UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE

ACADEMIEJAAR 2009 – 2010

THE RELATIONSHIP BETWEEN VC EXPERIENCE, MANAGEMENT

EXPERIENCE, AND PORTFOLIO COMPANY GROWTH.

Masterproef voorgedragen tot het bekomen van de graad van

Master in de Toegepaste Economische Wetenschappen

Jan Willems

Maarten Tollenaere

onder leiding van

dr. T. Vanacker

UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE

ACADEMIEJAAR 2009 – 2010

THE RELATIONSHIP BETWEEN VC EXPERIENCE, MANAGEMENT

EXPERIENCE, AND PORTFOLIO COMPANY GROWTH.

Masterproef voorgedragen tot het bekomen van de graad van

Master in de Toegepaste Economische Wetenschappen

Jan Willems

Maarten Tollenaere

onder leiding van

dr. T. Vanacker

PERMISSION

Ondergetekenden verklaren dat de inhoud van deze masterproef mag geraadpleegd en/of gereproduceerd worden, mits bronvermelding.

Jan Willems Maarten Tollenaere

I

CONTENT

ABSTRACT (NEDERLANDS)…………………….………………………………………………..…II

ABSTRACT (ENGLISH)………………………………………………………………………………II

1. Introduction…………………………………………………………………………………………1

2. Theory and Hypotheses…………………………………………………………………………..…4

2.1. Matching Model……………………………………………………………………………...4

2.2. Growth Model………………………………………………………………………………10

3. Data………………………………………………………………………………………………...17

3.1. Sample and Data Sources…………………………………………………………………...17

3.2. Measures…………………………………………………………………………………….19

3.2.1. VC Experience………………………………………………………………….….19

3.2.2. Management Experience………………………………………………………...…19

3.2.3. Portfolio Company Growth……………………………………………………..….20

3.2.4. Controls…………………………………………………………………………….21

3.3. Analysis…………………………………………………………………………………..…21

4. Results…………………………………………………………………………………………..…22

4.1. Matching Model…………………………………………………………………………….22

4.2. Growth Model……………………………………………………………………………….28

5. Discussion and Conclusion……………………………………………………………………….36

6. Appendix………………………………………………………………………………………..…39

References……………………………………………………………………………………………...IV

II

ABSTRACT (NEDERLANDS) In deze studie wordt er empirisch onderzoek gedaan op een kleine exploratieve steekproef bestaande

uit 43 portfoliobedrijven die we kunnen linken met hun respectievelijke hoofdinvesteerders uit de

eerste durfkapitaalronde. Op basis van enquêtes werd er gedetailleerde informatie verzameld voor een

selectiemodel dat enkele veelbelovende indicaties oplevert in verband met tweezijdige selectie op

gebied van specifieke ervaringsniveaus. Bovendien, aangezien er voor 41 van de 43 portfoliobedrijven

boekhoudkundige informatie verkregen is, wordt het onderzoek uitgebreid naar een groeimodel om de

mogelijke impact te meten van specifieke ervaringsgebonden selecties. Naast het vinden van

significante positieve groeiassociaties met de ervaringsniveaus van durfkapitalisten (zowel algemeen

als industriespecifiek) en de ondernemerservaring binnen portfoliobedrijven, denken we te kunnen

wijzen op mogelijke synergetische of verstorende effecten tussen bepaalde ervaringstypes in de

periode na de investering. Desalniettemin dient deze studie beschouwd te worden als zijnde van een

sterk exploratief karakter en wij willen er op wijzen dat onze resultaten zeker niet tot een exhaustieve

conclusie kunnen leiden.

ABSTRACT (ENGLISH)

In this study, we empirically investigate a small exploratory sample of 43 portfolio companies (PFCs)

that we can trace back to their initial lead venture capital (VC) investors. Using survey data, we get a

relatively in-depth view of experience types and levels of the PFC‘s management team at the time of

investment relationship formation. We use these detailed experience measures in a matching model

and find promising indications for specific experience-based matching. Furthermore, as we were able

to collect accounting data for 41 out of 43 PFCs, our research is extended towards a growth model that

allows for the empirical analysis of post-investment growth levels across experience-based

subsamples. Apart from significant positive associations with VC (both general and industry-specific)

experience and PFC entrepreneurial experience, we believe to have found potential synergetic as well

as interfering experience types in the post-matching (i.e. post-investment) stages. Nevertheless, we

acknowledge the fact that our results are far from exhaustive or conclusive, and should be seen as of a

rather exploratory nature.

III

Acknowledgements

This paper is the result of combined efforts from various people to whom we wish to offer our

deepest gratitude. First of all, we wish to thank our promoter, dr. Tom Vanacker, for giving us the

opportunity to explore the fascinating entrepreneurial setting. Without his professional guidance, it

simply would have been impossible for us to capture such a rich and complex research domain.

Secondly, we want to express our recognition for our dear colleagues, Jan Dufourmont and Jeroen

Baert, always in the mood for an interesting theoretical or methodological discussion. We were truly

fortunate to have such bright friends working on a similar subject. Thirdly, we strongly appreciate the

review efforts of Kirsten Timmermans, Arne De Smet and Michael Boschmans. Next, since this

dissertation provided us with many long days and nights, we want to thank our girlfriends for keeping

up with us all the time – we imagine that the financial lexicon can sometimes be somewhat less

attractive. Finally, as this master thesis can be seen as the result of several years of studying, we wish

to show our gratitude to our parents for supporting us throughout our academic studies and for

encouraging us to take up extensive extra-curricular activities.

1

1. Introduction

A great deal of previous academic research has led to an identification of both external and

internal factors that influence the growth of firms. Regarding the internal growth factors, it is

obviously impossible to rely on material assets only in order to explain the sustainable competitive

advantage of certain enterprises. In fact, strategic management research teaches us that it is vital for a

firm to possess difficult-to-imitate knowledge assets (Teece, Pisano and Shuen, 1997) and, more

specifically, numerous studies point out human capital as being one of the main contributors to

organizational performance (Brüderl, Preisendörfer, and Ziegler, 1992; Gimeno, Folta, Cooper, and

Woo, 1997; Pennings et al., 1998). Zingales (2000) even chooses to emphasize these statements by

proclaiming that the core of the ‗new firm‘ consists entirely of human capital.

If immaterial assets such as human capital are in fact able to explain or even predict

organizational performance, they are evidently of paramount importance to investors in the approach

of potential investments. A distinctive type of investors profoundly concerned with human capital

evaluation is to be found in the venture capital or private equity market. Indeed, for venture capitalists

(VCs), the assessment of a portfolio company‘s human capital is critical. First of all, during the due

diligence process, clear performance measures simply do not exist or are extremely difficult to detect

(DiMaggio and Powell, 1983; Podolny, 1993). In such circumstances, prior research has implicated

that human capital is one of the focal signals to which VCs direct themselves (Baum and Silverman,

2004). Secondly, the role of VCs is by any means broader than that of traditional financial

intermediaries in the sense that they are often actively involved in their investments (Gormann and

Sahlman, 1989). A particular aspect of this involvement is the professionalization of the portfolio

company (PFC) through the building of the management team (Hellmann and Puri, 2000). With

management turnover rate being positively associated with VC-backing (Hellmann and Puri, 2002), it

is clear that human resources are a serious issue during a VC‘s post-investment activities. More

specifically, as VC control increases, there appears to be a significant influence on the recruitment of

the senior managers (Kaplan and Strömberg, 2002). In fact, VCs even go that far as putting effort into

the search for professional managers in order to add value to the portfolio company (Hellmann, 1998).

To which point the selecting or building influence of VCs on top management teams (TMTs)

within their portfolio companies is valuable, is an interesting discussion. Some scholars believe that

human resources management is one of the key features to explain the success of the venture capital

industry (Gledson de Carvalho, Calomiris, and Amaro de Matos, 2008). However, the successful

selection or build-up of human resources is not straightforward with the venture capital industry as a

framework. Smart (1999) finds that – in a large number of deals – VCs are faced with significant

surprises regarding the outcomes of their assessments. Moreover, the ability for VCs to effectively

build a management team according to their preferences is defined by the financial contract, and the

latter document often prescribes several state-contingencies. Usually, as a portfolio company is

2

underperforming, VCs get more rights, thus upgrading their terms for exercising control in the field of

appointments or dismissals (Kaplan and Strömberg, 2003). However, it is sometimes possible that

state-contingencies exist, without being enforced by financial contracting. Since a substantial part of

the returns of venture capital funds is earned by exceptionally successful ventures (Sahlman, 1990)1, it

can occur that VCs will focus their value-adding services on those companies performing well

(Sapienza, Manigart, and Vermeir, 1996). In this case, we expect to see that high performing ventures

will get strong support to establish a suitable management team, whereas the bad performers will be

left aside.

When exploring the relationship between venture capital and portfolio companies along

human capital dimensions, we should take caution as not to oversee important characteristics of both

parties. For instance, we must never forget that a VC is also a market actor which, for its actions, is

largely dependent on human capital. After all, whatever structure a VC firm chooses to organize itself

in, it is always governed by an investment committee – a board of directors in charge of making

investment decisions. Obviously, the more investments they make, the more skilled VCs become. In

some cases, the mere potential to learn from an investment can be an incentive for investing itself

(Sørensen, 2007). It is clear that, given this perspective, VCs are not a homogenous group of

investors. Nevertheless, a lot of prior research has neglected this possible heterogeneity, merely

inserting a dummy-variable to indicate whether the investment was made by a VC or not. This is of

course problematic if one would take into account differential financing offers. Since prior research

has shown that offers from reputable VCs have far better chances to be accepted by a portfolio

company‘s management team (Hsu, 2004), an interesting question arises regarding the extent and the

consequences of possible deliberate matching behavior.

This study investigates the influences and consequences of differences along human capital

dimensions through the principles of matching. We acknowledge the possibility of two-sided selection

by both VCs and portfolio companies during their deal search. By measuring multiple types of

experience on both sides of the venture capital allocation process, we will search for a deliberate

matching behavior based on experience. Furthermore, we will present some post-matching figures and

characteristics of several subgroups in order to provide an insight into what type of matching could

bring favorable outcomes. To guide our research design, we propose the following two research

questions:

The first research question is: do VC firm experience and PFC management team experience influence

the matching between the two parties?

The second research question is: does this matching materially contribute to growth?

1 An important note to this view is the fact that ―successful ventures‖ are mostly considered as leading to IPOs

in the US framework, while this is less the case in the European context (Schwienbacher, 2008).

3

The remainder of the paper2 is organized as follows. We will first convert our research questions into

testable hypotheses using a theoretical framework. Given these hypotheses, we will introduce several

variables, allowing us to conduct the necessary research. Evidently, all the variable choices or possible

constructs will be clarified. Finally, a detailed disclosure of the empirical results will be followed by a

conclusion and a discussion.

2 From this point on all through the paper, we will reserve the word ―firm‖ to the venture capital investor, and

the word ―company‖ to the investee or portfolio company. Furthermore, whenever we use the term ―management

team‖, we refer to the (top or senior) management team of the portfolio company. In order to keep the text clear,

we sometimes use the following abbreviations: ‗VC‘ for ‗venture capitalist‘, ‗PFC‘ for ‗portfolio company‘ and

‗TMT‘ for ‗top management team‘ (of the portfolio company).

4

2. Theory and Hypotheses

2.1. Matching Model

The evolutionary models of entrepreneurship state that selection is generated through the

allocation of capital from external resource holders among alternative firms (Aldrich, 1999). In the

entrepreneurial setting, resource holders such as VCs are often regarded as providing a powerful

means of selection (Anderson, 1999). While VCs usually receive a lot of requests to take into

consideration, it is also important to acknowledge a two-sided matching in the venture capital market,

implying that multiple financing offers can reach one PFC in the same financing stage. This two-sided

matching concept, although difficult to detect the dominant direction (i.e. who selects who), is said to

lead to a sorting behaviour within the market for venture capital (Sørensen, 2007).

Since the matching between VCs and PFCs leads to a financial agreement, both parties are

faced with a moral hazard problem, bringing forward principal-agent conflicts. In fact, the VC

(principal) delivers valuable resources to be managed by the portfolio company‘s TMT (agent) and, in

such circumstances, a great deal of attention needs to be focused on the assessment and monitoring of

the investment. Therefore, when VCs approach new ventures during the due diligence process, a lot of

attention is given to start-up characteristics (Baum and Silverman, 2004). Previous studies have found

that management team experience, skills and personality are one of the key selection criteria

(MacMillan et al., 1985; Zacharakis and Meyer, 2000). The quality of a portfolio company‘s TMT is

claimed to be of such a great importance because other possible indicators of (future) performance are

difficult to determine in the pre-investment stage (DiMaggio and Powell, 1983; Podolny, 1993).

Moreover, in the post-investment stage, VCs are usually not too keen on high monitoring or

involvement costs (Kaplan and Strömberg, 2002). Although the accurate assessment of human capital

has often been regarded as almost impossible in economical literature, adequate and thorough

procedures are proven to exist. What is more, it has been demonstrated that VCs who use meticulous

TMT screening procedures are rewarded by above average returns on their investments (Smart, 1999).

According to traditional learning theory, it is likely that VCs will learn from successful behaviour and

repeat it in the future. Indeed, VCs learn from past investments (Sørensen, 2008) and it is reasonable

to believe that, as a VC gains experience, more qualitative TMT‘s will be selected. 3

In conventional financial markets, investors usually do not get closely involved with their

investments, at least not in the post-investment stage. To reduce the moral hazard problem, they count

on an initial risk assessment – comparable to a VC‘s due diligence process – and adequate financial

contracting. The venture capital market, however, is characterised by a far closer interaction between

the investor (VC) and investee (PFC) in the post-investment stage (Gorman and Sahlman, 1989;

Bygrave and Timmons, 1992; Sapienza, Amason and Manigart, 1994; Elango et al., 1995). In fact,

3 Although it is reasonable to accept that VCs learn from their investments, empirical evidence is sometimes

found to counter the traditional learning framework, especially in strategic contexts (Haleblian and Finkelstein,

1999; Shepherd, Zacharakis and Baron, 2003).

5

VCs are found to take up elaborate roles of monitoring, advisory and even recruiting senior executives

(Lerner, 1995; Sapienza, Manigart and Vermeir, 1996; Kaplan and Strömberg, 2002; Hellmann and

Puri, 2002; Baker and Gompers, 2003). From a competence-based perspective, it is then likely that, if

a PFC‘s top management team finds itself in a position to choose among several financing offers, it

will prefer an experienced VC to match with, hoping to create a sustainable competitive advantage.

However, VCs are not too keen on delivering all these ―extra-financial‖ services as they value their

time preciously and prefer not to be involved in a PFC on a day-to-day basis (Kaplan and Strömberg,

2002).4 This being the case, it is likely that only the highly experienced PFCs‘ top management teams

will be able to match with highly experienced VCs in order to get access to their valuable

competences.

For a matching between a VC and a PFC to take place, it is obvious that there needs to be an

opportunity for the two parties to meet and interact. Social networks, created and maintained by the

venture capital industry‘s widespread use of syndicated investing, are highly effective at information

diffusion (Sorenson and Stuart, 1999)5. Moreover, well-networked (i.e. high centrality) VCs are found

to outperform others and this can partially be explained by access to a better deal flow. Because

selecting skills are found to improve a VC‘s network position (Hochberg, Ljungqvist and Lu, 2007),

and investment experience drives selection skills (cf. supra), it is likely that more experienced VCs

will be able to match with better PFCs.6 Correspondingly to a VC‘s deal search, and because VCs are

renowned for their value beyond money, PFCs are also on the lookout to match with the best possible

partners. Since PFCs‘ management teams with prior (successful) experience have received visibility

through the years, it is reasonable to accept that they enjoy better negotiating positions (Hsu, 2007)

and thus are able to match with better VCs. Indeed, it is found that more experienced VC firms are

more likely to invest in serial entrepreneurs‘ start-ups (Gompers, Kovner, Lerner and Scharfstein,

2006). Evidence also supports that offers made by VCs with superior network resources are far more

likely to be accepted by a portfolio company (Hsu, 2004). The latter is of course not so surprisingly,

since network contacts can provide access to all kinds of resources such as information and the

recruitment of talented staff (Bygrave and Timmons, 1992; Hochberg, Ljungqvist and Lu, 2007).

Moreover, affiliation with reputable VCs is found to have a positive signalling effect (Megginson and

Weiss, 1991; Sorenson and Stuart, 1999).

According to classic financial theory, risk and return are positively correlated, implying that,

the higher the riskiness of the investment, the more return-on-investment an investor will expect and

4 The extent to which VCs get closely involved with their PFCs differs between the US or UK venture capital

market, and their counterparts in (Continental) Europe. It seems that in (Continental) Europe, VCs focus more on

monitoring than on other ―extra-financial‖ services (Sapienza, Manigart and Vermeir, 1996; Schwienbacher,

2008). 5 In the European context, portfolio diversification and risk sharing can be a more important motive to syndicate

than the access to a larger deal flow (Manigart et al., 2002). 6 Network effects are sometimes found to reduce or even eliminate investment experience as an explanation for

VC performance (Hochberg, Ljungqvist and Lu, 2007).

6

demand. During their risk assessment (due diligence process), VCs perform a thorough analysis where

the quality of the management team is an important asset (DiMaggio and Powell, 1983; MacMillan et

al., 1985; Podolny, 1993; Zacharakis and Meyer, 2000). Following the trade-off between risk and

return-on-investment, it is plausible that VCs will demand a higher return rate when the PFC‘s top

management team is less experienced. Furthermore, regarding the financial aspects of the contractual

agreement between VCs and PFCs, it is found that highly reputable VCs are able to obtain equity at

important discounts (Hsu, 2004). This implies that, if a less experienced PFC‘s top management team

would want to match with a highly experienced VC, it is likely to pay double for the disparity between

both parties. Therefore, it is reasonable to expect VC-PFC matches along equal experience levels.

Some factors that could be of importance during the investigation of matches between VCs

and PFCs are of a complete different nature than the traditional financial or organizational motives.

For instance, when we look at the interactions that take place in a market, an interesting aspect is the

preference of market actors to interact with similar others, called homophily. Using this rather

psychological approach, Franke et al. (2006) indeed find that, in the venture capital market, homophily

noticeably exists. Clearly, the otherwise relatively objective matching between VCs and portfolio

companies is distorted due to similarity biases. Because of the fact that VCs and portfolio companies‘

TMTs prefer to interact on the basis of similar characteristics, it is reasonable to expect once more a

matching between both parties along equal experience levels.

Many theoretical perspectives or empirical findings can be brought forward to support the

notion of a clear matching between VCs and PFCs along equal experience levels. Whether we use

financial, organizational, or even sociological and psychological motives, it seems that – in their deal

search – both parties are looking for the best possible partners to match with but sometimes find

themselves confronted with limitations, resulting in a rather intuitive sorting in the market or

equilibrium. Nevertheless, many of the above arguments are in fact double-edged swords and need

some nuance while some additional reasoning might explain a less clear-cut matching. As we explore

these alternative views, it becomes clear that questioning this ―deal search equilibrium‖ is far from a

trivial matter.

In any financial market, typical principal-agent conflicts can arise when the investor allocates

resources to the investee. Financial contracting theory, as a directive to mitigate these conflicts,

reveals that there are substantial contracting issues between management teams and external capital

providers. From the capital provider‘s perspective, it is clear that financial contracts are often

specifically aimed at providing incentives for optimal behaviour towards the management team.

Indeed, agency theory teaches us that it is important to align both parties‘ interests in order to

encourage corporate governance. Because of the specificity of the venture capital market with

complex situations where a substantial part of the venture‘s value is often tied up in human capital, the

nature of these financial agreements is particularly important (Kaplan and Strömberg, 2003). Apart

7

from resulting cash flow rights or managerial contingencies, these contracts often define extensive VC

control rights (Hellmann, 1998; Kaplan and Strömberg, 2003). More specifically, should a portfolio

company be underperforming, the VC will be able to upgrade its control rights, even to the extent of

dismissing or recruiting senior executives (Kaplan and Strömberg, 2003). Furthermore, when VCs

should exercise these vast control rights, empirical results show that they are actually able to attract

new, talented staff, thus building up an adequate top management team (Hellmann and Puri, 2000;

Baum and Silverman, 2004). This implies that, during the selection stage, the quality of the PFC‘s

initial top management team might not be of such extreme importance as often portrayed. It then

becomes clear that VCs might choose to match with less experienced PFC‘s top management teams

because the latter are expendable.7

The environment in which new enterprises face strategic, operational and financial challenges

is generally considered complex and many new ventures fail. Apart from their will and financial

power to boost young entrepreneurial companies, VCs are known to provide ―extra-financial‖

services. However, while a lot of studies have proven that VCs effectively possess the power to supply

all sorts of advisory or assistance, little is known about the exact costs of these services. Assuming that

more experienced VCs are better able to deliver such guidance, Hsu (2004) interestingly finds that

experienced VCs obtain large discounts on their acquisition of start-up equity. That being the case, an

essential question arises as to why PFCs‘ management teams with considerable experience should pay

a surplus for less indispensable VC servicing. In fact, from a competence-based approach it now

seems reasonable to expect highly experienced PFCs‘ top management teams to accept financing

offers from about any VC, or even from less experienced VCs if this should result in lower affiliation

costs.

When applying network theory to the venture capital industry, research has confirmed the

important role of social networks for information sharing and deal searching (Sorenson and Stuart,

1999). We can support the notion that PFCs will try to match with more experienced and thus well-

networked VCs in order to tap into their pool of information, expertise and contacts (cf. supra).

Nevertheless, using the same logic, it is arguable that highly experienced PFCs‘ top management

teams will actually match with more experienced VCs. In fact, when these TMTs possess of sufficient

experience, it is likely that they have a lot of social capital of their own. Indeed, Hsu (2007) finds that

previous founding experience leads to a larger social capital that can be used for, e.g. the recruitment

of senior executives. That being the case, the need for affiliation with well-networked or reputable

VCs is partially eliminated. Actually, using social capital as an argument, we can also argue if

experienced VCs are truly scouting for experienced PFCs‘ top management teams. Since experienced

VCs are so well-networked, chances are that they will feel more confident to recruit senior executives

7 The fact that a PFC‘s initial top management team is expandable or easily replaceable also depends on other

factors than solely the financial contract between the VC and PFC. For instance, institutional aspects might add

illiquidity to the labour market, as is the case for (Continental) Europe (Schwienbacher, 2008). Next, for a VC, in

the management of their long-term reputation, we imagine it to be important not to treat TMTs as expandable.

8

should the initial TMT turn out to be inadequate. This means that the quality of the (initial)

management team might not be of such prominent importance.

An interesting discussion among scholars is whether or not one should look more at the top

management team of a PFC (―bet on the jockey‖) or at the business idea (―bet on the horse‖) as an

indicator of future performance. It is clear that some VCs do not strictly look for the best management

teams alone. Kaplan, Sensoy and Strömberg (2007) also find that it can be beneficial to invest in a

good business idea even though the management team is less experienced. In fact, especially in early

stage investments, financial, technological and market criteria can sometimes be more important than

a complete or highly qualified management team (Baeyens, Vanacker and Manigart, 2006).

Furthermore, apart from their selective power, VCs also have the power to build management teams

should they not fully fit the required profile or lack of essential capabilities (Hellmann and Puri, 2002;

Baum and Silverman, 2004). Knowing that, particularly in early-stage investments, factors relating to

the business itself can prove to be sufficient reasons for sound investing, we must take into

consideration a distortion of the matching pattern along equal experience levels.

Another argument that can be brought forward in this debate, is the concept of adverse

selection. Adverse selection in this context implies that, because of the very nature of PFCs seeking

financial support within the venture capital industry, it is likely that a VC firm – be it experienced or

not – will almost never match with truly experienced TMTs. This so-called ―lemons effect‖ 8 can be

explained by the fact that the venture capital market is often serving the entrepreneurial community

with (expensive) financing in cases where traditional capital providers will not sign in for the

considerate financial risks. If on the other hand, TMTs are highly experienced, the business risk is

likely to decrease significantly and, in combination with their vast social capital, they are more able to

find cheaper financing alternatives or get funding within the traditional financial markets.

After having offered several views above, it is clear that we should question the nature of

matching between VCs and PFCs. Although the sorting in the venture capital market along experience

levels seems intuitively correct, there are many reasons as to why we can see distortions in the pattern.

Be it financial, organizational, sociological or psychological arguments, almost none are able to firmly

stand ground using a theoretical framework alone. Therefore, in this study, we will make an attempt to

empirically explore the matching pattern along human capital dimensions. We hereby present the first

hypothesis:

HYPOTHESIS 1A: In the pre-investment stage, VC firms will match with PFCs’ top management

teams on equal experience levels; more experienced VCs with more experienced PFCs’ TMTs, and

less experienced VCs with less experienced PFCs’ TMTs.

8 While Akerlof (1970) wrote about information asymmetries and ―lemons‖ using a car market example, an

identical rationale was used within the venture capital market by Amit, Glosten and Muller (1990).

9

Now that we presented the first hypothesis, it is crucial to make some refinements in order to

capture underlying differences in experience. For, whenever one is willing to understand and

effectively assess the impact of human capital on organizational behaviour, it is crucial to recognize

the multidimensional nature of this resource. In general, Becker (1975) demonstrated a very helpful

distinction between the generic and specific components. While the generic component includes

general knowledge built up by education and professional experience, the specific component is

fuelled by industry-specific and leadership experience. Interesting for our study, is to explore whether

there is a differential influence of general and industry-specific experience on the matching between

VCs and PFCs. Many of the arguments brought forward to open the debate regarding general

experience (cf. supra) are still valid in the industry-specific context. However, it might be useful to

point out some additional motives or explore further details using an industry-specific framework.

Prior research has established that industry spaces give shape to informational and

transmission boundaries (Sorenson and Stuart, 1999).9 This obviously implies that, for the venture

capital market, the window of potential investments might be narrowed as VCs specialize themselves

in a particular industry. Therefore, it is likely that, as a VC accumulates more industry-specific

experience, their deal search will focus mainly on ventures within the same industry. Furthermore,

empirical results already exist that confirm industry-specific homophily where entrepreneurs have a

significant preference to match with VCs with parallel backgrounds (Sapienza, Manigart and Vermeir,

1996). Hence, it is reasonable to expect that industry-specific experience from both parties provides

matching incentives along equal experience types and levels.

Industry specialization can certainly be beneficial for the performance of VCs (Sapienza,

Manigart and Vermeir, 1996; Gompers, Kovner, and Lerner, 2009). Nevertheless, it is also plausible

that too much industry specialization can lead to a very narrow perspective and ultimately to incorrect

judgements or suboptimal investment strategies. On the other hand, VCs that are aware of the

importance of a broad market scope might have a competitive advantage and are likely to reap rewards

of their exploratory behaviour. Indeed, empirical results confirm that VCs sometimes choose new

industries for the value of learning. Moreover, VCs that explore clearly appear to be more successful

(Sørensen, 2008).10

Evidently, building up axial positions in industry networks is a good choice for

VC firms that want to obtain valuable cross-boundary information (Sorenson and Stuart, 1999). Since

VCs have good incentives for keeping a healthy balance between specialization and exploration, it is

likely to expect quite a few matches outside a VC‘s focal industry. This implies that, if we use a

narrow, industry-specific framework, the intuitive matching pattern along equal (industry-specific)

experience levels might be distorted.

9 Sorenson and Stuart (1999) provide a more elaborate analysis of industry spaces and spatial distribution which

also includes geographical dimensions. 10

Although exploratory behavior can make VCs reap rewards, it is advised that this behavior is part of a sound

exploratory investment strategy instead of rather random investing (Sørensen, 2008).

10

It is clear that industry spaces cause several limitations and opportunities to arise in the

venture capital market. In our study, it is therefore useful to also focus on industry-specific matching

behaviour. Analogously to the theoretical and empirical foundation of the hypothesis regarding

general experience, it is not clear what kind of industry-specific matching pattern we should expect to

encounter. For this reason, we will test the following hypothesis:

HYPOTHESIS 1B: In the pre-investment stage, VC firms will match with PFCs’ top management

teams on equal industry-specific experience levels; more industry-specific experienced VCs with more

industry-specific experienced PFCs’ TMTs, and less industry-specific experienced VCs with less

industry-specific experienced PFCs’ TMTs.

2.2. Growth Model

When exploring the foundations of our Matching Model (cf. supra), we brought forward

various motives to provide an explanation for the matching behaviour between VCs and PFCs.

Clearly, if we accept a two-sided selection model, both parties will select each other during the pre-

investment stage on the basis of several characteristics and expectations, some of which are obviously

performance-related. In the post-investment stage, it is interesting to check how good those predictions

or choices turn out to be. Therefore, we will examine multiple rationales to explain PFC performance

(growth) as a result of experienced-based matching behaviour.

There are several reasons as to why VC and PFC experience levels matter in both the pre-

investment and post-investment stage. Obviously, the activities performed by both parties across those

two stages are likely to be reflected in the company‘s results. Intuitively, one can imagine a positive

relationship between VC or PFC experience and PFC performance. In reality, this reasoning has

certainly been supported by previous studies.

During the pre-investment stage, VCs focus themselves on deal flow creation, initial

screening, due diligence, valuation and financial contracting (Tyebjee and Bruno, 1984). Because it is

generally difficult for young companies to receive financing, VCs get offered a large number of

investment proposals every year. Consequently, it is crucial for VCs to be able to rely on adequate

routines to increase efficiency. Since these pre-investment activities can be handled in a chronological

order, it is less complicated to draw causal relationships between efforts and benefits (Zollo and

Winter, 2002). Therefore, the pre-investment process is likely to carry substantial learning potential

and more experienced VCs will probably have better selection skills. Thus, it is possible that an

experienced VC, mere by using superior ―scouting‖ abilities, will pick out a PFC with high-growth

potential.

Another pre-investment aspect that could potentially explain superior growth among PFCs is

due to a specific selection motive of a PFC‘s top management team. Obviously, if we acknowledge the

11

fact that PFCs can actually choose who they match with, it is reasonable that they will select a VC on

the basis of promising characteristics such as experience and reputation (Hsu, 2004). This is not

surprising, as new ventures often face a lot of credibility issues towards buyers, suppliers, employees,

and other potential investors. Indeed, the affiliation with experienced VCs can act as a proof of

legitimacy or an important ―signalling effect‖ that makes many stakeholders open up their doors

(Stuart, Hoang and Hybels, 1999). Thus, it is likely that the mere matching with an experienced VC

will cause growth within the venture.

In the post-investment stage, unlike traditional investors, VCs are known to take up more

active roles with regard to their investments (Gorman and Sahlman, 1989; Bygrave and Timmons,

1992; Sapienza, Amason and Manigart, 1994; Elango et al., 1995).11

Generally, VCs can play a

significant role in monitoring the management team of their portfolio companies (Lerner, 1995).

However, in certain cases, VCs even take up more elaborate value-adding responsibilities than mere

continuous screening. For instance, VCs can provide advice to guide the venture in strategic or

operational decisions (Lerner, 1995; Sapienza, Manigart and Vermeir, 1996; Kaplan and Strömberg,

2001). What is more, VCs are willing to actively help with establishing a strategic alliances network

(Sapienza, Manigart and Vermeir, 1996; Lindsey, 2002), something which is highly important given

the resource providers‘ aversion to quality uncertainty regarding new ventures (Stuart, Hoang, and

Hybels, 1999) and start-ups‘ imperfect markets for information (Aoki, 2000). Finally, VCs are

sometimes able to impose professionalization within the portfolio companies by shifting the structure

and experience of the top management team (Hellmann and Puri, 2002; Kaplan and Strömberg, 2002).

Baker and Gompers (2003) even emphasize the role of VCs in outlining the board of directors and

found that VC-backed boards are slightly larger than others.12

Evidently, this recruitment aid can

prove to be quite helpful since Beckman and Burton (2008) find that low-quality founding teams fail

to attract broadly experienced executives. In summary, all these VC post-investment involvements,

also called ―coaching‖, are likely to have an impact on a PFC‘s performance. Indeed, Hellmann and

Puri (2000) show that VC-backed companies are able to bring their products faster to the market. Prior

research has also established that the post-investment value-adding ability of a VC is positively related

to previous investment experience (Sapienza, Manigart and Vermeir, 1996). Clearly, the post-

investment relationship with its PFCs enables a VC to develop critical expertise and knowledge

regarding technological matters or PFC development strategies (Gupta and Sapienza, 1992; Wright

11

Even though VCs are known to exhibit a deeper investment involvement than traditional investors, it seems

that (Continental) European VCs tend to have a more hands-off approach compared to their US or UK

counterparts (Sapienza, Manigart and Vermeir, 1996; Schwienbacher, 2008). 12

One should use caution in interpreting large board sizes as a positive factor. The optimal size of the board of

directors is a trade-off between costs and benefits of additional directors and evidence has been provided for

decreasing performance levels at suboptimal board sizes (Yermack, 1996; Eisenberg et al., 1998). In this case,

we assume slightly larger boards to be more effective in relation to governance issues.

12

and Robbie, 1998; Sapienza and De Clercq, 2000).13

Moreover, each additional investment adds to a

VC firm‘s powerful business network and extends its competences (Sorenson and Stuart, 2001)14

.

Consequently, it is plausible that more experienced VCs are able to drive higher growth within their

ventures by means of a more effective ―coaching‖.15

In order to explain PFC performance, it is obvious that we need to open up the ―black box‖

and look at internal company qualities. While the resource-based view stresses the importance of

identification and possession of critical assets, the theory of the firm puts emphasis on effective asset

organization. Eesley, Hsu and Roberts (2009) find that neither isolated theory is strong enough to

explain successful performance of entrepreneurial companies. In fact, they plea for a strategic

integrative theory in which, apart from idea assets, human assets are crucial. Evidently, new ventures

face the ―liability of newness‖ in the sense that it is complicated for a young company‘s management

team to identify the critical assets and to establish priorities and contractual structures when

commercializing those assets. Prior research has clearly found evidence for such entrepreneurial skill

as it seems that serial entrepreneurs are more likely to be successful in succeeding ventures (Gompers,

Kovner, Lerner and Scharfstein, 2006). Since experienced TMTs have accumulated such skills

(Becker, 1964), it is plausible that they will enable higher PFC growth. Furthermore, as a TMT gains

experience, they will dispose of a larger social capital (Beckman, Burton and O‘Reilly, 2007). In line

with VC network effects, a PFC‘s own social capital can prove to be highly important for resource

acquisition (Fried and Hisrich, 1994). Indeed, having business connections can help to access valuable

resources such as (follow-on) finance, customers or senior executive recruitment (Florin, Lubatkin and

Schulze, 2003) – potentially implying higher PFC growth.

Evidently, many arguments can be used in support of a positive influence of both VC and PFC

experience on PFC performance. Whether it is due to higher strategic and operational competences of

both parties, or simply a matter of better pre-investment selection and deal making, quite intuitively,

more experience goes hand in hand with better performance. However, we can clearly distinguish

several grounds upon which the influence of both parties‘ experience levels can be nuanced, or even

transformed into a negative relationship with performance.

VCs often provide financing to young companies, usually without an extensive track record or

a substantial amount of assets in comparison with the investment. Therefore, perhaps more than any

13

An interesting question to ask is from what kind of PFC post-investment interactions a VC learns the most.

According to behavioural theory, PFCs that are underperforming will alert the VC to the ineffectiveness of

certain actions and provide a learning potential (Cyert and March, 1963; Levinthal and March, 1981).

Notwithstanding this, it is equally plausible that VCs will learn from exceptionally performing PFCs, especially

since there is a tendency to spend more time with PFCs that deliver high returns (Sapienza, Manigart and

Vermeir, 1996; De Clercq and Sapienza, 2005). 14

In the post-investment stage, the power of a VC‘s business network can be demonstrated by the use of all sorts

of service providers such as, e.g. patent lawyers or head hunters (Gorman and Sahlman, 1989; Sa hlman, 1990). 15

The effectiveness of ―coaching‖ is evidently limited by the spatial proximity of PFC and VC (Lerner, 1995;

Sorenson and Stuart, 1999).

13

other type of investor, VCs find themselves constantly faced with the difficulty of managing extreme

uncertainty. In the pre-investment stage, it is obvious that a VC‘s investment decisions are based on

perceptions of risks and returns (Tyebjee and Bruno, 1984). Prior research has revealed that more

experienced VCs are better at picking out potential serial entrepreneurs (Gompers, Kovner, Lerner and

Scharfstein, 2006). In addition to this, Vanacker (2009) finds that general investment experience will

lead a VC to identify portfolio companies with higher growth potential. Notwithstanding these

scouting upgrades, it is also possible that a higher VC experience level will result in less accurate

assessments during the pre-investment stage. This ―domain familiarity‖ problem occurs because there

appears to be less perceived risks in familiar domains than in unfamiliar ones (Sitkin and Pablo, 1992).

Furthermore, since VCs are operating in a high-risk environment, they are constantly looking for

exceptional returns. Moreover, especially reputable VCs will demand and receive an above average

return-on-investment (Hsu, 2004). According to classic financial notions of risks and returns, it could

be that more experienced VCs are actually taking higher risks. In combination with domain familiarity

issues (cf. supra), this might actually lead an experienced VC to pick out ―lemons‖.

PFCs, often young companies in search for recognition on the market, clearly have incentives

to engage in relationships with experienced VCs. The mere fact that such an experienced investor puts

trust in the PFC‘s business, often causes a ―signalling effect‖ towards other important stakeholders

(Stuart, Hoang, and Hybels, 1999). However, Hsu (2004) clearly demonstrates that the affiliation with

reputable VCs can turn out to be a costly affair. An important question that a PFC‘s top management

team should ask itself in this case is as to how much this affiliation is worth to their business. In fact,

young companies are often already severely constrained by a lack of resources – the last thing they

need is an investor that brings more costs than benefits. Especially since it is not straightforward for a

VC‘s ultimate goals to be perfectly aligned with a PFC‘s long term perspectives16

, it could sometimes

be possible that a reputable VC‘s involvement limits a PFC‘s abilities, implying suboptimal growth

rates.

Whether it is beneficial for VCs to focus on the potential of the venture‘s (idea) assets or the

quality of the venture‘s management team during due diligence processes, has always been an

interesting debate. Human capital theory strongly supports the need for high-quality management

teams and Zingales (2000) claims that, in modern companies, the core consists entirely of specific

human capital. However, Kaplan, Sensoy and Strömberg (2004) find that, among companies that grew

enormously (IPOs), the human assets were likely to change over time while the non-human assets did

not. Furthermore, for young (hi-tech) companies in extremely risky environments, prior research has

suggested that technological, market and business-related criteria are more important than specific

human capital since the latter can indeed be fuelled through VC involvement (Baeyens, Vanacker and

16

Zarutskie (2007) demonstrates that the typical lifespan of a VC investment is about three to five years.

Furthermore, evidence has been found for VC‘s to sometimes put pressure on their PFCs purely out of self-

interest (Gompers, 1996).

14

Manigart, 2006). Therefore, certainly in some cases, it might be that the management team, as long as

there is a sufficient fit with the company‘s business, does not influence a PFC‘s performance all that

much during the early stages.

Prior research has established that, in general, VCs learn from their investments and

sometimes the mere potential to learn can be an incentive for VC investing itself (Sørensen, 2008). In

the pre-investment stage it is clear that the use of homogeneous procedures or routines facilitates the

learning process (Zollo and Winter, 2002).17

The post-investment stage, however, is characterised by a

more complicated learning context. First of all, VCs are reluctant to spend too much time with their

PFCs after the investment is made (Kaplan and Strömberg, 2002). This implies that VCs will have far

less chances to learn from specific post-investment behaviour. Furthermore, there are hardly any

standard procedures possible since value-adding activities such as strategic or operational advisory

need serious customization over the various companies in a VC‘s portfolio. Following this lack of

homogeneity, the absence of clear sequential actions will again result in a lower learning potential for

a VC (Zollo and Winter, 2002). Clearly, it is plausible that, during the post-investment stage, a VC

will only partially be undergoing traditional learning effects. Consequently, it is not straightforward

for an experienced VC to be able to deliver superior post-investment value-adding involvement.

Another interesting view, comes from applying prospect theory to the financial reality of VC

firms, especially given their extreme risky investment environment. Since prior research has clearly

demonstrated that failure rates of new ventures are staggering (Timmons, 1990), portfolio risk

management is obviously of paramount importance for a VC.18

It seems that, no matter how good a

VC‘s selection skills are, it is likely that a (large) number of their investments will turn out to be a

waste of money. Not only do these losses need to be recovered, VCs are also bounded to delivering an

attractive return-on-investment to fund investors. Therefore, when managing their portfolio, it is

possible that a VC focuses its value-adding on those ventures that are performing exceptionally well

while the bad performers are turned into ―living dead‖ instead of being granted extra attention

(Sahlman, 1990; Ruhnka, Feldman and Dean, 1992; Sapienza, Manigart, and Vermeir, 1996).

Consequently, more experienced VCs might possess of special risk management procedures that limit

the losses within certain PFCs, instead of empowering growth. For instance, Dimov and Shepherd

(2005) found that law experience provides VCs with expertise in declaring bankruptcies within their

portfolio.

We have already opened up the ―black box‖ and explored internal factors to explain PFC

growth (cf. supra). Nevertheless, some company-specific competences might not be visible purely by

analyzing a certain PFC on the basis of TMT experience. In fact, an important aspect of resource

17

The amount of knowledge that a VC retrieves from the learning process is likely to vary over time. For

instance, among experienced VCs, there might exist a certain level of hubris in the sense that they feel as if they

have little more to learn (De Clercq and Sapienza, 2005). 18

Portfolio risk management is even demonstrated to be an incentive for syndication among VCs (Manigart et

al., 2002)

15

acquisition might be externally induced in the case of a corporate spin-off. Klepper (2001) found that

it is possible for a spin-off to inherit competences from the corporate partner. If a PFC is able to count

on a corporate parent‘s extensive competences, it might be possible to enjoy high growth rates while

being managed by a relatively inexperienced TMT. Analogously, it is plausible that a VC‘s experience

does not really matter in the case of corporate spin-offs.

Evaluating PFC performance in relation with VC and PFC experience certainly is a

complicated task. The difficulty lies in the fact that, if we measure PFC performance, we must be

aware of a multitude of reasons to explain the results. Nevertheless, it is clear that both the VC‘s as the

PFC‘s selection skills matter, as well as their respective strategic and operational capabilities.

Therefore, it is plausible that both parties‘ experience levels will have an influence on PFC

performance. Consequently, we offer the following hypothesis:

HYPOTHESIS 2A: The experienced-based matching between VC firms and PFCs’ top management

teams will have an impact on growth in the post-investment stage; highly experienced matches will

result in high PFC growth, whereas less experienced matches will result in lower PFC growth.

In general, we have offered several arguments both supporting and countering the notion of

experience-induced PFC growth. However, analogously to the theoretical foundations of our Matching

Model (cf. supra), we need to add some refinements to account for industry-specific experience with

possibly differential PFC performance levels as a result. Indeed, in order to capture the impact of

human capital on organizational behaviour, it is necessary to take into consideration the

multidimensional nature of this resource (Becker, 1975). Furthermore, since it is likely that large

quantities of human capital are allocated to a venture, differences in quantity may be less essential

than differences in quality (Dimov and Shepherd, 2005). Therefore, notwithstanding the above

arguments regarding general experience, we will offer an extension towards industry-specific

experience levels.

Bringing forward the theory of absorptive capacity, it is clear that not all types of experiences

lead to identical performance levels. In fact, experience is most functional when it is related to an

object that is already settled within the pool of knowledge (Cohen and Levinthal, 1990). To the point

of VCs‘ industry specialization, prior research has already established a significant impact on venture

performance. Specialized firms are found to generally outperform others by means of industry-specific

human capital developed by specialist venture investors within the firm (Gompers, Kovner, and

Lerner, 2009). This industry-specific human capital leads a VC to detect weakening performance

levels faster, as well as to better understand the organizational context of a particular industry (Gupta

and Sapienza, 1992; De Clercq et al., 2001). Evidently, when their industry experience and knowledge

fits with a PFC‘s focal industry, VCs find themselves in a position to add more value in the post-

16

investment stage (Sapienza, Manigart and Vermeir, 1996). The theory of absorptive capacity is

obviously also applicable to the competences at the level of a portfolio company‘s TMT. Indeed,

Colombo and Grilli (2005) provide evidence for a positive relationship between venture growth and a

management team‘s prior industry-specific working experience.

Notwithstanding the positive influences of industry specialization, the theory of absorptive

capacity might also explain another, less favourable effect of an accumulation of specific knowledge.

In fact, when VCs or PFCs are only able to count on an experience set within a very specific industry,

chances are that their rationales will become increasingly channelled and less flexible (Levinthal and

March, 1993). Indeed, highly specialized routines make it more difficult for both parties to evaluate

changing environments, thus slowing down necessary adaptation processes (Hayward, 2002).

Therefore, we must be careful as to look at industry-specific experience as an inherently positive

influence on value-adding and company growth.

Although industry-specific knowledge is likely to influence a VC‘s or PFC‘s selection,

strategic or operational skills, we would ignore an important knowledge base if we would assume that

industry boundaries are absolute and inevitable. In fact, knowledge-driven strategies exist to overcome

possible information transmission problems. More specifically, it is possible to fill potential

―knowledge gaps‖ by actively building and structuring alliances with parties from outside the focal

industry (Sorenson and Stuart, 1999; De Clercq and Dimov, 2008). Obviously, the ability to tap into

an external pool of expertise is likely to influence a PFC‘s growth rate without the help of an industry-

experienced TMT or VC.

Given the theoretical debate regarding the importance of industry-specific experience, and

parallel to the hypotheses in our Matching Model (cf. supra), we will offer an extension on the

relationship between experience and PFC growth. Hence we present our last hypothesis:

HYPOTHESIS 2B: The industry-specific experienced-based matching between VC firms and PFCs’

top management teams will have an impact on growth in the post-investment stage; highly industry-

specific experienced matches will result in high PFC growth, whereas less industry-specific

experienced matches will result in lower PFC growth .

17

3. Data

3.1. Sample and Data Sources

In order to conduct our study, we used data from several sources. First of all, to feed our

Matching Model, we needed to create a sample of portfolio companies that can be linked with their

lead investors in the initial venture capital financing round. To do this, we started off with a database

provided by the Belgian Venture Capital and Private Equity Association (B.V.A.). With 39 full-

member investment funds and 38 associated members in 2010, the B.V.A. is the foremost professional

association to include the Belgian private equity and venture capital community. The respective

database consisted of 109 portfolio companies that received initial venture capital financing between

1996 and 2004. We continued by expanding our portfolio company sample frame through the addition

of a second database (77 portfolio companies that received initial VC financing between 1988 and

2009) provided by Andy Heughebaert, doctoral researcher at Ghent University. After a duplicate

check, these two databases accounted for a total amount of 157 portfolio companies that could be

traced back to their initial lead VC investors. However, because of the typical high failure rate among

new ventures, we realized that it would be difficult to obtain a sufficient collection of human capital

survey data. Therefore, we decided to enlarge the sample frame even more by means of further

searches into VentureXpert (Thomson) and Zephyr (Bureau van Dijk), two electronic databases with

comprehensive investment deal information. Finally, we were able to construct a sample frame of 304

unique portfolio companies that received initial VC funding between 1996 and 2006. We were forced

to limit our deal selection to 2006 to allow for sufficient accounting data collecting later on (cf.

Growth Model). Once our initial sample frame was constructed, we needed to obtain the necessary

information by means of a survey. To design this questionnaire, we based ourselves primarily on the

variables used by Colombo and Grilli (2005) in their study on founder‘s human capital. Apart from a

selection of queries regarding the experience of the management team members, questions were added

to explore the management team‘s education, as well as the VC‘s pre- and post-investment influence

on the composition of the management team. Furthermore, because of the Belgian multilingual

context, we offered the survey in three languages (English, French and Dutch), carefully monitoring

for identical phrasing. Taking into account the absence of clear contact information for certain

portfolio companies that are no longer active or were part of a merger, we searched an online business-

oriented networking website (LinkedIn) for possible former top management team members.

Notwithstanding our efforts, it is obvious that our study will ultimately suffer from failure bias since

not all former top management team members are feasibly traceable. Finally, by means of e-mail, fax

machine, telephone and even actual visits, we believe19

to have reached 227 PFCs out of our total

sample frame and we have received 40 completed questionnaires, implying a response rate of 18% .

19

Since a substantial part of our survey work was carried out using e-mail, it is difficult to accurately estimate

the actual reach. Therefore, we took note of explicit non-responses and bounced e-mails.

18

Furthermore, we were able to add 6 more PFCs by carefully analyzing venture information in a special

conference brochure (BeneLux Venture Summit, Belgium, Brussels, 16th to 17

th February 2004)

20,

resulting in a total number of 46 PFCs. After thoroughly controlling for missing experience data, we

decided to use only 43 PFCs for our Matching Model.

Next, we needed to collect accounting data for our Growth Model. Our growth measures were

constructed using Delmar, Davidsson and Gartner (2003) as a guidance to take into consideration the

heterogeneity of company growth. We were able to collect yearly financial statement data for 41 out

of 46 portfolio companies for up to five years21

after the initial venture capital investment by using the

BelFirst database (Bureau van Dijk). The BelFirst database is able to deliver this type of information

because all companies incorporated under Belgian law are required to file their yearly financial

statements with the Belgian Central Bank. After a missing accounting data control, we found that 41

out of 43 PFCs in the Matching Model can be linked and used in our Growth Model.22

Immediately after the data collection, we were able to identify some core characteristics of the

total sample (N=46). At the year of the initial venture capital investment, the average age of the

portfolio companies in our sample was 6.53 years, ranging between a minimum of 0 and a maximum

of 23.25 years, and 33% of the portfolio companies raised venture capital funds during the foundation

year. At the same time, the average company in our sample has 4,058,278 euro of assets, reaches an

EBITDA of - 221,944 euro, accounts for an added-value of 701,611 euro, and employs 13 people (in

full-time equivalents). Common sectors included Life Sciences (20%), ICT (37%) and Business and

Industrial Products and Services (20%).23

Furthermore, the average portfolio company had about 3

(2.78) founders or top management team members. The average founding or management team had

13.50 years of work experience, 10.10 of which within the same industry as the venture in our sample.

Remarkably, at least 37%24

of the PFCs received multiple written offers in the first round and 74% of

all founding or management teams had prior founding experience. In addition, when we took a closer

look at the lead venture capital firms that provided initial finance to the companies in our sample,

some interesting figures arose as well. Regarding the lead VC‘s experience, we find that the average

VC firm has made 44 investments prior to the investment relationship formation on which we focus in

our sample. Furthermore, of all the lead venture capital firms, 85% has already invested in the same

industry as the venture in our study.

Since our research sample is comprised of firms and companies incorporated under Belgian

law, we must pay a great deal of attention to the generalization of possible results. Even though the

20

We are aware of the fact that comparisons between the interview data (40 PFCs) and the extra data (6 PFCs)

should not be taken for granted. Nevertheless, we only included PFCs of which we were able to complete as

good as the whole questionnaire. 21

We collected post-investment accounting data up to five years since the typical duration of venture capital

investments varies between three to five years (Zarutskie, 2007). 22

For a brief overview of subsamples, please consult Table 1, Table 2, Figure 1 and Figure 2 (Appendix). 23

EVCA Sectoral Classifications 24

We do not have the necessary information for the brochure data addition (6 PFCs of the BeneLux Venture

Summit) so we only counted 17 PFCs with multiple offers on a total of 46 PFCs.

19

Belgian venture capital and private equity market is at approximately the same level of development

as other Continental European venture capital and private equity markets, it is clear that these market

figures are not in the same league as the U.S. and U.K. statistics (EVCA25

, 2007). Furthermore,

because Belgium – as other Continental European countries – has a financial market that is mainly

bank-based, public equity and debt markets are typically less developed compared to their counterparts

in market-based financial systems such as the U.S. and U.K. (Demirguc-Kunt and Levine, 1999). The

latter implies that initial public offerings (IPOs) do not occur often and this is clearly reflected in

recent studies where they find that trade sales are the predominant type of exits in Europe

(Schwienbacher, 2008).

3.2 Measures

3.2.1. VC Experience

VC firm experience is captured using measurements at the time of investment relationship

formation. Notwithstanding the venture capital industry‘s omnipresence of syndicate investing, we

focus on the experience level of the lead investor. We acknowledge the fact that other syndicate

members might have an impact on the observed behaviour but, following prior research (e.g., Hsu,

2004; Dimov and Shepherd, 2005), we reckon that it is the lead investor who is responsible for the

pre-investment contacts and the post-investment interactions (Gorman and Sahlman, 1989).

VC General Experience is measured as the total amount of previous investments a particular

VC has made, regardless of the industry (Sørensen, 2007; Gompers, Kovner, Lerner and Scharstein,

2008). VC General experience ranges from 0 to 332 with a median value of 10 investments. VC

Industry-specific Experience is used to narrow down the general experience to investments made in

the venture‘s focal industry26

(Gompers, Kovner, Lerner and Scharfstein, 2008). VC Industry-specific

Experience ranges from 0 to 61 with a median value of 3 investments. Furthermore, to correct for the

decreasing marginal returns of experiential learning, the natural logarithm of both variables is used in

the following analyses (Pennings, Barkema and Douma, 1994).27

3.2.2. Management Experience

Management experience is measured along different dimensions from a human capital and

competence-based perspective (Colombo and Grilli, 2005). In brief, we captured prior work

experience and previous entrepreneurial experience. The respective data was collected using values at

the time of the investment relationship formation. Analogously to the VC Experience variables, we

25

The European Private Equity and Venture Capital Association (EVCA) is a member-based, non-profit trade

association and has over 1,200 members in Europe. 26

EVCA Sectoral Classification was used to measure VC Industry-specific Experience. 27

Given that the experience variables may be equal to zero, one is added to each variable before the logarithmic

transformation. For the variables with a negative value, we used the following approach: Ln [1+Z] if Z ≥ 0, but -

Ln [1+Z] if Z < 0 (Hand, 2000).

20

correct for the decreasing marginal returns of PFC experiential learning by using the natural

logarithm.28

PFC General Experience is operationalized as the average amount of work experience (in

years) between the founding or top management team members. PFC General Experience ranges from

1 to 29 years with a median value of 11.9 years. PFC Industry-specific Experience is used to narrow

down the general experience to the average years of work experience within the venture‘s focal

industry. PFC Industry-specific Experience ranges from 0.3 to 27.5 years with a median value of 6.7

years. Next, by means of our survey data, we are able to explore PFC Experience on a deeper level

using detailed experience measures. General Management Experience accounts for the average years

of work experience on a general management department. General Management Experience ranges

between 0 and 18.5 years with a median value of 2.1 years. R&D Experience accounts for the average

years of work experience on a research and development department. R&D Experience ranges

between 0 and 27 years with a median value of 4.8 years. Marketing/Sales Experience accounts for

the average years of work experience on a marketing or sales department. Marketing/Sales Experience

ranges between 0 and 8 years with a median value of 0 years. Accounting/Financial/Legal Experience

accounts for the average years of work experience on an accounting, financial or legal department.

Accounting/Financial/Legal Experience ranges between 0 and 10 years with a median value of 0

years. Furthermore, also using detailed survey information, we can focus on entrepreneurial

experience of the founding or top management team members. Entrepreneurial Experience – General

measures the average years of entrepreneurial experience, regardless of the industry. Entrepreneurial

Experience – General ranges from 0 to 2.5 years with a median value of 4 years. Entrepreneurial

Experience – Industry-specific measures the average years of entrepreneurial experience within the

venture‘s focal industry. Entrepreneurial Experience – Industry-specific ranges from 0 to 16.5 years

with a median value of 4 years. Previous Start-ups counts the total number of prior start-ups founded

between the venture‘s founding or top management team members. Previous Start-ups ranges from 0

to 51 with a median value of 1. Finally, since it is likely that the number of founding or top

management team members will have an influence on decision-making processes, we included a last

human capital variable. Number of Founders is the count of the venture‘s founding or top

management team members. Number of Founders ranges from 1 to 7 with a median value of 2.

3.2.3. Portfolio Company Growth

In contrast with many of the prior PFC growth studies, we do not focus on IPOs or trade sales

since such ―exits‖ can be a sign of either exceptional or deteriorating performance levels and thus

prove to be noisy proxies for returns (Schwienbacher, 2002; Hochberg, Ljungqvist and Lu, 2007).

Instead, the growth trajectory of the portfolio companies in our sample is studied from the year of

28

Given that the experience variables may be equal to zero, one is added to each variable before the logarithmic

transformation. For the variables with a negative value, we used the following approach: Ln [1+Z] if Z ≥ 0, but -

Ln [1+Z] if Z < 0 (Hand, 2000).

21

investment relationship formation until up to five years after the formation in order to capture the

typical duration of venture capital investments (Zarutskie, 2007). Since a VC‘s portfolio typically

consists of companies that need substantial investments while, in the meanwhile, cannot count on

immediate sale prospects (Puri and Zarutskie, 2008), the exclusive use of traditional accounting-based

profitability measures is rather inappropriate (Shane and Stuart, 2002). Therefore, to allow for an

adequate PFC growth exploration, we take a multidimensional perspective and use a set of four

different growth measures (Delmar, Davidsson and Gartner, 2003).

Total Assets Growth is constructed by taking the average total assets growth (in euros) of up

to five years after the investment relationship formation. Total Assets Growth ranges between -

1,229,200 and 26,961,800 euros with a median value of 410,500 euros. Employment Growth is

calculated by taking the average employment growth (in full-time equivalents) of up to five years after

the investment relationship formation. Employment Growth ranges between -146,400 and 1,748,600

euros with a median value of 76,000 euros. Value-added Growth is measured by the average value-

added growth (in euros) of up to five years after the investment relationship formation. Value-added

Growth ranges between -1,749,000 and 13,596,000 euros with a median value of 142,800 euros.

EBITDA Growth is constructed by taking the average EBITDA growth (in euros) of up to five years

after the investment relationship formation. EBITDA Growth ranges between -3,059,000 and

1,269,000 euros with a median value of 24,600 euros.

3.2.4. Controls

Because of the complex environment to which entrepreneurial growth studies are exposed, it is

important to correctly attribute certain VC or PFC characteristics to PFC growth. Therefore, we

inserted various control variables into our analysis. Since age effects are known to distort company

growth patterns (Jovanovic, 1982), we control for the difference in years between the PFC foundation

date and the time of investment relationship formation (PFC Age). Subsequently, from a path-

dependency perspective, we control for PFC size (in total assets) at the time of investment relationship

formation (PFC Initial Size). Next, we created a dummy variable (Hi-tech) to control for the different

growth patterns between companies in high-tech and low-tech industries (Harhoff, Stahl and

Woywode, 1998). Finally, we included a dummy variable to control for time effects (Year).

-------------------------------

Insert Table 3 about here

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-------------------------------

Insert Table 4 about here

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3.3 Analysis

Because of the fact that, to our best knowledge, little empirical research has been conducted

regarding experience-based matching behavior between VCs and PFCs, we will use several

explorative techniques to analyze our Matching Model. First of all, we will use a rather simple and

22

visual approach by plotting the VC-PFC dyads along (broad) general and industry-specific

dimensions. Secondly, we will offer univariate statistics of our detailed experience variables set.

Furthermore, bivariate statistics (Mann-Whitney U Tests) are used to search for significant matching

behavior. Finally, multivariate statistics (Probit Regression/OLS Regression) are included. Evidently,

the small sample size (N=43) calls for extreme caution when interpreting the results.

In our Growth Model, we will also use several analysis methods, more or less similar to some

of the previous entrepreneurial growth studies (e.g., Delmar, Davidsson and Gartner, 2003).

Specifically, we will analyze PFC growth along four dimensions (total assets, employment, EBITDA

and value-added) using univariate, bivariate (Mann-Whitney U Tests/Kruskal-Wallis Tests) and

multivariate statistics (OLS Regressions). Again, we wish to call for cautious interpretation of the

results because of the small sample size (N=41).

4. Results

4.1. Matching Model

Figure 3 and Figure 4 plot the VC-PFC matches along respectively general and industry-

specific experience. Using mean splits on (the natural logarithm of) both VC and PFC experience

variables, we created two subsamples for VCs and PFCs (LOW vs. HIGH) as well as four VC-PFC

matching quadrants (I. HIGH-HIGH, II. HIGH-LOW, III. LOW-LOW, IV. LOW-HIGH). Obviously,

these mean splits will be a helpful foundation in subsequent analyses where we will compare the

different subgroups along several dimensions. Regarding our Matching Model, hypotheses 1A and 1B

posit VC-PFC matching behaviour along equal experience levels. In anticipation of both hypotheses,

we would expect to see a balanced pattern in which the majority of the observed matches can be found

in quadrant I or III. In other words, we would expect to observe a cluster of less (general/industry-

specific) experienced VC-PFC matches in quadrant III and, analogously, a high (general/industry-

specific) experience matches cluster in quadrant I. Nevertheless, merely by looking at both graphs, no

distinct experienced-based matching pattern can be observed. In fact, if we count the matches in each

quadrant, we must conclude a relatively equal distribution across the four subgroups (Figure 3: I. 21%,

II. 33%, III. 23%, IV. 23%; Figure 4: I. 21%, II. 26%, III. 30%, IV. 23%). Clearly, on first sight, we

find no indication whatsoever of VC-PFC matching behaviour along equal experience levels.

Obviously, the absence of a clearly visible relationship between VC and PFC experience leaves us in a

doubtful position regarding hypotheses 1A and 1B – either the hypothesized relationship is inexistent

in our sample, or the experience measures are simply not adequate for exploring the hypothesized

relationship and need some fine-tuning.

23

Figure 3

Matching Model: General Exp. Matching Quadrants

Figure 4

Matching Model: Industry-specific Exp. Matching Quadrants

24

Since we find no evidence to support hypothesis 1A or 1B using a rather intuitive and visual

approach, we have conducted detailed investigations of the various subsamples. First of all, Table 5

and Table 6 present the univariate statistics for our complete experience variables set across the VC

and PFC subgroups. Using such a collection of detailed PFC experience measures clearly pays off as

we can observe substantial differences in values between the various subsamples. Furthermore, we

found quite a few differences between the subgroups in our sample to be statistically significant.

Table 7 reports the bivariate statistics for VCs‘ general experienced-based matching. We find that

more general experienced VCs match with PFCs whose TMT members have more experience in

general management (p < 0.10). Surprisingly, we find that more general experienced VCs match with

PFCs whose TMT members have less experience in R&D (p < 0.10). Interestingly, we also find a clear

matching relationship between more generally experienced VCs and TMT members with more

entrepreneurial experience (General: p = 0.140; Industry-specific: p < 0.05). In line with these

findings, more general experienced VCs match with PFCs whose TMT members have more previous

founding experience (p < 0.05). Finally, more general experienced VCs are found to match with PFCs

whose TMTs consist of more members (p < 0.05). Table 8 reports the bivariate statistics for VCs‘

industry-specific experience-based matching. Interestingly, all the above relationships remain

significant, while some even get accentuated. VCs with more industry-specific experience clearly

match with PFCs whose TMT members have more general management experience (p < 0.01). Next,

we also observe the opposite matching relationship between VC industry-specific experience and

TMT members‘ R&D experience (p < 0.10). Furthermore, we find a distinct matching between VCs

with more industry-specific experience and PFCs whose TMT members have more entrepreneurial

experience (General: p < 0.10; Industry-specific: p < 0.05). Finally, a clear matching pattern is found

again between VCs with more industry-specific experience and PFCs whose TMT members have

founded more start-ups in the past (p < 0.10).

After having found several interesting VC-PFC matches using bivariate statistics, we decided

to test these patterns in multivariate analyses (Table 9, Table 10, Table 11 and Table 12, Appendix).

Obviously, given the small simple size of our exploratory study, we wish to call for extreme cautious

interpretations of our regression results. However, notwithstanding these methodological concerns, we

find some experience-based matching relationships to exist on a significant level. Therefore, we

remain relatively convinced of the fact that, given the right set of detailed experience measures,

experience-based matching behaviour can indeed be found in the venture capital market. However,

instead of fully accepting hypothesis 1A and 1B, we wish to add some nuance to the stated

relationships. In fact, we do not believe in a frequent and straight-forward experienced-based matching

between VCs and PFCs. In contrast, we assume that matching occurs more often across distinct

qualitative experience dimensions.

25

Table 5

Matching Model: Univariate Statistics

Panel A: VC General Experience – LOW a

Mean S.D. Min. Max. Median

PFC Experience Variables

(1) PFC General Experience 14.78 8.04 2.50 29.00 14.00

(2) PFC Industry-specific Experience 11.17 7.78 0.30 27.00 9.15

(3) General Management Experience 3.05 4.65 0.00 18.00 0.13

(4) R&D Experience 8.06 7.89 0.00 27.00 6.58

(5) Marketing/Sales Experience 1.57 2.33 0.00 7.33 0.00

(6) Accounting/Financial/Legal Experience 1.09 2.52 0.00 10.00 0.00

(7) Entrepreneurial Experience – General 5.07 7.06 0.00 22.50 2.83

(8) Entrepreneurial Experience – Industry-specific 3.11 4.92 0.00 16.50 0.00

(9) Previous Start-ups 1.29 1.85 0.00 9.00 1.00

(10) Number of Founders 2.46 1.35 1.00 7.00 2.00

Panel B: VC General Experience – HIGH a

Mean S.D. Min. Max. Median

PFC Experience Variables

(1) PFC General Experience 13.18 8.85 1.00 27.50 11.50

(2) PFC Industry-specific Experience 9.51 7.21 1.00 27.50 6.50

(3) General Management Experience 5.17 5.61 0.00 18.50 2.66

(4) R&D Experience 3.78 3.65 0.00 10.40 3.33

(5) Marketing/Sales Experience 2.16 2.76 0.00 8.00 0.66

(6) Accounting/Financial/Legal Experience 1.25 2.16 0.00 6.25 0.00

(7) Entrepreneurial Experience – General 5.84 4.33 0.00 15.00 5.00

(8) Entrepreneurial Experience – Industry-specific 5.58 4.21 0.00 15.00 5.00

(9) Previous Start-ups 5.00 11.47 0.00 51.00 2.00

(10) Number of Founders 3.26 1.33 2.00 6.00 3.00

a Mean splits were used on the natural logarithm of VC General Experience.

26

Table 6

Matching Model: Univariate Statistics

Panel A: VC Industry-specific Experience – LOW a

Mean S.D. Min. Max. Median

PFC Experience Variables

(1) PFC General Experience 13.81 7.82 2.50 29.00 12.85

(2) PFC Industry-specific Experience 10.45 7.74 0.30 27.00 7.50

(3) General Management Experience 2.14 3.12 0.00 10.00 0.00

(4) R&D Experience 8.02 7.90 0.00 27.00 6.58

(5) Marketing/Sales Experience 1.73 2.63 0.00 8.00 0.00

(6) Accounting/Financial/Legal Experience 1.09 2.52 0.00 10.00 0.00

(7) Entrepreneurial Experience – General 4.57 6.51 0.00 22.50 2.83

(8) Entrepreneurial Experience – Industry-specific 2.86 4.59 0.00 16.50 0.00

(9) Previous Start-ups 1.33 1.90 0.00 9.00 1.00

(10) Number of Founders 2.50 1.35 1.00 7.00 2.00

Panel B: VC Industry-specific Experience – HIGH a

Mean S.D. Min. Max. Median

PFC Experience Variables

(1) PFC General Experience 14.41 9.17 1.00 27.50 13.80

(2) PFC Industry-specific Experience 10.42 7.37 1.00 27.50 6.60

(3) General Management Experience 6.33 6.24 0.00 18.50 3.25

(4) R&D Experience 3.83 3.69 0.00 10.40 3.33

(5) Marketing/Sales Experience 1.95 2.43 0.00 7.33 0.66

(6) Accounting/Financial/Legal Experience 1.25 2.16 0.00 6.25 0.00

(7) Entrepreneurial Experience – General 6.47 5.15 0.00 18.00 5.00

(8) Entrepreneurial Experience – Industry-specific 5.89 4.46 0.00 15.00 5.00

(9) Previous Start-ups 4.95 11.48 0.00 51.00 2.00

(10) Number of Founders 3.21 1.36 2.00 6.00 3.00

a Mean splits were used on the natural logarithm of VC Industry-specific Experience.

27

Table 7

Matching Model: Bivariate Statistics a

VC General Experience Mean Rank Sig.

LOW HIGH

PFC Experience Variables

(1) PFC General Experience 23.29 20.37 0.448

(2) PFC Industry-specific Experience 23.15 20.55 0.501

(3) General Management Experience 19.00 25.79 0.072*

(4) R&D Experience 25.15 18.03 0.063*

(5) Marketing/Sales Experience 20.58 23.79 0.360

(6) Accounting/Financial/Legal Experience 21.92 22.11 0.953

(7) Entrepreneurial Experience – General 19.52 25.13 0.140

(8) Entrepreneurial Experience – Industry-specific 18.17 26.84 0.020**

(9) Previous Start-ups 18.42 26.53 0.030**

(10) Number of Founders 18.48 26.45 0.031**

a Mann-Whitney U Test

*,**,*** indicate significance at respectively 10%, 5% and 1%.

Table 8

Matching Model: Bivariate Statistics a

VC Industry-specific Experience Mean Rank Sig.

LOW HIGH

PFC Experience Variables

(1) PFC General Experience 21.90 22.13 0.951

(2) PFC Industry-specific Experience 21.98 22.03 0.990

(3) General Management Experience 17.35 27.87 0.005***

(4) R&D Experience 25.00 18.21 0.076*

(5) Marketing/Sales Experience 20.92 23.37 0.484

(6) Accounting/Financial/Legal Experience 21.92 22.11 0.953

(7) Entrepreneurial Experience – General 19.06 25.71 0.080*

(8) Entrepreneurial Experience – Industry-specific 17.96 27.11 0.014**

(9) Previous Start-ups 18.65 26.24 0.042**

(10) Number of Founders 19.04 25.74 0.070*

a Mann-Whitney U Test

*,**,*** indicate significance at respectively 10%, 5% and 1%.

28

4.2 Growth Model

After empirically investigating the experienced-based VC-PFC matching behaviour, an

interesting extension of our research consists of a multidimensional exploration of PFC growth across

the various subgroups in our sample. Essentially, we are thus able to observe the potential growth

consequences of certain types of experience-based matching behaviour and, by doing so, we will try to

answer to hypotheses 2A and 2B as they posit higher growth levels across matches among high

(respectively, general and industry-specific) experienced parties.

We start our multidimensional PFC growth exploration by separately measuring Total Assets

Growth across the various VC and PFC (LOW vs. HIGH) subsamples. Interesting values arise when

we take a look at the descriptives in Table 12 and Table 13. In addition, bivariate analysis reports in

Table 14 allows us to detect several significant relationships between separate VC and PFC

experience levels and PFC growth. VCs with high levels of general experience have PFCs in their

portfolio that exhibit higher growth in total assets (p < 0.05). An identical relationship can be observed

among VCs with high levels of industry-specific experience and, what is more, the relationship seems

to be accentuated (p < 0.01). Regarding a positive relationship between a PFC‘s TMT experience and

PFC total assets growth, we are only able to find significant results for industry-specific

entrepreneurial experience (p < 0.10). Figure 5, Figure 6, and Figure 7 visually demonstrate the

significant associations.29

Figure 5

Growth Model: VC General Experience and PFC Total Assets Growth

29

We have controlled for initial PFC size before visualizing the associations in Figures 5, 6 and 7.

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Growth Model: VC Industry-specific Experience and PFC Total Assets Growth

Figure 7

Growth Model: VC Industry-spec. Entr. Exp. and PFC Total Assets Growth

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Next, we explored the PFCs‘ total assets growth trajectories using matched VC-PFC

experience sets along four quadrants (I. HIGH-HIGH, II. HIGH-LOW, III. LOW-LOW, IV. LOW-

HIGH). Table 18 gives a report of the bivariate analyses. Interestingly, we found that both a VC‘s

general as industry-specific experience stay significantly associated with a higher PFC‘s total assets

growth, albeit with an interaction of PFC experience variables. In fact, since the matching of VCs‘ and

PFCs‘ specific experience types and levels provides us with significantly different values across all

quadrants, we assume that, despite the insignificant results of most variables in separate bivariate

analyses, specific PFC experience levels can give rise to interference or synergetic effects within a

VC‘s experience-based relationship to PFC growth. In other words, even though we found most PFC

experience types to be insignificantly related to PFC growth, we must take into consideration the

possibility that some experience levels can actually improve or deteriorate the positive growth

association of an experienced VC. Indeed, while we clearly observe the highest PFC growth among

highly experienced VCs, depending on the specific type of PFC experience, we find both lower and

higher growth levels in cases where highly experienced PFCs‘ TMTs are matched. For instance, we

found superior growth to occur among both general and industry-specific high experience level

matches, as well as among more detailed matches such as R&D or entrepreneurial experience (p-

values < 0.10). In contrast, matches of highly (respectively, general and industry-specific)

experienced VCs with PFCs whose TMT members have high general management experience levels

seem to exhibit significantly lower growth in total assets (respectively, p < 0.10 and p < 0.05).

Furthermore, we found the same relationship to exist for matches between highly (respectively,

general and industry-specific experience) with PFCs whose TMTs have a lot of accounting, financial

or legal experience (respectively, p < 0.10 and p < 0.05).30

A final interesting observation we wish to

make is the fact that, although we expected to constantly witness inferior growth in the third quadrant

(III. LOW-LOW), this often turns out not to be the case in our sample. Figure 8 visually demonstrates

the possible synergetic effect of matching a highly general experienced VC with a PFC whose TMT

members have acquired substantial experience in R&D. In contrast, Figure 9 shows a potential

interference effect of matching a highly general experienced VC with a PFC whose TMTs possess of

high levels of experience in general management. Nevertheless, we choose to be very cautious in

bringing forward these interaction effects since we do not know to what extent the original TMT was

still active throughout our time frame. Finally, after having analyzed the various subsamples using

bivariate statistics, we have also conducted a multivariate analysis (Table 22, Table 23, and Table 24,

Appendix) Analogously to the analysis of the Matching Model, we call for a careful interpretation of

these regression results because of the small sample size (N=41).

30

We do wish to note that, for the matching with PFCs whose TMT members have a lot of accounting, financial

or legal experience, albeit significant, we only find a slight different in mean ranks compared to the other

quadrant with highly (general or industry-specific) experienced VCs. Nevertheless, we fail to observe a potential

synergetic interaction of high PFC experience levels, in contrast with several other PFC experience types.

31

Figure 8

Growth Model: Potential Synergetic Effects of Experience-based Matching

Figure 9

Growth Model: Potential Interference Effects of Experience-based Matching

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We repeated identical analyses for the three other growth dimensions that we measured in our

sample (Employment Growth, Value-added Growth and EBITDA Growth). However, we failed to find

any significant differences in growth across the various subsamples. Obviously, our results need to be

interpreted with care. Instead of simply rejecting relationships between VC or PFC experience levels

and PFC employment, value-added or EBITDA growth, we opted to question our time frame in

relation with these specific analyses. Although we have good reasons to limit our time frame to up to

five years after the initial VC investment (Zarutskie, 2007), it is perfectly possible that, especially for

value-added or EBITDA levels, the expected growth curves typically need more time to develop.

In summary, we are once more unable to deliver a straightforward answer with regard to the

existence of the various relationships suggested by our hypotheses. We have found that higher VC

experience (both general and industry-specific) is likely to be associated with higher PFC total assets

growth. In addition, whereas most PFC experience types seemed to be insignificantly related to PFC

growth (in total assets), we take into consideration the possible existence of both synergetic and

interference effects of specific types of PFC experience during the post-matching stages. We thus

conclude that hypothesis 2A and 2B cannot be accepted as such but, instead, they both need some

alterations to consider the differential influences of several experience types during the post-matching

interaction of both parties. Reflecting on hypotheses 1A and 1B, we find that more experienced VCs

might sometimes match with PFCs with certain TMT experience characteristics that could interfere

with a positive association between VCs and PFC growth. Finally, based on these results, we plea

once more for detailed measures in the complex setting of entrepreneurial (growth) studies. Indeed, we

are convinced that using a multidimensional approach enabled us to adequately explore several growth

relationships on a larger basis, keeping any generalizations or conclusions on a modest and safer level.

33

Table 12

Growth Model: Univariate Statistics

Panel A: VC General Experience – LOW

a

Mean S.D. Min. Max. Median

PFC Growth

(1) Asset Growth 554.76 1,502.25 -1,229.20 6,444.80 214.60

(2) Employment Growth 192.23 369.66 5.20 1,748.6. 69.28

(3) Value-added Growth 746.22 2,878.37 -411.00 13,596.00 127.80

(4) EBITDA Growth 28.51 300.60 -760.20 919.20 15.80

Panel B: VC General Experience – HIGH a

Mean S.D. Min. Max. Median

PFC Growth

(1) Asset Growth 2,961.83 6,256.55 -283.20 26,961.80 791.67

(2) Employment Growth 222.02 380.54 -146.40 1,425.00 92.60

(3) Value-added Growth 227.89 746.68 -1,749.00 1,949.60 222.00

(4) EBITDA Growth -128.59 990.84 -3,059.00 1,269.00 66.00

Panel C: VC Ind.-spec. Experience – LOW a

Mean S.D. Min. Max. Median

PFC Growth

(1) Asset Growth 532.39 1,511.10 -1,229.20 6,444.80 208.3

(2) Employment Growth 188.75 371.11 -12.20 1,748.60 69.28

(3) Value-added Growth 731.08 2,881.01 -411.00 13,596.00 101.40

(4) EBITDA Growth 22.65 306.36 -760.20 919.20 5.00

Panel D: VC Ind.-spec. Experience – HIGH a

Mean S.D. Min. Max. Median

PFC Growth

(1) Asset Growth 2,987.73 6,243.43 -175.60 26,961.80 791.67

(2) Employment Growth 226.05 378.51 -146.40 1,425.00 105.50

(3) Value-added Growth 245.42 747.24 -1,749.00 1,949.60 222.00

(4) EBITDA Growth -121.80 989.88 -3,059.00 1,269.00 107.60

a Mean splits were used on the natural logarithm of VC General and Industry-specific Experience.

34

Table 13

Growth Model: Univariate Statistics

Panel A: PFC General Experience – LOW

a

Mean S.D. Min. Max. Median

PFC Growth

(1) Asset Growth 1,233.72 2,158.83 -252.80 7,535.00 420.80

(2) Employment Growth 243.84 431.26 -146.40 1,748.60 97.60

(3) Value-added Growth 870.28 3,106.03 -411.50 13,596.00 112.80

(4) EBITDA Growth -90.72 650.70 -2,417.80 919.20 7.00

Panel B: PFC General Experience – HIGH a

Mean S.D. Min. Max. Median

PFC Growth

(1) Asset Growth 2,047.22 5,857.13 -1,229.20 26,961.80 267.37

(2) Employment Growth 173.38 315.24 -17.50 1,425.00 69.28

(3) Value-added Growth 191.43 632.74 -1,749.00 1,949.60 162.40

(4) EBITDA Growth -4.20 759.80 -3,059.00 1,269.00 60.20

Panel C: PFC Ind.-spec. Experience – HIGH a

Mean S.D. Min. Max. Median

PFC Growth

(1) Asset Growth 1,136.64 2037.24 -283.20 7,535.00 441.65

(2) Employment Growth 179.69 372.76 -146.40 1,748.60 89.30

(3) Value-added Growth 759.22 2,885.95 -411.50 13,596.00 147.40

(4) EBITDA Growth -40.53 628.16 -2,417.80 919.20 15.80

Panel D: PFC Ind.-spec. Experience – HIGH a

Mean S.D. Min. Max. Median

PFC Growth

(1) Asset Growth 2,288.07 6,282.20 -1,229.20 26,961.80 274.40

(2) Employment Growth 236.54 375.24 -17.50 1,425.00 72.80

(3) Value-added Growth 212.83 699.74 -1,749.00 1,949.60 142.80

(4) EBITDA Growth -48.65 800.49 -3,059.00 1,269.00 66.00

a Mean splits were used on the natural logarithm of PFC General and Industry-specific Experience.

35

Table 14

Growth Model: Bivariate Statistics a

TOTAL ASSETS

Mean Rank Sig.

LOW HIGH

VC Experience Variables

(1) VC General Experience 16.50 26.21 0.010**

(2) VC Industry-specific Experience 15.91 26.89 0.003***

PFC Experience Variables

(3) PFC General Experience 21.79 20.32 0.695

(4) PFC Industry-specific Experience 21.23 20.74 0.896

(5) General Management Experience 20.45 21.52 0.774

(6) R&D Experience 20.53 21.29 0.853

(7) Marketing/Sales Experience 20.63 21.53 0.812

(8) Accounting/Financial/Legal Experience 20.48 22.25 0.667

(9) Entrepreneurial Experience – General 18.60 22.38 0.330

(10) Entrepreneurial Experience – Industry-specific 17.18 23.71 0.085*

(11) Previous Start-ups 20.09 22.17 0.581

(12) Number of Founders 18.95 23.15 0.262

a Mann-Whitney U Test

*,**,*** indicate significance at respectively 10%, 5% and 1%.

Table 18

Growth Model: Bivariate Statistics a

TOTAL ASSETS

Mean Rank Sig.

MATCHING QUADRANTS

I. HIGH-HIGH

II. HIGH-LOW

III. LOW-LOW

IV. LOW-HIGH

MATCHING TYPES

(1) VC General Exp. – PFC General Exp. 29.11 14.23 19.78 23.60 0.031**

(2) VC Industry-specific Exp. – PFC Industry-specific Exp. 28.22 14.00 17.50 25.70 0.026**

(3) VC General Exp. – PFC Industry-specific Exp. 30.86 14.83 18.50 23.50 0.031**

(4) VC General Exp. – General Management Exp. 25.50 16.22 16.69 27.43 0.078*

(5) VC Industry-specific Exp. – General Management Exp. 26.31 13.75 17.14 28.17 0.028**

(6) VC General Exp. – R&D Exp. 30.89 15.53 18.57 22.00 0.022**

(7) VC Industry-specific Exp. – R&D Exp. 31.00 15.47 16.85 23.20 0.014**

(8) VC General Exp. – Marketing/Sales Exp. 24.67 18.00 15.64 27.60 0.066*

(9) VC Industry-specific Exp. – Marketing/Sales Exp. 27.11 15.25 16.29 26.70 0.035**

(10) VC General Exp. – Acc./Fin./Leg. Exp. 26.17 18.33 15.81 26.23 0.075*

(11) VC Industry-specific Exp. – Acc./Fin./Leg. Exp. 26.17 18.33 15.00 27.23 0.030**

(12) VC General Exp. – Entrepreneurial Exp. (Gen.) 26.93 16.18 16.82 23.50 0.073*

(13) VC General Exp. – Entrepreneurial Exp. (Ind.) 26.93 18.33 15.23 23.50 0.062*

(14) VC Industry-spec. Exp. – Entrepreneurial Exp. (Ind.) 28.40 15.89 15.92 21.25 0.021**

a Kruskal-Wallis Test

*,**,*** indicate significance at respectively 10%, 5% and 1%.

36

5. Discussion and Conclusion

The venture capital industry is an intriguing environment where investors are willing to

allocate valuable resources to ventures carrying high risks. Evidently, during the resource allocation

processes, the many information asymmetries (DiMaggio and Powell, 1983; Podolny, 1993) give rise

to potential moral hazard issues or principal-agent conflicts. Therefore, an adequate assessment of a

PFC‘s human capital is critical in order to minimize a VC‘s involvement costs (Kaplan and Strömberg,

2002) while hopefully maximizing portfolio performance. Notwithstanding their paramount

importance during due diligence processes (Baum and Silverman, 2004), such human capital

assessments often lack accuracy and many VCs face unpleasant surprises (Smart, 1999). Since VCs

are claimed to sometimes speed up professionalization (e.g., Lerner, 1995) while being renowned for

their extensive control rights (Hellmann, 1998; Kaplan and Strömberg, 2003), for a PFC‘s

management team, it is equally important to have a good understanding of the benefits and costs of

affiliating with certain types of VCs. Indeed, in theory, both parties have options to choose from and

this results in a sorting pattern where top-tier VCs match with better PFCs (Sørensen, 2007).

Nevertheless, during this matching, often a surplus has to be paid to the most reputable or experienced

VCs (Hsu, 2004), making a good understanding of VC influences on growth important for a PFC‘s

management team. Unmistakably, all these inherent complexities of the VC-PFC deal making process

are exactly what makes studying both parties‘ interaction behavior valuable.

In this study, we have made an attempt to provide a debate regarding the pre-investment

matching behaviour between VCs and PFCs, specifically aimed at human capital dimensions such as

experience levels. It is clear that, although the intuitive sorting along equal experience levels can count

on many supporting arguments, both in theory as through empirical findings, there are many reasons

as to why a distortion of this pattern can be expected. Not surprisingly, our empirical (yet exploratory)

results force us to take in a cautious position as to predict clear matching patterns based on VC and

PFC experience levels. Nevertheless, in our sample, we observed significant matching behaviour

along several experience-based dimensions. For instance, confirming prior research (Gompers,

Kovner, Lerner and Scharfstein, 2006), we detected matching between more (general and industry-

specific) experienced VCs and PFCs whose TMT members possess of substantial (general and

industry-specific) entrepreneurial experience or have founded previous start-ups. Furthermore, we

found the same relationship to exist between highly experienced (general and industry-specific) VCs

and PFCs whose TMT members are highly experienced in general management. We believe a

substantial explanation for the above results to be found in, for instance, VCs‘ due diligence

management assessments (Zacharakis and Meyer, 2000), both parties‘ superior network positions

(Hochberg, Ljunqvist and Lu, 2007) and increased visibility (Hsu, 2005), or simply homophily

(Franke et al., 2006). Surprisingly, we find an inverse matching association between VCs‘ (general

and industry-specific) experience and PFCs‘ R&D experience. Maybe we are allowed to assume that

37

more experienced VCs are confident enough to invest in less mature idea assets while less experienced

VCs prefer to invest in idea assets backed by sound technological expertise. Summarizing the results

of our Matching Model, we believe to have found promising indications of matching behaviour or

two-sided selection along specific experience types. Furthermore, as there still are lacunae in the

existing entrepreneurial literature regarding the detailed investigation of human capital dimensions,

our results point out an interesting avenue for future research using fine-tuned experience measures.

Nevertheless, our exploratory approach certainly has its limitations with regards to the above

conclusions. For instance, we only observed experience-based VC and PFC characteristics in our

Matching Model. However, it is perfectly possible that, for example, by measuring high TMT

experience levels for a PFC, we merely proxy for valuable venture idea assets to which VCs find

themselves particularly attracted.

Our research has also made an interesting extension towards the detection of potential VC or

PFC experience-related influences on PFC growth during the post-investment stage. In fact, using our

Growth Model, we found VC (general and industry-specific) experience levels to be positively

associated with PFC (total assets) growth in our sample. Whether this positive association with PFC

growth can be attributed to a VC‘s ―scouting‖ or ―coaching‖ skills (Baum and Silverman, 2004),

however, is a question that remains unanswered in our study. We also found evidence to support the

notion of ―entrepreneurial skill‖ (Gompers, Kovner, Lerner and Scharfstein, 2006) since we observed

significant positive relationships between entrepreneurial experience and PFC (total assets) growth.

Interestingly, while we often could not find any significant associations between other detailed PFC

experience types (e.g., general management experience or R&D experience) and PFC growth, we must

report the possible existence of synergetic and interference effects in relation to VC experience. For

instance, in our sample we believe to have detected high levels of PFCs‘ R&D experience to create

positive (total assets) growth variations across high VC experience subsamples. In contrast, we

observed possible growth restraining interactions among the high PFC general management

experience subsamples. Clearly, we must raise important questions as to exactly which types of both

parties‘ experiences can be related to a positive influence on PFC growth. What is more, reflecting on

our Matching Model, we noticed some VC-PFC matches to be potentially suboptimal in our sample,

that is if we can indeed accept the existence of such synergetic or interference effects. Notwithstanding

our interesting results, we wish to explicitly note the absence of clear information on the extent to

which both parties‘ experience-based actions can be attributable to the observed PFC growth

trajectories. In fact, from a theoretical perspective we have build a five-year time frame to capture VC

associations (Zarutskie, 2007) without controlling for more accurate and case-specific investment

durations. More problematic, however, is the fact that we do not know for how long the founding or

TMT members have remained to play a crucial role in their respective companies. Especially since

financial contracting allows VCs to make important changes to the composition of TMTs (Kaplan and

38

Strömberg, 2003), persistent TMT member influences should not be taken for granted in relation to

PFC growth.

In summary, we have offered a selective overview of the existing entrepreneurial literature

regarding human capital (and experience in particular) within the venture capital industry. What is

more, we pointed out several rationales to explain two-sided pre-investment selection motives and the

resulting consequences with regards to PFC post-investment growth. Nevertheless, we acknowledge

the fact that our debate is far from exhaustive, and should be seen as of a rather exploratory nature

than a conclusive one. Apart from the small sample size and the almost inevitable survivorship bias,

we believe the strongest limitation of our study to be the almost exclusive use of an (albeit relatively

detailed) experience-based approach in our models. Indeed, bringing forward the jockey-or-the-horse

debate, human capital is most likely to be important, yet there are enough motives for (experienced)

VCs to invest in strong (idea) assets, regardless of the backing by valuable management experience

types. Basically, this implies a noisy environment in which to draw conclusions from our Matching

Model. Furthermore, the results of our Growth Model should be interpreted with caution as this model

equally suffers from the complicated context. Indeed, because we are unable to accurately separate all

the possible differential influences on PFC growth, straightforward cause-and-effect relationships are

not likely to exist or are very difficult to demonstrate. Nevertheless, even in the extreme and highly

unlikely case that all experience measures in our sample can be reduced to proxies for other

characteristics, it remains interesting to observe the significantly differential growth trajectories across

the various subsamples. In fact, the differential growth levels across distinct matches still imply that

both VCs and PFCs are well advised to search for (specifically) experienced parties, regardless of the

extent to which experience levels themselves are the actual cause of higher growth. In other words,

since clear performance predictions are often inexistent in the venture capital industry (DiMaggio and

Powell, 1983; Podolny,1993), using detailed experience assessment methods might be able to reduce

the substantial uncertainty during both parties‘ deal search, even if these experience measures should

turn out to have only been proxies for other growth inducing characteristics.

39

6. Appendix

40

[ PREFACE ]

Dear sir or madam,

We send you this survey because we are doing research on venture capital

investments. In order for this study to succeed, it is vital that we collect some information

regarding companies that have engaged in investment relationships with venture capital

investors. Using publicly available data, your company has been selected in our research

sample. To ensure absolute confidentiality, all companies in our sample will be anonymously

put into our database. Furthermore, no company-specific information will be revealed in any

case without the explicit written permission of the respective company.

We realize that today‘s business leaders do not have time to spare. Therefore we

would be truly grateful if we could borrow only 5 minutes of your precious time. Not only

does this survey hopefully contribute to our successful graduation, we also wish that it will

ultimately lead to a better understanding of the venture capital market. Basically, there are

three ways of fulfilling this survey. First of all, you can fill out the questionnaire

electronically and send it back as an attachment to our email. Secondly, we have installed a

fax machine if you would prefer to fill out the survey by pen. Finally, we will be calling you

in the next three weeks to offer an opportunity for a very swift telephone interview. You will

find all the necessary contact details at the bottom of this page.

Should you have any further questions regarding our research, please do not hesitate to

send us an email. We will do our ultimate best to quickly address all potential issues and to

take into account your considerations. We would like to conclude by thanking you once more

for answering to our query and helping us succeed in our graduation.

Gratefully yours,

Jan Willems and Maarten Tollenaere

EMAIL janm.willems@ugent.be - maarten.tollenaere@ugent.be

FAX 09 / 264 35 77

41

[ SURVEY ]

TOP MANAGEMENT

How many people where part of the management team before entering

negotiations with the venture capitalist(s) that provided initial finance?

1. EDUCATION

For each member that was part of the management team before entering negotiations with the venture

capitalist(s) that provided initial finance, we would like to ask some questions with respect to their

level and type of education.

a) For each management team member, please thick the box with his/her highest level of

education.

MANAGEMENT

TEAM MEMBER A B C D E F G

high school

higher education

(non-university)

university

post-graduate master

MBA

Ph.D./MD

other

b) For each management team member, please thick the box with his/her main type of education.

MANAGEMENT

TEAM MEMBER A B C D E F G

economic/managerial

sciences/technical

other

………………………

42

2. EXPERIENCE

For each member that was part of the management team before entering negotiations with the venture

capitalist(s) that provided initial finance, we would like to ask some questions with respect to their

prior work experience at the time of the negotiations.

a) For each management team member, please fill in as accurately as possible the number of

years of experience in the appropriate categories. Your best guess is better than no answer.

MANAGEMENT TEAM

MEMBER A B C D E F G

within the current venture‘s focal

industry

within other industries

b) For each management team member, please fill in as accurately as possible the number of

years of experience in the appropriate business departments. Your best guess is better than no

answer.

MANAGEMENT TEAM

MEMBER A B C D E F G

general management

R&D/engineering/production

marketing/sales

accounting/finance/legal

other

c) For each management team member, please fill in as accurately as possible the number of

years of experience at the management level – regardless of the specific business

departments. Your best guess is better than no answer.

MANAGEMENT TEAM

MEMBER A B C D E F G

within the current venture‘s focal

industry

within other industries

43

d) For each management team member, please fill in as accurately as possible the number of

start-ups he/she has previously founded in the appropriate category.

MANAGEMENT TEAM

MEMBER A B C D E F G

within the current venture‘s focal

industry

within other industries

e) For each management team member, please fill in as accurately as possible the number of

years of entrepreneurial experience in the appropriate domains. Your best guess is better than

no answer.

MANAGEMENT TEAM

MEMBER A B C D E F G

within the current venture‘s focal

industry

within other industries

ADDITIONAL QUESTIONS

Please note once more that, whenever we use the term ‗management team‘, we refer to the

management team before entering negotiations with the venture capitalist(s) that provided initial

finance.

YES NO

Did one or more member(s) of the management team have experience in

negotiating with venture capital investors before the current company

raised venture capital?

Did one or more member(s) of the management team raise capital from

venture capital investors before the current company raised venture capital?

Did the current company receive any written offers from venture capital

investors other than those who provided the initial finance?

Did the venture capitalist(s) that provided initial finance bring changes to

the management team before they contributed capital?

44

On a 7-point scale, please indicate the extent to which the venture capitalist(s) has/have influenced the

composition of the management team after the initial investment. The scale ranges from 1 being ―no

influence at all‖ to 7 being ―heavily influenced‖.

NO

INFLUENCE

AT ALL

HEAVILY

INFLUENCED

1 2 3 4 5 6 7

RESPONDENT INFORMATION

COMPANY NAME……………………………………………………………………………………..

NAME OF RESPONDENT…………………………………..................................................................

FUNCTION OF RESPONDENT:………..…………………………………………………………......

REMARKS

……………………………………………………………………………………………………………

……………………………………………………………………………………………………………

……………………………………………………………………………………………………………

……………………………………………………………………………………………………………

……………………………………………………………………………………………………………

……………………………………………………………………………………………………………

45

Table 1

Matching Model: Industry Statistics

EVCA Sectoral Classification N %

Life Sciences 9 20.9

ICT 16 37.2

Business and Industrial Products and Services 8 18.6

Consumer Products, Services and Retail 3 7

Financial Services 3 7

Energy and Environment

Agriculture

2

2

4.7

4.7

Total 43 100

Figure 1

Matching Model: PFC Age Histogram

46

Table 2

Growth Model: Industry Statistics

EVCA Sectoral Classification N %

Life Sciences 8 19.5

ICT 15 36.6

Business and Industrial Products and Services 8 19.5

Consumer Products, Services and Retail 3 7.3

Financial Services 3 7.3

Energy and Environment

Agriculture

2

2

4.9

4.9

Total 41 100

Figure 2

Growth Model: PFC Age Histogram

47

Table 3

Matching Model: Definition of Variables a

Variable Definition

VC EXPERIENCE

(1) VC General Experience Number of total investments by the VC

(2) VC Industry-specific Experience Number of investments by the VC in the venture‘s focal

industry

PFC EXPERIENCE

(3) PFC General Experience Average work experience of the initial founding team/TMT

(in years)

(4) PFC Industry-specific Experience Average work experience of the initial founding team/TMT

in the venture‘s focal industry (in years)

(5) General Management Experience Average work experience of the initial founding team/TMT

on a general management department (in years)

(6) R&D Experience Average work experience of the initial founding team/TMT

on a research and development department (in years)

(7) Marketing/Sales Experience Average work experience of the initial founding team/TMT

on a marketing or sales department (in years)

(8) Accounting/Financial/

Legal Experience

Average work experience of the initial founding team/TMT

on an accounting, a finance or legal department (in years)

(9) Entrepreneurial Experience –

General

Average work experience of the initial founding team/TMT as

an entrepreneur (in years)

(10) Entrepreneurial Experience –

Industry-specific

Average work experience of the initial founding team/TMT as

an entrepreneur in the venture‘s focal industry (in years)

(11) Previous Start-ups Number of previous ventures started collectively by the initial

founding team/TMT

(12) Number of Founders Number of founding/TMT members

CONTROLS

(13) PFC Age Number of years since PFC foundation

(14) Hi-tech Dummy=1 if the PFC operates in a high-tech industry

a Variables measured at the time of investment relationship formation.

48

Table 4

Growth Model: Definition of Variables

Variable Definition

PFC GROWTH a

(1) Total Assets Growth Average growth in total assets

(2) Employment Growth Average growth in employment (based on employment cost)

(3) Value-added Growth Average growth in value-added

(4) EBITDA Growth Average growth in EBITDA

VC EXPERIENCE b

(5) VC General Experience Number of total investments by the VC

(6) VC Industry-specific Experience Number of investments by the VC in the venture‘s focal

industry

PFC EXPERIENCE b

(7) PFC General Experience Average work experience of the initial founding team/TMT

(in years)

(8) PFC Industry-specific Experience Average work experience of the initial founding team/TMT

in the venture‘s focal industry (in years)

(9) General Management Experience Average work experience of the initial founding team/TMT

on a general management department (in years)

(10) R&D Experience Average work experience of the initial founding team/TMT

on a research and development department (in years)

(11) Marketing/Sales Experience Average work experience of the initial founding team/TMT

on a marketing or sales department (in years)

(12) Accounting/Financial/

Legal Experience

Average work experience of the initial founding team/TMT

on an accounting, a finance or legal department (in years)

(13) Entrepreneurial Experience –

General

Average work experience of the initial founding team/TMT as

an entrepreneur (in years)

(14) Entrepreneurial Experience –

Industry-specific

Average work experience of the initial founding team/TMT as

an entrepreneur in the venture‘s focal industry (in years)

(15) Previous Start-ups Number of previous ventures started collectively by the initial

founding team/TMT

(16) Number of Founders Number of founding/TMT members

CONTROLS

(17) PFC Age Number of years since PFC foundation

(18) PFC Initial Size PFC total assets

(19) Hi-tech Dummy=1 if the PFC operates in a high-tech industry

(20) Year Year dummy

a Variable measured up to 5 years after the investment relationship formation.

b Variables measured at the time of investment relationship formation.

49

Table 9

Matching Model: Multivariate Statistics a

Dependent Variable = Pr (Funding by highly general experienced VC) N=43

Independent Variables

(1) PFC General Experience 0.230

(2) PFC Industry-specific Experience -0.270 *

(3) General Management Experience 0.033

(4) R&D Experience -0.682

(5) Marketing/Sales Experience -0.164

(6) Accounting/Financial/Legal Experience --0.0853

(7) Entrepreneurial Experience – General -1.161 **

(8) Entrepreneurial Experience – Industry-specific 1.388 **

(9) Previous Start-ups 0.033

(10) Number of Founders 0.020

Controls

(11) PFC Age 0.054

(12) Hi-tech 0.287

Constant -0.477

Prob > chi² 0.01

Log Likelihood -16.81

a Probit Regression

*,**,*** indicate significance at respectively 10%, 5% and 1%.

50

Table 10

Matching Model: Multivariate Statistics a

Dependent Variable = VC General Experience N=43

Independent Variables

PFC Experience Variables

(1) PFC General Experience -0.358

(2) PFC Industry-specific Experience 0.658

(3) General Management Experience 3.312

(4) R&D Experience 0.579

(5) Marketing/Sales Experience 2.821

(6) Accounting/Financial/Legal Experience -1.335

(7) Entrepreneurial Experience – General -2.824

(8) Entrepreneurial Experience – Industry-specific 3.940

(9) Previous Start-ups -0.315

(10) Number of Founders 7.743

Controls

(11) PFC Age -0.339

(12) Hi-tech -11.354

Constant -3.716

Prob > F 0.57

R² (Adj.) 0.26

a OLS Regression

*,**,*** indicate significance at respectively 10%, 5% and 1%.

51

Table 11

Matching Model: Multivariate Statistics a

Dependent Variable = Pr (Funding by highly industry-specific experienced VC) N=43

Independent Variables

(1) PFC General Experience 0.068

(2) PFC Industry-specific Experience --0.069

(3) General Management Experience 0.156

(4) R&D Experience -0.117

(5) Marketing/Sales Experience -0.199

(6) Accounting/Financial/Legal Experience -0.129

(7) Entrepreneurial Experience – General -0.438

(8) Entrepreneurial Experience – Industry-specific 0.555

(9) Previous Start-ups 0.035

(10) Number of Founders 0.200

Controls

(11) PFC Age 0.153 *

(12) Hi-tech 0.552

Constant -1.542

Prob > chi² 0.01

Log Likelihood -16.41

a Probit Regression

*,**,*** indicate significance at respectively 10%, 5% and 1%.

52

Table 12

Matching Model: Multivariate Statistics a

Dependent Variable = VC Industry-specific Experience N=43

Independent Variables

PFC Experience Variables

(1) PFC General Experience 0.995

(2) PFC Industry-specific Experience -0.610

(3) General Management Experience 0.475

(4) R&D Experience -0.132

(5) Marketing/Sales Experience -0.089

(6) Accounting/Financial/Legal Experience -0.364 **

(7) Entrepreneurial Experience – General -1.262

(8) Entrepreneurial Experience – Industry-specific 1.902

(9) Previous Start-ups 0.107

(10) Number of Founders 2.498

Controls

(11) PFC Age -0.245

(12) Hi-tech 3.543

Constant -4.936

Prob > F 0.42

R² (Adj.) 0.30

a OLS Regression

*,**,*** indicate significance at respectively 10%, 5% and 1%.

53

Table 15

Growth Model: Bivariate Statistics a

EMPLOYMENT

Mean Rank Sig.

LOW HIGH

VC Experience Variables

(1) VC General Experience 21.14 20.84 0.937

(2) VC Industry-specific Experience 20.59 21.47 0.814

PFC Experience Variables

(3) PFC General Experience 22.37 19.82 0.497

(4) PFC Industry-specific Experience 20.73 21.32 0.875

(5) General Management Experience 19.00 22.90 0.297

(6) R&D Experience 18.00 23.13 0.177

(7) Marketing/Sales Experience 21.17 20.76 0.916

(8) Accounting/Financial/Legal Experience 19.76 24.00 0.302

(9) Entrepreneurial Experience – General 19.20 22.04 0.465

(10) Entrepreneurial Experience – Industry-specific 18.65 22.67 0.290

(11) Previous Start-ups 19.09 23.44 0.248

(12) Number of Founders 20.33 21.70 0.715

a Mann-Whitney U Test

*,**,*** indicate significance at respectively 10%, 5% and 1%.

Table 16

Growth Model: Bivariate Statistics a

VALUE-ADDED

Mean Rank Sig.

LOW HIGH

VC Experience Variables

(1) VC General Experience 20.91 21.11 0.958

(2) VC Industry-specific Experience 20.23 21.89 0.657

PFC Experience Variables

(3) PFC General Experience 20.63 21.32 0.855

(4) PFC Industry-specific Experience 20.59 21.47 0.814

(5) General Management Experience 18.65 23.24 0.220

(6) R&D Experience 18.71 22.63 0.302

(7) Marketing/Sales Experience 19.33 23.35 0.290

(8) Accounting/Financial/Legal Experience 19.86 23.75 0.344

(9) Entrepreneurial Experience – General 20.80 21.12 0.935

(10) Entrepreneurial Experience – Industry-specific 20.24 21.54 0.731

(11) Previous Start-ups 19.30 23.17 0.306

(12) Number of Founders 20.00 22.05 0.584

a Mann-Whitney U Test

*,**,*** indicate significance at respectively 10%, 5% and 1%.

54

Table 17

Growth Model: Bivariate Statistics a

EBITDA

Mean Rank Sig.

LOW HIGH

VC Experience Variables

(1) VC General Experience 20.55 21.53 0.794

(2) VC Industry-specific Experience 19.82 22.37 0.497

PFC Experience Variables

(3) PFC General Experience 19.32 22.45 0.403

(4) PFC Industry-specific Experience 20.36 21.74 0.714

(5) General Management Experience 19.00 22.90 0.297

(6) R&D Experience 18.88 22.50 0.341

(7) Marketing/Sales Experience 20.17 22.18 0.597

(8) Accounting/Financial/Legal Experience 20.79 21.50 0.864

(9) Entrepreneurial Experience – General 21.07 20.56 0.978

(10) Entrepreneurial Experience – Industry-specific 20.47 21.38 0.812

(11) Previous Start-ups 20.70 21.39 0.854

(12) Number of Founders 21.24 20.75 0.896

a Mann-Whitney U Test

*,**,*** indicate significance at respectively 10%, 5% and 1%.

Table 19

Growth Model: Bivariate Statistics a

EMPLOYMENT

Mean Rank Sig.

MATCHING QUADRANTS

I. HIGH-HIGH

II. HIGH-LOW

III. LOW-LOW

IV. LOW-HIGH

MATCHING TYPES

(1) VC General Exp. – PFC General Exp. 20.89 19.08 24.11 20.80 0.814

(2) VC Industry-specific Exp. – PFC Industry-specific Exp. 24.56 18.40 22.42 18.70 0.615

(3) VC General Exp. – PFC Industry-specific Exp. 26.14 18.50 24.30 17.75 0.327

(4) VC General Exp. – General Management Exp. 22.92 22.89 19.92 17.29 0.727

(5) VC Industry-specific Exp. – General Management Exp. 23.77 21.50 20.07 16.50 0.650

(6) VC General Exp. – R&D Exp. 25.56 21.67 20.00 16.60 0.432

(7) VC Industry-specific Exp. – R&D Exp. 26.00 21.40 18.86 17.40 0.438

(8) VC General Exp. – Marketing/Sales Exp. 20.78 20.75 21.36 20.90 0.999

(9) VC Industry-specific Exp. – Marketing/Sales Exp. 23.22 18.00 22.07 19.90 0.799

(10) VC General Exp. – Acc./Fin./Leg. Exp. 21.17 26.83 19.00 20.69 0.598

(11) VC Industry-specific Exp. – Acc./Fin./Leg. Exp. 21.17 26.83 18.25 21.62 0.512

(12) VC General Exp. – Entrepreneurial Exp. (Gen.) 21.07 23.36 18.91 20.00 0.851

(13) VC General Exp. – Entrepreneurial Exp. (Ind.) 21.07 25.33 18.23 20.00 0.593

(14) VC Industry-spec. Exp. – Entrepreneurial Exp. (Ind.) 22.53 22.89 19.00 17.50 0.759

a Kruskal-Wallis Test

*,**,*** indicate significance at respectively 10%, 5% and 1%.

55

Table 20

Growth Model: Bivariate Statistics a

VALUE-ADDED

Mean Rank Sig.

MATCHING QUADRANTS

I. HIGH-

HIGH

II. HIGH-

LOW

III. LOW-

LOW

IV. LOW-

HIGH

MATCHING TYPES

(1) VC General Exp. – PFC General Exp. 22.23 20.62 21.33 20.00 0.977

(2) VC Industry-specific Exp. – PFC Industry-specific Exp. 24.00 19.20 21.08 20.00 0.835

(3) VC General Exp. – PFC Industry-specific Exp. 23.00 20.58 21.30 20.00 0.960

(4) VC General Exp. – General Management Exp. 24.58 21.44 20.54 15.14 0.427

(5) VC Industry-specific Exp. – General Management Exp. 24.62 21.00 19.79 16.00 0.499

(6) VC General Exp. – R&D Exp. 22.78 22.53 17.43 19.60 0.752

(7) VC Industry-specific Exp. – R&D Exp. 25.44 20.93 18.71 18.70 0.601

(8) VC General Exp. – Marketing/Sales Exp. 23.44 23.25 19.57 19.00 0.769

(9) VC Industry-specific Exp. – Marketing/Sales Exp. 23.89 22.75 18.79 20.10 0.746

(10) VC General Exp. – Acc./Fin./Leg. Exp. 25.00 22.50 20.31 19.31 0.783

(11) VC Industry-specific Exp. – Acc./Fin./Leg. Exp. 25.00 22.50 19.38 20.46 0.781

(12) VC General Exp. – Entrepreneurial Exp. (Gen.) 20.80 21.55 20.27 22.25 0.990

(13) VC General Exp. – Entrepreneurial Exp. (Ind.) 20.80 22.78 19.62 22.25 0.936

(14) VC Industry-spec. Exp. – Entrepreneurial Exp. (Ind.) 21.07 22.33 18.77 25.00 0.799

a Kruskal-Wallis Test

*,**,*** indicate significance at respectively 10%, 5% and 1%.

Table 21

Growth Model: Bivariate Statistics a

EBITDA

Mean Rank Sig.

MATCHING QUADRANTS

I. HIGH-HIGH

II. HIGH-LOW

III. LOW-LOW

IV. LOW-HIGH

MATCHING TYPES

(13) VC General Exp. – PFC General Exp. 25.33 20.46 20.67 18.10 0.616

(14) VC Industry-specific Exp. – PFC Industry-specific Exp. 25.33 18.50 20.92 19.70 0.630

(15) VC General Exp. – PFC Industry-specific Exp. 23.43 20.75 20.30 20.42 0.949

(16) VC General Exp. – General Management Exp. 24.75 20.44 20.62 16.00 0.488

(17) VC Industry-specific Exp. – General Management Exp. 24.85 19.75 19.86 17.00 0.527

(18) VC General Exp. – R&D Exp. 24.44 21.33 18.86 18.90 0.732

(19) VC Industry-specific Exp. – R&D Exp. 27.44 19.53 20.43 17.80 0.313

(20) VC General Exp. – Marketing/Sales Exp. 22.22 22.13 19.64 20.90 0.951

(21) VC Industry-specific Exp. – Marketing/Sales Exp. 22.11 22.25 18.43 22.60 0.804

(22) VC General Exp. – Acc./Fin./Leg. Exp. 23.17 19.83 20.81 20.77 0.967

(23) VC Industry-specific Exp. – Acc./Fin./Leg. Exp. 23.17 19.83 19.81 22.00 0.919

(24) VC General Exp. – Entrepreneurial Exp. (Gen.) 20.80 21.18 19.91 24.25 0.942

(25) VC General Exp. – Entrepreneurial Exp. (Ind.) 20.80 22.33 19.31 24.25 0.880

(26) VC Industry-spec. Exp. – Entrepreneurial Exp. (Ind.) 20.73 22.44 18.00 28.50 0.471

a Kruskal-Wallis Test

*,**,*** indicate significance at respectively 10%, 5% and 1%.

56

Table 22

Growth Model: Multivariate Statistics a

Dependent Variable = Total Assets Growth N=41

Independent Variables

VC Experience Variable

(1) VC General Experience 1.182

PFC Experience Variables

(2) PFC General Experience -0.547

(3) VC General Exp – PFC General Exp 0.642

Controls

(4) PFC Age 0.103

(5) PFC Initial Size -0.000

(6) Hi-tech -1.560

(7) Year 0.024

Constant 3.899

Prob > F 0.499

R² (Adj.) -0.15

a OLS Regression

*,**,*** indicate significance at respectively 10%, 5% and 1%.

Table 23

Growth Model: Multivariate Statistics a

Dependent Variable = Total Assets Growth N=41

Independent Variables

VC Experience Variables

(1) VC Industry-specific Experience 1.663 *

PFC Experience Variables

(2) PFC Industry-specific Experience 0.003

(3) VC Industry Exp – PFC Industry Exp 0.439

Controls

(12)PFC Age 0.136

(4) PFC Initial Size -0.000

(5) Hi-tech -1.992

(6) Year -0.058

Constant 4.501 *

Prob > F 0.339

R² (Adj.) 0.040

a OLS Regression

*,**,*** indicate significance at respectively 10%, 5% and 1%.

57

Table 24

Growth Model: Multivariate Statistics a

Dependent Variable = Total Assets Growth N=41

Independent Variables

VC Experience Variables

(1) VC General Experience 1.110

PFC Experience Variables

(2) PFC Industry-specific Experience -0.088

(3) VC General Exp – PFC Industry Exp 1.144

Controls

(12)PFC Age 0.083

(4) PFC Initial Size -0.000

(5) Hi-tech -1.859

(6) Year 0.008

Constant 4.418

Prob > F 0.409

R² (Adj.) 0.015

a OLS Regression

*,**,*** indicate significance at respectively 10%, 5% and 1%.

IV

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