Financing Unquoted High-Growth Companies: From Extending

Faculty of Economics and Business Administration
DEPARTMENT OF ACCOUNTING AND
CORPORATE FINANCE
Financing Unquoted High-Growth Companies:
From Extending Existing Finance Theory Towards an
Evolutionary Theory of the Finance Process
Submitted to the Faculty of Economics and Business Administration (Ghent University,
Belgium) in Fulfillment of the Requirements for the Degree of Doctor in Applied Economics
Tom Vanacker
Supervisor: Prof. dr. ir. Sophie Manigart
Funded by the Intercollegiate Center for Management Science (I.C.M.), Brussels, Belgium
& Impulsfonds
Ghent, May 2009

PhD Series - Ghent University, May 2009
Faculty of Economics and Business Administration
http://www.feb.ugent.be
© 2009, Tom Vanacker
All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any
means electronic or mechanical, including photocopying, recording, or by any other information
storage and retrieval system, without permission in writing from the author.

Financing Unquoted High-Growth Companies:
From Extending Existing Finance Theory Towards an
Evolutionary Theory of the Finance Process

Doctoral Jury
Prof. dr. Marc De Clercq (Ghent University, President)
Prof. dr. Patrick Van Kenhove (Ghent University, Secretary)
Prof. dr. ir. Sophie Manigart (Ghent University & Vlerick Leuven Gent Management School,
Supervisor)
Prof. dr. Ignace De Beelde (Ghent University)
Prof. dr. Frédéric Delmar (EM Lyon, France and Research Institute of Industrial Economics,
Sweden)
Prof. dr. Mirjam Knockaert (Ghent University)
Prof. dr. Hans Landström (Lund University, Sweden)
Prof. dr. Peter Roosenboom (Rotterdam School of Management, Erasmus University, The
Netherlands)
Prof. dr. Harry Sapienza (Carlson School of Management, University of Minnesota, US)
i

Acknowledgements
This dissertation is the creation of almost four years of intense doctoral studies. It is not produced in a
social vacuum, but a variety of people guided and supported me throughout this process. Without their
support, guidance and encouragement this dissertation would never be what it is today and I would
have missed many challenging but also rewarding experiences. I am grateful to all contributors and
although it is impossible to sum them all up, I would like to highlight my appreciation to some specific
individuals.
I have been privileged with Sophie Manigart as my supervisor. Sophie, I was afraid to start working
for you as a research and teaching assistant. After all, you were the professor who laughed at me when
I was unable to answer one of the follow-up questions during my oral exam as a Master student. I got
even more afraid when I received back your extensive comments on a draft of my very first academic
paper. However, for the same reasons I enjoyed working with you. I very much appreciate your
constant support and motivation to develop high-quality work. Thank you for all the time and energy
you put in (re)reading my papers and offering feedback. Moreover, you integrated me into a vibrant
community of researchers by providing me the ability to present on many international conferences
and bringing me into contact with Shaker Zahra and Harry Sapienza thereby allowing me to study in
the United States. I have truly enjoyed working together and hope this will continue in the future.
Moreover, the members of my outstanding doctoral guidance committee, Harry Sapienza and Hans
Landström, played a critical role in shaping my dissertation. Harry, thank you for giving me the
opportunity to visit the University of Minnesota. You have always been an inspiring force, extremely
considerate, and your positive comments made me feel proud of my own work. Hans, thank you for
reading my papers meticulously and providing me with detailed feedback. Your challenging questions
throughout our meetings shaped me into a more reflective researcher. I could not have imagined more
dedicated members in my doctoral committee.
Other members in my exam committee, namely Frédéric Delmar, Peter Roosenboom, Mirjam
Knockaert and Ignace de Beelde played an important role as well. Frédéric, I was extremely pleased
iii

you were willing to become part of my exam committee. I consider you to be one of the most
influential organizational growth scholars and knowing that you appreciate my research gives me even
more energy to rewrite my papers and target for high-quality publications. Peter, I first met you as a
discussant for one of my papers on the corporate finance day in Rotterdam where you provided me
extremely valuable feedback. I would like to thank you for your time and energy in evaluating and
improving this dissertation. Mirjam, I have been lucky to previously work with you on the Global
Entrepreneurship Monitor (Belgium) struggling through T.E.A. calculations. Thank you for your
constructive feedback not only during the final stages of writing my dissertation but on many
occasions. Ignace, I would like to thank you for your interest in my research. Moreover, as department
chair you provided me a great workplace over the past years.
My stay at the Carlson School of Management (University of Minnesota) provided me with
knowledge, professional contacts, but also deep friendship. I am especially appreciative to Shaker
Zahra and Patricia Zahra. Shaker and Patricia, thank you for making me feel more than welcome in
Minneapolis. From day one you have both amazed me with your hospitality and friendship. Thank you
for showing me around the many beautiful places in Minneapolis and surroundings, inviting me at
your home for lunch and dinner on many many occasions, being there to discuss my research and
having long talks about things besides research. You have made my visiting scholarship in the United
States an unforgettable experience. I am also grateful to Daniel Forbes, who contributed immeasurably
and beyond the call to the process of improving my case study paper. Dan, thank you for your interest
in my research and the fun times outside academia.
Veroniek, you deserve a special word of thanks. Some 10 years ago we had one thing in common: we
did not want to know each other. We could not have imaged this would quickly change when we
started our doctoral studies together. You have been an inspirational colleague: extremely intelligent
and dedicated. You have shaped my dissertation in more ways than you probably imagine. More
importantly, we developed a deep friendship and I am extremely proud you are my best friend. Even
after two years when we have hardly seen each other and were busy with studying and meeting new
people we did not loose contact. On the contrary, we had weekly and some weeks even daily telephone
iv

calls. We know everything about each other although we often mention the juicy details with (some)
delay. I am looking forward to having fun, laughing and traveling with you in the future.
I would also like to thank my current and former accounting and corporate finance colleagues, many
who became good friends. A special word of thanks goes to (in a random order): Elisabeth, Katleen,
Sofie, Wouter, Christof, Miguel, Sofie, Christophe, Dries, Lotte, Nick, David, Hannes, Andy,
Pieterjan, Arnout, Bivas and last but not least Sonia. Many of the doctoral students at Carlson also
deserve a special note. This not only for their valuable feedback during our entrepreneurship sessions,
but for some also for the Friday night social events: Jaume, John, Mazhar, Nachiket, Hans, Carla,
Zeke, Isil and Youngeun. I have been lucky Sofia joined me as a visiting doctoral student for a couple
of months. We combined hard work at our shared office with great experiences outside work. I would
also like to thank my friends from outside academia (sorry for not listing all your names, this does not
mean I do not appreciate you all).
This research and my stay at the Carlson School of Management would not have been possible without
the financial support of the Intercollegiate Center for Management Science (I.C.M.). A special thanks
goes to Françoise Degembe and Dirk Symoens. Financing from our Faculty of Economics and
Business Administration (including Impulsfonds) played a critical role in allowing me to optimize this
dissertation and attending international conferences as well.
Tenslotte wens ik mijn familie en in het bijzonder mijn ouders te bedanken. Ik wens jullie uit de grond
van mijn hart te bedanken voor jullie constant geloof in mijn kunnen en onvoorwaardelijke toewijding.
Ik apprecieer ontzettend dat jullie mij altijd de vrijheid hebben gegeven mijn eigen weg te gaan.
Ondanks de vele stemmen die twijfel hadden of ik zou slagen eerst in het secundair onderwijs, later op
universitair niveau, zijn jullie mij steeds blijven steunen zonder mij in een andere richting te willen
duwen. Zonder jullie was ik nooit geworden wie ik nu ben.
Tom Vanacker, May 2009
v

Table of Contents
Acknowledgements........................................................................iii
Table of Contents........................................................................... vi
List of Tables ................................................................................. xi
List of Figures..............................................................................xiii
Nederlandstalige samenvatting (Summary in Dutch) ................. xiv
Executive Summary....................................................................xvii
Chapter 1: General Introduction..................................................... 1
1.1. Finance and Company Growth.....................................................................................2
1.1.1. Study 1: Incremental Finance Decisions in High-Growth Companies: Pecking Order and
Debt Capacity Considerations ........................................................................................................ 3
1.1.2. Study 2: Seeking Experienced or Legitimate Partners? A Longitudinal Study on the Impact
of Venture Capital Firm Heterogeneity on Portfolio Company Growth ........................................ 4
1.1.3. Study 3: Early Differences and Persistence in the Entrepreneurial Finance Process:
Evidence from High- and Low-Growth Biotechnology Startups ................................................... 5
1.2. Key Dimensions of Financial Decision-Making and Foundational Finance Theories....6
1.2.1. Amount of Finance ............................................................................................................... 8
1.2.2. Type of Finance .................................................................................................................. 10
1.2.3. Source of Finance ............................................................................................................... 13
1.2.4. Dynamics ............................................................................................................................ 16
1.2.5. How the Studies in this Dissertation Cover the Different Dimensions of Financial
Decision-Making .......................................................................................................................... 17
1.3. The Link between Process Theories and Foundational Finance Theories....................18
vi

1.4. Company Growth as a Complex Organizational Process: What is High Growth? What
are High-Growth Companies?...........................................................................................23
1.4.1. Growth Indicators, Growth Formula and Timeframe......................................................... 24
1.4.2. How the Studies in this Dissertation Address the Multidimensional Nature of Growth .... 27
1.5. Research Context .......................................................................................................28
1.5.1. The Belgian Financial System ............................................................................................ 29
1.5.2. The Belgian Entrepreneurial Landscape............................................................................. 31
1.5.3. Implications of the Research Setting for the Studies in this Dissertation........................... 34
1.6. Data...........................................................................................................................35
1.6.1. BEL-FIRST Financial Accounts Database......................................................................... 36
1.6.2. Belgian Venture Capital & Private Equity Association (BVA) Database.......................... 38
1.6.3. Zephyr Database ................................................................................................................. 39
1.6.4. Interview data ..................................................................................................................... 41
1.7. Main Findings............................................................................................................42
1.7.1. Study 1: Incremental Finance Decisions in High-Growth Companies: Pecking Order and
Debt Capacity Considerations ...................................................................................................... 42
1.7.2. Study 2: Seeking Experienced or Legitimate Partners? A Longitudinal Study on the Impact
of Venture Capital Firm Heterogeneity on Portfolio Company Growth ...................................... 43
1.7.3. Study 3: Early Differences and Persistence in the Entrepreneurial Finance Process:
Evidence from High- and Low-Growth Biotechnology Startups ................................................. 45
1.8. Overall Academic Contributions ................................................................................46
1.8.1. Finance Literature............................................................................................................... 46
1.8.2. Growth Literature ............................................................................................................... 51
1.9. Structure of the Dissertation.......................................................................................54
References........................................................................................................................54
Chapter 2: Incremental Finance Decisions in High-Growth
Companies: Pecking Order and Debt Capacity Considerations .. 63
2.1. Abstract .....................................................................................................................64
2.2. Introduction ...............................................................................................................64
vii

2.3. Theory and Hypotheses..............................................................................................67
2.4. Method and Descriptive Statistics ..............................................................................74
2.4.1. Identifying High-Growth Companies ................................................................................. 75
2.4.2. Dependent Variables: Finance Events ................................................................................ 78
2.4.3. Independent Variables ........................................................................................................ 80
2.5. Empirical Results.......................................................................................................82
2.5.1. Independent Variables by Issue Type................................................................................. 82
2.5.2. Multivariate Analyses......................................................................................................... 83
2.5.3. Robustness Checks ............................................................................................................. 89
2.6. Discussion and Conclusions.......................................................................................90
References........................................................................................................................93
Chapter 3: Seeking Experienced or Legitimate Partners? A
Longitudinal Study on the Impact of Venture Capital Firm
Heterogeneity on Portfolio Company Growth ........................... 100
3.1. Abstract ...................................................................................................................101
3.2. Introduction .............................................................................................................101
3.3. Theory and Hypotheses............................................................................................104
3.3.1. Venture Capital Firm Experience and Portfolio Company Growth.................................. 105
3.3.2. Venture Capital Firm Legitimacy and Portfolio Company Growth ................................. 107
3.4. Data and Method......................................................................................................108
3.4.1. Sample .............................................................................................................................. 108
3.4.2. Measures........................................................................................................................... 109
3.4.3. Analysis ............................................................................................................................ 113
3.5. Results.....................................................................................................................116
3.5.1. Modeling Unconditional Change in Venture Capital Backed Companies ....................... 117
3.5.2. Modeling Conditional Change in Venture Capital Backed Companies ........................... 119
viii

3.5.3. Alternative Explanations for the Hypothesized Relationships: Investor Selection versus
Value Adding.............................................................................................................................. 124
3.6. Discussion and Conclusion ......................................................................................128
3.6.1. Academic Contributions ................................................................................................... 128
3.6.2. Implications for Practice................................................................................................... 131
References......................................................................................................................131
Appendix 1: Robustness Check.......................................................................................138
Chapter 4: Early Differences and Persistence in the
Entrepreneurial Finance Process: Evidence from High- and Low-
Growth Biotechnology Startups ................................................. 140
4.1. Abstract ...................................................................................................................141
4.2. Introduction .............................................................................................................141
4.3. Methods...................................................................................................................144
4.3.1. Research Design ............................................................................................................... 144
4.3.2. Data Sources ..................................................................................................................... 149
4.3.3. Data Analysis.................................................................................................................... 153
4.4. How does the Finance Process of High-and Low-Growth Ventures Differ?..............154
4.4.1. Amount of Finance ........................................................................................................... 154
4.4.2. Type of Investor................................................................................................................ 157
4.5. How do Early Differences in the Finance Process Originate? ...................................159
4.5.1. Initial Differences in Quality and Growth Potential......................................................... 159
4.5.2. Initial Differences in Growth Ambition............................................................................ 162
4.5.3. Local Search ..................................................................................................................... 162
4.6. How do Early Differences in the Finance Process Persist across Time?....................167
4.6.1. Venture Professionalization.............................................................................................. 168
4.6.2. Stunted Learning............................................................................................................... 170
4.6.3. Investor Syndication Preferences...................................................................................... 172
ix

4.7. Discussion ...............................................................................................................175
4.7.1. Reframing the Entrepreneurial Finance Process as an Evolutionary Model .................... 177
4.7.2. Resource Mobilization and Initial Network Formation.................................................... 179
4.8. Conclusion...............................................................................................................181
References......................................................................................................................182
Chapter 5: Limitations, Avenues for Future Research and
Implications for Practice............................................................. 190
5.1. Limitations and Avenues for Future Research ..........................................................191
5.2. Practical Implications...............................................................................................195
5.2.1. Entrepreneurs.................................................................................................................... 196
5.2.2. Investors............................................................................................................................ 197
5.2.3. Policy makers.................................................................................................................... 198
References......................................................................................................................199
x

List of Tables
Chapter 1
Table 1.1 The Three Studies and their Focus on the Different Dimensions of the Finance Process
Table 1.2 Overlap (%) between Different Groups of High-Growth Companies using Different
Growth Indicators and Growth Formula
Table 1.3 The Three Studies and Multidimensional Nature of Growth
Table 1.4 Description of the Samples and Databases Used
Chapter 2
Table 2.1 Distribution of Sample Firms by Industry
Table 2.2 Sample Split according to Finance Type
Table 2.3 Correlation Matrix
Table 2.4 Variables by Issue Type
Table 2.5 Logistic Regression of the Determinants of Outside Finance Use
Table 2.6 Logistic Regression of the Determinants of External Equity Use
Chapter 3
Table 3.1 Correlation Matrix
Table 3.2 Descriptive Statistics for Employment and Total Assets
Table 3.3 Unconditional Analysis for Employment and Total Assets
Table 3.4 Conditional Analysis for Employment and Total Assets
Table 3.5 Portfolio Company Characteristics by Type of Venture Capital Firm
xi

Table 3.6 Conditional Analysis for Employment and Total Assets
Chapter 4
Table 4.1 Theoretical Sampling Procedure
Table 4.2 Description of Biotechnology Cases Studied
Table 4.3 Sources of Data Biotechnology Cases
Table 4.4 Finance Process by Type of Investor and Venture Growth
Table 4.5 Early Signals of Growth Potential and Quality: Alliance Capital, Technology and
Target Market at Startup
Table 4.6 Local Search and the Impact of the Social Context on Initial Financing
Table 4.7 Key Changes in the Management Team Relating to the CEO and CFO
xii

List of Figures
Chapter 1
Figure 1.1 The Static Trade-off Theory of Capital Structure
Figure 1.2 Firm Continuum and Sources of Finance: The Financial Growth Cycle Paradigm
Figure 1.3 Process Models of Organizational Development and Key Finance Theories
Figure 1.4 Early-Stage Entrepreneurial Activity Rates for 2002/03, 2004/05 and 2006/07 in High-
Income Countries
Figure 1.5 Relative Prevalence of High-Growth Expectation in Early-Stage Entrepreneurship,
Chapter 3
2000–2006
Figure 3.1 Unconditional Models: Observed and Predicted Means
Figure 3.2 Conditional Models: Predicted Means for Companies Backed by Investors with High
Industry Deal Experience versus Low Industry Deal Experience
Figure 3.3 Conditional Models: Predicted Means for Companies Backed by Old versus Young
Chapter 4
Venture capital firms
Figure 4.1 Cumulative Amount of Pre-Exit Finance Raised across Time
Figure 4.2 Summarizing Framework of the Finance Process of High-and Low-Growth
Companies
xiii

Nederlandstalige samenvatting (Summary in Dutch)
Snelgroeiende bedrijven zorgen voor een disproportioneel deel van de innovaties,
tewerkstellingscreatie en waardegeneratie binnen een economie. Ondernemingsgroei is dan ook een
centraal thema binnen academisch onderzoek en een belangrijk aandachtspunt van beleidsmakers.
Financiering is een belangrijke factor die potentieel kan leiden tot blijvende verschillen in groei tussen
bedrijven. Financiële middelen zijn immers essentieel daar ze groeigerichte bedrijven toelaten om te
investeren. Een suboptimale financiële structuur, als gevolg van een gebrekkige toegang tot
financiering of onvoldoende kennis van financieringsalternatieven, kan leiden tot een beperkte
mogelijkheid voor verder groei, financiële moeilijkheden en zelfs een faillissement. Ondanks het
belang van de relatie tussen financiering en groei is onze kennis hieromtrent beperkt. Het doel van de
drie studies in dit doctoraat is dan ook om een meer inzichten te verwerven in de relatie tussen
financiering en groei.
De eerste studie gaat na hoe snelgroeiende bedrijven zich financieren en hoe de kenmerken van
snelgroeiende bedrijven hun financieringskeuzes beïnvloeden. Ondanks de focus op het belang van
nieuw aandelenkapitaal in de oprichting en verdere groei van bedrijven in de huidige literatuur, toont
de eerste studie aan dat het merendeel van de managers van snelgroeiende bedrijven nooit een beroep
doen op nieuw aandelenkapitaal om groei te financieren. Snelgroeiende bedrijven verkiezen om -
indien mogelijk- hun investeringen intern te financieren en schuldcapaciteit te vrijwaren. Indien
interne middelen ontbreken gaan snelgroeiende bedrijven met een laag risicoprofiel eerder geneigd
zijn om additionele schulden aan te trekken. Nieuw aandelenkapitaal wordt voornamelijk
aangetrokken door snelgroeiende bedrijven met een hoog risicoprofiel: niet-winstgevende bedrijven
met lage kasstromen, een hoge schuldgraad, een hoge kans op faling en significante investeringen in
immateriële activa. Verder blijkt dat éénmaal bedrijven een bepaald type externe financiering
aantrekken er een hogere kans is dat ze hetzelfde type financiering terug zullen aantrekken in de nabije
toekomst.
xiv

De tweede studie gaat na hoe verschillende types van investeerders het groeipatroon van bedrijven
beïnvloeden. Bedrijven die initiële financiering aantrekken van venture capital investeerders met meer
ervaring binnen de sector van hun portefeuille bedrijven, vertonen een snellere groei in vergelijking
met bedrijven die financiering aantrekken van investeerders met minder ervaring. Daarnaast vertonen
bedrijven die financiering aantrekken van oudere investeerders en investeerders die meer in de media
verschijnen eveneens een snellere groei. Initiële beslissingen met betrekking tot het type investeerder
van wie bedrijven financiering aantrekken, beïnvloeden de mogelijkheid van bedrijven om additioneel
personeel en activa aan te trekken en dit tot vijf jaar na de initiële beslissing. Meer financiering is dus
niet noodzakelijk beter voor de groei van een bedrijf; minstens even belangrijk is van welke
investeerder men initiële financiering verkrijgt.
De derde studie toont aan hoe meer ervaren investeerders niet noodzakelijk toegang hebben tot
bedrijven met het hoogste groeipotentieel. Ondernemers beperken hun zoektocht naar
opstartfinanciering namelijk vaak tot één of een beperkt aantal investeerders en vermijden losse
contacten met vele investeerders. De zoektocht naar opstartfinanciering is lokaal en ondernemers
benaderen typisch enkel investeerders die ze kennen via vroegere werkervaringen of investeerders die
een indirecte band hebben met het bedrijf, zoals spin-offs die in eerste instantie een universitair fonds
zullen aanspreken. Initiële verschillen in het financieringsproces hebben een blijvende impact op latere
financieringsbeslissingen. Zo krijgen bedrijven die starten met ervaren investeerders typisch grote
bedragen aan opvolgfinanciering van andere ervaren investeerders, terwijl de bedrijven die starten met
onervaren investeerders veel minder opvolgfinanciering ophalen en dit meestal van andere onervaren
investeerders. De processen die ervoor zorgen dat initiële verschillen in het financieringsproces zich
blijven verder zetten doorheen de tijd worden geïdentificeerd en besproken.
Samen tonen de drie studies aan hoe we onze kennis omtrent financiering binnen niet-beursgenoteerde
ondernemingen sterk kunnen verbeteren door deze te kaderen binnen een evolutionaire theorie. Een
evolutionaire theorie stelt dat bedrijven strijden om toegang te krijgen tot beperkte middelen. Er wordt
typisch vanuit gegaan dat investeerders bedrijven selecteren die het best passen binnen hun
xv

espectievelijke omgeving. De verschillende studies binnen dit doctoraat tonen echter aan hoe
ondernemers eveneens hun bedrijven selecteren als kandidaten voor het ontvangen van financiering
van bepaalde investeerders. Alhoewel deze resultaten in lijn liggen met recent onderzoek, gaat mijn
onderzoek een stap verder door aan te tonen hoe de rol van ondernemers de beslissing om al dan niet
externe financiering aan te trekken overstijgt. Verder tonen de verschillende studies aan hoe initiële
verschillen in het financieringsproces tussen bedrijven zich verder zetten doorheen de tijd. Initiële
financieringsbeslissingen beïnvloeden latere financieringsbeslissingen en zelfs de ontwikkeling het
volledig bedrijf. De processen die verklaren hoe verschillen ontstaan en hoe deze een blijvende impact
hebben omvatten het lokale karakter van de zoektocht naar financiering, gebrekkig leren, verschillen
in de professionalisering van bedrijven onder impuls van heterogene investeerders en routines die
gehanteerd worden door investeerders om co-investeerders te selecteren. Deze processen staan in
schril contrast met de focus in traditioneel financieringsonderzoek op neoklassieke economische
concepten waaronder rationaliteit, homogeniteit van investeerders en perfecte contracten.
xvi

Executive Summary
High-growth companies contribute disproportionately to innovation, employment creation and value-
added generation in most modern economies. Hence, firm growth is a central area of academic
research and a major policy concern. Financial resources may cause sustainable differences in venture
development, as financial resources allow ventures to invest in strategic assets. A suboptimal financial
structure caused by finance constraints or limited knowledge of finance alternatives may constrain the
future growth potential of a business and may even cause failure. Despite its importance our
knowledge on relationship between finance and growth remains limited. The goal of the three studies
in this dissertation is to further extend our knowledge on this relationship.
The first study examines the importance of internal finance, debt finance and new equity finance in
high-growth companies. Moreover, the study demonstrates how company characteristics influence the
finance strategies of high-growth companies. While prior research has mainly focused on the role of
new equity finance in the creation and growth of new ventures, this study demonstrates how the
majority of high-growth companies never raise new equity finance. High-growth companies prefer to
finance investments internally -whenever possible- thereby retaining debt capacity. When high-growth
companies with a low risk profile lack internal finance they become more likely to raise debt finance.
New equity issues, however, are especially important for high-growth companies with a high risk
profile: unprofitable companies with low cashflows, high leverage, high risk of failure and significant
investments in intangible assets. The study also demonstrates how high-growth companies that raised
a particular type of finance become more likely to raise the same type of finance again in the future.
The second study examines how particular types of investors affect the growth pattern of
entrepreneurial ventures. Companies that initially raise finance from venture capital firms with more
industry deal experience exhibit higher growth across time. This is not the case, however, for
entrepreneurial companies that raise initial finance from investors with more overall deal experience.
Moreover, companies that initially raise finance from older venture capital firms and venture capital
xvii

firms that appear more frequently in the media also exhibit higher growth across time. The initial
decision from whom to raise finance influences the ability of entrepreneurial ventures to attract
employees and mobilize assets and this for up to five years after the initial decision. Hence, more
financial resources are not necessarily better; at least as important is the source from which ventures
raise finance.
The third study demonstrates how more experienced investors not necessarily have access to
entrepreneurial companies with the highest growth potential. This is because entrepreneurs typically
limit their search for finance to one or a few investors and refrain from having loose contacts with
many potential investors. The search for early finance is local and entrepreneurs generally search
finance from investors they know from prior work experiences or investors that have an indirect tie
with the company, such as university spin-offs who restrict their search for finance to the university
fund. Initial differences in the finance process have a long-term impact on subsequent finance
decisions. Companies that raise early finance from experienced investors typically raise large amounts
of follow-on finance from other experienced investors, while companies that raise early finance from
relatively inexperienced investors attract limited amounts of follow-on finance from other
inexperienced investors. We theorize on the processes that cause early differences in the finance
process to persist across time.
Overall, the three studies indicate how our knowledge on financial decision-making may benefit from
using an evolutionary lens. In such a perspective resource scarcity creates selection environments
within which investors decide to invest in the companies that best fit within the environment.
Nevertheless, the different studies in this dissertation demonstrate how entrepreneurs themselves also
select their ventures as candidates for receiving finance from particular investors. While these findings
confirm recent research, they also extend them in an important way by demonstrating how the role of
entrepreneurs is more important than merely deciding whether they want to raise outside finance or
not. Moreover, the studies demonstrate how early differences between companies persist across time.
Early finance decisions are likely to influence subsequent finance decisions and even affect company
xviii

development as a whole. The processes that cause early differences to originate and persist across time
include local search, imperfect learning, differential professionalization of entrepreneurial ventures by
heterogeneous investors and investor routines used to select potential syndication partners. These
processes stand in stark contrast to current research in finance based on neoclassical paradigms such as
comprehensive search, rationality, investor homogeneity and optimal contracts.
xix

Chapter 1: General Introduction
Company growth is a central area of academic research and a major policy concern, as it provides
insights into the dynamics of the competitive process, strategic behaviour, the evolution of market
structure, and even the growth of the aggregate economy (Carpenter and Petersen, 2002a). A recent
literature review by Henrekson and Johansson (2009) demonstrates how only a small group of high-
growth companies contribute disproportionately to employment generation, innovation and wealth
creation in most modern economies. Research in the U.K. for example demonstrates how only 4% of
startups create more than half of the jobs created by all startups together (Storey, 1994). Even more
striking findings emerge from the U.S., where research shows that some 2-3% of all companies create
almost all net jobs in the economy (Acs, Parsons, and Tracey, 2008). Yet despite its importance the
growth literature is fragmented and much remains unknown about the antecedents of company growth
(O’Regan, Ghobadian, and Gallear, 2006; Delmar, Davidsson, and Gartner, 2003; Carpenter and
Petersen, 2002a).
Financial capital is one of the crucial resources companies require to realize high growth. Financial
capital allows companies to carryout crucial investments in tangible assets, intangible assets and
working capital (Gilbert, McDougall, and Audretsch, 2006). An unsuitable financial structure, caused
by finance constraints or inadequate knowledge about finance alternatives amongst other issues, may
cause a limited potential for future growth, financial distress and even company failure (Cassar, 2004;
Carpenter and Petersen, 2002b; Van Auken, 2001; Himmelberg and Petersen, 1994). The financial
structure may not only constrain subsequent growth, but might also act as a facilitator. Scholars have
indicated that the finance process operates as a key external prompt, which initiates sustainable
differences in venture growth (Maurer and Ebers, 2006). These scholars, however, have not addressed
what it is exactly that is different in the financing of high- and low-growth companies. The overall
goal of the different studies in this dissertation is to extend our knowledge on the interrelatedness
between the entrepreneurial finance process and the growth of unquoted companies.
1

The rest of this introduction is structured as follows. First, I introduce the different studies and
highlight the main purposes of each study. Second, I define the key dimensions of financial decision-
making and describe the most influential theories in modern business finance. The goal of this section
is to offer a common frame of reference, present the most influential theories relevant to the different
studies in this dissertation and highlight how the different studies address different dimensions of
financial decision-making. Third, I introduce process theories into the finance literature and link
existing finance theories to these process theories. While finance scholars have generally ignored
dynamics in financial decision-making, this section presents multiple frameworks proposed by
organizational scholars to study change or dynamics across time. Fourth, I define entrepreneurial
growth as a multidimensional process, identify the key dimensions of growth and its implications for
the studies in this dissertation. Fifth, I offer an overview of the research context, including the Belgian
financial system, the Belgian entrepreneurial landscape and its implications for the studies in this
dissertation. Sixth, I discuss the datasets used in this dissertation and reflect on their advantages and
disadvantages. Seventh, I summarize the main findings of each individual study. Next, I highlight the
overall contributions of this dissertation to both the finance and growth literature. Finally, I discuss the
structure of the rest of this dissertation.
1.1. Finance and Company Growth
In this section, I start by introducing the three studies included within this dissertation. We have no
choice but to cut down on the complexity of a problem by putting some processes in the foreground
and others in the background (Van de Ven, 2007). Prior research demonstrates the critical nature of
financial resources as they may both constrain and facilitate venture growth. Hence, my decision to
more fully focus on the finance process in all studies and research how finance relates to company
growth. Below I stipulate the main questions addressed by each study individually.
2

1.1.1. Study 1: Incremental Finance Decisions in High-Growth Companies:
Pecking Order and Debt Capacity Considerations
Managers of high-growth companies typically have multiple types of finance available to finance new
investment projects, including internal finance, debt finance and new equity finance. However,
scholars have typically assumed that internal finance, if available, will be largely insufficient to
finance high growth (Michaelas, Chittenden, and Poutziouris, 1999; Gompers, 1995). Furthermore,
scholars argue that high-growth companies consistently use less debt finance as they grow across time
(Barclay, Smith, and Morellec, 2006). Debt finance is sometimes even considered inappropriate,
especially for high-growth companies active in technology-based industries, given their small or
negative operational cashflows and lack of tangible assets that could serve as collateral (Carpenter and
Petersen, 2002b). Instead, it is proposed that high-growth companies actively use new equity, which is
the most suitable source of finance for high-growth companies (Fama and French, 2005; Frank and
Goyal, 2003; Carpenter and Petersen, 2002b).
Few studies on financial policies in high-growth companies, however, consider a broad range of
finance alternatives, including inside finance, debt finance and new equity finance (Eckhardt, Shane,
and Delmar, 2006), but instead focus on the role of new equity in the creation and growth of new
companies (Davila, Foster, and Gupta, 2003; Carpenter and Petersen, 2002b; Gompers, 1995).
Nevertheless, one of the most influential theories in modern business finance, namely the pecking
order theory, argues that high-growth companies will avoid using new equity when internal finance or
debt finance is available (Myers, 1984). Overall, while prior research stresses the role of new equity in
the financing of high-growth companies, finance theory indicates a more central role for internal funds
and debt finance in companies that are characterized by high informational asymmetry. A more
comprehensive study on the role of different types of finance in high-growth companies is therefore
timely.
3

More significantly, finance scholars have typically treated high-growth companies as a homogenous
group within a larger sample of companies. Few studies have addressed how different high-growth
companies are more or less likely to use these different types of finance. Nevertheless, such a
perspective is critical as high-growth companies are heterogeneous in nature and hence do not all grow
in the same way (Delmar, Davidsson, and Gartner, 2003). As a result, one might expect high-growth
companies with different characteristics to resort to different types of finance.
The goal of the first study in this dissertation “Incremental finance decisions in high-growth
companies: pecking order and debt capacity considerations” is twofold. First, I offer a more
comprehensive insight into how high-growth companies are financed across time and offer more
detailed insights into the importance of internal finance, debt finance and new equity finance. Second,
I demonstrate how particular high-growth companies are more or less likely to use different types of
finance.
1.1.2. Study 2: Seeking Experienced or Legitimate Partners? A Longitudinal
Study on the Impact of Venture Capital Firm Heterogeneity on Portfolio
Company Growth
Most finance research has focused on broad types of finance, such as debt finance and equity finance
(Frank and Goyal, 2005). Thereby these studies largely ignore from which source finance is raised and
treat all debt and equity providers as homogenous (Kochhar and Hitt, 1998). Nevertheless, the value of
investors often lies in providing not only money, but also value added services, such as monitoring,
offering contacts with other businesses and offering business advice (Sapienza, Manigart, and
Vermeir, 1996; David, O’Brien, and Yoshikawa, 2008). As investors come in all shapes and sizes, one
might expect that they will not all contribute equally to company development. This makes the choice
of managers with respect to the source of finance they target an important strategic decision.
4

Although more recent studies address the consequences of investor heterogeneity, nearly all of these
studies have put the investor in the foreground and the company in the background (Sorensen, 2007;
Hochberg, Ljungqvist, and Lu, 2007; Dimov and De Clercq, 2006; Dimov and Shepherd, 2005). In
other words, most of these studies have focused on the impact of investor heterogeneity on investor
performance, but have largely ignored the impact on portfolio company growth and performance
(Hochberg, Ljungqvist, and Lu, 2007). In the second study in this dissertation, I focus on the
perspective of the entrepreneurial venture that raises finance instead of the investor that infuses
finance in entrepreneurial ventures. This is vital as studying organizational processes from different
perspectives may offer different insights into the phenomenon studied (Van de Ven, 2007).
Moreover, although scholars agree that particular investors are more beneficial than others, it remains
unclear which types of investors will contribute most to company development. The second study in
this dissertation titled “Seeking Experienced or Legitimate Partners? A Longitudinal Study on the
Impact of Venture Capital Firm Heterogeneity on Portfolio Company Growth” hence focuses on how
investor heterogeneity affects portfolio company growth.
1.1.3. Study 3: Early Differences and Persistence in the Entrepreneurial Finance
Process: Evidence from High- and Low-Growth Biotechnology Startups
A common assumption by finance scholars is that all financial decision-makers are economically
rational and make comprehensive finance decisions in such a way that company value is maximized
(Subrahmanyam, 2007). Nevertheless, the second study in this dissertation demonstrates that growth-
oriented companies end-up with different types of investors and that these investors have a different
impact on subsequent company growth. If all entrepreneurs are rational then why do they not all raise
finance from high quality investors and why are rents related to affiliating with these better investors
not entirely reduced through competition in the factor market? An important question that arises is
how entrepreneurs structure their search for finance and why they raise finance from particular
investors?
5

Furthermore, in light of current finance theories the finding in the second study that early finance
decisions have a long-lasting impact on company growth and development is remarkable. Optimal
capital structure theories indicate that all companies can relatively easily rebalance their financial
structure and all move towards an optimal target financial structure (Leary and Roberts, 2005;
Hovakimian, Opler, and Titman, 2001). This assumption precludes a long-term impact of the financial
structure on company development. Hence, a more balanced vision is required which offers more
insight why early finance decisions facilitate and/or constrain the subsequent finance process.
The final study in this dissertation “Early Differences and Persistence in the Entrepreneurial Finance
Process: Evidence from High- and Low-Growth Biotechnology Startups” focuses on dynamics in the
finance process. The goal of this study is three-fold. First, it studies the finance process from startup
through development of both high- and low performing entrepreneurial companies and identifies key
differences in the finance process. Second, it demonstrates how early differences in the finance
process originate between these high- and low-growth companies. Finally, I theorize on the processes
that explain why early finance decisions may both facilitate and constrain subsequent finance
decisions.
1.2. Key Dimensions of Financial Decision-Making and Foundational
Finance Theories
Modern theory on business finance started with Modigliani and Miller (1958). They demonstrated that
in a perfect market characterized by free and directly available information, no transaction costs,
rational actors, homogenous expectations and no taxes amongst other assumptions, finance behavior is
irrelevant and does not influence firm value. It does not matter whether companies will raise debt or
equity finance and short-term or long-term finance to give just two examples. Why? When investors
have equal access to financial markets, investors can create any financial structure that is optimal for
them, even if the company itself does not offer it. Investors will not pay for something they can create
6

themselves and hence the financial structure will not be valued. While the Modigliani and Miller
theorem does not provide a realistic description of actual finance behavior, it did provide a theoretical
framework for finding reasons why finance matters (Frank and Goyal, 2005). Financial markets are
not perfect and imperfections, such as taxes, bankruptcy costs, agency conflicts and imperfect
information have all been advanced to explain why finance does matter in the value creation process.
Financial decision-making is complex and multidimensional in nature. Most studies focus on at least
one of the following dimensions: (a) the amount of finance raised, (b) type of finance raised, (c) the
source from which finance is raised and (d) dynamics in the finance process. The finance process
relates to the amount, type and source of finance raised across time and how these finance decisions
interrelate 1 . Financial decision-making and the finance process can be studied from different
perspectives. The majority of studies have focused on the finance obtained (i.e. the intersection
between the demand and supply side of the market) and how financial intermediaries make decisions
on whether or not to provide finance (i.e. supply side). Less is known about how entrepreneurs
structure their search for finance (i.e. demand side). Hence, researchers have typically put investors–as
the more established resource-rich firms–in the foreground and put entrepreneurial companies–as the
more passive resource-poor firms–in the background when studying the financing of entrepreneurial
companies (Cassar, 2004; Katila, Rosenberger, and Eisenhardt, 2008).
I will discuss the different dimensions of financial decision-making in more detail below. The goal of
this section is to offer a common frame of reference to the reader. It is beyond the scope of this
section, however, to offer a complete overview of the vast literature on company finance (Harris and
Raviv (1991) and more recently Frank and Goyal (2005) offer a more comprehensive overview of this
literature). Rather, I will focus on the most influential theories in corporate finance that have direct
relevance to the subsequent studies included in this dissertation.
1 I do not focus on financial bootstrap strategies, which include more or less creative techniques that reduce the need for
external finance (Winborg and Landström, 2001). Nevertheless, such techniques may have a significant impact on company
growth. I address the impact of financial bootstrap strategies on startup growth in a paper that is not included in this
dissertation (Manigart, Vanacker, Meuleman and Sels, 2009). It is available upon simple request.
7

1.2.1. Amount of Finance
A first dimension of financial decision-making is the amount of finance. The amount of finance
required by companies depends on a number of factors. I briefly discuss the main factors influencing
the finance needs, namely the characteristics of the operations, working capital policies, profitability
and growth (Smith and Smith, 2000). First, the characteristics of the operations, such as whether
companies are active in capital-intensive versus labor-intensive industries, minimum efficient scale
and time to first customer, all have an important impact on the finance needs of companies (Porter,
1980). For example, manufacturing companies generally require significant amounts of finance to
carryout upfront investments in production facilities and equipment, while service companies require
relatively little upfront investments in equipment and can expand more gradually. Thus, the average
capital requirement at startup between capital- and labor-intensive industries differs markedly
(Chandler and Hanks, 1998).
Second, working capital policies are related to the finance needs of companies. Young and small
ventures generally operate in industries that are characterized by particular norms, which are difficult
to change by these companies (Smith and Smith, 2000). Companies need to offer credit to customers
or otherwise potential customers will not be willing to do business with the company. Highly uncertain
companies may be forced to pay their resource providers before they will offer goods or services. All
these requirements will increase the amount of finance needed by companies. Nevertheless, when
companies are confronted with external finance constraints, some companies may more actively
implement more or less creative policies to increase the speed with which inventories and accounts
receivable are transformed into cash inflows and accounts payables are transformed into cash outflows
(Winborg and Landström, 2001). Hence, companies may change their working capital policies to
increase the amount of internal funds available and reduce its need for external finance.
Finally, profitability and firm growth significantly influence the financial needs of companies. All else
equal, the more profitable a company, the lower its need for external finance (Titman and Wessels,
8

1988). Retained earnings represent inside equity created by profitable companies (Frank and Goyal,
2005). However, if a company’s growth rate is high relative to its profitability, internal funds may not
be sufficient and external finance will be necessary to support company growth (Smith and Smith,
2000). Hence, scholars generally assume that internal finance will be insufficient to finance a high
growth strategy and that high-growth companies are particularly likely to raise outside debt or equity
finance in order to realize high-growth (Michaelas, Chittenden, and Poutziouris, 1999).
Nevertheless, not all companies will have automatic access to sufficient outside financial resources in
order to carryout crucial investments in tangible and intangible assets needed to achieve high growth.
Given the existence of market imperfections, such as asymmetric information and transaction costs,
external finance is costly, if available at all (Berger and Udell, 1998). Asymmetric information entails
that outside investors have less information about company prospects than company managers. This
implies that external investors do not know whether the investment projects for which companies
search finance are as valuable as managers claim. These information problems are especially
important for uncertain companies, such as young and small companies, which lack a track record of
past performance and where publicly available information is sparse. Investors may require a risk
premium, thereby offering only an “average” price for the securities offered by the company, to
compensate for the risk of selecting companies with poor projects. Moreover, the perceived risk of
adverse selection (i.e., the risk of selection companies with bad projects) may become so high that
investors decide to ration credit to these informationally opaque companies (Stiglitz and Weiss, 1981).
Additionally, public equity and debt finance are subject to significant costs, including market due
diligence, distribution and security registration, which are essentially fixed and create economies of
scale in issue size (Berger and Udell, 1998). These economies of scale in issue create a barrier which
small and young companies may not be able to overcome. Hence, transaction costs make particular
types of external finance extremely costly. Overall, the process of obtaining sufficient outside finance
is a process fraught with difficulties and this especially for new and small ventures.
9

Empirical research confirms that companies are generally confronted with external finance constraints.
Himmelberg and Petersen (1994) demonstrate for a panel of US companies active in high-technology
industries that investments in Research and Development (R&D) are positively related to the
availability of internal finance. Manigart, Baeyens and Verschueren (2002) offer similar findings for
unquoted Belgian companies and demonstrate how investments in tangible fixed assets are positively
related to the availability of internal finance. Using the growth of the whole company instead of an
individual component, such as R&D-investments or investments in fixed tangible assets, Carpenter
and Petersen (2002a) confirm that the growth of small manufacturing companies is constrained by the
availability of internal finance.
1.2.2. Type of Finance
A second dimension of financial decision-making relates to the type of finance. Two of the most
influential theoretical frameworks on finance behavior relate to the type of finance, i.e. the static trade-
off theory and pecking order theory (Fama and French, 2005; Frank and Goyal, 2005; Cassar, 2004).
Both grew out of the debate sparked by Modigliani and Miller. The static trade-off theory indicates
that companies will trade-off the benefits of debt against the costs of debt finance. Debt finance has an
important advantage compared to equity finance under the corporate tax system in most developed
countries: while the interests that businesses pay are tax-deductible expenses, dividends and retained
earnings typically are not or less so. Hence, debt yields a benefit in that it shields earnings from
corporate taxes (Modigliani and Miller, 1963). Moreover, an agency perspective indicates an
additional benefit related to debt finance as it is proposed to reduce the free cashflow problem in
companies with large amounts of cashflows (Jensen, 1986). The fixed debt-related payments force
managers to distribute excessive cashflow to debtholders instead of using it to invest in pet projects or
other value destroying investments.
One would expect companies to entirely finance their operations with debt if there would be no
offsetting costs (Figure 1.1.). However, excessive use of debt finance creates bankruptcy costs. Debt
10

finance entails fixed debt-related payments; when a company is unable to fulfill these payments both
indirect and direct bankruptcy costs will decrease company value (Figure 1.1.). Additionally, highly
levered companies with significant growth opportunities may face an underinvestment problem and
company shareholders may forego value-creating investments (Myers, 1977). In these highly levered
companies additional investments may disproportionately benefit debtholders, while shareholders have
to carry the entire cost of new investments. Companies may avoid that agency conflicts emerge
between debtholders and shareholders by reducing debt levels. In the static trade-off theory companies
have an optimal capital structure where the marginal cost of an additional euro of debt finance equals
the marginal benefit of an additional euro of debt finance (Figure 1.1). Moreover, it is proposed that
companies make incremental finance decisions in such a way that they gradually move towards their
target (optimal) debt ratio.
Market value of
firm
Source: Shyam-Sunder and Myers (1999), pp. 220.
FIGURE 1.1
The Static Trade-off Theory of Capital Structure
PV interest tax shields
Firm value under all-equity financing
Optimum
PV costs of financial distress
A different perspective is the pecking order theory (Myers, 1984; Myers and Majluf, 1984). In a
pecking order framework, the cost related to the existence of informational asymmetries between
company managers and investors is of first-order importance, while the costs and benefits proposed in
Debt
11

the static trade-off theory are only of second-order importance. Informational asymmetry entails that
while business managers have private information on the value of assets in place and future growth
options, outside investors can merely estimate these values. When outside investors underestimate the
value of debt or equity issued by a company, this company may decide not to accept the finance and
scale down or completely abandon valuable investment projects. The undervaluation of the securities
offered by a company may be so severe that when the company accepts prohibitively expensive
external finance to execute new investment projects this may damage company value. Companies may
avoid these problems by financing projects internally (Myers, 1984). Moreover, debt finance will be
preferred to new equity finance because the information problems are lower when companies issue
new debt (Myers, 1984). When companies have no cashflow problems debtholders are guaranteed
companies will be able to fulfill the fixed debt-related payments. Nevertheless, new shareholders are
not sure that companies will distribute sufficiently high dividends over time as these are neither fixed
nor required.
In summary, companies prefer to finance new investments with internal funds, which are not subject
to asymmetric information problems. When internal funds are insufficient to meet the finance needs,
managers will turn to more costly outside funds. In this situation, companies are expected to issue the
safest securities first as these will suffer less from information asymmetries (Myers, 1984). This
implies managers will first raise debt finance and only consider new equity as a final option. Finally, it
is important to note that in a pecking order framework companies have no target debt ratio. Debt ratios
merely reflect companies’ cumulative past requirements for external finance (Myers, 1984).
These two different perspectives, often presented as competing frameworks, sparked a vast stream of
large-sample empirical studies testing which framework performs best in explaining real-world
financing behavior (López-Garcia and Sogorb-Mira, 2008; Fama and French, 2005; Frank and Goyal,
2003; Shyam-Sunder and Myers, 1999). The standard versions of both theories, however, appear to be
inadequate. A particularly important problem for the static trade-off theory is that some of the most
successful companies –e.g. Microsoft– succeed by using no or limited debt finance thereby foregoing
12

valuable interest tax shields (Brealey and Myers, 2000). More significantly, debt ratios did not change
considerably in the last decades despite significant changes in corporate tax levels over the same
period (Frank and Goyal, 2005; Brealey and Myers, 2000). A particularly important problem for the
standard pecking order theory is that debt ratios are typically low in high-technology, high-growth
industries, despite significant external finance needs (Fama and French, 2005; Frank and Goyal, 2005;
Brealey and Myers, 2000). Both static trade-off and pecking order proponents are fine-tuning
abovementioned standard versions of their theories to account for known facts. Trade-off theorists are
currently developing dynamic tradeoff models, while pecking order theorists are focusing their efforts
on the development of an extended pecking order theory -incorporating the notion of debt capacity-
and the development of more complex adverse selection models (Frank and Goyal, 2005).
The first study “Incremental Finance Decisions in High-Growth Companies: Pecking Order and Debt
Capacity Considerations” builds further on this tension in modern business finance. It contributes to
the literature on the extended pecking order theory by more fully developing the notion of debt
capacity. While prior research generally defines debt capacity as sufficiently high debt ratios that
restrain a company from raising additional debt finance, this paper demonstrates the key role of
cashflows necessary to fulfill the fixed debt-related payments. The goal of this study is not to test
whether the pecking order or static trade-off theory performs best in a sample of high-growth
companies. Rather it demonstrates the value of combining both perspectives in order to increase our
understanding of financial decision-making.
1.2.3. Source of Finance
A third important dimension of financial decision-making relates to the finance source. Particularly
influential is the financial growth cycle as proposed by Berger and Udell (1998). Based on pecking
order considerations and optimal security design this life-cycle model indicates that different sources
of finance become more or less important when companies become larger, older and more
informationally transparent. Figure 1.2 indicates that startups, which are highly uncertain and lack a
13

track record, will heavily rely on inside finance and trade credit. For companies with high-growth
ambitions and limited debt capacity (due to, for example, a lack of collateral) outside equity finance
from business angels and venture capital investors will be a particularly important source of finance.
When a company has valuable assets, which can serve as collateral, bank debt will become available.
Only when companies develop a track record of past performance and thereby become more
informationally transparent public equity and debt markets will become important sources of finance
to finance future growth.
Firm Size
Firm Age
FIGURE 1.2
Firm Continuum and Sources of Finance: The Financial Growth Cycle Paradigm
Information Availability
Very small firms,
possibly with no
collateral and no
track record.
Initial Insider Finance
Small firms, possibly
with high growth
potential but often with
limited track record.
Source: Berger and Udell (1998), pp. 623.
Medium-sized firms.
Some track record.
Collateral available, if
necessary.
Angel Finance Venture Capital Public Equity
Short-Term Financial Institutions Loans
Trade Credit
Intermediate-Term Financial Institutions
Mezzanine
Private Placements
Large firms of known
risk and track record
Commercial paper
Medium Term
Notes
Public Debt
14

Although the financial growth cycle paradigm is generally accepted in the finance literature, it has not
been subject to much empirical scrutiny (Gregory, Rutherford, Oswald, and Gardiner, 2005).
Moreover, the financial growth cycle does not address more fine-grained questions with respect to the
source of finance. For example, which particular source of finance are companies more likely to
target? Are companies more likely to target venture capital investor A or venture capital investor B?
This is a problem characterizing most finance theories given the central role of rationality and investor
homogeneity in mainstream finance theories (Subrahmanyam, 2007).
Eckhardt, Shane and Delmar (2006), for example, consider both the demand and the supply side of the
market and propose that the finance process is a two-stage process. First, companies decide whether to
raise external finance or not based on the quality perception of their venture. Second, investors select
from the pool of companies that are willing to raise external finance based on their own quality
assessment. Implicit in these models is that all companies with external finance needs target all
possible external investors and/or investors screen all entrepreneurial companies with external finance
needs. Furthermore, entrepreneurs are proposed to rank order all finance offers received from investors
according to a particular criterion. Companies are expected to rank order finance offers based on the
price offered as indicated by traditional finance theory or the reputation of the investor as
demonstrated more recently by Hsu (2004). This results a very deterministic view on financial
decision-making.
In the second study “Seeking Experienced or Legitimate Partners? A Longitudinal Study on the Impact
of Venture Capital Firm Heterogeneity on Portfolio Company Growth” I offer new evidence on the
importance for the type of investor within a particular subset of investors, namely venture capital
investors, on the growth of portfolio companies. Moreover, the third study “Early Differences and
Persistence in the Entrepreneurial Finance Process: Evidence from High- and Low-Growth
Biotechnology Startups” offers additional insights into how entrepreneurs structure their search for
early finance thereby contributing to a general gap in our understanding.
15

1.2.4. Dynamics
Companies have to make decisions with respect to the amount, type and source of finance at different
points in time throughout their development. However, dynamics in the finance process or how
financial decisions interrelate have received comparatively little research attention (Eckhardt, Shane
and Delmar, 2006; Gregory, Rutherford, Oswald, and Gardiner, 2005; Berger and Udell, 1998). This
relates to the lack of longitudinal data especially on small and unquoted entrepreneurial companies.
While the financial growth cycle theory, for example, indicates that companies will prefer different
sources of finance as they develop and grow, this is traditionally tested using a cross sectional research
design. A good example is the study by Gregory, Rutherford, Oswald, and Gardiner (2005) who claim
to provide one of the first empirical tests of the financial growth cycle theory use a cross sectional
dataset. This study analyzes the effect of company age, size and information availability on the use of
one of three finance types (insider funding, venture capitalist/mid-term loan or public equity/long-term
loan). Although such cross-sectional studies are informative about differences between companies,
they simply cannot study how companies make finance decisions over time since the response
variables are measured at a single occasion (Fitzmaurice, Laird, and Ware, 2004).
More significantly, traditional capital structure theories generally ignore the role of company history
on future finance behavior (Hovakimian, Opler and Titman, 2001). Traditional finance scholars, who
build upon the idea proposed by the static trade-off theory that companies have an optimal financial
structure and will make finance decisions in such a way that they reach this optimum, are more
interested on how quickly companies move towards their target financial structure. Leary and Roberts
(2005) indicate that companies move relatively quickly towards their optimum financial structure.
Moreover, it is proposed that companies not only manage their financial structure actively, but also
rebalance towards a target that is more or less time-invariant (Lemmon, Roberts, and Zender, 2008).
In the third study “Early Differences and Persistence in the Entrepreneurial Finance Process:
Evidence from High- and Low-Growth Biotechnology Startups” I focus explicitly on dynamics in the
16

finance process. The study demonstrates how differences originate in the finance process and
elucidates the processes that cause initial finance decisions to constrain or facilitate subsequent
financial decision-making.
1.2.5. How the Studies in this Dissertation Cover the Different Dimensions of
Financial Decision-Making
Table 1.1 offers an overview of the different studies and the dimensions of financial decision-making
on which they focus. By design, the first study does not focus on the amount of finance raised, but
rather focuses on the type of finance raised and more specifically on the choice between internal
finance, debt finance and outside equity finance. Contrary to traditional capital structure research,
however, which often treats all equity finance as homogenous, this study makes a distinction between
inside finance (retained earnings) and outside equity finance. Although this study focuses on the
source of finance, or the distinction between inside and outside sources of finance, this distinction
remains rather crude. The study controls for the impact of prior finance decisions on future finance
decisions.
The second study focuses on the initial source of finance and its impact on company growth. It studies
the impact of venture capital firm heterogeneity on company growth. It controls for both the effects of
the amount of finance by including a control variable measuring the amount of startup finance raised.
Furthermore, the second study controls for the impact of the type of finance by focusing on a
homogenous group of venture capital backed companies, which all raise initial venture capital. The
study largely ignores the impact of initial finance decisions on the subsequent finance process.
The third study focuses explicitly on dynamics in the finance process and studies how the finance
process differs with respect to the amount of finance and source of finance raised between high- and
low-growth entrepreneurial ventures. It demonstrates how early finance decisions may both facilitate
and constrain future finance decisions. The third study controls for the impact of the type of finance by
17

focusing on the acquisition of venture capital finance across time within new biotechnology ventures.
Other more traditional sources of finance, such as internally generated funds and bank debt, are known
to be unavailable for this type of ventures.
TABLE 1.1
The Three Studies and their Focus on the Different Dimensions of the Finance Process a
Study 1: Incremental finance decisions in high-growth
companies: pecking order and debt capacity considerations
Study 2: Seeking Experienced or Legitimate Partners? A
Longitudinal Study on the Impact of Venture Capital Firm
Heterogeneity on Portfolio Company Growth
Study 3: Early Differences and Persistence in the
Entrepreneurial Finance Process: Evidence from High- and
Low-Growth Biotechnology Startups
Amount Type Source Dynamics
0 • • C
C C • 0
• C • •
a Where • indicates this aspect of the finance process is the focus of the study, c implies the study controls for this aspect of
the finance process and 0 indicates the study does not focus on this aspect of finance process.
1.3. The Link between Process Theories and Foundational Finance
Theories
In the prior section, I argued how current finance research has generally failed to addresses dynamics
in financial decision-making. In this subsection, I start by depicting the finance process as an
organizational process and present multiple models advanced by organizational scholars to grasp the
reality of complex organizational processes. I link these basic organizational process models to
foundational finance theories discussed above. Four basic process models have been proposed, which
each depict the process of development as unfolding in a fundamentally different progression of
change events, governed by different generative mechanisms or motors of change (Van de Ven and
Poole, 1995). Figure 1.3 summarizes these four basic models and relates them to existing finance
theory. I elaborate on the four process models below:
18

First, a teleological (or planned change) model depicts development as movement towards attaining a
goal or desired end-state. These models posit a set of goals desired by entrepreneurs, which they have
to attain in order to realize their aspirations. Development is viewed as a cycle of goal formulation,
implementation, evaluation and modification of goals and stresses the purposeful enactment of
entrepreneurs as the motor for development. Teleological models incorporate the systems theory
assumption of equifinality, which implies there are several equally effective ways to achieve a goal.
Hence, there is no assumption about historical necessity and teleological models do not specify what
future trajectories will follow. At best, these models list a set of possible trajectories by relying on
norms of decision and action rationality (Van de Ven, 2007).
Static trade-off and pecking order theories are the most influential theories in modern corporate
finance (Myers, 1984). Although these theories have been depicted as competing frameworks in
numerous empirical finance studies (e.g., López-Garcia and Sogorb-Mira, 2008; Shyam-Sunder and
Myers, 1999), both the static trade-off theory and pecking order theory fit within the definition of a
teleological model. The static trade-off theory and pecking order theory both assume that
entrepreneurs have one overriding goal, namely value maximization (Subrahmanyam, 2007). Hence,
entrepreneurs structure their search for finance in such a way that they minimize the cost of finance.
More specifically, the static trade-off theory indicates managers will trade-off the benefits of debt
against the cost of debt and choose a financial structure where the cost of finance is minimized. A
pecking order theory proposes entrepreneurs will minimize the costs of informational asymmetry by
avoiding external finance, and especially new equity, whenever possible. It is only when entrepreneurs
are confronted with finance needs that they will act. When internal finance is available, they will use
these funds to finance investments. When internal funds are not available, entrepreneurs will start the
search for debt finance, and only when debt finance is unavailable or prohibitively costly will they
consider external equity (Myers, 1984).
19

Moreover, historical finance decisions have no impact on future finance decisions in the static trade-
off and pecking order theory (Hovakimian, Opler, and Titman, 2001). More specifically, the static
trade-off theory proposes that managers move towards an optimal financial structure with more or less
difficulty (Leary and Roberts, 2005). Movement towards the target financial structure is hence
independent from prior financial decisions. In a pecking order framework, when managers are
confronted with finance needs they observe at that time whether sufficient internal funds are available.
If that is the case, they will use internal funds to finance the investments, irrespective of how the
company was financed before. If internal funds are not sufficient, companies will consider raising
outside finance (preferably debt) and this irrespective of prior finance decisions.
Second, next to teleology, life-cycle models are perhaps the most common explanation of
development in the broader management and finance literature. A life-cycle (or regulated change)
model depicts the process of change in an entity as progression through a necessary sequence of
stages. An institutional, natural or logical program prescribes the specific contents of these stages. It is
this institutional, natural or logical program that moves the company from a given point towards an
end-state. The final end-state is prefigured and requires a specific historical sequence of events (Van
de Ven, 2007). The most influential life-cycle theory in corporate finance is probably the financial
growth cycle (Berger and Udell, 1998). This theory indicates that the availability and suitability of
different finance sources changes as companies age, become larger and more transparent. Hence, the
motor for change in the financial growth cycle is the uncertainty of companies and the availability of
information.
Third, dialectical models are founded on the assumption that companies exist in a pluralistic world of
colliding events, forces or contradictory values that compete with each other for dominance and
control. Change occurs when opposing values, forces or events gain sufficient power to confront the
status quo. The conflicts that emerge between entities espousing opposing thesis and antithesis collide
to produce a synthesis (i.e. a change in the finance process for the better or worse), which in time
20

ecomes the thesis for the next cycle. Confrontation and conflict between opposing entities are central
within these models and generate a dialectical cycle of change (Van de Ven, 2007).
A key finance theory focusing on the design of alternative governance structures to mitigate agency
conflicts arising from the possible divergence of interest between shareholders, managers and
debtholders, is agency theory (Jensen and Meckling, 1976). Agency theory, for example, presents debt
finance as a governance device useful in reducing conflicts between shareholders and managers
(Jensen, 1986). Agency theory indicates that companies with plenty of internal funds will attract debt
finance as a mechanism to reduce the risk of managers investing in pet projects. This is because the
managers within companies that raise additional debt finance will have to use excessive funds to fulfill
the fixed debt-related payments.
Finally, an evolutionary model of business development and change incorporates both the ideas of
“organizational selection” or the role of the external market environment in determining the survival
and growth of companies (Van de Ven, 2007) and “organizational genetics” or the transmission of
organizational traits through time (Helfat, 1994). The variation-selection-retention framework is a key
element within an evolutionary perspective (Aldrich, 1999). Variation involves the creation of novel
organizations. Selection occurs primarily through competition among companies for scare resources,
such as financial resources. The environment selects those companies that best fit the resource base of
that specific environment. Retention involves the forces including inertia and persistence, which
perpetuate and maintain certain organizational forms. Overall, an evolutionary theory proposes that
early differences, which emerge between companies operating within the same industry, are likely to
persist across time (Nelson and Winter, 1982).
21

I. TELEOLOGY
FIGURE 1.3
Process Models of Organizational Development and Key Finance Theories a
Dissatisfaction
Implement Search/
Goals Interact
Set/Envision
Goals
Motors of change: Purposeful enactment and equifinality.
Finance theory: Pecking order theory and static tradeoff theory
(Myers, 1984)
III. DIALECTIC
Thesis
Antithesis
Conflict Synthesis
Motors of change: Diversity, confrontation and conflict.
Finance theory: Agency theory (Jensen and Meckling, 1976)
II. LIFE CYCLE
Stage 4 (Terminate)
Stage 3 Stage 1
(Harvest) (Startup)
Stage 2
(Grow)
Motors of change: Immanent program, regulation and compliant
adaptation.
Finance theory: Financial growth cycle (Berger and Udell, 1998)
IV. EVOLUTION
Variation Selection Retention
Motors of change: External market and organizational “genetics”.
Finance theory: ---
a Largely adapted from Van de Ven, A.H. and Poole M.S. (1995) “Explaining development and change in organizations.” Academy of Management Review, 20: 520.
22

While current finance theories fit within a teleological, life cycle or dialectical model, few theory building
and empirical research has focused on the finance process as an evolutionary process. Nevertheless, recent
research demonstrates that such a perspective may be particularly valuable in order to further develop our
understanding of financial decision-making. Eckhardt, Shane and Delmar (2006) demonstrate that the
finance process is characterized by double selection, where not only investors as resource providers, but
also entrepreneurs play a key role. Entrepreneurs will first select whether or not they want to raise outside
funds, based on their perceptions of company quality, and it is only from the pool of ventures that is
willing to raise outside finance that investors will subsequent be able to select the ventures to which they
will offer finance (Eckhardt, Shane and Delmar, 2006). As will be demonstrated later on, the overall
contribution of this dissertation lies in further developing the entrepreneurial finance process as an
evolutionary process.
1.4. Company Growth as a Complex Organizational Process: What is High
Growth? What are High-Growth Companies?
Although organizational growth is a central area of academic research, this research has failed to develop
a cumulative body of knowledge and remains rather fragmented (O’Regan, Ghobadian, and Gallear, 2006;
Delmar, Davidsson, and Gartner, 2003). A reason, which is typically advanced for this fragmentation, is
that organizational growth is a multidimensional concept (Weinzimmer, Nystrom, and Freeman 1998). It
is the failure to recognize the multidimensional nature of organizational growth that has led to confusion
and even conflict in both theory building and empirical research (Delmar, Davidsson, and Gartner, 2003).
Below I first discuss the multidimensional nature of growth, before highlighting its consequences for the
different studies 2 .
2 This section mainly focuses on the methodological issues in growth studies. It is beyond the scope of this section to offer an
overview of foundational growth theories as well. Wiklund, Patzelt and Shepherd (2009) provide an excellent review of growth
theory. I do relate my findings back to growth theory when discussing the overall contributions of this dissertation (section 1.8.2.).
23

1.4.1. Growth Indicators, Growth Formula and Timeframe
When studying high-growth companies, three dimensions merit particular attention: (a) the growth
concept, (b) the growth formula and (c) dynamics in growth (Davidsson and Wiklund, 2006; Weinzimmer,
Nystrom, and Freeman 1998). I discuss these elements in more detail below. First, different growth
indicators have been used in prior research (Delmar, Davidsson, and Gartner, 2003) and the most popular
include growth in sales, growth in employment and growth in assets (Weinzimmer, Nystrom, and Freeman
1998). Other relevant growth indicators include growth in value added and growth in cash flow amongst
other.
It is crucial to acknowledge that each growth indicator has shortcomings. Growth in total assets will be
more relevant for capital-intensive industries and as a result using total assets as a growth concept is likely
to ascribe higher growth to capital-intensive companies compared to service companies. Employment
growth will be more relevant in labor-intensive industries; using employment as a growth concept is likely
to ascribe higher growth to more labor-intensive companies. Sales is not a perfect growth concept either,
as it is sensitive to inflation and currency exchange rates (Delmar, Davidsson, and Gartner, 2003).
Moreover, many R&D-intensive companies, such as biotechnology ventures, have no sales for long
periods, while they might develop themselves and grow in other dimensions. As a result, organizational
scholars advise to use multiple growth indicators, which should give richer, more robust and more
comparable findings, compared to studies resorting to only a single growth indicator (Delmar, Davidsson,
and Gartner, 2003; Weinzimmer, Nystrom, and Freeman, 1998).
Second, organizational growth scholars mostly discuss two growth formulas, namely absolute growth and
relative growth. This distinction is vital, as absolute growth measures tend to attribute higher growth rates
to larger companies, while relative growth measures tend to attribute higher growth to smaller companies.
As an example, it is easier for a small company with only 10 employees to grow with 100% (i.e. relative
24

terms) to a company with 20 employees, than for a company with 500 employees to growth with 100% to
a company of 1,000 employees. For a company with 500 employees, however, it will be easier to add 20
people (i.e. absolute terms) to its workforce, compared to a small company employing only 10 people.
Using the dataset from the first study in this dissertation, Table 1.2 demonstrates the critical nature of the
decision with respect to the growth concept and the growth formula researchers use. The overlap between
companies selected by using different growth indicators is low. For example, only 29% of the companies
selected as high-growth companies based on absolute growth in sales are also selected as high-growth
companies based on absolute growth in assets. This indicates that growth in sales is quite different from
growth in assets. These findings correspond with Delmar, Davidsson, and Gartner, (2003) for Swedish
companies.
Table 1.2 further demonstrates that the overlap between companies selected by using absolute or relative
growth measures is low. For example, only 19% of the companies selected as high growth based on
absolute growth in employment are also selected as high growth based on relative growth in employment.
Hence, the distinction between absolute and relative growth measures is critical. Interesting is the
observation that the overlap between the different groups of absolute growth companies is higher than the
overlap between the different groups of relative growth companies. It indicates that the typical absolute
growth company grows over more dimensions than the typical relative growth company.
25

TABLE 1.2
Overlap (%) between Different Groups of High-Growth Companies using Different Growth Indicators and
Growth Formula a
Absolute growth
in total assets
Absolute growth in total assets 100 *** *** *** *** ***
Absolute growth in sales 29 100 *** *** *** ***
Absolute growth in employment 19 37 100 *** *** ***
Relative growth in total assets 11 5 4 100 *** ***
Relative growth in sales 8 16 13 13 100 ***
Relative growth in employment 3 5 19 8 26 100
a Growth is measured as a three year moving average over a ten-year timeframe. Five growth indicators (assets, sales,
employment, value added and cash flow) and two growth formula (absolute and relative) are used. This gives 10 listings offering
a growth figure for all Flemish and Brussels companies that employ at least 10 people. High-growth companies are defined as
those companies that are among the first percentile in one of those listings for at least two years. This results in a dataset of 2,106
companies. Reported here are the three most commonly used growth indicators (i.e. total assets, sales and employment).
Source: Van Eeckhout, Clarysse, Vanacker and Manigart (2006) “Op Safari in Vlaanderen: De identificatie en kenmerken van
gazellen” In: Durven groeien in Vlaanderen: Een boek voor gevorderden (Eds. B. Clarysse) Roularta Books, Roeselare.
Third, organizational growth is an inherently dynamic measure of change across time (Davidsson and
Wiklund, 2006; Weinzimmer, Nystrom, and Freeman, 1998). A five-year period has been the time frame
most widely used in prior organizational growth studies (Weinzimmer, Nystrom, and Freeman 1998).
Surprisingly, scholars generally neglect the central role of the time frame of a study on its findings
(Zaheer, Albert and Zaheer, 1999). More surprisingly, despite the inherently dynamic nature of growth,
growth studies typically use only first and last year sizes. Consequently, these studies do not fully capture
the growth of companies, because they ignore development in between these two time points (Delmar,
Davidsson, and Gartner, 2003; Weinzimmer, Nystrom, and Freeman 1998). As a result, most growth
Absolute growth
in sales
Absolute growth
in employment
Relative growth
in total assets
Relative growth
in sales
Relative growth
in employment
26

studies make –often-implicit– assumptions about the temporal pattern of growth in between two points in
time. Some researchers assume that growth occurs as one large quantum size leap over the period studied
while others assume that growth is a linear process (Davidsson and Wiklund, 2006).
1.4.2. How the Studies in this Dissertation Address the Multidimensional Nature of
Growth
Organizational growth scholars argue that researchers should be more explicit about how they measure
growth (Davidsson and Wiklund, 2006). Table 1.3 offers an overview of the different studies and how
they incorporate the multidimensional nature of growth. The first study defines high-growth companies by
using five growth indicators, which include employment, revenues, total assets, added value and cashflow.
Moreover, growth is measured in both absolute and relative terms. Given that the goal of the first paper
was to obtain a broad sample of high-growth companies in order to study their finance policies, we
combined multiple measures and growth formula. Although, this study is longitudinal in the sense that I
follow the financial decisions of high-growth companies for up to eight years, dynamics in company
growth were not the focus of the study.
In the second study, I used two growth indicators, namely growth in employment and growth in total
assets (excluding cash and cash equivalents). These two growth indicators are particularly suitable to
study the development of venture capital backed companies on which I focus in this study. Venture capital
backed companies typically develop new products or services without any immediate sale prospects even
in low-tech sectors (Puri and Zarutskie 2008). Given that many venture capital backed companies in the
sample are startups, I decided to focus on absolute growth, as it is impossible to calculate relative growth
when initial values are zero. The second study explicitly models the dynamic and non-linear nature of
growth by using an appropriate longitudinal technique called Linear Mixed Model (LMM).
27

Study 1: Incremental Finance Decisions in High-Growth
TABLE 1.3
The Three Studies and Multidimensional Nature of Growth
Companies: Pecking Order and Debt Capacity Considerations
Study 2: Seeking Experienced or Legitimate Partners? A
Longitudinal Study on the Impact of Venture Capital Firm
Heterogeneity on Portfolio Company Growth
Study 3: Early Differences and Persistence in the Entrepreneurial
Finance Process: Evidence from High- and Low-Growth
Biotechnology Startups
Indicator Formula Dynamics
Multiple indicators Absolute and relative
Multiple indicators
growth
Absolute growth only
Ignored
Explicitly modeled
through LMMs
Multiple indicators Absolute growth only Explicitly studied
through longitudinal
case studies
The third study makes a distinction between high- and low-growth biotechnology startups. Three
performance measures are used including employment growth, growth in total assets and patenting rate.
The focus is again on absolute growth since all ventures are startups where initial values equal zero.
Within the high- and low-growth ventures, we study the accumulation of financial resources across time.
We explicitly theorize on the dynamics in the finance process, which foster and/or constrain ventures in
their ability to raise follow-on finance.
1.5. Research Context
All studies in this dissertation focus on unquoted Belgian companies. In this section, I first describe the
Belgian financial system and demonstrate it is a typical example of a bank-based financial system.
Second, I describe the entrepreneurial landscape in Belgium and compare it with the entrepreneurial
landscape in other high-income countries. Finally, I discuss the implications of the research setting for the
studies in this dissertation.
28

1.5.1. The Belgian Financial System
Belgium has a bank-based financial system and banks play a central role in mobilizing savings, allocating
capital, exerting corporate control and providing risk management vehicles (Demirguc-Kunt and Levine,
1999). Other examples of so-called bank-oriented countries include Germany, France, Italy, Spain and
Japan (Demirguc-Kunt and Levine, 1999). In market-oriented countries like the U.K., U.S. and Canada,
securities markets (together with banks) play a central role in mobilizing and allocating capital, corporate
control and risk management (Demirguc-Kunt and Levine, 1999). Rajan and Zingales (1995) argue that
the difference between the two types of financial systems is not so much present in the level of debt
companies use, but is more likely reflected in the choice between public finance (e.g. stocks and bonds)
and private finance (e.g. bank loans and venture capital finance).
The following figures illustrate the central role played by banks in the Belgian financial system. While
Belgian GDP equaled 314.1 billion euro in 2006, the amount of assets controlled by the Belgian Bank
sector equaled 1,422 billion euro by the end of December 2006 (NBB, 2008). Loans by banks account for
over 60% of their assets, a figure that has remained quite stable over the last years (NBB, 2008). The two
most common parties to whom banks grant loans are other financial intermediaries and companies. They
each represent about one third of the loan portfolio. Moreover, private individuals represent another fourth
of the loan portfolio (NBB, 2008). The Belgian banking sector is highly developed as measured by its
size, activity and efficiency (see Demirguc-Kunt and Levine (1999) for cross-country comparisons). This
is definitely the case when compared by the development of its public equity markets.
The amount of finance raised through public equity markets equaled 1.3 billion euro in 2006, of which -
some 700 million euro- was raised by one financial holding. Only 14 companies conducted an Initial
Public Offering (IPO) in 2006. In 2005, 1.8 billion euro was raised through public equity markets and
eight companies conducted an IPO (NBB, 2008). These figures demonstrate that public equity is only
29

accessible for a limited number of companies. Moreover, IPOs are rare events in the Belgian financial
market. The value of public bond issues increased from 5 billion in 2005 to 5.5 billion euro in 2006. It is
important to note, however, that over 90% of the corporate bond issues relate to foreign companies,
including a number of subsidiaries of Belgian financial intermediaries (NBB, 2008). Hence, public bond
issues are extremely rare for Belgian non-financial companies.
The Belgian venture capital and private equity market, although less developed than the U.S. and U.K.
markets, is quite similar compared to other Continental European venture capital and private equity
markets (Baeyens, 2006). In 2006, 721.7 million euro of venture capital and private equity was raised, a
sharp increase from the 352.7 million raised in 2005 (EVCA, 2007). Banks contribute a significant
amount of funds to the venture capital and private equity industry, with 78.8% and 28.3% of the funds
raised in respectively 2006 and 2005. This is further evidence pointing towards the key role played by
banks in the Belgian financial system.
In 2006, 940 million euro was invested through 349 investments in 244 companies (EVCA, 2007).
Although public sector investors used to dominate the Belgian venture capital and private equity market
(Baeyens, 2006), more recently independent funds are the most common type of investor, representing
45% of the amount invested and 57% of the number of investments in 2006 (EVCA, 2007). Only 4% of
the amount invested and 12% of the number of investments are in the seed and startup stage. This is a
significant drop from 2005, where some 32% of the amount invested and 39% of the investments were in
the seed and startup stage. In 2006, high-technology investments represented 12% of the amount invested
and 47% of the number of investments. In 2005, the amount invested in high-technology companies still
represented 44% of the amount invested. This sharp drop in the amount invested in high-technology
industries is mainly due to a significant reduction in the amount invested in the biotechnology industry.
Divestments by trade sale, public offering, write-off and sale to financial institutions (the most important
options) represented respectively 14.7%, 14.7%, 11.8% and 20.6% of divestments in 2006.
30

Overall, the Belgian financial system is a typical example of a bank-based financial system (Demirguc-
Kunt and Levine, 1999). It allows us to provide more insight into the finance process in a different setting
compared to the majority of prior empirical studies. Prior studies more often analyze the finance process
of large Anglo-Saxon companies, which often raise funds by issuing corporate debt and equity on public
capital markets (Zingales, 2000).
1.5.2. The Belgian Entrepreneurial Landscape
The Global Entrepreneurship Monitor (GEM) offers an annual assessment and review of entrepreneurial
activity in countries participating in the GEM project. In 2007, the ninth annual assessment, 42 countries
participated. A central indicator is the early-stage entrepreneurial activity index. This index represents the
percentage of the working population (i.e., people between 18 and 64 years old) of a country that is
currently involved in setting up a company (nascent entrepreneurs) or recently started a new company
(owner-managers of companies up to 42 months old).
In 2007, the early-stage entrepreneurial activity index equaled 3.2% in Belgium. This implies that on
average 3.2% of the working population indicated they were involved in setting up a business or recently
founded a new business. The rate of entrepreneurial activity in Belgium lies significantly below the
European average (5.3%) and global average (8.9%). Figure 1.4 reports the early-stage entrepreneurial
activity rates for 2002/03, 2004/05 and 2006/07 and demonstrates how Belgium can be consistently found
in the lower rankings on the entrepreneurial activity scale in high-income countries for each of the three
periods studied. These figures are worrying as research indicates a positive relationship between the
prevalence of entrepreneurial activity in high-income countries and economic growth (van Stel, Carree,
and Thurik, 2005).
31

FIGURE 1.4
Early-Stage Entrepreneurial Activity Rates for 2002/03, 2004/05 and 2006/07 in High-Income Countries
Source: Global Entrepreneurship Monitor Adult Population Survey (Global Report, 2007, pp. 17).
While the early-stage entrepreneurial activity index reflects the prevalence of entrepreneurship in general,
the focus of the different studies in this dissertation is on high-growth companies. It is generally
acknowledged that only a small fraction of new firms contribute disproportionately to economic growth
(Storey, 1994). Achieving high-growth is non-trivial and does not happen by accident. On the contrary, it
requires significant investments both in tangible and intangible assets. These investments are unlikely to
be carried out if the entrepreneur has no or only limited growth ambition (Wiklund and Shepherd, 2003).
Autio (2005) defines high-growth expectation entrepreneurship as the percentage of entrepreneurs that
expect to employ at least 20 people within a timeframe of five years after founding.
Autio (2005) demonstrates how the overall early-stage entrepreneurial activity negatively relates with
high-growth expectation entrepreneurship. So, while the general entrepreneurial activity in a country may
be quite low, a large fraction of this activity may be high-growth expectation entrepreneurship.
Unfortunately, figure 1.5 demonstrates how Belgium also hinges behind in terms of the relative
prevalence of high-growth expectation entrepreneurship.
32

FIGURE 1.5
Relative Prevalence of High-Growth Expectation in Early-Stage Entrepreneurship, 2000–2006
Source: Global Entrepreneurship Monitor Adult Population Survey (Global Report, 2007, pp. 27).
In 2007, on average 6.9% of Belgian entrepreneurs are involved in establishing a new business and expect
to employ more than 20 people in five years time. Alternatively, only 0.2% of the 18-64 Belgian
population is involved in high-growth expectation entrepreneurship. In Europe, on average 9.6% of
entrepreneurs are involved in establishing a new business with high-growth expectations. This represents
0.48% of the 18-64 European population.
Both the absolute level of high-growth expectation entrepreneurship and especially the relative level of
high-growth expectation entrepreneurship are positively related with the business angel activity and the
availability of finance within countries (Autio, 2005). While prior research stresses the role of founder
experience in securing resources from professional investors (Hallen, 2008; Hsu, 2007), the majority of
people indicate they lack the necessary skills to start a new business. The 2007 GEM survey indicates that
only 35% of the Belgians perceive themselves as capable and believe they have the necessary skills to
start a business. This figures lies in line with other European countries, including Finland (31%), France
(32%), Denmark (33%), the Netherlands (33%), Sweden (39%) and Spain (41%).
33

Overall, a low level of entrepreneurial activity and low prevalence of high-growth expectation
entrepreneurship characterize the Belgian context. Mobilizing sufficient and adequate financial resources
is a key element of the venture creation process. However, many people express they lack the necessary
skills and knowledge to start a new company. Hence, studying the relationship between the finance
process and company growth is particularly acute in such a setting. It is my hope that entrepreneurs,
investors and policymakers can gain a more thorough insight into the financial policies that relate to high
growth.
1.5.3. Implications of the Research Setting for the Studies in this Dissertation
In this section, I discuss how the Belgian financial system and entrepreneurial context bear implications
for the different studies in this dissertation. In the first study, I focus on the role of different types of
finance within high-growth companies. While prior studies have touted that new equity finance may be
particularly suitable for high-growth companies, debt finance may play a more crucial role in Continental
European countries, as banks play a central role in the mobilization and allocation of capital. Both private-
and public equity markets are less developed in Continental European countries. This makes it more
difficult for entrepreneurs to tap finance from these sources. It calls for a broader study on the role of
different types of finance (including debt and equity finance) for high-growth companies within a
Continental European context and demonstrates the limits to generalize results from studies in an Anglo-
Saxon setting to a Continental European setting and vice versa.
In the second paper, I study the growth of venture capital backed companies and the impact of investor
heterogeneity on the relationship between venture capital and growth relationship. Nearly all studies have
focused on the impact of investor heterogeneity on the relative amounts of exits. In these studies, which
are often in an Anglo-Saxon context, it is assumed that IPOs are the most favorable exit, while M&As are
only a second-best option. In the Belgian and more broadly continental European context these
34

assumptions are problematic. First, in order to conduct an IPO an active stock market is required. As
previously indicated the Belgian stock market is relatively underdeveloped and only few companies go
public. Second, M&As are often the preferred exit route both for very promising and less promising
ventures. This is why I focus on the perspective of the entrepreneurial company in the second paper and
study how different initial investors in these ventures help to accumulate more human resources and assets
across time.
In the third study, we make a distinction between young biotechnology ventures which exhibit high or low
growth based on changes in employment, changes total assets and patenting rate. I previously argued that
many Belgian entrepreneurs setup ventures with low growth-ambitions from startup. Hence, it will be
necessary to consider the growth ambitions of the entrepreneurs that setup these new ventures. The study
argues that entrepreneurial ventures that raise finance from experienced investors at startup raise more
follow-on finance compared to ventures that raise early finance from relatively inexperienced investors. A
particular challenge in this study will hence be to assure that both venture backed by more experienced
and less experienced investors have high-growth ambitions in order to rule this out as an alternative
explanation for the findings.
1.6. Data
All three studies in this dissertation use a different longitudinal dataset. The goal of this section is to offer
more detail on the basic building blocks of the different datasets used within this dissertation. No data
source is perfect and in this section I reflect on the advantages and disadvantages of the data sources on
which my studies build. Table 1.4 summarizes the samples used in each study and the databases on which
they build. Note that the first study is fundamentally different in design from the second and third study.
In the first study, I only select companies that realized high growth during one particular period over the
35

timeframe of the study 3 . However, the goal of this study was not to explain how finance decisions cause
high growth or vice versa. Rather it was to study how a broad sample of high-growth companies financed
themselves across time. The second study and third study, however, comprise both companies that
realized high growth and companies that failed to realize high growth despite having growth potential.
TABLE 1.4
Description of the Samples and Databases Used a
Sample Description
Study 1 Incremental finance decisions of 2,077
high-growth companies for up to eight
years (1997-2004)
Study 2 94 companies receiving initial venture
Study 3
capital between 1999 and 2003. These
companies are tracked for 5 years after the
initial investment resulting in 487 firm-year
observations
Longitudinal case study of 9 young Flemish
biotechnology ventures active in research
and development
BEL-FIRST
BVA
Zephyr
Interviews
• 0 0 0 None
• • • 0
• 0 0 •
a Where • indicates the database is used in a particular study and 0 indicates it is not used.
1.6.1. BEL-FIRST Financial Accounts Database
Other Data Sources
European Patent Database, trade
directories
European Patent Database,
statutory required publications,
business plans, websites, press
releases
The BEL-FIRST database maintained by Bureau van Dijk offers electronic access to detailed yearly
financial accounts of Belgian companies. An advantage of the Belgian setting is that all limited liability
companies –irrespective of their size– are required to file detailed financial accounts with the Belgian
3 This does not imply that I only study successful companies, as my sample also includes high-growth companies that eventually
go bankrupt.
36

Central Bank. Hence, rich longitudinal data is available for all companies, which provides a unique setting
to study the finance process of unquoted high-growth companies.
Not all companies are required to file complete financial statements, however. SMEs are allowed to report
abbreviated financial statements, which are still rather extensive compared to the information that is
typically available to researchers studying small unquoted ventures. One major difference between
abbreviated and complete financial statements is that sales levels only have to be disclosed in complete
financial statements. Fortunately, both complete and abbreviated formats report net added value, which
may be used as an alternative output measure to sales.
Investors heavily use and value general accounting information from financial statements. The decision to
lend and the terms of the loan contract are often based on the strength of the balance sheet and income
statements (Berger and Udell, 2002). Financial statements also play an important role in the screening and
selection of companies by venture capital investors. Falconer, Reid, and Terry (1995) report that venture
capital investors are heavy users of financial statement information and more specifically, that this
information is a key component to evaluate ventures ex-post. Manigart, De Waele, Wright, et al. (2000)
show that when venture capital investors have a financial or banking background they emphasize
accounting and financial statement information even more and this both in screening and monitoring.
Overall, financial statement data is expected to play a key role in the relationship between investors and
entrepreneurs.
Most financial account data from small and unquoted companies is unaudited, which may raise concerns
about the reliability of the data. In many small ventures the boundaries between entrepreneurs and their
ventures is blurry and entrepreneurs often use firm assets for private purposes. Moreover, reliable
financial accounts require sophisticated accounting infrastructures which many small businesses lack
(Berger and Udell, 2002). Nevertheless, the most important data with respect to the finance process that
37

are collect through the financial accounts (for example, external debt and external equity) are relatively
difficult to manipulate. Moreover, there is no reason to expect entrepreneurs to provide more accurate
financial information through a survey, which is one of the main alternatives to obtain financial data from
entrepreneurs. Surveys would also have an additional disadvantage in that it would be particularly difficult
for entrepreneurs to recall key financial indicators for different periods in time.
In the different studies the research context further limits some of the concerns on the reliability of the
financial account data raised above. The first paper excludes those companies that employ less than 10
people. From this size category onwards, ventures typically have developed internal specialization, there
is an identifiable management function, and there is usually at least some separation of ownership and
employees, in the sense that not all employees are also owners of the company (Autio, 2005). In the
second and third paper, I focus on venture capital backed companies. Prior research demonstrates how the
quality of financial account data reported by venture capital backed companies is higher compared to non-
venture capital backed companies as venture capital firm monitoring is influencing the financial reporting
discipline of their portfolio companies (Beuselinck, Deloof and Manigart, 2009).
1.6.2. Belgian Venture Capital & Private Equity Association (BVA) Database
The BVA was founded in 1986 and is the professional association that represents the Belgian private
equity and venture capital community. The BVA currently has 36 venture capital firms as full members.
Members are well balanced with respect to the type of investors (private and public institutions),
background (financial and industrial), focus (venture and development capital as well as buy-outs), timing
of exit (open ended and closed) and nationality of investors (Belgian and foreign ultimate investors).
The raw database constructed by the BVA forms the building block of the second study in this
dissertation. It comprises a sample of companies that received an initial venture capital investment
38

etween 1999 and 2003. The dataset is quite straightforward and includes the year of the initial venture
capital investment, portfolio company name, portfolio company VAT number (which allows linking the
BVA database to the BEL-FIRST database) and name of the lead investor (which allows linking the BVA
database to the Zephyr database). The 94 companies selected represent roughly 1/3 of all initial
investments by the BVA members over the period studied. It is a representative sample including
companies that eventually fail.
The structure of the database forces me to focus on the impact of the lead investor on venture
development. Although it is common for venture capital investors to invest in a syndicate, the focus on the
lead investor is in line with prior studies (e.g., Gompers, 1996; Dimov and Shepherd, 2005). Moreover,
the focus on the lead investor is motivated by the fact that it is this investor, who is typically responsible
for the main contacts with the portfolio company. The content of the database also implies that I am
focusing on investors that are members of the BVA. Venture capital investors may affiliate with
professional organizations in order to gain legitimacy. Hence, I am likely to focus on a sample of more
legitimate investors. Given that I study the impact of investor legitimacy on portfolio company growth this
selection is important. However, the selection of investors that may on average be more legitimate is only
likely to decrease the probability of finding a relationship between legitimacy and venture growth.
1.6.3. Zephyr Database
Venture capital firm characteristics play a central role in the second study within this dissertation. In order
to obtain longitudinal data on investor characteristics, such as overall deal experience and industry deal
experience, I used the Zephyr database. Zephyr is a database of private equity deals (similar to
Thomson.One) with a special focus on pan-European transactions. The Zephyr database starts to cover
venture capital backed deals from 1997.
39

Two drawbacks of the Zephyr database are important to acknowledge. First, given that the coverage of the
Zephyr database only starts from 1997, I miss the experience of particular venture capital firms
accumulated before that period. Hence, in the second study I focus on the more recently accumulated
experience. However, it is especially this experience that is thought to be important, given that the
knowledge accumulated through experience has been shown to depreciate over time (Darr, Argote, and
Epple, 1995). Second, there is a tendency of the Zephyr database to focus on larger more visible deals.
Again, we limit this drawback in the second study by relying not only on the Zephyr database, but adding
information from the BVA to our dataset.
While databases such as Zephyr and Thomson.One form the building block of many datasets in current
venture capital research, one should be particularly careful as research solely relying on these databases
might lead to the wrong conclusions due to abovementioned shortcomings. For example, one
biotechnology venture was both in the dataset of the second paper and included as a longitudinal case
study in the third paper. The Zephyr database indicated this company raised 5,000,000 from startup from
both local and international investors. At that time, a highly experienced manager was already in the
venture. This observation might guide researchers to conclude that experienced managers are able to raise
significant amounts of finance from highly experienced investors. Detailed interviews on the finance
process, however, indicated that the company was founded by purely scientific entrepreneurs who first
raised a limited amount of seed finance from a local experienced investor. This local investor helped with
hiring a professional biotechnology manager. Once a highly experienced manager joined the company the
local investor committed more funds and helped in the search for other finance from international
investors. Hence, a different story emerges: inexperienced entrepreneurs are able to raise finance from
experienced investors, who help the venture to professionalize and subsequently raise more finance from
other (international) experienced investors.
40

1.6.4. Interview data
In the third study, I address how the finance process differs between high- and low-growth companies and
why early differences have a tendency to persist across time. Case studies are particularly apt to address
these how and why questions on which traditional finance frameworks offer little guidance. Central within
the longitudinal case studies are my interviews with venture capital investors and entrepreneurs.
All investors and entrepreneurs are active in the biotechnology industry and all cases are young Flemish
biotechnology companies active in research and development. Homogenous case selection allows
reducing the non-measured variance as much as possible. Moreover, biotechnology ventures typically
require large amounts of finance, hence, the cases should allow me to study multiple finance events within
one venture. A drawback is that the external validity of the findings for other types of ventures (i.e. ICT
ventures, low technology-based ventures) might be questioned. Nevertheless, Yin forcefully argues that
“you should try to aim toward analytical generalization in doing case studies…” (Yin, 2003: 33).
Consequently, case studies are used to make generalizations to theory and leave it up to theory-testing
research to conclude whether findings can be generalized to other settings (i.e. other industries, older
companies).
Particular attention was given to minimize retrospective biases, amongst other the capacity of informants
to accurately remember past finance events. First, finance decisions are important decisions made by
entrepreneurs. Biotechnology entrepreneurs are expected to devote considerable time and energy to raising
finance, which should assure that they remember their finance decisions more fully compared to routine
decisions. Second, the selected biotechnology ventures are maximum 5 years old at the time of the first
interviews. It should be easier for entrepreneurs to remember less distant decisions. Third, interviews
focused on facts rather than feelings or believes. Moreover, information on the finance obtained could be
at least partially double-checked with detailed financial accounts data. If entrepreneurs had difficulties in
41

accurately remembering finance events we would have noted important differences between these two
sources of data. This was not the case, however.
1.7. Main Findings
In this section, I present the main findings and contributions of each study individually. The studies have
been organized in such a way that they gradually move from extending existing finance theory, pointing
towards gaps in existing finance theory and developing an alternative framework to study the
entrepreneurial finance process.
1.7.1. Study 1: Incremental Finance Decisions in High-Growth Companies:
Pecking Order and Debt Capacity Considerations
The first study focuses on the role of retained earnings, debt finance and new equity finance in high-
growth companies. At least three fundamental insights emerged from this study. First, the majority of
high-growth companies realize high-growth without raising new equity finance. This is remarkable given
the focus in prior research on the role of new equity finance as the most suitable type of finance for high-
growth companies. Almost 45% of the finance events observed relate to high-growth companies that raise
additional debt finance. Moreover, nearly 39% of finance events relate to high-growth companies that
decide to retain their earnings within the business. Retained earnings provide profitable companies with
internal finance which allows them to conduct additional investments without the need to raise outside
finance.
However, high-growth companies are heterogeneous in nature and different types of high-growth
companies are more or less likely to resort to different types of finance. A second fundamental insight is
that profitable high-growth companies prefer to finance investments with retained earnings. Surprisingly,
42

these companies are less likely to raise additional debt and thereby prefer to retain their debt capacity.
While this finding is in line with the pecking order theory, it is contrary to the prediction of the static
trade-off theory, which indicates that especially profitable high-growth companies will raise additional
debt finance to fully take advantage of the interest tax-shield related to debt finance. This latter behavior is
not observed in our sample of high-growth companies.
Third, while new equity issues are less widespread in high-growth companies as initially expected they are
important for a particular subset of companies. Unprofitable high-growth companies with high debt levels,
limited cash flows, high risk of failure and significant investments in intangible assets are more likely to
raise new equity. These findings indicate that high-growth companies are pushed towards new equity
when there are few alternatives, such as internal finance and debt finance. Hence, new equity issues allow
companies to grow beyond their debt capacity. This behavior is in line with the extended pecking order
theory. More significantly, the findings demonstrate that debt capacity is not only determined by leverage,
as implicit in prior studies, but is also determined by the ability to fulfill the fixed debt-related payments
through internally generated cashflows.
1.7.2. Study 2: Seeking Experienced or Legitimate Partners? A Longitudinal Study
on the Impact of Venture Capital Firm Heterogeneity on Portfolio Company
Growth
A basic assumption in foundational finance theories, such as the pecking order theory, is that investors are
homogenous. In the second study, I demonstrate the impact of investor heterogeneity on subsequent
company growth. Overall, results demonstrate how the initial source of finance has a significant impact on
subsequent company growth and has the potential to create sustainable differences in development
between companies. First, venture capital firms with more prior experience within the industry of their
portfolio companies contribute disproportionately to the growth of these companies. However, the overall
43

experience of venture capital firms does not influence the subsequent growth of portfolio companies.
Results further demonstrate how older venture capital firms and venture capital firms that appear more
often in the business press (i.e. more legitimate investors) also contribute significantly to the growth of
portfolio companies.
Second, investors with more industry deal experience do not select companies with higher intangible
assets on total assets ratios or more patents granted before the venture capital investment compared to
their less experienced peers. More legitimate investors, however, select companies with lower average
intangible assets on total assets ratios and fewer patents granted before the venture capital investment
compared to their less legitimate peers. This indicates that companies backed by more experienced or
more legitimate venture capital investors have lower or similar growth potential before the venture capital
investment, compared to companies that raise initial finance from less experienced or less legitimate
investors. Despite similar or lower growth potential at startup, portfolio companies of more experienced
and more legitimate investors grow at a higher rate. These results hint that more experienced and more
legitimate investors add value after their investment and help companies to realize higher growth rates.
The study also addresses two related shortcomings in organizational growth studies. First, growth studies
typically only look at first and last year sizes and ignore development in between these two time points.
This is problematic as growth is an inherently dynamic measure of change across time. This study
explicitly models the dynamic nature of growth and resorts to modern longitudinal data analysis
techniques for that purpose. Second, researchers typically make rather simplistic assumptions about the
growth pattern across time. It is common to assume that growth occurs as a quantum size leap at one point
in time or that growth is linear. Results indicate that the growth pattern of companies is typically more
complex and that models assuming linear growth trajectories are generally misspecified.
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1.7.3. Study 3: Early Differences and Persistence in the Entrepreneurial Finance
Process: Evidence from High- and Low-Growth Biotechnology Startups
In this paper, we study the finance process of both high- and low-growth biotechnology ventures from
startup through development. Three major findings emerged. First, high- and low-growth biotechnology
ventures did not differ systematically in the amount of finance raised at startup. Some high-growth
companies raise even less initial finance compared to similar low-growth companies. More crucial than
the amount of finance raised was the source from which finance was raised. High-growth companies
typically raise finance from highly experienced venture capital investors in their first or second finance
round, while low-growth companies typically raise early finance from relatively inexperienced investors.
Second, these initial differences in the finance process between high-and low growth companies are
unlikely caused by differences in growth potential or growth ambition at startup. High- and low- growth
biotechnology ventures did not differ systematically in their human capital, alliance capital, technology
development and market orientation at startup. Moreover, even the low-growth companies had high-
growth ambitions and were striving to develop into professional biotechnology ventures. Early differences
in the finance process are more likely the result of the entrepreneurs themselves who engage in a local
search, which pushes them towards one or a few investors, they happen to know.
Third, these early differences in the finance process persist across time. The high-growth companies raise
more follow-on finance from other experienced investors, while the low-growth companies raise less
follow-on finance from other relatively inexperienced investors. This is explained by venture
professionalization, stunted learning and preferences in investor syndication which all facilitate and
constrain the future finance process and thereby may act as isolating mechanisms making it difficult for
entrepreneurs to replicate successful finance strategies from their peers. We demonstrate, for example,
how more experienced investors are more likely to initiate changes in the management team from
45

founding. The increased professionalization within these portfolio companies puts them in a competitive
advantage when raising follow-on finance.
1.8. Overall Academic Contributions
This dissertation lies at the crossroad of both the finance literature and the organizational growth
literature. All studies tackle the interrelatedness between the finance process of unquoted companies and
company growth. In this section, I start with discussing the overall contribution to the finance literature,
before discussing the overall contributions to the organizational growth literature.
1.8.1. Finance Literature
A deep understanding of the entrepreneurial finance process requires multiple theoretical frameworks. I
previously linked key finance theories, such as the pecking order theory, static trade-off theory, the
financial life cycle theory and agency theory to basic process theories, such as teleological, life cycle and
dialectical models. All studies in this dissertation, however, indicate that foundational assumptions in
current finance theories are violated in entrepreneurial ventures or at least that there are key elements
missing in these theories.
While both the static trade-off theory and pecking order theory have provided valuable insights into
financial decision-making, which was further demonstrated in the first study in this dissertation, these
teleological models also have problems in explaining some of the key observations within this
dissertation. First, the third study demonstrated how entrepreneurs are not comprehensive in their search
for finance, despite the critical nature of finance decisions. Rather, it was observed how entrepreneurs
approach only one or a few investors when searching for startup finance and decided to accept finance
from the investor(s) without considering the long-term implications of early finance decisions. Hence, the
46

underlying assumption in teleological models that entrepreneurs are rational and structure their financial
decision-making in such a way that company value is maximized seems inappropriate.
Second, as demonstrated in the first and third study finance decisions are interrelated. The first study
showed how companies that raise a particular type of finance at one point in the past are more likely to
raise the same type of finance in the future. Moreover, the third study demonstrated how entrepreneurs
that raise early finance from experienced investors are more likely to raise experienced finance in the
future, something that is extremely difficult for the entrepreneurs that raised early finance from relatively
inexperienced investors. Overall, contrary to the assumption in teleological models the studies in my
dissertation indicate that it is possible to predict future finance trajectories based on past financing
behavior.
Several findings in this dissertation also fail to fit within life-cycle models, such as the financial growth
cycle theory. Life-cycle models indicate that companies, which have an ambition to IPO, will generally
search for venture capital in order to reach the IPO stage. However, the third study in this dissertation
indicated that despite the ambition of companies to conduct an IPO some companies that raise early
venture capital were unable to go public, while others were successful. Life-cycle models offer no insight
into what is driving these differences in the finance process between companies. Furthermore, some of the
companies that went public in the third study could hardly be defined as old, large or informational
transparent. On the contrary, the third study demonstrated that it is no exception to see biotechnology
ventures that are only five to seven years old, without products on the market and employing less than 50
people to go public. Finally, the life-cycle models offer little to no guidance in understanding why
particular investors are more or less valuable
Several observations are also problematic for the dialectical models, such as agency theories in finance.
The first study indicated that high-growth companies with plenty of internal funds prefer to finance
47

investments internally and not to attract additional debt finance. This is contrary to the prediction of
agency theory, which indicates that exactly these high-growth companies should attract debt finance to
mitigate potential conflicts between managers and shareholders. Moreover, while entrepreneurs and
investors are often portrayed as organizational actors with different (incompatible) goals, in many young
ventures they act as a syndicate and jointly work towards the attainment of organizational goals (see
Graebner and Eisenhardt, 2004). In the second and third study, for example, it is indicated how
experienced investors help their portfolio companies with mobilizing key resources, such as human
capital, financial resources and other (in)tangible assets.
All studies in this dissertation point towards the relevance of an evolutionary theory on the finance
process. Nevertheless, an evolutionary perspective is probably the least developed perspective in current
finance theory. The evolutionary perspective explains change as a recurrent, cumulative and probabilistic
progression of variation, selection and retention among ventures generated by the competition for scarce
resources (Aldrich, 1999; Van de Ven, 2007). The population of new ventures exhibits significant
variation as different types of entrepreneurs are known to start-up and develop ventures with different
strategies, structures and forms (Eckhardt, Shane and Delmar, 2006).
Selection principally occurs through the competition among ventures for scarce resources. It is generally
proposed that the environment selects those ventures that are best suited to the resource base of an
environmental niche (Van de Ven, 2007). Recent studies however demonstrate how the finance process is
essentially a staged selection process with two sequential selection events. First, entrepreneurs select their
ventures as candidates for receiving external finance based on their own assessment of the venture.
Second, investors use objective characteristics of the ventures to select which ventures to finance from the
pool of ventures that entrepreneurs have put forth as candidates for external finance (Eckhardt, Shane, and
Delmar, 2006). The first and third study in this dissertation confirm the critical role played by the
entrepreneur in this double selection process and extend the findings by Eckhardt and colleagues.
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The first study demonstrates how selection by entrepreneurs is more important than initially indicated by
Eckhardt and colleagues. The latter assume that once entrepreneurs are willing to raise external finance,
they will consider the entire range of external investors and external investors will be able to select from
the entire pool of ventures that are willing to raise external finance. The first study, however, demonstrates
how ventures with sufficient debt capacity are unlikely to consider the use of new equity finance. Hence,
equity investors may only have access to a more limited pool of high potential ventures with limited debt
capacity.
The third study offers even more striking results on the role of entrepreneurs in the entrepreneurial finance
process. Even within the pool of ventures that are willing to raise venture capital finance, not all venture
capital investors will have access to the entire pool of ventures. Entrepreneurs typically approach only one
or a few venture capital investors during the startup phase. Hence, the search for finance is local. They
avoid loose contacts with many potential investors and rather prefer to negotiate with investors known
from prior experiences or investors that are related in one way or another. Overall, this dissertation points
towards the central role of selection by entrepreneurs in the entrepreneurial finance process.
Finally, retention within the evolutionary perspective involves the forces, including inertia and
persistence, which perpetuate and maintain differences between ventures. All studies in this dissertation
demonstrate how early finance decisions have a sustainable impact on subsequent finance decisions and
even company development as a whole. The first study demonstrates how high-growth companies that
previously raised debt finance are more likely to raise debt finance again. Moreover, companies that
previously raised new equity are more likely to raise new equity in the future. However, high-growth
companies that previously raised additional debt finance are not necessarily more likely to raise new
equity in the future. These results at least suggest the existence of path dependent learning: once
entrepreneurs learn how to negotiate and price a particular type of finance this does not necessarily
49

contribute to the acquisition of other types of finance and as a result entrepreneurs are likely to stick to the
finance types previously raised.
The second study demonstrates how initial finance decisions may even have a long-lasting impact on
company development as a whole. Experienced and legitimate investors individually contribute
disproportionately to the growth path of their portfolio companies. The initial investor affects the ability of
entrepreneurial ventures to attract employees and assets for up to five-years after the initial investment.
Hence, relatively small differences in the early finance process or the source from which ventures raise
initial finance, may cause not only persistent, but also increasing differences in the development of
entrepreneurial ventures.
The third study demonstrates how the finance process differs between high- and low-growth companies
from startup through development. Results confirm with the second study as it is shown how ventures that
raise early finance from experienced investors raise more follow-on finance from other experienced
investors. Ventures that raise early finance from inexperienced investors, however, raise limited follow-on
finance typically from other inexperienced investors. Moreover, the third study delves deeper into the
processes that cause this persistence in the finance process. Differences in venture professionalization,
imperfect learning and investor syndication preferences maintain differences in the finance process.
Overall, a core contribution of the dissertation is that it develops an alternative framework to study the
finance process. This evolutionary framework indicates that one way to increase our understanding of how
financial decisions are made may well lie in systematically studying the finance process right from the
start. Moreover, it offers a more behaviorally accurate view of the finance process by demonstrating the
existence of evolutionary trajectories and the central role of local search, investor heterogeneity and prior
finance decisions in the origination and persistence of these trajectories. These ideas stand in stark contrast
50

to traditional corporate finance theories in which neoclassical concepts, such as comprehensive search,
value-maximization, investor homogeneity and optimal contracts hold sway.
1.8.2. Growth Literature
The resource-based view (RBV) is one of the key theoretical perspectives on company growth. The RBV
states that building distinctive resources and capabilities will benefit company growth and performance
(Barney, 1991; Wernerfelt, 1984). Companies that possess valuable, scarce, unique and imperfectly
mobile resources and capabilities will be able to realize a sustainable competitive advantage and
subsequently generate above-normal returns compared to their competitors (Barney, 1991). Resources and
capabilities that received particular attention in the strategy field include human capital, including
knowledge, skills and experience (Chandler and Hanks, 1998), networks resources (Baum, Calabrese, and
Silverman, 2000) and intellectual capital (Baum and Silverman, 2004).
In the traditional RBV literature financial resources are generally not considered to provide a competitive
advantage (Lee, Lee, and Pennings, 2001). RBV scholars build upon early finance theory, which indicates
that there is enough finance for value-creating projects in perfect financial markets (Modigliani and
Miller, 1958). Moreover, in perfect financial markets investor homogeneity is assumed: the role of
financial intermediaries is restricted to offering finance without active involvement in portfolio
companies. This makes financial resources non-distinctive, not scarce, not unique and perfectly mobile.
Hence, financial resources are not expected to provide ventures with a sustainable competitive advantage.
Financial markets, however, are not perfect and imperfections, such as information asymmetries and
transaction costs, make that the finance process of small ventures is fraught with difficulties (Berger and
Udell, 1998; Cassar, 2004). Finance is scarce and entrepreneurial ventures may find it difficult to attract
sufficient and adequate finance under acceptable conditions (Guiso, 1998), ultimately leading to finance
51

constraints (Himmelberg and Petersen, 1994). Hence, entrepreneurship scholars building upon the RBV
theory indicate that the amount of finance raised at startup is expected to offer startups a competitive
advantage as startups that raise more initial finance are likely to accumulate a larger stock of strategic
resources compared to their finance constrained peers (Lee, Lee, and Pennings, 2001).
A basic premise of the RBV is that more resources and capabilities will increase the efficiency and
effectiveness of firms (Barney, 1991). Having more resources is typically considered to be better than
having fewer resources (see for example Cooper, Gimeno-Gascon and Woo, 1994). Recent research
started to challenge this basic assumption. Katila and Shane (2005) demonstrate how in particular
environments resource constraints promote innovation. Mishina, Pollock, and Porac (2004) demonstrate
how human resource slack enhances short-term market expansion, but slows down short-term product
expansion. The studies in this dissertation have joined the few voices that have expressed reservations
about the view that more resources are necessarily better for venture growth.
The second study in this dissertation, for example, demonstrates how controlling for the amount invested
by venture capital investors the characteristics of these investors heavily influence the subsequent growth
of venture capital backed companies. Hence, two companies raising the same amount of finance may
exhibit a completely different growth pattern depending on the type of investor that offered early finance.
The third study indicates that raising relatively large amounts of startup finance from inexperienced
investors may even constrain the subsequent finance process. Ventures that raise smaller amount of
finance from highly experienced investors, however, may find it easier to raise follow-on finance and
exhibit significantly higher growth rates.
Overall, while entrepreneurship scholars have introduced financial resources as an important resource
within the RBV literature they have almost exclusively focused on the role of the amount of finance. The
implicit assumption in most research is that more financial resources, especially at startup, are better than
52

fewer financial resources. This dissertation encourages researchers to more fully explore the role of
finance beyond the amount of finance on venture growth.
Moreover, I make an important methodological contribution to growth research. While growth is a
fundamentally dynamic measure of change over time, almost no growth research has focused on the
temporal pattern of change within companies (Delmar, Davidsson, and Gartner, 2003; Weinzimmer,
Nystrom, and Freeman, 1998). All studies in this dissertation indicate the inadequacy of prior studies,
which typically assume that growth occurs as a quantum size leap over time or that growth is a linear
process (see Davidsson and Wiklund (2006) for a more comprehensive discussion). First, growth curves
are non-linear and much more complex than often assumed. Second, different companies exhibit very
different growth curves. Hence, while one growth model may be particularly appropriate for one
company, it may be inadequate for another company.
Although the argument that the growth process is a non-linear and complex process may not be entirely
new, few scholars have offered a suitable alternative of how to proceed and solve this drawback. The
second study in this dissertation demonstrates how modern longitudinal techniques, such as Linear Mixed
Models, may be particularly helpful to capture the dynamic nature of change in organizations more fully.
To my knowledge, and supported by the literature overview provided by Davidsson and Wiklund (2006),
this is the first study to use these advanced longitudinal methods to study company growth. Moreover, the
third study demonstrates how qualitative longitudinal case studies may be used to complement
quantitative longitudinal techniques to obtain a better understanding of critical junctures in company
growth. It is my hope this dissertation has set a standard for future empirical research on high-growth
companies.
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1.9. Structure of the Dissertation
The remainder of this dissertation is organized as follows. Chapter 2 contains the first study: “Incremental
Finance Decisions in High-Growth Companies: Pecking Order and Debt Capacity Considerations”. This
study investigates the discrete finance choices of 2,077 high-growth companies for up to eight years. It
extends the traditional pecking order theory and more fully develops the notion of debt capacity. Chapter 3
comprises the second study: “Seeking Experienced or Legitimate Partners? A Longitudinal Study on the
Impact of Venture Capital Firm Heterogeneity on Portfolio Company Growth”. Using a longitudinal
dataset of 94 venture capital backed companies, I examine which investors contribute most to company
growth. Thereby this study addresses the foundational assumption of investor homogeneity in traditional
finance theories. Chapter 4 includes the third study: “Early Differences and Persistence in the
Entrepreneurial Finance Process: Evidence from High- and Low-Growth Biotechnology Startups”. This
longitudinal case study examines the finance process of high- and low-growth biotechnology ventures
from startup through development. In this study, an alternative lens is developed to study the finance
process. Finally, chapter 5 formulates the limitations of this dissertation, avenues for further research and
implications for entrepreneurs, investors and government officials.
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Chapter 2: Incremental Finance Decisions in High-Growth
Companies: Pecking Order and Debt Capacity Considerations
Tom Vanacker
Ghent University, Department of Accounting and Corporate Finance, Kuiperskaai 55E, 9000 Gent, Belgium;
TomR.Vanacker@UGent.be
Sophie Manigart
Ghent University, Department of Accounting and Corporate Finance, Kuiperskaai 55E, 9000 Gent, Belgium &
Vlerick Leuven Gent Management School, Reep 1, 9000 Gent, Belgium; Sophie.Manigart@UGent.be
A version of this paper is forthcoming in Small Business Economics under the title “Pecking order and debt
capacity considerations for high-growth companies seeking financing”. A preliminary version of this paper is also
published in the 2006 edition of Frontiers of Entrepreneurship Research (ISBN 0-910897-27-1). The authors
appreciate helpful comments and suggestions from Rajesh Aggarwal, Gavin Cassar, Wouter De Maeseneire, Miguel
Meuleman, Hans Landström, Harry Sapienza and anonymous referees. We also thank participants from the 2008
Academy of Management Meeting, the 2006 Babson College Entrepreneurship Research Conference and the
Finance Seminars at Ghent University and the Katholieke Universiteit Leuven for their valuable feedback. We are
indebted to Bart Clarysse and Caroline Van Eeckhout for help in constructing the high-growth company database.
The financial support of the Intercollegiate Center for Management Science (I.C.M.) and “Steunpunt
Ondernemingen, Ondernemerschap en Innovatie” is gratefully acknowledged.
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2.1. Abstract
This study examines incremental finance decisions within high-growth companies. A large longitudinal
dataset, free of survivorship bias, to cover finance events of high-growth companies for up to 8 years is
analyzed. The empirical evidence shows that profitable high-growth companies prefer to finance
investments with retained earnings, even if they have unused debt capacity. External equity is particularly
important for unprofitable high-growth companies with high debt levels, limited cash flows, high risk of
failure and significant investments in intangible assets. These findings are consistent with the extended
pecking order theory controlling for constraints imposed by debt capacity. It suggests that new equity
issues are particularly important to allow high-growth companies to grow beyond their debt capacity.
2.2. Introduction
Although few in number, high-growth companies contribute disproportionately to employment and wealth
creation in an economy (Henrekson and Johansson, 2009; Storey, 1994). This makes organizational
growth a central area of research in entrepreneurship and a major policy concern. Proper financial
management, including raising suitable finance, is one of the key factors shaping high-growth companies
(Maurer and Ebers, 2006). The purpose of this paper is to offer an insight into the discrete finance
decisions taken within high-growth companies. Information asymmetries are thought to be particularly
severe in this setting (Frank and Goyal, 2003), causing a substantial wedge between the costs of internal
and external (debt and equity) finance (Carpenter and Petersen, 2002a). We therefore focus on the pecking
order theory to explain the finance choices of high-growth companies. The pecking order theory predicts
the existence of a finance hierarchy, where business managers avoid the cost of external finance if
possible. As a result, they will first prefer to use internal funds, then debt and finally outside equity as a
last resort to finance investments (Myers, 1984; Myers and Majluf, 1984).
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The impact of company characteristics on financial decision-making may vary according to the research
setting (Harris and Raviv, 1991). It is therefore important to test financial theories in settings where our
knowledge is limited to determine the generalizability of the theories across different settings (Cassar,
2004). Although high-growth companies are subject to the same market forces as any other company,
studying the finance decisions of high-growth companies is germane for a number of reasons.
First, a recent stream in the finance and growth literature discusses the importance of external equity from
private equity investors, like venture capitalists and business angels in the financing of high-growth
companies (e.g., Baum and Silverman, 2004; Davila, Foster, and Gupta, 2003). Conversely, it is assumed
that bank debt is an unsuitable source of finance, especially for innovative entrepreneurial companies
(e.g., Audretsch and Lehmann, 2002; Carpenter and Petersen, 2002b; Gompers and Lerner, 2001). Most
studies in entrepreneurial finance have therefore focused on private equity finance, ignoring other
potentially important sources of finance such as retained earnings and debt finance (Eckhardt, Shane, and
Delmar, 2006). In contrast to most contributions on the financing of high-growth companies, we do not
limit ourselves to external equity finance, but empirically consider a diverse range of finance choices,
covering internally generated funds, bank finance and new equity finance.
Second, some entrepreneurs may never consider the use of outside debt or equity finance (Howorth,
2001). The fear of loosing control and independence may inhibit entrepreneurs from raising outside
finance and especially outside equity finance (Manigart and Struyf, 1997). Most high-growth companies,
however, have considerable outside finance needs. Internal finance is often insufficient to finance high
growth (Michaelas, Chittenden, and Poutziouris, 1999; Gompers, 1995). Hence, we may expect financial
decision-makers in companies that realized high growth to at least consider a broader range of finance
alternatives compared to those in “Mom and Pop” companies.
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Companies do not continuously raise finance, which implies that finance behavior is characterized by
discrete “jumps” (Jansson, 2002). We therefore study the discrete decision (i.e., whether a particular type
of finance is raised or not) rather than the continuous decision (i.e., how much of a particular type of
finance is raised). Discrete models may be criticized because they use less information compared to
continuous models. However, models of continuous decisions ignore the inherently qualitative aspects of
finance behavior, which may introduce serious misspecification problems (Jansson, 2002). Moreover, by
studying incremental finance decisions, our research addresses a number of drawbacks of previous
research focusing on capital structure.
First, traditional capital structure research does not distinguish between internal finance and external
equity finance. Nevertheless, this distinction may be particularly important in our setting characterized by
high informational asymmetry (de Haan and Hinloopen, 2003). For example, an informationally opaque
company with sufficient internal funds should be more likely to finance investments internally and hence
should be less likely to attract additional external equity finance. Hence, company characteristics may
have a different impact on the probability of using internal finance or external equity finance.
Second, the capital structure of a company is the aggregate of its past finance decisions 4 . Therefore,
capital structure research generally masks information on the timing of the finance acquired. The
characteristics of a business change as it grows, which affects the availability and suitability of different
finance options (Berger and Udell, 1998; Gompers, 1995). For example, as a company grows it may invest
more in tangible assets, which can serve as collateral and make more and cheaper bank finance available.
It is therefore important to take into account the dynamic nature of company characteristics and finance
choices.
4 Rather than focusing on incremental finance decisions, one might also focus on the number of times companies raise a particular
type of finance across time. However, such a count measure is also an aggregate measure and hence is subject to the same
problems as described below for traditional capital structure research.
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Further, while most studies research finance choices of quoted companies (Fama and French, 2005; Frank
and Goyal, 2003; de Haan and Hinloopen, 2003; Shyam-Sunder and Myers, 1999; Helwege and Liang,
1996), we focus on finance choices of predominantly unquoted companies. Quoted companies have,
however, more finance options due to lower information asymmetries (Berger and Udell, 1998; Harris and
Raviv, 1991). This may lead to different finance strategies for quoted and unquoted companies.
From a methodological perspective, the lack of longitudinal studies in entrepreneurship research has been
described as a major weakness (Davidsson and Wiklund, 1999). Our study analyses the incremental
finance decisions of companies over a period of up to 8 years, during which all companies in our sample
have grown extensively. We include start-ups, failed and merged companies, if they have grown
considerably after start-up or before disappearing as independent entities. This implies that our study
limits survivorship bias.
The study starts with a discussion of the theoretical background and development of the hypotheses. Next,
we discuss the data set, where we describe in detail how high-growth companies are identified, how
finance decisions are defined and how independent constructs are measured. Thereafter, we present our
research findings, followed by conclusions and avenues for further research.
2.3. Theory and Hypotheses
In perfect financial markets, companies will always find sufficient and suitable finance for value-creating
investments (Modigliani and Miller, 1958). Hence, finance decisions are irrelevant, and internal finance
and external debt and equity finance are perfect substitutes. Real-world financial markets, however, are
not perfect, and market imperfections cause finance decisions to matter for business development and
company value. Unsuitable finance decisions may have major consequences, such as limited potential for
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future expansion, financial distress and even company failure (Carpenter and Petersen, 2002b; Michaelas,
Chittenden, and Poutziouris, 1999).
It is well established in the finance literature that market imperfections cause a considerable wedge
between the costs of internal finance and outside finance (see Colombo and Grilli, 2007; Carpenter and
Petersen, 2002a; Berger and Udell, 1998). Asymmetric information is probably one of the most important
reasons why outside funds are thought to be substantially more costly compared to internal funds (Berger
and Udell, 1998). Informational asymmetry entails that while managers have private information about the
value of assets in place and future growth options, outside investors can merely estimate these values.
Faced with the risk of adverse selection, outside investors will demand a ‘‘lemons’’ premium for the
securities offered by companies (Akerlof, 1970). The more risky the securities, the higher the premium
will be, as risk exacerbates the effects of information asymmetry (Myers, 1984). As a result, companies
prefer to finance new investments with retained earnings, which are not subject to asymmetric information
problems. When internal funds are insufficient to meet the finance needs, managers will turn to more
costly outside funds. In this situation companies are expected to issue the safest securities first as these
will suffer less from information asymmetries and hence be subject to lower premiums (Myers, 1984;
Myers and Majluf, 1984). This implies managers will first raise debt finance and only consider new equity
as a last resort. The resulting finance hierarchy is often referred to as a pecking order and is one of the
most influential theories in the finance literature (Frank and Goyal, 2003) 5 .
5 We acknowledge that information asymmetries do not necessarily lead to a finance hierarchy (Halov and Heider, 2004) and the
existence of asymmetric information may not be the only reason why there exists a finance hierarchy. First, transaction costs may
also contribute to the wedge between the costs of internal and outside finance (Myers, 1984). Second, there is a potential cost of
loosing control over the business when resorting to outside finance. These factors may further inhibit business managers from
issuing outside finance and even constrain company growth (Manigart and Struyf, 1997). Finally, the existence of a knowledge
gap from the entrepreneur’s perspective may cause a finance hierarchy, as business owners are typically most familiar with
traditional sources of funding, such as inside finance and debt, but less familiar with capital commonly used to fund growth, such
as venture capital and business angel finance (Van Auken, 2001).
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In this paper the focus is on the pecking order theory as a framework to understand incremental finance
decisions. Consequently, this research is in line with prior studies, such as Helwege and Liang (1996);
Shyam-Sunder and Myers (1999) and Frank and Goyal (2003), which focus on the finance choices of
quoted American companies. Their direct tests of the two main tenets of the pecking order model—i.e.,
(1) companies prefer to finance new investments with retained earnings and (2) external equity is only
issued as a last resort if outside funds are needed—have offered inconclusive and even contradictory
results, however.
Helwege and Liang (1996), studying a panel of US companies that conducted an IPO in 1983, find that the
probability of obtaining outside funds is not related to a shortfall in internally generated funds, which is in
contrast with predictions of the pecking order theory. However, consistent with pecking order predictions,
they find that companies with a cash surplus avoid outside finance. Finally, companies accessing the
capital market do not follow a pecking order when choosing the type of security to offer. Shyam-Sunder
and Myers (1999), however, draw a different picture of the predictive power of the pecking order model.
Based on a sample of 157 US companies that traded continuously between 1971 and 1989, they conclude
that ‘‘the pecking order theory is an excellent first-order descriptor of corporate finance behavior, at least
in our sample of mature corporations’’ (Shyam-Sunder and Myers, 1999, p. 242). Frank and Goyal (2003)
show that for a more elaborate sample of publicly quoted US companies, the greatest support for the
pecking order theory is found among large firms. Smaller firms, which are expected to be more likely to
be subject to information asymmetries, do not seem to follow a pecking order. Additionally, the pecking
order theory prediction that high-growth companies will end up with high debt ratios because of their
large finance needs is questioned (Fama and French, 2005). Barclay et al. (2006) demonstrate that high-
growth companies consistently use less debt finance as they grow. These findings have led some scholar
to conclude that ‘‘the pecking order theory works well when it should not and not so well when it should’’
(Heider 2003, p. 3).
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Despite contradictory empirical findings, the motivation behind our choice of the pecking order as the
main theoretical framework is clear-cut. Particularly, small and high-growth companies are subject to
significant information asymmetries (Frank and Goyal, 2003; Carpenter and Petersen, 2002a), causing a
substantial wedge between the costs of internal and external finance (Carpenter and Petersen, 2002a).
Consequently, based on theoretical grounds one would expect the pecking order theory to be particularly
useful to explain financing behavior in our sample of mostly unquoted high-growth companies. As a
result, we expect these firms to prefer internally generated funds to external funds if possible. This
suggests that in profitable companies internally generated funds will gradually replace outside debt and
equity finance. This leads to our first hypothesis:
Hypothesis 1: High-growth companies that have more internal funds will be less likely to raise additional
external debt or equity finance.
Our first hypothesis is non-trivial in light of previous contradictory empirical evidence. Furthermore, the
main competing framework to the pecking order theory, the static trade-off theory, predicts a different
behavior. The static trade-off theory states companies will trade off the benefits of debt, especially tax and
agency benefits, against the cost of debt, especially bankruptcy and agency costs of debt (Modigliani and
Miller, 1963; Titman, 1984; Myers, 1977). The static trade-off theory predicts companies will make
incremental finance decisions in such a way that an optimal capital structure is obtained. This optimal
capital structure is obtained when the marginal benefit of an additional dollar amount of debt finance
equals its marginal cost. Following the static trade-off theory, we would expect companies with a lot of
internal funds to rebalance their capital structure and issue additional outside debt finance. First,
companies with plenty of internal funds or financial slack are less likely to fail, reducing the bankruptcy
costs associated with debt finance. Financial slack buffers companies from shocks in the environment and
allows them to survive during turbulent times (Sharfman, Wolf, Chase, and Tansik, 1988). Further,
additional outside debt finance may mitigate potential agency conflicts resulting from abundant internal
70

funds (Jensen, 1986). Hence, the static trade-off theory indicates that internal finance and outside debt
finance are complements rather than substitutes. Profitable companies that built up internal equity capital
in the past are predicted to be particularly likely to attract additional debt finance in the future.
When internal funds are insufficient to finance company growth, the question whether to raise additional
debt or new equity becomes critical. Bank finance is an important source of finance for young and
growing companies in Continental Europe and is expected to be widely available (Manigart and
Meuleman, 2004). Bank debt is considered to be the cheapest source of outside finance, as banks only
require an interest on their loan and do not expect to share in the value creation, as equity investors do.
Banks only have a limited return on their investment (i.e., interest margin) and as a result are expected to
focus primarily on low-risk projects in companies with sufficient cash flow to fulfill the fixed debt-related
payments (Carey, Post, and Sharpe, 1998). Furthermore, banks typically require collateral and may
include restrictive debt covenants in the debt contract to reduce adverse selection and moral hazard
problems (Berger and Udell, 1998). However, as leverage increases, the probability of financial distress
and moral hazard problems increase, and hence the marginal cost of debt finance may increase rapidly
(Carpenter and Petersen, 2002b).
At a certain point the cost of additional debt may be excessively high or debt finance may simply be
unavailable, and company owners may turn to new equity as a last resort. Contrary to additional financial
debt, new equity issues do not increase the probability of failure, do not accentuate moral hazard problems
and do not require collateral (Carpenter and Petersen, 2002b). Hence, new equity issues may allow high-
growth companies with limited debt capacity to conduct additional value-creating investments, which
would not be possible, when the company solely relied on debt finance. However, the cost of issuing new
equity is thought to be significant, especially for small and unquoted companies. Venture capital investors,
for example, require an average yearly return of more than 20% for later stage investments and as much as
55% for early stage investments (Sapienza, Manigart, and Vermeir, 1996). A higher expected return
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lowers company value and in turn increases the equity stake required by the outside investor. This
explains why managers will avoid new equity issues whenever possible.
A particularly important problem for the traditional pecking order theory, however, is exactly the wide use
of outside equity finance despite its high cost (Fama and French, 2005; Frank and Goyal, 2005).
Significant external equity issues by high-growth companies are considered to refute the pecking order
theory (Frank and Goyal, 2003). The extensive use of new equity by especially small and high-growth
companies may, however, be explained in a pecking order framework by taking into account debt
capacity. Myers (1977) defines debt capacity as the point at which additional debt issues would reduce the
total market value of a firm’s debt. Lemmon and Zender (2004) are among the first to empirically examine
the impact of debt capacity considerations on finance decisions in a pecking order framework. Using a
sample similar to Frank and Goyal (2003), they show that the pecking order theory is a good predictor of
financing behavior for a broad cross-section of firms when controlling for debt capacity. This is referred to
as the extended pecking order theory (Lemmon and Zender, 2004). Furthermore, they show that high-
growth companies have more restrictive debt capacity constraints and hence have lower debt capacity.
Consequently, exactly high-growth companies will reach their debt capacity quickly and will issue outside
equity as a last resort. Significant outside equity issues by high-growth companies are hence not
necessarily in contradiction with the pecking order theory when taking debt capacity considerations into
account.
Current studies have defined debt capacity as the point at which companies reach ‘‘sufficiently high debt
ratios’’ that curtail further debt issues or make them prohibitively expensive (Fama and French, 2005;
Shyam-Sunder and Myers, 1999). Fama and French (2005) interpret their finding that more than half of
small, unprofitable high-growth companies issue outside equity as invalidating the extended pecking order
theory. They argue that these firms start with low levels of leverage, hence they should be able to raise
cheaper additional debt (Fama and French, 2005). This conclusion reflects a more limited view on debt
72

capacity than the traditional notion of debt capacity as coined by Myers (1977), which also takes into
account the possibility of the company repaying the debt from operational cash flows. Banks are ‘‘cash
flow lenders,’’ implying that they emphasize firms’ cash flows as the ultimate source of interest and
principal repayment, rather than ‘‘asset lenders’’ which emphasize collateral (Carey, Post, and Sharpe,
1998).
Undoubtedly ‘‘sufficiently high debt ratios’’ may make it more difficult to get additional debt finance.
Companies with high leverage have a higher financial risk, implying less protection for debt holders,
because of the smaller equity buffer on which debt holders can rely in case of liquidation (Ooghe and Van
Wymeersch, 2003). However, contrary to the narrow definition of debt capacity, even companies with low
leverage may have no or very limited debt capacity. Take, for example, privately held biotechnology
companies. These companies typically require large amounts of outside finance (Corolleur, Carrere, and
Mangematin, 2004). Despite this, European biotechnology companies exhibit very low leverage ratios
(EuropaBio, 2006). However, typically no or insufficient cashflows are available (Corolleur, Carrere, and
Mangematin, 2004). This implies that while debt levels are low, these companies have no capacity to
attract financial debt because of their inability to carry out debt-related payments.
Accumulating fixed commitments, which increase liquidity risk (i.e., a company’s inability to carry out
the fixed payments causing financial distress), will at a certain point reduce the total value of debt. As a
result, firms with low levels of cashflows may find that issuing additional debt is not advantageous
compared to outside equity or is even impossible to raise (Helwege and Liang, 1996). Therefore, they will
move further down the pecking order to outside equity. Hence, we argue that debt capacity is not only
determined by a firm’s leverage, but also by its capacity to carry out the fixed debt-related payments.
High-growth companies with limited debt capacity are expected to issue external equity as a last resort
(Lemmon and Zender, 2004). This leads to our second hypothesis:
73

Hypothesis 2: High-growth companies with high leverage and/or limited cash flows will be more likely to
raise additional external equity finance instead of additional external debt finance.
In the above, we argue that companies will only issue new equity as a last resort (i.e., when internal
finance and debt finance are unavailable). This might give a negative connotation to new equity issues.
However, it is important to note that the pecking order theory indicates that raising new equity may be in
the best interest of existing owners if growth opportunities are high. By raising new equity companies may
invest in value-creating investment projects for which it is difficult to raise debt finance, such as additional
research and development projects, which would not materialize without raising new equity finance.
Hence, having no access to new equity finance would constrain future company growth.
2.4. Method and Descriptive Statistics
The empirical evidence of this paper is based on detailed yearly financial statement data of all Belgian
companies, as provided in the BEL-FIRST database (Bureau Van Dijk). All Belgian limited liability
companies, irrespective of their size, have to file detailed financial statement information. For each year
between 1997 and 2004, we select all firms that are (1) active in Flanders and Brussels (the two most
developed regions in Belgium comprising the majority of economic activity) and (2) employ at least ten
people (in order to exclude micro-companies and companies founded for non-economic purposes). Both
independent companies and companies that belong to a company group structure are included.
Additionally, both companies established within the time frame of this study and firms disappearing from
the database, because they failed or were taken over, are included. Thereby, we limit survivorship bias in
our study, which is an important advantage compared to the majority of other finance studies (see Cassar,
2004). This results in a data set of 32,754 companies active over at least some period during the time
frame of our study.
74

Belgium has a bank-based financial system. The most important source of external finance within firms is
bank debt. Issuing public debt is a rare event for Belgian companies. Furthermore, only a minority of
Belgian companies are quoted on the stock exchange. The private equity market, however, is quite well
developed compared to some other Continental European countries (Reynolds et al., 2000).
In what follows, we first discuss how high-growth companies are identified. Second, we develop the
measures of finance events. Finally, we discuss the independent variables.
2.4.1. Identifying High-Growth Companies
Prior organizational growth research is often criticized because it does not take into account the
multidimensional nature of growth. The classification of a company as a high-growth company depends
on the growth concept and growth formula used (Delmar, Davidsson, and Gartner, 2003). We explicitly
take into account the multidimensional nature of growth. First, we use different growth indicators, such as
sales, employees, total assets, cash flow and added value. The use of different concepts gives richer
information and is therefore better than the use of a single indicator (Weinzimmer, Nystrom, and Freeman,
1998). Second, we use both absolute and relative growth measures following Davidsson and Wiklund
(1999). While absolute growth measures tend to favor larger companies, relative growth measures tend to
favor smaller companies. Although compound measures have been developed to tackle this problem (e.g.,
the Birch index), these measures lack a conceptual basis (Davidsson and Wiklund, 1999).
We calculated the growth of all companies in our database using five growth indicators (sales, employees,
total assets, cash flow and added value) and two growth formulas (absolute and relative). Growth in each
year is measured as a moving average of the growth rate in the previous three years. From each of ten
yearly rankings (two growth formulas x five growth indicators), we selected the growth champions, i.e.,
the first percentile of growers. In order to be selected in our sample as a high-growth company, a company
75

had to be for at least two years among the first percentile 6 . More specifically, this means that a company
has to be minimum twice among the top 250 companies in Flanders and Brussels in one of ten growth
dimensions. 2,077 companies were selected in this way. Only 57 of the selected companies are quoted on
a stock exchange, the other high-growth companies are privately held companies.
The yearly absolute growth in added value is at least €5,256,179 for added value growers, the cash flow at
least €4,067,419 for cash flow growers, the total assets at least €52,269,000 for total asset growers, the
revenues at least €21,619,000 for revenue growers and 46 employees for employment growers. The cut off
yearly relative growth rate lies between 317% (for revenue growers) and 2406% (for added value
growers). These descriptive statistics clearly indicate that only top growth companies are considered.
Similar growth rates have been reported by Markman and Gartner (2002) when studying the link between
extraordinary growth and profitability in Inc. 500 high-growth companies.
Some descriptive statistics on the sample of high-growth companies are interesting in their own respect. In
line with the results of Delmar, Davidsson and Garter (2003) the growth concept and growth formula have
a profound impact on the companies selected as high-growth companies. The overlap between the
different types of high-growth companies is low, especially for relative growth. The highest overlap
between the different samples of high-growth companies is between absolute growth in added value and
absolute growth in revenues, which is 54%. The average age of the high-growth companies in the sample
is 22 years. However, there is a remarkable difference between absolute growers and relative growers with
the average age being respectively 28 and 16 years. Although the majority (71%) of high-growth
companies are in existence over the entire time frame of the study, 22% of the companies are founded in
1997 or later and 7% of the companies disappear due to company failure or a takeover.
6 We use a three-year moving average of the growth rate and require a company to be at least twice among the first percentile of
companies in order to exclude erratic or one-shot growth companies. The stability of the group of high-growth companies is an
interesting question in its own respect. Few companies are able to report high-growth rates over a long period of time, especially
when using relative growth rates. More details on the stability of the sample of high-growth companies may be found in Van
Eeckhout et al. (2006).
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Table 2.1 shows the sector distribution of the high-growth companies. It is noteworthy that high-growth
companies are active in all sectors of the economy, with a high prevalence in the transportation and
communication sector (31.49%), the building and civil engineering industry (23.83%) and in the
distributive trades, hotels, catering and repairs sector (13.34%). Moreover, around 80% of the high-growth
companies are active in industries that are generally considered low-tech. This finding confirms prior
growth research, which indicates that high-growth companies are not overrepresented in high-tech
industries, but rather exist in all industries and if anything appear overrepresented in services (Henrekson
and Johansson, 2009).
TABLE 2.1
Distribution of Sample Firms by Industry
0 Agriculture, hunting, forestry and fishing 14 0.67%
1 Energy and water 103 4.96%
2
Extraction and processing of non-energy producing minerals and
derived products; chemical industry
225 10.83%
3 Metal manufacture; mechanical and instrument engineering 87 4.19%
4 Other manufacturing industries 134 6.45%
5
Building and civil engineering 495
23.83%
6 Distributive trades, hotels, catering, repairs 277 13.34%
7 Transport and communication 654 31.49%
8 Banking and finance, insurance, business services, renting 27 1.30%
9 Other services 61 2.94%
TOTAL 2077
100.00%
77

2.4.2. Dependent Variables: Finance Events
The dependent incremental finance event variables are constructed following Marsh (1982). A first way to
finance projects is with retained earnings. Retained earnings represent inside equity created by profitable
companies (Frank and Goyal, 2005). When the retained earnings within a year exceed 5% of total assets,
we define this as an internal finance event. Second, companies are coded as using financial debt if there is
a yearly net increase of outstanding financial debt (both short-term and long-term), which exceeds 5% of
total assets. Finally, companies are coded as using new equity finance, when there is a net increase in
external equity of at least 5% of total assets 7 . The threshold value of 5% is well established in the
incremental finance literature and is used first to assure that the focus of the analyses is on relatively
substantial finance events and second to guarantee consistency with previous studies (Hovakimian, Opler
and Titman, 2001; de Haan and Hinloopen, 2003).
High-growth companies may use different types of finance together. As a result, finance events are not
mutually exclusive. When companies raise multiple types of finance together, even if the relative share of
debt and equity remains unchanged, these finance events are included in the analyses. Furthermore, all
finance events are measured on a yearly basis. Consequently, multiple finance events in one year are
coded as one large issue. This is appropriate as firms actively rebalance their capital structure on average
about once a year (Leary and Roberts, 2004). Finally, this approach makes that we study finance decisions
conditional upon companies’ finance needs, which is conventional in related studies (see for example, de
Haan and Hinloopen, 2003, pp. 667).
The descriptive statistics of the dependent discrete finance events are reported in Table 2.2. Each year,
more than half of the high-growth companies resort to internal finance or raise external finance. Over the
eight year frame of our study, 92% of the companies have at least one finance event and the median
7 Unfortunately, the identity of the finance provider(s) is not available. Hence, we are unable to make a distinction between
investors that previously offered finance to the company and new investors.
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number of finance events equals 4. This suggests that being able to attract sufficient financial resources
and high growth are interrelated.
Table 2.2 shows that financial debt is the most common financing route, accounting for almost 45% of the
finance events. This is interesting in light of prior studies claiming that high-growth companies seem to
consistently use less debt finance across time (Barclay et al., 2006). Internal finance is the second most
frequently used way to finance growth: nearly 39% of the finance events relate to the decision to retain
earnings. Despite the assumption of prior research that rapidly growing companies have insufficient
internal funds to finance their growth internally (Michaelas, Chittenden, and Poutziouris, 1999; Gompers,
1995), it remains the second most common financing route. In the later years of our study (2003-2004),
internal funds are even the most prevalent financing route. Finally, only 16% of the finance events relate
to raising external equity finance. However, nearly 44% of the companies in our sample raise at least once
outside equity finance. Consistent with Ou and Haynes (2006), who study the acquisition of external
equity capital by small companies, the descriptive statistics indicate that even for high-growth companies
the current emphasis on external equity finance in the literature may be somewhat overstated. Although
external equity is undeniably an important source of finance, almost 85% of the finance events relate to
retained earnings and financial debt.
Table 2.2 further shows the average size of the finance events (excluding the cases without a finance
event). The median size of internal finance events equals 15% of total assets, while the median debt issue
equals 14% of total assets. Consistent with previous research (Colombo and Grilli, 2007; Helwege and
Liang, 1996), the largest median issue size is for external equity issues with 21% of total assets. This
shows that although new equity is issued less often than debt, the average equity issue size is larger than
that of debt issues or internal funds.
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Percentage of
firms using:
TABLE 2.2
Sample Split according to Finance Type a
Internal finance Financial debt External Equity
% of firms in
sample with new
issue:
Number of
finance events
Number of firms in
sample:
1997 25.17% 33.59% 11.66% 55.03% 1021 1450
1998 25.61% 30.35% 11.59% 53.71% 1055 1562
1999 26.09% 32.57% 12.60% 56.21% 1188 1667
2000 25.33% 35.98% 12.36% 56.93% 1329 1804
2001 24.44% 31.48% 12.86% 55.50% 1289 1874
2002 24.27% 27.35% 10.04% 51.51% 1161 1883
2003 26.43% 24.67% 9.20% 50.83% 1127 1869
2004 30.01% 21.42% 6.61% 49.84% 913 1573
Total number of
finance events:
% of finance
events:
3538 4056 1489 9083
38.95% 44.66% 16.39% 100.00%
Issue size/Total
assets (%):
Median 14.70% 14.42% 21.20%
a Note that companies may issue several types of finance within one year.
2.4.3. Independent Variables
All independent variables are lagged one year in order to avoid problems of reverse causality. Where
appropriate, independent variables are scaled by total assets in order to standardize the variables and make
them comparable for companies with a different size. Furthermore, all the independent variables are
calculated using book values 8 . Below we define the independent and control variables.
As proxies for the amount of internal finance available within the venture, we use its profitability ratio,
measured as earnings on total assets and the amount of cash and marketable securities on total assets.
Finally, the pay-out ratio, measured as dividends on total assets, indicates lower internal finance. Debt
capacity is proxied by leverage and cash flow. Leverage is operationalized as a company’s debt ratio
(financial debt on total assets). Furthermore, we include a variable indicating if debt is greater than total
assets (negative stockholders’ equity dummy variable). Cash flow is operationalized by using the cash
8 We are mainly studying unquoted high-growth companies and hence market values are generally unavailable.
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flow ratio (i.e. internally generated cash flow on total assets), indicating a company’s ability to support
additional debt related payments.
We additionally include a number of control variables related to the static trade-off theory, which is the
main competing theoretical framework to the pecking order theory (Frank and Goyal, 2005). It is
interesting to include these variables in the model and see if the pecking order variables hold in a nested
model including the variables proposed by the static trade-off theory (Frank and Goyal, 2003). Under the
static trade-off theory tax shields, financial distress and agency costs are expected to determine finance
decisions. We include two types of tax shields, debt tax shields (interests on total assets) and non-debt tax
shields (depreciations on total assets). The expected cost of financial distress depends on the probability
of trouble and the value lost if trouble comes (Myers, 1984). Our proxy for the probability of financial
distress is the OJD-score, is similar to the Altman Z-statistic, but adapted to the Belgian context (Ooghe
and Van Wymeersch, 2003). A lower score indicates a higher risk of failure. Furthermore, we use asset
structure operationalized as the ratio of property, plant and equipment to total assets, as a proxy for the
cost of financial distress. A lower ratio indicates a higher cost of financial distress. Agency costs are
particularly prevalent in a setting characterized by considerable future growth options. Firms generally
engage in research and development to generate growth options (Titman and Wessels, 1988).
Consequently, we use the ratio of intangible assets on total assets to operationalize growth potential and
the corresponding agency costs.
Other general control variables, including organizational size (i.e., natural logarithm of total assets),
previous debt finance (i.e., dummy variable equal to 1 if the venture acquired debt financing in the
previous year, zero otherwise) and previous external equity finance (i.e., dummy variable equal to 1 if the
venture acquired external equity in the previous year, zero otherwise) are included in the model.
Furthermore, we included year and industry dummy variables in the analysis to control for time and
industry effects. Table 2.3 reports the correlations for the continuous independent and control variables.
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Noteworthy is the high correlation between the probability of financial distress (OJD-score) and the debt
ratio, which equals -0.61. All other correlations are sufficiently low and do not indicate potential
multicollinearity problems.
Internal finance:
Profitability ratiot-1
Cash and cash equivalentst-1
Pay-out ratio t-1
Debt capacity:
Debt ratio t-1
Cash flow ratio t-1
Control variables:
Tax shields
Debt tax shields t-1
Non-Debt tax shields t-1
Financial distress
Probability of distress (OJD) t-1
Cost of distress (Asset structure) t-1
Agency costs
Intangible assets t-1
(1) 1.000
TABLE 2.3
Correlation Matrix a
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
(2) -0.055 1.000
(3) 0.203 0.019 1.000
(4) -0.364 -0.017 -0.065 1.000
(5) 0.161 0.102 0.030 0.119 1.000
(6) -0.055 -0.162 -0.039 0.331 -0.083 1.000
(7) -0.138 0.028 -0.053 0.192 0.291 0.062 1.000
(8) 0.300 0.012 0.046 -0.609 0.042 -0.308 -0.027 1.000
(9) -0.082 -0.087 -0.080 0.164 0.066 0.071 0.497 -0.004 1.000
(10) -0.126 0.005 -0.033 0.068 0.015 -0.013 0.262 -0.026 -0.006 1.000
Size t-1 (11) 0.135 -0.369 0.117 -0.157 -0.285 0.298 -0.209 0.125 -0.140 -0.054 1.000
a Bold: Correlation is significant at the 0.05 level (2-tailed).
2.5. Empirical Results
2.5.1. Independent Variables by Issue Type
Table 2.4 shows the independent and control variables by issue type. While the average earnings on total
asset ratio is positive for companies resorting to internal finance, companies issuing external debt and
equity finance have on average negative earnings. This suggests that positive earnings replace bank loans
and new equity issues. The median amount of cash and cash equivalents on total assets is quite similar for
the different types of issuers. Companies issuing external finance have on average lower payout ratios
than companies resorting to internal finance.
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Companies issuing external finance have higher debt ratios compared to companies resorting to internal
finance. Particularly noteworthy is that more than 20% of external equity issues are related to companies
that had a negative book value of shareholders’ equity in the previous year 9 . This is a considerable group
of companies which previous studies in capital structure research sometimes explicitly remove from their
dataset (e.g. Heyman, Deloof, and Ooghe, 2008; Sogorb-Mira, 2005), leading to potentially biased results.
Furthermore, external equity issuers have much lower internally generated cash flow ratios compared to
debt issuers. These findings suggest that companies issuing new equity are closer to their debt capacity.
Some interesting observations occur with respect to the control variables related to financial distress and
intangible assets. Companies issuing external equity have a higher risk of failure compared to internal
finance users and debt issuers. Furthermore, on average external equity issuers hold less tangible assets,
which can serve as collateral, compared to debt issuers. Finally, companies issuing external equity finance
have the highest average ratio of intangible assets on total assets.
2.5.2. Multivariate Analyses
We start by addressing the question when high-growth companies utilize outside debt or equity finance.
For this purpose, we report logistic regression models for the choice between internal finance and external
finance (Table 2.5). Next, we address the use of external equity and report logistic regression models
analyzing the characteristics of companies utilizing external equity (Table 2.6). All reported models are
significant. The McFadden’s pseudo R² in the full models equal respectively 0.18 (Table 2.5-M5) and 0.12
(Table 2.6-M5).
9 In the entire sample less than 9% of all finance events are related to companies that have negative shareholders’ equity in the
previous year.
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Internal finance:
TABLE 2.4
Variables by Issue Type
Internal Finance (IF) Financial debt (FD) External equity (EE) Significance a
Median Mean S.D. Median Mean S.D. Median Mean S.D. IF-FD IF-EE FD-EE
(EARNINGS/TOTAL ASSETS)t-1 0.0412 0.0640 0.1463 0.0080 -0.0170 0.2164 0.0000 -0.0891 0.3142 ** ** **
(CASH AND EQUIVALENTS/TOTAL ASSETS) t-1 0.0156 0.0698 0.1349 0.0143 0.0556 0.1198 0.0141 0.0767 0.1613 ** **
(DIVIDENDS/TOTAL ASSETS) t-1 0.0000 0.0212 0.0614 0.0000 0.0111 0.0402 0.0000 0.0089 0.0332 ** ** †
Debt capacity:
(FINANCIAL DEBT/TOTAL ASSETS) t-1 0.5982 0.5532 0.3130 0.8003 0.7863 0.5664 0.7207 0.7184 0.6466 ** ** **
(Negative shareholders’ equity) t-1 0.0124 0.1152 0.2058 ** ** **
(INTERNALLY GENERATED CASH
FLOW/TOTAL ASSETS) t-1 0.2835 0.5275 0.7604 0.2202 0.4305 0.6722 0.0871 0.2723 0.5996 ** ** **
Control variables:
Tax Shields
(INTERESTS/TOTAL ASSETS) t-1 0.0051 0.0109 0.0147 0.0091 0.0155 0.0205 0.0046 0.0135 0.0218 ** ** **
(DEPRECIATIONS/TOTAL ASSETS) t-1 0.0184 0.0411 0.0569 0.0284 0.0512 0.0667 0.0160 0.0437 0.0661 ** **
Financial Distress
(OJD-SCORE) t-1 1.0200 1.2190 1.5480 0.3500 -0.1130 6.5410 0.2700 -0.7990 10.8810 ** ** *
(PROPERTY, PLANT AND EQUIPMENT/TOTAL
ASSETS) t-1 0.0494 0.1369 0.1999 0.1034 0.2223 0.2728 0.0450 0.1832 0.2669 ** ** **
Agency costs
(INTANGIBLE ASSETS/TOTAL ASSETS) t-1 0.0000 0.0147 0.0582 0.0000 0.0195 0.0710 0.0000 0.0277 0.0845 ** ** **
Ln(TOTAL ASSETS) t-1 10.6050 10.2060 2.7680 9.9554 9.7008 2.8614 9.9554 9.5220 3.2184 ** ** †
Previous debt financing 0.2340 0.4530 0.3509 ** ** **
Previous external equity financing 0.0707 0.1384 0.2928 ** ** **
a Where † indicates significant at 0.1, * significant at 0.05 and ** significant at 0.01 (t-tests). Non-parametric Mann-Whitney tests offer similar results, except that the
differences in median between cash and cash equivalents on total assets and dividends on total assets are not significantly different between the different finance issuers.

We correct for the dependence among finance outcomes for the same company. Consequently, we take
into account that the observations are not independent within companies. This affects the estimated
standard errors and variance-covariance matrix of the estimates (VCE). The Huber-White-Sandwich
estimator of variance is used in order to obtain robust variance estimates.
Table 2.5 reports logistic regression models with internal finance as the base outcome. We report several
model specifications, starting with control variables (M1), and gradually adding internal finance variables
(M2), debt capacity variables (M3), static trade-off variables excluding the probability of financial distress
(i.e. OJD-score) (M4) and the probability of financial distress (M5). The probability of financial distress is
included separately, because doing so reduces the sample size significantly. This is explained by the
presence of missing data needed to calculate this measure.
Supporting hypothesis 1, all model specifications indicate that more profitable companies prefer to finance
investments internally 10 . This evidence suggests the existence of a possible substitution between internal
and outside finance. This finding is consistent with prior capital structure research on privately held
companies reporting a negative relationship between profitability and the debt ratio (for such evidence see
Heyman, Deloof, and Ooghe, 2008; Sogorb-Mira, 2005). Companies with more cash and marketable
securities are less likely to issue outside finance according to specifications (M2) to (M4), but are more
likely to issue outside financing according to specification (M5) 11 . The ratio of dividends on total assets
never shows a significant coefficient.
10 The interpretation of the estimated regression coefficients in the fitted logistic models is not the straightforward interpretation
of the parameters in a linear regression model (see Neter, Kutner, Nachtsheim and Wasserman, 1996). In the following, we focus
on the sign of the estimated coefficients. As an alternative, one might also report the estimated odds ratios, which are obtained by
taking the exponent of the coefficients. In model 5-5, for earnings on total assets, the odds of a company issuing outside finance is
0.2516 (exp(-1.38) = 0.2516) times higher or alternatively 3.9749 (1/0.2516) times lower for a one-percentage point increase in
earnings on total assets.
11 The mean ratio of cash and cash equivalents on total assets is lower for debt issuers compared to companies resorting to equity
finance (Table 2.4). The difference in the mean ratio of cash and cash equivalents on total assets for debt issuers compared to
equity issuers is more pronounced when excluding these cases where data on financial risk is not available (not reported). This
indicates that the change in sign may be due to a relatively larger number of companies with higher cash and cash equivalents
issuing debt finance that are dropped because of missing values when including the financial risk measure.
85

Internal finance:
(EARNINGS/TOTAL ASSETS)t-1
TABLE 2.5
Logistic Regression of the Determinants of Outside Finance Use
M1 M2 M3 M4 M5
(CASH AND EQUIVALENTS/TOTAL ASSETS) t-1
(DIVIDENDS/TOTAL ASSETS) t-1
Debt capacity:
(FINANCIAL DEBT/TOTAL ASSETS) t-1
(Negative shareholders’ equity) t-1
(INTERNALLY GENERATED CASH FLOW/TOTAL ASSETS) t-1
(FINANCIAL DEBT/TOTAL ASSETS) t-1 x (INTERNALLY GENERATED
CASH FLOW/TOTAL ASSETS) t-1
Control variables:
Tax Shields
(INTERESTS/TOTAL ASSETS) t-1
(DEPRECIATIONS/TOTAL ASSETS) t-1
Financial distress
(OJD-SCORE) t-1
(PROPERTY, PLANT AND EQUIPMENT/TOTAL ASSETS) t-1
Agency costs
(INTANGIBLE ASSETS/TOTAL ASSETS) t-1
Ln(TOTAL ASSETS) t-1
Previous debt finance
Previous external equity finance
Year dummies
Industry dummies
Coef. Coef. Coef. Coef. Coef.
-3.82 ** -2.71 ** -2.69 ** -1.38 **
-1.10 ** -0.87 ** -0.67 ** 1.62 **
-0.11 0.07 0.42 0.25
1.71 ** 1.50 ** 0.64 **
1.18 ** 1.12 ** 0.94 **
-0.42 ** -0.42 ** -0.27 **
0.91 ** 1.06 ** 0.40
7.67 ** 7.84 **
-0.79 -2.36 *
-0.59 **
0.87 ** 1.23 **
0.65 1.89 *
-0.09 ** -0.09 ** -0.89 ** -0.09 ** -0.01
0.94 ** 0.78 ** 0.51 ** 0.50 ** 0.48 **
1.10 ** 0.86 ** 1.09 ** 1.07 ** 0.70 **
Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes
Number of obs. 8067 7995 7951 7896 5962
Prob. 0.00 0.00 0.00 0.00 0.00
Pseudo R² 0.07 0.10 0.15 0.15 0.18
Results indicate that companies using internal finance have a higher debt capacity (M3 to M5). Companies
with lower debt ratios, positive stockholders’ equity or higher cash flow are more likely to resort to
internal finance. These findings are contrary to the predications of the static trade-off theory. One would
expect companies with a lot of debt capacity to attract financial debt. This would allow profitable
companies to benefit from the tax advantage of additional interest payments. Consistent with Lemmon and
Zender (2004), we find evidence that profitable high-growth companies with a lot of debt capacity prefer
86

to retain their debt capacity. This evidence again points to a substitution between outside finance and
internal finance.
When examining the control variables it is noteworthy that high-risk companies, as reported in
specification (M5), are more likely to attract outside finance. Companies that have more tangible assets,
which may serve as collateral, are more likely to attract outside finance (M4 and M5). We find marginal
evidence that high-growth companies with investments in intangible assets are more likely to issue outside
finance (M5). Companies that previously issued debt or equity finance are more likely to issue outside
finance again in the future. This is strongly supported in all model specifications.
Table 2.6 reports logistic regression models exploring the determinants of external equity issuers. We
report several model specifications, starting with control variables (M1), and gradually adding internal
finance variables (M2), debt capacity variables (M3), static trade-off variables excluding the probability of
financial distress (M4) and the probability of financial distress (M5).
The results consistently show that less profitable companies are more likely to finance new investments
with external equity. External equity issuers do not differ significantly in terms of cash and cash
equivalents from non-external equity issuers in model specifications (M2) to (M4), but are more likely to
issue additional equity in model specification (M5). The negative coefficient of the debt ratio variable
indicates that companies with higher debt ratios are less likely to issue new equity (M2 to M5). However,
high-growth companies with extremely high debt ratios as proxied by the negative stockholders’ equity
dummy are more likely to issue external equity (M3 to M5). Furthermore, companies with more cash
flows, which are required to fulfill the fixed debt-related payments, have a lower probability of issuing
external equity. We also included an interaction term between debt and cash flow in the model. Graphical
representation of the interaction term indicates that as cash flow increases (and hence the ability to repay
fixed debt-related payments increases), companies are less likely to raise external equity, but this decrease
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in the probability of raising external equity is smaller for companies that have higher debt ratios (M3 to
M5) 12 . Overall, these findings support for hypothesis 2.
Looking at the control variables proposed by the static trade-off theory we find no significant impact of
debt and non-debt tax shields on the decision to issue new equity (M4 and M5). In line with the static
trade-off theory, companies with a high risk of failure are more likely to attract external equity finance
(M5). This finding is consistent with the results from Baum and Silverman (2004) and indicates that
private equity investors are investing in companies on the edge of short-term failure. Although companies
with more tangible assets are more likely to issue outside funding (Table 2.5), the level of tangible assets
does not discriminate between new equity issuers and non-issuers (Table 2.6, M4 and M5). The results
strongly support the idea that companies with more intangible assets are more likely to fund their
investment projects with external equity finance rather than with debt finance or internal equity (M4 and
M5). This last finding offers further evidence that the debt capacity of growth options may not only be
lower compared to the debt capacity of assets in place, but may actually be negative (Barclay et al., 2006).
Finally, it is worth noting the coefficients of the previous debt and external equity finance dummies.
Similar to Helwege and Liang (1996) and de Haan and Hinloopen (2003), the results show that companies
previously issuing external equity finance are more likely to do so in the future (M1 to M5). However,
previous debt finance has no significant impact on the probability of subsequently raising new equity.
These results indicate the existence of persistence in the finance process, which may be due to a learning
effect in the search for finance (e.g. de Haan and Hinloopen, 2003; Jansson, 2002).
12 Graphical representation of the interaction term in logit models as advocated by Norton, Wang and Ai (2004) is available from
the authors upon request.
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Internal finance:
(EARNINGS/TOTAL ASSETS)t-1
TABLE 2.6
Logistic Regression of the Determinants of External Equity Use
(CASH AND EQUIVALENTS/TOTAL ASSETS) t-1
(DIVIDENDS/TOTAL ASSETS) t-1
Debt capacity:
(FINANCIAL DEBT/TOTAL ASSETS) t-1
(Negative shareholders’ equity) t-1
(INTERNALLY GENERATED CASH FLOW/TOTAL ASSETS) t-1
(FINANCIAL DEBT/TOTAL ASSETS) t-1 x (INTERNALLY GENERATED
CASH FLOW/TOTAL ASSETS) t-1
Control variables:
Tax Shields
(INTERESTS/TOTAL ASSETS) t-1
(DEPRECIATIONS/TOTAL ASSETS) t-1
Financial distress
(OJD-SCORE) t-1
(PROPERTY, PLANT AND EQUIPMENT/TOTAL ASSETS) t-1
Agency costs
(INTANGIBLE ASSETS/TOTAL ASSETS) t-1
Ln(TOTAL ASSETS) t-1
M1 M2 M3 M4 M5
Coef.
Coef.
Coef. Coef. Coef.
-1.63 ** -1.12 ** -1.25 ** -0.92 **
0.22
-1.34
0.36 0.41
-0.71 -0.52
0.82 *
-0.76
-0.31 ** -0.24 † -0.43 *
1.47 ** 1.40 ** 1.64 **
-0.46 ** -0.45 ** -0.28 **
0.50 ** 0.71 ** 0.59 *
1.71
-1.35
0.15
-0.96
-1.41
-0.09 *
0.19
1.47 ** 1.91 **
-0.06 ** -0.03 † -0.03 * -0.03 * 0.02
Previous debt finance 0.01 -0.09 -0.10 -0.12 † -0.03
Previous external equity finance 1.28 ** 1.09 ** 1.04 ** 1.02 ** 1.05 **
Year dummies Yes
Industry dummies Yes
2.5.3. Robustness Checks
Yes
Yes
Yes
Yes
Yes
Yes
Yes Yes
Number of obs. 8067 7995 7951 7896 5962
Prob. 0.00 0.00 0.00 0.00 0.00
Pseudo R² 0.05 0.07 0.10 0.11 0.12
We first replaced the separate logistic regression models with a multinomial logit model, which has three
possible outcomes: internal finance, financial debt and new equity finance. This model offered similar
results to those of the separate logistic regression models reported in this paper.
89

Additional analyses on smaller sub-samples have been conducted to test for the robustness of the main
results. We conducted separate analyses for the companies that were in the database over the total
observation period (thereby excluding failed companies and companies that are no longer independent),
and we contrasted small versus large high-growth companies (median split on total assets) and absolute
versus relative high-growth companies. We also excluded the 57 public high-growth companies from the
analyses. Finally, left censoring of a population’s history requires researchers to the cautious in drawing
inferences especially about the effects of aging (Aldrich, 1999). Remedial measures include the collection
of additional data from before the timeframe of this study or exclude the older companies for which we
lack data. We chose for the last option and selected a sample of younger companies where left censoring
should be less problematic. Results remained qualitatively similar. First, less profitable companies are
more likely to issue outside financing and prefer to retain debt capacity. Second, companies with limited
debt capacity are more likely to issue external equity.
Removing the companies with negative shareholder’s equity (i.e., companies with excessive debt levels)
from the analyses slightly changes the results in favor of the traditional pecking order theory. High-growth
companies end up with high debt ratios. Companies with lower cash flow ratios, however, remain more
likely to issue outside equity finance. These results confirm that debt capacity is not only determined by
the level of debt, but also by the capacity to carry additional debt-related payments. This finding shows
that removing companies with negative equity from the sample may bias results.
2.6. Discussion and Conclusions
Financial capital is one of the key resources companies require to support their growth. This paper
researches financial decision making in high-growth companies. Although previous research indicates that
financial management is of paramount importance in a high-growth setting, little is known about financial
policy of high-growth companies. Previous studies focused mainly on professional venture capital and
90

more broadly new equity issues as a key finance option for high-growth companies, ignoring other
frequently used alternatives. Using the pecking order theory, we provide testable hypotheses of finance
behavior in a high-growth setting. Our results are based on a large longitudinal dataset of finance
decisions made by high-growth companies over an eight year period. Startups and companies disappearing
from our database, because of company failure or mergers and acquisitions, are included.
Our results are consistent with the extended pecking order theory, taking into account constraints due to
limited debt capacity. More profitable companies prefer to finance investments internally. Companies
using internal finance stockpile debt capacity and are less likely to attract debt finance. Companies with
excessive leverage and lower cash flow ratios are more likely to issue outside equity finance. It is striking
that stockholders’ equity is negative in more than 20% of the companies in the year prior to getting
external equity. Companies issuing external equity may therefore be unable to attract more debt finance
given the excessive leverage and difficulty to support additional debt-related payments. Companies with a
high risk of failure are more likely to issue new equity. Finally, companies that invest more heavily in
intangible assets are more likely to attract additional external equity.
Together these results indicate that retained earnings will gradually replace outside finance when possible.
Bank debt is primarily used by low risk companies, while external equity is used by high-risk companies.
These findings are consistent with prior research focusing on financial decisions in private equity versus
non-private equity backed firms in Belgium (Baeyens and Manigart, 2005). External equity is particularly
important for unprofitable companies, companies investing more heavily in intangible assets, companies
with high debt levels and limited cash flows or companies subject to high risks of failure. Consequently,
new equity is crucial as it allows some high-growth companies to undertake investments and grow beyond
their debt capacity.
91

Our results are important for academics. External equity finance is undeniably important for high-growth
companies needing finance beyond their debt capacity. However, contrary to the current focus in the
literature on external equity finance for high-growth companies, internal finance and financial debt are the
most frequently used finance alternatives. Pecking order proponents are currently developing and
integrating the notion of debt capacity in the standard pecking order theory. Contrary to assumptions in
prior studies (e.g., Fama and French, 2005), we claim that debt capacity is not only determined by
leverage, but also by the capacity to fulfill fixed debt-related payments. Hence, even companies with low
leverage may be unable to attract more debt finance.
Despite its contributions, this research is not without limitations. First, we study finance decisions within
companies that realized high-growth over the timeframe of our study. As a result, we are unable to identity
differences in financial policies between high- and low-growth companies. Hence, while this study
indicates that growth and finance are interrelated, we do not provide insight into the questions whether
access to finance causes growth or whether it is growth that requires finance. Second, similar to the
majority of other studies on company finance, we focus on the finance obtained. Therefore, we are unable
to answer questions such as: What does the search for financing look like? Do company managers prefer
to target one or a few investors, or do they target multiple potential investors? How does the initial search
for finance influence the subsequent search for finance? Our results do hint that there is persistence in the
acquisition of financial resources. Third, we focus on the decision of managers to use retained earnings,
financial debt or new equity. We do not focus on the amount of finance managers attract. This would be a
valuable avenue for further research. Finally, there are other potentially important sources of finance, such
as trade credit and subsidy finance, on which we decided not to focus. The need to focus on one (or in
exceptional cases, two) sources of finance is directly attributable to theoretical tractability (i.e. one cannot
model everything). We cover the key finance alternatives advanced by the pecking order theory, which is
a broader coverage compared to what one typically finds in the literature and leave it up to further
research to examine the role of other finance alternatives in high-growth companies.
92

Our results are also important for management practice. By studying the finance behavior of high-growth
companies, entrepreneurs can gain a more thorough insight in the financial policies related to high growth.
Our results are important for policymakers as well. Our research indicates the importance of retained
earnings in the financing of investment projects by high-growth companies. Debt finance, however, has an
important advantage compared to equity finance under the corporate tax system in most developed
countries: while the interests that companies pay are tax-deductible expenses, dividends and retained
earnings are not. The Belgian government recently implemented a unique tax incentive that reduces the
fiscal discrimination between debt and equity finance. This incentive, called ‘notional interest deduction’
allows companies subject to Belgian corporate tax to deduct from their taxable income an amount equal to
the interest they would have paid on their capital (corrected equity capital more specifically, including
capital and carry-forward profits, among other) if that capital was long-term debt finance. Given our
research findings, tax incentives, such as the notional interest deduction, which increase the incentive to
retain earnings within companies, are well taken.
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Chapter 3: Seeking Experienced or Legitimate Partners? A
Longitudinal Study on the Impact of Venture Capital Firm
Heterogeneity on Portfolio Company Growth
Tom Vanacker
Ghent University, Department of Accounting and Corporate Finance, Kuiperskaai 55E, 9000 Gent,
Belgium; TomR.Vanacker@UGent.be
I thank Sophie Manigart, Hans Landström, Harry Sapienza and Mirjam Knockaert for helpful comments. This paper
received one of the awards reserved for top papers presented by doctoral students at the 2009 meetings of the
Midwest Finance Association (Chicago, March 4-7) and is selected for presentation at the 2009 Annual Meeting of
the Academy of Management (Chicago, August 7-11). A prior version of this paper was presented at the 2008
Babson College Entrepreneurship Research Conference (The University of North Carolina at Chapel Hill, US) and is
published in the 2008 edition of Frontiers of Entrepreneurship Research. The financial support of the
Intercollegiate Center for Management Science (I.C.M.) and Impulsfonds are gratefully acknowledged.
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3.1. Abstract
Although scholars agree that not all partners are equal, it remains unclear whether companies will
particularly benefit from forming relationships with more experienced or more legitimate firms. This
study examines the impact of venture capital firm experience and legitimacy on portfolio company
growth. For this purpose, I track 94 companies forming initial investment relationships with venture
capital firms for up to five years after the initial investment. Linear Mixed Models (LMMs) are used to
gain more insight into the non-linear growth trajectories of portfolio companies. Findings indicate that
both companies backed by venture capital firms with more industry experience, but not overall
experience, and companies backed by more legitimate venture capital firms exhibit higher growth curves.
3.2. Introduction
The lack of established working relationships, social approval, tested routines, and the resulting high risk
of failure make resource mobilization a key challenge and a process fraught with difficulties within young
and small entrepreneurial ventures (Stinchcombe, 1965). Nevertheless, the mobilization of sufficient
external resources is critical and ventures which are able to mobilize more strategic resources at startup
are likely to develop a competitive advantage over their resource-constrained peers (Lee, Lee and
Pennings, 2001). Interorganizational relationships have become an attractive way to obtain resources
(Baum, Calabrese and Silverman, 2000). Hence, these relationships are likely to be particularly beneficial
to young, resource-constrained ventures (Stuart, Hoang and Hybels, 1999; Stuart, 2000). This study
focuses on investment relationships between professional venture capital firms and their portfolio
companies as a representative form of interorganizational relationships (Hallen, 2008) and studies the
impact of venture capital firm heterogeneity on portfolio company growth.
Although scholars have recognized that certain relationships are more valuable than others, it remains
unclear which type of partners contribute most to the development of young entrepreneurial ventures
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(Rindova, Williamson, Petkova and Sever, 2005). I focus on learning and signaling theory, which stress
different mechanisms through which partners might influence venture development. Organizational
learning theory stresses the importance of accumulated experience of partners through past actions (Hoang
and Rothaermel, 2005). Firms are likely to learn how to identify and nurture promising ventures through
repeated interactions with these ventures (Cohen and Levinthal, 1990) and are likely to develop a
repertoire of proven routines (Nelson and Winter, 1982). Hence, partners will become more effective and
efficient in their tasks as they accumulate experience. Signaling theories stress the transfer of legitimacy
from more established partners to ventures that lack legitimacy in the marketplace (Stuart, Hoang and
Hybels, 1999). In signaling theories it is the information exchange and social influence, which results
from affiliating with more legitimate partners, that changes the perception of stakeholders about venture
quality and makes them more likely to transact with an informationally opaque venture (Rao, 1994;
Pollock and Rindova, 2003). The goal of this study is to examine whether entrepreneurial companies
particularly benefit from forming investment relationships with more experienced or more legitimate
venture capital firms.
Empirical work on venture capital firm heterogeneity has almost exclusively put venture capital firms in
the foreground by focusing on venture capital fund performance and put portfolio companies in the
background (Dimov and Shepherd, 2005; Dimov and De Clercq, 2006; Sorensen, 2007; Hochberg,
Ljungqvist and Lu, 2007; Bottazzi, Da Rin and Hellmann, 2007). Nearly all our knowledge on the
consequences of venture capital firm heterogeneity comes from the proportion of initial public offerings
(IPOs), mergers and acquisitions (M&As) and failures in the portfolios of venture capital funds. It is
assumed that IPOs are the most lucrative exit, while M&As are only a second-best option (Berger and
Udell, 1998). This raises two concerns. First, for venture capital firms to be able to exit from promising
companies through an IPO, an active stock market is required, which is generally not the case in bank-
based financial systems, including most Continental European countries (Black and Gilson, 1998).
Second, M&As do not distinguish between successful and unsuccessful companies, as it represents a
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common exit route both for very promising companies and less promising ones (Schwienbacher, 2002;
Graebner and Eisenhardt, 2004).
More importantly, studying organizational processes from different perspectives may offer different
insights (Van de Ven, 2007). A successful exit from the perspective of the venture capital firm may not
always be successful from the perspective of the portfolio company. Exits by venture capital firms bear
risks for entrepreneurs, like losing control and major changes in board composition (Schwienbacher,
2002). While some entrepreneurs may have an active interest in selling their companies others may show
strong opposition to an acquisition (Graebner and Eisenhardt, 2004). Additionally, venture capital firms
sometimes have perverse incentives and act in their self-interest. Gompers (1996) shows how young
venture capital firms bring their portfolio companies to the market prematurely in order to establish
legitimacy. Thereby these young venture capital firms do not maximize the value of the IPO from the
perspective of their portfolio companies and additionally may put a serious burden on these less mature
companies, as the costs of maintaining stock exchange listings are very high (Berger and Udell, 1998).
This study departs from extant research on venture capital firm heterogeneity which is generally interested
in fund performance and studies the impact of investor heterogeneity from the perspective of the portfolio
company thereby focusing on portfolio company growth or its ability to accumulate key resources.
I use a unique longitudinal database, free of survivorship bias, tracking 94 Belgian venture capital backed
companies for up to five years after the initial venture capital investment. LMMs are used as an
appropriate longitudinal technique to model the dynamic (non-linear) nature of growth. This is an
important methodological contribution to organizational growth research that typically measures growth
as the difference in size between two points in time, thereby ignoring development in-between these two
points (Weinzimmer, Nystrom and Freeman, 1998; Delmar, Davidsson and Gartner, 2003). Results
demonstrate how companies which connect with venture capital firms with more industry experience, but
not overall experience, and companies which connect with more legitimate venture capital firms exhibit
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higher growth paths both in employment and total assets. It is unlikely that only selection is driving these
results. I find evidence indicating that more experienced and legitimate venture capital firms also provide
superior value-added services to their portfolio companies and provide stronger signals to outside
stakeholders which benefits venture growth.
The rest of the paper is organized as follows. I first present a theoretical framework on the impact of
venture capital firm experience and legitimacy on portfolio company growth and develop specific
hypotheses. Next, I outline the methods, including the sample, measures and method of analysis. Then, I
present the main research findings. Finally, I conclude by discussing the results from both a theoretical
and practical perspective.
3.3. Theory and Hypotheses
Relationships with venture capital firms are one of the earliest and most critical relationships formed,
especially within growth-oriented entrepreneurial ventures requiring quick access to a variety of resources
(Katila, Rosenberger and Eisenhardt, 2008). Venture capital firms not only contribute well-needed
financial resources to their portfolio companies, but generally also contribute knowledge-based resources
like advice, referrals for executive hires and industry connections (Sapienza, Manigart and Vermeir,
1996). Most prior research has treated venture capital firm participation as a dummy variable (Hsu, 2004),
thereby assuming that all venture capital firms have equal capacity to source high quality companies and
equal ability to nurture companies through active involvement (Megginson and Weiss, 1991; Kortum and
Lerner, 2000; Hellmann and Puri, 2002; Davila, Foster and Gupta, 2003; Baum and Silverman, 2004).
Nevertheless, venture capital firms exhibit significant heterogeneity both in their selection behavior
(Muzyka, Birley and Leleux, 1996) and post-investment assistance to their portfolio companies (Elango,
Fried, Hisrich and Polonchek, 1995). Grounded in learning and signaling theory, I develop testable
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hypotheses on the role of venture capital firm experience and legitimacy for portfolio company
development. Experience and legitimacy constitute two kinds of intangible resources playing a central role
in the venture capital industry. Their impact on the growth path of portfolio companies is discussed in
more detail below.
3.3.1. Venture Capital Firm Experience and Portfolio Company Growth
Learning theory indicates that firms learn how to manage relationships through repeated engagements
with other ventures. Firms are likely to absorb and accumulate knowledge through prior relationship
formation (Cohen and Levinthal, 1990). Firms are also likely to develop routines based on past
experiences (Nelson and Winter, 1982). The routines that become part of an organization’s repertoire are
those that previously produced favorable outcomes (Levinthal and March, 1993). Moreover, the
application of routines increases their efficiency and the likelihood of a desirable outcome (Levitt and
March, 1988).
As venture capital firms gain experience they may become more capable at selecting the best ventures.
The decision accuracy of venture capitalists increases with experience, although too much experience is
not necessarily beneficial as experienced decision-makers may get trapped in their current modes of
thought (Shepherd, Zacharakis and Baron, 2003). Despite the potential downside of having too much
experience for decision-making accuracy, Sorensen (2007) demonstrates the existence of a monotonically
increasing relationship between overall venture capital firm experience and the probability of portfolio
companies going public 13 . The main driver behind the results is investor selection; more experienced
venture capital firms invest in better companies (Sorensen, 2007).
13 It should be noted that the precise shape of the relationship between experience and company growth -that is, whether it is
monotonically increasing or an inverted U-shape- is a separate empirical issue. A priori, I would expect the monotonically
increasing relationship to materialize in our research setting, as the Belgian venture capital industry although developed is still
significantly less mature than its U.S. and U.K. equivalents. Hence, it is unlikely that Belgian venture capital firms already moved
down to the flatter part of the learning curve (see more on this issue later).
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The accumulated experience may not only influence venture capital firms’ ability to select more
promising ventures, but may also contribute to the quality of the extra-financial, knowledge-based
resources they offer to their portfolio companies (Baum and Silverman, 2004). Venture capital firms
typically play an important role in monitoring the management and progress of their portfolio companies
(Lerner, 1995; Fried, Bruton and Hisrich, 1998) and often help their portfolio companies with
professionalizing, for example, by influencing the structure and experience of the management team
(Hellmann and Puri, 2002). The effectiveness of post-investment management is likely to be dependent
upon venture capital firm ability (Dimov and De Clercq, 2006). Overall, both the expected ability of more
experienced venture capital firms to source high quality companies and their ability to nurture companies
through active involvement lead to the following hypothesis:
Hypothesis 1A: Companies backed by venture capital firms with high overall experience will exhibit
steeper growth curves compared to companies backed by venture capital firms with low overall
experience.
Not all experience is the same, however, and learning performance is expected to be the greatest when the
object of learning is related to what is already known (Cohen and Levinthal, 1990). Scholars have argued
that it is difficult to learn from experience when performing heterogeneous tasks (Zollo and Winter, 2002).
Extending this idea, it may be difficult for venture capital firms to learn from prior experience if they form
investment relationships with very different types of companies. Alternatively, venture capital firms
investing in a more homogenous group of companies may maximally benefit from learning curve effects
through the accumulation of superior knowledge. An important specialization strategy for venture capital
firms is to invest in only one or a few industries (Norton and Tenenbaum, 1993).
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There are several ways through which specialized industry expertise held by venture capital firms may
positively influence portfolio company growth. First, specialized industry expertise may allow for a better
understanding of the intricacies associated with investing in particular industries. This may facilitate the
selection of more promising ventures, as it is not because venture capital firms are good in selecting
companies with high potential in for example the wholesale industry, that they will be equally able to do
so in the biotechnology industry. Second, increased industry expertise may also benefit specialized
venture capital firms in performing their governance role and value adding activities (Sapienza, Manigart
and Vermeir, 1996). Venture capital firms with high industry deal experience may be better connected, for
example, drawing on a greater number of contacts with relevant suppliers, customers, investors and
managers for their portfolio companies (Hochberg, Ljungqvist and Lu, 2007). This leads to the following
hypothesis:
Hypothesis 1B: Companies backed by venture capital firms with high industry experience will exhibit
steeper growth curves compared to companies backed by venture capital firms with low industry
experience.
3.3.2. Venture Capital Firm Legitimacy and Portfolio Company Growth
Signaling theory indicates that company stakeholders, like customers, suppliers, employees and investors -
especially those that are risk averse- will be more likely to transact with entrepreneurial ventures after they
have been endorsed by established firms (Stuart, Hoang and Hybels, 1999). Davila, Foster, and Gupta
(2003) illustrate that venture capital firms as a group signal company quality to the labor market and
thereby influence the ease with which companies attract key employees. Janney and Folta (2003) show
how in a sample of publicly quoted technology companies, private equity placements send positive signals
to the market and increase company value. This effect more pronounced when these quoted companies
raise finance from more established investors (Janney and Folta, 2006). It indicates that the decision to
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offer finance by venture capital investors informs stakeholders about company quality and this manifests
itself in increased growth.
Not all venture capital firms are likely to provide credible signals as many venture capital firms still need
to establish legitimacy in the marketplace themselves (Gompers, 1996). When entrepreneurial companies
connect with partners that lack legitimacy it is unlikely that this will change the perception of outsiders in
any significant way. However, the legitimacy of more established venture capital firms is likely to transfer
to their portfolio companies that lack legitimacy in the marketplace (Stuart, Hoang and Hybels, 1999;
Stuart, 2000). This should allow these portfolio companies to mobilize more resources from key
stakeholders across time which should contribute to company growth. Accordingly, I put forth the
following hypothesis:
Hypothesis 2: Companies backed by more legitimate venture capital firms will exhibit steeper growth
curves compared to companies backed by less legitimate venture capital firms.
3.4. Data and Method
3.4.1. Sample
This study partially builds on a database provided by the Belgian Venture Capital and Private Equity
Association linking venture capital backed companies to their lead investors. I track 94 companies that
received initial venture capital finance between 1999 and 2003. Deals are only selected until 2003 in order
to have at least three-year financial figures for the companies selected at the end of this timeframe, as the
last financial figures available at the time of data collection were those of 2006.
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For each portfolio company I collected detailed yearly financial statement data for up to five years after
the initial venture capital investment. This was possible as all Belgian limited liability companies,
irrespective of their size, are required to file financial statements with the National Bank. It offered 487
firm year observations. The average age of the portfolio companies at baseline (i.e. the year of the initial
venture capital firm investment) equals 3.46 years, with a minimum of zero and maximum of 15 years. At
baseline, the average company employs 9.78 people and has 3,905,500 euro of assets. Some 60% of the
companies in the sample are active in four sectors, namely computer and related activities (24%),
biotechnology (12%), manufacturing (11%) and wholesale (11%).
In order to collect data on the lead venture capital firm providing initial venture capital, I combined
multiple sources including the Zephyr database (a database of private equity deals with a special focus on
pan-European transactions), the Belgian Venture Capital and Private Equity Association database and
trade directories. The lead venture capital firms providing initial finance range from small venture capital
firms with only six million euro of assets under management to venture capital firms with more than one
billion euro of assets under management. The majority of lead investors offering initial finance to Belgian
companies are domestic investors. Only two companies raised initial finance from international venture
capital funds.
3.4.2. Measures
Dependent variable. Organizational scholars argue that growth studies should be longitudinal because of
the dynamic nature of growth (Weinzimmer, Nystrom and Freeman, 1998). I study the temporal growth
pattern of venture capital backed companies from the year of investment relationship formation up to five
years after the initial investment. This is important as the typical lifespan of venture capital investments is
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around three to five years (Zarutskie, 2007). Furthermore, a five-year period has been the time frame most
widely used in prior organizational growth studies (Weinzimmer, Nystrom and Freeman, 1998).
Growth is multidimensional in nature and hence the classification of a company as a high growth
company depends on the growth concept and growth formula used (Delmar, Davidsson and Gartner,
2003). I take into account the multidimensional nature of growth by using two different growth indicators.
Venture capital investors typically invest in companies which require large investments in employment
and total assets but without immediate sale prospects, even in low-tech industries (Puri and Zarutskie,
2008). This explains my choice to study growth in employment (in full time equivalents) and growth in
total assets. I decided to focus on absolute changes in employment and total assets. Relative measures are
not as suitable in the current research setting, since many variables may have a value equal or close to zero
during the initial stage of venture capital firm involvement (Baum, Calabrese and Silverman, 2000).
Independent variables. The key independent variables are correlates of experience and legitimacy of the
lead venture capital firm measured at baseline. Overall experience is operationalized as the total number
of investments made by the venture capital firm prior to the focal investment (Gompers, Kovner, Lerner
and Scharfstein, 2008; Sorensen, 2007). It includes both the number of investments prior to the focal
investment within the timeframe of this study (i.e., 1999-2003) and the number of investments made by
the venture capital firm before this timeframe. Overall deal experience ranges from 1 to 90 investments
with a median value of 9 investments. Industry experience, is constructed similarly to overall experience,
but only examines investments in the same industry (2-digit industry code) as the focal company
(Gompers, Kovner, Lerner and Scharfstein, 2008; Sorenson and Stuart, 2001). Industry deal experience
ranges from 1 to 26 investments with a median value of 2 investments. The natural logarithm of overall
and industry experience was used in subsequent analysis because doing so captured the decreasing
marginal returns that experiential learning is subject to (Pennings, Barkema and Douma, 1994; Hoang and
Rothaermel, 2005). Similar results were obtained using the non-transformed measures.
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As a proxy for venture capital firm legitimacy I construct a dummy variable which equals one when a
venture capital firm is older than seven years (median value) and zero otherwise 14 . Older venture capital
firms are like to be more legitimate compared to younger venture capital firms for at least two reasons.
First, when venture capital firms invest it takes several years before the first results of the initial
investments can be observed by outsiders (Zarutskie, 2007). Contrary to older venture capital firms with
rich historical backgrounds, younger venture capital firms typically lack a track record of past
performance. Second, many venture capital firms periodically raise follow-on funds to remain active in
venture capital financing and firms generally have two or three overlapping funds each starting three to six
years after the previous fund (Gompers and Lerner, 1996). Hence, older venture capital firms are likely to
have demonstrated they conform to the generally accepted industry norms and practices, which should
increase their legitimacy in the marketplace (Oliver, 1997).
In the above, experience and legitimacy are portrayed as two independent constructs. While the
experience of a venture capital firm reflects its ability to conduct its tasks better (i.e., being good), the
legitimacy of a venture capital firm reflects its collective recognition in the eyes of outsiders including the
financial community (i.e., being known). Nevertheless experience and legitimacy are likely to be related.
Venture capital firms that accumulate experience in the venture capital industry are likely to develop
legitimacy within the industry, and vice versa, legitimate venture capital firms are likely to have
accumulated experience over time. Hence, empirically the two constructs will be related and are not fully
orthogonal.
In order to alleviate the concern that venture capital firm age reflects accumulated experience rather than
legitimacy, I ran models that control for venture capital firm experience in order to pick up any residual
14 Following Gompers (1996), I prefer to use a dummy variable in order to address potential nonlinearities. Nevertheless, similar
results were obtained when using a continuous venture capital firm age variable instead of a dummy variable.
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effects of this potential confound. Additionally, as an alternative proxy for venture capital firm legitimacy,
I looked up the number of times venture capital firms were cited in Belgian financial newspapers over the
period 1995 until the year of initial investment. The media presents stakeholders with information that
affects impression formation and legitimization of companies (Pollock and Rindova, 2003).
Control variables. Prior studies have advanced important company and venture capital firm
characteristics associated with growth. It is well established that age effects may cause differences in
growth patterns (Jovanovic, 1982). Company age at baseline is measured as the difference between the
year of initial investment and company founding year. Similarly, high-tech companies may exhibit a
different growth path compared to low-tech companies (Harhoff, Stahl and Woywode, 1998). A high-tech
dummy variable is equal to one when the company is active in a high tech sector and zero otherwise. The
classification of an industry as a high-tech industry is based on a classification scheme provided by the
Belgian government and is based on two digit industry codes. It includes industries, such as
biotechnology, computer and related activities.
Companies investing in intangible assets typically do this to generate future growth (Titman and Wessels,
1988). Rather than the absolute level of intangible assets, scholars mainly emphasize the importance of
intangible assets relative to tangible assets as one of the main drivers of the sustainability of performance
(Villalonga, 2004). The ratio of intangible assets on total assets is used as a proxy for growth potential. I
also control for the amount of equity finance received by the portfolio company at baseline. It is calculated
based on financial accounts as the net increase in outside equity in the year of venture capital firm
participation.
It is also necessary to control for size differences between venture capital firms, as there are multiple
reasons why larger venture capital firms may have fast growing companies in their portfolio besides
experience and legitimacy (Sorenson and Stuart, 2001). Larger venture capital firms, for example, have
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more possibilities to offer large amounts of follow-on finance compared to smaller venture capital firms.
Venture capital firm size at baseline is measured as the natural logarithm of capital under management.
Finally, I include year and industry dummy variables in the analysis to control for potential time (year of
initial investment) and industry effects. Table 3.1 reports the correlation matrix for the continuous
variables.
1. Employment 1.000
2. Total Assets 0.342 1.000
3. Overall Deal Experience 0.092 0.057 1.000
TABLE 3.1
Correlation matrix a
1 2 3 4 5 6 7 8 9
4. Industry Deal Experience 0.015 -0.088 0.648 1.000
5. Venture Capital Firm Age 0.185 0.137 0.469 0.288 1.000
6. Company Age 0.377 0.246 -0.088 -0.153 -0.048 1.000
7. Intangible Assets Ratio -0.293 -0.290 0.031 0.152 -0.215 -0.165 1.000
8. Initial Investment Size 0.196 0.616 -0.005 0.029 0.056 0.055 -0.021 1.000
9. VCF Size 0.247 0.420 0.432 0.169 0.349 0.126 -0.264 0.274 1.000
10. VCF Media Citations 0.271 0.300 0.690 0.323 0.270 0.112 -0.070 0.197 0.534
a Bold: Correlations (time=1) are significant at the 0.05 level (2-tailed).
3.4.3. Analysis
Linear Mixed Models (LMMs) for repeated measures are used to study change in employment and total
assets (Weiss, 2005; Fitzmaurice, Laird and Ware, 2004). It is the mix of fixed and random effects in the
same model that is the basis of the name Linear Mixed Model. Scholars have often used General
Multivariate Regressions Models. These require longitudinal data where all companies have the same
number of repeated measures, taken at time points, which are also the same for all companies
(Fitzmaurice, Laird and Ware, 2004). These strict assumptions are rarely fulfilled in longitudinal (growth)
studies and are not required when modeling LMMs 15 .
15 All reported results remain robust when using General Linear Models (GLMs) with repeated measures covariance structures.
Contrary to LMMs the structure of the covariance matrix in GLMs is not dictated by random effects, but the structure is selected
to best model the empirical covariance model.
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It is conceptually convenient to depict LMMs as multilevel models (Fitzmaurice, Laird and Ware, 2004).
The multilevel perspective is most useful if one assumes that companies randomly vary in terms of their
initial size and growth trajectory. This assumption seems reasonable for many applications in
organizational studies. Individual profile plots (not presented) confirm significant heterogeneity in initial
company size and how companies evolve over time. For this purpose, I discuss two levels of equations.
The first-level in the hierarchy is the individual-level model, which specifies the nature of change for each
individual company. The simplest model of individual company change is the straight-line (linear) growth
model:
Yij = β1i + β2i tij + eij (1)
where Yij is the ith company’s employment or total assets at the jth time point. tij is the linear time coding
(0, 1, 2 … 5) used to fit a linear trend to the ith company’s data across time. β1i and β2i are the company
specific intercept and linear coefficient, respectively. The values of the βs can vary among the companies.
The eij are the residuals. Equation (1) illustrates the flexibility of LMMs. Companies can have different
number of time points, they may be measured at different times and each company can have a different
growth trajectory (Fitzmaurice, Laird and Ware, 2004). LMMs can also accommodate non-linear change.
The simplest non-linear model is a quadratic model, which is specified by adding β3i tij² to equation (1) 16 :
Yij = β1i + β2i tij + β3i tij² + eij (2)
The second-level in the hierarchy are the group-level models. Though individual regression equations are
informative, researchers are usually interested in group effects. Conceptually, the random change
parameters from the individual-level model (e.g. β1i, β2i and β3i or company specific intercept, linear
coefficient and quadratic coefficient respectively) are treated as response variables in a second set of
models. Considering the equation (2) quadratic individual change model, the group level equations are:
16 A point of confusion is that LMMs can be used to model non-linear change across time. The term linear in Linear Mixed Model
refers to the linearity of the parameters and does not refer to the type of change that is modeled.
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β1i = β1 + b1i (3)
β2i = β2 + b2i (4)
β3i = β3 + b3i (5)
β1, β2 and β3 are the fixed intercepts in the level 2 equations and thus the averages of the individual-level
parameters. β1, β2 and β3 indicate the nature of change for the group as a whole, where β1 is the group
mean intercept or mean initial size, β2 is the group mean linear change and β3 is the group mean quadratic
change. These βs are known as fixed effects, because they do not vary among companies. b1i, b2i and b3i
are the level 2 residual terms reflecting individual company differences from the fixed effects.
An extension of the unconditional model discussed above is to incorporate one or more static covariates.
A static covariate of change is a predictor of change that does not vary over the course of the study. The
key covariates in this paper are overall deal experience, industry deal experience, venture capital firm age
and venture capital firm media citations measured at baseline. That is, I examine whether the individual
change parameters (e.g. β1i, β2i and β3i) vary as a function of the experience and legitimacy of the lead
investor backing the company. Overall deal experience, industry deal experience, age and media citations
are measured at baseline and consequently do not vary across time. Hence, they are incorporated in the
group-level equations. Consider the individual-level quadratic change model (2) above. The group level
equations studying change conditional on overall deal experience, for example, then become:
β1i = β1 + β4 odei + b1i (6)
β2i = β2 + β5 odei + b2i (7)
β3i = β3 + β6 odei + b3i (8)
where odei is the value of overall deal experience of the lead investor measured at baseline for the ith
company. β4 is the relationship between overall deal experience and intercept (initial size), β5 is the
relationship between overall deal experience and linear change and β6 is the relationship between overall
deal experience and quadratic change. β4 is also known as the deal experience by intercept interaction, as
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it indicates how the mean initial size (or intercept) of companies is dependent on overall investor
experience. β5 is known as the overall experience by linear trend interaction, as it indicates how the mean
linear trend is dependent on investor overall experience. Similarly, β6 is the overall deal experience by
quadratic trend interaction.
There is obviously natural heterogeneity among companies in many extraneous variables. Although these
extraneous variables may not be of any substantive interest they can potentially have an impact on the
growth curve of companies. The beauty of the longitudinal research design adopted in this paper is that
any extraneous factors (regardless of whether they have been measured or not) that influence the growth
profile of companies and whose influence persists, but remains relatively stable throughout the duration of
the study, are eliminated or blocked out when the size of companies is compared at several occasions. By
eliminating these major sources of variability from the estimation of within-company change, a precise
estimate of change can be obtained (Fitzmaurice, Laird and Ware, 2004).
3.5. Results
Table 3.2 shows descriptive information on employment and total assets. It reports unconditional means
across time. The bottom of Table 3.2 shows the sample sizes at each time point. The study does not suffer
from survivorship bias, as failing companies are included in the analysis. This information is typically
unavailable to other researchers (see Cassar (2004) for a discussion on survivorship bias when examining
startup financing). The sample size decreases at the end of the time frame for two main reasons. First,
some companies are too young to have four or five-year observations. Second, due to M&As some
companies stop to exist as independent entities. The companies are included in the analysis for the years
before the M&A, but excluded as the companies stop to exist as separate entities after the M&A.
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The LMM methodology assumes that the response variable is normally distributed. I check the conditional
and unconditional distributions of the raw employment and total assets values using longitudinal box plots
(Weiss, 2005). These show that employment and total assets are positively skewed (not presented).
Therefore, the natural logarithm of employment and the natural logarithm of total assets are used as
normalizing transformations for all subsequent analyses.
TABLE 3.2
Descriptive Statistics for Employment and Total Assets a
Time
Measure Baseline 1 2 3 4 5
Sample means (s.d.)
Employment
Total Assets
N (% Missing)
Employment
Total Assets
9.78
(14.81)
3,905
(6,678)
83
(5.68)
88
(0.00)
12.76
(18.59)
4,561
(7,287)
91
(3.19)
94
(0.00)
14.82
(24.22)
4,780
(7,165)
92
(0.01)
93
(0.00)
16.88
(29.40)
6,096
(10,574)
86
(0.01)
87
(0.00)
19.79
(36.04)
7,742
(16,074)
72
(0.00)
72
(0.00)
a Employment is in full time equivalents and total assets (excluding cash and cash equivalents) in 1,000EUR.
Note: s.d. = standard deviation
3.5.1. Modeling Unconditional Change in Venture Capital Backed Companies
22.47
(45.52)
6,087
(10,299)
53
(0.00)
53
(0.00)
I first develop models for unconditional change in employment and total assets. Unconditional models do
not have static covariates of change. Therefore, unconditional models focus on mean change in the entire
group of venture capital backed companies. Thereby, more insight is gained in the temporal pattern of
growth within venture capital backed companies, which is a critical step in order to be able to start
answering questions about what the effects are of particular covariates, such as venture capital firm
experience and legitimacy, on this growth pattern (Weiss, 2005).
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The results of the unconditional analysis appear in Table 3.3. I start by testing an elaborate model
including a linear, quadratic and cubic term. Models higher than third order are rarely used in social
sciences. Following Peixoto (1987), testing involves backward elimination, starting with the highest order
polynomial, in order to avoid bias. If the highest order term is significant, all lower order terms are left in
the model regardless of their significance, because only the highest order term is interpretable, in the sense
that it does not change when the time metric is arbitrarily changed (Peixoto, 1987). The unconditional
model for loge employment has a linear, quadratic and cubic term. The omnibus null hypothesis of no time
effects is rejected, F(3,79) = 14.32, p

Natural log employment
2.2
2.1
2
1.9
1.8
1.7
1.6
FIGURE 3.1
Unconditional Models: Observed and Predicted Means
0 1 2 3 4 5
Time
Observed Means Predicted Means
Natural log total assets
7.3
7.2
7.1
7
6.9
6.8
6.7
6.6
6.5
0 1 2 3 4 5
Time
Observed Means Predicted Means
For loge total assets, the cubic term is not significant in the highest order polynomial tested (Table 3.3).
However, the omnibus null hypothesis of no time effects is rejected, F(3,86) = 3.25, p

significant quadratic trend. In this section, conditional LMMs are estimated, which allow to study the
impact of venture capital firm experience and legitimacy on the shape of the growth curve of venture
capital backed companies, controlling for a-priori covariates, such as company age, initial investment size
and venture capital firm size amongst other controls.
The results of the conditional analyses are reported in Table 3.4. Separate models for growth in
employment (E) and total assets (TA) are reported. The relatively high bivariate correlation between
overall deal experience and industry deal experience (0.648) is not a cause for concern because these
variables were not entered simultaneously into any of the estimated models. Hypotheses are tested by
focusing on the highest order terms in the LMMs. In polynomial models only the highest order terms are
interpretable (Peixoto, 1987). The lower order terms may be informative about the shape of the growth
curve, but only when considered with the higher order terms. Moreover, as previously indicated, if the
highest order terms are significant, all lower order terms are left in the model regardless of their
significance, because only the highest order terms are interpretable, in the sense that it does not change
when the time metric is arbitrarily changed (Peixoto, 1987). In the following I will discuss the role of
respectively overall deal experience, industry deal experience, venture capital firm age and venture capital
firm citations on the shape of the growth curve of venture capital backed companies.
In models E1 and TA1 the effect of overall deal experience on the shape of the growth curve in terms of
employment and total assets is tested. In model E1 the overall deal experience by linear trend interaction
is positive, but not significant (0.0597; p>0.1). It provides evidence that companies backed by investors
with high overall deal experience do not exhibit a significantly steeper growth curve in employment
compared to companies backed by less experienced investors. These results are confirmed for growth in
total assets. In model TA1 the overall deal experience by linear trend interaction is positive, but not
significant (0.1176; p>0.1). Overall, I find no support for hypothesis 1A.
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TABLE 3.4
Conditional Analysis for Employment and Total Assets
Employment Total Assets
E1 E2 TA1 TA2
INTERCEPT -0.9047 -0.8600 1.2383 1.2656
Overall Deal Experience -0.1514 -0.1424
Industry Deal Experience -0.2135 -0.4045
VCF Age (Dummy) 0.4405 † 0.3834 † 0.5330 † 0.5189 *
Company Age 0.1109 *** 0.1088 *** 0.0587 † 0.0545 †
High Tech (Dummy) 0.4067 * 0.4263 * -0.2120 -0.1396
Intangible Assets Ratio -0.0071 † -0.0072 * -0.0120 ** -0.0114 **
Initial Investment Size 0.1217 † 0.1243 † 0.5932 *** 0.5948 ***
VCF Size 0.1390 0.1268 0.1972 † 0.2043 *
LINEAR TREND 0.5341 *** 0.5445 *** 0.1317 0.0792
Overall Deal Experience*Linear Trend (H1A) 0.0597 0.1176
Industry Deal Experience*Linear Trend (H1B) 0.0940 † 0.2539 *
VCF Age (Dummy)*Linear Trend -0.2211 * -0.1936 * -0.4581 * -0.4198 *
Company Age*Linear Trend -0.0139 * -0.0127 * 0.0010 0.0041
QUADRATIC TREND -0.1921 *** -0.1921 *** -0.0705 * -0.0702 *
VCF Age (Dummy)*Quadratic Trend (H2) 0.0424 ** 0.0418 ** 0.0700 † 0.0695 †
CUBIC TREND 0.0191 *** 0.0191 *** --- ---
Null Model Likelihood Ratio Test
Chi-Square 723.20 *** 723.40 *** 455.85 *** 452.63 ***
N 477
† p < .10, * p < .05, ** p < .01, *** p < .001 (Conservative two-tailed tests)
Next, I focus on the role of industry deal experience on the growth curve of venture capital backed
companies. The industry deal experience by linear trend interaction is positive and significant both in
model E2 (0.0940; p

exhibit steeper growth curves both in employment and total assets 18 . I find support for hypothesis 1B both
for growth in employment and growth in total assets.
FIGURE 3.2
Conditional Models: Predicted Means for Companies Backed by Investors with High Industry Deal
Natural log employment
2,6
2,4
2,2
2
1,8
1,6
1,4
Experience versus Low Industry Deal Experience
0 1 2 3 4 5
Time
Pred. Means (Unconditional)
Pred. Means (Companies backed by VCFs with high
industry deal experience)
Pred. Means (Companies backed by VCFs with low
industry deal experience)
Natural log total assets
7,4
7,3
7,2
7,1
7
6,9
6,8
6,7
6,6
6,5
0 1 2 3 4 5
The effect of venture capital firm legitimacy is tested by using the venture capital firm age dummy. The
venture capital firm age dummy variable has an effect on the quadratic growth trend in all reported
models. These models control for overall and industry deal experience respectively, which should alleviate
the concern that venture capital firm age proxies for experience. The venture capital firm age dummy by
quadratic term interaction is positive and significant in the employment models E1 (0.0424; p

and significant. Figure 3.3 plots the predicted means from the unconditional model and the predicted
means for companies backed by old versus young venture capital firms (based on a median split). It shows
considerable differences in the growth path of companies backed by old versus young venture capital
firms, where the growth path of companies backed by older more legitimate venture capital firms lies
consistently above that of companies backed by younger less legitimate venture capital firms. All models
offer support for hypothesis 2.
Natural log employment
FIGURE 3.3
Conditional Models: Predicted Means for Companies Backed by Old versus Young Venture capital firms
2.6
2.4
2.2
2
1.8
1.6
1.4
0 1 2 3 4 5
Time
Pred. Means (Unconditional)
Pred. Means (Companies backed by young VCFs)
Pred. Means (Companies backed by old VCFs)
7.4
7.3
7.2
7.1
7
6.9
6.8
6.7
6.6
6.5
0 1 2 3 4 5
As a robustness check, I ran additional models testing the impact of the natural logarithm of the number of
citations to the respective venture capital firms on portfolio company growth (see Table 3.6 in Appendix
A). Portfolio companies backed by investors that appear more frequently in the financial press exhibit a
growth curve with a higher linear trend in employment (0.0996; p0.1). Overall, findings demonstrate the
Natural log total assets
Time
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obustness of the positive relationship between venture capital firm legitimacy and company growth
reported above and this especially for growth in employment. It offers further support for hypothesis 2.
Finally, some of the results of the control variables also merit attention. Company age by intercept
interactions indicate that older companies are larger at baseline. The high tech dummy by intercept
interactions are only significant in the employment models. Companies active in high technology
industries employ more people at baseline. However, companies with more intangible assets are smaller in
term of number of employees at baseline and smaller in term of total assets. Companies receiving a larger
equity investment at baseline are larger in terms of employment and total assets. Larger venture capital
firms have a tendency to invest in companies that have more assets at baseline. Finally, in the employment
models, the negative and significant company age by linear trend interactions indicate that older
companies have a lower linear growth rate.
3.5.3. Alternative Explanations for the Hypothesized Relationships: Investor
Selection versus Value Adding
Two distinct processes may explain why the companies backed by investors with more industry
experience and more legitimate investors exhibit higher growth across time. One explanation is that more
experienced and legitimate venture capital firms have access to companies with different characteristics
and select companies with higher growth potential. An alternative explanation is that more experienced
and legitimate venture capital firms add more value post investment. More experienced venture capital
firms, for example, may be better business advisors and have a larger network, while more legitimate
venture capitals firms may convey a stronger signal to outside stakeholders.
Amit, Brander and Zott (1998) argue that although venture capital firms exist because of their capacity to
reduce informational asymmetry, venture capital firms will still prefer to select those companies where
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selection costs are relatively low. In other words, venture capital firms will prefer to invest in those
companies where the cost of informational asymmetry is less severe. Information problems are thought to
be particularly severe in young, small and technology-based companies (Berger and Udell, 1998;
Carpenter and Petersen, 2002). Moreover, it should be easier for venture capital firms to select companies
that eventually realize high growth when these are characterized by lower informational asymmetry
(Lerner, 1999). Following this line of reasoning, if more experienced and legitimate investors select those
companies that are more informationally transparent and hence easier to identify as potential high growth
companies one would expect these investors to prefer older, larger and low-tech companies.
Moreover, selection entails that more experienced and legitimate venture capital firms identify and invest
in those companies that have more growth opportunities already present in the company prior to the
investment (Sorensen, 2007) or alternatively that companies with high growth potential prefer to raise
finance from more experienced venture capital firms (Hsu, 2004). Companies invest in intangible assets to
generate future growth and hence, if selection is primarily driving the results, one might expect that more
experienced and legitimate venture capital firms will match with those companies that have higher
intangible assets on total assets ratios. Furthermore, it has been demonstrated that entrepreneurial ventures
use patents to signal their value and commercial potential to outside stakeholders, including venture
capital investors (Hsu & Ziedonis, 2007). As a robustness check, I looked up whether companies in the
sample were granted patents up until one year after the initial investment using the European Patent Office
database. If the more experienced and legitimate venture capital firms select those companies with higher
growth potential, one would expect them to prefer to invest in companies that were granted more patents
before the investment.
Table 3.5 reports Non-parametric Mann-Whitney Tests, which indicate few differences in initial
characteristics between the companies backed by investors with high versus low (based on median split)
industry deal experience (Panel A). Both groups of companies do not differ in terms of age, total assets,
125

employment and intangible assets ratio 19 . However, venture capital firms with high industry deal
experience are more likely to invest in biotechnology ventures. The biotechnology industry is one of the
riskiest and most uncertain industries in our modern knowledge-based economy. This finding is
remarkable as selection costs will be particularly high in the biotechnology industry and it should be as
easy, if not easier, to select companies with high growth potential in other industries.
TABLE 3.5
Portfolio Company Characteristics by Type of Venture Capital Firm a
Low Industry Deal High Industry Deal
Mann-Whitney
Test
Panel A
Experience
Experience
(Two-tailed)
Mean Median Mean Median
Information asymmetry
Age 3.250 2.000 3.730 2.000
Ln(Total Assets) 7.012 6.892 6.519 6.444
Employment (FTE) 7.000 3.000 6.530 3.000
Computer 0.210 0.270
Biotechnology 0.020 0.220 **
Manufacturing 0.150 0.050
Wholesale 0.130 0.050
Growth potential
Intangible Assets Ratio 0.109 0.001 0.112 0.006
Patents 0.040 0.000 0.070 0.000
Young Venture Capital Old Venture Capital
Mann-Whitney
Test
Panel B
Firms
Firms
(Two-tailed)
Mean Median Mean Median
Information asymmetry
Age 3.830 2.500 3.100 1.500
Ln(Total Assets) 6.575 6.423 7.009 6.975
Employment (FTE) 4.840 3.000 8.800 3.000
Computer 0.280 0.190
Biotechnology 0.110 0.100
Manufacturing 0.090 0.120
Wholesale 0.150 0.040 †
Growth potential
Intangible Assets Ratio 0.144 0.063 0.078 0.001 *
Patents 0.110 0.000 0.000 0.000 *
a I do not differentiate between companies backed by investors with high versus low overall deal experience, as these
ventures do not differ in their subsequent growth. Companies backed by investors with high versus low media citations
only differ in terms of industry focus, where investors with more citations in the media are more likely to invest in
biotechnology ventures.
† p < .10, * p < .05, ** p < .01 (Conservative two-tailed tests)
19 Multivariate logit models confirm the reported results. Company characteristics, such as size and intangible assets ratio, which
may be influenced by the financing received are measured in the year prior to the investment (when possible) in order to avoid
problems of reverse causality.
126

I also compared the companies backed by young versus old (based on median split) venture capital firms
(Panel B). Both groups of companies do not differ in terms of age, total assets, employment and industry
focus. Young, less legitimate venture capital firms select companies with higher intangible asset on total
asset ratios. Moreover, I find that younger, less legitimate venture capital firms are also more likely to
select companies that successfully applied for patents. It indicates that younger less legitimate venture
capital firms are more likely to select ventures with higher growth potential as poxied by their intangible
asset ratio and patenting activity. Conversely, older more legitimate venture capital firms select companies
with lower growth potential as proxied by their intangible asset ratio and patenting activity. Despite lower
or at least equal initial growth potential the companies backed by more legitimate venture capital investors
exhibit higher subsequent growth 20 .
Why would young venture capital firms be more likely to invest in companies with high growth potential
compared to their older counterparts? Young venture capital firms have incentives to grandstand or take
actions that signal their ability to their own potential investors (Gompers, 1996). This is important as
previous research indicates that past performance influences fundraising ability (Lakonishok, Shleifer,
Thaler, and Vishny, 1991). Hence, younger venture capital firms have an incentive to bring companies
public earlier than older venture capital firms, this in order to establish legitimacy in the marketplace and
successfully raise capital for new funds (Gompers, 1996). Extending this line of reasoning, young venture
capital firms may have a large incentive to invest in companies with high growth potential that can be
brought public quickly.
20 The characteristics included in Table 3.5 are only coarse characteristics (i.e. intangible assets on total assets, company age,
company size…). Venture capital firms may also select on unobserved characteristics. However, the results reported here are in
line with the findings reported in Chapter 4. In this chapter, detailed case study evidence indicates that biotechnology ventures
backed by more experienced versus less experienced venture capital investors did not differ systematically in terms of human
capital, alliance capital, technology and market-orientation at the time of the first venture capital investment. Hence, more
experienced investors do not necessarily select the best companies ex-ante. However, the more experienced investors were more
active in professionalizing the management team of their portfolio companies and helped their portfolio companies extensively
with raising follow-on finance from other experienced investors among other value adding activities.
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3.6. Discussion and Conclusion
Most prior research treated venture capital firms as a homogenous group, thereby obscuring experience
and legitimacy differences between venture capital firms (Hsu, 2004). It remains unclear whether it is
particularly the experience or legitimacy of the venture capital firm which will benefit venture
development. Moreover, recent studies explicitly incorporating venture capital firm heterogeneity almost
exclusively focus on explaining fund performance. In this study, I take the perspective of the portfolio
company and examine the impact of venture capital firm heterogeneity on the growth pattern of venture
capital backed companies.
Using learning and signaling theory, I provide testable hypotheses on the impact of venture capital firm
experience and legitimacy on portfolio company growth. I use a unique longitudinal database tracking
employment and total assets within 94 companies for up to five years after the initial investment. Results
indicate that both investor experience and legitimacy significantly influence the (non-linear) growth path
of venture capital backed companies. However, not all experience matters. Companies backed by
investors with more overall deal experience do not exhibit different growth curves compared to companies
backed by investors with limited overall deal experience. I find no support for hypothesis 1A.
Nevertheless, companies backed by investors with high industry experience exhibit steeper growth curves
both in employment and total assets, thereby offering support for hypothesis 1B. Furthermore, companies
backed more legitimate venture capital firms exhibit higher growth curves compared to companies backed
by venture capital firms that lack legitimacy in the marketplace thereby offering support for hypothesis 2.
3.6.1. Academic Contributions
From a theoretical standpoint this study contributes in at least three ways. First, prior research has
demonstrated a tension between economists and institutional theorists on who the most valuable partners
are for young resource-constrained ventures (Rindova, Williamson, Petkova and Sever, 2005). It remains
128

unclear whether entrepreneurial ventures will particularly benefit from connecting with partners that are
good or known in the marketplace? Economists stress the role of experienced firms which are likely to
provide higher quality tangible and knowledge-based resources through learning based on past actions.
Institutional theorists stress the role of partner legitimacy which is transferred to entrepreneurial ventures,
thereby influencing stakeholder perceptions making the latter more likely to transact with the venture.
This study demonstrates the artificial nature of this tension, as the development of entrepreneurial ventures
benefits both from connecting with more experienced and legitimate partners. Hence, both experience and
legitimacy plays a complementary role and entrepreneurial ventures are likely to benefit both from
forming relationships with good and well-known partners.
Second, prior research unambiguously demonstrated that more experienced and legitimate firms perform
better (Pollock and Rindova, 2003; Hoang and Rothaermel, 2005; Sorensen, 2007). More experienced
and legitimate firms are, for example, able to ask a higher premium from entrepreneurial ventures that are
willing to connect with them (Hsu, 2004). Given the cost of affiliating with more experienced and
legitimate partners, it is important to consider how relationships with these partners affect the
development of entrepreneurial companies. This study demonstrates how entrepreneurial ventures
connecting with more experienced and legitimate partners are able to accumulate more human resources
and total assets. Hence, as growth in employment is a good proxy for the value of private entrepreneurial
companies (Davila, Foster and Gupta, 2003), the premium required by more experienced and legitimate
investors may be worthwhile.
Third, this study also starts to disentangle the processes underlying the higher growth of entrepreneurial
ventures backed by more experienced and legitimate partners. Experienced and legitimate partners may be
able to both select more promising ventures and contribute better intangible knowledge-based resources. It
is unlikely that selection drives our results. The companies getting finance from more experienced and
legitimate investors do not differ systematically in age and size prior to the initial investment. Results do
129

suggest that venture capital firms with high industry deal experience are more likely to invest in the
biotechnology industry, which is one of the riskiest industries in our modern economy. Furthermore,
results suggest that companies getting finance from more legitimate venture capital firms may have lower
growth opportunities prior to the investment as proxied by the intangible assets ratio and patenting
activity. In sum, this indicates that results are not entirely driven by more experienced and legitimate
investors selecting more promising companies before the investment, but also by their contribution of
superior knowledge-based resources and the stronger signals they convey to outside stakeholders after the
investment.
From an empirical standpoint this study addresses two related shortcomings in organizational growth
studies. First, growth studies often look only at first and last year sizes and ignore development in between
these two time points (Delmar, Davidsson and Gartner, 2003; Weinzimmer, Nystrom and Freeman 1998).
Second, researchers typically make highly simplistic (and often implicit) assumptions about the temporal
growth pattern across time. It is common for researchers to assume that growth occurs as a quantum size
leap at one particular point in time or that growth is a linear process (See Davidsson and Wiklund, 2006
for a literature overview and critical comment). While nearly all organizational scholars agree that growth
is a dynamic non-linear process, few scholars explicitly incorporate this idea in theory building and
empirical testing.
This study explicitly models the dynamic nature of growth and demonstrates how LMMs can be used for
that purpose. LMMs allow modeling non-linear change trajectories and account for individual differences
between companies as well as similarities among groups of companies (Weiss, 2005; Fitzmaurice, Laird
and Ware, 2004). Despite major advances in statistical methods for longitudinal analysis in recent years,
the methods have not been widely used (Fitzmaurice, Laird and Ware, 2004).
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3.6.2. Implications for Practice
Entrepreneurs typically have to balance the pressure of running out of cash and the time needed to search
for desirable investors. This study indicates that the decision from which investor to raise finance may
have a long-term impact, besides the provision of cash at the time of the investment. Hence, raising initial
finance from desirable venture capital firms may be worth the higher cost (Hsu, 2004). Moreover, a
complaint often heard from entrepreneurs is that experienced and legitimate venture capital firms are
especially wary to offer finance to young and small entrepreneurial companies. I did not find evidence,
however, that more experienced and legitimate venture capital firms select older or larger entrepreneurial
companies. Overall, my findings are encouraging for entrepreneurs as they suggest that even young and
small entrepreneurial ventures have the potential for receiving investments from desirable investors.
Results may also be informative for government officials. All over the world programs are implemented to
increase the supply of venture capital, especially to young, innovative and growth-oriented entrepreneurial
companies, which are considered to be the motor of any modern economy. Policy makers should take into
account that not only to supply of financial capital as a commodity good is important. Policy measures
targeting experienced and legitimate investors may have disproportionally positive effects on employment
generation and asset accumulation within an economy.
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Appendix 1: Robustness Check
In the paper I used venture capital firm age (dummy) as a proxy for legitimacy. An alternative proxy for
legitimacy is the number of times a venture capital firm has been named in Belgian financial newspapers
over the period 1995 until the year of investment. The media presents stakeholders with information that
affects impression formation and the legitimization of firms (Pollock and Rindova, 2003). The number of
venture capital firm citations ranges from zero to 654. Given this high dispersion I use the natural
logarithm of this measure in subsequent analyses. It is important to note that venture capital firm citations
is highly correlated with overall deal experience (0.690). Hence, venture capital firms that appeared more
in the media conducted more investments. The correlation between venture capital firm citation and
industry deal experience (0.323) and venture capital firm citations and venture capital firm age (0.270) is
much lower.
Table 3.6 presents additional LMMs modeling change in employment and total assets conditional upon
venture capital firm citations. The positive and significant venture capital firm citation by linear trend
interaction in the employment model indicates that companies backed by investors that appear more
frequently in the financial press exhibit a steeper growth curve in employment. These results are not
confirmed for growth in total assets. Interestingly, venture capital firm citations remain significant in a
nested model including venture capital firm age (not reported). It indicates that legitimacy provided by age
and legitimate provided by the media may act as complements.
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TABLE 3.6
Conditional Analysis for Employment and Total Assets
Employment Total Assets
E3 TA3
INTERCEPT -0.1007 1.7155
VCF Citations 0.2773 0.1807
Company Age 0.1113 *** 0.0572 †
High Tech (Dummy) 0.2616 -0.3328
Intangible Assets Ratio -0.0089 *** -0.0138 **
Initial Investment Size 0.1048 0.5686 ***
VCF Size 0.4502 0.1542
LINEAR TREND 0.4502 *** 0.07684
VCF Citations*Time 0.0996 * 0.0971
Company Age*Time -0.1363 * 0.0011
QUADRATIC TREND -0.1652 *** -0.0389 †
CUBIC TREND 0.0179 ** ---
Null Model Likelihood Ratio Test
Chi-Square 663.3 *** 441.96 ***
n 477
† p < .10, * p < .05, ** p < .01, *** p < .001 (Conservative two-tailed tests).
487
139

Chapter 4: Early Differences and Persistence in the
Entrepreneurial Finance Process: Evidence from High- and
Low-Growth Biotechnology Startups
Tom Vanacker
Ghent University, Department of Accounting and Corporate Finance, Kuiperskaai 55E, 9000 Gent, Belgium;
TomR.Vanacker@UGent.be
Sophie Manigart
Ghent University, Department of Accounting and Corporate Finance, Kuiperskaai 55E, 9000 Gent, Belgium and
Vlerick Leuven Gent Management School, Reep 1, 9000 Gent, Belgium; Sophie.Manigart@UGent.be
Miguel Meuleman
Vlerick Leuven Gent Management School, Reep 1, 9000 Gent, Belgium and Nottingham University, Jubilee
Campus, Nottingham NG8 1BB, United Kingdom; Miguel.Meuleman@Vlerick.be
The paper benefited from the comments of Gavin Cassar, Daniel Forbes, Hans Landström, Peter Roosenboom, Harry
Sapienza and Shaker Zahra, as well as from seminar participants at University of Minnesota (Carlson School of
Management), Erasmus University (Rotterdam School of Management) and Ghent University. A prior version of this
paper was presented at the 2008 Academy of Management Meeting and selected for inclusion in the 2008 Process
Research Workshop. We thank Katleen Baeyens for excellent research assistance and Bart Clarysse for generously
granting access to data on Flemish research-based startups. The financial support of the Intercollegiate Center for
Management Science (I.C.M.) is gratefully acknowledged.
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4.1. Abstract
We study financial resource mobilization from startup through development in both high- and low-growth
entrepreneurial ventures. For this purpose, we use nine longitudinal case studies of young biotechnology
ventures. Contrary to the most influential theories in corporate finance, which portray the finance process
as either a teleological or a life-cycle model, we reframe this process as an evolutionary model. More
broadly, we demonstrate that with whom entrepreneurs connect early-on influences how easily their
ventures mobilize future resources. While entrepreneurial ventures are often depicted as passive in
relationship formation with more established resource-rich firms, we show how entrepreneurs play a key
role in early resource mobilization from these firms. More specifically, entrepreneurs engage in a local
search and only target one or a few related firms to mobilize early resources. Moreover, we identify
multiple processes explaining why early relationships that were critical to mobilize resources during the
startup phase may both facilitate and constrain entrepreneurs in their resource mobilization efforts during
the development phase.
4.2. Introduction
Young ventures typically lack social approval, tested routines and sufficient resources, which increases
their risk of failure compared to more established companies (Stinchcombe, 1965). Young ventures may
reduce these liabilities of newness by forming relationships with more established resource-rich firms
(Baum, Calabrese, and Silverman, 2000). These relationships not only allow direct access to vital
resources (Stuart, 2000), but also upgrade the focal venture’s own reputation, which makes risk-averse
stakeholders, like customers, financiers and employees, more likely to transact with the venture (Stuart,
Hoang, and Hybels, 1999; Stuart, 2000; Davila, Foster, and Gupta, 2003). Ventures that are able to
mobilize more strategic resources at startup are likely to develop a competitive advantage over their
resource-constrained peers (Lee, Lee, and Pennings, 2001). The goal of this study is to investigate
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financial resource mobilization from startup through development in both high- and low-growth
entrepreneurial ventures.
To date, research has only provided partial guidance on how different resource positions originate (Noda
and Collis, 2001; Ahuja and Katila, 2004; Hsu, 2007). While young ventures are often depicted as passive
bystanders in relationship formation, it is generally assumed that established resource-rich firms dominate
relationship formation (Katila, Rosenberger, and Eisenhardt, 2008). The latter are more likely to provide
resources to entrepreneurs with proven reputations and prior direct or indirect working relationships (Uzzi,
1999; Shane and Cable, 2002; Hsu, 2007; Hallen, 2008). If entrepreneurs lack prior experience and
working relationships, an alternative path to mobilize resources from established resource-rich firms is to
first demonstrate outstanding venture accomplishments (Hallen, 2008). Nevertheless, it is problematic that
the majority of entrepreneurs lack prior business experience (Westhead, Ucbasaran, and Wright, 2003)
and often require substantial amounts of outside resources from startup (Puri and Zarutskie, 2008). A key
challenge for entrepreneurs is hence to convince established resource-rich firms to form a relationship and
provide the needed resources (Hsu, 2004). Overall, our knowledge of the mechanisms initiating
differences in early resource positions between ventures remains limited, especially in settings where
entrepreneurs lack prior business experience and working relationships.
Besides focusing on how different resource positions originate, research needs to further explore dynamics
in resource mobilization. Early relationships with established resource-rich firms may invoke a cycle of
accumulating advantages for young ventures thereby expediting the mobilization of resources across time
(Stuart, Hoang, and Hybels, 1999). However, the same relationships that were critical to mobilize startup
resources may also constrain ventures in their subsequent resource mobilization and development (Maurer
and Ebers, 2006). Hence, more insight is needed in the processes affecting resource mobilization and the
contingencies under which early relationships become an asset or a liability (Hoang and Antoncic, 2003).
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In this study, we focus on the mobilization of financial resources from startup through development. The
finance process is critical for at least two reasons. First, organizational studies offer suggestive evidence
that the finance process may act as a key prompt initiating sustainable differences in development between
high- and low-growth ventures. The finance process is likely to initiate changes in how ventures arrange
and reconfigure their social capital (Maurer and Ebers, 2006) and influences the structure and experience
of the management team (Beckman and Burton, 2008). Second, ties to outside investors are typically the
first and one of the most critical ties formed by early-stage, high-potential ventures (Katila, Rosenberger,
and Eisenhardt, 2008).
We ask three related questions: (a) how does the finance process differ between high- and low-growth
ventures, (b) how do differences originate and (c) how do differences persist across time? We chose to
conduct comparative longitudinal case studies of high- and low-growth entrepreneurial ventures, because
of our interest in looking at rarely explored questions for which extant theory did not appear to be
particularly revealing. In such situations, case studies are likely to generate novel and more accurate
insights into the phenomenon under study compared to other research methods (Eisenhardt, 1989a).
In contrast with current theories in corporate finance that portray the finance process as either a
teleological or a life-cycle model, we reframe the finance process as an evolutionary model. It indicates
that one path to increase our understanding of how financial decisions are made may well lie in
systematically studying the finance process right from the start. More broadly, we contribute to the
literature on resource mobilization and initial network formation within entrepreneurial ventures. We
demonstrate the key role played by entrepreneurs in shaping early differences in resource positions. More
specifically, entrepreneurs engage in a local search and prefer to connect to related firms when mobilizing
startup resources. We demonstrate how early decisions from whom entrepreneurs mobilize resources may
both facilitate and constrain ventures in their subsequent resource mobilization and identify several
143

processes that make it difficult for ventures to replicate successful resource mobilization strategies from
their peers.
The paper first explains the research method. We then present the nine entrepreneurial ventures, and the
three major findings that relate to how the finance process differs between high- and low-growth ventures,
how differences originate and how differences persist across time. We conclude by discussing theoretical
contributions to corporate finance and, more broadly, organizational research.
4.3. Methods
4.3.1. Research Design
The research design is a longitudinal, multiple case study involving nine young biotechnology ventures.
Multiple longitudinal cases are particularly suitable to study dynamic processes (Van de Ven, 2007) and
enable a replication logic in which each case serves to confirm or disconfirm inferences drawn from the
others (Yin, 1984). We employ an embedded design (i.e. multiple levels of analysis) that includes
entrepreneurs, ventures, investors and the finance process. Although an embedded design is more complex
than a holistic design, it permits the development of richer, more reliable models (Yin, 1984).
We chose young biotechnology ventures as our research setting for at least two reasons. First, it allows us
to focus on the mobilization of venture capital (e.g., professional venture capital investors, business angels
and corporate investors), without ignoring other potentially important finance alternatives. More
traditional sources of finance, such as internal finance and debt finance are typically unavailable and even
unsuitable for most young biotechnology ventures (Pisano, 2006). Furthermore, in order to get at a stage
where it is possible to raise finance from large pharmaceutical companies (if that is part of the strategy of
the venture) and public equity finance, it is typically necessary to first raise several rounds of venture
144

capital (Nilsson, 2001). Second, young biotechnology ventures are characterized by high risk and
uncertainty, which makes finance constraints particularly acute in this setting (Himmelberg and Petersen,
1994). Hence, mobilizing financial resources is of heightened importance and entrepreneurs are expected
to devote considerable energy and time to raising finance (Greene, 1999).
We selected cases based on a theoretical sampling procedure (Eisenhardt, 1989a). To generate insights on
how the finance process of young biotechnology ventures relates to growth, we selected matched pairs of
biotechnology ventures that operated under similar conditions and regulatory regimes but differed
considerably with respect to their growth (Table 4.1). Young biotechnology ventures were labeled as high-
or low-growth based on widely applied proxies, such as employment growth, total asset growth and
patenting rate (Maurer and Ebers, 2006). These dimensions gauge biotechnology startups’ growth across a
diverse range of dimensions critical to early success, such as success in recruiting human resources,
investments in tangible and intangible assets, and development of intellectual property (Baum, Calabrese,
and Silverman, 2000).
All cases are dedicated Flemish biotechnology ventures active in research and development (R&D). We
do not consider ventures from traditional sectors whose activities only partially involve biotechnology. In
order to be included in our research, the ventures had to undertake R&D to develop new innovative
products or services. This ensures that we study a more homogenous group of ventures with high growth
ambitions and large financing needs (Nilsson, 2001). We make sure that the cases are active in R&D by
asking a filter question through preliminary telephone interviews.
We restrict our cases to Flemish ventures to control for institutional and spatial contingencies that may
affect venture founding, development and growth (Maurer and Ebers, 2006). The Flemish biotechnology
cluster is vibrant, but developing. The number of biotechnology ventures per inhabitant and the number of
employees per biotechnology venture lies above the European average (Flanders Institute for
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Biotechnology, 2002). The biotechnology industry, however, is relatively young. Most of the medical
biotechnology ventures have been founded in 1995 or later. This implies that Flemish biotechnology
ventures are typically founded by pure scientists. There is still no well-developed market of “second-
generation” biotechnology managers (Flanders Institute for Biotechnology, 2002). This is similar to other
Continental European biotechnology industries, but stands in contrast to the UK and US biotechnology
industries where scientific entrepreneurs accumulated founding and management experience through
earlier foundings (Maurer and Ebers, 2006). Limiting our cases to Flemish biotechnology ventures thus
has the advantage that founding teams are rather similar: all are characterized by limited prior business
experience and limited formal education in business administration. Our findings should therefore be
interpreted in the context of an emerging industry.
We selected matched pairs of academic spin-offs, corporate spin-offs and spin-offs from specialized
research institutes (Table 4.1). The origin of one venture, Myosic, is dual: a university and a corporate
jointly developed its core technology. Given the R&D-intensive nature, it is unlikely that entrepreneurs
will start an independent biotechnology venture. As a result, most biotechnology ventures originate from
academia or the business community. Ventures from different origins need and acquire different types of
resources due to their different background and hence their finance process might be different (Lindelof
and Lofsten, 2005). Our theoretical sampling procedure allows us to control for possible differences in
venture origin.
Finally, it is well established that venture capital investments show a cyclical pattern (Gompers and
Lerner, 1998). We control for the availability of venture capital by sampling matched pairs raising their
first venture capital during “hot” and “cold” markets (Table 4.1). Pharmaleads and Theraptosis raised
initial venture capital during 2000. This was a record year, both in terms of amount of finance raised by
European venture capital investors and in terms of amount invested in their portfolio companies
(European Venture Capital Association, 2005). Entomed, Myosic, AC Pharma, Irogene, Genom, I-Zyme
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and Aptanomics raised initial venture capital after 2000, a period during which venture capital investments
decreased dramatically, especially in high-tech sectors (European Venture Capital Association, 2005).
Most of our cases raised initial finance during “cold” markets. This is because our research interest was in
studying dynamics in the finance process of young ventures that were maximum five years old at the time
of first data collection. During “cold” markets, less finance is available compared to “hot” markets, which
might lead entrepreneurs to devote more time to locate and negotiate with potential investors.
DEDICATED FLEMISH
BIOTECHNOLOGY VENTURES
ACTIVE IN R&D
MARKET
SENTIMENT
HOT MARKET
(1999-2000)
COLD
MARKET
(2002-2003)
TABLE 4.1
Theoretical Sampling Procedure a
FIRM ORIGIN
a The names of the ventures are disguised to guarantee anonymity.
LOW GROWTH
COMPANIES
HIGH GROWTH
COMPANIES
ACADEMIC Pharmaleads Theraptosis
ACADEMIC Entomed, (Myosic) AC Pharma
CORPORATE Irogene, (Myosic) Genom
RESEARCH INSTITUTE I-Zyme Aptanomics
The characteristics of the cases are summarized in table 4.2. The oldest venture is Genom, founded in
1999, while the youngest venture is Myosic, founded in 2003 21 . Ventures are active in areas such as
diagnostics, therapeutics, environment and technology platforms. We ensured that matched pairs of cases
are active in similar areas, as a venture’s activity within the biotechnology sector might influence both the
availability of finance and venture growth (Maurer and Ebers, 2006). Nevertheless, a perfect match was
not always possible.
21 Before the legal founding of ventures, scientists typically worked on the technology for many years within the university or the
parent company. Hence, the legal founding year may be arbitrary and ventures that are legally founded in the same year may be in
a different stage of development at that time. This explains why we prefer to select matched cases that raised initial venture
capital in the same year, which indicates all ventures were deemed “investor-ready” at that point in time. Nevertheless, in most
cases the year of initial venture capital investment and founding year are the same.
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Table 4.2 differentiates between high- and low-growth ventures. The initial financial accounts indicate
ventures were rather similar in terms of employment in their first year of operation. Furthermore, from the
initial accounts we do not observe a systematic difference in the amount of total assets accumulated in the
first year of operation 22 . At startup, ventures had no patents on their name yet, but all had an exclusive
license on a patent or patent portfolio from the university or parent company. All cases appeared in a
confidential listing of ventures that applied for R&D subsidies from the most important subsidy-granting
agency in their region of operation, which further confirms their active involvement in R&D. Based on the
2006 financial accounts, high-growth ventures that survived as independent entities employ between 33
and 67 people, while low-growth ventures only employ between three and four people. The amount of
assets ranges from €9,848,000 to €119,559,000 for high-growth cases, while the maximum amount of
assets equals only €657,000 for the surviving low-growth cases. High-growth ventures were granted a
minimum of 10 and a maximum of 22 new patents up to 2006. Low-growth ventures have not been
granted patents since founding, except Entomed with two patents granted.
The median number of finance rounds equals two, with a minimum of one and a maximum of four. In
none of the cases had an investor exited at the time of our main data collection effort (interviews).
However, in some cases investors realized a partial or full exit while we followed the cases in real time
and this under different forms such as an IPO, trade sale or failure. This implies that our findings do not
suffer from a success or survivorship bias.
22 One should be careful when comparing initial figures across cases, as there may be important differences in the length of the
initial reporting period, which ranges from a minimum of seven months to a maximum of 23 months.
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Case
Founding year
Activity profile
TABLE 4.2
Description of Biotechnology Cases Studied a
Length first reporting period
(months)
Employment
(first reporting period, FTE)
Employment
(2006, FTE)
Total Assets (first reporting
period, in 000 EUR)
High Growth Companies
Theraptosis 2000 Therapeutics 23 3 33 691 9,848 17 3 600 28,800 IPO
(2007)
AC Pharma 2002 Platform technologies
and therapeutics
Genom 1999 Platform technologies
and therapeutics
Aptanomics 2002 Platform technologies,
diagnostics and
therapeutics
Pharmaleads 2000 Therapeutics and
platform technologies
Total Assets (2006, in 000
EUR)
Cumulative number of
patents granted (until 2006)
Number of finance rounds
First round finance
(in 000 EUR)
Pre-exit finance
(in 000 EUR)
12 2 10 b 4,081 2,248 b 19 2 4,500 6,750 Trade
Sale
(2006)
18 16 67 4,550 119,559 10 2 4,450 29,400 IPO
(2005)
18 6 59 6,207 29,285 22 4 62 70,000 IPO
(2007)
Low Growth Companies
14 5 3 604 81 0 2 1,100 2,600 No
Entomed 2002 Platform technologies 13 1 4 536 657 2 3 470 1,189 No
Myosic 2003 Diagnostics 7 1 3 b 1,140 894 b 0 1 1,500 1,500 Bankrupt
(2006)
Irogene 2002 Therapeutics 14 4 N.A. 371 31 0 1 350 350 No
I-Zyme 2002 Platform technologies,
industrial and
environmental biotech
7 2 N.A. 101 18 0 1 62 62 No
a N.A. indicates recent employment data was not available for Irogene and I-Zyme. However, when looking at the wages and remunerations paid
by these ventures, it is highly unlikely that employment increased.
b Data from 2005, as more recent data was not available given that the company failed or stopped to exist as a separate entity.
Source: financial accounts, interviews, European Patent Office and company websites
4.3.2. Data Sources
We used several data sources (Table 4.3) including (1) quantitative and qualitative data from semi-
structured interviews with key financial decision makers from both biotechnology ventures and their
investors, (2) excerpts from initial business plans used to raise startup finance, (3) e-mails and phone calls
Exit
149

to follow-up interviews, (4) financial statement data and statutory required publications on capital
increases and shareholder structure and (5) archival data, including Web sites, business publications and
materials provided by informants. The use of multiple data sources increases the validity and reliability of
the evidence (Yin, 1984).
Case
Entrepreneurs
Theraptosis CEO, 2
interviews
AC Pharma CEO, 2
interviews
TABLE 4.3
Sources of Data Biotechnology Cases
Interviews:
Follow-up
Entrepreneurs Investors
2 Investor
B, D and G
- Investor
B and D
Genom CEO 1 Investor
F
Aptanomics CEO and
Business
Developer
- Investor
F and H
Pharmaleads CEO and CSO - Investor
D and G
Entomed CEO, 2
interviews
- Investor
C and I
Myosic CEO 1 Investor
A and E
Irogene CEO, 2
interviews
High Growth Companies
Archival Documents:
Detailed yearly financial accounts, statutory required publications,
press releases, prospectus, (slides) management road show
Detailed yearly financial accounts, statutory required publications,
press releases, confidential reports venture capital investor
Detailed yearly financial accounts, statutory required publications,
press releases, (slides) presentation CEO at Belgian Biotechnology
Association, (slides) presentation CFO at university seminar
Detailed yearly financial accounts, statutory required publications,
press releases, (slides) presentation CEO at Belgian Biotechnology
Association
Low Growth Companies
Detailed yearly financial accounts, statutory required publications,
press releases, excerpts initial business plan (university report)
Detailed yearly financial accounts, statutory required publications,
press releases, internal university reports, excerpts initial business
plan
Detailed yearly financial accounts, statutory required publications,
press releases, initial business plan, confidential reports venture
capital investor
- Parent Detailed yearly financial accounts, statutory required publications,
press releases
I-Zyme CEO 1 - Detailed yearly financial accounts, statutory required publications,
press releases
Total Number:
(excl. pilot
interviews)
15
5
10
Part of this study is historical, in the sense that we rely on interviews and archival data to obtain a
retrospective account of the finance process. However, we also tracked changes in the finance process in
real time after the interviews. In total, we conducted more than 40 interviews. All interviews were
150

conducted by at least two researchers, where one researcher exclusively dealt with making field notes.
Additionally all interviews were taped and then transcribed in the hours following the interview. The first
phase included pilot interviews with biotechnology entrepreneurs, biotechnology investors, policy makers
and members of the biotechnology association. During the pilot interviews, we fine-tuned the interview
guides targeting entrepreneurs and investors. The pilot interviews indicated that the Chief Executive
Officer (CEO) eventually helped by a Chief Financial Officer (CFO) typically make finance decisions.
Other individuals, such as senior scientists, generally have less detailed knowledge of the finance events
taking place.
We conducted 15 semi-structured interviews with key informants in biotechnology ventures (Table 4.3).
The interviewees who were our informants were the founding entrepreneurs and senior management. We
typically conducted multiple interviews in each venture; there was a first round of interviews in 2003 and
a follow-up round in 2004. The interviews generally took between 90 and 120 minutes and followed an
interview guide that had variations for the different cases. The interviews began with background
information, such as management composition and prior experiences, core technology, products in the
pipeline, market characteristics and alliances. We then asked informants to chronologically discuss the
history of the venture with respect to its financing. Access to detailed yearly financial accounts and
statutory required publications on capital increases allowed interviewers to prepare a finance timeline in
advance of each interview. This finance timeline captured the history of the finance obtained and was used
during the interview to crosscheck information provided by our informants. During the interviews, we
additionally discussed the finance unsuccessfully applied for and the finance sources which entrepreneurs
were not willing to consider.
Throughout our interviews with entrepreneurs, we took steps to minimize informant biases. Given that we
study young biotechnology ventures that were maximum five years old at the time of the first interview
and that capital acquisition decisions are major decisions, we limit recall bias (Neisser, 1982). If
151

informants had difficulties in accurately remembering finance events, we would expect differences in the
information they provided with data from other sources. This was not the case, however. We also focused
on facts during the interview process. An emphasis on facts is likely to be less subjective to both cognitive
biases and impression management as opposed to beliefs or feelings (Miller, Cardinal, and Glick, 1997).
The information given was quite objective, e.g. whether entrepreneurs applied for different sources of
finance, whether they had prior ties to the investors from which they raised finance and how investors
helped or constrained the venture when raising follow-on finance. To further motivate our informants to
give accurate data we promised confidentiality.
We also conducted 10 semi-structured interviews with selected investors that offered finance to the cases
studied (Table 4.3). Given the sensitive nature of the finance process, we did not require investors to
discuss individual portfolio companies. We focused on the general investment process and discussed the
different phases in the investment process from deal origination through exit. This strategy allowed us to
link detailed investor characteristics and insights from investors to the cases. However, during the
interviews we motivated investors to offer examples involving the cases studied. Other investors were
even willing to provide confidential reports on their portfolio companies at the end of the interview. We
interviewed independent and private, independent and quoted, university-related, bank-related and
corporate venture capital investors that invested as little as €500,000 to as much as €194,000,000 in
biotechnology ventures. Furthermore, we interviewed one parent company that offered startup finance.
After the interviews and up to December 2007, we tracked changes in the finance process in real time.
Given the information requirements in our research setting (i.e. even private ventures are required by law
to file detailed yearly financial statements to the National Bank), we are confident that we observed all
important finance rounds that occurred after our interviews. If important changes took place in the finance
process, we collected press releases, asked additional questions through e-mail or telephone and attended
management road shows amongst other.
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4.3.3. Data Analysis
Transcriptions alone totaled 700 double spaced pages. In order to manage the large amount of data
collected, we started by building individual case studies synthesizing the interview transcripts and archival
sources. Case histories were used for two analyses: within-case and cross-case. The within-case analysis
focused on the finance timeline, which depicted all finance events, unsuccessful finance events and
important facts and conclusions for the individual cases. Within-case analysis allowed us to describe the
finance process in detail as experienced by a single venture. Through this process, we noted patterns
within each case. Next, the conclusions from each individual case were considered information needing
replication by other cases (Yin, 1984).
Cross-case analysis began after all cases were finished (Eisenhardt, 1989a). Using cross-case analysis
techniques, we looked for similar constructs and relationships across cases. We heavily relied on cross-
case synthesis techniques by using tables and graphs that display the data according to a uniform
framework. The tables were used to study whether different groups of cases were more similar and
whether particular subgroups or categories of general cases could be identified (Yin, 1984).
Our analysis led to an emerging framework depicting the finance process as an evolutionary model. It
describes, first, how high- and low-growth ventures differ in their mobilization of financial resources. We
demonstrate how early differences in the experience of investors with whom entrepreneurs connect affect
the amount of follow-on finance raised and venture growth. Second, the framework addresses how early
differences originate. The social context in which entrepreneurs make early finance decisions, and more
specifically venture origin, pushes entrepreneurs towards related investors and this through a local search.
Third, we show that early differences in the finance process are likely to persist across time, because of
venture professionalization, stunted learning and investor syndication. These processes make it difficult
153

for ventures to replicate the successful finance strategies of their peers and may therefore act as isolating
mechanisms.
4.4. How does the Finance Process of High-and Low-Growth Ventures
Differ?
In this section, we study how the finance process of high- and low-growth ventures differs from startup
through development. We start by focusing on the amount of finance raised. Next, we study the impact of
the type of investor that offers finance. Although high- and low-growth ventures do not differ
systematically in the amount of startup finance raised, the high-growth ventures raise considerably larger
amounts of finance across time compared to their low-growth peers. We show how ventures raising early
finance from highly experienced investors attract more follow-on finance and exhibit high growth, while
ventures raising early finance from relatively inexperienced investors attract less follow-on finance, if any,
and exhibit low growth. We discuss the role of the amount of finance and type of investor in more detail
below.
4.4.1. Amount of Finance
Although financial resources are typically portrayed as a commodity good, the amount of finance raised at
startup might offer startups a competitive advantage (Lee, Lee, and Pennings, 2001). Startups raising more
initial finance are likely to accumulate a larger stock of strategic resources, including intangible assets and
human resources, compared to their finance constrained peers (Dierickx and Cool, 1989; Hubbard, 1998;
Lee, Lee, and Pennings, 2001). Figure 4.1, however, shows how high- and low- growth cases do not differ
systematically in terms of amount of startup finance raised.
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Cumulative amount of pre-exit finance (in 000 EUR)
100000
10000
1000
100
10
1
5
9
13
17
21
25
High Growth Companies
29
33
37
a Note that a logarithmic scale was used for ease of representation.
41
45
49
53
FIGURE 4.1
Cumulative Amount of Pre-Exit Finance Raised across Time a
Source: Statutory required publications on capital increases, financial statements, interviews.
57
61
IPO
65
69
Time (Months since startup financing)
Aptanomics Genom Theraptosis AC Pharma
M&A
73
77
IPO
81
IPO
85
89
93
97
101
100000
10000
1000
100
10
Low Growth Companies
Bankruptcy
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101
Time (Months since startup financing)
Pharmaleads Myosic Irogen I-Zyme Entomed
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A good illustration is the difference in initial finance between Pharmaleads and Aptanomics. Pharmaleads,
an academic spin-off founded in 2000, raised some €1,100,000 from three university funds. Despite the
relatively large amount of initial finance raised, this venture failed to realize its high growth ambitions.
Aptanomics, founded in 2002, received only €62,000 initial finance from a domestic investor. This
investor decided, in close cooperation with the specialized research institute from which Aptanomics
originated, to first search for an experienced CEO before offering more finance. Once an experienced
CEO was hired, the domestic investor together with the CEO searched for additional finance. They
targeted venture capital investors that typically co-invest with this domestic investor. It is only some 15
months after founding that Aptanomics raised an additional €5,000,000 of finance from domestic and
international investors and started to focus on the development of its operations. This indicates that
ventures do not necessarily require large amounts of finance to startup and exhibit high subsequent
growth.
Nevertheless, the cumulative amount of finance raised and venture growth are related. Figure 4.1 shows
how large differences in the amount of finance raised by high- and low-growth ventures become apparent
some two to four year after raising startup finance. High-growth ventures attract large amounts of follow-
on finance. Aptanomics, Genom and Theraptosis raised a cumulative amount of venture capital exceeding
€25,000,000. AC Pharma raised €6,750,000 before it merged with another biotechnology venture. These
ventures are also more likely to conduct an Initial Public Offering (IPO). Aptanomics, Genom and
Theraptosis conducted an IPO between five and seven years after founding. Going public is a momentous
event for biotechnology ventures: it gives access to capital, increases legitimacy and is an important exit
mechanism (Deeds, Decarolis, and Coombs, 1997). This contrasts with low-growth cases, which typically
attracted limited amounts of follow-on finance, if any. Pharmaleads raised the largest cumulative amount
of venture capital, which equals only €2,600,000. Although most low growth companies are still
surviving, recent financial figures demonstrate that these ventures are severely undercapitalized.
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Pharmaleads, for example, burned nearly all of its capital raised and reported total assets equal to merely
€81,000 at the end of 2006.
4.4.2. Type of Investor
Focusing only on the amount of finance raised ignores the type of investors that offer finance. Table 4.4
shows the type of investors and the finance rounds in which they participated. Highly experienced venture
capital firms (VCFs) are investors that (a) actively invest in biotechnology ventures and (b) have a team
dedicated to evaluate and follow-up biotechnology proposals. Relatively inexperienced venture capital
investors comprise university funds, venture capital subsidiaries of banks and other venture capital firms
investing only sporadically in biotechnology ventures without a team dedicated to biotechnology. Next,
business angels (BAs) are defined as wealthy individuals investing their personal funds in privately-held
ventures. Finally, in the case of corporate spin-offs, the parent company is typically involved in startup
finance.
We observe that the high-growth ventures generally raise finance from more experienced investors during
the first or second financing round and raise large amounts of follow-on finance (Table 4.4). Aptanomics
and AC Pharma raised finance from more experienced investors at startup. Genom initially raised
convertible debt from its two parent companies, both successful biotechnology ventures. It further raised
initial venture capital finance from highly experienced venture capital investors some three years after
founding. Theraptosis raised venture capital from an experienced investor in its second finance round.
The low growth ventures typically start with relatively inexperienced investors and raise limited amounts
of follow-on finance, if any (Table 4.4). Pharmaleads and Entomed raised initial finance from relatively
inexperienced venture capital investors. Irogene and I-Zyme were the first spin-offs from their parents,
who also financed these ventures. Myosic raised finance from an inexperienced academic investor and a
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corporate investor related to a big pharmaceutical company. This last case indicates that receiving initial
finance from a more experienced investor (i.e., the corporate investor) is no guarantee for success.
Case
Domestic
highly
experienced
VCFs
TABLE 4.4
Finance Process by Type of Investor and Venture Growth a
Type of investor and finance round in which they participated:
International
(experienced)
VCFs
Other
relatively
Academic
VCFs
inexperienced
Bank VCFs VCFs
High Growth Companies
BAs
Parent
company /
institute
Theraptosis 2,3 2,3 1,2,3 2,3 3 - - 600 28,800
AC Pharma 1,2 - 1,2 1,2 - - - 4,500 6,750
Genom - 2 - - - - 1 4,450 29,400
Aptanomics 1,2,3,4 2,3,4 - 4 - - - 62 70,000
Low Growth Companies
Pharmaleads - - 1,2 2 - - - 1,100 2,600
Entomed - - 2 - 1, 2,3 2,3 - 470 1,189
Myosic - - 1 - - - 1 1,500 1,500
Irogene - - - - - - 1 350 350
I-Zyme - - - - - - 1 62 62
a The numbers in bold indicate finance rounds that occurred before the main data collection (the interviews). The numbers in
italics indicate finance rounds that occurred afterwards. All cases were monitored in real time up until the end of 2007. If cases
raised new finance, we should notice this in the statutory required publications or financial statements which ventures are obliged
to complete by law. The number of finance rounds in this table do not necessarily correspond with the number of increases in the
cumulative amount of finance raised as depicted in Figure 4.1. This is because investors may decide to stage finance, thereby
gradually offering the finance if milestones are reached instead of offering all finance from the start.
The observation that the high-growth ventures raised early equity finance from experienced investors is
consistent with prior studies, which highlight that affiliation with prominent investors entails benefits to
their portfolio companies (Stuart, Hoang, and Hybels, 1999; Hochberg, Ljungqvist, and Lu, 2007;
Sorensen, 2007). Findings further indicate that with whom ventures connect early-on influences the
resources they subsequently mobilize and venture development. Ventures starting early with more
experienced investors typically attract large amounts of follow-on finance and exhibit high growth, while
ventures starting with relatively inexperienced investors typically attract limited amounts of follow-on
Startup finance
(in 000 EUR)
Pre-exit finance
(in 000 EUR)
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finance and exhibit poor growth. This extends prior studies by demonstrating how the finance process of
ventures backed by more and less experienced investors evolves differently across time.
4.5. How do Early Differences in the Finance Process Originate?
An important question that emerges is why some ventures raise early finance from highly experienced
investors, while others raise early finance from relatively inexperienced investors. On the surface, it would
seem that initial differences in quality and growth ambition initiate early differences in the finance process
(Sorensen, 2007; Hallen, 2008). Yet, there was no consistent pattern linking quality and initial growth
ambition to growth. Instead, we demonstrate how entrepreneurs engage in local search when mobilizing
early financial resources, which pushes them towards related investors. We expand on the impact of
differences in quality, differences in growth ambition and local search on the origination of early
differences in the finance process below.
4.5.1. Initial Differences in Quality and Growth Potential
Prior research indicates experienced investors end up with ventures of high quality, while less experienced
investors end up with ventures of poorer quality (Sorensen, 2007). This is explained as follows. Once
entrepreneurs are willing to attract outside finance they consider the entire range of investors. Conversely,
investors screen the entire pool of ventures that try to raise outside finance (Eckhardt, Shane, and Delmar,
2006; Sorensen, 2007). When entrepreneurs are confronted with multiple offers, they engage in an
optimization exercise, where they rank investors by experience and eventually accept the offer of the most
experienced investor (Hsu, 2004; Sorensen, 2007). An investor with less experience is therefore pushed
down in relative rankings and is left with ventures of worse quality (Sorensen, 2007).
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In contrast to the image portrayed above, our cases indicate that it is unlikely that experienced investors
have automatic access to high quality startups, while relatively inexperienced investors are generally left
with startups of worse quality. Ventures did not differ systematically in terms of quality and growth
potential when raising their first finance. We looked at four early signals of quality and growth potential
measured at startup: human capital, alliance capital, technology and target market (Baum and Silverman,
2004). Prior organizational studies indicate that investors are more likely to offer finance to ventures
founded by entrepreneurs with prior founding experience (Hallen, 2008; Hsu, 2007). All ventures in our
study, however, are founded by scientists with limited, if no, business experience. If an experienced
manager is recruited, this is typically under the impetus of the early investor. In the case of AC Pharma,
for example, the founders were all pure scientists without any prior business experience. The initial
venture capital investors helped with recruiting an experienced biotech manager before proceeding with
further investments.
Table 4.5 summarizes the configuration of each venture’s alliance capital, the scope and development of
its technology and its target market. All ventures typically start with a single formal R&D collaboration
agreement with a university or a specialized research institute. Furthermore, both in high- and low-growth
cases do ventures start with a technology that is still in the idea phase. For example, the CEO of Genom, a
venture experiencing tremendous growth since its founding, indicated that when the venture was founded
its technology was still in the idea phase: “The idea was rudimentary”. Hence, the image that successful
ventures have a more developed technology at startup is not necessarily true. Also noteworthy is that both
ventures with a broad technology scope and ventures that focus on one product are able to raise finance
from more experienced investors. Finally, both high and low growth ventures target niche markets and
large mainstream markets.
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TABLE 4.5
Early Signals of Growth Potential and Quality: Alliance Capital, Technology and Target Market at Startup a
Case
Alliance capital Technology
market
Technical R&D with
Commercialization development universities or Stage in product
activities activities research institutes development cycle Technology scope Target
High Growth Companies
Theraptosis 0 0 2 Idea Phase 4 Niche market
AC Pharma 0 0 0 Proof of concept 1 Mainstream market
Genom 0 0 0 Idea Phase 5 N.A.
Aptanomics 1 0 1 Proof of concept 5 Mainstream market
Low Growth Companies
Pharmaleads 0 0 1 Proof of concept 4 Niche market
Entomed 0 0 0 Concrete market-
ready product
2 Niche market
Myosic 0 0 1 Idea Phase N.A. Niche market, move to
mainstream market later
Irogene 0 1 1 Idea Phase 5 Niche market
I-Zyme 0 0 1 Proof of concept 5 Niche market, move to
mainstream market later
a N.A. indicates that data was not available for the case. Technology scope at founding was measured on a five-point scale with
1= focus on one specific product and 5 = very broad technology platform with several applications.
Source: Interviews.
Evaluating venture quality and predicting future growth is extremely difficult in high-tech industries
(Lerner, 1999). It is probably even more difficult in the biotechnology industry where even the more
established ventures operate under considerable uncertainty as they typically lack products and still have
to generate their first revenues (Pisano, 2006). Our findings correspond with this view, as we do not find
systematic differences in early signals of quality and growth potential between the high- and low-growth
cases. Both the more experienced and relatively inexperienced investors contributed startup finance to
ventures which had not yet demonstrated accomplishments and were operating under considerable
uncertainty.
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4.5.2. Initial Differences in Growth Ambition
Another possibility is that early differences in growth ambition between entrepreneurs drive our findings.
Research confirms that growth ambition has a positive impact on realized growth (Wiklund and Shepherd,
2003) and hence will influence the need for financial resources to support this growth (Sapienza,
Korsgaard, and Forbes, 2003). Entrepreneurs in the low-growth cases may have lower growth ambitions
compared to their peers in the high-growth cases and may attract finance from less experienced investors
as they do neither envisage the need to raise significant amounts of follow-on finance nor require active
involvement from their investors. Indeed, the majority of entrepreneurs set up new businesses to provide
an income or support a desired lifestyle (Berger and Udell, 1998). This may even be true for technology-
based startups which raise venture capital finance (Heirman and Clarysse, 2004). Growth ambitions will
be lower in these ventures as entrepreneurs sacrifice growth for lifestyle choices (Berger and Udell, 1998).
Nevertheless, this explanation seems unlikely as initial business plans and early internal documents
demonstrate that even entrepreneurs in low-growth cases had high growth ambitions. Entomed, for
example, planned to develop into a professional biotechnology venture in two to three years after
founding, employing 25 to 30 people. In Myosic, the founder envisioned to raise some 10 million euro in
follow-on finance and subsequently conduct an IPO. The CEO of Pharmaleads envisaged the same future
for his venture as that of another academic spin-off from the same university, which experienced dramatic
growth and conducted an IPO. Despite their high growth ambitions at startup, these entrepreneurs were
unable to realize them.
4.5.3. Local Search
We find that early differences in finance are largely explained by the local search behavior of
entrepreneurs. Surprisingly, entrepreneurs are not comprehensive at all in their search for startup finance
and this despite the heightened importance of finance decisions in our setting. Entrepreneurs unexpectedly
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limit their search to only one or a few investors (Table 4.6). The social context within which financial
resources are mobilized guide entrepreneurs in their search for finance. The pre-founding context has an
important impact on financial resource acquisition at startup. Firm origin and existing contacts of
entrepreneurs through previous employment largely determine which investor is targeted (Table 4.6).
A good illustration is Genom, a corporate spin-off funded by the parent companies at startup. The CEO
indicated that it was not necessary to further search for startup finance when it became clear that the
parent companies were willing to offer startup finance. Hence, the CEO did not search for credible finance
alternatives. Nevertheless, the CEO later indicated that the initial finance decision constrained his venture,
because the venture was “too dependent on its parent companies”. Therefore, the CEO decided to target
independent venture capital investors for the first time in a subsequent round of finance.
Table 4.6 demonstrates that the search for startup finance by entrepreneurs is local. The notion of local
search is a relative term and presumes a broader context (Stuart and Podolny, 1996). We define local
search in the finance process as the search for finance from investors related to the venture through pre-
existing ties as opposed to unrelated investors. Our results offer additional evidence on the importance of
the social context in the finance process and puts nuance to prior studies, which indicate that investors are
more likely to offer finance to entrepreneurs with whom they have pre-existing ties (Uzzi, 1999; Shane
and Cable, 2002; Hallen, 2008). We demonstrate that these results might also be explained by
entrepreneurs, who are more likely to initiate relationships with related investors. The social context
within which finance decisions are embedded determines from whom entrepreneurs will search for
finance. This largely explains early differences in the finance process 23 .
23 Note that we do not argue that entrepreneurs remain local in their search for finance. As ventures mature and uncertainty is -at
least partially- reduced, we observe that entrepreneurs become more comprehensive in their search for follow-on finance. It is not
uncommon that entrepreneurs contact over 50 venture capital investors when preparing follow-on finance rounds. Nevertheless,
although entrepreneurs become more comprehensive in their search for finance across time, other processes enhance and/or limit
the number of viable finance options in the future. These are discussed later in this chapter (§4.6.).
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TABLE 4.6
Local Search and the Impact of the Social Context on Initial Financing
Theraptosis -University spin-off getting finance from
university fund.
-Did not look for other potential equity
investors.
AC Pharma -University spin-off getting finance from
university fund and its shareholders.
Genom -Corporate spin-off getting finance from
parent company
-Did not look for other potential equity
investors.
Aptanomics -Aptanomics raised finance from a highly
experienced investor. This experienced
investor has financed nearly all spin-offs from
the research institute.
Pharmaleads -University spin-off getting finance from
university funds.
-No other equity investor looked for besides
the university fund
Facts Illustrative Quotes
High Growth Companies
"It was the logical choice in the case of a
spin-off."
"The firm is a university spin-off and one
of our investors is the university spin-off
fund…All initial investors in our firm are
also shareholders of the university fund."
"The CEO did not need to look for money,
it was provided by the two parent
companies"
Low Growth Companies
Entomed -CEO contacted a business professor
-There was a friendship between the university
professor contacted by the CEO and the senior
investment manager of the initial investor.
-Relied solely on university professor to locate
an investor.
Myosic -Corporate/University spin-off getting finance
from a corporate fund and university fund.
-No other equity investors considered
Irogene -Corporate spin-off getting finance from
parent company.
- Tried to attract financing from a small
number of investors, besides the mother
company, but was unsuccessful.
I-Zyme -Spin-off getting startup finance from
research institute.
-Did not look for other potential equity
investors.
Source: Interviews.
"It is the norm for spin offs from
universities to start talking to the seed
funds of universities. With the university
fund we had the most obvious link."
"When searching for funding it appeared to
be logical to have resources from the
investors with whom the company and
entrepreneur already had contacts."
"Contacts were first initiated with a
university seed fund. When they were not
willing to invest the parent company
provided financing."
"The institute had the idea to spin-off the
company and was therefore willing to
invest. Afterwards, there were some
contacts with business angels, but not
more, things did not get any further."
Firm Origin
√
√
√
√
√
Other preexisting
relationship
√
√ √
√
√
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Why do entrepreneurs limit their search for finance to one or a few investors with whom they have pre-
existing ties despite the importance of finance decisions? One possible explanation is that ventures had no
alternative. While possible, this explanation is doubtful. First, entrepreneurs generally do not test the
market for credible alternatives. The CEO of Pharmaleads, for example, did not contact other investors.
The CEO stated:
“The venture did not need to contact other venture capital investors or business angels because we were able to raise
equity from the university fund.”
Second, finance is more readily available during “hot” markets. We might hence logically expect that
entrepreneurs will be less dependent on related investors to mobilize startup capital during these times.
Nevertheless, both ventures raising their initial finance during “hot” and “cold” markets exhibit the same
local search behavior.
The cases suggest that the social context is important as it (a) helps in locating potential investors, (b)
helps in reducing the fear of expropriation and (c) shapes taken-for-granted assumptions about what
constitutes appropriate search behavior. We expand on these three mechanisms below.
Locating investors. Prior research at least implicitly assumes a market with complete information where
entrepreneurs are aware of the full spectrum of finance alternatives (Brealey and Myers, 2000; Eckhardt,
Shane, and Delmar, 2006; Sorensen, 2007). However, the acquisition of finance in imperfect markets is a
function of the amount of information that is available to the entrepreneur: information deficiencies limit
entrepreneurs’ set of choices. Inexperienced entrepreneurs are therefore expected to have a more limited
set of finance choices (Van Auken, 2001). Continental European biotechnology ventures are a typical
example of ventures founded by entrepreneurs with little prior business and finance experience. Social
capital will benefit these entrepreneurs in identifying where the needed resources are available (Hite and
Hesterly, 2001; Rangan, 2000). As a result, entrepreneurs are more likely to search for finance from
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investors related to the venture, as pre-existing direct or indirect ties increase the likelihood that the
entrepreneur will identify these investors as potential exchange partners. The CEO of AC Pharma, for
example, indicated:
“There are two reasons why the firm found its initial investors relatively easily. One reason is that the firm is a
university spin-off and one of our investors is the university spin-off fund… All initial investors in our firm are also
shareholder of the university fund.”
Reducing the fear of expropriation. It is well-established that informational asymmetries between
entrepreneurs and investors make investors reluctant and even unwilling to provide finance (Stiglitz and
Weiss, 1981). Adverse selection and moral hazard problems, which relate to the existence of informational
asymmetry, are viewed as a major problem for investors when evaluating entrepreneurial ventures
(Eisenhardt, 1989b). Entrepreneurs are depicted as possessing private information, which they may
potentially use to mislead investors (Eisenhardt, 1989b). Nevertheless, entrepreneurs also need to evaluate
investors and informational asymmetry is also a problem for them (Sahlman, 1990). For instance, as an
entrepreneur and venture capitalist interact, the latter becomes more informed and may continue the
project without the entrepreneur (Ueda, 2004). The risk of expropriation makes entrepreneurs reluctant to
disclose information when searching for finance especially when the other party is unknown. The
following quotes illustrate:
“Particularly pre-investment, entrepreneurs are wary to disclose all information to us. Information transfer is always
very delicate. We live in a competitive world and have a large number of portfolio companies. It happens that a
company that looks for financing is a competitor of one of our portfolio companies or that there is at least a partial
overlap. Even when two companies start very differently, they may gradually become more similar over a four to
five year timeframe. Therefore, information transfer is often a problem... However, when we do not receive all the
information, we will not invest.” (Senior investment manager, Investor F, Highly Experienced VCF)
“Unwillingness to provide information is something which happens more often with external entrepreneurs. It is less
of a problem if we work with scientist from our own university.” (Managing Partner, Investor A, Academic VCF)
Taken-for-granted assumptions. According to an institutional view, the motives of human behavior
extend beyond economic optimization to social justification (Zukin and DiMaggio, 1990). When
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managers justify actions with the claim that “everybody does it this way”, they refer to institutionalized
activities (Oliver, 1997). Our cases demonstrate that once entrepreneurs have located an investor through a
network search, they typically do not engage in a broader search. In Pharmaleads and Theraptosis, two
university spin-offs, the CEOs justified why they only searched for finance from university funds at
startup by indicating that it is the norm for spin-offs. The CEO of Pharmaleads, for example, stated: “It is
the norm for university spin-offs to start talking to the seed fund of universities”. This demonstrates that
entrepreneurs take a lot of implicit finance decisions without considering the long-term impact of their
early decisions.
In summary, early differences in the finance process are largely shaped by the social context, which
pushes ventures towards related investors. It indicates that the social context creates a barrier that
precludes perfect competition in the factor market. It is noteworthy that local search is not necessarily
inefficient and may even be a cost effective and speedy way to secure finance and value-added services.
Especially entrepreneurial ventures that are not connected to highly experienced investors may benefit
from a more comprehensive search at startup. In the following section, we demonstrate the importance of
early finance decisions as these may both facilitate and constrain the future finance process.
4.6. How do Early Differences in the Finance Process Persist across Time?
Corporate finance theory generally assumes limited influence from history on the finance process, as
ventures actively rebalance their financial structure towards an optimum (Hovakimian, Opler, and Titman,
2001; Leary and Roberts, 2005). In contrast, we identify a number of processes that may both facilitate
and constrain the subsequent finance process, including venture professionalization, stunted learning and
investor syndication preferences. These processes explain how early differences in the finance process
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persist across time and suggest it is difficult for ventures to imitate others and duplicate successful finance
strategies. We elaborate on these processes below 24 .
4.6.1. Venture Professionalization
Hellmann and Puri (2002) indicate that venture capitalists as a group play an important role in venture
professionalization by bringing outsiders into the position of CEO. More recently, Beckman and Burton
(2008) suggest that venture capitalists may influence the entire structure and experience of teams within
ventures. Our cases illustrate an important contingency and highlight how especially more experienced
venture capital investors play a vital role in professionalizing ventures by shaping their management team.
Table 4.7 summarizes key changes in management. We focus on changes in the management team
involving the recruitment of new CEOs and CFOs, as these are the most influential positions that shape
the future finance process.
In Aptanomics, the experienced founding venture capital investor played a crucial role in hiring a
professional manager with considerable experience in the biotech industry shortly after founding. This
manager previously held key positions in several biotechnology ventures, both Belgian and foreign, since
the early 1980s. In the beginning of 2006, Aptanomics appointed a new CEO and its first CFO. The
purpose was to further strengthen the management team with international experience in both private and
public financing 25 . The same story holds for AC Pharma. In Genom, an experienced manager was
transferred from one of the highly successful biotechnology parents.
24 Besides venture professionalization, stunted learning and syndication preferences other processes may enforce persistence in
the finance process. Venture capital deals are characterized by extensive contracts and venture capital investors, for example, will
typically insist on rights of first refusal, which give the venture capital investor the right to invest in subsequent rounds (Sahlman,
1990). Such contract clauses add to persistence in the finance process.
25 Interestingly, Aptanomics’ former CEO now took a leading role in a new spin-off from the research institute from which
Aptanomics originated and the investor offering startup capital to this new spin-off was the same as the first round investor in
Aptanomics. It indicates that highly experienced venture capitalists have access to a pool of experienced managers, which they
transfer between their portfolio companies.
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TABLE 4.7
Key Changes in the Management Team Relating to the CEO and CFO
Professional CEO hired/changes Professional CFO hired/changes
High Growth Companies
Theraptosis Startup year CEO hired with consulting
background in the biotech industry.
AC Pharma Startup year
2 years after
startup
CEO hired under impetus initial
investor with consulting background
in the biotech industry.
New CEO hired with consulting
background in biotech industry.
Previously, informal advisor to
venture capital funds.
Genom Startup year CEO hired from parent company.
Limited experience in raising
financing.
Aptanomics Startup year
4 years after
startup
Pharmaleads None
Entomed None
Myosic 2 years after
startup
Irogene None
I-Zyme None
Source: Interviews, press releases.
CEO hired, with extensive
experience in both European and US
biotechnology startups under
impetus initial investor.
New CEO hired with a successful
track record in creating company
value and attracting finance both in
private and public area.
Low Growth Companies
New CEO hired under impetus
initial investor. Previously cofounded
an ICT company and a
medical software venture.
7 years after
startup
5 years after
startup
7 years after
startup
4 years after
startup
Professional CFO hired
None
Professional CFO hired
New Professional CFO hired
Professional CFO hired
Although relatively inexperienced investors also initiate changes at startup (see for example Therapthosis),
these changes are not common among the low-growth cases that are backed by inexperienced investors. In
Pharmaleads and Entomed, both ventures backed by inexperienced venture capital investors, no changes
took place in team formation across time. The same holds for Irogene, which was financed by its poorly
None
None
None
None
None
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performing biotechnology parent company and I-Zyme, the first spin-off from a research institute, which
was financed by this institute.
Furthermore, experienced investors do not necessarily initiate changes in the management team at startup.
In Myosic, for example, the relatively inexperienced academic investor and more experienced corporate
investor had doubts about the management qualities of the founder. Despite these concerns, the investors
accepted that he took the lead over the venture. It was only some two years after founding, once the
scientific founder’s shortcomings became visible through weak progress, that a more experienced CEO
was hired.
Strengthening the founding team with experienced managers and finance specialists is likely to benefit
ventures in their search for follow-on financing. Maurer and Ebers (2006) indicate that high-growth
ventures are more likely to introduce specialization within the entrepreneurial team compared to their low-
growth peers. Our cases are generally consistent with this finding and extend them by indicating that
especially more experienced venture capital investors are at the origin of professionalization within
entrepreneurial teams thereby offering the ventures they back a competitive advantage when mobilizing
subsequent financial resources.
4.6.2. Stunted Learning
Hiring experienced managers and finance specialists is only one path to increase the likelihood of
mobilizing follow-on finance. Learning may be an alternative path. Learning is cumulative in nature and
repetition of the same task will benefit people in performing those tasks (Cohen and Levinthal, 1990). As
a result, knowledge gained from interacting with investors is expected to accumulate across time and
entrepreneurs are likely to learn many facets of dealing with investors through their early experiences with
financiers. As a result, entrepreneurs may develop knowledge based on prior experiences that will benefit
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them in their search for follow-on financing. The following quote by the CEO-founder of Myosic
illustrates:
“The first financing round helped me to improve the way I present and negotiate with investors. I now feel it will be
easier to locate new financing sources for future financing rounds.” (CEO-founder Myosic)
Nevertheless, learning performance is greatest when the object of learning is related to what is already
known. Conversely, learning will be more difficult in novel domains (Cohen and Levinthal, 1990). In our
cases, founders are typically pure scientists, who lack experience in raising finance. Although some
founders perceive that they have learned more about finance through prior contacts with investors,
investors themselves indicate that many founders fail to fully develop the competencies required to
manage a business, including the ability to negotiate and raise large amounts of follow-on finance.
Investors, for example, often complain that the business plans of biotechnology ventures are not accessible
because they are too focused on the technology, which is the scientific founder’s main line of expertise.
The following quotes illustrate:
“I can give you examples of business plans from ventures that exist for over five years…when you look at those
business plans, which are written to attract follow-on financing, it still looks like these are written by scientists that
work on a scientific paper.” (Investment manager, Investor H, Highly Experienced VCF)
“One of the most important shortcomings of biotech business plans is an under developed business model. While
there is often a very interesting scientific concept, people have not thought adequately about how the science can be
translated into an economic model. Inexperience causes that timelines are unrealistic, the financial plan is
unrealistic…” (Senior investment manager, Investor E, Corporate VCF)
Investor heterogeneity compounds learning difficulties. Venture capital investors exhibit significant
heterogeneity in their selection behavior (Muzyka, Birley, and Leleux, 1996), level of experience (Hsu,
2004) and goals for investing in ventures (Hellmann, Lindsey, and Puri, 2008). This will influence the pre-
investment process of venture capital investors, which is a lengthy process containing several hurdles,
such as initial screening, due diligence, valuation and contracting (Fried and Hisrich, 1994). As a result,
approaching different types of investors requires different strategies, knowledge and skills.
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“In our venture capital firm the investment managers have a technical background and no financial background,
which is different from ‘traditional’ venture capital investors…we evaluate investment opportunities which
‘traditional’ venture capital investors ignore…” (Investment manager, Investor H, Highly Experienced VCF)
In an emerging industry where founders generally lack experience, our cases indicate that learning is an
inferior path compared to venture professionalization when developing a business. Ventures where pure
scientists take a leading role are at a competitive disadvantage because of stunted learning, which
constrains scientific founders in their search for follow-on finance. Furthermore, knowledge gained
through prior contacts with investors will not necessarily be suitable to approach new investors due to
different investor profiles. This makes it difficult for ventures backed by relatively inexperienced investors
to attract finance from highly experienced investors in the future, which further causes persistence in the
finance process.
4.6.3. Investor Syndication Preferences
It is common for entrepreneurial ventures to receive finance from multiple investors (a syndicate) over
several investment rounds, where later rounds typically involve continued investments from prior
investors and one or more new investors (Lerner, 1994). Entrepreneurs and investors indicate that
syndication preferences both facilitate and constrain future financing decisions. More specifically,
investors prefer to syndicate with (a) investors known from previous investments and (b) knowledgeable
counterparts.
Consistent with prior organizational studies, our cases indicate that investors are more likely to collaborate
with investors they know from previous investments (Hallen, 2008). We observe how investors
consistently form a syndicate with the same group of local and foreign investors. An investment manager
at a more experienced venture capital firm describes:
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“You always see particular funds investing together…You almost always see investor F investing first [a domestic
fund] and investors X and Z [other Continental European funds] offering follow-on financing.” (Investor H)
Why do investors prefer to form a syndicate with other investors they know from prior investments? One
possible explanation advanced by prior research is reciprocity, where lead investors invite other investors
to join the syndicate in the hope these investors will return the favor in the future (Sorenson and Stuart,
2001). Reciprocity will thus lead to clusters of investors emerging in different portfolio companies over
time. This picture is incomplete, however. Prior research indicates embedded relationships are preferred
because these reduce the risk and uncertainty associated with interorganizational exchange (Chung, Sing
and Lee, 2000; Wright and Lockett, 2003; Sorenson and Stuart, 2008). Our cases demonstrate that
embedded relationships are chosen as the development of relationship specific heuristics reduces the
uncertainty surrounding the behavior of other investors. One important risk perceived by biotechnology
investors, for example, is that other investors do not allocate sufficient funds for follow-on financing. This
may cause distress and even failure and consequently increases the risk to any single finance provider
(Oakey, 1995). By deciding to co-invest with investors known through previous mutual investments in the
biotechnology sector, investors limit this risk. A senior investment manager at a more experienced venture
capital firm describes his experience:
“It is important to know who your co-investors are…You want to know before you enter a venture how your coinvestors
will react when problems emerge, follow-on financing is needed at a later stage… If you look at our
investment portfolio you will see a number of co-investors emerging frequently…” (Investor F)
Highly experienced investors, because of their active involvement in the industry, typically have more ties
to other investors within the biotechnology investment community. This will benefit ventures that raise
finance from more experienced investors when searching for follow-on finance. Ventures connecting with
relatively inexperienced investors early-on will be more constrained in their options for follow-on finance.
Hence, not only the history of the venture itself, but also the history of its investors is likely to influence
the subsequent finance process.
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Additionally, our cases illustrate how initial investors that have experience with biotechnology ventures
attract other experienced investors in the future. Starting with investors that do not have a track record of
past biotechnology investments, however, makes it more difficult to attract investors with deep knowledge
in the biotech industry in the future. A senior investment manager at a more experienced venture capital
firm indicates his preference:
“Assume we receive two similar proposals: one from company X and one from company Y. However, in company X
we have an experienced investor and in company Y we have a fund that invested for the first time in biotechnology.
We will definitely select company X and I believe we will be very hesitant to invest in company Y.” (Investor F)
Hence, investors prefer to form a syndicate with similar others. Much more than a mere sociological
argument, such as homophily (e.g., Sorenson and Stuart, 2001), results are more likely to be driven by
resource considerations (Brander, Amit, and Antweiler, 2002). Experienced investors typically prefer to
form a syndicate with other experienced investors, as they are more confident in their ability to help build
a venture through the provision of extra-financial services.
“If you start with the wrong investors the company will go nowhere. Everything goes wrong! If you start with good
investors the Board of Directors can be used as a forum. With the wrong investors, the Board becomes an empty
thing…What you ask from investors in a young company is not only the money, but also their expertise… If you
start with experienced and well-networked investors, it is easy to attract similar investors in the future. If you start
with inexperienced and poorly networked investors, nobody wants to join the syndicate later-on.” (CEO Aptanomics)
“If an investor obtains a seat in the Board of Directors, you want investors who can offer an important contribution,
investors who know the sector… In a syndicate you want to have investors that can add value to the firm.”
(Investment manager, Investor H, Highly Experienced VCF)
The conditions prompting investors to initiate relationships with more distant investors instead of
investors that are known from prior investments are not well understood (Baum, Rowley, Shipilov, and
Chuang, 2005; Sorenson and Stuart, 2008). Our cases hint that when an investor trusts another investor in
its capacity to build a young biotechnology venture, because of its prior experience, the investor will be
more likely to form a syndicate irrespective of whether the investors know each other from prior
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investments. Hence, ventures backed by highly experienced investors will find it relatively easier to attract
follow-on finance from other experienced investors compared to their peers backed by relatively
inexperienced investors.
4.7. Discussion
Our study highlights three key findings. First, mobilizing finance from highly experienced investors
during the startup phase affects the ease with which ventures mobilize follow-on finance during the
development phase. Second, entrepreneurs mobilize early finance through a local search thereby limiting
the number of investors they target to one or a few investors that are related to the venture. Third, early
differences in the finance process are likely to persist, as venture professionalization, stunted learning and
investor syndication preferences make it difficult for ventures to replicate successful finance strategies
from their peers. The key findings are summarized in Figure 4.2.
The proposed framework presented in figure 4.2 is not entirely deterministic, however, and does not
suggest that when a venture raises initial finance from a relatively inexperienced venture capital investor it
will automatically be locked-in. It may still be possible for ventures to raise more experienced finance
early-on despite the presence of a relatively inexperienced investor in the venture. Theraptosis is a good
illustration. The venture raised startup finance from a relatively inexperienced venture capital firm at
startup, but raised finance from highly experienced investors in the next finance round. Three elements are
worth noting. First, although the venture started with a relatively inexperienced investor, an experienced
manager was hired early-on working shoulder to shoulder with the scientific founder. Second, with the
startup finance raised, the venture reached all important milestones, hence demonstrating its managerial
and technological abilities. As a result, the initial investor actively supported the venture. Third, the CEO
used a particularly insightful strategy to approach highly experienced investors. While the CEO was in a
weak position to approach highly experienced investors directly, he resorted to network leverage strategies
175

(Gargiulo, 1993). He approached not only the shareholders of the university-related investor (mostly bank-
related venture capital investors), but also approached more experienced investors that frequently co-
invest with the shareholders of the university investor. Therapthosis is the only case, however, which
started with a relatively inexperienced venture capital investor and subsequently raised finance from
highly experienced investors. The case indicates that although possible, it is difficult to change a venture’s
evolutionary path. It requires relatively quick action, demonstration of performance, investor support and
clever management.
Amount of venture
capital finance raised
FIGURE 4.2
Summarizing Framework of the Finance Process of High-and Low-Growth Companies
Similar growth
potential and growth
ambitions
LOCAL SEARCH: SELECTION
OF EARLY INVESTOR(S) BY
THE ENTREPRENEUR
Startup Development
High-growth company
Early investor: highly experienced
PERSISTENT DIFFERENCES CAUSED BY:
. Venture professionalization
. Stunted Learning
. Investor syndication preferences
Low-growth company
Early investor: relatively inexperienced
Time
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Although the above indicates that entrepreneurial ventures which demonstrate performance may change
their evolutionary path under some conditions, it is unlikely that performance is entirely driving the
subsequent finance process rather than the type of investor that contributed early finance. First, during the
early startup phase, our cases did not differ systematically in their potential to develop into professional
biotechnology ventures. Nevertheless, in the companies backed by experienced investors much more
effort was put under the impetus of these investors to professionalize, which substantially influenced
subsequent venture development. Second, not all ventures backed by inexperienced investors are
necessarily badly performing ventures. Although Entomed, for example, failed to realize its high growth
ambitions the company was able to raise follow-on finance on two occasions from other relatively
inexperienced investors. This indicates that despite the inherent potential within the venture, Entomed is
unable to translate this potential in high growth.
4.7.1. Reframing the Entrepreneurial Finance Process as an Evolutionary Model
Two of the most influential theories in modern corporate finance that are generally used to frame finance
decisions are the static trade-off and pecking order theory (Cassar, 2004). Most researchers have treated
these theories as competing theories, which resulted in numerous large-sample studies empirically testing
which framework performs best (Fama and French, 2005; Frank and Goyal, 2005; Shyam-Sunder and
Myers, 1999). Nevertheless, the similarities between these theories have typically been ignored. Both
theories are teleological models. First, they depict the finance process as a process where managers make
optimal finance decisions with value maximization as the ultimate goal (Myers, 1984). Second, they
assume limited influence from prior finance decisions on future finance decisions as venture will actively
rebalance their financial structure towards an optimum (Hovakimian, Opler, and Titman, 2001; Leary and
Roberts, 2005). Also popular are life-cycle models. The financial growth cycle is probably the most
influential life-cycle model. It combines pecking order theory and optimal security design and indicates
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that the availability and suitability of different sources of finance change as a venture develops and grows
(Berger and Udell, 1998).
Although current teleological and life-cycle models advanced our understanding of the finance process
significantly, much remains unknown. Current theories are not able to explain all stylized facts in
corporate finance (Frank and Goyal, 2005). Furthermore, these theories are unable to explain the key role
of the finance process in shaping sustainable differences in venture development (Subrahmanyam, 2007).
Without in any way dismissing the valuable insights offered by teleological and life-cycle models, this
study suggests that we can further increase our knowledge of the finance process -a key organizational
process- by using a different lens. Our findings indicate an evolutionary model may be particularly
insightful. Hence focusing on early differences in the finance process and on the persistence of finance
decisions across time may be a particularly insightful strategy to gain a more comprehensive
understanding of the finance process. It corresponds with the view that organizational processes should be
studied from different lenses, which each focus on different generative mechanisms explaining change
(Van de Ven and Poole, 1995).
An important consideration is whether evolutionary trajectories in the finance process are limited to our
setting of biotechnology startups. We believe our findings relate to a broader set of entrepreneurial
ventures. First, most of the biotechnology entrepreneurs in our cases are scientists with limited finance
experience, which might limit their search for finance. However, the majority of entrepreneurs are
inexperienced and lack networks in the investment community (Westhead, Ucbasaran, and Wright, 2003).
Moreover, entrepreneurs are generally less familiar with finance sources commonly used to fund growth,
such as venture capital, compared to traditional finance sources, such as internal finance and bank debt
(Van Auken, 2001).
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Second, it is remarkable that biotechnology entrepreneurs limit their search for startup finance to only a
few related investors they happen to know given that finance decisions are very important in the
biotechnology context (Greene, 1999). If in this setting entrepreneurs limit their search for finance, then
what should we expect from entrepreneurs in an average venture, where finance decisions are considered
to be less critical? Indeed, the concept of local search in the search for finance corresponds with the
stylized fact that the majority of entrepreneurs start with finance from known investors, i.e. family and
friends (Berger and Udell, 1998). An important boundary condition for local search behavior is probably
the entrepreneurial setting. Large and quoted corporations typically employ multiple finance specialists.
Hence, we might expect a more comprehensive search for finance that focuses more on value
maximization in these corporations.
Finally, we focus on the mobilization of venture capital finance across time. Recent studies have pointed
out the importance of heterogeneity among venture capital investors (Hsu, 2004; Sorensen, 2007). It is a
common belief that debt financing is more homogenous than equity finance. Nevertheless, recent studies
indicate that debt is also heterogeneous in nature and more importantly that this heterogeneity also has
important implications for R&D investments and firm growth (David, O’Brien, and Yoshikawa, 2008).
Hence, not only differences in the equity finance process are likely to persist, but also differences in the
debt finance process are likely to have a long-term impact on venture development. This implies that our
findings also bear relevance beyond ventures that mobilize venture capital finance.
4.7.2. Resource Mobilization and Initial Network Formation
We contribute to a broader literature on resource mobilization and initial network development by using
the finance process as the research context. Relationships with venture capital investors are one of the
earliest and most critical relationships formed, especially within young technology-based ventures, as
these ventures need swift access to outside finance to support further development (Katila, Rosenberger,
179

and Eisenhardt, 2008). In this context, investors are typically portrayed as the more powerful, desirable
and resource-rich firms, while entrepreneurial ventures are typically portrayed as passive bystanders that
are in need of outside resources.
The mainstream view is that it are the more powerful, established and rich-rich firms that are particularly
likely to contribute resources to ventures headed by entrepreneurs with prior experience and ventures that
have demonstrated outstanding accomplishments since founding (Cable and Shane, 2002; Eckhardt,
Shane, and Delmar, 2006; Sorensen, 2007; Hsu, 2007; Hallen, 2008). Contrary to prior research, our study
indicates that the more established and resource-rich firms do not necessarily have access to the entire
pool of entrepreneurial ventures with high growth potential. Entrepreneurs heavily affect early relationship
formation by not engaging in a comprehensive search for startup resources. They avoid loose contacts
with many potential resource providers and instead target only one or a few related firms to acquire startup
resources.
External relationships with firms create dyadic ties, which are the basic building block of the external
network for new ventures (Hite and Hesterly, 2001). Two opposing views exist on how the use of
embedded ties to mobilize startup resources may influence subsequent resource mobilization and venture
development. Network closure theorists propose that a close-knit network of embedded ties are beneficial
as these ties create an environment of trust and cooperation, which is needed to mobilize critical resources
(Coleman, 1990). Structural hole theorists, however, propose that embedded ties act as a source of inertia
thereby constraining subsequent resource mobilization (Gargiulo and Benassi, 2000). Loose connections
with multiple firms, which offer ventures more diversity in information and brokerage opportunities
created by the lack of connection between separate clusters, are particularly valuable (Burt, 1992).
This paper contributes to bridging the tension between these two views. Our cases indicate how early
embedded ties both constrain and facilitate ventures in their subsequent resource mobilization and
180

development. Both high-growth ventures and low-growth ventures resorted to embedded firms to mobilize
startup resources. Whether early embedded ties foster or hinder ventures in their subsequent development
is likely to be contingent on the compositional quality of these ties. Compositional quality refers to the
extent to which ties can provide the needed resources (Hite and Hesterly, 2001). In our research context,
for example, it refers to an investor’s ability to provide not only tangible financial resources, but also
better-quality services, such as professionalization of the venture and access to a network comprising other
prominent actors.
Our cases demonstrate how early ties to firms that are high in compositional quality facilitate subsequent
resource mobilization and venture development. Experienced investors, for example, typically helped in
raising large amounts of follow-on finance by initiating venture professionalization and offering ventures
access to their network in the financial community. Early ties to firms that are low in compositional
quality, however, constrained subsequent resource mobilization. Relatively inexperienced investors, for
example, generally contribute little to venture professionalization and repel other investors from
contributing financial resources in the future. Our cases demonstrate that not only the history of the
venture itself matters when mobilizing resources. An important factor that may facilitate or hinder a
venture in its ability to mobilize resources is the history of the firms from which the venture mobilized its
early resources.
4.8. Conclusion
Overall, our theoretical contribution lies in reframing the entrepreneurial finance process as an
evolutionary process. We demonstrate persistence in the finance process and the central role of local
search behavior and investor heterogeneity. These ideas stand in stark contrast to current corporate finance
theories in which economic rationality and optimal contracts dominate. More broadly, we add to a dearth
of research on resource mobilization and network formation in new ventures. While entrepreneurial
181

ventures are often depicted as passive in their efforts to mobilize resources from more established
resource-rich firms, we highlight the key role of entrepreneurs in the mobilization of startup resources by
restricting their search to related firms. Moreover, we bring to light the processes explaining why early
affiliation to some firms led to an accumulation of advantages, while early affiliation to other firms led to
an accumulation of disadvantages in subsequent resource mobilization efforts.
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Chapter 5: Limitations, Avenues for Future Research and
Implications for Practice
The three studies included in this dissertation all investigate the relationship between the entrepreneurial
finance process and company growth in a Continental European setting. Study 1 looks at incremental
finance decisions in high-growth companies. In particular, it investigates the relationship between high-
growth company characteristics and the use of internal finance, debt finance and new equity finance. We
empirically test the pecking order theory and extend the notion of debt capacity. Study 2 focuses on
investor heterogeneity and examines which investors contribute most to company development. I look at
the role of venture capital firm experience and legitimacy on the growth pattern of portfolio companies.
Finally, study 3 offers a dynamic view of the finance process. It demonstrates how the finance process
differs between high- and low-performing entrepreneurial ventures, how those differences originate and
how differences are likely to persist across time.
The lack of longitudinal data on unquoted businesses is an important shortcoming characterizing most
research in entrepreneurship, finance and organizational growth (Davidsson and Wiklund, 2006). Most of
our knowledge relates to companies that have had some successful conditional outcome like going public
and joining a strategic alliance (Puri and Zarutskie, 2008) and research on small company finance is often
subject to a survivorship bias (Cassar, 2004). Each study in this dissertation builds on a different hand-
collected longitudinal dataset comprising high-quality (financial) data on unquoted companies. Moreover,
all datasets comprise companies that eventually fail and hence findings are not subject to survivorship
bias. The datasets permit to provide important insights in the finance process of unquoted companies and
its relationship with company growth in a Continental European research context.
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The remainder of this chapter is structured as follows. I start by discussing the limitations of this
dissertation and suggest some promising avenues for further research. Next, I discuss the implications for
entrepreneurs, investors and policy makers.
5.1. Limitations and Avenues for Future Research
All studies have their limitations and this is not different for the studies in this dissertation. The first study
used a large database of high-growth companies and focused on financial decision making within this
setting. Although I had access to yearly financial statement data for all Belgian companies, including the
ones identified as high-growth companies, not all companies are required to report sales. This implies that
the group of high-growth companies identified by using the sales concept is likely to be incomplete. To
mitigate this problem, I used added value as an alternative concept, which all companies are required to
report. Sales are an important input variable in the calculation of added value and hence the correlation
between the two growth indicators was rather high.
Another shortcoming of the first study was that it only focused on how high-growth companies are
finance. Hence, this study is unable to distinguish between financial policies in high- and low-growth
companies. Moreover, it does not provide insights into questions such as whether access to finance causes
growth or growth requires finance. Despite its shortcomings, the first study demonstrated that high-growth
companies with low leverage do not necessarily have debt capacity. Prior research has equated high
leverage ratios with limited debt capacity. Scholars have raised doubts about the validity of the pecking
order theory, as high-growth companies with low leverage should be able to attract additional debt
finance, but many of these companies unexpectedly raise new equity (Fama and French, 2005). The first
study demonstrated that such claims reflect a limited view on debt capacity. Not only leverage, but
leverage and cashflows will determine whether companies are able to attract additional debt finance. A
good real-world example is provided by young biotechnology ventures. These ventures have low leverage
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and despite these low leverage ratios are unable to attract additional debt finance. This is because they
lack the necessary cashflows to fulfill the fixed debt-related payments. The first study calls for more
research, which further develops the notion of debt capacity.
The second study focused on the impact of the experience and legitimacy of the initial lead venture capital
investor on subsequent company development by using appropriate longitudinal techniques. A particular
methodological challenge was to distinguish investor experience from investor legitimacy. Relatively high
correlation between the measures forced me to carefully build models were multicollinearity concerns
were limited. Nevertheless, I reported robust findings indicating that portfolio companies benefited from
receiving finance from investors with high industry experience and legitimate investors (i.e. older
investors and investors that appear more frequently in the media). An interesting observation, however,
was that even within the group of companies backed by similar types of investors there was significant
variability in how firms grow. While some firms backed by a particular type of investor experienced
dramatic growth and went public others backed by the same type of investors were outright failures. This
raises the question if and when the effect of connecting with particular types of investors varies for
different types of firms, and so yields different consequences for firm growth.
One particularly interesting contingency is the effect of founder experience on the relationship between
venture capital and growth. Organizational theory indicates that experienced founders will be particularly
likely to gain access to experienced venture capital finance (Hallen, 2008). However, experienced
entrepreneurs probably require less knowledge-based resources from venture capital investors given their
own background and experience. One might expect that companies founded by inexperienced
entrepreneurs may particularly benefit from the infusion of knowledge-based resources to complement the
lack of founder experience. Moreover, connecting with particular investors, such as highly experienced
venture capital investors is costly (Hsu, 2004). Again especially inexperienced investors will be willing to
pay a premium for the advice and other services provided by more experienced investors, while
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organizational theory indicates they are less likely to obtain finance from these more experienced
investors. Overall, it remains unclear whether investor experience and entrepreneurial experience will act
as substitutes or complements.
The third study used longitudinal case studies to gain a deeper understanding of the finance process in
high- and low-growth ventures. It demonstrated how early differences originate in the finance process and
how differences persist across time between high- and low-growth ventures. A particular challenge to
generate insights on how the finance process of ventures relates to growth was to select matched pairs of
ventures that operated under similar conditions but differed considerably with respect to their growth.
Although a perfect match was never possible, I believe that consistent findings across multiple comparable
cases increase the reliability and validity of the findings. The third study indicated how early investors
may both facility and constrain ventures in the subsequent mobilization of financial resources.
This third study calls for more research on when ventures resort to existing or new investors to obtain
follow-on finance. Two opposing perspective may be found in the literature. One perspective based on
agency conflicts indicates that insiders may continue to invest in projects with significant probabilities of
destroying value (Admati and Pfleiderer, 1994). Once entrepreneurs have raised venture capital, the early
investors become insiders. The early investors may learn of problems within the company through time.
Nevertheless, these problems are not necessarily visible for outsiders, due to the existence of asymmetric
information. Early investors now have the incentive to spend other investors’ money and hence may help
or force entrepreneurs to search for new finance from other investors. In this perspective, early investors
will keep good companies for their own and try to attract as much finance from colleague investors for
bad quality companies.
A different perspective indicates that early investors will invite colleague investors to join high-quality
companies, in the hope that these investors will return the favor in the future (Sorenson and Stuart, 2001).
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Additionally, early investors may invite other high-quality investors to invest in their portfolio companies
in order to infuse more (complementary) tangible and intangible resources, which contribute to the
development of these companies (Brander, Amit, and Antweiler, 2002). The case study evidence provided
in the third paper provides limited evidence for the agency perspective. Rather it points more towards the
vision that especially high-quality companies will raise finance from (existing and) new investors.
Moreover, I want to highlight two main limitations of all studies in this dissertation. First, all studies have
a limited geographical coverage and focus on the finance process of unquoted Belgian companies. We
previously argued that this context has many advantages. First, single country studies reduce non-
measured variance and thereby control for country-specific determinants, such as the financial context and
legal context. Second, all Belgian companies are required to file detailed financial statement data with the
National Bank, which implies we have access to high-quality longitudinal data on unquoted companies.
Finally, while most studies have focused on financial policies in market-based economies, Belgium is a
typical example of a bank-based economy. Despite its advantages, a drawback of single-country studies is
that that the external validity of the findings may be low for other regions. Nevertheless, reflecting on this
drawback, I believe that Belgian companies are probably more similar to the average company in many
other regions, compared to prior studies that exclusively focused on quoted U.S. companies (Shyam-
Sunder and Myers, 1999) or entrepreneurial companies operating in very specific, one of a kind
environments, such as Silicon Valley (Hallen, 2008).
Second, while all studies in this dissertation put the finance process on the foreground, they put other
important organizational processes, such as the development of the entrepreneurial management team and
technology in the background. Nevertheless, as indicated in the final study, the finance process may, for
example, influence the structure and experience of the management team and vice versa. A more complete
understanding of the relationship between different organizational processes in high- and low-growth
companies may significantly enhance our understanding of the emergence of high-growth companies.
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However, we have no choice but to cut down on the complexity of a problem but to cut down on the
complexity of the problem domain by putting some processes in the foreground and others in the
background (Van de Ven, 2007).
Overall, this dissertation offered a broader framework to grasp the complexity of the entrepreneurial
finance process and indicated how the development of evolutionary models -incorporating foundational
behavioral concepts– may be a particularly valuable research strategy to increase our understanding of the
entrepreneurial finance process. It is important to note that I do not argue that evolutionary models are
superior to other models such as teleological models. On the contrary, all models are valuable and direct
researchers’ attention to particular motors of change.
To organizational growth scholars the dissertation indicated that future growth studies should more fully
incorporate other dimensions of the finance process besides the amount of finance raised. More financial
resources at startup are not necessarily better. Other dimensions, such as the type of investor that
contributes finance, might offer ventures a competitive advantage as well. Moreover, future research
should address the temporal pattern of growth. For this purpose, longitudinal dataset will be required. This
will require scholars to become better acquainted with the appropriate longitudinal techniques to study
longitudinal datasets. Recent years have seen considerable progress in the development of statistical
methods for the analysis of longitudinal data. These modern techniques will allow organizational scholars
to focus on different questions, which more fully incorporate the essence of organizational growth; namely
change across time.
5.2. Practical Implications
In this section, I discuss the practical implications of the different studies in this dissertation for
respectively entrepreneurs, investors and policy makers.
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5.2.1. Entrepreneurs
By studying the finance process of (small) unquoted companies and its relationship with company growth,
growth-oriented entrepreneurs can gain a more thorough understanding of the financial policies related
with high-growth companies. First, despite the media visibility and research attention towards companies
that raise large-amounts of venture capital or raise public equity finance, outside equity is not a
prerequisite to realize high growth across time. Over 80% of finance events in high-growth companies
relate to internally generated funds and debt finance. It is only when debt capacity is exhausted and large
amounts of finance are needed that high-growth companies move to outside equity investors.
Second, many entrepreneurial companies are constantly strapped for cash and hence entrepreneurs are
under pressure to accept finance when and where they can find it irrespective of the source of finance.
Nevertheless, the experience and legitimacy of the investors from whom entrepreneurs raise early finance
has a long-lasting impact on company development. Companies that raise finance from investors with
high industry experience and legitimacy in the marketplace are able to recruit more people, mobilize more
assets and raise more follow-on finance to fuel growth.
Third, entrepreneurs are generally local in their search for early finance and target only one or a few
related investors. Although this may be a cost-effective and speedy way to obtain access to well-needed
financial resource, it might also be advisable to conduct a broader search and test the market for credible
alternatives. Especially entrepreneurial companies that are not connected to highly experienced investors
may benefit from a more comprehensive search.
Forth, a common believe among entrepreneurs is that highly experienced and legitimate investors select
larger more mature companies and typically avoid investing in highly risky companies that are still in the
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seed phase. Our studies indicate that more or less experienced and legitimate investors do not select
different companies in terms of initial size in employment and total assets. Moreover, when studying the
finance process of new biotechnology ventures, we observed that even highly experienced investors
contributed seed finance to new biotechnology ventures.
Finally, early investors will not necessarily facilitate the acquisition of future financial resources, but may
also repel future investors from contributing financial resources to the company. Our interviews indicated
how some entrepreneurs started with relatively inexperienced investors, but wanted to raise finance from
more experienced investors in the future. This is extremely difficult, however, and most companies fail in
this endeavor. When lucky, they can still find additional finance from other inexperienced investors, but
many companies become what may be called “living-death”, i.e. the company survives, but is unlikely to
ever realize its initial ambitions.
5.2.2. Investors
Insights from the different studies in this dissertation are important for professional investors as well.
First, outside equity investors do not have automatic access to all types of companies. Companies that
search for outside equity are typically those companies that are unprofitable, have limited debt capacity,
invest significantly in intangible assets and are characterized by a high risk of failure. Moreover,
entrepreneurs are generally local in their search for finance especially during the startup phase. Most
entrepreneurs limit their search for finance to those investors with whom they have at least a superficial
direct or indirect relationship. Hence, active deal origination remains important for those investors that
want to play a key role in building young high-potential companies.
Second, inexperienced venture capital investors sporadically contributing capital to young knowledge-
intensive companies operating in complex industries such as biotechnology may question their current
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investment strategy. Given the required expertise and network structure to guide these companies through
different stages of development, it might be a better strategy for inexperienced investors to only act as a
non-lead investor in a syndicate. I did note that when large amounts of finance are needed in the later
stages of development, highly experienced venture capital investors invited relatively inexperienced
investors to join an investment syndicate.
5.2.3. Policy makers
The different studies in this dissertation also offer some important insights and recommendations for
public policy. Two important topics are highlighted in this subsection. First, policy makers should be
aware that although outside equity is important to allow companies to grow beyond their debt capacity,
high-growth companies prefer to use retained earnings as an internal source to finance investments
whenever possible. Hence, recent initiatives by the Belgian government, such as the ‘notional interest
deduction’ are probably well-taken. This policy measure reduces the fiscal discrimination between debt
and equity finance by allowing companies to deduct from their taxable income an amount equal to the
interest they would have paid on their capital (including retained earnings) if that capital was long-term
debt. This measure increases the incentive of companies to retain earnings and increase the amount of
internal funds that are available within the company instead of transferring them to government. The value
of the ‘notional interest deduction’ is highly debated. Some politicians call to abolish this measure, as
costs are high for government and taxpayers, while it would especially benefit large and rich corporations
without creating additional investments. I provide evidence that such claims are shortsighted, as retained
earnings are the preferred path used by high-growth companies to finance growth in employment, total
assets, value-added, sales and cash flow.
Second, government officials are increasing the supply of outside equity finance to especially young
innovative and high-growth oriented companies by contributing significant amounts of finance into the
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venture capital industry. The main rational for this government intervention in the venture capital market
is the role of venture capital in providing funds for companies that find it difficult to attract finance from
more traditional financiers such as banks and hence reduces the finance constraints (Lerner, 1999). It is
further motivated by the observation that a mature venture capital market in the U.S. played a critical role
in the emergence of new industries such as biotechnology (Berger and Udell, 1998). However, policy
makers should realize that venture capital investors not only contribute financial resources. Policy
measures targeting experienced and legitimate investors may have a disproportionate positive effect on
employment generation and asset accumulation in an economy.
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