they prioritize founder functional experience, founder job match, and company growth lower than average. However, private VCs do not rate founder performance significantly more important than their colleagues at captive funds. None of the interaction terms is significant at the 10% level. Thus, the data compiled from the conjoint experiment do not support my theoretical reasoning where I stated that private VCs are likely to be more performance oriented than their colleagues at captive funds.39 One possible though tentative interpretation of this finding might be that at captive funds investment managers also put high emphasis on founder performance, e.g. because their personal incentives are closely linked to the fund’s portfolio return. This would explain why the personal preferences of all VCs are rather similar even though the respective institutional setups are different.

37

38 39

Hypothesis 1 states that turnover decisions are more often taken in private VC firms than in captive VC firms. The reasoning underlying this hypothesis is based on the assumption that private VCs need to achieve higher performance goals than captive VCs (cp. section 3.1). For details on the construction of the VC type dummy refer to paragraph 4.2.2.2. Moreover, a joint test of all five interaction variables yields a chi2-value of 8.930 (with a p-value of 0.112). Thus, there is no significant difference between the two groups either.

94 Table 4.5:

4 Analysis of VCs’ Founder Turnover Decisions Rank ordered logistic regression including interaction variables

The sample consists of 54 rank-ordered subsets of 10 choices each. The dependent variable is the rankordering. Independent variables include the 5 scenario attributes, i.e., founder performance, founder functional experience, founder firm-specific know-how, founder job match, and company growth, as well as 5 interaction variables. Rankings are grouped, observations from identical VCFs are clustered. The table reports coefficients, as well as the relative importance of independent variables. The relative importance is the share of the maximim utility achievable. The relative importance for the group of private VCs is calculated as the value of any coefficient of the interaction terms devided by the sum of all coefficients of the interaction terms. Dependent variable: rank-ordering Coefficient/ [Standard Error] Founder performance Founder functional experience Founder firm-specific know-how Founder job match Company growth Founder performance x Private VC Founder functional experience x Private VC Founder firm-specific know-how x Private VC Founder job match x Private VC Company growth x Private VC

1.419*** [0.187] 0.884*** [0.141] 0.799*** [0.103] 2.000*** [0.276] 0.745*** [0.144] 0.518 [0.396] -0.288 [0.232] 0.067 [0.198] -0.319 [0.397] -0.362 [0.255]

Log pseudolikelihood Wald Chi2 Prob > chi2 Observations Number of groups Number of clusters * significant at 10%; ** significant at 5%; *** significant at 1%

Relative importance 0.292 0.133 0.144 0.328 0.102 0.355 0.109 0.159 0.308 0.070 -607.491 236.800 0.000 540 54 22

4.5 Implications for Subsequent Course of Analysis

95

4.5 Implications for Subsequent Course of Analysis The conjoint experiment yields preferences from real time VC decision making which can be expected to be more reliable than preferences reported post hoc. Since I do not use conjoint analysis for validating but rather for exploring VCs’ decision behavior here, I interpret my results in the light of VC influence on turnover decisions in start-up companies. As I have argued above, the experiment yields substantial support for the importance of several hypothesized determinants of turnover. Figure 4.19 summarizes the implications of the obtained results in terms of their support of the theoretical model. Out of five propositions three are supported from the VC perspective, while two are not. The results obtained in the conjoint analysis mainly contribute to this thesis and its subsequent course of analysis. The findings outlined above provide an understanding of the VC’s perspective on founder turnover decisions. Especially their decision preferences with respect to specific attributes of turnover situations could be revealed. In the further course of analysis the results obtained from the conjoint experiment will serve as important points of reference and allow for comparisons between the VC perspective and the perspective of entrepreneurs. However, even though the conjoint experiment yields first promising empirical support for several of my theoretical propositions, revealed preferences obtained from an experimental environment are hard to interpret with respect to empirical reality. Therefore, I aim at drawing a more complete picture of founder turnover by broadening the empirical foundation of the thesis. Thus, in the subsequent step, I complement my analysis with field data collected among entrepreneurs before accurately testing all hypotheses in multivariate estimations.

96

4 Analysis of VCs’ Founder Turnover Decisions

VC influence

+ (H1)

+ (H2) Private VC

+ (H12)

Turnover

- (H3a) Company (hyper) growth

+ (H3b) Control variables

+ (H8)

Company performance

Founder job match

- (H4)

Founder performance

- (H5)

Founder professional experience

- (H6)

+ (H10)

Founder firm-specific know-how

- (H7)

- (H11)

(H13)

Rotation/ Departure

Founder CEO

VC procedural justice

- (H9)

H: Support from the VC perspective H: No support from the VC perspective

Figure 4.19: Contributions of conjoint experiment to the theoretical model

5 Determinants and Impact of Founder Turnover While my analyses in chapter 4 were exclusively based on data retrieved from an conjoint design, this chapter analyzes the determinants and the impact of founder turnover based on field data. It is structured as follows. In section 5.1, I introduce the research design used to collect data among entrepreneurs. Section 5.2 covers the description of the dataset including a discussion of sample selection, the data collection process, as well as selection and response biases. I describe all dependent, independent, and control variables used in subsequent analyses in section 5.3. Section 5.4 is concerned with descriptive statistics of the dataset. In section 5.5, I study the determinants of founder turnover, while in section 5.6 I look at performance implications of founder turnover in start-up companies. Section 5.7 summarizes the results in the light of the theoretical model.

5.1 Research Design In this chapter, I use a post-hoc design. I implemented an online survey among entrepreneurs in order to collect company and person specific data regarding founder turnover in German venture capital funded start-ups over the past 10 years. The subsequent analyses are based on this unique cross-sectional data set. For several reasons I used a structured online survey to collect data among entrepreneurs. First, given the prior understanding of the topic at hand I was able to formulate concise questions suiting a survey format. Second, using a survey in general is an efficient tool in terms of cost incurred and time spent on performing data collection (Ruane 2005). Third, my target group of entrepreneurs can be regarded as Internet-savvy which justifies the use of an online tool rather than a paper version of the survey to be mailed out. After extensive pre-testing in personal meetings and phone calls with 10 entrepreneurs, the online survey was launched in October 2006. Every founder included in the population received an invitation to visit a website and to com-

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5 Determinants and Impact of Founder Turnover

plete the survey. A unique password to enter the survey was assigned to every participant allowing me to track individual answering behavior. For a full transcript of the online survey refer to Appendix 7. Sample screenshots of the online form are displayed in Appendix 8 and Appendix 9. Answering time to complete the questionnaire varied between 10 and 20 minutes according to the respondents’ selections in filtering questions.

5.2 Dataset This section introduces the dataset used for the subsequent analyses. First, I look at sample selection in paragraph 5.2.1, followed by a description of the data collection process in paragraph 5.2.2. Finally, in paragraph 5.2.3, I investigate whether or not sample selection and non-response lead to any considerable systematic biases in the data. 5.2.1 Sample Selection

In order to assess founder turnover in German venture capital financed start-up companies through a survey, several pieces of information had to be combined. In essence, I needed to know (i) which investors could be regarded as VCs investing in German start-ups, (ii) which companies these VCs had invested in, (iii) who the founders (or more precisely the founding directors) of those companies were, (iv) which period of time I had to include in the analysis, and (v) how one could possibly identify and address those founders in order to ask them to participate in a survey. Those five issues will briefly be discussed here, introducing the sources of data used for the identification of founders surveyed in this study. First, I needed to come up with a full list of venture capital investors investing in German start-up companies. The most comprehensive list of that kind is kept at the Centre for European Economic Research (ZEW) in Mannheim, Germany. In a working paper, Engel (2002b) describes how this list has been compiled. According to the author, the list contains all members of the German Venture Capital Association (BVK) accounting for those VCs being based in Germany, all members of the European Private Equity and Venture Capital As-

5.2 Dataset

99

sociation (EVCA) for those venture capital investors with a European background, and all VCs of the National Venture Capital Association (NVCA). The full list comprises all VCs registered with the three associations and all investment vehicles under their management. Additionally, about 200 investors active in the US market for venture capital in 1998 were included in the list. Engel (2002b) reports a total of 1,273 VCs found following this algorithm.40 Second, to identify portfolio companies, the full list of VCs was matched against the data pool of CREDITREFORM, the leading German credit rating agency. Their data comprise information about virtually every single company in Germany, including information on the company’s private and institutional equity holders. Ever since 1991 the ZEW has received semi-annual updates of the CREDITREFORM data from which a longitudinal panel has been built up. Taking the names of all venture capital firms registered in the list, start-ups having those VCs among their shareholders could be identified as portfolio companies.41 Third, once the portfolio companies were identified, I was able to name all venture capital companies involved and all individuals actively engaged in those firms. The CREDITREFORM database contains information about the roles individuals play in a company. The two important roles in search for potential founders are the ones of shareholders and managing directors.42 This is based on the simple reasoning that one can expect a founder to be invested in his own company and to be active as a managing director at the same time. In order to identify the founders of the portfolio companies, I took a two-step approach. In a first step, every person labelled shareholder and managing director at the same point in time was considered a potential founder. In a second step, only those potential founders who occupied both roles immediately after the founding of the company were selected. Consequently, only individuals occupying both roles, 40

41 42

The list was updated according to the very same logic in 2004. This latest version of the list is used in the research presented here. A very detailed description of the matching process is provided by Engel (2002b). For the German „Gesellschaft mit beschränkter Haftung (GmbH)“ a shareholder is identified as „Gesellschafter“ and a managing director as „Geschäftsführer“. For the „Aktiengesellschaft (AG)“ the respective roles are „Aktionär“ and „Vorstand“. All portfolio companies identified in the CREDITREFORM database are registered under either of those legal forms.

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5 Determinants and Impact of Founder Turnover

the role of shareholder and of managing director within a defined time frame after the precisely known founding date of the company were considered to be founders. Since the CREDITREFORM data – e.g., information on the roles of people involved in the registered companies – is partially taken from the German commercial register (“Handelsregister”), a time lag between the initial documentation in the register and the registration of that information at CREDITREFORM needs to be taken into account. To calculate the average time lag, I looked at 10 Munich-based venture capital financed start-up companies by comparing their commercial register files kept at the Munich local court (“Amtsgericht”) with corresponding entries in the CREDITREFORM database. By doing so, I compiled a hand-collected sample of 46 documented management changes that were first recorded in the commercial register and some time thereafter in the CREDITREFORM database.43 In fact, all changes documented in the commercial register were recorded in the CREDITREFORM database. This allows for the assumption that the CREDITREFORM database holds a complete record of turnover cases in German companies. Based on the sample of 46 observations I calculated the average time-lag. The results are shown in Figure 5.1. The average time lag between the initial documentation in the commercial register and the registration of the same information at CREDITREFORM is about half a year (173 days, i.e., 5 months and 23 days). As the value of the standard deviation suggests, there are several observations not lying in the 6 months interval but showing even longer time lags. To make sure I capture all management team information around the time of foundation, I decided to allow for a 12 months range after foundation in which I identified individuals as founders if they simultaneously occupied the roles of shareholder and managing director in their companies.44

43

44

Note that not all those management changes were founder turnovers according to my definition but that any changes in the management of the selected companies were considered. Based on the sample depicted in Figure 5.1, a 12 months range captures 95% of observations. Though the hand-collected sample cannot be claimed to be representative, the covered fraction is reasonably high.

5.2 Dataset

101

Deviation in days_

600 500 400 300 200 100 0 1

6

11

16

21

26

Observed management changes Figure 5.1:

31

36

41

46

(N=46, mean=173.2, std=113.9)

Deviation of commercial register against CREDITREFORM

Fourth, as mentioned above, the data set kept at ZEW dates back to the year of 1991. However, venture capital as an asset class only came into existence in Germany around the mid 1990s. Accordingly, I needed to determine a relevant starting point for the analysis of founder turnover in venture capital financed companies. Engel (2002b) finds that 70% of all investments identified in his 2002 paper were made between 1998 and 2000. Moreover, he states that in 1997 the number of investments for the first time increased dramatically. Concluding that a 10 year time period would hence cover the full era of venture capital investments in Germany, I exclusively look at founders of start-up companies founded between 1996 and 2006. Fifth, I had to find a way how to contact the selected founders in order to make them take part in the online survey. The CREDITREFORM database not only contains names of founders, but also their private postal mail addresses mostly originally taken from the commercial register. However, in order to increase the expected number of returned answers to survey invitations sent out, I researched email addresses via the internet. If no email address could be identi-

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5 Determinants and Impact of Founder Turnover

fied, the social network platform XING45 was used as an alternative way of identifying and contacting people. Only if no email and no contact options at XING were found, a physical letter was mailed to the founders.46 Figure 5.2 shows this process in detail. 5.2.2 Data Collection

Out of the 1,136 founders in the relevant set, 50 proved to be redundant, incomplete or irrelevant entries. The remainder of 1,086 founders was contacted according to the process illustrated in Figure 5.2 below. Overall, 480 emails, 372 letters and 97 XING messages were successfully sent out to participants. A total of 137 founders could not be reached through any of the three channels, because their email and postal addresses could not be identified or were no longer correct. Nor could those individuals be identified within the XING network. According to the flowchart, those entries were labelled as not identifiable. I designed the data collection process in several waves of invitation. Overall, four different waves were launched. In what follows, I describe the invitation mailing process. The first wave was a test run comparing postal mail versus email invitations. With this trial, I wanted to find out how both channels performed in terms of submitted questionnaires. I did this to evaluate whether the arguably higher performance of email would justify the use of different channels in order to address all 1,086 individuals. To this end, I compiled two randomly selected subsamples. The first sub-sample included 50 founders whose email addresses I had identified through Internet research, the second one 50 founders whose postal addresses I had compiled from different online and offline directories after an email addresses could not be identified. I found that out of 45 non-bounced letters 45

46

XING is an online platform helping business people to get and stay in touch with each other. Their website www.xing.com demonstrates how it works and what the features are (last visit: September 17th, 2007). The original postal addresses taken from the CREDITREFORM database were all checked and updated. To do so, the online directories www.telefonbuch.de and www.goyellow.de (last visit of both websites: October 7th, 2007) as well as the offline directories D-Info (Buhl Data Service GmbH 2006) and klickTel (klickTel AG 2006) were used.

Figure 5.2:

no

Count as contacted by email (N=480 founders)

(N=137 founders)

no

Count as not identifiable

yes

Does email bounce?

Does postal mail bounce?

yes

Person’s current email address available?

Email to available address

yes

Postal mail to address given in ZEW dataset

no

Person‘s current postal address, email, or XING profile identifiable online?

START

yes

no

Flowchart illustrating invitation mailing process STOP

(N=97 founders)

Count as contacted via XING

Send message via XING

yes

XING personal message enabled?

yes

Person’s XING page available?

no

no

Count as contacted by postal mail (N=372 founders)

no

Does postal mail bounce?

Postal mail to available address

Person’s current postal address available

yes

5.2 Dataset 103

104

5 Determinants and Impact of Founder Turnover

only 5 submitted online questionnaires were generated. Email invitations were around twice as successful. 49 emails were delivered to recipients and 13 of them filled in and submitted the online form. Thus, while postal mail yielded a rate of return of around 11%, about 27% of all email invitations motivated founders to participate in the survey. Based on this result, I decided to avoid a media break from a written letter to the browser based survey and to contact founders by email or XING wherever possible. Figure 5.3 shows the start of both trials at the beginning of the data collection interval in October 2006. An additional learning from the trials was that some respondents tend to reply to invitations well after deadlines communicated to them. In mid November 2006 I launched the second wave. I performed a stress test to the designed process by sending out emails, XING messages and letters within a few consecutive days.47 At this point, I had contacted around one fourth of the founders. According to the flowchart presented in Figure 5.2, I handled bounced letters and emails in a way that the founder was addressed through a different channel within no more than 7 days after the initial letter had bounced. Building on the fine-tuned process implemented in the second wave, I launched the third wave with all remaining individuals to be addressed in December 2006. As a result, by the end of the year 2006, there were 115 submitted questionnaires stored in the database. To further increase the rate of participation, I launched a last wave reminding everybody who had received an invitation through any of the three channels but who had not submitted a questionnaire yet, to participate in the survey. Between the end of February 2007 and mid May 2007 another 74 founders filled in the online survey which yielded a total of 189 responses. However, due to the fact that some participants did not fulfil crucial criteria48, only 154 questionnaires were included in the final dataset. 47

48

There is a limitation of 20 XING messages per day implemented in the XING program. Consequently, I had to distribute the invitations sent out through XING across several days. There were three initial questions, all participants had to answer. First, participants were asked if they were equity holders. Second, they were required to having been active as manager right after company inception. Third, they also had to confirm they were managers at the time when VCs entered the business. If any of those criteria was not met, the observation was excluded from the dataset.

30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0

Figure 5.3:

Answering behavior of survey participants

03/10/06 Postal Mail Trial

17/11/06 30/11/06 Stress Deadline Test Stress Test 31/10/06 08/12/06 Deadline 1. Invitation Trials

20/10/06 E-Mail Trial

31/12/06 Deadline 1. Invitation 27/02/07 2. Invitation

31/03/07 Deadline 2. Invitation

15/05/07 End of Data Collection

5.2 Dataset 105

Number of submitted questionnaires

106

5 Determinants and Impact of Founder Turnover

Thus, as of May 15th, 2007 a total of 189 out of 949 contacted founders submitted the questionnaire, which equals an overall return rate of 19.9%. In more detail, out of the 480 emails a total of 123 responses were generated (25.6%). 17 of the 97 individuals contacted through the XING network answered the questionnaire (17.5%) and 372 letters delivered to founders yielded a return of 49 answers (13.2%).49 Given the sensitivity of the surveyed information and the time needed to complete the online questionnaire, this return rate is notably high. The return rate is especially satisfactory, because respondents were obliged to fill in all questions in the survey, which resulted in fully completed questionnaires. Figure 5.3 illustrates the number of submitted questionnaires over time. Refer to Appendix 4 for a sample letter used in the first and second wave of invitations, to Appendix 5 for a sample letter from the third wave and to Appendix 6 for sample letters used in the fourth wave of invitation mailings. 5.2.3 Selection Bias and Response Bias

This paragraph discusses two different types of potential biases that might be present in the collected data. The first bias to be discussed may result if the selected sample frame shows systematic differences from the population it intents to represent. It is therefore referred to as the selection bias. The second bias is the response bias which may occur if the group of respondents systematically differs from the group of non-respondents (Sapsford 1999). It can be assumed that the list of VC investors compiled from the ZEW database covers the population of German VCs (Engel 2002a). This allows for a quasi perfect identification of portfolio companies. Moreover, the CREDITREFORM database in fact is among the most complete sources of general company information including information on entrepreneurs. This allows for a precise identification of founding directors who I address in my survey. Given that I intend to look at founders who had institutionalized VCs among their investors the data kept at the ZEW arguably is the most complete and comprehensive sam49

With respect to my initial thoughts on the use of different media to contact founders it can be said that email is about twice as powerful as postal mail in terms of responses generated through the respective channel.

5.2 Dataset

107

ple frame to build on. As a consequence, selection effects resulting from frame imperfections and thus from sample biases can safely be neglected in the research design presented here. In other words, there is no reason to worry about a systematic selection bias introduced by the construction of the sample frame. However, a response bias may occur assuming that a given set of individuals from the ZEW sample might systematically not take part in the survey. A nonresponse analysis compares characteristics of entrepreneurs who received an invitation to the online survey and actually submitted their answers to those who were invited but did not take the online survey. The aim of a non-response analysis is to figure out if the probability of taking the survey is random or if there are systematic factors that explain (non-) response behavior. Random non-response can be considered irrelevant because the resulting sample is a random sub-set of the sample frame which in the first place is expected to perfectly represent the target population. Non-random non-response however distorts estimations based on the sample which then would not allow statistical inference to the entire population. Therefore, it is important to review if systematic non-response behavior is present. To perform a non-response analysis it is necessary to have identical information on both groups – respondents and non respondents. Given the fact that the ZEW dataset includes company and founder related information for each unit of the sample frame, I am able to perform a comprehensive non-response analysis. I include two founder-related, two company-related, and one environment-related variable into the non-response analysis. This allows for a copious analysis taking into account aspects along several important dimensions. With regards to founders, one may expect that older founders are less likely to fill in an online questionnaire than younger founders who might be more Internet savvy. A systematic over-representation of young entrepreneurs would be the result. Therefore, I include the founder’s age into the non-response analysis. The founder’s age is measured as the difference between the year 2006 and the founder’s year of birth. H N1: The probability of response decreases with founder age.

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5 Determinants and Impact of Founder Turnover

Moreover, I suspect that founders with an academic background are more adept in the use of surveys and are more willing to contribute to scientific projects than people without an academic background. If this was true, the actual sample would include more people with a doctoral or a professorial degree than the sample frame. I include a dummy variable with a value of 1 for founders with an academic title and value 0 for all others in order to test for the statement that: H N2: The probability of response increases for founders with a doctoral or a professorial degree.

With regards to the company level, two more variables should be included into the non-response analysis. First, considering company performance, it can be expected that founders of companies currently performing well are more likely to take the time to answer a survey than those who struggle with their start-up. The CREDITREFORM database includes a variable compiled from a multitude of company information indicating a company’s economic condition in terms of the CREDITREFORM credit rating (Creditreform 2005).50 Based on this variable I compare respondents and non respondents assuming that: H N3: The probability of response increases with superior company credit rating.

Second, one may expect that founders of small companies are more willing to spend time on an online survey than founders of rather large firms who can be expected to be more involved in their top management positions. Thus, I include the number of employees as an additional measure here. The dataset at hand includes a size range indicating the number of employees for each company. 1 stands for 1 employee, 2 for 2 to 4 employees, 3 for 5 to 9 employees, 4 for 10 to 19 employees, 5 for 20 to 49 employees, 6 for 50 to 99 employees, 7 for 100 to 199 employees, 8 for 200 to 499 employees, 9 for 500 to 999 employees, and 10 50

The credit rating is an index ranging from 600 for the worst potential rating to 100 for the best achievable rating. It aggregates company-related information on assets and loans, returns, liquidity, structural characteristics, payment habits, as well as industry risks into one measure.

5.2 Dataset

109

for more than 1000 employees. I use the respective mean values of each bracket for further calculations, i.e., I translate 1 as 1, 2 as 3, 3 as 7, 4 as 14.5, 5 as 34.5, 6 as 74.5, 7 as 149.5, 8 as 349.5, 9 as 749.5, and 10 as 1000. The sample would be biased due to systematic non-response if the following was true: H N4: The probability of response decreases with the number of employees.

Finally, looking at environmental factors, I compare companies from the Eastern part of Germany with such from the Western part of the country. In order to reject biases resulting from systematic regional distortion in response behavior, the following proposition would have to be rejected: H N5: The probability of response differs between Eastern and Western Germany.

In order to test potential response biases resulting from the mentioned five factors, I run a probit estimation51 with the invitees’ actual response behavior as a dependent variable and the five factors just mentioned as independent variables. The resulting estimation equation has the following functional form: 5

prob( y 1)

prob(D  ¦ x iEi  H ! 0)

(5.1)

i 1

where P is the dichotomous dependent variable indicating participation taking on the value of 1 for all respondents and value 0 for all non respondents. Į is a constant, i is an index, ȕi is a vector of coefficients, and İ is the error term. The independent variables xi are: x1 = Founder age x2 = Founder academic title (dummy variable with 1 = yes and 0 = no) x3 = Company credit rating

51

For a more detailed discussion of the probit estimator refer to paragraph 5.5.1 of this book.

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5 Determinants and Impact of Founder Turnover

x4 = Company number of employees x5 = Region (dummy variable with 1 = Eastern Germany and 0 = Western Germany) The dataset I use is compiled from the 949 founders that I had been able to contact through any of the three channels. All data used stem from the original CREDITREFORM dataset kept at the ZEW. The values of all founder-related variables are those from the last record in which the founder had been registered as a member of the management team. Consequently, those data represent the last available information on the founder. The values of all company-related variables – including the region dummy – date from the last record stored at the ZEW, i.e., the second half of the year 2006.52 The following Table 5.1 gives an overview of the variables included in the non-response analysis. In this first estimation I neither include the company credit rating, nor the number of employees. The reason for the exclusion of both variables is the limited number of available observations (689 observations for company credit rating, 889 observations for the number of employees). In order to lose as few degrees of freedom as possible I run a regression without those two variables first before including all five variables in a separate estimation. It can be seen that one out of the five propositions formulated above is supported by the probit estimation. Founders with an academic title were more likely to participate in the survey. This result is statistically significant at the 5% level. One potential explanation might be the increased willingness of academically trained founders to support academic projects. Also note that the control variable measuring company age suggests a significantly higher participation rate by founders of older companies.

52

In order to control for the time of company existence I include the control variable “company age” into the regression. This variable is calculated as the difference between the year 2006 and the founding year of each company.

6.843

Company age (control) -

1.773

-

-

9.238

-

7

0

0

43

Std Median 0.400 0

Standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%

Observations Log likelihood LR chi2 Prob > chi2 Pseudo R2

-

0.236

Region (1=East)

Constant

0.232

44.896

Founder age

Founder academic title (1=yes)

Mean 0.199

-

1

0

0

24

Min 0

-

10

1

1

83

Max 1

-

943

949

949

924

N 949

-0.005 [0.005] 0.273** [0.110] -0.144 [0.114] 0.048* [0.028] -0.975*** [0.278] 918 -456.280 10.050 0.040 0.011

Probit DV (1=yes)

Table 5.1:

Variable Participation

The sample consists of 949 founders. 918 observations are included in the regression. The estimation is based on a probit function in which the dependent variable is a dummy indicating founders' participation in the online survey. Independent variables are founder age, founder academic title, region, and company age. The table reports descriptive statistics as well as the probit estimation results.

5.2 Dataset 111

Variables and results of non-response analysis I

-

1.773

-

80.677

Standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%

Observations Log likelihood LR chi2 Prob > chi2 Pseudo R2

-

6.843

Company age (control)

90.679 0.236

Constant

-

9.238

Std 0.400

330.363 144.261

Region (1=East)

Company number of employees

Company credit rating

0.232

44.896

Founder age

Founder academic title (1=yes)

Mean 0.199

-

1

0

1

154

0

24

Min 0

-

10

1

749.5

600

1

83

Max 1

-

943

949

889

689

949

924

N 949

-0.005 [0.007] 0.090 [0.129] 0.000 [0.000] -0.001 [0.001] -0.092 [0.131] 0.018 [0.033] -0.703*** [0.361] 665 -341.880 3.370 0.762 0.005

Probit DV (1=yes)

Table 5.2:

Variable Participation

The sample consists of 949 founders. 665 observations are included in the regression. The estimation is based on a probit function in which the dependent variable is a dummy indicating founders' participation in the online survey. Independent variables are founder age, founder academic title, company credit rating, company number of employees, region, and company age. The table reports descriptive statistics as well as the probit estimation results.

112 5 Determinants and Impact of Founder Turnover

Variables and results of non-response analysis II

Including the company’s credit rating as well as the number of employees in the equation yields a strongly reduced regression sample of 665 observations (cp. Table 5.2). Based on this selected sample, none of the variables shows significant coefficients. The originally significant effect associated with the founder’s academic title cannot be replicated by the reduced sample.

5.3 Variable Description

113

Even though only one variable (founder academic title) and one control variable (company age) report significant differences between the group of respondents and the group of non respondents in Table 5.1, it cannot be excluded the possibility that other variables not included in the model would unveil additional response biases. In fact, the non-response analysis shows that I cannot claim the sample to be a random draw from the sample frame. The data selection at hand is significantly biased by an over-representation of answers from founders with an academic degree and from founders of older companies.

5.3 Variable Description In this section, I introduce the measurement of the variables used in the subsequent empirical analyses. Each variable is related to the combined theoretical model introduced in section 3.4 thereby delivering an operationalization of the constructs used in the formulated hypotheses. For an easy reference to theory, this paragraph is structured in accordance with the hypotheses compiled in chapter 3 with the exception of the dependent variables being described first. Finally, I describe some additional control variables that I will use in subsequent estimations. 5.3.1 Dependent Variables

According to the conceptual framework, there are dependent variables in all three steps illustrated in Figure 1.1. Considering the first step, the general turnover decision as the selection result is the first dependent variable to be defined. At the outcome level, which is the second step of the framework, the decision to rotate or to depart, i.e., the observable type of turnover is regarded as the dependent variable. Finally, in the last step of the framework I look at performance as dependent variable. In what follows, the measurement of those three variables – turnover decision, turnover type, and performance – is presented. If not otherwise stated, variable construction and measurement are based on data retrieved from the survey I conducted among German venture capital funded entrepreneurs (cp. paragraph 5.2.1).

114

5.3.1.1

5 Determinants and Impact of Founder Turnover

Turnover Decision

I consider turnover as the founder’s leaving from his job position initially taken when the first VC entered the company. To define participants’ turnover experience, I combine their answers as to rotation and departure. If the founder experienced at least one of the turnover types, i.e., rotation or departure, I label him as a turnover case. Turnover is a dummy variable taking on the value of 1 in case of turnover and the value of 0 in case of survival. The variable exists for all 154 observations. 5.3.1.2

Turnover Type

This variable is a dummy variable composed of two primary variables from the questionnaire. Those variables are departure and rotation. Departure I asked respondents if they had left the company after the first VC had entered the business, i.e., had resigned from their initial job and left the firm subsequently. Those founders positively answering this question are considered departure cases. Departure is a dummy variable with value 1 for departure and value 0 for all other cases. For all of the 154 observations this variable is observed. Rotation Additionally, I asked participants to indicate whether they had ever changed their position inside the company after the first VC had become a shareholder. Those entrepreneurs confirming they had taken on a different job internally in times when VCs were among their shareholders are labelled rotation cases. If founders first rotated and later departed from the company they are considered departure cases. Thus, rotation is a dummy variable taking on the value of 1 in case of rotation without subsequent departure and the value of 0 for all other case. It exists for all 154 observations. For all cases of rotation the variable “turnover type” takes on the value of 0. For all cases of departure it takes on the value of 1. Cases with neither rotation, nor departure are not captured by turnover type, since turnover type is only regarded contingently on the initial selection of turnover from survival cases. Consequently, the variable exists for 61 observations and has missing values for

5.3 Variable Description

115

sequently, the variable exists for 61 observations and has missing values for the remaining 93 observations. 5.3.1.3

Company Performance

There is an abundant literature about how to measure new venture performance (Delmar and Shane 2006). A commonly mentioned argument is that purely financial measures such as EBIT or ROI are not appropriate for the determination of new venture success. It is also popular among entrepreneurship researchers to employ subjective performance measures (Haber and Reichel 2005). In this book, I use a widely employed performance measure appropriate for the assessment of the performance of venture capital funded start-ups. I consider postturnover company growth measured in terms of the total number of employees as the key performance variable. In fact, growth can be regarded as a short-term measure of performance, while financial measures are especially useful to determine long-term company performance. Especially for venture capital funded companies there is a clear argument to use (post-turnover) company growth as a performance measure: companies receiving venture capital are considered to have massive growth potential. It is the inherent logic of venture capital investments to finance a company’s growth until the business breaks even. Company growth thus is a viable measure of performance for venture capital funded start-ups.53 In the questionnaire, I asked participants to indicate the number of employees being employed with the company from inception until 5 years thereafter. Founders were given brackets they could choose from. The brackets offered to respondents were “0-5 employees”, “6-10 employees”, “11-20 employees”, “21-30 employees”, “31-40 employees”, “41-50 employees”, “51-75 employees”, “76100 employees”, “101-150 employees” and “151 employees and above”. Moreover, respondents could indicate if their company did not exist any more after a certain number of years or if it was younger than 5 years. For the CAGR calcula53

Note that sales growth could possibly be used as an alternative measure. However, I do not refer growth to sales given the fact that my sample includes companies from different industries. Accounting for the fact that revenue patterns over time are obviously different for e.g. IT and biopharma companies, I prefer employee growth which is a more general measure of growth across industries.

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5 Determinants and Impact of Founder Turnover

tion I took the mean value of each bracket (2.5 for “0-5 employees”, 8 for “6-10 employees”, 15.5 for “11-20 employees”, 25.5 for “21-30 employees”, 35.5 for “31-40 employees”, 45.5 for “41-50 employees”, 63 for “51-75 employees”, 88 for “76-100 employees”, 125.5 for “101-150 employees” and 151 for “151 employees and above”). Based on this, the post-turnover employee growth variable is constructed as follows. If turnover happened within 5 years after company foundation I calculate the post-turnover employee CAGR54 based on the survey employee numbers. For instance, if turnover happened one year after foundation I employ the number of employees at one year after foundation and today’s number of employees, i.e., the number of employees in the year 2006. If turnover happened two years after foundation I refer to the number of employees in the second year after foundation and today’s number of employees, and so on. Since I know the year of turnover I am able to calculate the employee CAGR for each turnover case. Lacking detailed information on the number of employees as of year 6 after foundation I use employee CAGRs based on employee numbers of year 5 for turnovers after 6, 7, and 8 years, too.55 For survival cases a turnover date obviously does not exist. Therefore, as a point of reference, I take the sample average time until turnover (3.38 years, cp. Figure 5.31) to determine the time of a hypothetic event. In fact, for founders surviving in their job positions I calculate the employee CAGR between the third year after foundation and today. Consequently, I do not calculate post-turnover growth for companies that have not existed for less than 2 years. For all calculations I set today’s number of employees to 1 if the company died before 2006. Following this procedure, I obtain 138 calculated CAGRs and 16 missing values. 5.3.2 Independent Variables

In this paragraph, I describe the measurement of all independent variables employed in subsequent hypothesis testing.

54

55

The CAGR is calculated as (number of employees today / number of employees in the year of turnover) ^ (1 / number of years between turnover and today) – 1. Differences of more than 8 years from inception to turnover were not observed.

5.3 Variable Description

5.3.2.1

117

VC Influence

To test hypothesis 1 which states that higher VC influence increases the founder’s hazard of turnover, a measure of VC influence is needed. I use the VCs’ ability to dominate the company to operationalize VC influence. More precisely, I look at VCs’ voting rights over the first three years to assess the degree of VC influence. In the online survey, I directly measured VCs’ share of voting rights asking participants to indicate its importance over time – from the time of first VC involvement until 5 years later. I provided 5 possible brackets respondents could choose from, including “under 10%”, “10% up to 25%”, “25% up to 50%”, “50%” and “over 50%”. From this information given on an annual basis I calculate a dummy variable taking on the value of 1 if VCs’ share of voting rights was 50% or more. The independent variable “VC influence” takes on the value of 1 if any of those dummies equals 1 in the first year, the second year, or the third year after the initial VC involvement. Thus, provided VCs at some point over the first three years of VC involvement ever held 50% or more of voting rights I declare them to be specifically influential. The variable is defined for 151 observations. 3 observations are missing due to the fact that respondents did not provide any information on the share of voting rights held by their VCs within the time span of the first three years of VC involvement.56 5.3.2.2

VC Type

In order to measure VC types as needed to test hypothesis 2, I asked founders to indicate whether the group of investors involved in their companies were rather private VCs, corporate VCs, bank-associated VCs, or government-associated VCs. I combine respondents’ answers in a dummy variable with value 1 for private VCs and value 0 for the remaining three types of VCs (captive VCs). The variable has values 0 or 1 for all 154 observations.

56

The question for the share of VCs’ voting rights included an answering option “company not that old / company dead” (cp. Appendix 7).

118

5.3.2.3

5 Determinants and Impact of Founder Turnover

Company Growth

I use an operationalization of company growth based on the company’s number of employees in order to test hypothesis 3a and 3b. The exact measure used to operationalize company growth is the 3 years employee CAGR which is calculated as follows. I take the mean value of each bracket (2.5 for “0-5 employees”, 8 for “6-10 employees”, 15.5 for “11-20 employees”, 25.5 for “21-30 employees”, 35.5 for “31-40 employees”, 45.5 for “41-50 employees”, 63 for “51-75 employees”, 88 for “76-100 employees”, 125.5 for “101-150 employees” and 151 for “151 employees and above”) for the founding date, 1 year after foundation, 2 years after foundation and 3 years after foundation. If all four data points are available I calculate the 3 years CARG. If any data point is missing, e.g. because the company had been closed down after less than 3 years or was less than 3 years old, I calculate the CAGR for the reported time span instead. I classify the founder’s company as a strongly growing company if the calculated employee CAGR is higher than 100%, i.e., if the number of employees on average doubled every year over the first 3 years. The dummy variable takes on a value of 1 for strongly growing companies. All remaining observations take on the value of 0 (CAGR below 100%). Likewise, as a measure of hyper growth (cp. hypothesis 3b) I define a dummy variable with value 1 for all companies exceeding an employee CAGR of 200%. Values to compile both growth dummy variables are available for all 154 observations. 5.3.2.4

Founder Job Match

In accordance with hypothesis 4 and its sub-hypotheses formulated above I determine four types of matches for each respondent – an educational match, an entrepreneurial match, an industry match, and a functional match. Each of those four variables is a dummy variable with value 1 if there is a match between the founder’s prior activities and its initial position taken in the start-up company and value 0 otherwise. The respective four dummies exist for all 154 observations.

5.3 Variable Description

119

Educational Match I declared the founder to have a high educational match inside the company if he indicated to have an educational specialization in economics and if he started as the CEO, the CFO or the CSO of the company. Founders with an educational specialization in natural sciences and a leading position in R&D were also regarded to be in good job matches. The same was assumed for founders with an educational background in engineering taking on the COO or CTO position. If answers provided by the founders yielded none of those combinations, their education was not considered to match with their job. Entrepreneurial Match To determine the founder’s entrepreneurial match, I asked respondents to indicate whether they had ever been active as an entrepreneur before founding the company under observation. If they had founded a company before I assumed them to have an entrepreneurial match, otherwise I did not. Industry Match I grouped industries into four different fields. Provided founders indicated they were active in an industry of the same field prior to foundation and within the start-up, I expected them to have a high industry match. I define “service” as the first field of industries, including banking and financial services, retail, media and entertainment, as well as consulting. The second field of industries was “biopharma” including biotechnology, chemistry, as well as pharmacy and medical products. The third field was “engineering” with electrical engineering and mechanical engineering. The last field of industries I define was “IT” including hardware, software, Internet and telecommunications. Functional Match I asked founders to indicate in which function they had been working in their most important job prior to founding the company. By comparing this function with their function inside the start-up team I determined their functional match. I assumed CFOs who had been active in finance or financial control, COOs with prior experience in production and logistics, CSO who came from sales and marketing, chief executives for R&D with prior R&D exposure and CEOs with a former position in management and strategy to form good matches.

120

5.3.2.5

5 Determinants and Impact of Founder Turnover

Founder Performance

To measure founder performance as needed to test hypothesis 5 I asked participants for a self-assessment. Respondents had to estimate their level of accomplishment with regards to the expectations their investors had had towards them during the time they had been active in their initial management position. They had to choose from a 6 bracket scale including “100% and over”, “80% to 99%”, “60% to 79%”, “40% to 59%”, “20% to 39%” and “0% to 19%” as possible answers, thereby indicating their personal level of accomplishment. I translate those brackets into a 6 point scaled variable with value 1 for “0% to 19%” and value 6 for “100% and over”. Values for all 154 respondents exist. 5.3.2.6

Founder Professional Experience

According to hypotheses 6 and 8 which – including their sub-hypotheses – both cover the founder’s professional experience, I compile four different variables. I introduce years of professional experience as a general effect measure and project management experience, people management experience, as well as process design experience for the effects postulated by the sub-hypotheses. None of the four variables shows any missing values. Years of Professional Experience I asked respondents to indicate the number of years of job experience they had made prior to founding. With a scale from 0 years to 10 years being truncated at more than 10 years and about 50% of respondents indicating they had more than 10 years of job experience, I transform the variable into a dummy with value 1 for founders with more than 10 years of job experience and value 0 for those with 10 years and less. Project Management Experience I asked participants to indicate their level of project experience when founding their company on a 5 point rating scale ranging from “no experience” to “very extensive experience” which I translate into a variable taking on values between 1 for “no experience” to 5 for “very extensive experience”. In order to measure if founders had relevant project management experience I transform this variable

5.3 Variable Description

121

into a dummy with value 1 if founders reported they had very extensive experience and value 0 otherwise. People Management Experience Participants were asked to indicate their level of people management experience prior to company foundation on a rating scale ranging from “no experience” to “very extensive experience”. I translated “no experience” as 5 and “very extensive experience” as 0. Based on the resulting variable I composed a dummy with value 1 for founders with very extensive experience in people management and value 0 for the remainder of founders. Process Design Experience Along with the prior experience variables, I construct a dummy variable for founders’ process design experience. If participants indicated they had very extensive experience in process design prior to founding, the dummy takes on the value of 1. For all other founders indicating lower levels of experience on a 5 point rating scale the dummy takes on the value of 0. 5.3.2.7

Founder Firm-Specific Know-How

To measure firm-specific know how as proposed in hypotheses 7 and 9 I analyzed German and European patent data. For each of the 154 founders I checked the online database of both the German Patent and Trademark Office (“Deutsches Patent- und Markenamt”)57 and the online database of the European Patent Office58 for the inventor’s name. If I was able to identify at least one patent held by the founder that was technologically relevant in the context of the start-up company under observation, I labelled patent holders as having knowledge specific to the firm. My underlying assumption is that patent holders are the inventors of a key technology used by the company. I introduce a dummy variable taking on the value of 1 for patent holders and the value of 0 for non-patent holders. 57

58

Cp. http://publikationen.dpma.de/DPMApublikationen/qry_pat_xpt.do (last visit August 4th, 2007). Cp. http://ep.espacenet.com/advancedSearch?locale=en_EP (last visit August 4th, 2007).

122

5.3.2.8

5 Determinants and Impact of Founder Turnover

Founder CEO

In the survey, I asked participants to indicate their management position at the time when the first VC entered the company. Founders could choose from a range of C-level positions or indicate they had been active in some other top management position. If respondents chose “CEO” they were labelled as chief executive. For a measure appropriate to test hypothesis 10 I construct the variable as a dummy with value 1 for founder-CEOs and value 0 for all other founders. 5.3.2.9

VC Procedural Justice

In order to test hypothesis 11 I finally had to operationalize the investors’ procedural justice as perceived by founders. In the questionnaire, I asked respondents to indicate their level of agreement to the statement that VCs’ decision processes regarding the founder’s person were always perceived as fair. Founders could answer on a 5 point rating scale ranging from “fully disagree” to “fully agree”. I translate those answers into a discrete variable with values from 1 to 5, 1 indicating the lowest level of agreement and 5 the highest level. The variable does not show any missing values. 5.3.2.10

Turnover Decision and Turnover Type

I use the same variables I consider as dependent in the analysis of determinants of founder turnover as independent variables in the analysis of founder turnover on performance impact. While the turnover decision variable is used in exact the same specification as described above, I split the turnover decision dummy into two variables. For those cases with an ultimate outcome of rotation, I introduce a separate rotation dummy with value 1 if the ultimate outcome is rotation and value 0 otherwise. In the same manner I construct a dummy for the ultimate outcome of departure, with value 1 for the ultimate outcome of departure and value 0 otherwise.

5.3 Variable Description

123

5.3.3 Control Variables

Control variables are such variables that are not part of the theoretical model but may nevertheless have an influence on the dependent variable of a statistical model. In multivariate statistics, those variables can control for potential explanations of the variation in the dependent variable that are different from the hypothesized ones (Neumann 1997). Since I do have to control for several effects in the subsequent statistical analyses, I introduce a set of control variables describing how they are constructed as well as what they are supposed to control for. Three groups of factors that may affect founder turnover as well as company performance can be extracted from previous research: (i) individual characteristics attributes, (ii) structural characteristics59 and strategies of the new business itself, and (iii) conditions characterizing the environment of a new firm (Brüderl et al. 1992). This paragraph introduces a set of founder-related control variables first, followed by company-related and environment-related control variables. I will refer to those variables in the respective context of their use in the multivariate analyses in section 5.5 and 5.6. 5.3.3.1

Founder-Related Control Variables

The following variables measure effects which are directly linked to the person of the entrepreneur. Some of those variables are used as dependent variables in the explanation of founder turnover, too. I rely on the same measures to control for influences of founder characteristics on firm performance. Age of Founder I asked founders to indicate their year of birth from which I calculate their age. I did so by subtracting their year of birth from 2006. The age for every participant is known, i.e., all 154 observations are nonnegative integers.

59

With a cross sectional dataset at hand this also includes controls for differences in time.

124

5 Determinants and Impact of Founder Turnover

Founder Educational Background Provided participants had indicated they had a graduate degree, the survey included a follow-up question about the field of education the founder had received this degree in. Respondents could choose between economics, engineering, natural sciences, humanities and other as the field they were mainly educated in. From this information, I construct two dummy variables for founders with their prime education in engineering and natural sciences in order to control for the influence of rather technical education. Those dummies take on the value of 1 if the founder is educated in the respective field and the value of 0 if he is not. There are no missing values in any of the two variables. Founder Academic Background I asked entrepreneurs to indicate whether they held a doctoral or professorial degree. From this information I compile a dummy variable. The dummy takes on the value of 1 for any founder who has a doctoral or professorial degree – or both. It takes on the value of 0 if the founder doesn’t hold any such degree. Values for all 154 observations exist. Founder Entrepreneurial Experience I use the same variable by which I operationalize an entrepreneurial job match (cp. paragraph 5.3.2) as a control variable measuring the founder’s entrepreneurial experience. Founder Firm-Specific Know-How To control for the founder’ firm-specific know-how I use the dummy identifying the founder as a patent holder. This variable is also used as an independent variable in the analysis of determinants of turnover (cp. paragraph 5.3.2). 5.3.3.2

Company-Related Control Variables

The control variables subsequently described capture effects which are related to the structure and the strategy of the companies founded by respondents. Again, some of those variables are also used as dependent variables in the explanation of founder turnover.

5.3 Variable Description

125

Size of Founding Team I asked respondents to indicate the size of the founding team they were part of. The variable is a count variable of nonnegative values ranging from 1 to 11 with integers 1 to 10 indicating the respective number of team members and 11 indicating more than 10 members. The variable has no missing values for any of the 154 observations. Number of VCs at Foundation 152 out of 154 respondents indicated the number of VCs initially involved in the company. Values of the variable are between 0 and 5. Values 1 to 4 indicate the respective number of investors and value 5 identifies 5 or more investors. VC Share of Voting Rights at Foundation I measured the VCs’ share of voting rights at foundation. Respondents were given a choice of 5 possible brackets, including “under 10%”, “10% up to 25%”, “25% up to 50%”, “50%” and “over 50%”. I transformed those brackets into their mean values, i.e., 5 for “under 10%”, 17.5 for “10% up to 25%”, 37.5 for “25% up to 50%”, 50 for “50%” and 51 for “over 50%”. 152 out of 154 participants provided information on the share of voting rights at company inception. Number of Employees at Foundation In the online survey, I asked participants to indicate the number of employees being employed with the company at the time of its inception. Answer brackets available to respondents were “0-5 employees”, “6-10 employees”, “11-20 employees”, “21-30 employees”, “31-40 employees”, “41-50 employees”, “51-75 employees”, “76-100 employees”, “101-150 employees” and “151 employees and above”.60 I translate those brackets as their mean values, i.e., I replaced “0-5 employees” for 2.5, “6-10 employees” for 8, “11-20 employees” for 15.5, “21-30 employees” for 25.5, “31-40 employees” for 35.5, “41-50 employees” for 45.5, “51-75 employees” for 63, “76-100 employees” for 88, “101-150 employees” for 60

Even though the number of employees at foundation typically is rather small, the scale ranges up to more than 150 employees. This is due to the fact that the very same scale was used to make respondents plot the development of the number of employees over the first five years of firm existence in which a total staff of 150 and more may be attained.

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5 Determinants and Impact of Founder Turnover

125.5 and “151 employees and above” for 151. There is one missing value among the 154 observations. Sales at Foundation I also asked founders to indicate the amount of sales in terms of million Euros they realized in the year of company inception. In line with the above, I translate the given brackets into mean values. For “under 0.5M EUR” I take 0.5, for “over 0.5M EUR to 1.0M EUR” 0.75, for “over 1.0M EUR to 2.5M EUR” 1.75, for “over 2.5M EUR to 5.0M EUR” 3.75, for “over 5.0M EUR to 10.0M EUR” 7.5, for “over 10.0M EUR to 20.0M EUR” 15, for “over 20.0M EUR to 30.0M EUR” 25, for “over 30.0M EUR to 40.0M EUR” 35, for “over 40.0M EUR to 50.0M EUR” 45, for “over 50.0M EUR to 75.0M EUR” 62.5, for “over 75.0M EUR to 100.0M EUR” 87.5, for “over 100.0M EUR to 150.0M EUR” 125, for “over 150.0M EUR” 150. The variable shows 152 existing and two missing values. Lifetime Company I asked participants in the online survey to indicate the foundation date of their company as well as the date of company shut-down in case the company no longer existed. For all dead companies I calculate the difference between the date of company shut-down and the foundation date. For the remaining living companies I subtract the foundation date from 2006. Thus, the variable measures the lifetime of companies. Nonnegative values exist for all 154 observations. 5.3.3.3

Environment-Related Control Variables

Variables described in this paragraph are used in the subsequent analyses to control for environmental effects. In the context of the research at hand all environmental control variables refer to the industry the start-up is active in. Industry Dummies I introduce industry dummies for the two largest industries represented in the sample. First, I subsume the hardware, software, Internet and telecommunications industries in the field of “IT”. If respondents indicated their company was mainly active in one of those industries it was labelled as an IT company. I define a dummy variable which takes on the value of 1 for IT companies and the value of 0 for non IT companies. The second field of industries is “biopharma”

5.4 Descriptive Statistics

127

including biotechnology, chemistry, as well as pharmacy and medical products. If companies had their main activities in any of those industries I label them as biopharma firms. The respective dummy takes on the value of 1 for biopharma companies and value 0 otherwise. Both industry dummies have values 0 or 1 for all 154 observations.

5.4 Descriptive Statistics This paragraph summarizes important descriptive statistics covering descriptions of the independent variables used in the subsequent multivariate estimations. The variables discussed are primarily those put forward in the theoretical model. They are complemented by some additional descriptions providing a more complete understanding of the information contained in the dataset. This section is structured as follows: I first provide descriptive statistics on respondents, i.e., entrepreneurs in paragraph 5.4.1, followed by statistics on startup companies in paragraph 5.4.2, and descriptions of VCs in paragraph 5.4.3. Subsequently, in paragraph 5.4.4 I look at founder performance. Finally, descriptive statistics on performance are provided in paragraph 5.4.5. 5.4.1 Descriptive Statistics Regarding Entrepreneurs

In the following, I present insights about the survey participants, precisely about the types and quality of their job matches, their professional experience, their performance on the job, as well as their firm-specific know-how. 5.4.1.1

Job Matching

The following paragraphs and figures illustrate key information related to the participants’ job positions and their job matches. Figure 5.4 shows a breakdown of the sample according to the initial position taken by entrepreneurs. 60% of all participants were CEOs, 12% CTOs, 10% Chief Sales Officers and a smaller percentage CFOs, COOs, Chief Scientific Officers or other C-level or non C-level officers. According to hypothesis 10, I expect different turnover dynamics – mainly in rotation versus departure decisions – for CEOs and other types of officers.

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5 Determinants and Impact of Founder Turnover

4% 5% 5%

3% 1% 60%

CEO CTO Chief Sales Officer CFO

10%

Other non C-level position COO Chief Scientific Officer

12%

Other C-level position N=154

Figure 5.4:

Founder survey: respondents’ initial position at company

It is worth commenting on the distribution shown in Figure 5.4 which indicates a very high fraction of CEOs in the sample. This may be due to several effects. First, with an average founding team size of around 3 (cp. Figure 5.15) and at least one CEO per company it is a statistical consequence that at least one third of all respondents are CEOs. Second, with start-ups having very weak hierarchical structures there might even be more than one person considering himself a CEO initially.61 Third, a response bias cannot be ruled out62, i.e., it might be possible that if several founders of the same company received an invitation to participate in the online survey, they agreed on rather having the CEO answer the survey instead of other C-level directors.63 61

62

63

This in fact would increase the probability of rotation events at some point in time (cp. paragraph 5.4.4). However, given that the information whether a founder is the CEO is not given in the CREDITREFORM dataset, I was not able to analyze this potential response bias in paragraph 5.2.3 earlier. For more information on the CREDITREFORM dataset refer to section 5.2. Note that a systematic selection bias in the dataset can be excluded as an additional explanation here. According to several VC experts the assumption that every founder is listed as a director in the CREDITREFORM database (“Geschäftsführer” for GmbHs and “Vorstand” for AGs) is valid (cp. paragraph 5.2.1).

5.4 Descriptive Statistics

129

Also note the interesting fact that the arguably most important functions in start-ups, namely technology/product development and sales are strongly represented in terms of C-level founder responsibility. Along the same line of reasoning there is only a small fraction of CFOs which supports the common view that this function is typically added to the top management team at a later point in the company lifecycle. The following charts provide a detailed description of the educational, functional and industry background of participants. The subsequently described job matches are derived from this data. First, looking at respondents’ education, the majority of founders hold a university degree – 58% when accounting for universities only, 75% when also considering universities of applied sciences. This indicates that venture capital funded entrepreneurs in Germany are highly educated and tend to have an academic background. This view is supported by the fact that 45% of the respondents hold a second academic degree and 40% a doctoral or a professorial degree. With regards to the broader educational background, the sample contains founders from different disciplines. Figure 5.5 illustrates the distribution of founders across educational backgrounds. Management studies, engineering and natural sciences each account for about 30% of the sample, while founders with their mayor education in the field of humanities are an exception (2%). 9% come from other backgrounds. This suggests that founders tend to have a background in any of the three fields of business, science, or engineering. Apparently, venture capital funded teams combine complementary know-how from various disciplines resulting in a top management with business and technical competencies. A very similar picture emerges when looking at founder’s functional backgrounds. Based on their most important job position prior to foundation 37% of all respondents stated they had a technology/R&D background, 33% a management/strategy background and 22% a sales/marketing background. 8% indicated their background was in different fields. Again, these results show the importance of technologically oriented and trained founders among German venture capital funded entrepreneurs. Figure 5.6 summarizes those results.

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5 Determinants and Impact of Founder Turnover

9%

Engineering

2%

30% Management Studies Natural Sciences

29% Humanities Other 30% Figure 5.5:

N=129

Founder survey: respondents’ educational background

5% 2%1%

Technology/R&D 37%

22%

Management/Strategy Sales/Marketing Production/Logistics Finance/Accounting Human Resources

33% Figure 5.6:

Founder survey: founders’ functional background

N=147

5.4 Descriptive Statistics

Figure 5.7:

131

Founder survey: respondents’ industry background

Third, I asked participants to indicate in which industry they had mainly worked prior to company inception. Even when consolidating industry classification – which I do here in a comprehensive way64 – it is evident that founders have worked in many different industries before starting their own businesses. However, it also becomes clear that founders come from rather young than mature and thus rather dynamic than static industries. The most important industries are IT (25%), consulting (13%), engineering (12%) and biopharma (11%). Science, telecommunications, media & entertainment and other industries rank below the 10% mark. Compare Figure 5.7 for an illustration of respondents’ industry background.

64

Based on the industry specification used in the online questionnaire (cp. Appendix 7) I summarized “IT Hardware”, “IT Internet”, and “IT Software” into “IT”. “Engineering” is combined from “Electrical Engineering” and “Mechanical Engineering”. “Biopharma” captures “Biotechnology”, “Chemical Industry”, and “Pharma/Medical Products”. “Consulting”, “Science”, “Telecommunications”, and “Media/Entertainment” are directly taken from the survey. “Other” comprises all remaining industries.

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5 Determinants and Impact of Founder Turnover

Educational Match

Entrepreneurial Match

21%

44% 56%

79%

Functional Match

Industrial Match 29%

47%

53% 71%

Match Figure 5.8:

No match

N = 154

Founder survey: types and quality of job matches

Having looked at the educational, functional and industry backgrounds of entrepreneurs I now proceed to the issue of job matching. Hypothesis 4 postulates a relation between the quality of the job match experienced by the founder and his likelihood of survival in his initial job position. To provide a comprehensive understanding of job matching in the present context, the following charts illustrate types of job matches and their quality as experienced by the sampled founders. I account for educational, entrepreneurial, functional, and industry matches according to the operationalization described in paragraph 4.2.2.2. Figure 5.8 shows the percentage of the sample with an educational, an entrepreneurial, a

5.4 Descriptive Statistics

133

functional as well as an industry match. It can be seen that in their initial position 21% of all participants had an educational background which matched their job, 44% had prior entrepreneurial experience, 29% found themselves in a similar function in which they had gained experience in their most important prior job and 47% had founded their company in the same industry which they had largely worked in before. Interestingly, an ex ante functional match is only observable in 29% of all cases while an industry match can be found in 47% of the sample. Thus, founders are much more likely to have worked in a different function than they work in at their start-up. The same holds true with regards to industry, though about half of the founders stay within the industry they have mostly worked in before. It is worth mentioning that 44% of venture capital funded entrepreneurs have gained entrepreneurial experience before starting their current business. One may expect that venture capitalists tend to appreciate entrepreneurial experience when funding start-up teams. However, note that the percentages do not indicate how important any of the four matches is in terms of founder turnover. I will more deeply analyze this aspect in the subsequent multivariate analyses. 5.4.1.2

Personal Performance

Hypothesis 5 states that there is a negative relation between the founder’s personal performance and his turnover probability. The following Figure 5.9 shows how respondents’ assess their own performance in their initial position at the start-up company. 21% believe they performed to the maximum measured against the expectations their investors had had towards them during the time they had been active in their initial management position. The majority (36%) agreed they had met 80% to 99% of their targets, 26% estimated their performance between 60% and 79%, 9% between 40% and 59%, 3% between only 20% and 39% and 5% between 0% and 19% of the investors’ expectations. Even though this variable can be assumed to be somewhat biased it also shows that respondents where able to differentiate between 6 brackets given to them which is documented in the full use of the scale across participants. In the further course of analysis, I will investigate in how far this self-reported performance helps explain founder turnover.

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3%

5%

21%

9%

(6) 100%+ of target (5) 80%-99% of target (4) 60%-79% of target (3) 40%-59% of target

26%

(2) 20%-39% of target (1) 0%-19% of target 36% N=154, mean=4.51, std=1.25

Figure 5.9:

5.4.1.3

Founder survey: respondents’ personal performance

Professional Experience

With reference to hypotheses 6 and 8, I describe several variables being related to founders’ professional experience. I look at the number of years of professional experience for each entrepreneur and his experience in project management, people management as well as process design. Figure 5.10 shows evidence for the fact that the vast majority of respondents disposes of large professional experience. 44% of the founders had gained more than 10 years of professional experience before founding their company. However, there are also some very inexperienced entrepreneurs among the respondents. 12% replied they had 2 years and less of work experience. This may either indicate that VCs in the “boom” phase unwisely invested their funds in inexperienced teams or that there are specific business ideas that are best pursued by very young and thus necessarily inexperienced entrepreneurs.65 65

Such ideas may be Internet based business ideas. A recent example of professionally inexperienced entrepreneurs is the management team of the online community StudiVZ (funded by Holtzbrinck Ventures GmbH in 2006), all aged between 26 and 28. Another case was the alando founding team (funded by Wellington Partners in 1999), who all came strait from university.

5.4 Descriptive Statistics

135

0 years of experience

5% 7%

1-2 years of experience 10%

3-4 years of experience 5-6 years of experience

44%

7-8 years of experience 12% 9-10 years of experience >10 years of experience 12% 10%

N=154, mean=7.79, std=3.70

Figure 5.10: Founder survey: respondents’ professional experience

While the number of years of professional experience provides a good general understanding of the level of work experience, a more detailed look at the type of experience can further sharpen the picture. The following figures provide details on founders’ experience in project and people management, as well as process design.66 Figure 5.11 shows that most founders have gained broad (32%) or even very broad (48%) experience as project managers. Only 4% stated they had very little or little experience in project management.

66

Note that values for those three variables only exist for 149 observations. This is due to the fact that participants who indicated to have no professional experience were not asked the respective questions in the online survey (cp. Appendix 7). However, respondents without professional experience are accounted for in the construction of the dummy variables (cp. paragraph 5.3.2).

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5 Determinants and Impact of Founder Turnover

Number of respondents_

70 60 50 40 30 20 10 0 (1) very little

(2) little

(3) some

Level of experience

(4) broad

(5) very broad

N=149, mean=4.22, std=0.89

Figure 5.11: Founder survey: respondents’ experience in project management

Figure 5.12 illustrates respondents’ experience in people management. Apart from the “very little experience” option chosen by only 5% of respondents answers are somewhat equally distributed. While 31% indicated they had very broad people management experience, 22% replied they had broad experience. 25% said they only had some experience of this type and 17% little experience. With respect to process design experience the majority of respondents (34%) said they had only some experience in this field. This is particularly interesting acknowledging the fact that growing companies require more and more process building and thus process design capabilities within the top management. However, nearly half (a total of 48%) indicated they had broad or very broad process design experience, while only 18% said they had little or very little experience of this type. Figure 5.13 summarizes the results. In essence, those distributions show that there is a large part of founders with extensive experience in all three of the presented fields. Looking at mean values, the average entrepreneur has broad experience in project management (μ=4.22), and some experience in people management (μ=3.56) and process design (μ=3.48). However, there are also a number of founders with rather little

5.4 Descriptive Statistics

137

Number of respondents_

70 60 50 40 30 20 10 0 (1) very little

(2) little

(3) some

Level of experience

(4) broad

(5) very broad

N=149, mean=3.56, std=1.24

Figure 5.12: Founder survey: respondents’ experience in people management

Number of respondents_

70 60 50 40 30 20 10 0 (1) very little

(2) little

(3) some

Level of experience

(4) broad

(5) very broad

N=149, mean=3.48, std=1.13

Figure 5.13: Founder survey: respondents’ experience in process design

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5 Determinants and Impact of Founder Turnover

experience as indicated by standard deviations (between 0.89 and 1.24). Both mean values and standard deviations suggest that broad or very broad people management and process design experience are harder to obtain than an equal level of project management experience. In the subsequent multivariate analysis I aim at studying in how far those facets of professional experience do matter in terms of founder turnover and turnover type. 5.4.1.4

Firm-Specific Know-How

In my hypotheses 7 and 9 I postulate the influence of founders’ firm-specific know-how on turnover decisions and the type of turnover occurring subsequently. As laid out in paragraph 4.2.2.2 I use relevant patents held by the founders as an operationalization of firm-specific know-how. The following chart in Figure 5.14 shows that there are 31% of founders who in fact are inventors of patented items. Figure 5.14 additionally provides information on the number of patents held by inventors. As can be seen from the pie chart, the majority of inventors hold one patent (37%), 11% hold two patents, 9% three patents and smaller fractions of the sample hold more than three patents. In the sample, there is one founder holding 15 patents, one holding 20 and one holding as many as 37 patents relevant for the business of his start-up. Summing up, this paragraph showed that German VC backed start-up entrepreneurs tend to be highly academically educated and highly professionally experienced. They often come from a technical or scientific background and young, dynamic industries but do not necessarily find themselves in positions clearly matching their professional profiles when starting their own companies. About one third of the founders in my sample are inventors and can be assumed to hold important firm-specific know-how.

5.4 Descriptive Statistics

139

Figure 5.14: Founder survey: patent holdings among respondents

5.4.2 Descriptive Statistics Regarding Start-Up Companies

The following paragraph describes the start-up companies included in the sample. I look at the size of founding teams, the dates of inception and death of companies, their industry affiliation and their growth over time. 5.4.2.1

Team Size

Figure 5.15 shows that 136 start-ups (88%) have been initiated by founding teams of two or more founders. About half of the founding teams (81, i.e., 53%) are made up of two or three entrepreneurs. Surprisingly, there are five companies with more than 10 founders which must be regarded as very uncommon. The results support the well known fact that venture capitalists tend to fund teams rather than single entrepreneurs. The average team size in my sample is 3.38 members per company which is in line with prior findings (Amason and Sapienza 1997).

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5 Determinants and Impact of Founder Turnover

Number of respondents_

45 40 35 30 25 20 15 10 5 0 1

2

3

4

5

Number of team members

6

7

8

10

(N=154, mean=3.38, std=2.03)

Figure 5.15: Founder survey: size of founding teams

Figure 5.16 depicts a well-known effect. There is a “start-up boom” in 1999 and 2000 and a “start-up bust” in 2001 and 2002. 81 firms, i.e., 53% of all companies in the sample were founded in 1999 or 2000. 47% of all companies (23 firms) that went out of business did so in 2001 or 2002. Between 2000 and 2001 both, the increase in start-up failures and the decrease in company inceptions were higher than ever before or afterwards. Including the date of turnover in the picture, it seems that the “boom” and “bust” phases show different dynamics with regard to founder turnover. Figure 5.16 clearly shows that there is a steep increase of founder turnover in 2000, i.e., the last year of the boom. Throughout the bust phase in 2001 and 2002 as well as in its aftermath the absolute number of turnover cases remains stable at around 10 per year. In 2005 the number decreases and again reaches the pre-boom level of around 2 or 3 turnover cases. Those results allow for several tentative interpretations. First, the time-lagged increase of start-up failures after the steep increase of foundations indicates that many of the new ventures founded in the boom phase were not able to develop a sustainable business model. The strong increase of founder turnover at the end of

141

50 45 40 35 30 25 20 15 10 5 0

be fo re 1

99 5 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07

Number of companies_

5.4 Descriptive Statistics

Inception (N=154)

Death (N=49)

Turnover (N=61)

Figure 5.16: Founder survey: dates of inception, death and turnover

the boom and its high level throughout the years of 2000 to 2004 might be explained by two effects. First, there is no doubt that some founders left their positions in the course of company shutdowns. However, in the period of 2000 to 2004 there are only 7 observations in the dataset in which the date of company death and the turnover date are the same. Thus, the level of turnover constantly elevated over the level of company shutdowns cannot be explained by this effect only. Therefore, I suspect a second effect induced by venture capitalists. When the market turned bad in late 2000 they possibly pushed for more turnover decisions in their portfolio companies than before, aiming at improving performance through modified management team compositions. However, it seems that founder turnover decisions were taken with a considerable time-lag, i.e., the implementation of founder turnovers took several years in some cases. Interestingly, the turnover phenomenon quasi does not exist in the early years of the existence of the German VC industry. This allows for the conjecture that VC investors have professionalized their activities in terms of actively changing management teams in their portfolio companies over the first 10 years of their existence. The reduction of founder turnover cases after 2005 may be explained by both, im-

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5 Determinants and Impact of Founder Turnover

proved team selection in the post-boom years and by a lack of data at the actual rim (cp. Figure 5.1 and the related discussion in paragraph 5.2.1). 5.4.2.2

Industry Affiliation

Figure 5.17 shows main industry affiliations of the sampled start-up companies. In essence, about one third of the companies are active in the IT business (34%). The second-largest fraction belongs to the field of biopharma (19%), while engineering forms the third-largest group (12%). The remainder is mainly active in different other industries (34%). Thus, with respect to industry affiliation, there are three important groups of companies in the sample: IT companies, biopharma companies, and engineering companies. Roughly speaking, this corresponds well to the founders’ industry backgrounds (cp. Figure 5.7) which explains the relatively high industrial match compared to other types of job matches as described in Figure 5.8. As it can be expected for venture capital funded start-ups, the large majority of the sampled firms is active in growing and dynamic industries while traditional and mature industries are underrepresented.

Figure 5.17: Founder survey: industry affiliations of companies

5.4 Descriptive Statistics

5.4.2.3

143

Company Growth

Number of respondents_

In hypothesis 3a, I formulate a negative relation between company growth and turnover. The following chart in Figure 5.18 illustrates the CARG of employees over the first three years of company existence. Companies with more than 100% of average annual growth are denominated as strongly growing companies. In fact, this definition applies to one third of the sample, equalling 50 companies. It can also be seen that 8 companies (5%) on average more than tripled their number of employees every year over the first three years (CAGR > 2.0) I denote these firms as hyper growth companies. The fact that there are only very few hyper growth businesses in my sample renders the statistical testing of the influence of hyper growth on turnover (cp. hypothesis 3b) very difficult. I will elaborate on this problem in more detail later. This view is supported by looking at the headcount of companies reported by respondents for the time of answering the survey (“today”, cp. Appendix 7). Figure 5.19 illustrates that the sample includes companies from below 5 to up to over 150 employees. 28% of all companies do not exist any more today. 58%

40 35 30 25 20 15 10 5 0

GAGR 0

0.2

0.4

0.6

0.8

103 slowly growing companies (67%)

1

1.2

1.4

1.6

1.8

2

2.2

2.4 >2.5

50 strongly growing companies (33%) N = 153

Figure 5.18: Founder survey: slowly and strongly growing companies

5 Determinants and Impact of Founder Turnover

76 -1 00 10 115 0

51 -7 5

41 -5 0

Number of employees

co >1 m 50 pa ny de ad

N=154, mean=32.05, std=40.65

31 -4 0

21 -3 0

11 -2 0

610

45 40 35 30 25 20 15 10 5 0

05

Number of respondents_

144

Figure 5.19: Founder survey: number of employees today

have not made it to more than 50 employees since foundation. Only 14% have grown to more than 50 employees. Even though the limit of 50 employees is somewhat arbitrarily chosen here, those percentages show the typical distribution of “write-offs”, “living deads”, and “stars” in VC portfolios (Manigart et al. 2002).67 Summing up, the sampled companies can be described as founded by entrepreneurial teams of mostly 2, 3 or 4 individuals. Many of them were founded in the boom phase of 1999 and 2000, several died in the subsequent downturn. About one third of the companies are strongly growing firms with more than 100% of average annual employee growth over the first three years of their existence. Today, only a few companies can be characterized as “stars”, while many have died or are somewhat small “living deads”.

67

The sample includes companies of different age. For example, a company having received venture capital in 2006 might be in the 31-40 employees bracket and still have the chance to become a “star”. Given that venture capital funds also include early investment and later investment companies, Figure 5.19 well represents a typical VC portfolio.

5.4 Descriptive Statistics

145

5.4.3 Descriptive Statistics Regarding VCs

In this paragraph I take a look at investors. First, I describe VCs as characterized by survey participants. Second, I investigate VCs’ influence in their portfolio companies and third, I describe VCs’ procedural fairness as perceived by founders. 5.4.3.1

VC Types

The sample includes managers whose firms were financed by four types of VCs – private VCs, bank-affiliated VCs, government-affiliated VCs, and corporate VCs. Figure 5.20 gives an overview of the respective type of VC dominating the group of investors in the sampled start-up companies. Private VCs account for the majority of 47%. Interestingly, 37% of all entrepreneurs name either bank- or government-affiliated VCs as their most important investor types. This is most probably a result specific for Germany where governmental support of companies, especially small and medium-sized firms, is traditionally rooted in the ERP established by the US government after WW II (Behrmann 2007). I have postulated in hypothesis 2 that private VCs with much higher return expectations than captive VCs (such as bank- or government-affiliated investors) can be expected to behave differently with respect to founder turnover decisions. Comparing private VCs against the remaining groups of investors one can see that 54% of founders with predominantly private VCs experienced turnover while only 46% of founders with other types of VCs as their main shareholders did so. Although this hints at differences in the aggressiveness of turnover decisions taken by private and captive funds, Pearson’s chi-square test only yields a chi2-value of 1.479 (p-value = 0.224), which indicates that both groups are not significantly different. In the subsequent multivariate analysis I will nevertheless inspect in how far hypothesis 2 holds to statistical testing. 31% of the founders in my sample raised money once, 31% twice, 25% three times, and 13% more often. Even without assuming perfect representation of the population by the sample, this allows for two tentative interpretations. First, as is known (Neher 1999), VCs stage their investments, i.e., they continue financing their portfolio companies after a certain time, provided their development re-

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5 Determinants and Impact of Founder Turnover

16% Private VC

47%

Bank-affiliated VC Governmentaffiliated VC

23%

Corporate VC

14%

N=154

Figure 5.20: Founder survey: type of VC dominating the investor group

mains economically promising. Second, the chance of receiving money more than three times seems to be rather limited. Looking at living companies, this means that financing contracts seem to be designed in a way that after two or three rounds of funding businesses should be able to break even. For unsuccessful start-ups this implies that VCs possibly write off companies if a certain amount of investment does not yield positive economic results. However, in order to assess these indeed tentative interpretations, a broader and more complete empirical foundation than the one at hand would be needed. There is another interesting effect associated with the number of financing rounds and the probability of turnover. Comparing survival and turnover cases one can easily see that the percentage of turnover cases increases with additional rounds of funding (23% turnover with 1 round versus 34% with 3 rounds). Consequently, the number of survival cases drops with the acquisition of additional VC money (34% with 1 round versus 20% with 3 rounds). Table 5.3 summarizes the results. However, with a chi2-value of 5.933 (p-value = 0.204) in Pearson’s

5.4 Descriptive Statistics

147

7% 5% 30%

1 round 2 rounds 3 rounds

26% 4 rounds 5 and more rounds

32%

N=154, mean=2.28, std=1.16

Figure 5.21: Founder survey: number of financing rounds

Table 5.3:

Number of financing rounds and turnover

Frequency 1 round Percent Frequency 2 rounds Percent Frequency 3 rounds Percent Frequency 4 rounds Percent Frequency 5 and more rounds Percent Frequency Total Percent Pearson chi2 = 5.933 Pr = 0.204

Survival 32 34.41 32 34.41 19 20.43 5 5.38 5 5.38 93 100

Turnover 14 22.95 17 27.87 21 34.43 3 4.92 6 9.84 61 100

Total 46 29.87 49 31.82 40 25.97 8 5.19 11 7.14 154 100

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5 Determinants and Impact of Founder Turnover

chi-square test the difference between the survivor and the turnover group is not statistically significant. This might be driven by two effects. First, additional rounds of financing can be regarded as a sign for good company development and firm growth. Growth, however, may worsen the founder’s job match and increase his turnover probability. Second, the described effect might be attributable to the fact that VCs’ influence in the company increases in the course of additional funding. As a consequence, they may change the composition of the top management team more easily. Further following this line of reasoning, I analyze the development of VC influence next. 5.4.3.2

VC Influence

In the following, I illustrate the increasing influence of VCs in their portfolio companies over time. First, I look at the number of investors, second at their holdings of voting rights over time. Starting with the number of investors, Figure 5.22 shows the number of VCs involved in the sampled companies over the first 5 years after foundation. Over time most companies have 1 investor on board, an important number of firms have 2 and a smaller number have 3 investors among their shareholders. The number of companies without any investor steeply decreases over the first 3 years. Looking at the average number of investors, it becomes evident that surviving companies add new investors to their shareholder base over the first 3 years after initiation. By acquiring additional investors, entrepreneurs more than double the average number of VCs invested in their companies with an average number of 0.7 investors at foundation and an average of 1.7 investors 3 years later. Several results can be obtained from the graph. First, it seems that after foundation, entrepreneurs initially need some time to attract investors to their venture. However, after 2 years, only 13% (20 companies) of the sampled firms did not attract any investor at all. Thus, the number of companies without any investor decreases rapidly over the first years. Second, assuming that companies go through several rounds of financing thereby adding new investors it is also according to intuition that the number of

149

90

2

80

1.8

70

1.6 1.4

60

1.2

50

1 40

0.8

30

0.6

20

0.4

10

0.2

0

Avg. number of VCs_

Number of companies _

5.4 Descriptive Statistics

0 0

0 VCs 2 VCs n.a.

1

2

1VC 3 VCs 5 yrs. avg.

3

4

5

Years after company inception

N=154

Figure 5.22: Founder survey: number of VCs involved in companies over time

single investor companies increases with companies closing their first round of financing – e.g. with a single investor – in their first years of existence and decreases with additional rounds and additional investors in later years. However, while one might expect a reduction of single investor companies along with an increase in multiple investors companies this effect is only partially supported by the data. The number of companies with 2 investors increases in years 1 and 2, falling thereafter. The number of companies with 3 investors increases until year 3 after foundation. On the one hand, those results do support the view that growing companies over time in fact attract additional investors. On the other hand there is no intuitive reason why the average number of investors should fall after year 3, which it surprisingly does.68 In fact, the graph shows that the average number of investors slightly decreases in later years (1.6 investors after 4 years, 1.5 investors after 5 years).

68

However, a simple t-test reveals that the difference in the mean values of the number of investors in the third and the fourth year is not statistically significant (t-value = 1.368, p-value = 0.174).

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5 Determinants and Impact of Founder Turnover

Number of respondents_

70 60 50 40 30 20 10 0 0 to <1

1 to <2

2 to <3

Avg. number of investors over first 3 yrs.

3 to <4

4 to 5

N=154, mean=1.44, std=1.00

Figure 5.23: Founder survey: average number of VCs over first 3 years

One possible explanation for a reduction in the number of investors might be the fact that the sample covers the boom and the bust phase as described above (cp. discussion of Figure 5.16). While in 1999 and 2000 many new VCs entered the market and invested their funds, a lot of them had to be liquidated in 2001 and 2002 or later. At that time, the surviving VCs or the founders potentially bought back equity shares from those VCs going out of business which in a market consolidation concentrated equity shareholding in the hands of fewer investors. With most companies founded in the boom phase, this would explain the reduction of the number of investors in many companies around 3 years after foundation. Figure 5.23 shows the number of VCs averaged over the first 3 years of company existence. In accordance with Figure 5.22 most companies (47%) have an average of 1 or 2 VCs among their shareholders. There are 21% with on average less than 1 investor. Those firms mostly did not attract investors around the time of founding but in later years. 23% of the companies have an average of between 2 and 3 investors, 10% on average count more than 3 investors over the first three years of their existence. I will use this variable as a control variable in later analyses (cp. paragraph 5.3.3).

5.4 Descriptive Statistics

151

Number of companies _

90

30

80 70

25

60

20

50 40

15

30

10

20

5

10 0

Avg. percentage of VC voting rights

35

100

0 0

1

2

3

4

5

Years after first VC entry below 25%

above 50%

5 Yrs. Avg.

N=111 (year 5) to 152 (foundation)

Figure 5.24: Founder survey: VCs’ voting rights in companies over time

Turning to VC voting rights, Figure 5.24 shows that while the average number of investors decreases in years 4 and 5, the average share of voting rights with VCs remains rather constant in later years. This provides some evidence for my interpretation of a concentration of shares with a reduced number of investors due to market consolidation as laid out above.69 Figure 5.24 depicts the development of VCs’ voting rights over time.70 The commonly acknowledged fact that VCs tend to increase their voting power in their portfolio companies as

69

70

The slight drop in the average shareholdings of VCs which can be observed in Figure 5.24 in years 4 and 5 might result from the fact that distressed VCs were not only bought out by other VCs but also by entrepreneurs. On average, this would lead to an increase of shareholdings by entrepreneurs. Note that I asked survey participants to indicate the number of investors as of the year of firm inception, while I asked them to describe the development of voting rights starting with the year the first VC invested in the company. Those different points of reference in fact deliver more specific results with regards to the information I intended to obtain. With regards to the number of investors the answers provided show how long it takes entrepreneurs to acquire VC money while with respect to the voting power associated with the investors, information before the first actual external investment would not yield any insight at all.

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5 Determinants and Impact of Founder Turnover

those grow successfully is supported. In fact, the number of companies in which VCs hold a minority share decreases by 51% from 88 companies to 43 over the first 5 years after signing the first VC contract. Over the same period, the number of companies in which VCs hold a majority share of voting rights surges by 150% from 12 to 30. Between the first and the fifth year, the average percentage of voting rights held by VCs increases from 25% to 31%. Those results provide strong support for inter-temporal changes of power distribution in terms of voting rights in venture capital funded start-ups which I assume to affect founder turnover decisions and their implementation (cp. hypothesis 1). In order to measure VC influence in one comprehensive variable I differentiate those firms in which VCs had held 50% of voting rights or more at some point in time over the first three years of VC involvement from those firms where this was not the case (cp. paragraph 5.3.2). In 30% of the companies VCs held the majority of voting rights in at least one year during the first three years of VC involvement in the firm. In the remaining 70% of companies this was not the case. This shows that there is a minority of companies – in total 30% – in which investors clearly dominated the firm at some point in time. I will scrutinize in the following analysis whether this can help explain founder turnover decisions. 5.4.3.3

VC Procedural Justice

58% of founders fully agreed or rather agreed that their VCs had been fair in their decision making towards them. 23% rather disagreed or fully disagreed and 19% were unsure. Figure 5.25 shows the distribution. This variable is used in the further course of this study to examine the influence of procedural justice on the type of turnover chosen by the founder as proposed in hypothesis 11. Summing up this paragraph, VCs can basically be divided into four different groups, while private investors form about half of the sample. They typically fund their portfolio companies over several rounds thereby increasing their influence in the firms in terms of number and importance of voting rights. While most entrepreneurs rather agree on fair decision making processes on the side of VCs, a considerable number of them do not seem to perceive their investors as being procedurally just.

5.4 Descriptive Statistics

153

Number of respondents_

60 50 40 30 20 10 0 5 (fully agree)

4 (rather agree)

3 (unsure)

VC perceived as fair

2 (rather disagree)

1 (fully disagree)

N=154, mean=3.60, std=1.28

Figure 5.25: Founder survey: VCs’ fairness towards founder

5.4.4 Descriptive Statistics Regarding Founder Turnover

Founder turnover is among the core dependent variables in this research. Therefore, in the following, I take a look at turnover in general and rotation and departure as well as considered rotation and departure in particular. Considered rotation and departure describe situations in which turnover was taken into account, but finally did not take place. Figure 5.26 illustrates that 40% of founders experienced turnover while VCs were among their shareholders. In another 17% of all cases some sort of founder turnover was considered but not effected. The remaining respondents (43%) replied that turnover was never an issue for them. This first of all underpins that founder turnover as studied in this thesis is largely prevalent in venture capital funded firms. With regards to effected turnover, 28 founders (39% of all turnover cases or 18% of the sample) reported they took another job internally (rotation), while 43 founders (61% of all turnover cases or 28% of the sample) indicated they left the company during the time VCs were among their shareholders.

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5 Determinants and Impact of Founder Turnover

Figure 5.26: Founder survey: turnover, rotation, and departure among founders

Looking at considered turnover, 13 founders (50% of the considered turnover cases or 8% of the sample) reported that there had been discussions about them taking on another job internally. Another 13 said there had been discussions about them leaving the company. However, those individuals ultimately did not experience the discussed type of turnover. Second, it also becomes evident that both, internal and external turnover play an important role. This supports the logic of my conceptual framework (cp. Figure 1.1) in which I argue that a distinction in the type of turnover matters and that there are good reasons for founders to stay in their firms or to leave them.

5.4 Descriptive Statistics

155

11% <10% (=5%)

14%

36%

10% to <25% (=17.5%) 25% to <50% (=37.5%) 50% and over (=51%)

39%

N=28, mean=35.98,std=15.55

Figure 5.27: Founder survey: VCs’ share of voting rights at effected rotation

The sample includes both types of turnover which allows me to empirically test the hypothesized selection and outcome structure. The four figures to follow illustrate shares of VC voting rights at the time of both, effected and considered turnover. I discuss those graphs in order to find first support for my assumption that VCs have a major influence on turnover and might especially drive rotation and departure once their voting rights are sufficiently high. The following charts might allow for first careful interpretations in this regard. A deeper analysis of this conjecture is needed and follows in subsequent multivariate analyses. Figure 5.27 illustrates that in 36% of all rotation cases VCs held 50% and more of the voting rights. In another 39% of internal turnover cases VCs were granted between 25% and 50% of voting rights. In only 25% of the 28 observed rotation cases the investors held below 25% of voting rights. Thus, the data de-

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5 Determinants and Impact of Founder Turnover

2% 14% <10% (=5%)

10% to <25% (=17.5%)

19%

65%

25% to <50% (=37.5%) 50% and over (=51%)

N=43, mean=42.70, std=13.22 Figure 5.28: Founder survey: VCs’ share of voting rights at effected departure

scription suggests that rotation occurs when VC influence is rather high than low. I will scrutinize this conjecture in the subsequent multivariate analysis. Figure 5.28 shows the respective percentages for effected departure, i.e., external turnover. 65% of all managers left their companies while their VCs held the majority of voting rights. This indicates that departure is most likely in cases in which VCs are in control. Even though one might expect that high VC voting rights explain an increased hazard of turnover, this interpretation is not viable at this point of the analysis. For a multivariate analysis of this supposition refer to paragraph 5.5.3. Figure 5.29 and Figure 5.30 depict VC voting rights at the time when rotation or departure were considered but ultimately not effected. With just 13 observations each, both sub-samples are very small. Therefore, results based on those data need to be taken with caution and might only serve as rather vague indications.

5.4 Descriptive Statistics

157

8% <10% (=5%)

31%

10% to <25% (=17.5%)

38%

25% to <50% (=37.5%) 50% and over (=51%)

23%

N=13, mean=31.38, std=16.52

Figure 5.29: Founder survey: VCs’ share of voting rights at considered rotation

In Figure 5.29 it can be seen that on average VCs’ voting rights are considerably lower in those cases in which rotation was considered but not effected than in those where internal turnover actually took place. A preliminary interpretation might be that VCs tend to initiate discussions about changes in the management team, even if they might not be able to enforce rotation. This might occur for two reasons. VCs may want to put pressure on the entrepreneur by explicitly formulating potential sanctions, or discussions about potential rotation may be a standard part of VCs’ professional management development schemes.71

71

In my explorative interviews several VCs indeed indicated that their role often was to rather propose founder turnover as a suggestion of improvement than enforcing management changes. They also pointed out that they often were not powerful enough to enforce turnover on the basis of their voting rights alone. However, in many cases they were considered the most influential investor whose suggestions other investors as well as entrepreneurs followed in decision making processes.

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5 Determinants and Impact of Founder Turnover

15% <10% (=5%)

47%

15%

10% to <25% (=17.5%) 25% to <50% (=37.5%) 50% and over (=51%)

23% N=13, mean=35.65, std=18.17 Figure 5.30: Founder survey: VCs’ share of voting rights at considered departure

Figure 5.30 illustrates that the importance of VCs’ voting rights in cases of considered departure is also comparably high (47%) with respect to the importance of VCs’ voting rights in cases of effected departure (65%). It seems that VCs only raise the issue of potential departure when this means a credible threat towards the entrepreneur. Nevertheless, VCs’ ability to actually enforce turnover – in this case departure – seems to be reduced with only 45% of cases where VCs hold a majority share compared to 65% of cases when rotation was effected. The further multivariate analyses will help to scrutinize those suppositions. Figure 5.31 plots the number of years between company inception and the time of implementation of turnover. It can be seen that the majority of observed turnover cases happens quite early after foundation, which supports the view of high dynamics in growing companies. This also indicates that VCs implement management change in early years of the investment.

5.4 Descriptive Statistics

159

Number of respondents_

20

15

10

5

0 1

2

3 4 5 Number of years

6 7 8 N=61, mean=3.38, std=1.82

Figure 5.31: Founder survey: number of years between foundation and turnover

Summing up the descriptive results concerning founder turnover, it can be said that turnover in VC backed start-ups is a prevalent phenomenon and mostly happens during the first 3 years of firm existence. First descriptive results strongly suggest that the power of VCs inside their portfolio companies is related to the implementation of turnover. 5.4.5 Descriptive Statistics Regarding Performance

In this paragraph I describe company performance. As I have argued earlier (cp. paragraph 5.3.1), I refer to post-turnover company growth as the key performance metric. Figure 5.32 shows those growth rates summarized in 10%-classes. Figure 5.32 shows that a large number of companies do not grow at all after turnover (or after the third year of company existence in the cases of surviving founders). There are more companies with negative post-turnover growth than with positive post-turnover performance (cp. negative mean value reported in Figure 5.32). In order to more deeply analyze this surprising result I look at mean values for turnover, rotation, and departure cases.

5 Determinants and Impact of Founder Turnover

>0 .7 0

to 0.6 0

to 0.5 0

to 0.4 0

to 0.7 0 >0 .6 0

>0 .5 0

>0 .4 0

to 0.3 0

>0 .3 0

to 0.2 0

to 0.1 0

Post-tunover growth rate

>0 .2 0

>0 .1 0

0

0

0

to -0 .1 0

>0 .0 0

<0 .0 0

to - 0. 2 <- 0.1 0

to - 0. 3 <- 0.2 0

50 to - 0.

to - 0.4

<- 0.3 0

0 to - 0. 7

to- 0 .6 0

<- 0.4 0

<- 0.5 0

<- 0.7 0

to - 0.8

0

45 40 35 30 25 20 15 10 5 0

<- 0.6 0

Number of respondents_

160

N=134, mean=-0.08, std=0.34

Figure 5.32: Founder survey: post-turnover employee CAGRs of companies

Table 5.4 shows a growth rate of -14.5% for turnover cases and a growth rate of -3.7% for companies where founders did not leave their initially taken job positions. The numbers are in line with Figure 5.32 and moreover suggest that turnover might be detrimental to subsequent company growth. However, the growth difference between the turnover and the survival group marginally is not significant at the 10% level. Comparing rotation and departure, it becomes clear that rotation cases demonstrate a positive growth rate of 5.4% while departure cases show strongly negative growth of -22.7%. This allows for a tentative and preliminary – though very interesting – interpretation. In line with hypothesis 13, it seems that rotation and departure have opposite effects on company growth. As proposed by the t-test statistics, for the case of departure the measured performance impact is even significantly negative. Looking at company survival as another interesting indicator of firm performance it can be seen from Figure 5.33 that about one third (32%) of all companies have gone out of business while the remainder is still alive.

5.4 Descriptive Statistics Table 5.4:

161

Post-turnover growth by turnover, rotation, and departure

Turnover Rotation Departure**

Mean -0.037 -0.145 -0.100 0.054 -0.022 -0.227

no yes no yes no yes

Std 0.401 0.345 0.394 0.240 0.380 0.351

N 79 55 118 16 95 39

Mean value t-test Pr (|T|>|nT|) = 0.108 Pr (|R|>|nR|) = 0.157 Pr (|D|>|nD|) = 0.004

** significant at 5%

Looking at the distribution of the time of survival, it becomes clear that venture capital financed firms die young. The mean value of survival for dead companies is below 4 years with a rather small standard deviation of below 2 years. This indicates that both, entrepreneurs and VCs may try to abandon unsuccessful projects rather quickly in order to turn to more attractive alternatives. Comparing the distributions of the time of survival of founders (cp. Figure 5.31) and the time of firm survival (cp. Figure 5.33) one may be tempted to conclude that turnover goes together with company shutdowns. However, again, it is important

32% have gone out of business

Number of respondents_ __

68% are alive

15

10

5

0 0

1

2 3 4 5 6 Number of years of survival

Figure 5.33: Founder survey: survival of companies

7 8 9 10 N=49, mean=3.82, std=1.88

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5 Determinants and Impact of Founder Turnover

to note that this is not the case. A comparison of turnover cases and survival cases in fact even shows that the probability of turnover is higher in firms that still exist today than in firms that have gone out of business, though this difference is not statistically significant.72 5.4.6 Summary of Variables Used in Subsequent Empirical Analysis

The following Table 5.5 summarizes key descriptive statistics for the variables used in the subsequent multivariate analysis. Apart from two determinants (founder performance and VC procedural justice) all variables are dummy variables. In order to develop a better understanding of the data at hand, I expand the description by splitting the turnover from the survival group and compare mean values of both groups. Table 5.6 gives an Table 5.5:

Description of variables included in selection and outcome estimation I

Variable Turnover decision (D)* Turnover type (D - rotation or departure)* (1) VC influence (D) (2) Founder professional experience (D) (3) Firm-specific know-how (D) (4) VC type (D) (5) Company growth (D) (6) Founder performance (7) Founder CEO (D) (8) VC procedural justice (9) Educational job match (D) (10) Entrepreneurial job match (D) (11) Functional job match (D) (12) Industry job match (D) (13) Project management experience (D) (14) People management experience (D) (15) Processes design experience (D)

Mean 0.396 0.705 0.298 0.442 0.305 0.481 0.331 4.513 0.591 3.604 0.214 0.442 0.292 0.468 0.461 0.299 0.227

Std 1.254 1.275 -

Min 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0

Max 1 1 1 1 1 1 1 6 1 5 1 1 1 1 1 1 1

N 154 61 151 154 154 154 154 154 154 154 154 154 154 154 154 154 154

(D) = Dummy variable, * = Dependent variable

72

The mean value of the turnover dummy is 0.367 for dead firms and 0.410 for living firms. A Pearson chi2-test testing if both values are significantly different yields a chi2value of 0.249 (p-value of 0.618). Thus, the mean values are not significantly different.

Turnover 0.443 0.311 0.213 0.541 0.426 4.410 0.508 3.344 0.279 0.410 0.230 0.492 0.475 0.262 0.180

(D) = Dummy variable, *** = significant at 1%, ** = significant at 5%, * = significant at 10%

(1) VC influence (D)*** (2) Founder professional experience (D)*** (3) Firm-specific know-how (D)** (4) VC type (D) (5) Company growth (D)** (6) Founder performance* (7) Founder CEO (D)* (8) VC procedural justice (9) Educational job match (D) (10) Entrepreneurial job match (D) (11) Functional job match (D) (12) Industry job match (D) (13) Project management experience (D) (14) People management experience (D) (15) Processes design experience (D)

Std Min Max N No No No No No turnover Turnover turnover Turnover turnover Turnover turnover Turnover turnover 0.200 0 0 1 1 61 90 0.527 0 0 1 1 61 93 0.366 0 0 1 1 61 93 0.441 0 0 1 1 61 93 0.269 0 0 1 1 61 93 4.581 1.216 1.280 1 1 6 6 61 93 0.645 0 0 1 1 61 93 3.774 1.389 1.171 1 1 5 5 61 93 0.172 0 0 1 1 61 93 0.462 0 0 1 1 61 93 0.333 0 0 1 1 61 93 0.452 0 0 1 1 61 93 0.452 0 0 1 1 61 93 0.323 0 0 1 1 61 93 0.258 0 0 1 1 61 93

Mean

chi2 p-value 10.230 0.001 6.932 0.008 4.039 0.044 1.480 0.224 4.121 0.042 10.395 0.065 2.859 0.091 7.157 0.128 2.488 0.115 0.412 0.521 1.920 0.166 0.239 0.625 0.084 0.772 0.639 0.424 1.268 0.260

Pearson chi2 test

Table 5.6:

Independent variable

5.4 Descriptive Statistics 163

Description of variables included in selection and outcome estimation II

164

5 Determinants and Impact of Founder Turnover

overview of the results. It can be seen that VC influence is significantly higher in the group of turnover cases. Moreover, turnover is significantly higher in strongly growing companies. The founder’s professional experience as well as his personal performance is significantly lower in turnover cases. Founder CEOs are significantly underrepresented in the turnover group. This descriptive analysis provides some hints with respect to the theoretical model and the framework of hypotheses. However, in order to allow for statistical inference multivariate estimations are needed. Therefore, an in-depth empirical analysis including multivariate modelling and hypothesis testing follows next.

5.5 Empirical Analysis of Determinants of Founder Turnover Based on the data described above, this section provides insights into two of the research questions put forward in section 2.2:

–

What drives founder turnover in venture capital financed start-up companies?

–

Under which conditions will internal turnover prevail over external turnover and vice versa?

To assess these questions in more detail, I estimate a bivariate probit selection model which sheds light on both questions. In accordance with the conceptual framework laid out in section 1.4, the econometric model used here assumes two consecutive decisions. Figure 5.34 shows how this section fits the overall conceptual framework. It covers the first two steps and prepares for a discussion of performance implications in the subsequent section 5.6. As suggested by Figure 5.34, a general decision on whether a founder leaves his initial position is taken first. According to the literature and theory presented above, founder turnover should primarily be attributable to either job matching or corporate governance related reasons. Job matching theory suggests that venture growth and associated changes in job designs may require founder turnover.

5.5 Empirical Analysis of Determinants of Founder Turnover

165

Performance

Outcome

Selection

Antecedents of Turnover

Tier 1

Survival

Turnover

Tier 2 Rotation

Departure

Tier 3 Performance

Figure 5.34: Antecedents of turnover in the conceptual framework

In addition, corporate governance theory includes such potential drivers of turnover as deterioration in firm performance and increase in control by VCs as the company matures (cp. discussion of Figure 2.1). At the second stage, a decision as to internal or external turnover is taken. Besides job matching theory, the third theoretical dimension of organizational psychology needs to be considered at the outcome stage. Thus, besides the founder’s professional experience and firm-specific know-how, the existence or absence of procedural justice, i.e., the fairness in decision making processes in the relationship between the VC and the entrepreneur and the founder’s experienced job satisfaction are expected to determine the outcome at stage two (cp. discussion of Figure 2.1). In what follows, I formally describe and specify the selection and outcome model. Based on this, I present selected results obtained from multivariate analysis designed to empirically test hypotheses 1 to 11. I discuss each hypothesis and

166

5 Determinants and Impact of Founder Turnover

describe the importance of my results with regards to founder turnover decisions in venture capital funded start-ups. 5.5.1 Estimation Method

Given the fact that the sample of turnover cases studied at the second stage of the model is non-random, I need to take sample selection into account when estimating selection outcomes. The basic structure of a sample selection model is that the outcome variable y – in my case the decision of the founder to stay with the company or to leave it – is only observed if some criterion, defined with respect to a variable z, is met. In the given context, this variable z is the turnover decision. The common form of the model therefore has two stages. At the first stage, a dichotomous variable z determines whether or not y is observed, y being observed only if z=1. At the second stage, I estimate the expected value of y, conditional on its being observed. In the model, there is the observable dummy variable z, i.e., turnover, which is a realization of a latent continuous variable z*. For values of z=1, y as the realization of a second latent variable y* is observed. In the context of the present research it is reasonable to think of z* as the utility derived by investors from letting the founder stay in his position versus making him leave his job. Similarly, y* can be interpreted as the founder’s utility from both rotation and departure.73 There are several assumptions underlying this model (Wooldridge 2002) which shall be summed up here: first, zi (the participation indicator) and xi (the vector of dependent variables at the outcome stage) are always observed, which emphasizes the sample selection nature of the problem. Second, the error terms of both variables, ei and ui, are independent of xi with zero mean, which expresses the exogeneity of xi. Third, with regards to the distribution of the error terms, ei is supposed to be normally distributed, while ui does not necessarily

73

Note that I argued above that VCs are more likely to be the drivers of the initial turnover decision, while founders can be expected to determine the ultimate type of turnover (cp. section 2.3).

5.5 Empirical Analysis of Determinants of Founder Turnover

167

have to be normally distributed. However, the combined distribution of the error terms is assumed to be bivariate normal. Unlike in the classic model74, I use a binomial probit at the outcome stage given that the outcome of my model is either rotation or departure.75 Thus, the outcome equation for the model used here is specified as Pr( y i

1)

) ( x i' ß) ,

(5.2)

where yi is the binary outcome variable, ĭ the cumulative distribution function of the standard normal distribution, xi’ a 1 x K vector of covariates, and ß a K x 1 vector of the corresponding coefficients. Given the probit model specification76, yi is the realization of a latent variable yi* given by y*i

xi 'ß  ui .

(5.3)

With respect to yi it holds that

yi

­1 if y* ! 0 ° i . ® *d0 0 if y °¯ i

(5.4)

At the first stage of the model, the selection stage, the model is also specified as a binary probit function. This estimator determines whether y will be observed in the outcome equation (second stage). The probit function for stage one is specified as

74

75

76

The first to describe this type of model was James Heckman (1979).74 In the original Heckman model the outcome equation, i.e., the equation at the second stage, is a simple OLS regression of the form yi = xi’ß + ui. In this equation yi denotes the dependent variable. xi’ is a vector of covariates, and ß the corresponding coefficients. This probit model with selection is implemented in STATA by the heckprob command. I make use of this estimator to account for the fact that my dependent variable is dichotomous at the outcome stage. Even though an error term is not necessary to provide stochastic nature for this model, it is typically conceptualized through an underlying model that does contain an error term, in which an unobservable – or latent – index is specified as a linear function of explanatory variables plus an error term (Kennedy 1998).

168

5 Determinants and Impact of Founder Turnover

Pr(z i

1)

) ( w i' O ) .

(5.5)

Again, the latent variable z* is estimated by z*i

w i ' O  ei ,

(5.6)

where zi* is the dependent variable, wi’ a 1 x K vector of covariates for unit i in the selection equation, Ȝ a K x 1 vector of the corresponding coefficients, and ei the term for random disturbance for unit i. The dependent variable zi* triggers the selection at stage one according to the function: zi

­1 if z * ! 0 ° i . ® * °¯0 if z i d 0

(5.7)

In the following analysis I use this selection model in order to estimate antecedents of turnover (wi’Ȝ) as well as the determinants of rotation and departure (xi’ß) in venture capital backed start-ups. 5.5.2 Estimation Equation

This paragraph describes the estimation function used on the basis of the selection model introduced above. I separately present the results for the selection and the outcome function. All variables used are described in section 5.3 above. Starting with the specification of the selection equation at the first stage, the linear estimation function for the latent variable y* is specified as follows. y i * D  E1i VCI + E 2i VCT  E 3i CGR  E 4i FJM  E 5i FPE  E 6i FFK  E 7i FEX  E 8i LTC  u i

(5.8)

with VCI standing for VC influence, VCT for VC type, CGR for company growth, FJM for founder job match, FPE for founder performance, FFK for founder firm-specific know-how and FEX for founder professional experience. LTC stands for lifetime company, which is a control variable. At stage 2 of the model accounting for the probability of rotation versus turnover outcomes I specify the following estimation function:

5.5 Empirical Analysis of Determinants of Founder Turnover

z i * D  O1i FFK + O 2i FEX  O 3i FCE  O 4i VCP  e i

169

(5.9)

where FFK denotes the founder’s firm-specific know-how, FEX the founder’s professional experience, FCE that the founder is a CEO and VCP the level of VC procedural justice as perceived by the entrepreneur. 5.5.3 Multivariate Analysis

Based on the empirical specification described above, this paragraph presents multivariate analyses testing the hypotheses formulated in sections 3.1 (selection-related hypotheses) and 3.2 (outcome-related hypotheses). First, I analyse correlations between independent variables used in the subsequent analysis by computing and discussing a correlation matrix. Second, I estimate a bivariate probit selection model based on the theory previously discussed. 5.5.3.1

Correlation Matrix

Out of the 15 variables described in Table 5.5, 13 are dichotomous and two are interval scaled – one from 1 to 5 (VC procedural justice) and one from 1 to 6 (founder performance). This setup of ordered-category data calls for the calculation of polychoric correlation coefficients in order to determine the strength of variable interrelation (Olsson 1979; Drasgow 1988). Assuming that each of the ordinal variables is obtained from categorizing a normally distributed latent variable, the polychoric correlation is derived as the maximum likelihood estimate of the correlation between any two variables.77 The correlation matrix (cp. Table 5.7) reveals that there are several independent variables that are importantly correlated. I briefly comment on those correlation coefficients which are below –0.2 or above 0.2, assuming that coefficients in between those borders can be neglected for their lack of importance.78 77

78

If each of the ordinal variables has only two categories, then the correlation between the two variables is referred to as tetrachoric. This is the case for the correlation between all 13 dummy variables included in the dataset at hand. It is convenient to only pay attention to bivariate relationships with absolute correlation coefficient values above 0.2 (Harhoff and Reitzig 2004). I neglect correlations below this threshold. Note that apart from very few exceptions this heuristic leads to a discussion of all statistically significant correlations (cp. Table 5.7).

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5 Determinants and Impact of Founder Turnover

The growth dummy (5) is negatively correlated with the founder’s professional experience (2) and the founder’s firm-specific know-how (3). It is positively correlated with VC influence (1) and the private VC dummy (4). Somewhat surprisingly, the negative correlations seem to suggest that more experienced founders and entrepreneurs with more firm-specific know-how are comparably less likely to launch fast growing businesses. The positive coefficients indicate that private VCs are more capable than captive funds to select future high-growth ventures which would also explain their hypothesized ability to achieve returns above those of captive – especially governmental – funds.79 Moreover, it seems that VCs push for growth if they are able to mainly influence company development. The founder CEO (7) and VC type (4) variables are positively correlated, which may suggest that companies financed by private VCs do install CEOs earlier than other companies, which may longer act in a quasi hierarchy-free setup. Additionally, private VCs (4) are perceived as less procedurally just (8) than other types of investors, as suggested by the negative correlation between both dummies. Looking at the variables included in the analysis to measure specific facets of both, job matching and professional experience, several coefficients deserve attention. First, I look at correlations between the group of matching variables (9, 10 and 11) and experience variables (12, 13 and 14). The educational job match variable (9) and the VC influence variable (1) are positively correlated. A possible explanation for this may be that VCs invest heavily in companies in which founders show good job matches, especially if they are adequately educated. However, it must be noted that the remaining match dummies – (10) and (11) – do not show high correlation coefficients with the VC influence dummy (1), which renders this interpretation somewhat ambiguous. Again surprisingly at first sight, the negative correlation between the founder’s educational job match (9) and his firm-specific know-how (3) suggests

79

Refer to my prior reasoning that private VCs in fact need to be able to achieve higher returns than captive funds in order to sustain their business model (cp. derivation of hypothesis 2 for details).

1.000 0.151 -0.028 -0.428 0.152 0.035 -0.018 -0.146 0.246 0.196 0.294 0.535 0.676 0.448

(2)

1.000 -0.167 -0.239 0.058 0.059 -0.046 -0.204 0.105 0.014 -0.139 -0.031 0.155 0.080

(3)

1.000 0.331 -0.108 0.261 -0.223 0.064 0.096 0.113 0.017 0.159 -0.052 0.064

(4)

1.000 0.187 -0.143 -0.029 -0.058 0.112 0.057 0.142 -0.023 -0.117 -0.035

(5)

1.000 -0.172 0.136 0.009 0.188 0.089 0.124 0.240 0.182 0.270

(6)

(8)

1.000 -0.121 1.000 -0.141 0.113 0.035 0.174 0.266 0.013 0.103 -0.135 -0.124 0.119 0.040 0.120 -0.147 0.092

(7)

1.000 -0.146 0.481 0.032 0.265 0.194 -0.115

(9)

1.000 0.242 0.009 0.068 0.354 0.296

(10)

1.000 0.046 0.247 0.239 -0.014

(11)

1.000 0.354 0.255 -0.020

(12)

1.000 0.608 0.658

(13)

1.000 0.612

(14)

The correlation matrix shows tetrachoric correlation coefficients for bivariate variables and polychoric correlation coefficients for variables of more than two categories (N=154). Statistically significant correlation coefficients (10% level and below according to a chi2 test) are in bold.

(1) 1 -0.081 -0.151 0.074 0.259 0.059 -0.123 -0.156 0.421 -0.110 -0.193 0.007 0.055 0.070 -0.073

1.000

(15)

Table 5.7:

(1) VC influence (D) (2) Founder professional experience (D) (3) Firm-specific know-how (D) (4) VC type (D) (5) Company growth (D) (6) Founder performance (7) Founder CEO (D) (8) VC procedural justice (9) Educational job match (D) (10) Entrepreneurial job match (D) (11) Functional job match (D) (12) Industry job match (D) (13) Projects management experience (D) (14) People management experience (D) (15) Processes design experience (D)

5.5 Empirical Analysis of Determinants of Founder Turnover 171

Correlation matrix of variables used in binary probit selection regressions

172

5 Determinants and Impact of Founder Turnover

that founders carrying high firm-specific know-how tend to be in comparably inferior educational job matches. This may be explainable thinking of e.g. natural scientists taking on the role of CEO after founding their company. The correlation between the founder’s functional job match (11) and the founder CEO dummy (7) suggests that CEOs often have been in a general management function before. In general, founders with more professional experience (2) show a better industry job match (12). Naturally, professional experience (2) is highly correlated with all three variables measuring specific facets of experience (13, 14, and 15). The same holds true for professional experience (2) and the founder’s entrepreneurial job match (10). It is evident that founders with prior entrepreneurial experience are generally more professionally experienced than founders without prior entrepreneurial activity. Founders with a high level of performance (6) seem to have increased project management (13) and process design (15) experience as suggested by the respective correlation coefficients. Additionally, there are some important correlations within the matching and the experience variables. The functional job match variable (11) and the educational (9) as well as the entrepreneurial (10) job match variables show important positive correlations. This is not surprising since it can be expected that founders in a given job may have chosen this job due to their educational as well as their functional background, both often being associated with each other.80 Moreover, founders with prior entrepreneurial experience can be expected to be specifically likely to receive venture capital funding for additional entrepreneurial projects. Founders with high educational job matches (9) seem to have increased project management experience (13) when founding a company. Entrepreneurs with prior entrepreneurial experience (10) seem to dispose of specific people management (14) and process design capabilities (15). High project management (13) and high people management (14) experience are both positively correlated with the founder’s functional (11) and industry 80

Take a CFO for instance who has studied economics (educational match with the CFO post) in order to work as an investment banker (functional match with the CFO post) thereafter or a CTO who studied engineering (educational match with the CTO post) and worked at the F&E department of a big corporation (functional match with the CTO post).

5.5 Empirical Analysis of Determinants of Founder Turnover

173

(12) job matches. In addition, the three rather specific variables measuring professional experience are highly correlated with each other (cp. bottom right corner of the correlation matrix (12, 13 and 14)). The respective correlation coefficients are all above 0.6 which indicates that founders with high experience in one field are highly experienced in the remaining two fields as well. Overall, the correlation matrix shows that the independent variables used in the subsequent estimations are partially correlated with each other, which I will pay attention to in the subsequent model specification and discussion. 5.5.3.2

Estimation Results

In the following, I describe the specification of the binary selection model subsequently used. I start with an extensive specification as proposed by my theoretical model in Figure 3.1 (Model 1) which I gradually reduce (Models 2 and 3). In a final step, I estimate a specification which I refer to as the restricted model (Model 4). In what follows, I illustrate and describe those four specifications. Table 5.8 shows all four estimates of the selection (Pr (Decision)) and outcome (Pr (Type)) model. In the initial model I intended to include as many measures for the sub-hypotheses associated with job matching (cp. hypothesis 4) as well as professional experience (hypotheses 6 and 8) as possible. However, including all four job matching measures into the selection regression yields a ȡvalue with boundaries –1 and 1 (model not reported) which would suggest that the error terms in the selection and outcome equations are perfectly correlated. This in turn indicates model instability. This result is most likely attributable to the fact that functional job match and educational job match are highly correlated (cp. Table 5.7). Given the high correlation of both variables, I do not include the educational job match variable into the specification. Exclusively incorporating functional match, industry match, and entrepreneurial match into the selection equation leads me to Model 1. Also due to multicollinearity (cp. bivariate relations in Table 5.7) a more extensive specification including any of the specific experience variables (project management experience, people management experience, or process design experience) is not possible.81 Thus, those variables 81

In most cases, STATA either does not allow for estimation or does not achieve convergence. Otherwise the output indicates ȡ-values with boundaries –1 and 1.

174

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are not included in the initial model specification, neither at the selection, nor at the outcome stage. As a consequence, professional experience is only measured in its more general sense based on the years of work experience in Model 1. In order not to measure similar effects in different variables I exclude industry job match and entrepreneurial job match from the selection equation (Pr (Decision))82, which yields Model 2. The resulting specification still includes variables for each hypothesis put forward in my theoretical model.83 In a subsequent step, I reduce Model 2 by excluding the professional experience variable at the outcome stage (Pr (Type)). The resulting specification can be seen in Model 3. Finally, I further reduce Model 3. I limit the selection equation (Pr (Decision)) by two variables (Model 3). The coefficients of the variables VC type and company growth are not significant in Model 3 and are therefore taken out of the equation. I do not remove the variable founder performance for the sake of model stability.84 Testing Model 1 against Model 4 in an LR test for the joint significance of the variables taken out of the specification reveals that the restricted model is not significantly different from the full model (LR chi2-value = 3.17, p-value = 0.673). Thus, the variables excluded from Model 1 do not add to the explanation of the selection and outcome effects. In essence, the obtained results indicate that the restricted model (Model 4) is an appropriate reduction of the full model (Model 1 as the most complete specification of the theoretical model). With ȡ-value remaining within the boundaries of –1 and 1 in all specifications, the model can be regarded as quite stable. This is especially noteworthy given the small sample size and the complexity of the estimator. I use Model 4 for hypothesis testing in the further course of analysis.

82

83

84

The coefficients of both variables are insignificant which allows me to take them out of the regression. Note that in the case of the founder’s professional experience the sub-hypotheses laid out in the theoretical model are neglected here. For the founder’s job match only the functional job match is considered. Taking the variable out of the specification results in a ȡ-value with boundaries –1 and 1. For this reason, I carry on the analysis with the specification as shown in Model 4.

Table 5.8:

Standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%

The sample consists of 61 turnover and 90 survival cases. The model is a two-stage outcome and selection estimation with two probit regressions. The dependent variables are turnover or survival at the selection stage and rotation or departure at the outcome stage. Independent variables are founder CEO, VC procedural justice, firm-specific know-how, founder professional experience, VC influence, functional job match, founder performance, VC type, company growth, entrepreneurial job match, and industry job match. Lifetime company is a control variable. Dependent variables: turnover vs. survival for Pr(Decision) / rotation versus departure for Pr(Type) Model 2 Model 3 Model 4 Model 1 Pr (Type) Pr (Decision) Pr (Type) Pr (Decision) Pr (Type) Pr (Decision) Pr (Type) Pr (Decision) Founder CEO (D) 0.321 0.309 0.311 0.299 [0.277] [0.276] [0.282] [0.294] VC procedural justice -0.185* -0.193* -0.198* -0.205* [0.106] [0.105] [0.104] [0.108] Firm-specific know-how (D) 0.218 -0.355 0.213 -0.371 0.191 -0.374 0.19 -0.419* [0.338] [0.249] [0.332] [0.248] [0.335] [0.247] [0.335] [0.248] Founder professional experience (D) 0.147 -0.452* 0.155 -0.416* -0.373* -0.416* [0.316] [0.241] [0.307] [0.232] [0.216] [0.218] VC influence (D) 0.712*** 0.710*** 0.719*** 0.715*** [0.221] [0.218] [0.217] [0.229] Functional job match (D) -0.307 -0.302 -0.302 -0.267 [0.232] [0.233] [0.232] [0.237] Founder performance -0.087 -0.081 -0.081 -0.072 [0.082] [0.081] [0.081] [0.078] Lifetime company -0.015 -0.013 -0.009 -0.026 [0.048] [0.047] [0.045] [0.053] VC type (D) 0.202 0.203 0.209 [0.209] [0.208] [0.208] Company growth (D) 0.202 0.222 0.224 [0.231] [0.227] [0.229] Entrepreneurial job match (D) 0.055 [0.206] Industry job match (D) 0.12 [0.225] Constant 1.543*** 0.122 1.574*** 0.149 1.646*** 0.103 1.690*** 0.371 [0.487] [0.531] [0.490] [0.526] [0.466] [0.513] [0.517] [0.489] Number of observations 151 151 151 151 151 151 151 151 Log likelihood -121.53 -121.7 -121.83 -123.12 -0.919 [-0.998 to 0.438] -0.954 [-0.999 to 0.993] -0.946 [-0.999 to 0.798] -0.922 [-0.999 to 0.424] rho [Conf. interval rho] Test (rho=0) Prob > chi2 0.025 0.020 0.020 0.026 Wald chi2 0.172 0.139 0.092 0.069

5.5 Empirical Analysis of Determinants of Founder Turnover 175

Regression results of bivariate probit selection models

Standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%

Number of observations Log likelihood Test rho=0 (Prob > chi2) Wald chi2 (Prob > chi2) LR chi2 (Prob > chi2) Pseudo R2 (Prob > chi2)

Constant

Lifetime company

Founder performance

Functional job match (D)

VC influence (D)

Founder professional experience (D)

Firm-specific know-how (D)

VC procedural justice

Founder CEO (D)

Turnover Type Turnover Decision Model 4 (1) Pr (Type) Pr (Decision) 0.299 [0.294] -0.205* [0.108] 0.19 -0.419* [0.335] [0.248] 0.715*** [0.229] -0.267 [0.237] -0.416* [0.218] -0.072 [0.078] -0.026 [0.053] 1.690*** 0.371 [0.517] [0.489] 151 151 -123.120 0.026 0.069 -

Turnover Type Turnover Decision Independent Binary Probits (2) Pr (Type) Pr (Decision) 0.462 [0.366] -0.214 [0.136] 0.042 -0.393 [0.437] [0.242] 0.694*** [0.234] -0.218 [0.244] -0.494** [0.222] -0.059 [0.086] 0.014 [0.045] 1.059* 0.098 [0.580] [0.527] 61 151 -34.145 -91.446 0.126 0.002 0.077 0.102

151 -122.845 0.000 0.115

-0.227 [0.215] -0.221*** [0.083] -0.409* [0.237] 0.703*** [0.223] -0.274 [0.239] -0.501** [0.217] -0.062 [0.086] -0.006 [0.044]

Turnover Ordered Probit (3)

Table 5.9:

Dependent Variable

The sample consists of 61 turnover and 90 survival cases. The models are a two-stage outcome and selection estimation with two probit regressions (1), two separate probit regressions (2), as well as an ordered probit regression (3). The dependent variable is turnover or survival at the selection stage and rotation or departure at the outcome stage (1), turnover or survival in the first and rotation or departure in the second probit estimation (2), and turnover in the ordered probit model (3) respectively. Independent variables are founder CEO, VC procedural justice, firm-specific know-how, founder professional experience, VC influence, functional job match, and founder performance. Lifetime company is a control variable.

176 5 Determinants and Impact of Founder Turnover

Regression results of selection model and alternative models

5.5 Empirical Analysis of Determinants of Founder Turnover

177

It must be noted that in all four specifications shown in Table 5.8 the sample selection effect discovered by the model is significant at the 5% level (see Table 5.8, Test (ȡ=0) statistics). The measure of correlation between the error terms in the selection and the outcome equation (ȡ) indicates a significant selection effect. To further investigate on the selection effect and the robustness of the restricted model (Model 4) I estimate a set of specifications ignoring sample selection. Although the coefficients in the outcome equations are necessarily affected by selection bias, the results provide a useful point of comparison to the sample selection model. Table 5.9 shows the restricted model like in Table 5.8. I compare two independent probit regressions and an order probit regression against this specification.85 As can be seen in Table 5.9, I include all variables from the outcome equation as independent variables in the first probit regression (Pr (Type)) and the type of turnover (rotation versus departure) as dependent variable. The second probit regression (Pr (Decision)) comprises the variables from the former selection equation. The dependent variable is the turnover decision (turnover versus survival). In the ordered probit regression I include all independent variables from both stages of the bivariate probit selection model and introduce a dependent variable with three ordered categories. The dependent variable takes on the value of 0 in the case of survival, 1 in the case of rotation as ultimate outcome and 2 for the ultimate outcome of departure. This ordering is reasonable thinking of rotation as a less severe managerial change than departure. Comparing results from the independent probit regressions to the selection model reveals that all coefficients at the selection stage carry the same sign and are about the same magnitude.86 The levels of significance as well as standard errors also remain the same in both specifications. While no differences can be expected at the selection stage, some difference may occur at the outcome stage. 85

86

For details on the probit model refer to paragraph 5.5.1. The ordered probit model essentially is a response model in which responses are hierarchically ordered (Wooldridge 2002). The only exception is the control variable “Lifetime company” which changes from a very small negative coefficient in the selection and outcome model to a very small positive coefficient in the independent probit regression.

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While signs remain the same, the magnitude of coefficients and their standard errors are clearly different in both specifications. Moreover, the VC procedural justice variable is significant at the 10% level in the selection specification, but not in the independent probit. The ordered probit model leads to similar results. Again, the coefficients and standard errors at the selection stage are more or less the same for both the restricted selection and the ordered probit model with identical levels of significance for three variables. However, the coefficients of the variables at the outcome stage are largely different from those in the selection model, again indicating that it is important to account for sample selection. For instance, the ordered probit model is not able to separate the significant effect patent holding has at the selection stage from the insignificant effect of patent holding on the type of turnover. For the founder CEO variable it even predicts an inverse – though not significant – negative effect. A last consistency check of the specification should be performed in order to avoid that the model is identified solely on distributional assumptions (Sartori 2003). The selection model requires an exclusion restriction, i.e., a variable which theoretically explains selection but not outcomes. In the context of my theoretical model there are two such exclusion restrictions, which are also referred to as identifiers. First, I state that VC influence has an impact on turnover decisions but not on the turnover type (hypothesis 1). Second, I postulate that the quality of job matching determines a founder’s chance to survive in his initial position (hypothesis 4). However, I do not expect job matches to be relevant for the founder’s decision to either stay with the company or to leave it.87 For a test of both assumptions I conduct a two-step procedure. First, based on Model 4 (cp. last column of Table 5.8) I estimate a specification also including VC influence in the outcome equation. As can be seen in Table 5.10 VC influence has no significant effect. Including functional job match in the outcome

87

Note that the third variable – founder professional experience – which is included in the selection equation but not in the outcome equation in Model 4 cannot serve as an identifier here. This is due to the fact that the variable was excluded from the specification for statistical reasons earlier.

5.5 Empirical Analysis of Determinants of Founder Turnover

179

Table 5.10: Test of exclusion restrictions The table reports coefficients, standard errors and p-values for VC influence and founder functional job match in a specification of the bivariate selection model including the respective variable in the outcome equation. The LR test statistics are based on a comparison of a specification including VC influence and founder functional job match versus the specification presented in Model 4 above. Variable VC influence Founder functional job match

Coefficient Standard Error 0.432

0.507

-0.283

0.372

p-value

LR test

0.394 LR chi2=2.28 0.447 p-value=0.319

equation of a separate estimation yields the same result. As predicted by theory, this indicates that none of the two exclusion restrictions are statistically important at the second stage of the model. Second, I compared a specification including both identifiers, i.e., VC influence and founder functional job match against the specification of Model 4 in an LR test. Testing for a possible joint significance of both variables, the LR test yields insignificant results (cp. last column of Table 5.10). Thus, the theoretical reasoning which led me to specify VC influence and founder functional job match as identifiers in Model 4 is supported by the statistical results. In a final step, I calculate the marginal effects for Model 4. I assume all other variables but the variable under observation to be at their mean when calculating marginal effects. The computation of marginal effects yields the results shown in Table 5.11.88 Looking at the marginal effects shown in Table 5.11 it can be seen in how far the theoretical model suggested in section 3.4 is supported by the data. In what follows I will comment on the results with respect to the hypotheses laid out earlier in this book.

88

I use the mfx2 command in STATA to calculate marginal effects.

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Table 5.11: Marginal effects of restricted binary probit selection model The table shows marginal effects for the variables in the selection and the outcome equation of Model 4. Marginal effects are calculated with all other variables kept constant at their means. For dummy variables, marginal effects are computed for a change from 0 to 1. Marginal Effects (Model 4) Founder CEO (D) VC procedural justice Firm-specific know-how (D)

Pr (Type) 0.061 [0.060] -0.041* [0.022] 0.036 [0.606]

Founder professional experience (D) VC influence (D) Functional job match (D) Founder performance Lifetime company

Pr (Decision)

-0.155* [0.088] -0.157** [0.080] 0.277*** [0.087] -0.100 [0.087] -0.027 [0.030] -0.010 [0.020]

Standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%

5.5.3.3

Review of Hypotheses

I first discuss hypotheses at the selection stage, followed by hypotheses at the outcome stage. Results at the selection stage of the model are the following: Hypothesis 1 suggests that higher VC influence increases the hazard of turnover. This proposition cannot be rejected. The variable indeed is significant at the 1% level. The marginal effect is by far the largest of all effects covered by the model. In fact, if VCs hold a share of more than 50% of voting rights during the first three years of VC involvement in a company, founders are 27.7% more likely to face founder turnover compared to companies in which VCs do not hold the majority of voting rights. This very clearly supports the theoretical reasoning that VCs enforce management change if they have the power to do so.

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181

Hypothesis 2 states that private VCs are more likely to push for turnover decisions in their portfolio companies. This conjecture is not supported. The variable is included in Models 1, 2 and 3 as described in Table 5.8. Even though the coefficient is positive, the variable is not significant in any of the specifications. It is therefore dropped from further analysis. Thus, there is not support for the hypothesis that private VCs with their more pronounced performance orientation enforce more turnover than other types of VCs. Hypothesis 3a proposes that in strongly growing companies there is a lower chance for turnover than in other companies. The data does not support this proposition. Company growth was included in the more extensive Models 1, 2 and 3 but did not prove to be significant. Hypothesis 3b states that in hyper growth companies there is an increased turnover hazard for founders. Including the hyper growth dummy in Model 1 as presented in Table 5.8, the variable shows a highly insignificant coefficient (pvalue = 0.879, not reported). Thus, hypothesis 3b cannot be supported. However, given that in my sample there are only 8 hyper growth companies, this result must be handled with caution. In fact, a meaningful assessment of hypothesis 3b is not possible based on the data at hand. Hypothesis 4 suggests that a better job match reduces the founder’s hazard of experiencing turnover. Though Models 1, 2 and 3 in the regression analysis show a negative coefficient for functional job match, the variable is not significant. Since none of the specific match variables (educational match, entrepreneurial match, functional match, or industry match) improve the model (cp. Model 1 in Table 5.8), the hypothesis is not supported. Hypothesis 5 states that the founder’s personal performance is negatively related to his hazard of turnover, i.e., high-performers are expected to be turned over less often than low-performing entrepreneurs. The regression coefficient in all specified models is negative. However, the variable has no explanatory power due to its lack of significance. The hypothesis cannot be supported. Hypothesis 6 postulates that founders with a lot of professional experience are less likely to face turnover in their start-up firms than founders with little professional experience. The empirical analysis very strongly supports this conjecture. Table 5.11 shows that the professional experience dummy is significant at the 5% level. The marginal effect indicates that founders with more than 10

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years of professional experience are 15.7% less likely to leave their initial job positions than founders with less than 10 years of professional experience. This is a strong effect supporting the theoretical reasoning that professionally more experienced founders are more capable to cope with the tasks associated with the development of a start-up business. Hypothesis 7 proposes that founders with firm-specific know-how are less likely to be turned over. This is supported by the data. Looking at the marginal effect displayed in Table 5.11 it can be seen that founders who are patent holders are 15.5% less likely to leave their initial job positions. However, this result is statistically significant at the 10% level only. The following hypotheses are related to the outcome stage. Consequently, the dependent variable those propositions are related to is the type of turnover, i.e., rotation or departure. Results at the outcome stage of the model are the following: Hypothesis 8 suggests that founders with more professional experience are more likely to choose the option of departure. While the negative effect of professional experience on turnover is significant at the selection stage, the observable positive effect on departure is not significant at the outcome stage (cp. Models 1 and 2 in Table 5.8). As a consequence, hypothesis 8 cannot be supported. Hypothesis 9 postulates that founders with high firm-specific know-how are more likely to quit the company after a turnover event. Though the restricted model (Model 4 in Table 5.8) exhibits a positive coefficient, there is no significant contribution of the variable to explain the outcome. Thus, the data do not provide support for hypothesis 9. In hypothesis 10 I formulate that founders who entered their start-up as CEOs are more likely to leave the firm after having been turned over than other C-level executives. Though the effect in the restricted model (Model 4 in Table 5.8) is positive, there is no significant effect. Consequently, the hypothesis cannot be supported. Hypothesis 11 – the proposition that VCs’ procedural justice towards the founder increase his propensity to stay with the firm after having left his initial position – is supported. Though one might be tempted to conclude that compared to the revealed effects at the selection stage, the effect shown here is compara-

5.6 Empirical Analysis of Performance Impact of Founder Turnover

183

tively small, this conclusion is not valid. While the marginal effects considered before are based on a change in the dummy variable from 0 to 1, procedural justice is a quasi continuous variable. Therefore, the marginal effect must be interpreted as a percent change in the dependent variable, i.e., the probability of leaving the company, given a 1% change in the independent variable. Thus, a 1% increase in VC’s procedural justice as perceived by the founder leads to a 4.1% increase in the founder’s likelihood to stay with the company instead of leaving it after turnover. This is a very strong effect. The result is significant at the 10% level. In essence, out of the 9 hypotheses at the selection stage 3 are supported, while 6 are not supported. At the outcome stage 1 out of 4 hypotheses is not supported. However, all coefficients in the restricted model carry the expected sign.

5.6 Empirical Analysis of Performance Impact of Founder Turnover Subsequent to the previous analysis of determinants of founder turnover in venture capital backed start-ups this section aims at answering the final research question formulated in section 2.2: Which impact on performance do turnover, rotation, and departure have in start-up companies? Figure 5.35 shows how this final step of the analysis is linked to the conceptual framework elaborated in section 1.4. As can be seen, this section examines the performance impact (i) of general turnover decisions taken in start-ups (the selection decision discussed above in section 5.5), (ii) of founder rotation as well as (iii) founder departure (the outcome of the two-staged model discussed in section 5.5). On the following pages, I describe two OLS regression models to estimate the performance impact of turnover, rotation, and departure. Subsequently, I present results derived from multivariate analyses designed to empirically test hypotheses 12 and 13.

5 Determinants and Impact of Founder Turnover

Tier 1

Survival

Performance

Outcome

Selection

184

Turnover

Tier 2 Rotation

Departure

Tier 3 Performance

Performance Impact of Turnover

Figure 5.35: Performance impact of turnover in the conceptual framework

5.6.1 Estimation Method

In order to address the research question formulated above I rely on two similar OLS regression models. The first specification contains turnover as an independent variable, the second one rotation and departure. While the first regression estimates the effect of the general turnover decision on company performance, the second regression divides the effect of rotation from the one of departure.89 In both models I assume turnover, rotation, and departure to be exogenous factors influencing company performance. In what follows, I introduce the OLS model. Subsequently, I discuss the most important assumptions underlying this model and comment on their importance

89

Note that separating both equations is necessary here given that rotation and departure only exist contingent on an individual’s selection into the group of turnover cases. Therefore, I analyze the performance effect of selection (turnover) first before studying the impact of outcome effects (rotation and departure) in a separate model.

5.6 Empirical Analysis of Performance Impact of Founder Turnover

185

in the context of the research at hand. In large parts of this paragraph I follow Wooldridge’s (2003) and Greene’s (1997) description and discussion of the OLS model.90 The OLS model has the general form of yi

D  x i 'E  Hi

(5.10)

where Į is a constant – also referred to as the intercept – xi‘ is a 1 x K vector of variables with a K x 1 vector of coefficients ȕ, and İi is the error term. There are several assumptions associated with the model. The most important ones are to be discussed next.91 I comment on the scale of the dependent variable, the linearity assumption, the normal distribution of errors assumption, the homoskedasticity of errors assumption, and on multicollinearity between independent variables. Speaking of the dependent variable, the OLS regression model necessitates an interval-scaled dependent variable. Since I use the post growth variable as a measure of firm performance my model specification meets this prerequisite (cp. Table 5.12). Moreover, the OLS model assumes a linear function to be estimated. This implies the model to be linear in its parameters. In the models to be subsequently specified I assume additive estimation functions in which the sum of the effects in the independent variables explains changes in the dependent variable. Thus, my specifications meet the linear model assumption (cp. equations (5.11) and (5.12) below). In the OLS model, errors are not only assumed to have zero mean but also to be normally distributed. With a sample size of well above 100, this assumption can safely be made here. This results from the central limit theorem (CLT) as discussed by Greene (1997). The homoskedasticity of errors assumption states that the variance of error terms must be constant. If error terms are heteroskedastic, the variance of errors changes across different segments of the population and thus the sample – pro90

91

Note that the use of variable names and indices is not in line with either of the books but consistent with the denomination used in this thesis. A more detailed discussion of the OLS assumptions can be found in Wooldridge (2003) and Greene (1997).

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5 Determinants and Impact of Founder Turnover

vided random sample selection. However, because homoskedasticity is needed for statistical testing and inference it must be tested if the homoskedasticity hypothesis holds for any given dataset used for OLS estimation. I do test for heteroskedasticity using the Cook-Weisberg test (1983). Finally, independent variables included in OLS regression models must not be too strongly collinear. Even though OLS estimation is possible under the presence of multicollinearity, too, high correlations between the independent variables may result in imprecise coefficient estimations and wrong significance levels. I will therefore comment on selected correlation coefficients hinting at the importance of multicollinearity in my specifications. By using OLS estimation one aims at a model specification to be BLUE (best linear unbiased estimator, Wooldridge (2003)). If any of the assumptions underlying the OLS model are not met, the resulting estimates will not be BLUE. This may result in an inefficient linear estimator with low predictive power. However, based on the discussion above, there is good reason to assume a good fit of the OLS model for the econometric problem at hand. In the following paragraphs I will therefore specify the estimation functions and estimate the respective models. 5.6.2 Estimation Equation

Based on the general description of the OLS model I specify the functions to be estimated subsequently. Generally speaking, I estimate two similar regressions, one analyzing the impact of turnover on subsequent company growth and another modelling the impact of rotation and departure on post-turnover performance. The following specifications are full models which include all control variables introduced in paragraph 5.3.3. Even though I use restricted forms of the models in the subsequent multivariate analysis, it is helpful to present the extensive estimation functions. The first function to be estimated is y1

D  JT  E1FA  E 2 FEBengin  E 3 FEBnatscie  E 4 FAB  E 5 FEE  E 6 FSK  E 7 SFT  E8 NVC  E 9SVC  E10 NEM  E11SAL  E12 LTC  E13 IT  E14 BIO  E15 ENG  H

(5.11)

5.6 Empirical Analysis of Performance Impact of Founder Turnover

187

The slightly adapted full model estimating the influence of rotation and departure on post-turnover growth has the following functional form: y2

D  J1R  J 2 D  E1FA  E 2 FEBengin  E 3 FEBnatscie  E 4 FAB  E 5 FEE  E 6 FSK  E 7 SFT  E 8 NVC  E 9SVC 

(5.12)

E10 NEM  E11SAL  E12 LTC  E13 IT  E14 BIO  E15 ENG  H

In both equations Į is a constant. T denotes turnover in equation (5.11), R is rotation and D is departure in equation (5.12). In both estimation functions FA denotes the founder’s age, FEBengin the founder’s educational background in engineering, FEBnatscie the founder’s educational background in natural sciences, FAB the founder’s academic background, FEE the founder’s entrepreneurial experience, FSK the founder’s firm-specific know-how, SFT the size of the founding team, NVC the number of VCs at foundation, SVC the VCs’ share of voting rights at foundation, NEM the number of employees at foundation, SAL sales at foundation, and LTC the lifetime of the company. IT, BIO, and ENG are industry dummies indicating that a firm is active in the IT, biopharma, or engineering industry respectively. İ is the error term. In the following I will refer to those models based on equation (5.11) as turnover specifications and to models rooted in equation (5.12) as rotation and departure specifications. However, before estimating regressions based on the dataset collected through the online survey among entrepreneurs (cp. section 5.2), it is helpful to have a look at a description of all the independent variables used in both OLS estimations. Table 5.12 gives an overview of the regressors.92 Because the performance variable only has 138 observations and due to some missing values in several of the dependent variables the number of observations is reduced to 134.93 92

93

Note that the maximum values of the VCs’ voting rights at foundation (12), the number of employees at foundation (13), and the sum of sales at foundation (14) are surprisingly high. Looking at the respective mean values reveals that the maximum values can be considered outliers that do not harm the goodness of fit of the regression model in the case of robust standard errors. In fact, four variables have less than 154 observations (number at VCs at foundation with 152 observations, VCs’ share of voting rights at foundation with 152 observa-

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Table 5.12: Description of variables included in OLS estimations Variable Post-turnover growth* (1) Turnover decision (D) (2) Rotation (D) (3) Departure (D) (4) Founder age (5) Founder educational background engineering (D) (6) Founder educational background natural sciences (D) (7) Founder academic background (D) (8) Founder serial entrepreneur (D) (9) Founder firm-specific know-how (D) (10) Size of founding team (11) Number of VCs at foundation (12) VCs' share of voting rights at foundation (13) Number of employees at foundation (14) Sales at foundation (15) Lifetime of company (16) Company in IT industry (D) (17) Company in biopharma industry (D) (18) Company in engineering industry (D)

Mean -0.082 0.410 0.119 0.291 44.597 0.224 0.224 0.321 0.463 0.299 3.373 0.687 25.127 4.437 1.983 6.231 0.119 0.403 0.194

Std 0.382 8.970 1.976 1.036 15.084 7.647 13.085 2.169 -

Min -0.875 0 0 0 26 0 0 0 0 0 1 0 5 3 1 0 0 0 0

Max 2.200 1 1 1 70 1 1 1 1 1 11 5 51 63 150 11 1 1 1

N 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134

(D) = Dummy variable, * = Dependent variable

After having introduced the general properties of the OLS model, the estimation functions, as well as simple descriptive statistics of the variables used in the subsequent analyses I will present and discuss results from statistical estimations next. 5.6.3 Multivariate Analysis

Based on the specification described above, this paragraph presents multivariate analyses testing the hypotheses formulated in sections 3.3 (performance-related hypotheses). First, I estimate two sets of OLS regressions including the variables described in Table 5.12 (with either turnover or rotation and departure). Second, I interpret the results with respect to the performance-related hypotheses.

tions, number of employees at foundation with 153 observations, sales at foundation with 152 observations).

5.6 Empirical Analysis of Performance Impact of Founder Turnover

5.6.3.1

189

Estimation Results

In what follows, I describe estimation results for turnover specifications (cp. Table 5.13). All models shown in the table include turnover as an independent variable, because it is my primary intention to study the influence of founder turnover on subsequent company growth. Besides the turnover variable, Model 1 includes founder-related control variables. Due to the lack of significance of the founder’s academic background, the founder’s entrepreneurial experience, and the founder’s firm-specific know-how I drop those variables from the model. Even though none of the variables is significant, it is necessary to test their joint significance. An F-test on the common influence of the three variables yields an F-value of 0.68 (p-value = 0.568). Thus, given the lack of joint significance of the variables taken out of Model 1, the restricted Model 2 is not significantly different from Model 1. Consequently, Model 2 can be accepted as a valid simplification of Model 1. Model 3 includes company-related control variables, as well as some characteristics of the VCs invested in the company. According to the same logic previously described I reduce Model 3 by taking out the insignificant variables (number of VCs at foundation, VCs’ share of voting rights at foundation, sales at foundation). As revealed by an F-test, those variables are not jointly significant either (F-value = 0.06, p-value = 0.942). Model 5 includes environment-related control variables, i.e., industry dummies. Even though only two of them are significant, I keep all three to be integrated into the combined model. The combined model can be seen in Model 6. It includes all those control variables not excluded from the analysis earlier. Model 6 will be the basis for hypothesis testing later. It can be seen from Model 6 that turnover has no significant influence on subsequent company growth. However, several of the control variables do have an impact. With regards to the founder-related control variables one can conclude that founders’ age is weakly positively but highly significantly related to post-turnover company performance. Founders’ education in both, engineering and natural sciences has an important positive influence. With respect to the company-related control variables it becomes evident that the size of the founding team, as well as the number of employees at company inception have a

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5 Determinants and Impact of Founder Turnover

slightly negative impact. Contrarily, a slightly positive effect can be attributed to the amount of sales at foundation as well as to company lifetime. None of the other control variables show significant coefficients. In the very same fashion as described above I run several rotation and departure estimations (cp. Table 5.14). Thus, I replace turnover for rotation and departure, which leads to very similar results in Models 1 to 5 (cp. coefficients in Table 5.13 and Table 5.14 below).94 However, the estimates for rotation and departure in Model 6 in Table 5.14 split the turnover effect into a rotation and a departure effect. It can be seen that both coefficients are significant, with a positive sign for rotation and a negative sign for departure. I will comment on and further discuss this result in the light of hypothesis testing below. Before interpreting the results, I briefly comment on two issues addressed above: multicollinearity and heteroskedasticity. I have pointed out that the regressors in OLS estimations must not be too highly correlated in order to generate reliable results. Looking at the correlations of independent variables in both specifications (cp. Model 6 in Table 5.13 and Table 5.14 above), the large majority of variables are weakly correlated with values above –0.2 and below 0.2. The highest correlation can be observed between the size of the founding team and the number of employees at foundation (0.46).95 However, this is clearly exceptional. For instance, the correlation matrices for both models only show two more coefficients with values below -0.3 or above 0.3 each. Given that I do not intent to interpret the impact of control variables, I do not report, nor discuss those matrices here. However, it may be concluded at this point, that there are very few variables being strongly correlated. As a consequence, multicollinearity can be assumed not to be detrimental to the precision of the OLS estimators. The second issue to be addressed is heteroskedasticity. As previously mentioned, I use the test proposed by Cook-Weisberg to test for heteroskedasticity.96

94

95 96

An F-test between Model 1 and Model 2 yields an F-value of 0.59 and a p-value of 0.626. The corresponding results for an F-test between Model 3 and Model 4 are 0.03 (F-value) and 0.973 (p-value). The measures used here are simple Pearson correlation coefficients. The test is implemented in STATA with the estat hettest command.

5.6 Empirical Analysis of Performance Impact of Founder Turnover

191

Table 5.13: Results of OLS regressions including turnover as regressor The sample consists of 134 observations. All models shown are OLS regressions. The dependent variable in all models is postturnover growth. The independent variable turnover is common to all five specifications. Model 1 additionally includes founderrelated control variables (founder age, educational background engineering,educational background natural sciences, founder academic background, founder serial entrepreneur, founder firm-specific know-how). Model 2 is a restricted version of Model 1. Model 3 includes company-related control variables (size of founding team, number of VCs at foundation, VC share of voting rights at foundation, sales at foundation, lifetime company). Model 4 is a restricted version of Model 3. Model 5 includes environmentrelated control variables (company in engineering industry, company in IT industry, company in biopharma industry). Model 6 is a combination of Model 2, Model 4, and Model 5. All six models are estimated with robust standard errors accounting for heteroskedasticity.

Turnover Founder age Educational background engineering Educational background natural science Founder academic background Founder serial entrepreneur Founder firm-specific know-how Size of founding team Number of VCs at foundation VC share of voting rights at foundation Number of employees at foundation Sales at foundation Lifetime of company Company in engineering industry Company in IT industry Company in biopharma industry Constant Number of observations R2

Dependent variable: post-turnover growth EnvironmentFounder-related control Company-related control related variables variables control variables Model 1 Model 2 Model 3 Model 4 Model 5 -0.117* -0.110* -0.081 -0.083 -0.089 [0.064] [0.063] [0.065] [0.064] [0.060] -0.006* -0.006** [0.003] [0.003] 0.248** 0.263*** [0.109] [0.097] 0.107 0.170** [0.083] [0.071] 0.091 [0.067] -0.019 [0.067] -0.017 [0.070] -0.02 -0.02 [0.017] [0.016] -0.006 [0.024] 0.000 [0.002] -0.006* -0.006** [0.003] [0.003] 0.003*** 0.003*** [0.000] [0.000] 0.061*** 0.062*** [0.014] [0.014] 0.283** [0.143] 0.002 [0.070] -0.139* [0.082] 0.136 0.148 -0.331* -0.349** -0.053 [0.141] [0.139] [0.181] [0.150] [0.055] 134 134 134 134 134 0.124 0.115 0.174 0.174 0.11

Robust standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%

Combined model Model 6 -0.073 [0.056] -0.007** [0.003] 0.178** [0.088] 0.176*** [0.061]

-0.026* [0.015]

-0.005** [0.002] 0.003*** [0.000] 0.051*** [0.016] 0.249 [0.154] -0.003 [0.062] -0.102 [0.072] -0.027 [0.192] 134 0.309

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5 Determinants and Impact of Founder Turnover

Table 5.14: Results of OLS regressions including rotation and departure as regressors The sample consists of 134 observations. All models shown are OLS regressions. The dependent variable in all models is postturnover growth. The independent variables rotation and departure are common to all five specifications. Model 1 additionally includes founder-realted control variables (founder age, educational background economics, educational background engineering,educational background natural sciences, founder academic background, founder serial entrepreneur, founder firmspecific know-how). Model 2 is a restricted version of Model 1. Model 3 includes company-related control variables (size of founding team, number of VCs at foundation, VC share of voting rights at foundation, sales at foundation, lifetime company). Model 4 is a restricted version of Model 3. Model 5 includes environment-related control variables (company in engineering industry, company in IT industry, company in biopharma industry). Model 6 is a combination of Model 2, Model 4, and Model 5. All six models are estimated with robust standard errors accounting for heteroskedasticity.

Rotation Depature Founder age Educational background engineering Educational background natural science Founder academic background Founder serial entrepreneur Founder firm-specific know-how Size of founding team Number of VCs at foundation VC share of voting rights at foundation Number of employees at foundation Sales at foundation Lifetime of company Company in engineering industry Company in IT industry Company in biopharma industry Constant Observations R2

Dependent variable: post-turnover growth EnvironmentFounder-related control Company-related control related variables variables control variables Model 1 Model 2 Model 3 Model 4 Model 5 0.068 0.077 0.077 0.076 0.136* [0.065] [0.066] [0.071] [0.070] [0.079] -0.194*** -0.189*** -0.148** -0.149** -0.175*** [0.074] [0.071] [0.074] [0.073] [0.066] -0.006* -0.006** [0.003] [0.003] 0.248** 0.261*** [0.107] [0.095] 0.088 0.144** [0.084] [0.070] 0.086 [0.067] -0.004 [0.067] -0.017 [0.070] -0.021 -0.02 [0.017] [0.016] -0.004 [0.024] 0.000 [0.002] -0.005 -0.005 [0.003] [0.003] 0.003*** 0.003*** [0.000] [0.000] 0.058*** 0.059*** [0.014] [0.014] 0.292** [0.143] -0.033 [0.069] -0.142* [0.077] 0.129 0.142 -0.315* -0.328** -0.041 [0.137] [0.136] [0.182] [0.151] [0.053] 134 134 134 134 134 0.163 0.156 0.203 0.203 0.165

Robust standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%

Combined model Model 6 0.101* [0.059] -0.145** [0.069] -0.008*** [0.003] 0.175** [0.085] 0.146** [0.062]

-0.026* [0.015]

-0.005** [0.002] 0.003*** [0.000] 0.048*** [0.016] 0.262* [0.155] -0.031 [0.064] -0.094 [0.071] 0.016 [0.195] 134 0.342

5.6 Empirical Analysis of Performance Impact of Founder Turnover

193

An insignificant result of this test indicates a lack of heteroskedasticity, i.e., the presence of equal variance of the residuals as required in the OLS model. Without robust standards errors the test yields a chi2-value of 12.44 (p-value = 0.000) for Model 6 in Table 5.13 and a chi2-value of 9.44 (p-value = 0.002) for Model 6 in Table 5.14. Those test statistics clearly prove the presence of heteroskedasticity in both regressions. In order to correct for heteroskedasticity in all estimations, I ran the regression models with robust standard errors. The results shown in Table 5.13 and Table 5.14 are based on those robust specifications eliminating heteroskedasticity from the estimations. 5.6.3.2

Review of Hypotheses

Due to the linear nature of the OLS model, coefficients can be interpreted as marginal effects. Thus, the results displayed in Table 5.13 and Table 5.14 allow for a direct interpretation of the coefficients. Hypothesis 12, which states that founder turnover has a positive influence on company performance, cannot be confirmed. Model 6 in Table 5.13 even shows a weak negative coefficient. However, since the negative coefficient is not significant, it must be concluded that based on the given dataset no influence of turnover on subsequent company growth can be shown. This is a surprising result which I will discuss later. Hypothesis 13 postulates that rotation and departure have opposite effects on post-turnover company performance. According to H13a and H13b founder rotation is beneficial while founder departure is detrimental to post-turnover company performance. Model 6 in Table 5.14 provides some support for these hypotheses. The effect of rotation on performance is significant (at the 10% level) and positive while the effect of departure is significant (at the 5% level) and negative. Post-turnover employee-growth is 10.1% higher if the chosen turnover type is rotation. In line with theoretical predictions, post-turnover employee-growth is reduced by 14.5% if the founder opts to depart after the turnover event. Even though both effects can be considered as strong, it is worth mentioning that the rotation effect only becomes evident after the inclusion of all important control variables isolated in Model 2, 4, and 5. Those statistical results strongly suggest that turnover per se has no influence on subsequent company performance. However, comparing the rotation and

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5 Determinants and Impact of Founder Turnover

departure subgroups of turnover cases one can conclude that there are two opposed effects leading to the insignificant turnover coefficient in the regression (cp. Model 6 in Table 5.13). There is a significantly positive effect of rotation and a significantly negative effect of departure (cp. Model 6 in Table 5.14) on performance. Thus, it seems that if founders stay with the company, their rotation from the initial to another position is beneficial for the future development of the company. However, provided the founder departs from the firm, the company loses this core person which in turn is detrimental to its future growth. I will elaborate on this result later in section 6.4 where I discuss alleys for future research.

5.7 Results in the Light of the Theoretical Model The empirical results obtained in section 5.5 and 5.6 allow for a comprehensive view on the theoretical model and the underlying framework of hypotheses. This final section illustrates the results from chapter 5 in the light of the theoretical model. Figure 5.36 gives an overview of field data support of the theoretical model. In essence, it can be said that hypotheses 1, 6, 7, 11, and 13 are supported by the dataset collected among German start-up entrepreneurs. Hypotheses 2, 3, 4, 5, 8, 9, 10, and 12 are not supported by those field data. In essence, I am able to reveal several effects at all three tiers of the conceptual framework. With respect to selection, VC influence, the founder’s firmspecific know-how, as well as his professional experience proved to be relevant in determining founders’ propensity of turnover. Regarding outcomes, VCs’ procedural justice is essential to keep a founder with the firm. Finally, looking at performance implications, rotation – contrary to departure – proves to be performance-enhancing in terms of post-turnover company growth.

5.7 Results in the Light of the Theoretical Model

VC influence

195

+ (H1)

+ (H2) Private VC

Company (hyper) growth

+ (H12)

Turnover

- (H3a) + (H3b) Control variables

+ (H8)

Company performance

Founder job match

- (H4)

Founder performance

- (H5)

Founder professional experience

- (H6)

+ (H10)

Founder firm-specific know-how

- (H7)

- (H11)

(H13)

Rotation/ Departure

- (H9) H: Support from field data H: No support from field data

Figure 5.36: Support of the theoretical model from online survey

Founder CEO

VC procedural justice

6 Conclusion This final chapter concludes my thesis on founder turnover in venture capital funded start-ups. I summarize the results obtained in section 6.1 and I derive recommendations for both, founders and VCs, in section 6.2. In section 6.3, I present several caveats to consider along with the results. Finally, section 6.4 gives a glance at potential paths of future research.

6.1 Results In this section, I summarize the results by looking at selection, outcome, and performance. I combine the results from both, the conjoint experiment as well as the online survey in the discussion of the determinants of turnover. Subsequently, I briefly comment on the results regarding outcomes, i.e., turnover types, as well as performance, i.e., post-turnover company growth. A very important contribution of this thesis is the theoretical development and empirical confirmation of a selection effect inherent in turnover decision making. I was able to show that turnover decision (selection) and turnover type (outcome) can be seen as separate decision stages. In the following, I aim at combining the results obtained from the conjoint experiment (cp. chapter 4) and the online survey (cp. chapter 5). To this end it is worth comparing the hypotheses tested in both studies.97 Given the fact that VC influence (cp. hypothesis 1) is the strongest significant effect found in the analysis of survey data, it is worth comparing both perspectives because VC decision criteria can be expected to be important in real-life turnover decisions.

97

Note that only selection-related hypotheses (H2 to H7) were tested with data from both, the conjoint experiment and the online survey. Outcome- (H8 to H11) and performance-related (H12 and H13) hypotheses were exclusively analyzed based on the online survey dataset.

198 Table 6.1:

6 Conclusion Comparison of results from conjoint experiment and online survey

Hypothesis and effect

Support from conjoint experiment (VCs)

Support from survey data (entrepreneurs)

Hypothesis 2: Private VC -> turnover (+)

No

No

Hypothesis 3(a): Company growth -> turnover (-)

Yes

No

Hypothesis 4: Founder job match -> turnover (-)

Yes

No

Hypothesis 5: Founder performance -> turnover (-)

Yes

No

Hypothesis 6: Founder firm-specific knowhow -> turnover (-)

Yes

Yes

Hypothesis 7: Founder professional experience -> turnover (-)

Yes

Yes

As can be seen in Table 6.1, none of the two studies reveals differences in the preferences of private and captive VCs. With respect to company growth, the results from the experiments with VCs suggest that strong company growth reduces the risk of turnover. Both studies find a negative effect of the quality of job match and of high founder performance on turnover. While both effects are significant in the conjoint analysis, they lack significance in the selection equation. Significant negative effects of founders’ firm-specific know-how and professional experience on turnover are found in both studies. Thus, generally speaking, both studies in part support the (selection) and causal effects predicted by theory. Looking at the magnitude of effects, from the VC perspective, the founder’s expected future job match as well as his personal performance are the most important determinants of turnover (cp. relative importance reported in Table 5.8). Interestingly, based on the data collected among entrepreneurs the founder’s firm-specific know-how and his professional experi-

6.2 Recommendations

199

ence prove most important (cp. marginal effects reported in Table 5.11). Thus, even though both studies emphasize different aspects, they provide a coherent picture of the determinants of turnover. Summarizing the effects found at the outcome stage, the statistical importance of VCs’ procedural justice must be outlined. Founders are more likely to stay with the company if the turnover decision is perceived as being taken in a procedurally just manner. However, even though all coefficients have the sign predicted by the theoretical framework, none of the remaining variables proves to have a statistically significant influence on the type of turnover. In part, this is due to the small dataset my analyses are based on. Finally, with respect to post-turnover performance, the hypothesis that turnover per se has a positive effect on subsequent company performance is not supported. Looking at the influence of the type of turnover on company growth, I am able to clearly separate two opposing effects. While rotation has a statistically significant positive influence on post-turnover company growth, the impact of departure is negative and statistically significant. The most important question that remains unanswered at this point is what can be learned from those results. Therefore, in the following section, I will derive recommendations based on my findings.

6.2 Recommendations From the results presented above several recommendations for both, VCs and entrepreneurs can be derived. VCs may benefit from the results obtained throughout this book by considering the following recommendations. First, VCs should understand the performance impact of founder turnover. According to my results, turnover per se does not improve company performance. Thus, taking a founder out of his position is not necessarily helpful in terms of financial goals. This is in contrast to many VCs’ assertion that turnover decisions are taken for the sake of subsequent company growth and development. My results show that there is a positive effect from rotation and a negative effect from departure. Therefore, I recommend VCs to make specific efforts to keep founders inside the company in the aftermath of a turnover decision. This is

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6 Conclusion

mainly achievable through fair and transparent decision making, i.e., procedural justice by the VCs towards the founder. Second, I recommend that VCs should very proactively plan founder successions in their portfolio companies. Founder succession planning may be as important as the initial selection of new venture management teams. However, German VCs today hardly have systematic procedures in place that would allow for turnover planning. Especially accounting for the fact that almost all investors said turnover decisions were taken too late while founders tend to hold on to their positions too long (cp. Figure 4.15), a more systematic and early arrangement of founder transition may improve the timing and thus the performance outcome of turnover decisions. Third, VCs should integrate founders in such succession planning early on. By involving founders into the turnover decision process, the level of conflict associated with founder succession may be reduced which in turn might motivate founders to stay with the firm. Thus, I recommend VCs to reflect about founder succession together with the entrepreneurs as early as possible. Together with founders, investors should elaborate turnover and succession decisions in a procedurally just manner, so that investors may gain early support by founders. For entrepreneurs I derive the following recommendations. Primarily, entrepreneurs have to be very conscious about the fact that accepting venture capitalists as investors often results in a distribution of power and voting rights that may enable VCs to make founders leave their initially taken positions. Thus, in the terminology proposed by Wasserman (2006) founders should decide whether they want to be “rich” or “king”. Having VCs among their investors may in fact put the founder’s position as the king of its company at the disposal of VCs. Second, founders having taken VCs on board should agree on regular reviews with their VCs in order to assess management team composition and their personal job match. In their own financial interest, founders should accept successors with a suitable skill set and relevant professional experience. Defining a new role for them in the company helps founders to leave their initial positions in a smooth process of transition. Founders should therefore even proactively address theirs VCs if they feel they might no longer be the ideal person in a given position. It can be expected that founders are the first to actually realize their being

6.3 Caveat

201

overstrained, which makes it recommendable for them to seek external managers before their personal and the company’s entire performance suffer. Last, founders should appreciate their often “evangelistic” role for their companies. It should not be ignored by founders that remaining inside their start-up after leaving a top management position is of high importance for company development. Thus, founders should accept and be open to the fact that they might add most value to their company in different positions over the company lifecycle. After all, financially speaking, accepting turnover and remaining with the firm can be expected to be the most reasonable course of action for founders to follow.

6.3 Caveat Some aspects need to be considered when interpreting the results. In fact, there are a few issues that may limit the validity of my findings to some degree. I will therefore outline the most important caveats that I recommend the reader to consider. I only invited German VCs to take part in the conjoint experiment and the online survey was exclusively targeted to founders of Germany-based start-ups. Even though the economic and psychological dynamics of founder turnover in venture capital funded start-up companies can be regarded as universally valid, it cannot be eliminated the possibility that German VCs act differently than VCs in other countries. For instance, US VCs are anecdotally said to be more professional with regards to founder succession planning which may affect their turnover decisions. Thus, one must be conscious when transferring the findings of this thesis to other countries. Another very prominent issue to be addressed here is sample size. With a total of 154 observations and 61 turnover cases my sample is rather small. Acknowledging this, it is not surprising that several independent variables are not significant in the regression models, even though they show positive or negative signs in accordance with the theoretical model. Thus, the results obtained here are somewhat limited through the small sample size. A similar study with a more

202

6 Conclusion

extensive database would most likely help to validate – and partially improve – the results. However, a larger sample for Germany is hard to obtain. Finally, it must be noted that in my analysis of the impact of founder turnover on company growth I assume turnover events to be exogenous. This is a rather strong assumption given the possibility that turnover decisions might themselves be driven by company performance. In order to account for potential endogeneity, an instrumental variable for turnover would have to be identified. Ideally, this variable would have to be correlated with turnover but not with the error term in the OLS equation.

6.4 Future Research With this book I have tapped into the wide field of founder research focussing on a specific issue: founder turnover in venture capital backed start-ups. However, founder turnover has a variety of facets and consequences within the corporate context hat I was not able to shed light on in this thesis. Along two dimensions I suggest several promising avenues of future research as extensions to the issues discussed in this book. First, I consider aspects which I recommend future researchers to scrutinize more deeply when studying founder turnover as such. Second, I introduce fields of interest adjacent to and influenced by founder turnover that are promising areas of future research. Starting out with the issues in the field of founder turnover that would deserve some more consideration I propose paying more attention to the team context. While I looked at the individual founder, it is recommendable for future researchers to explore if and how patterns of founder turnover differ in specific team setups and how founder turnover decisions in different types of entrepreneurial teams affect company development. For instance, the phenomenon of founder turnover and its impact on firm development might be different in teams of rather young versus rather old founders or in teams with different levels of complementarity and diversity. Such research would have interfaces with topics recently studied by entrepreneurship researcher like Ucbasaran (2003) and Beckman et al. (2005).

6.4 Future Research

203

Additionally, I did not study team additions in this book. While on the one hand those are sometimes present in the context of founder turnover decisions, team additions might also explain why founders actually survive in their initial positions. In any case it would be worth understanding the dynamics of hiring additional managers in order to strengthen the founding team. As a last subject, I suggest to study decision processes in founder turnover situations more deeply. I have shown that fair procedures are important for the founder to stay with the company, which in turn has a positive impact on firm performance. Given this importance, it would be worthwhile focusing future research on questions related to decision processes. Besides those issues immediately linked to founder turnover there are some fields of research affected by founder turnover. The relevance of founder turnover for those fields should also be investigated in future research. First, one topic to be studied in more detail is post-succession in start-up companies. The integration of new members as well as the re-integration of founders after rotation are issues which entrepreneurship researcher know very little about today. It would moreover be promising to relate findings from founder turnover research and the leadership literature. This is especially true given the tremendous influence of rotation versus departure on company performance. It seems that if founders stay within the firm, the company is much more successful afterwards while it loses on performance if founders leave the start-up. This strong performance difference suggests that the founder plays an important role as a leader – even after having left his initial position. Finally, a comparative study between Germany and the US would be a promising endeavor. It would especially be interesting to analyze in how far more experienced VCs – like those active in the US – have learned to manage founder turnover processes and in how far VCs with institutionalized experience are able to actually increase company performance in the aftermath of turnover events.

Appendix Appendix 1:

Task description handed out to participants in the conjoint experiment Beschreibung

Ihre Situation: Sie evaluieren als einer der Hauptinvestoren das Management eines Ihrer Portfolio-Unternehmen. Dabei überdenken Sie die Besetzung der CEO-Position. Konkret überlegen Sie, ob der bestehende CEO, der einer der Unternehmensgründer ist, in seiner derzeitigen Position verbleiben oder diese in den folgenden Monaten verlassen sollte (Turnover). Im Fall eines Turnovers ist nicht klar, ob der Gründer das Unternehmen verlassen oder in einer anderen Position im Unternehmen verbleiben wird. Ihre Aufgabe: Schritt 1: Es werden Ihnen nun 10 verschiedene Szenarien vorgegeben. Versetzen Sie sich kurz in jedes dieser Szenarien. Bitte reihen Sie anschließend die 10 Szenario-Karten gemäß der Wahrscheinlichkeit, mit der Ihrer Einschätzung nach ein Turnover stattfindet. Ordnen Sie die Karten so an, dass das Szenario mit der höchsten Turnover-Wahrscheinlichkeit an erster Stelle und das mit der geringsten Turnover-Wahrscheinlichkeit an letzter Stelle liegt. Schritt 2: Bitte füllen Sie den beiliegenden Fragebogen vollständig aus. Ihr Ansprechpartner: Dipl.-Kfm. Martin Heibel, [email protected], 0179/1489821

206

Appendix

Appendix 2:

Sample scenario card used in the conjoint experiment Karte 1

• Der Gründer hat die Ziele des Business Plans in den vergangenen 12 Monaten weitgehend verfehlt. • Der Gründer hat umfangreiche Berufserfahrung in der von ihm im Unternehmen ausgeübten Funktion. • Der Gründer verfügt über hohes unternehmensspezifisches Wissen, das für das Unternehmen von hohem Wert ist. • Beim Gründer sind die in seiner Position für die weitere Entwicklung des Unternehmens benötigten Fähigkeiten erkennbar ausgeprägt. • Das Unternehmen weist gemäß Planung in Zukunft ein sehr starkes Wachstum auf.

Appendix

207

Appendix 3:

Questionnaire used in the conjoint experiment

Kurzfragebogen für Venture Capitalists Forschungsprojekt „Founder Turnover in Venture Capital Backed Start-Up Companies“

Dipl.-Kfm. Martin Heibel

Bearbeitungshinweis: Dieser Kurzfragebogen erfasst ergänzende Informationen zu Ihrer Person und zu Ihrer Erfahrung als Venture Capitalist. Abschließend werden Sie gebeten, den Grad Ihrer Zustimmung zu einigen Aussagen anzugeben. Bitte beantworten Sie alle Fragen. Ihre Angaben werden ausschließlich anonymisiert ausgewertet und nur im Rahmen meiner Dissertation verwendet. Ich danke Ihnen sehr für Ihre Teilnahme.

208

Appendix

Appendix

209

210

Appendix

Appendix 4:

Sample letter used in first and second wave of online survey invitations

INSTITUT FÜR INNOVATIONSFORSCH UNG, TECHNOLOGIEMANAGE MEN T & ENTREPRENE URSHIP (INNO-TEC)

Ludwig-Maximilians-Universität · INNO-tec · Kaulbachstr. 45 · 80539 München

Herrn Vorname Nachname persönlich/vertraulich Adresse

Dipl.-Kfm. Martin Heibel Telefon +49 (0)89 2180-5609 Telefax +49 (0)89 2180-6284 [email protected] www.inno-tec.de Postanschrift: Kaulbachstr. 45 80539 München

München, 03.10.2006

Teilnahme an einer wissenschaftlichen Befragung der LMU München Sehr geehrter Herr Nachname, Als Gründer des Unternehmens Musterfirma gehören Sie zu den rund 1.000 Unternehmensgründern in Deutschland, die zwischen 1996 und 2006 in der Geschäftsleitung eines Venture Capital-finanzierten Start-Ups tätig waren oder noch heute sind. Daher wende ich mich an Sie mit der Bitte, an einer wissenschaftlichen Befragung teilzunehmen, die ich im Rahmen meiner Dissertation bei Herrn Prof. D. Harhoff, Ph.D. an der Ludwig-MaximiliansUniversität München durchführe. Ihr Beitrag ist für den Erfolg des Forschungsprojekts von sehr großer Bedeutung. Ziel meiner Arbeit ist es, die berufliche Entwicklung von Unternehmensgründern im eigenen Unternehmen zu analysieren. Dabei sollen Entwicklungspfade von Gründern aufgezeigt und Empfehlungen für Gründer und Investoren zur personellen Entwicklung von Führungsteams gegeben werden. Als Unternehmensgründer können Sie hierzu einen wichtigen Beitrag leisten. Als Dankeschön für Ihre Unterstützung erhalten Sie auf Wunsch einen Ergebnisreport von mir. Bitte geben Sie in Ihren Internetbrowser folgenden Link ein, um zum Fragebogen zu gelangen: www.inno-tec.bwl.uni-muenchen.de/survey/ oder www.inno-tec.de/survey/ Das Schlüsselwort lautet LMU3742. Zur Beantwortung des Fragebogens benötigen Sie ca. 20 Minuten Zeit. Ihre Angaben unterliegen dem Datenschutz und werden vollständig anonym ausgewertet und ausschließlich für wissenschaftliche Zwecke verwendet. Sofern Sie Fragen haben, können Sie sich jederzeit gerne an mich wenden (089/2180-5609 oder [email protected]). Ich danke Ihnen herzlich für Ihre Unterstützung und bitte Sie um die Beantwortung des Fragebogens bis zum 31. Oktober 2006. Mit freundlichen Grüßen aus München,

Dipl.-Kfm. Martin Heibel Wissenschaftlicher Mitarbeiter und Doktorand Institut für Innovationsforschung, Technologiemanagement und Entrepreneurship, Ludwig-Maximilians-Universität München

Appendix

Appendix 5:

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Sample letter used in third wave of online survey invitations

INSTITUT FÜR INNO VATIO NS FO RS CH UNG, TECHNO LO GIEM ANAGE MEN T & ENTREPRENE URSHIP (INNO -TEC)

Ludwig-Maximilians-Universität · INNO-tec · Kaulbachstr. 45 · 80539 München

Herrn Vorname Nachname persönlich/vertraulich Adresse

Dipl.-Kfm. Martin Heibel Telefon +49 (0)89 2180-5609 Telefax +49 (0)89 2180-6284 [email protected] www.inno-tec.de Postanschrift: Kaulbachstr. 45 80539 München

München, 08.12.2006

Teilnahme an einer wissenschaftlichen Befragung der LMU München Sehr geehrter Herr Nachname, Als Gründer des Unternehmens Musterfirma gehören Sie zu den rund 1.000 Unternehmensgründern in Deutschland, die zwischen 1996 und 2006 in der Geschäftsleitung eines Venture Capital-finanzierten Start-Ups tätig waren oder noch heute sind. Daher wende ich mich an Sie mit der Bitte, an einer wissenschaftlichen Befragung teilzunehmen, die ich im Rahmen meiner Dissertation bei Herrn Prof. D. Harhoff, Ph.D. an der Ludwig-MaximiliansUniversität München durchführe. Ihr Beitrag ist für den Erfolg des Forschungsprojekts von sehr großer Bedeutung. Ziel meiner Arbeit ist es, die berufliche Entwicklung von Unternehmensgründern im eigenen Unternehmen zu analysieren. Dabei sollen Entwicklungspfade von Gründern aufgezeigt und Empfehlungen für Gründer und Investoren zur personellen Entwicklung von Führungsteams gegeben werden. Als Unternehmensgründer können Sie hierzu einen wichtigen Beitrag leisten. Als Dankeschön für Ihre Unterstützung erhalten Sie auf Wunsch einen Ergebnisreport von mir. Bitte geben Sie in Ihren Internetbrowser folgenden Link ein, um zum Fragebogen zu gelangen: www.inno-tec.bwl.uni-muenchen.de/survey/ oder www.inno-tec.de/survey/ Das Schlüsselwort lautet LMU9131. Zur Beantwortung des Fragebogens benötigen Sie ca. 20 Minuten Zeit. Ihre Angaben unterliegen dem Datenschutz und werden vollständig anonym ausgewertet und ausschließlich für wissenschaftliche Zwecke verwendet. Sofern Sie Fragen haben, können Sie sich jederzeit gerne an mich wenden (089/2180-5609 oder [email protected]). Ich danke Ihnen herzlich für Ihre Unterstützung und bitte Sie um die Beantwortung des Fragebogens bis zum 31. Dezember 2006. Mit freundlichen Grüßen aus München,

Dipl.-Kfm. Martin Heibel Wissenschaftlicher Mitarbeiter und Doktorand Institut für Innovationsforschung, Technologiemanagement und Entrepreneurship, Ludwig-Maximilians-Universität München

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Appendix 6: Sample letter used in fourth wave of online survey invitations

INSTITUT FÜR INNOVATIONSFORSCH UNG, TECHNOLOGIEMANAGE MEN T & ENTREPRENE URS HIP (INNO -TEC)

Ludwig-Maximilians-Universität · INNO-tec · Kaulbachstr. 45 · 80539 München

Herrn Vorname Nachname persönlich/vertraulich Adresse

Dipl.-Kfm. Martin Heibel Telefon +49 (0)89 2180-5609 Telefax +49 (0)89 2180-6284 [email protected] www.inno-tec.de Postanschrift: Kaulbachstr. 45 80539 München

München, 27.02.2007

Teilnahme an einer wissenschaftlichen Befragung der LMU München - 1. Follow-Up

Sehr geehrter Herr Nachname, vor einigen Wochen wandte ich mich mit der Bitte an Sie, an einer wissenschaftlichen Befragung der Ludwig-Maximilians-Universität München teilzunehmen. Diese Erhebung führe ich im Rahmen meiner Dissertation im Bereich der Entrepreneurship-Forschung bei Herrn Prof. D. Harhoff, Ph.D. durch. Als Gründer des Unternehmens Musterfirma gehören Sie zur für meine Fragestellung relevanten Gruppe der ca. 1.000 Unternehmensgründer in Deutschland, die zwischen 1996 und 2006 in der Geschäftsleitung eines Venture Capital-finanzierten Start-Ups tätig waren oder dies noch heute sind. Daher bitte ich Sie mit diesem Schreiben nochmals um Ihre Teilnahme an der Befragung. Ihre Antworten sind für den Erfolg meines Forschungsprojekts von sehr großer Bedeutung, auch wenn Ihr Unternehmen heute nicht mehr existieren sollte. Bislang haben ca. 100 Unternehmer den Fragebogen ausgefüllt. Um die statistischen Ergebnisse abzusichern, benötige ich jedoch zwischen 150 und 200 Antworten. Daher bitte ich Sie nochmals sehr um das Ausfüllen des Online-Formulars. Bitte geben Sie in Ihren Internetbrowser folgenden Link ein, um zum Fragebogen zu gelangen: www.inno-tec.bwl.uni-muenchen.de/survey oder www.inno-tec.de/survey Das Schlüsselwort lautet LMU25. Zur Beantwortung des Fragebogens benötigen Sie max. 20 Minuten Zeit. Ihre Angaben unterliegen dem Datenschutz und werden vollständig anonym ausgewertet und ausschließlich für wissenschaftliche Zwecke verwendet. Als Dankeschön für Ihre Unterstützung erhalten Sie auf Wunsch einen Ergebnisreport von mir. Sofern Sie Fragen haben, können Sie sich jederzeit gerne an mich wenden (089/2180-5609 oder [email protected]). Ich danke Ihnen herzlich für Ihre Unterstützung und bitte Sie um die Beantwortung des Fragebogens bis zum 31. März 2007. Mit freundlichen Grüßen aus München,

Dipl.-Kfm. Martin Heibel Wissenschaftlicher Mitarbeiter und Doktorand Institut für Innovationsforschung, Technologiemanagement und Entrepreneurship, Ludwig-Maximilians-Universität München

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Appendix 9: Sample screenshots of online survey – excerpt of section C.1

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