the importance of protection ability as selection

UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE

ACADEMIEJAAR 2009 – 2010

The importance of protection ability as selection criterion

for venture capitalists.

Masterproef voorgedragen tot het bekomen van de graad van

Master in de Toegepaste Economische Wetenschappen

Annelore Huyghe

onder leiding van

Prof. Dr. Mirjam Knockaert

PERMISSION

The undersigned author declares that the content of this master thesis may be consulted and/or

reproduced, provided that the source is mentioned.

Annelore Huyghe

I

ACKNOWLEGDEMENTS

In order to obtain the degree of Master in Applied Economics, I deliberately used the opportunity to

suggest an own topic for my master thesis, after consulting Prof. Dr. Mirjam Knockaert. The research

question is related to two aspects that already appealed to me during the Bachelor-courses of

‘Entrepreneurship’ and ‘Business Planning’ where we had to develop an innovative business plan.

Two critical decisions for entrepreneurs are considering what is the appropriate source of funding

and whether or not intellectual property protection is advantageous. This paper starts from the point

of view of venture capitalists, which are often the only source of finance for risky new ventures. This

allows me to get more insight into the behavior of an important group of investors that many

entrepreneurs have to deal with. Writing this dissertation was an instructive and interesting

experience to complete my Master education.

I wish to thank my supervisor, Prof. Dr. Mirjam Knockaert, for the time she spent to the guidance of

my master thesis as well as for the database and the other useful information she provided to me.

Moreover, I thank my mother, Sabine Devos, whose feedback helped me a lot during the writing of

this paper and whose moral support was irreplaceable during my education at university in general. I

thank my both parents for investing in my future. A special word of thanks also goes to my brother

Dieter Huyghe, Mathias Haentjens and Sara Speltdoorn for their encouragement and valuable

comments.

II

TABLE OF CONTENTS

ACKNOWLEGDEMENTS ............................................................................................................................ I

TABLE OF CONTENTS ............................................................................................................................... II

LIST OF ABBREVIATIONS USED ............................................................................................................... III

LIST OF TABLES AND FIGURES ................................................................................................................ IV

NEDERLANDSTALIGE SAMENVATTING (SUMMARY IN DUTCH) .............................................................. V

ABSTRACT ................................................................................................................................................ 1

1. INTRODUCTION ............................................................................................................................... 2

2. LITERATURE REVIEW ....................................................................................................................... 4

2.1. The venture capitalist investment decision ............................................................................ 4

2.1.1. Processual research – What process do VCs use to evaluate potential investments? .... 4

2.1.2. Criteria research - What criteria do VCs use to evaluate potential investments? ........... 6

2.2. Protection ability and importance for new ventures and VCs .............................................. 16

2.2.1. Protection ability ........................................................................................................... 16

2.2.2. Importance of protection ability in entrepreneurship literature ................................... 17

2.2.3. Importance of protection ability in venture capital literature ....................................... 18

3. THEORETICAL FRAMEWORK AND HYPOTHESES ........................................................................... 20

3.1. Fund characteristics and importance of protection ability ................................................... 21

3.1.1. Source of VC funds ......................................................................................................... 21

3.1.2. Experimental learning ................................................................................................... 23

3.2. Investment manager characteristics and importance of protection ability ......................... 25

3.2.1. Industry-specific human capital ..................................................................................... 27

3.2.2. Task-specific human capital .......................................................................................... 27

3.2.3. General human capital .................................................................................................. 28

4. RESEARCH METHODOLOGY .......................................................................................................... 29

4.1. Sample ................................................................................................................................... 29

4.2. Data collection ....................................................................................................................... 30

4.3. Measures ............................................................................................................................... 31

4.3.1. Dependent variable ....................................................................................................... 31

4.3.2. Independent variables ................................................................................................... 31

4.3.3. Control variables ............................................................................................................ 33

5. RESULTS ........................................................................................................................................ 35

6. CONCLUSIONS AND LIMITATIONS ................................................................................................ 39

7. IMPLICATIONS AND DIRECTIONS FOR FURTHER RESEARCH ........................................................ 42

REFERENCES .......................................................................................................................................... 44

III

LIST OF ABBREVIATIONS USED

VCs Venture capitalists

NTBFs New technology-based firms

IPO Initial public offering

IPRs Intellectual property rights

R&D Research and development

EVCA European Venture Capital Association

OLS Ordinary Least Squares

PhD Doctor of Philosophy

IV

LIST OF TABLES AND FIGURES

Table 1: Prior findings on stages of venture capitalists’ decision process 6

Table 2: Prior findings on venture capitalists’ investment criteria 12

Table 3: Hypothesized impact of independent variables on importance of protection ability as selection criterion 28

Table 4: Descriptive statistics and correlations of continuous variables 35 Table 5: Regression analysis for importance of protection ability 38

V

NEDERLANDSTALIGE SAMENVATTING (SUMMARY IN DUTCH)

Tot op heden heeft een groot aantal academische onderzoekers zich reeds geconcentreerd op het

verkrijgen van inzicht in het selectiegedrag van risicokapitaalinvesteerders. Een onderscheid kan

gemaakt worden tussen enerzijds studies die zich focussen op de verschillende fases en activiteiten

in het beslissingsproces van de venture capitalist, en anderzijds studies die de selectiecriteria

identificeren die risicokapitaalinvesteerders hanteren tijdens het evalueren van potentiële

portfoliobedrijven. Hoewel de taken van een VC kunnen opgedeeld worden als activiteiten voor en

na het tot stand komen van de investeringsovereenkomst, betreft deze studie de initiële stadia in het

proces, namelijk het screenen en beoordelen van nieuwe ondernemingen. Uit voorgaand onderzoek

kunnen vier categorieën van criteria onderscheiden worden die risicokapitaalinvesteerders hierbij

gebruiken : kenmerken van de ondernemer en het managementteam, overwegingen aangaande het

product of de dienst, markt- en concurrentievoorwaarden en financiële en ondernemings-

gerelateerde criteria.

In de huidige literatuur ontbreekt echter onderzoek naar de determinanten die ertoe leiden dat

venture capitalists belang hechten aan welbepaalde selectiecriteria en aan beschermbaarheid in het

bijzonder. Beschermbaarheid kan omschreven worden als de beschikbaarheid van middelen die het

mogelijk maken winstgevende innovaties te beschermen tegen imitatie door concurrenten.

Meerdere studies toonden reeds aan dat risicokapitaalinvesteerders de mogelijkheid om de

technologie of het product te beschermen in beschouwing nemen tijdens de investeringsbeslissing.

Uit de ondernemerschaps- en de venture capital-literatuur blijkt dat de bescherming van

intellectuele eigendom integraal deel uitmaakt van de waardecreatie en het strategisch succes van

nieuwe ondernemingen en dienst doet als een signaalfunctie om VC investeerders te overtuigen van

het groeipotentieel. Bijgevolg is beschermbaarheid een centraal element in het verkrijgen van VC

financiering. Deze masterproef heeft als doel nagaan welke en in welke mate verschillen tussen

venture capitalists bepalend zijn voor de verschillen in de klemtoon die de investeerders leggen op

beschermbaarheid van de technologie bij het evalueren van mogelijke portfoliobedrijven.

Vanuit een theoretisch raamwerk worden zowel fondskarakteristieken als het human capital van de

investeringsmanagers, die verantwoordelijk zijn voor het selecteren van investeringsopportuniteiten,

verondersteld het belang van beschermbaarheid als selectiecriterium voor venture capitalists te

beïnvloeden. Hiertoe wordt een uniek samengestelde, reeds bestaande database gebruikt,

opgebouwd uit 68 Europese risicokapitaalverschaffers die investeren in hoogtechnologische

ondernemingen die zich in een vroege fase van ontwikkeling bevinden.

VI

Wat de fondskarakteristieken betreft, toont deze studie aan dat het percentage aan publieke

financiering waarover venture capitalists beschikken, een positieve invloed heeft op de mate waarin

de investeerders gebruik maken van beschermbaarheid als selectiecriterium tijdens hun

beslissingsproces. Gezien intellectuele eigendomsrechten kunnen beschouwd worden als de output

van succesvolle R&D-activiteiten, wijst deze bevinding erop dat venture capitalists die financiële

middelen ontvangen van publieke bronnen ervoor kiezen te investeren in nieuwe

hoogtechnologische ondernemingen met veel innovatief potentieel. Op die manier trachten ze de

objectieven van het overheidsingrijpen te realiseren, namelijk het reduceren van marktimperfecties

waarmee hoogtechnologische bedrijven tijdens vroege fasen van ontwikkeling geconfronteerd

worden, alsook het stimuleren van de economische groei via technologische innovatie. De

beschikbaarheid van publieke fondsen blijkt er dus toe te leiden dat venture capitalists

investeringsvoorstellen op een andere manier beoordelen en dat technologische selectiecriteria een

belangrijkere rol spelen.

Daarnaast geeft dit onderzoek aan dat risicokapitaalinvesteerders met een groter aantal

investeringen sinds de oprichting van het fonds minder aandacht zullen besteden aan de

beschermbaarheid van de technologie of het product tijdens de investeringsbeslissing. Deze

bevinding bevestigt het plaatsvinden van ervaringsleren en het minder belangrijk worden van

patenteerbaarheid als selectiecriterium naarmate de ervaring van het fonds toeneemt. Dergelijk

ervaringsleren betreft het proces waarin ondernemingen hun taken herhaaldelijk uitvoeren en hun

huidige acties aanpassen aan ervaringen uit het verleden, om op die manier te leren door

ondervinding en organisatorische kennis op te bouwen. Naarmate een venture capitalist meer

contracten heeft gesloten, raakt het fonds vertrouwder met het beslissingsproces en stijgt de

bekwaamheid om de meest geschikte portfoliobedrijven te identificeren. Daarom kunnen venture

capitalists met een groot aantal investeringen op hun eigen selectie-capaciteiten vertrouwen en

worden verhandelbare activa zoals patenten minder noodzakelijk om zich in te dekken tegen de

mogelijke risico’s en agency problemen die verbonden zijn aan vroege-fase, hoogtechnologische

investeringen.

Steunend op de notie van self-efficacy onderzocht deze studie ook de relatie tussen sectorspecifiek,

taakspecifiek en algemeen human capital van de investeringsmanagers en de mate waarin venture

capitalists de beschermbaarheid van de technologie benadrukken tijdens hun investeringsbeslissing.

Self-efficacy betreft het vertrouwen van een individu in zijn/haar capaciteiten om een specifieke taak

te organiseren en uit te voeren zodat vooropgestelde performantieniveaus en doelstellingen

gerealiseerd worden. Afhankelijk van de mate waarin men over self-efficacy beschikt, zullen

VII

personen verkiezen activiteiten uit te voeren en in sociale omgevingen te opereren die ze oordelen

te kunnen managen.

Tegen de verwachtingen in, werd geen significante impact gevonden van sectorspecifiek human

capital op het belang van beschermbaarheid bij het screenen van investeringsopportuniteiten door

venture capitalists. Ondanks de kennis en self-efficacy die ze bezitten met betrekking tot

hoogtechnologische gebieden, blijken investeringsmanagers met een technische of academische

achtergrond niet meer belang te hechten aan een technisch-gerelateerd selectiecriterium zoals

patenteerbaarheid. Een mogelijke verklaring voor deze bevinding is het feit dat de strategie en

ervaring op fondsniveau alsook eerdere VC-ervaring van investeringsmanagers een grotere impact

kunnen hebben op de gebruikte selectiecriteria dan de persoonlijke vertrouwdheid van de

investeringsmanagers met de hoogtechnologische context van de investeringsvoorstellen.

Verder blijkt de aanwezigheid van taakspecifiek human capital bij de investeringsmanagers positief

geassocieerd te zijn met het belang van beschermbaarheid als selectiecriterium voor VCs. Deze

bevinding is consistent met het self-efficacy aspect van human capital dat veronderstelt dat meer

ervaring in relevante taken toelaat deze effectiever uit te voeren. Investeringsmanagers die in het

verleden reeds VC fondsen hebben gemanaged, beschikken over een grotere bekwaamheid en self-

efficacy om de meest interessante portfoliobedrijven te selecteren en hebben de neiging om meer

aandacht te besteden aan beschermbaarheid. Immers, meer ervaren investeringsmanagers kunnen

beter omgaan met technologische onzekerheid en hebben bovendien een beter begrip van de

waarde en de voordelen die met de beschikbaarheid van intellectuele eigendomsrechten gepaard

gaan.

Ten slotte bewijst deze studie dat algemeen human capital van investeringsmanagers een negatieve

invloed heeft op het belang van beschermbaarheid als selectiecriterium voor VCs. Dit resultaat

bevestigt de verwachtingen. Aangezien algemeen human capital geen kennis verstrekt op

hoogtechnologisch gebied noch ervaring biedt in het managen van een VC fonds, zijn dergelijke

investeringsmanagers minder in staat om het innovatief potentieel en de voordelen van een

beschermde technologie of product te beoordelen en bijgevolg besteden ze minder aandacht aan

beschermbaarheid als selectiecriterium.

De resultaten van deze masterproef leiden tot een aantal nuttige inzichten en suggesties voor

risicokapitaalinvesteerders en hun investeringsmanagers, voor hoogtechnologische ondernemers,

voor de overheid alsook voor toekomstige onderzoekers.

1

ABSTRACT

The objective of this study is to fill a gap in literature by providing a better understanding of why

certain venture capital (VC) investors use protection ability as decision making criterion when they

evaluate technology based companies searching for early stage financing. Using a unique dataset of

68 European early stage high tech investors, this paper examines to which extent VC fund and

investment manager related factors influence the importance attached to protection ability as

selection criterion in the VC investment decision. At the fund level, the results show that publicly

funded VCs and public-private partnerships put a higher value on a new venture’s ability to own

patents and trade secrets than private VC funds. VCs with more fund experience, acquired through a

large number of investments since founding, were found to put less emphasis on protection ability

when evaluating potential portfolio companies. Furthermore, with respect to human capital

characteristics, investment executives that had managed VC funds in the past and possess task-

specific human capital, tend to emphasize protection of the technology. Moreover, the findings

indicate that general human capital is negatively associated with the importance of protection ability

as selection criterion for VCs. Finally, no indication was found that industry-specific human capital

relating to high-tech investments affects the importance attached to protection ability. This study

has a number of implications for VC firms that look for interesting investment opportunities, for high

tech start-ups that seek VC funding, for policy makers that attempt to overcome the financial gaps

for NTBFs, and for future researchers.

Key words: venture capital, early stage high tech firms, selection behavior, protection ability, human

capital, fund characteristics

2

1. INTRODUCTION

Venture capital firms are “those organizations whose predominant mission is to finance the founding

or early growth of new companies that do not yet have access to public securities or to institutional

lenders such as banks and insurance companies” (Gupta and Sapienza, 1992, p. 349). As venture

capitalists (VCs) are often the only or most appropriate source of funding, they play a key role in the

start-up and growth phases of risky entrepreneurial firms. Especially high tech new ventures, that

require substantial amounts of financing to get started and to realize growth, face difficulties to

obtain the necessary funding and typically appeal to VC firms. Consequently, VCs do not only

function as risk financiers, but also assume responsibility for innovation, technological renewal and

growth in the overall economy.

Empirical studies on VCs can be classified into six domains, thereby following Fried and Hisrich

(1988): portfolio of VC firms, investment decision, operations, strategy, impact on the entrepreneur

and public policy. Despite numerous researchers examining the multi-stage process and the criteria

used in the VC investment decision, the existing academic literature does not provide a clear and

comprehensive understanding of the determinants of why VCs put emphasis on selection criteria.

This paper aims to fill this important gap in literature with respect to the drivers of differences in the

importance attached by VCs to one particular selection criterion, namely protection ability. The focus

on the intellectual property criterion can be justified easily, as patents and trade secrets are an

integral part of value creation in high tech ventures and thus are a critical element in attracting VC

financing.

Some researchers already examined the influence of the VC’s experience and background on the

evaluation of business proposals (e.g. Dimov, Shepherd and Sutcliffe, 2007; Franke, Gruber, Harhoff

and Henkel, 2006; 2008; Patzelt, zu Knyphausen-Aufseß and Fischer, 2009). Other researchers

revealed that fund characteristics such as age, size or ownership structure determine to a large

extent which portfolio companies VCs decide to invest in (e.g. Gupta and Sapienza, 1992; Elango,

Fried, Hisrich and Polonchek, 1995; Hall and Tu, 2003). Only a limited amount of research about VC

investment decisions has yet integrated both human capital characteristics and VC fund features (e.g.

Clarysse, Knockaert and Lockett, 2006). This paper also tries to fill this second omission in literature

by taking into account both as drivers of the emphasis put on protection ability in the VC decision

process.

As noted by Clarysse et al. (2006), several authors underlined that the venture capital industry is

heterogeneous. Previous studies showed that early stage VCs differ from late stage VCs (Sapienza,

Amason and Manigart, 1994; Elango et al., 1995; Mayer, Schoors and Yafeh, 2005), and revealed

3

differences between high tech and non-high tech VCs (Murray and Lott, 1995; Lockett, Murray and

Wright, 2002; Baum and Silverman, 2004). Nevertheless, up to now research on VC investment

selection behavior mainly focused on the VC industry as a homogeneous group. By using a unique

dataset of 68 European early stage high tech investors, this paper only concentrates on a specific

group of VCs that invest in high tech start-ups at early stages. High tech VCs are crucial for the

funding of new technology-based firms (NTBFs). Start-ups with VC-funding tend to outperform non

VC-backed ventures in terms of a higher survival rate (Zacharakis and Meyer, 1998), growth and

innovative activity (Kortum and Lerner, 2000), access to lower bank interest rates (Gompers and

Lerner, 1999) and the time to bring a product to the market (Hellman and Puri, 2000).

This thesis starts from the premise that VCs are also heterogeneous in terms of their hierarchy of

decision criteria when evaluating business proposals. Specifically, this study addresses the following

research question: what drives the differences in the importance of protection ability as selection

criterion across early stage high tech venture capitalists?

The structure of this paper is as follows. The following section provides a summary of the current

status of empirical research on the investment decision of VCs and on the importance of protection

ability. Building on agency theory and human capital theory, part three of this study hypothesizes

that variations in the importance of protection ability in the VC investment decision may be affected

by both the human capital of the investment manager and VC fund characteristics. Next, research

methodology, data collection and measures are described in section four. Fifth, the results of the

analyses are presented and discussed. Finally, the paper ends with conclusions and limitations,

implications and some prospects for possible further research.

4

2. LITERATURE REVIEW

This section provides an overview of the existing literature with regard to the VC investment decision

and the importance of protection ability as asset for new ventures and as a selection criterion for

VCs. This literature review must allow to have a better understanding of both research topics and of

the existing gap. Additionally, this summary facilitates comprehending the contribution of this paper

to the current literature before examining the actual research question.

2.1. The venture capitalist investment decision

Since the 1970s, academic researchers tried to explain and to get insight into the selection behavior

of venture capitalists and this research domain has been intensively studied by scholars until the

present. A distinction can be made between researchers who focus on the successive phases and the

several activities in the decision-making process of venture capitalists on the one hand (e.g. Wells,

1974; Tyebjee and Bruno, 1984; Silver, 1985; Hall, 1989; Fried and Hisrich, 1994; Boocock and

Woods, 1997; Bliss, 1999; Silva, 2004; Klonowski, 2007) and researchers who attempt to identify the

criteria on which VCs base their investment selection decisions on the other hand (e.g. Wells, 1974;

Poindexter, 1976; Tyebjee and Bruno, 1981; 1984; MacMillan, Siegel and Subbanarasimha, 1985;

1987; Hutt and Thomas, 1985; Khan, 1987; Robinson, 1987; Sandberg, Schweiger and Hofer, 1988;

Keeley and Roure, 1989; Hisrich and Jankowicz, 1990; Riquelme and Rickards, 1992; Hall and Hofer,

1993; Meyer Zacharakis and De Castro, 1993; Fried and Hisrich, 1994; Muzyka, Birley and Leleux,

1996; Zacharakis and Meyer, 1998; 2000; Shepherd, 1999; Kaplan and Strömberg, 2000; Kakati, 2003;

Silva, 2004).

2.1.1. Processual research – What process do VCs use to evaluate potential investments?

The research stream focusing on the stages in the VC decision process originates from the study by

Wells (1974) which divided the activities that a VC carries out into six categories. These stages are the

search for investment opportunities, the screening of proposals, the evaluation of proposals, venture

board meetings and follow-up, dealing with venture operations and finally the cashing out of the

ventures. The findings resulting from this pioneering study were adapted by Tyebjee and Bruno

(1984). Although their study mainly focused on examining the VC selection criteria, Tyebjee and

Bruno (1984) modeled the investment activities of venture capitalists as a sequential process

involving five steps. The first step is deal origination or how exactly deals enter into consideration as

potential investments. The second step consists of a broad screening of the deals to limit

investments to fields which the venture capitalist is familiar with. The next phase is the evaluation

procedure during which proposals that passed through the screening are examined in a much more

5

detailed way and where VCs subjectively judge the venture based on a multidimensional set of

characteristics. The fourth step concerns deal structuring where the venture capitalist and the

entrepreneur negotiate and have to agree on the investment in terms of amount, form and price.

Eventually, the last step relates to post-investment activities which may vary from a rather passive

role to close contact with the venture and active involvement in its day-to-day operations. Hall

(1989) described the VC decision process using eight successive stages, of which the first is the

generation of a deal flow. The second stage is a brief proposal screening, followed by a more detailed

assessment of the business plan. During the next step of project evaluation, the venture capitalist

really visits the business and/or meets the entrepreneurial team. After that, the remaining

investment proposals undergo due diligence. When an agreement is reached about the deal, the deal

structuring stage takes place and venture operations start. The final phase encompasses the ending

of the VC’s involvement in the new venture and is called the cash out stage. Fried and Hisrich (1994)

also modified the 1984 version of Tyebjee and Bruno and modeled the VC decision-making process as

consisting of six stages: origination, VC firm-specific screen, first-phase evaluation, second-phase

evaluation and closing. Boocock and Woods (1997) supported the prior models which suggest that

the VC decision process comprises different successive stages. Their paper models the VC activities as

generating a deal flow, initial screening, first meeting, second meeting, board presentation, due

diligence, and finally the deal structuring stage. Additionally, their study found that the criteria may

differ in their level of significance depending on the stage of the VC decision-making process. The

main objective of Bliss (1999) was to extend the model of Fried and Hisrich (1994) to transitioning

economies. Their VC decision-making process differs in two important ways: variation in the deal

origination stage and a lack of firm-specific screening. The case study of Silva (2004) demonstrated

that the VC investment process is characterized by more and earlier interaction between the VC and

the entrepreneur(s) than in previous models. Contrary to the findings in prior literature, the research

revealed that the several activities in the stages of the decision-making process arise simultaneously

rather than consecutively.

Table 1 provides the different phases found in the studies about VCs’ decision-making from a

cognitive process perspective. As noted by Hall and Hofer (1993, p.29), “the most relevant findings

revealed by the studies and agreed by all the researchers are: (1) that the process consists of multiple

stages and (2) that the venture evaluation itself involves at least two different stages, i.e. screening

and evaluation.” Although the tasks of VCs can be categorized as pre-and post-investment activities,

this study deals with the initial stages of the decision making process.

6

Wells (1974)

Tyebjee & Bruno (1984)

Silver (1985)

Hall (1989)

Fried & Hisrich (1994)

Boocock and Woods

(1997)

Bliss (1999)

Silva (2004)

Klonowski (2007)

Search Deal origination

Search Generating a deal flow

Deal origination

Generating a deal flow

Origination Deal origination

Deal origination

Screening Deal screening

Initial screen

Proposal screening

Firm-specific screen

Initial screening

Generic screen

Informal screening

Initial screening

Proposal assessment

Generic screen

First meeting

Formal screening

Feedback investment committee and due diligence phase I

Evaluation Deal evaluation

Due diligence

Project evaluation

First-phase evaluation

Second meeting

First-phase evaluation

Evaluation Pre-approval completions

Board presentation

Due diligence

Second-phase valuation

Due diligence

Second-phase valuation

Formal approvals and due diligence phase II

Deal structuring

Deal structuring

Deal structuring

Closing Deal structuring

Closing Closing Deal completion

- Venture board meetings and follow-up - Venture operations

Post-investment activities

Monitor progress

Venture operations

/ / / / Monitoring

Cashing out / / Cashing out / / / / Exit

TABLE 1: Prior findings on stages of venture capitalists’ decision process

(own extended and completed version of tables in Hall and Hofer (1993), Silva (2004)

and based on the articles referred to in the first row)

2.1.2. Criteria research - What criteria do VCs use to evaluate potential investments?

Since VCs spend a lot of time and effort on the screening of business proposals, a second stream of

empirical research on the VC decision-making process attempts to discover the criteria most

commonly used by VCs in assessing potential investments.

Wells (1974) found a weighted list of criteria. According to this pioneering study, VCs consider

management commitment as the criterion with the highest weight in their evaluation of business

proposals, followed by product, market and marketing skills. Poindexter’s study (1976) resulted in a

ranked list with the quality of management as the most important selection criterion. The expected

rate of return and the expected risk were respectively on the second and third rank. The study by

Tyebjee and Bruno (1981) identified six dimensions by which venture capitalists characterize and

assess entrepreneurial opportunities: profitability of the venture, market factors, management

quality, uncontrollable risks, cash-out factors (or exit-opportunities) and viability of the venture. They

found management skills of the prospective entrepreneur to be the most influential determinant of a

positive investment decision. In a following study, Tyebjee and Bruno (1984) concluded that low

perceived risk and high expected return increase the likelihood that a deal will be accepted by VCs.

Their results showed that the quality and the skills of the management team are fundamentally

important during the deal evaluation stage. The extent to which the organization is resistant to

7

environmental threats, such as the entry of new competitors, economic cycles or technology

obsolescence, is a second important factor that determines the perceived risk to failure. Their

research also demonstrated that two considerations have a significant impact on the expected rate

of return associated with the deal: attractive market conditions and a highly differentiated product.

Cash-out potential, reflecting the VC’s ability to liquidate the investment, was not significantly

related to either assessment. Consistent with previous findings, MacMillan et al. (1985) confirmed

that the management skills of the entrepreneur ultimately determine the investment decision of VCs.

Their study identified the top ten criteria most commonly rated as essential by VCs and

demonstrated that five of them are related to the entrepreneur’s experience or personality:

capability of sustained effort, demonstrated leadership in the past, track record relevant to the new

venture, good evaluation of and reaction to risk, and capability of articulating the venture well. By

means of cluster analysis, proof was produced for significant differences in the importance of

selection criteria among different VCs. MacMillan et al. (1985) also noted that, in the mid-1980s, a

shift can be established in VCs’ expectations from specific skills of the entrepreneur towards

capabilities of the management team. According to Hutt and Thomas (1985), the management

team’s previous track record is a key factor in the evaluation by VCs. Other important criteria

resulting from their study are the degree of product differentiation and a profound understanding of

market demand and level of competition.

After these first studies on VCs’ investment criteria, a large number of academic researchers had the

goal to develop a better understanding on how VCs actually select their investments, by using

significantly different methodologies. In a subsequent study of MacMillan et al. (1987), the authors

tried to answer the question if the criteria derived from previous studies (especially from MacMillan

et al. (1985)) are useful to discriminate between successful and unsuccessful ventures. The aim of

their study was to discover the determinants of high and low performance rather than the evaluation

criteria of VCs. They examined 25 criteria covering four areas: entrepreneurial team characteristics,

product/service characteristics, market characteristics and financial considerations. Again, the quality

of the management team reveals to be the most important criterion: the difference between

successful and unsuccessful ventures generally was a flaw in the entrepreneurial team. Moreover,

two major criteria are identified as the most critical predictors of new venture performance, namely

the extent of competitive threat and the degree of demonstrated market acceptance of the venture’s

product. These two decision criteria were not highly weighted in their previous study, therefore

much more importance needs to be attached to these market-related criteria in screening venture

proposals. Entrepreneur/team-related criteria that have been heavily weighted in their prior study

were no good performance predictors. The researchers explained this finding by the fact that the

8

“VCs had already applied them to weed out undesirable ventures” (MacMillan et al., 1987, p. 134).

Khan (1987) developed actuarial decision models – conjunctive (noncompensatory) and disjunctive

(compensatory) – to describe the assessment of potential investments by venture capitalists and

their attempt to identify the most successful ones. In arriving at their judgment, VCs tend to put

emphasis on the entrepreneur’s desire for success and the uniqueness of the product or service

relative to competition. Additionally, the study also suggests that VCs’ judgment is poorly related to

actual outcomes and thus is not a good predictor of venture success. The most important investment

predictor is the creativity and ingenuity of the entrepreneur.

As noted and criticized by several researchers (Sandberg et al., 1988; Hall and Hofer, 1993; Shepherd,

1999; Zacharakis and Meyer, 1998; Silva, 2004), although providing key insights into the criteria used

in the VC decision-making process, past research efforts suffer from certain common weaknesses and

may be somewhat misleading. First, the majority of prior studies rely on post-hoc data collection

techniques. Such retrospective collection methods include questionnaires and interviews in order to

gather data on VCs’ past investment decisions and may be subject to post hoc rationalization and

recall biases (Sandberg et al., 1988; Shepherd and Zacharakis, 1999). Moreover, many of previous

studies are exposed to errors and biases as a result of self-reporting on decision criteria (Sandberg et

al., 1988; Hall and Hofer, 1993). As indicated by Macmillan et al. (1985) and Sandberg et al. (1988),

research participants may give these answers they believe to be the correct or desirable responses.

Self-reporting may also lead to overstatement of the number of criteria actually used by VCs and

understating of the weightings of the most significant criteria; for example, VCs generally put

emphasis on management capabilities as the most fundamental consideration while more than one

criterion actually matters in the investment decision. Finally, prior research did not look at the

decision process itself. (Sandberg et al., 1988) Some authors attempted to overcome these

methodological limitations and potential biases of existing research by using real time research

methods, such as verbal protocols and repertory grid analysis (e.g. Sandberg et al., 1988; Hisrich and

Jankowicz, 1990; Hall and Hofer, 1993; Zacharakis and Meyer, 2000) or policy capturing and

experiments in order to conduct conjoint analysis (e.g. Riquelme and Rickards, 1992; Muzyka, Birley

and Leleux, 1996; Zacharakis and Meyer, 1998; Shepherd, 1999). The results of such real-time studies

allow to extend the knowledge on the VC investment decision and are further discussed below.

In order to obtain insight into the constructs of the VC decision process, Hisrich and Jankowicz

(1990) used in-depth interviews with five VCs and repertory grid methodology. Their results showed

that the issues of interest in the investment decision can be grouped in three areas: management,

unique opportunity and appropriate return. According to their study, venture capitalists mostly

attach importance to management consisting of different constructs: the general traits of the

9

proposer, the experience of the principal, the characteristics of the management team, and

continuity of the company/market. The unique opportunity was also found to be generally used in

the evaluation of investment proposals and was primarily associated with finding a market niche and

the uniqueness of the product.

According to the study of Riquelme and Rickards (1992), a VC’s decision-making process can be

modeled with hybrid conjoint models. The use of a real-time technique – conjoint analysis – allowed

them to better capture the complexity of VCs’ priorities and trade-offs among criteria in their

evaluation of new venture proposals. The research demonstrated that initially, VCs focus on a small

subset of criteria. In a second phase, VCs end a detailed examination by choosing the most preferred

ventures through the acceptance of a lower value on one criterion compensated by a high value on

another. The models confirmed the emphasis that VCs put on the entrepreneur’s experience during

the first stage of the evaluation of business proposals (the ‘screening step’ of Tyebjee and Bruno

(1984)). In addition, unique features of the product and the presence of a functioning prototype

proved to be important criteria as well. The main considerations identified in the second stage (the

evaluation phase) are patent for product protection from competition and product gross profit

margin.

Hall and Hofer (1993) conducted a series of interviews and verbal protocol analysis to determine the

criteria used by VCs in the proposal screening stage of new venture evaluation. The key criteria used

by VCs to make investment decisions are the fit with the venture’s lending policy and the long term

growth and profitability of the industry in which the prospective firm will operate. In the next phase

of proposal assessment, the source of the business proposal was considered to play an important

role too: proposals reviewed by trustworthy people are perceived as more interesting potential

investments. In contrast with previous findings and thus rather unexpected, the results from the

study revealed that the entrepreneurial team, financial factors (such as risk and return on

investments) and the business strategy of the new venture are of restricted importance to venture

capitalists, at least during the early stages of their decision-making process. The authors suggested

that these criteria are typically assessed at later stages of the VC decision-making process such as

project evaluation and/or deal structuring. As a result, the importance of criteria may differ in

different stages of the decision-making process and the weighting of entrepreneurial and team

characteristics in the investment decision should be revised.

Fried and Hisrich (1994) elaborated on the three basic constructs of Hisrich and Jankowicz (1990) in

order to find generic evaluation criteria that all VCs use. Their research highlights that the

10

management team is not the only investment criterion, but that venture capitalists carefully examine

the potential rate of return and the business concept of the ventures too.

Most of previous studies were conducted for US-based venture capitalists. Additionally, Muzyka et al.

(1996) indicate that in general the data collection of these studies was exploratory and a single

hierarchy of decision criteria was assumed. Therefore, Muzyka et al. (1996) used conjoint analysis as

a valuable tool to examine trade-offs in 35 selection criteria made by European venture capitalists. In

sum, this first cross-national comparison revealed that VCs evaluate potential investments in terms

of a capable management team and acceptable financial and product-market criteria, while overall

fund and deal characteristics seem to be second-order issues. Consistent with previous studies, the

‘human factor’ was judged to be the most important criterion in the VC investment decision, since all

five management team criteria were at the top of the rankings. These criteria included the leadership

potential and the track record of the lead entrepreneur and the management team as well as the

presence of recognized industry expertise in the team. In addition, via cluster analysis three groups

of VCs were identified: investors who prefer a national location, a second group that focuses

predominantly on the characteristics of the deal and mainstream investors who consistently and

instinctively rank the five management team criteria at the top of their list.

The findings of Zacharakis and Meyer (1998) showed that venture capitalists are not good at

introspection. Although they lack understanding of their own intuitive decision-making process, VCs

are consistent in applying their decision procedures. The study draws on social judgment theory in

order to provide a theoretical framework, which was lacking so far in studies on VC decision criteria.

In contrast with the findings from prior studies using post-hoc methodology and consistent with the

real-time study of Hall and Hofer (1993), entrepreneurial and team characteristics were found not to

be the most important consideration in investment decisions when VCs have sufficient information

about market characteristics. As indicated by Zacharakis and Meyer (1998), prior research needs to

be reevaluated in terms of the importance attached to different criteria and the amount of

information that VCs actually use in the evaluation process. Zacharakis and Meyer (2000) used

conjoint analysis and also showed that the entrepreneur is not that important as in previous studies.

Market and competition considerations were far more critical.

Kaplan and Stromberg (2000) tried to explain how venture capitalists screen potential portfolio

companies. Consistent with prior research, the attractiveness of the opportunity – the market size,

the business model, the technology, a high likelihood of customer adoption, and competition – the

management team and the deal terms proved to be essential evaluation criteria for VCs. Additionally,

this study highlighted the importance of the VCs’ initial assessment of the management quality for

11

subsequent performance as the probability of an IPO (initial public offering) is higher for portfolio

companies with strong management teams. Although Silva (2004) did not explicitly indicate the

importance of the selection criteria, the case study agreed with prior literature that VCs heavily pay

attention to information about the entrepreneurs, particularly their knowledge and contacts, their

degree of commitment to and their understanding of the business idea. The business idea itself was

considered as an important criterion too, while financial projections do not seem to have a significant

influence on the investment decision.

Several of the identified key criteria were consistent across various studies and can be classified in 4

distinct categories: (1) entrepreneur/team characteristics, (2) product/service considerations, (3)

market/competitive conditions, and (4) financial and firm criteria. Table 2 presents an overview of

prior research into the selection criteria that VCs employ when evaluating venture proposals. This

summarizing table specifies the author(s) and year of publication, the sample size, the method of

data collection and statistical analysis as well as the criteria examined in the studies. The most

consistent conclusion across venture capital literature on criteria is the major importance that VCs

attach to the managerial capabilities of the entrepreneurial team.

12

Study Wells (1974)

Poindexter (1976)



MacMillan et al. (1985)

Hutt & Thomas (1985)

Method of data collection Personal interviews

Questionnaire Telephone interviews

Telephone & personal

interviews + questionnaire

Questionnaire Questionnaire

Sample size (VCs) 8 97 46 46 (study I), 41 (study II)

102 4

Method of statistical analysis - Descriptive statistics - Content analysis

- Descriptive statistics

- Descriptive statistics - Content analysis

- Descriptive statistics - Factor analysis - Discriminant analysis

- Descriptive statistics - Factor analysis - Cluster analysis

- Descriptive statistics

Criteria

Entrepreneur/team characteristics

Management skills/experience X X X X X X

Venture team X X

References/Track record X X X

Management stake in the firm X X

Other - Management commitment

- Personality of entrepreneur

Product/service considerations

Proprietary X X

Product attributes X X X X

Degree of product differentiation X X X

Market acceptance X

Other - Prototype

Market environment

Market size X X X X

Market growth X X X X

Barriers to entry X X

Competitive threat X X

Other -Industry/ technology

- Market niche/ position

- Environmental threat resistance - Existence of market need

Financial and firm criteria

Cash-out potential/Liquidity X X X

Expected rate of return X X X X

Expected risk X X X

Venture development stage X X X

Size of investment X X X

Geographic location X X

Other - % equity share - Financial provision for investor rights

- Financial history - Growth potential

Protection/Patent ability X X X

TABLE 2: Prior findings on venture capitalists’ investment criteria (1/4)

(own extended and completed version of tables in Hall and Hofer, 1993; Zacharakis and Meyer, 1998;

Shepherd and Zacharakis, 1999; Silva, 2004 and based on the articles referred to in the first row)

13

Study MacMillan et al. (1987)

Khan (1987)

Robinson (1987)

Timmons et al. (1987)

Sandberg et al. (1988)

Keeley & Roure (1989)

Method of data collection Questionnaire Interviews + Questionnaire

Questionnaire Unstructured interviews

Interviews, simultaneous

verbal protocols

Archival data from business

plans

Sample size (VCs) 67 36 53 47 1 4

Method of statistical analysis - Descriptive statistics - Factor analysis - Cluster analysis - Regression analysis

- Noncompensatory actuarial decision models (conjunctive and disjunctive)

- Descriptive statistics - Factor analysis

- Content analysis

- Content analysis

- Descriptive statistics - Regression analysis

Criteria



Venture team X X X X

References/Track record X X X

Management stake in the firm X

Other - Entrepreneur’s personality

- Desire for success - Tenacity/courage - Enthusiasm/ capacity for work

-Personal motivation


Proprietary X

Product attributes X X

Degree of product differentiation X X X X

Market acceptance X X

Other - Prototype - Product line growth path

Market environment

Market size X X

Market growth X X X


Competitive threat X X X X X

Other


Cash-out potential/Liquidity X X X

Expected rate of return X X

Expected risk

Venture development stage X

Size of investment X

Geographic location

Other - Substantiated growth objectives

- Value added stream

Protection/Patent ability X X


14

Study Hisrich &

Jankowicz

(1990)

Riquelme &

Rickards

(1992)

Hall & Hofer

(1993)

Meyer et al.

(1993)

Fried & Hisrich

(1994)

Muzyka et al.

(1996)

Method of data collection Series of in-

depth

interviews,

repertory grid

technique

Experimental

questionnaire

(full profiles)

Semistructured

interviews,

verbal

protocols

In-depth

structured

interviews

Personal

interviews +

questionnaire

Personal

interviews +

experimental

questionnaire

(trade-offs)

Sample size (VCs) 5 13 4 5 18 73

Method of statistical analysis - Factor

analysis

- Cluster

analysis

- Conjoint

analysis

- Content

analysis

- Content analysis - Descriptive

statistics

- Content

analysis

- Conjoint analysis

- Cluster analysis

Criteria



Venture team X X X

References/Track record X X

Management stake in the firm

Other - General

traits of the

proposer

- Management

strategy


Proprietary

Product attributes X X X

Degree of product differentiation X X X

Market acceptance X

Other - Use of

technology

- Functioning

prototype

- Product timing - Sustainable

competitive

advantage

Market environment

Market size X


Barriers to entry X

Competitive threat X X

Other - Market

uniqueness

- Profitability

of industry

- External market

conditions

- Seasonality and

sensitivity to

economic cycles


Cash-out potential/Liquidity X X

Expected rate of return X X X X

Expected risk X X

Venture development stage X

Size of investment X X

Geographic location X X

Other - Venture’s

lending policy

- Venture’s

business

strategy

- Time to

breakeven

- Time to payback

Protection/Patent ability X


15

Study Zacharakis & Meyer (1998)

Shepherd (1999)

Kaplan & Strömberg

(2000)

Zacharakis & Meyer (2000)

Kakati (2003)

Silva (2004)

Method of data collection Experiment (full profiles),

policy capturing

methodology

Experiment (trade-offs) + questionnaire

Interviews Experiment (full profiles),

policy capturing

methodology

Interviews Interviews, participant observation

Sample size (VCs) 51 66 10 53 27 10

Method of statistical analysis - Regression analysis

- Conjoint analysis

- Descriptive statistics - Content analysis - Regression analysis

- Conjoint analysis - Regression analysis

- Descriptive statistics - Cluster analysis - Factor analysis - Regression analysis

- Descriptive statistics - Content analysis

Criteria



Venture team X X X

References/Track record X X X X

Management stake in the firm

Other - Industry-related competence

- Desire for success - Creativity - Enthusiasm/ capacity for work - Personality

- Entrepreneur’s commitment and personality


Proprietary X

Product attributes X X

Degree of product differentiation X X X X

Market acceptance X X

Other - Time to development

- Strategy/ business model

- Competitive strategy - Prototype

- Time to market - Growth potential

Market environment

Market size X X X X X



Competitive threat X X X X X X

Other - Timing entry - Lead time - Scope

- Existence of and access to established distribution channel

- Industry situation - Access to distribution channel


Cash-out potential/Liquidity X X

Expected rate of return X X X

Expected risk

Venture development stage

Size of investment X

Geographic location

Other - Deal terms - Financial market conditions

Protection/Patent ability X


16

2.2. Protection ability and importance for new ventures and VCs

2.2.1. Protection ability

The main problem analyzed in this paper is by which VCs protection ability is considered as an

important selection criterion in their screening and evaluation of new venture proposals. Protection

ability is the availability of means by which innovations and their profitability can be protected

against imitation. In previous research, the concept has generally been referred to as a crucial factor

in the appropriability regime of a firm or industry. A regime of appropriability corresponds with

environmental dimensions, apart from firm and market structure, that manage an innovative start-

up’s ability to capture the economic returns generated by its technological capabilities (Teece, 1986;

Lee, Lee and Pennings, 2001). Consequently, appropriability is an essential element of the

sustainability of a company’s competitive advantage and the defensibility of innovations.

A classification of appropriability regimes is possible based on the nature of the innovative

technology – product, process, tacit or codified know-how – and the efficacy of the legal instruments

for intellectual property protection – patents, trade secrets or copyrights (Teece, 1986). Accordingly,

a simplified distinction can be made between environments with ‘tight’ appropriability regimes,

where technology can be protected quite easily and is difficult-to-imitate, and ‘weak’ appropriability

regimes, where technology protection is almost impossible and imitation concerns are salient (Teece,

1986).

Levin, Klevorick, Nelson and Winter (1987) argued that companies employ a variety of methods to

protect the competitive advantages of new or improved processes and products. The most important

mechanisms through which companies can enlarge their potential to appropriate the profits of their

innovations are: obtaining patents to prevent duplication or to secure royalty income, enforcing

secrecy, gaining lead time, exploiting learning curve advantages, superior sales or service efforts,

establishing a dominant design and raising imitation cost and imitation time (Levin et al., 1987;

Harabi, 1995; Cohen, Nelson and Walsh, 2000). According to Cohen et al. (2000), these mechanisms

can be classified into three main ‘strategies’: the exploitation of complementary capabilities and lead

time, patent mechanisms and secrecy. Several researchers have examined the effectiveness of

different appropriability mechanisms and have shown that patents are rather weak compared with

secrecy and lead time (e.g. Levin et al., 1987; Harabi, 1995; Cohen et al., 2000). Hurmelinna-

Laukkanen and Puumalainen (2007) revised and extended previous findings. Next to intellectual

property rights, tacitness of knowledge, secrecy and lead time, they also considered contracts, labour

legislation and human resource management as appropriability mechanisms in their study.

17

This study focuses on intellectual property rights (IPRs), since it is a fundamental aspect of the

appropriability regime and one of the most widely studied mechanisms for the protection of

innovations. Means of intellectual property protection include patents, copyrights, trademarks, trade

secrets, and other IPRs. Patents are a frequently used mechanism, especially by larger firms,

although the limitations and inter-industry variations of their effectiveness in preventing imitation by

competitors (Teece, 1986; Levin et al., 1987; Cohen et al., 2000; Hurmelinna-Laukkanen and

Puumalainen, 2007). As indicated in Gallini (2002), a range of policy changes to stimulate innovation

since the 1980s extended and strengthened the relative protection that patents provide. In previous

literature, patent applications and patents granted are often regarded as the output of successful

R&D activities and as an indicator of innovative performance of companies (Ernst, 2001; Hagedoorn

and Cloodt, 2003). A positive relationship between patents and subsequent changes of economic

performance on the firm level, such as market value, sales or profit increases, has been established

several times in prior empirical research (Scherer, 1965; Comanor and Scherer, 1969; Narin, Noma

and Perry, 1987; Griliches, 1990; Ernst, 1995, 2001). Cohen et al. (2000) pointed to the growing

importance of secrecy as appropriability mechanism.

2.2.2. Importance of protection ability in entrepreneurship literature

Product innovation and technological protection through patents are critical for new technology-

based firms in order to realize a competitive advantage and to create value (McCann, 1991; Lee et

al., 2001). Intellectual property protection allows start-ups to commercialize the toils of their new

product development efforts, seize market opportunities, and differentiate themselves from

incumbents. Competitive advantages which are not protected are vulnerable to imitation or

replication by competitors, and thus diminish a new venture’s appropriability regime. (Lee et al.,

2001)

Gans and Stern (2003) argued that the problem of small new ventures is not so much invention but

rather commercialization. Start-ups must make a strategic trade-off between entering the market for

products and competing against incumbents versus choosing for the market for ideas and

collaborating with established companies. The role of intellectual property in strategy was

highlighted by Teece (1986) and Gans and Stern (2003): appropriability regimes and the firm’s

position relative to critical complementary assets are important considerations influencing the choice

of commercialization strategy. In case of a strong appropriability regime and when industry

incumbents possess requisite specialized complementary assets that function as a barrier to entry,

start-ups will generate higher profits from cooperation than from direct competition in product

markets. Strong and formal intellectual property protection (such as patents) increases the returns to

18

innovation as it is an important mechanism by which start-up innovators can overcome

expropriation threats arising from cooperation with industry incumbents (Teece, 1986; Gans and

Stern, 2003). Both new entrants and incumbents may benefit from the existence of a patent portfolio

and collaborative activity, and from playing on the market of ideas instead of product markets (Gans

and Stern, 2003). Arora, Fosfuri and Gambardella (2001) also highlight the rising importance of

operating through markets for technology as commercialization strategy for start-ups, since the

existence of strong intellectual property rights can facilitate entry in markets for technology. IPR

protection may accelerate the diffusion of technological knowledge through avoidance of

opportunistic behavior by licensees, and so encourage firms to license rather than to keep their

innovations and knowledge within the boundaries of the company. Their conclusion is in contrast

with previous findings of Levin et al. (1987) which suggest that strong IPR stimulate innovation but

restrain the diffusion of knowledge.

Although protection of technologies or products can be limited within geographic boundaries and in

time, it may allow entrepreneurial companies to gain a greater market share and to grow faster than

competition. This is because intellectual property rights enable NTBFs to have a head start in

comparison with other players in the industry and/or to pursue a differentiation strategy. As noted

above, intellectual property rights can also serve as tradable assets that facilitate successful

commercialization. Lee et al. (2001) proved that the protection ability of the technology of a new

venture is positively related to the start-up’s performance.

2.2.3. Importance of protection ability in venture capital literature

Several previous studies in venture capital literature contain an indication that protection ability may

actually play a part when VCs evaluate potential portfolio companies. Tyebjee and Bruno (1984)

already took into account the patentability of the product as evaluation criterion, being an element

of a broader factor labeled as product differentiation. In MacMillan et al. (1985), the extent to which

the product is proprietary or can otherwise be protected was revealed to be an essential product

consideration for VCs. Moreover, a lack of product protection was shown to be one of the features of

unsuccessful ventures (MacMillan et al., 1987). Other criteria studies (Hutt and Thomas, 1985;

Riquelme and Rickards, 1992; Kakati, 2003) also examined whether the protectability of the

product/service is assessed in the evaluation of business proposals by VCs. For each of the six case

studies of venture capital investments in European NTBFs by Murray (1996), patent protection was

mentioned as an important criterion for VCs and judged to be a key part in the success of the new

venture.

19

According to the study of Baum and Silverman (2004), intellectual capital characteristics have a

significant impact on the decision-making of venture capitalists. The presence of patents is

considered as an indicator of innovative potential and increases the probability that start-ups will

receive financing from VCs. The case studies of Mann (2005) indicated that patents or the prospect of

patents may help new ventures to obtain VC funding by convincing VCs that their firm can

sustainably differentiate itself from competition.

Clarysse et al. (2006) applied cluster analysis to examine the degree to which early stage high

technology VCs are heterogeneous with respect to their selection process. Three types of investors

were distinguished: VCs that primarily attach importance to human capital and team features, a

second group of investors which mostly emphasize financial data and a cluster that puts technology

characteristics on the top of their list of selection criteria. The final group of VCs differs from the

others as they especially focus on the extent to which the technology of the new venture can be

protected or patented.

Engel and Keilbach (2007) found that firms with a high number of patents have a higher likelihood of

obtaining VC funding. After the involvement of a VC, the firm’s innovative output (measured by the

number of patents granted) did not differ significantly from other companies. Consistent with

Hellman and Puri (2000), these findings indicate that VCs actually consider the firm’s innovativeness

in their decision-making process and put emphasis on commercialization of existing innovations and

growth perspectives of the firm. This suggests that only firms that convince VCs of their growth

potential by having patent applications are able to attract venture capital. As VC investors are faced

with considerable uncertainty, they seem to rely on patents as quality signals when trying to assess

the prospects and growth potential of potential portfolio companies.

Having identified the VC investment decision and the importance of protection ability for

entrepreneurs and VCs, the next section provides a theoretical framework to understand what

makes some VC firms put more emphasis on this selection criterion than others.

20

3. THEORETICAL FRAMEWORK AND HYPOTHESES

As outlined above, a large amount of prior literature has mainly concentrated on how VCs evaluate

potential portfolio companies and what criteria they utilize in their investment decisions. So far, little

research has dealt with a better understanding of the determinants of VC selection behavior.

A few studies have examined which factors determine how VCs build their investment portfolio. As

suggested by upper echelon theory, the choice of the VC’s portfolio strategy is a central strategic

decision which is taken by the entire top management team rather than the judgment of an

individual investment manager (Hambrick and Mason, 1984; Dimov et al., 2007; Patzelt et al., 2009).

Venture capitalists can either invest with a focus on early stage ventures or also include more mature

companies in their portfolios (Robinson, 1987; Elango et al., 1995; Manigart et al., 2002).

Furthermore, some VC firms may decide to diversify their portfolio companies across industries,

while others may prefer specialization in one or a small number of industries (Gupta and Sapienza,

1992; Norton and Tenenbaum, 1993; Knockaert, Lockett, Clarysse and Wright, 2006). Moreover, VCs

may select investments with a narrow geographic scope or choose to invest internationally or even

globally (Gupta and Sapienza, 1992; Hall and Tu, 2003). On the one hand, prior researchers

demonstrated the impact of fund characteristics such as source of funds, size and age on these

strategic investment decisions. On the other hand, another group of researchers analyzed whether

the background, education and experience of a VC’s management team explain variance in the

investment decisions by VC firms (e.g. Dimov et al., 2007; Franke et al., 2006; 2008; Patzelt et al.,

2009).

Up till now, few researchers have considered both the heterogeneity of VC fund characteristics as

well as the human capital of investment managers as drivers of investment decisions. This study

seeks to contribute to the entrepreneurship and VC literature by taking into account both factors

throughout the analysis of why some VCs put more emphasis on protection ability as selection

criterion than others. In this section, a theoretical framework is built in order to examine the

following specific research questions:

- To what extent is the importance of protection ability as selection criterion of different early stage

high tech VCs associated with differences in their fund characteristics?

- To what extent is the importance of protection ability as selection criterion of different early stage

high tech VCs associated with differences in the human capital of their investment managers?

http://www.sciencedirect.com/science/article/B6VDH-4ST4CCB-1/2/8629d8986366bb4ed0155bc96ee3328c?&zone=raall#bbib40

21

3.1. Fund characteristics and importance of protection ability

3.1.1. Source of VC funds

As highlighted by a large number of researchers, new technology-based firms are often confronted

with serious problems to raise external funding, especially debt financing (e.g. Storey and Tether,

1998; Murray, 1999; Lerner, 1999; Carpenter and Petersen, 2002; Di Giacomo, 2004; Colombo, Grilli

and Verga, 2007). A first reason concerns the uncertainty with regard to the investment risk and the

expected returns associated with high tech start-ups. At early stages, it is difficult for external

investors to make reliable judgments of demand for the NTBFs’ products and services because

markets are highly immature. Moreover, technology-based sectors are rapidly changing thereby

adding a risk of accelerated redundancy. Secondly, NTBFs are characterized by a lack of collateral for

potential investors, since they are mainly composed of intangible or firm-specific assets. Finally,

information asymmetries for external financiers are often more significant in case of innovative firms

in high tech sectors than for other investments. Accordingly, venture capital is usually considered to

be the primary source of external financing for NTBFs (Gompers and Lerner, 2001; Kaplan and

Strömberg, 2001; Botazzi and Da Rin, 2002). VC firms understand how to overcome the risks

associated with financing such firms in order to achieve commercial success, and are able to add

value to their portfolio companies (Harrison and Mason, 2000). Besides, venture capitalists serve as

financial intermediaries which are especially suitable to promote the innovative performance and

development of early stage high tech companies (Bygrave and Timmons, 1986; Hellmann and Puri,

2000, 2002; Kortum and Lerner, 2000). Although VC funds may provide a solution to NTBFs’ financial

constraints, Murray and Lott (1995) and Lockett et al. (2002) proved that VCs show a negative bias

against investing in high tech start-ups.

The rejection of early stage high tech portfolio companies by VCs can be explained from an agency

point of view. A large amount of academic literature deals with the principal-agent problem in

financial contracting and concentrates on the conflicts of interest between the agent, who is an

entrepreneur with a new venture searching for funding, and the principal, who is the external

financier (e.g. Jensen and Meckling, 1976; Myers and Majluf, 1984; Hart, 2001; Kaplan and

Strömberg, 2001). Entrepreneurs are more closely involved and possess more knowledge about the

invention and patents than investors providing funds for the new venture. Due to such information

asymmetries, problems of adverse selection and moral hazard can arise for VCs (Amit, Brander and

Zott, 1998; Svennson, 2007). Consequently, information asymmetries may result in agency conflicts

and costs, that are more probable to occur for new high tech ventures as it is difficult for external

financiers to evaluate the technological feasibility and the commercial implications of strategic

22

choices during the earliest stages of development (Gompers, 1995; Cumming, 2006; Knockaert et al.,

2006; Murray, 2007). Especially in these early stages, the search and transaction costs associated

with the identification of interesting investment projects and the assessment of their technical and

commercial potential are large for VCs (Svensson, 2007).

VCs as principals are able to diminish information asymmetries in entrepreneurial environments, and

thus can mitigate agency problems (adverse selection and moral hazard) in three ways: through

sophisticated contracting, by developing specialized abilities in selecting portfolio companies or by

devoting a substantial amount of time to monitoring and value-adding activities (Amit et al., 1998;

Lerner, 1999; Kaplan and Stromberg, 2001; Cumming, 2006; Knockaert, Clarysse, Wright and Lockett,

2008). In other words, next to structuring the financial contracts, they can address agency problems

by intensively scrutinizing entrepreneurial projects before providing funds (Fried and Hisrich, 1994)

or by engaging in post-investment activities. Nevertheless, VC firms still prefer investment

opportunities with minimal information asymmetries and they remain reluctant towards the

allocation of capital in the earliest stages (seed and start-up) of the technology investment cycle

(Lockett et al., 2002). The outcome is a restriction in the range and availability of finance for high

tech start-ups due to the stage of the business and the technology involved, referred to as ‘the equity

gap’ (Murray, 1997; Harrison and Mason, 2000; Mason, 2009).

NTBS are believed to be a key source of economic growth and job creation and governments

consider the funding gaps for such companies as market failure. This belief explains the rise of

various government initiatives offering public support to overcome market imperfections for new

high tech firms: a large number of policy makers around the world have established programs to

promote VC financing of innovations and thereby encourage economic development (Aernoudt,

1999; Lerner, 1999; Harrison and Mason, 2000; Di Giacomo, 2004; Cumming, 2007; Murray, 2007;

Mason, 2009). Wright, Lockett, Clarysse and Binks (2006) provide an overview of six different types

of public intervention to stimulate early stage high tech financing: 100% publicly owned funds and

public-private partnerships, refinance and guarantee schemes, as well as the provision of fiscal

incentives and incubation schemes. Most commonly, governments provide VC financing through the

creation of totally public funds which invest directly in new ventures, or by granting public financing

to existing private sector VC funds (Di Giacomo, 2004; Wright et al., 2006; Cumming, 2007).

Based on the findings by Mayer et al. (2005) and as shown by Clarysse et al. (2006), the source of

VCs’ funds may have a significant impact on the decisions of the investment managers, such as the

hierarchy of selection criteria used in the investment decision. As a result, the source of funds that

VCs have at their disposal is a potential determinant of the importance attached to protection ability.

23

Strategic investment theory (Hellmann, 2002) concentrates on the different objectives that

shareholders of funds can pursue as well as the differences in the measures used to evaluate a fund’s

success. In particular, publicly funded VCs focus on other objectives than purely realizing financial

returns. The purpose of governments when providing public money to early stage high tech start-ups

is supporting the growth (especially job creation) of the regional economy (Harrison and Mason,

2000; Di Giacomo, 2004; Engel, 2004). Therefore, VCs that invest funds from public initiatives rather

take into account factors such as a venture’s capacity of technological breakthrough and renewal, as

this is expected at the macro-level to influence employment rates and to stimulate economic growth

(Botazzi and Da Rin, 2002; Knockaert et al., 2006).

Given that publicly funded VCs emphasize a venture’s ability to maintain and encourage

technological innovation and that protection ability is an indicator capturing a start-up’s innovative

performance (Ernst, 2001; Hagedoorn and Cloodt, 2003), this study expects that VCs which receive

public funding will consider protection ability of the technology as a more important selection

criterion. This leads to the following hypothesis:

Hypothesis 1 :

There will be a positive relationship between the availability of public funding in a VC firm’s capital

and a VC’s emphasis on the protection ability of the technology as selection criterion.

3.1.2. Experimental learning

The link between the experience of a VC fund and the importance of protection ability as selection

criterion can be understood in the context of agency theory as well.

High tech new ventures are typically characterized as risky investments with potentially high agency

costs because of the uncertainty with respect to the market, the management and the technology

(Storey and Tether, 1998). This study assumes that there exist at least two reasons why VC funds may

however decide to invest in NTBFs. As outlined in prior research and in the development of the first

hypothesis, the source of funds may help venture capitalists to overcome the risks and agency

problems associated with high tech start-ups, since government intervention aims at changing the

risk-averse mentality and at facilitating investments in early stage and high tech firms (Lerner, 1999;

Di Giacomo, 2004). Even though public instruments cannot mitigate moral hazard and adverse

selection problems, they may allow funds to share or offset the agency risk and potential agency

costs with private investors (Knockaert et al., 2008). An alternative way to deal with the risks related

to funding NTBFs is when VC funds possess adequate experience in making investment decisions.

Experienced VCs are more familiar with the decision process and structure and are better at

24

identifying suitable portfolio companies and at engaging in post-investment activities as their

experience increases (Shepherd, Zacharakis and Baron, 2003).

Building on an experimental learning perspective, a VC fund’s experience in making investment

decisions can be operationalized as the number of investments made since founding. Prior literature

describes organizational learning as the process through which firms acquire, create, transfer and

apply organizational knowledge in their activities (Dodgson, 1993; Grant, 1996). Firms learn from

their own direct experiences as well as from the experiences of others, they form interpretations and

draw causal inferences from these earlier events, and store their accumulated knowledge in the

organizational memory as a guide for future behavior (Levitt and March, 1988). Next to expanding

their stock of knowledge through the learning process, organizations are also able to develop new

competencies or to extend existing ones (Levitt and March, 1988; Dodgson, 1993; Zahra, Neilsen and

Bogner, 1999). Consequently, organizational learning is beneficial to organizations and is a key source

of competitive advantage, as it can improve a firm’s intelligence and performance (Levitt and March,

1988). Zahra et al. (1999) argued that organizational learning can be classified into two types:

experimental learning and acquisitive learning. Experimental learning refers to a learning-by-doing

process in which new organizational knowledge is generated from individual or group experiences. In

this process, firms learn from their own experience by carrying out activities repeatedly and adapting

to past experience. Applied to the context of VC investing, VC funds derive knowledge from prior

investments and use their ability to understand and exploit their accumulated knowledge in the

evaluation, selection and management of investment opportunities (De Clercq and Dimov, 2008).

A key aspect in the experimental learning process is the experience intensity or the number of

instances of repetition in the learning-by-doing process. This is because accumulating knowledge

through trial and error requires repetition. Firms have to evaluate the outcomes of their decisions

and generalize causal relationships between courses of actions and outcome responses (Yang,

Narayanan and Zahra, 2009). Frequently repeating such evaluations and generalizations enables

organizations to revise and adapt their actions in order to increase the probability of securing desired

incomes (March and Olsen, 1975; Van de Ven and Polley, 1992).

Logically, VC funds with a larger number of investments since founding will have had more

opportunities to evaluate outcome responses and to draw generalizations than funds with a smaller

number of investment deals. The more deals a VC fund has closed, the higher the experience

intensity and the more the VC may have learned from prior experience. Through repetitive

investment activities, VCs are able to evaluate successes and failures with respect to prior

investments and in such a way, they develop fund experience and competencies that allow them to

25

make better investment decisions. Since such experienced VCs may rely more on their own

capabilities to select portfolio companies with great potential and to monitor them effectively

afterwards, they will probably attach less importance to obtaining risk-decreasing, commercially

tradable assets such as intellectual property rights.

Consequently, given that the number of investments since founding raises the VCs’ experience and

selection capabilities, this study expects that their need for commercially tradable assets will reduce

and so, protection ability will become a less important investment criterion. Hence:

Hypothesis 2:

There will be a negative relationship between the number of investments since the founding of the VC

fund and a VC’s emphasis on the protection ability of the technology as selection criterion.

3.2. Investment manager characteristics and importance of protection ability

Prior studies on the VC investment decision have mostly examined the responses of one investment

manager, but have drawn conclusions for the decision-making of the VC as a whole. Human capital

theory offers a foundation of why individual judgments may deviate from a fund’s selection behavior,

and this allows to examine investment manager characteristics as drivers of the differences in the

importance attached to protection ability.

Numerous studies have demonstrated the importance of human capital as key contributor to an

organization’s competitive advantage and performance (Bruderl, Preisendorfer and Ziegler, 1992;

Cooper, Gimeno-Gascon and Woo, 1994; Gimeno, Folta, Cooper and Woo, 1997; Pennings, Lee and

Witteloostuijn, 1998; Dahlqvist, Davidsson and Wiklund, 2000; Beckman, Burton and O’Reilly, 2007).

With regard to venture capitalists, the importance of human capital has been shown by Dimov and

Shepherd (2005) in their study of the relationship between the education and experience of the top

management teams of venture capital firms and the firms’ performance. Human capital theory

suggests that more or higher quality human capital allows investment managers to achieve higher

performance in executing relevant tasks such as pre- and post-investment activities. This results in a

lower proportion of portfolio companies going bankrupt, a higher proportion of portfolio companies

going public, and therefore better performance of the VC firm (Becker, 1975; Dimov and Shepherd,

2005).

The notion of human capital consists of two key demographic characteristics, formal education and

personal experience (Becker, 1975). Dimov and Shepherd (2005) applied the human capital

perspective in a VC context. They made a distinction between general and specific human capital,

based on whether the VC’s top management team’s human capital in a particular domain provides

26

skills that are directly used in the selection or monitoring of portfolio companies. General human

capital relates to the overall education and practical experience of the investment executive which

are useful across a wide range of occupational alternatives, while specific human capital concerns the

education and experience that is only applicable in a particular activity or context, such as human

capital specific to a firm, industry or task (Becker, 1975; Gimeno et al., 1997; Dimov and Shepherd,

2005; Zarutskie, 2010). In this study, the commonly used concepts of specific and general human

capital are applied to a high tech VC context. Furthermore, consistent with Zarutskie (2010), specific

human capital is split up into industry-specific human capital and task-specific human capital. As this

paper focuses on high tech investing, industry-specific human capital is defined as technical

education or prior experience in high tech research domains, while task-specific human capital refers

to prior experience as VC investment manager. General human capital comprises education in

business administration, and experience in finance, consulting, general management and

entrepreneurial experience.

This thesis builds on self-efficacy theory to explain how the human capital of investment managers

may influence the importance of protection ability as selection criterion for VCs. The concept of self-

efficacy lies at the center of social cognitive theory, which explains psychosocial functioning in terms

of a triadic reciprocal causation in which behavior, the environment, cognitions and other personal

factors interact and influence each other in a dynamic way (Bandura, 1977, 1986; Wood and

Bandura, 1989; Gist and Mitchell, 1992). Self-efficacy concerns an individual’s beliefs in its

capabilities of organizing and performing a specific task necessary to achieve certain performance

levels and to accomplish desired goals (Bandura, 1977; Wood and Bandura, 1989). According to self-

efficacy theory, people who believe in their capacity to perform well at a specific task actually do

better than those who think they will fail (Gist and Mitchell, 1992). Consequently, an individual’s

judgment of self-efficacy influences his or her choice of activities and environments; people are more

likely to execute activities and to select social environments they consider themselves capable of

managing and controlling (Wood and Bandura, 1989). This reasoning is consistent with agency

theory, which implies that VC firms will opt for those portfolio investments where selection and

monitoring costs are rather small or where the potential costs due to asymmetric information are

less severe (Amit et al., 1998). Additionally, the study by Shepherd et al. (2003) demonstrated that

experience affects the VC decision-making process. These findings suggest that human capital has an

impact on the selection behavior of the investment manager.

27

3.2.1. Industry-specific human capital

Early stage high tech investors typically have to deal with great uncertainty about the commercial,

technical and managerial achievability of new ventures (Storey and Tether, 1998). Building on self-

efficacy theory, a technological background or academic experience may allow VC investment

managers to better judge the potential of a new technology and to select those portfolio companies

they believe to be aligned with their coping capabilities. Investment executives that have specific

high tech expertise at their disposal are better able to estimate the importance and the value of

intellectual property protection and therefore, they will have more confidence in evaluating the risk

and potential return related to investments.

Accordingly, this paper expects that investment managers who are more familiar with high tech

domains, and thus own industry-specific human capital, to put more emphasis on the protection

ability of the technology in the VC selection process, as this is a tech-related criterion.

Hypothesis 3a :

There will be a positive relationship between the degree of industry-specific human capital and a VC’s

emphasis on the protection ability of the technology as selection criterion.

3.2.2. Task-specific human capital

The degree of task-specific human capital will be positively associated with the probability of

acquiring personal mastery experiences, which are utilized in the development of self-efficacy (Gist

and Mitchell, 1992; Knockaert et al., 2006). Investment executives who have managed VC funds in

the past, are more familiar with the investment management tasks and possess more self-efficacy

due to a combination of both more knowledge regarding which companies to invest in and how to

actively manage those investments as well as networking advantages (Manigart, Baeyens and Van

Hyfte, 2002; Zarutskie, 2010). While rather inexperienced investment executives might have

difficulties with the assessment of market acceptance, technological uncertainty and the value of

patents during early stages of development, investment managers with prior VC fund experience are

more likely to have a better understanding of emerging environments and possess knowledge of the

advantages related to protection ability. Since experienced fund managers know that protection

allows high tech firms to profit from their inventions through a collaborative commercialization

strategy without losing value to industry incumbents (Teece, 1986; Gans and Stern, 2003), they might

put a high value on the protection ability of the technology that potential portfolio companies aim to

commercialize.

28

Building on self-efficacy theory, we can hypothesize that experienced investment executives who are

more competent at pre-investment activities, and thus have obtained task-specific human capital,

may have a tendency to emphasize protection ability as selection criterion in their investment

decision. Therefore:

Hypothesis 3b :

There will be a positive relationship between the degree of task-specific human capital and a VC’s


3.2.3. General human capital

Since general human capital does neither provide substantial knowledge in high tech domains nor

experience in managing a VC fund, it is unlikely that VC managers are able to evaluate the

technological potential and risks or to determine the benefits associated with technology protection.

Consequently, this study expects that investment managers with general human capital will consider

protection ability as a less important selection criterion.

Hypothesis 4:

There will be a negative relationship between the degree of general human capital and a VC’s


The expected relationships are summarized in table 3.

Fund characteristics

Percentage public capital (H1) + Number of investments since founding fund (H2) -

Human capital characteristics

Industry-specific human capital (H3a) + Task-specific human capital (H3b) + General human capital (H4) - TABLE 3: Hypothesized impact of independent variables on importance of protection ability as selection criterion

29

4. RESEARCH METHODOLOGY

4.1. Sample

The conducted analysis is based on a unique, already constructed dataset of 68 European early stage

high tech VC investors. The dataset is unique because it contains the required and detailed

information on both VC fund characteristics and human capital characteristics of the investment

managers, which none of the publicly available databases (e.g. VentureEconomics or VentureOne) or

other sources on European VC activity provide. The data were hand-collected by Knockaert M. and

colleagues in 2003. For the sake of completeness, a sample description and their method of data

collection are explained below.

As this research primarily concerns early stage high tech VC investors, the potential number of

respondents within one country, beside the US, would be too small. Consequently, collecting an

international database is necessary. For the dataset a stratified sample was drawn from the seven

regions across Europe with the highest R&D intensity and VC presence: Cambridge/London region

(UK), Ile de France (France), Flanders (Belgium), North Holland (the Netherlands), Bavaria (Germany),

Stockholm region (Sweden) and Helsinki region (Finland).

For each region, both small and large funds with various degrees of public funding are represented in

the sample. The directory information from EVCA (European Venture Capital Association) was

combined with information of the various regional venture capital associations and information

obtained from academics with specific regional expertise and contacts. This was done because a

random sample based upon the EVCA fillings, the most widespread available sample frame, would

have resulted in a sample biased towards the large private venture capital firms. This procedure

resulted in a population of 220 early stage and high tech funds across the seven European regions.

The sample frame was stratified into different groups or subpopulations according to the scale of the

funds and their institutional investors. In terms of scale, 33 funds were small, 21 were large and 14

were mega funds1. In reference to institutional investors, 6 funds were private equity arms of banks,

9 funds were public funds, 12 were public/private partnerships and the others were private funds.

1 Venture funds having a fund size between 100 million Euro and 250 million Euro are considered to be large funds for

venture investments. Mega funds are those funds having a size of more than 250 million Euro, small funds have less than 100 million Euro under management (EVCA definition).

30

4.2. Data collection

Interviews were conducted by Knockaert M. and colleagues between January and December 2003

and lasted on average 90 minutes. The first part of the interviews provided information on the

resource-based characteristics of the VC fund and on the investment manager’s human capital. The

VC characteristics include information about fund size, number of years since establishment, origin of

the funds, the availability of public funds, sectors and geographic regions of investment, number of

investments made in early stage high tech etc.. Information on the interviewed investment manager

includes the education, experience and his or her industry focus. In the second part, information was

collected on the selection behavior of the venture capitalists. Most prior studies about the decision

making of venture capitalists use post-hoc methods of data collection. Because of the reliance on

retrospective and self-reported data, these methods may generate biased results and may result in

questionable validity (supra, p. 8). To overcome these methodology limitations of previous research,

conjoint analysis was applied to analyze the decisions of venture capitalists in their attempt to

predict which new ventures are most likely to succeed.

Conjoint analysis is a general term referring to a technique that requires respondents to make a

series of judgments based on a combination of attributes. From these judgments, the underlying

structure of the “captured” decision processes can be decomposed (i.e. the attributes’ significance in

the judgment, how these attributes affect the judgment and the relative importance of each

attribute in the decision process) (Shepherd and Zacharakis, 1999). Although conjoint analysis has

mainly been used for market research problems, the method can be applied in any scientific domain

where measuring people’s judgments is necessary. As demonstrated in prior studies, VCs evaluate

potential investments in a process comprising different phases (supra, p.4). This research focuses on

the initial stage of the VC decision-making process: the assessment of new venture proposals. Using

the conjoint method, 27 fictitious business proposals that differ across a range of attributes were

presented to the VC investment managers and evaluated on a 5-point Likert scale (1= bad investment

opportunity and I would certainly not invest; 5= major investment opportunity and large chance of

investing). The 12 attributes and their different levels (or possible events) were derived from

previous research and after consultation with practitioners (2 VCs, 1 business angel and 3 VC

experts): team, entrepreneur, contact with the entrepreneur, uniqueness of the product, protection

of the product, market acceptance, general purpose technology, location, size and growth of the

targeted market, time to break-even and return on investment. The total number of profiles resulting

from all possible combinations of the 30 levels of the 12 attributes would become too great for

respondents to score in a meaningful way. For that reason, a fractional factorial design was used

based on Addelman’s basic plans (Addelman, 1962) for designing an orthogonal main effects plan.

31

The outcome was the 27 business proposals that were presented to the respondents. Next, the

scores of the investment managers were translated into derived utility scores for each attribute by

means of a conjoint analysis. These utility scores measure how important each characteristic is to the

respondent’s overall preference of a new venture proposal. Importance scores were calculated by

taking the utility score for a specific characteristic and dividing it by the sum of all utility scores and

give an insight into the relative importance of each selection criterion in the final VC decision.

4.3. Measures

4.3.1. Dependent variable

The dependent variable is the importance of protection ability as selection criterion in the VC

investment decision. The importance score, linked to the protectability of the product and derived

from the conjoint analysis described above, is used as measure for this variable. In the business

proposals presented to the respondents, protection ability was defined as the ability to protect the

technology by patents or trade secrets. The average importance score in the sample is 7,76%, with a

minimum of 0% and a maximum of 30,14%. 3 out of 68 VC funds do not attach any importance to

protection ability in their investment decision. With respect to the multivariate analysis, a

transformation of this variable was carried out and the square root of the importance score has been

taken into account in order to make sure that all assumptions to conduct ordinary least squares (OLS)

linear regression (in particular the normality of residuals) are fulfilled.

4.3.2. Independent variables

FUND CHARACTERISTICS

Percentage public capital

The variable percentage public capital was constructed to capture the extent to which a VC fund

received public funding. This measure ranges between 0% and 100%, with 100% indicating that the

VC is entirely funded by public means. 47 out of 68 VC funds are entirely privately funded, 9 are

completely funded by public means, whereas the other 12 funds have received some public funding

(ranging from 15% to 70%).

Number of investments since founding fund

A second fund characteristic is VC fund experience, measured by the absolute number of proposals

to which the VC firm has already provided funds since the founding. On average, the VCs in the

sample have made 38,63 investments since they were set up. The lowest number of investments

since founding is 3, while the maximum of the sample is a VC fund with 400 investments.

32

HUMAN CAPITAL CHARACTERISTICS

Industry-specific human capital

Industry-specific human capital is operationalized as the extent to which the investment executive

possesses human capital related to high tech investing. Therefore, two variables are included in the

analysis: academic experience and technical education. The former measures how many years of

experience the investment manager has with high technology, by means of a PhD or a research

position at a university or other research institute. Only a minority of the sample, or 11 investment

executives, have academic experience at their disposal. The investment managers in the sample have

on average 1,18 years of academic experience, with a range from 0 to 20 years. Consistent with

Dimov and Shepherd (2005), the second variable, called technical education, takes the form of a

dummy and is coded one when the investment manager has attained any bachelor or master

degrees in mathematics, natural sciences and engineering. 52,9% of the investment managers in the

sample, or 36 investment managers, has a technical education.

Task-specific human capital

Task-specific human capital concerns prior experience related to managing investment funds.

Investment management experience indicates how many years of experience investment executives

have as VC investment manager. The interviewed investment managers of all 68 VC funds in the

sample hold investment management experience, with an average of 4,86 years and a range from 1

to 17 years.

General human capital

General human capital refers to the overall background and experience of the investment executives,

in particular human capital not related to high tech investing or VC investment management. In line

with prior research (e.g. Knockaert et al., 2008) and following the definitions by Dimov and Shepherd

(2005), 5 variables are utilized and classified as general human capital. Financial experience is

measured as the number of years the investment managers worked in commercial, investment and

merchant banking prior to joining the VC industry. The majority of the sample (89,7%) has financial

experience. The investment managers interviewed have on average 6,90 years of financial

experience, with a range from 0 to 35 years. The second indicator of general human capital,

consulting experience, reflects the number of years an investment manager has worked for a

company providing consulting services. In the sample of this study, 19 respondents have experience

as consultant with an average of 1,07 years. Entrepreneurial experience measures how many years

the investment managers have previously been involved as entrepreneur or founder of a new

venture, which is on average 1,15 years for the sample. 15 of the 68 interviewed investment

executives own entrepreneurial experience. Next, Dimov and Shepherd (2005) labeled law industry

33

experience as a general human capital variable. However, only one investment manager in the

sample had worked for a law firm in the past. As 30 interviewed executives have prior experience as

manager, it was more relevant to include management experience as additional variable. The

variable is measured as the number of years experience in general management, which is on average

4,12 years for the sample and has a range from 0 to 24 years. Finally, business administration

education reflects all MBA degrees as well as degrees in art or social sciences (excluding economics)

and takes the form of a dummy variable. 46 out of 68 investment managers have had such an

education.

4.3.3. Control variables

Based on prior research, several VC fund characteristics are included as control variables in the

analysis in order to eliminate alternative explanations for differences in VC decision making, in

particular for differences in the importance of protection ability.

VC fund size

This study controls for the size of the VC fund, operationalized by the capital managed, which

previous research has found to influence a VC firm’s investment strategy (Gupta and Sapienza, 1992;

Elango et al., 1995; Hall and Tu, 2003). Moreover, Engel (2004) assumed that due to economies of

scale larger VC funds have an advantage in organizing their activities of project search and evaluation

compared to smaller VCs, which may result in different selection behavior. The average fund size is

269,04 million Euro. The smallest fund in the sample has a size of 0,9 million Euros, while the largest

fund manages a capital of 4.400 million Euros.

VC fund age

Additionally, a control variable is included for the age of the VC fund, calculated as the number of

years that had passed by since the founding of the VC, because prior research has shown that this

factor might potentially influence the VC investment selection activity (Hall and Tu, 2003; Dimov and

Murray, 2008). Because of the benefits associated with learning by doing, older VC funds may be

more willing to be confronted with risks. As their expertise allows them to deal more easily with the

uncertainty inherent to early stage high tech investments, fund age might have an impact on their

selection behavior. The age of the VC firms in the sample varied from 1 to 58 years and is on average

8,06 years.

ICT focus

Finally, although this paper focuses on a relatively homogeneous group of investors, i.e. early stage

high tech VCs, differences with respect to the sectoral focus of the fund may occur and play a role

34

too. As noted by several researchers (Mann, 2005; Mann and Sager, 2007; Gans, Hsu and Stern,

2008), protectability of the technology has a relatively unimportant role in the ICT sector, because

product life cycles are short and software patents are either weak or copyright protection can serve

as substitute. The weak appropriability regime and the availability of complementary assets affect

the choice of commercialization strategy. The probability that new ventures in the ICT industry will

play on the market for ideas is rather small and ICT start-ups are more likely to enter the market for

products and to compete against industry incumbents. Consequently, VC funds investing in ICT

business proposals may put less emphasis on technology characteristics such as patents and trade

secrets during the evaluation of potential portfolio companies. Other characteristics of ICT start-ups,

such as management skills and business connections, that allow to compete with existing firms

through a short time to market will presumably be more important selection criteria. That is why a

dummy variable is included to control for whether or not the VC fund invests in portfolio companies

active in ICT (grouping communications, computer related and other electronics related). 85,3% of

the respondents indicated that the VC fund invests in ICT.

35

Median Mean S.D. 1 2 3 4 5 6 7 8 9 10

Independent variables

(1) Percentage public capital 0,00 20,21 35,37 1,000

(2) Number of investments since founding fund 23,00 38,63 56,57 0,115 1,000

(3) Academic experience 0,00 1,18 3,36 0,091 -0,176 1,000

(4) Investment manager experience 3,50 4,86 3,84 0,104 0,134 -0,095 1,000

(5) Financial experience 5,00 6,90 6,72 0,361** 0,034 -0,176 0,510** 1,000

(6) Consulting experience 0,00 1,07 2,24 -0,067 -0,060 0,064 -0,225 -0,246* 1,000

(7) Entrepreneurial experience 0,00 1,15 3,00 0,198 -0,141 0,519** 0,063 -0,037 -0,033 1,000

(8) Management experience 0,00 4,12 6,33 0,051 0,025 0,313** -0,036 -0,273* 0,054 0,068 1,000

Control variables

(9) VC fund size (capital managed in million €) 116,78 269,04 649,35 -0,157 0,177 -0,105 -0,087 -0,103 -0,053 -0,106 -0,076 1,000

(10) VC fund age 5,00 8,06 9,45 0,048 0,286* -0,151 0,255* 0,205 -0,082 -0,103 -0,100 0,414** 1,000

Dependent variable

Importance protection ability (square root) 2,54 2,53 1,17 0,145 -0,172 0,130 -0,010 -0,190 0,154 -0,159 0,001 0,163 0,035

TABLE 4: Descriptive statistics and correlations of continuous variables

Pearson correlations level of significance (two-tailed): * p < 0,05, ** p < 0,01; n=68

36

5. RESULTS

The descriptive statistics and correlations for the continuous variables used in this study are

presented in table 4. Pearson correlations between independent variables are all below 0,6.

Moreover, the condition index is below 30 (maximum value of 12,88) and all tolerance values are

above 0,30 (minimum value of 0,485), suggesting that multicollinearity is not an issue (Janssens,

Wijnen, De Pelsmacker and Van Kenhove, 2008).

OLS regression analysis was used to test the hypotheses. In order to assure the validity and reliability

of the results, diagnostic tests were used to make sure that the data did not violate the assumptions

lying at the basis of the performance of regression analysis (Janssens et al., 2008). Using Z scores and

a visual inspection of histograms, the linear relationship between the dependent and independent

variables proved to be the correct functional form. Residuals were tested for independence,

normality and homoscedastiscity. After transformation of the dependent variable by taking the

square root, all necessary conditions were met.

Table 5 reports the results of the multivariate analyses. The base model only contains control

variables, VC fund age, VC fund size and ICT focus, and is statistically significant (adjusted R² = 0,098;

p < 0,05). The statistical significance of the model as well as the variance explained increase when

fund characteristics are also included in the OLS regression analysis (adjusted R² = 0,162; p < 0,01).

The base model including human capital characteristics is statistically significant too, but only at the

10% level (adjusted R² = 0,122). Eventually, the full model is significant at the 0,001 level and 33,2%

of the total variance is explained.

With respect to the fund characteristics, hypothesis 1 states that publicly funded VCs will attach

more importance to protection ability as selection criterion than private VC funds. The regression

results provide support for this hypothesis as for percentage public capital a significantly positive

coefficient is found in the full model (β = 0,469; p < 0,001). Hypothesis 2 assumes that VC funds with

a larger number of investments since founding will put less emphasis on protection of the technology

as selection criterion than rather inexperienced VCs. The results of the regression analysis support

this hypothesis about experimental learning (β = -0,288; p < 0,05).

Concerning the investment manager characteristics, hypothesis 3a considers the relationship

between industry-specific human capital and the importance of protection ability in the VC

investment decision. In the full model, no significant coefficients are found neither for academic

experience nor for technical education (p > 0,10). This suggests that the degree of human capital in

high tech domains has no significant impact on the attention paid to patents and trade secrets in the

37

investment decision. Hypothesis 3b deals with the impact of task-specific human capital on the

importance attached to protection ability in the selection process. As expected, the full model shows

a significantly positive coefficient for investment manager experience (β = 0,233; p < 0,10), which

indicates that investment managers with prior VC fund experience put a higher value on the

protection ability of the technology of potential portfolio companies. Finally, hypothesis 4 states that

a higher degree of general human capital by VC investment managers will lead to fewer emphasis on

the protection ability of the technology as selection criterion. The full model provides partial support

for this hypothesis: the coefficients are significantly negative for financial experience (β = -0,420; p <

0,01), entrepreneurial experience (β = -0,306; p < 0,05), general management experience (β = -

0,191; p < 0,10) and business administration education (β = -0,248; p < 0,10). The last measure of

general human capital, namely consulting experience, was not found to have a significant impact (p >

0,10). With regard to the control variables, VC fund age was not found to have a significant impact on

the importance of protection ability in the VC investment decision. In the full model, both VC fund

size and ICT focus have significant coefficients at the 5% level with values of respectively 0,268 and -

0,275 for β.

In summary, the results of the OLS regression analysis suggest that both fund characteristics and

human capital characteristics of the investment managers affect the attitude towards protection

ability as selection criterion to some extent.

Given that Shepherd et al. (2003) demonstrated that inexperienced VCs as well as highly experienced

VCs make decisions in less reliable or effective ways than moderately experienced VCs, this study

also tested for the existence of such curvilinear relationship between experience and the importance

attached to protection ability in the VC investment decision. Therefore, squared terms of the human

capital variables and of the fund experience variable were added to the full model. No indication of a

curvilinear relationship was found. A possible explanation for this finding is the relatively young and

emerging nature of the European VC market (Botazzi and Da Rin, 2002; Martin, Sunley and Turner,

2002). Shepherd et al. (2003) showed that greater experience in the VC task is beneficial to the

quality of VC decision making, but only up to a specific point, followed by a decline in decision

effectiveness and that this optimal level of VC experience is about 14 years. In the sample of this

study, investment managers had on average 6,90 years of financial experience, of which 4,86 years in

the VC industry. Moreover, only 4 out of 68 respondents had more than 14 years experience as

investment manager, which might justify the absence of a curvilinear relationship.

38

Finally, the full model was reestimated and interaction terms were added for human capital

characteristics and the availability of public funds as well as for the fund experience proxy and the

availability of public funds. However, no significant interaction effects were found (p > 0,10).

Base model Base model + Fund

characteristics

Base model + Human capital characteristics

Full model

Fund characteristics

Percentage public capital 0,254** 0,469****

Number of investments since founding fund -0,198* -0,288**

Human capital characteristics

Industry-specific human capital

Academic experience 0,209 0,142

Technical education -0,095 -0,069

Task-specific human capital

Investment manager experience 0,138 0,233*

General human capital

Financial experience -0,194 -0,420***

Consulting experience 0,164 0,147

Entrepreneurial experience -0,213 -0,306**

Management experience -0,138 -0,191*

Business administration education -0,168 -0,248*

Control variables

VC fund size 0,201 0,270** 0,195 0,268**

VC fund age 0,005 0,022 -0,004 0,010

ICT -0,338*** -0,348*** -0,275** -0,275**

Constant 3,376**** 3,342**** 3,664**** 3,882****

Model

F-statistic 3,437** 3,594*** 1,845* 3,561****

R-squared 0,139 0,225 0,266 0,462

Adjusted R-squared 0,098 0,162 0,122 0,332

TABLE 5: Regression analysis for importance of protection ability

Standardized regression coefficients are displayed in the table.

Levels of significance: * p < 0,10, ** p < 0,05; ** p < 0,01; **** p < 0,001; n=68

39

6. CONCLUSIONS AND LIMITATIONS

Using a unique hand collected dataset of European early stage high tech VCs, this paper has

examined what determines the differences between VC firms in the importance they attach to

protection ability as selection criterion in their investment decision.

So far, a substantial amount of VC literature has dealt with the decision making process and the

criteria used in VCs’ assessment of new venture proposals. Several researchers have shown that

technological considerations, and patentability in particular, are an important category of selection

criteria in the VC investment decision. By focusing on the drivers of VCs’ importance attached to

protection ability, this paper offers a more detailed understanding of desired technological

characteristics than prior research. From a theoretical perspective, two different factors were

derived that could drive the VCs’ attention paid to protection ability of the technology in proposal

screening decisions: VC fund level characteristics and the human capital of the investment managers

responsible for choosing portfolio companies. The majority of the formulated hypotheses received

empirical support. Consequently, this study yields some important results that explain the variations

in VCs’ importance attached to protection.

Concerning fund characteristics, the findings suggest that the percentage of public capital that VC

funds have at their disposal positively influences the extent to which VCs utilize protection ability as

selection criterion in their decision making process. This indicates that public funds choose to invest

in start-ups with high innovative potential thereby realizing the objectives of government

intervention, namely rectifying market imperfections for high tech ventures in early stages of

development and stimulating economic growth through technological innovation. This finding is

consistent with prior research by Mayer et al. (2005) and Clarysse et al. (2006) that points to the fact

that the availability of public financing causes VC funds to look at investment opportunities in a

different way. Secondly, this research shows that VC funds with a larger number of investments since

founding will put less emphasis on the protection ability of the technology as selection criterion. This

suggests that VC funds with a large number of investment deals have developed fund experience and

selection capabilities that allow them to overcome the risks and agency problems related to early

stage high tech investments. Consequently, relying on tradable assets of potential portfolio

companies such as intellectual property rights might be less necessary.

Building on a human-capital perspective and self-efficacy theory, this study also examined the role of

industry-specific, task-specific and general human capital in the decision policies of early stage high

tech VC investors when evaluating the protection ability of the technology. Surprisingly, industry-

specific human capital was not found to have a significant impact on the importance of protection

40

ability in VCs’ screening of investment opportunities. Despite having a technical education or

academic experience and thus being more familiar with high tech domains, investment managers do

not seem to put emphasis on the tech-related selection criterion of patents or trade secrets. A

possible explanation for this finding is that the strategy and experience at fund level as well as prior

experience of investment managers in performing VC tasks will probably have a greater impact on

the evaluation criteria used than the acquaintance of investment managers with the high tech

context of the business proposals. As expected, it was found that the degree of task-specific human

capital of investment executives is positively associated with the importance attached to protection

in the VC investment decision. This finding is consistent with the self-efficacy aspect of human capital

which states that more experience in performing relevant tasks enables actors to perform more

effectively (Dimov and Shepherd, 2005). Investment managers who have managed VC investment

funds in the past have developed self-efficacy and tend to put a higher value on a venture’s ability to

acquire patents and trade secrets. This finding indicates that because experienced VC investment

managers are better able to deal with technological uncertainty and have a better understanding of

the benefits associated with intellectual property protection, they will lay more emphasis on the

protection ability of the technology. Finally, general human capital of investment managers was

found to have a negative impact on the importance of protection ability as selection criterion for VCs.

This study, as with all studies, has a number of limitations. A first potential limitation of this paper is

the question whether the selection behavior resulting from conjoint analysis corresponds with the

actual VC investment decisions. Several researchers pointed out that the selection process and

criteria that VCs declare to use deviate from the real investment process and criteria. In particular,

Shepherd (1999) indicated that there is a gap between VCs’ “espoused” and “in use” decision

policies. As highlighted by Zacharakis and Meyer (1998), VCs may have a lack of insight into their

intuitive decision making process, especially when noise is caused by information overload. When

the amount of information available for venture capitalists enlarges, the gap between “espoused”

and “in use” decision policies increases as well. Secondly, since VCs typically operate in information-

rich environments, this may lead to overconfidence on behalf of VC decision makers, i.e. the

tendency to overestimate the likely occurrence of a set of events (Zacharakis and Shepherd, 2001).

Greater information enables VCs to establish potential pitfalls and to create more confidence, but at

the same time it makes decisions more complex which may result in lower decision accuracy.

Therefore, this study additionally examined whether the VCs that expressed to emphasize protection

ability in their selection process according to the conjoint analysis actually invested in companies

with the ability to obtain patents or trade secrets. The investment managers were asked in mid-2004

to provide a list of portfolio companies for which they had been involved in the selection process.

41

This resulted in a list of 171 portfolio companies provided by 36 VC investment managers. Escapenet

was used to look up whether these portfolio companies possess any patents and thus have the ability

to protect their product, service or technology. Subsequently, an extra variable was created for those

36 remaining VCs in the sample, showing the percentage of a VC’s portfolio companies that have

patents. The percentage of a VC’s portfolio companies that has IPRs correlated significantly positive

with the importance score of protection ability (correlation of 0,403; p < 0,05). This finding indicates

that the selection behavior observed by conjoint analysis is in line with the in use selection behavior

of the investment managers: when the importance score for protection ability was shown to be high,

the VCs actually invested to a large extent in early stage high tech ventures with the ability to protect

the technology by patents. A second possible limitation of this study is that for each VC fund only one

investment manager was interviewed. It might have been better to capture the human capital and

the investment decision of all investment managers within the VCs. This would indicate whether all

investment managers of a VC use the same selection criteria and would provide information on fund

level decision making. Interviewing all investment executives involved might help to discriminate

between selection behavior influenced by the human capital of the investment manager and

selection behavior determined by fund characteristics. Actually, a proportion of the investment

manager’s selection behavior might be affected by the VC fund’s imposed investment strategy.

Finally, although conjoint analysis is a strong tool that accounts for a number of biases and errors in

other research methods and allows to get useful insights into VCs’ use of selection criteria, the

limitations of this methodology must be noted too. As indicated by Shepherd and Zacharakis (1999)

and Shepherd (1999), a first issue is the reliance on hypothetical ventures and environments that

may reduce the external validity of the research results. However, the same researchers partially

invalidated this first criticism with prior evidence that hypothetical representations are useful for

capturing real policies (Brehmer, 1988; Riquelme and Rickards, 1992) and consequently, conjoint

analysis is a step towards actual decision making. A second limitation related to conjoint analysis is

that the use of dichotomous attributes may preclude perceptual requirements from the task, thereby

placing less emphasis on respondents’ ability to extract attribute information. Besides, investment

managers may attach importance to certain attributes just because they are presented in the

fictitious business proposals. However, the attributes and their different levels in this study have

content validity, as they were derived from academic literature and tested with experts. Therefore,

the second limitation has been taken into account when collecting the data.

Despite these possible limitations, the study provides interesting insights into the heterogeneity of

VC selection behavior and implications for practitioners as well as future research opportunities are

suggested.

42

7. IMPLICATIONS AND DIRECTIONS FOR FURTHER RESEARCH

This study has some important implications for VCs and their investment managers, for high tech

entrepreneurs as well as for policy makers.

First, for VC firms, this research provides a better understanding of what has an impact on their

decision policies with respect to preference of protected technologies. Unlike what one would

expect, VC investment managers with a background or experience in high tech domains were not

found to attach more importance to protection ability. Since investment executives are not biased by

their industry-specific human capital, this suggests that other human capital and fund characteristics

drive the fact that some VCs emphasize the intellectual property criterion when they screen portfolio

companies. This study shows that especially investment managers with previous task-specific

experience attach importance to this criterion, presumably because they are better able to evaluate

the value of patents and trade secrets. The opposite seems to apply to investment managers

possessing general human capital. It might be interesting for VCs to take these findings into account

when recruiting investment managers. The results also provide insight into how the availability of

public funding and the extent to which the fund has learnt by doing may affect their investment

selection. Accordingly, this study allows VCs to understand which other VC firms are competitors in

closing interesting high tech investment deals and which other VC firms are potential syndication

partners, based on whether they possess similar or different characteristics and stress similar or

different selection criteria.

From the perspective of new high tech ventures looking for early stage financing, they should be

aware of the differences within the early stage high tech VC industry and be able to identify those

VCs that put emphasis on protection ability in their investment decision. The results of this study

offer entrepreneurs a more detailed understanding of how VCs’ selection behavior with respect to

protection is influenced by the source of funds, the human capital of the investment manager, the

extent of experimental learning of the VC as well as the size and sector focus of the VC fund. These

insights allow high tech start-ups to select the most appropriate investor given the technology

characteristics of their business proposal, and thus to increase their chances of obtaining VC

financing. For instance, a new venture that lacks a capable management team and clear financial

projections but that has a protected technology is more likely to attract funding from publicly owned

VCs or public-private partnerships than from private VC funds. Analogously, business proposals that

contain protection ability have a greater chance of getting funding from VC funds with only a small

number of investment deals since founding. Moreover, not only choosing the right VC firm seems to

matter, but also submitting the business plan to an investment manager whose human capital and

43

evaluation criteria fit best with the characteristics of the start-up. Therefore, it proves to be useful

for entrepreneurs to hold knowledge about the fund characteristics of potential VC financiers as well

as about their managers’ expertise and background. Taking into account that the majority of new

venture proposals is rejected during the initial stages of the investment decision, it is worth to

address a ‘good’ VC as soon as possible.

Thirdly, this paper also has implications for policy makers that aim at rectifying the market

imperfections that exist for early stage high tech companies. The results of this study indicate that

(partially) publicly funded VCs stress protection ability when evaluating investment opportunities,

which suggests that innovative ventures do have access to VC financing and thus that government

initiatives succeed in overcoming ‘the equity gap’ for NTBFs. It might be particularly interesting for

policy makers to possess knowledge about whether their public money is utilized and deployed for

the preset goals, because high tech start-ups are essential for technological renewal and the

stimulation of economic growth.

Finally, future research should take into account the heterogeneous nature of the early stage high

tech VC industry and consider both fund level and human capital characteristics simultaneously.

Moreover, in further research all investment managers of the VC firms should be interviewed since

different selection criteria may be emphasized within one fund. As outlined above, this may also

allow to make a distinction between behavior because of the human capital that the investment

managers possess and behavior due to the funds’ imposed investment strategy. Furthermore, this

paper did only attempt to understand the drivers of putting emphasis on patents or trade secrets of

potential portfolio companies. Future studies may examine whether employing protection ability as

selection criterion in the investment decision actually results in better VC fund performance.

44

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