Should Private Equity Investors Bet on the Jockey or the Horse? An empirical analysis on the influence of the investment stage on SMEs’ growth

(1)

Should Private Equity Investors Bet on the

Jockey or the Horse? An empirical analysis

on the influence of the investment stage

on SMEs’ growth

Master Thesis Small Business and Entrepreneurship

Name:

Sander de Boer

Student nr.: S3030849

Supervisor: Dr. S. Murtinu

2

nd

_{Reader: Dr. O. Belousova}

(2)

Abstract

An increasingly important discussion among private equity (PE) investors’ concerns the relative importance of the business (“the horse”) or the management team (“the jockey”) when making an investment decision. This study contributes to this debate by investigating the influence of the investment stage on the growth of Dutch SMEs. This has been empirically tested a sample of 188 investments in Dutch SMEs made by 72 Dutch PE investors from 2008-2017. The results suggest that PE investors should place more weight on the jockey than on the horse. Furthermore, late-stage investments lead to higher relative investee firm growth than early-stage investments. The results also build on the theories about the organizational development of the firm.

Acknowledgement

(3)

1. Introduction

Private Equity (PE) refers to equity – that is, shares representing a firm’s ownership – that is not publicly listed or traded (Nederlandse Vereninging voor Participatiemaatschappijen (NVP), 2017). PE funds financed ventures being responsible for 10% of employment in the Dutch private sector and 16% of the Gross Domestic Product (GDP) (NVP, 2017). The interest of private equity for Small and Medium Sized Enterprises (SMEs) became more important in the financial crisis that started in 2008 (Roosenboom, 2014). The Dutch financial landscape changed drastically, and SMEs had to deal with stricter loan conditions and banks that were reluctant to lend funds. Therefore, SMEs had (and still have to) innovate the way they finance themselves (Roosenboom, 2014). Because PE offers SMEs another way of financing it is valuable to gain insight into these investors, more specifically the value they place on the management team (jockey) or the business/market (horse) when making an investment decision. Actually, one of the major ongoing debates among PE investors’ concerns the relative importance of the management team or the business characteristics for a company’s growth (Mitteness, Baucus and Sudek, 2011). While investors try to invest in SMEs with both strong business ideas and strong management, different investors weigh one or the other more heavily at the margin (Kaplan, Sensoy, and Strömberg, 2009). This debate is often characterized as whether one should bet on the jockey (management) or the horse (the business/market). Most of the research relating this discussion shows that PE investors weight the management team more heavily than the business/market when investing in a company (Haines, Madill, and Riding, 2003; Harrison and Mason, 2002; Mason and Harrison, 1996; Van Osnabrugge, 1998; Gompers, Gornall, Kaplan and Strebulaev, 2017). However, another group of studies presents results contrary to this conclusion, indicating that PE investors place greater weight on the strength of the business idea or market potential rather than the management team (Fiet, 1995; Hall and Hofer, 1993; Kaplan, Sensoy and Stromberg, 2004; Zacharakis and Meyer, 1998). The management team is considered as one of the key factors, because it shapes behaviour, organizational structure and as a result firm growth (Eesly, Hsu and Roberts, 2013). On the contrary, the business and the market it operates in appears to be more stable than the management team which is valued highly by PE investors (Kaplan, Sensoy, and Stromberg, 2009).

(5)

2 This Thesis has three main goals. The first goal is to contribute to the ongoing debate between the so-called ‘jockey’ or ‘horse’ view among PE investors by empirically investigating the influence of the stage of investment on the relationship between the view of the investor and the firm growth of the investee. The second goal is to consider how our findings can be interpreted in relation to two existing theories, which have not been used in PE research. The path dependency theory and the life-cycle theory are actually related to this debate in the sense that the theories have opposing views on the development of the firm, and I argue that this latter is related to the investment stage (Pintado, Lema and Van Auken, 2007).

The third goal is to provide Dutch SMEs with an updated picture of Dutch PE investors. This is relevant since earlier studies focused on the jockey versus horse discussion are mainly explorative and focused on American investors which are very different from European investors. (Tyebee and Bruno, 1984; MacMillan, Siegel and Narasimha, 1985; Carter and Auken, 1992; Fiet, 1995; Kaplan, Sensoy and Stromberg, 2009). The difference can be found, for instance, in the investment cycle and the average amount invested in the investee company (Basta, 2017).

This research fills a gap in the literature by exploring how the investment focus may vary depending on the investment stage and which are the consequences for the PE-backed company’s growth. The results may assist further development of government policy that facilitates PE investing. A better understanding of PE investing can help Dutch SMEs to develop better and more targeted signals to attract capital. Also, PE firms can use the information from this study to better understand their decision processes.

Central to this research is the following research question:

Is there a difference in firm growth of the investee company depending on the view of the investor and on the investment stage in SMEs?

(6)

3

2. Theoretical framing

In this section, the path dependency theory and the life-cycle theory are discussed to develop insights about the relationship between the focus of the investor on investee firm growth and the influence of the stage of investment. These two theories are especially relevant for this research setting since they describe the development of the firm, which is determinative for the stage of the investment.

2.1 Path dependency theory

The original concept of path dependence emerged from a study by David (1985), which sought to explain the adoption and persistence of nonoptimal technological standards. The example David (1985) used in his study is the well-known standard of the QWERTY keyboard and its predominance for over 100 years. This standard has spread around the world and has never been seriously challenged by all the newly developed, technically more efficient alternatives. According to David (1985) this predominance is the result of a path-dependent process.

Arthur (1989) was the first to model a formal theory of path dependence and to expose increasing returns as the major process driver. Later, this thinking was extended to economics of institutions by North (1990). And after a while the theory received prominent attention in the fields of economic and sociological research (Garud and Karnoe, 2001; Sydow, Schreyögg, and Koch, 2009).

The starting point of any path dependence process stresses the importance of past events for future action or, in more focused ways, of foregoing decisions for current and future decision making (Geroski, Mata and Portugal, 2010). This essential insight has advanced the understanding of emerging organizational phenomena and has helped to overcome the ahistorical view of rational choice thought (Sydow, Schreyögg, and Koch, 2009). The theory also learned that history can be quite important for explaining strategic choices and organizational failures. It is therefore in organizational and strategic management research widely acknowledge that decision making and realized strategies of organizations depend upon the past of the organization (Kimberly, 1979; Sydow, Schreyögg, and Koch, 2009; Garud and Karnoe, 2001).

(7)

4 Eisenhardt and Schoonhoven (1990) found similar results and showed that founding teams exert permanent effect on the performance of a firm. Over time the factor of human capital became an increasingly important subject within the path dependency theory (Geroski, Mata and Portugal, 2010; Eesly, Hsu and Roberts, 2013; Burton and Beckman, 2008).

Geroski, Mata and Portugal (2010) explored the issue of whether the conditions into which a firm is born have an effect on its survival chances and how long their effect last. Their findings showed that the effect of founding conditions upon survival decreases over time. However, although their effect is not permanent, many factors like human capital and firm size seem to have relatively long-lived effects on survival. The results point to the conclusion that the human capital at the foundation of the firm exert effects on firm survival for at least 10 years after they are born.

Eesly, Hsu and Roberts (2013) also studied the impact of the conditions in which a firm is born, but related to firm performance instead of firm survival. The authors focus also on human capital in the sense that they studied the impact of founding team composition through a process of imprinting, which largely relies on path dependency mechanisms. According to their study the founding teams are the firms first top-management team of an organization. The decisions regarding the composition of the founding team shapes future behaviour, organizational structure, and, as a result firm performance. Therefore, Eesly, Hsu and Roberts (2013) concluded that the founding team must be aligned with the strategy and the business environment to produce long-term organizational performance.

Another study relating the composition of top-management teams from a path dependence view is conducted by Burton and Beckman (2008). In their article the breadth of founder prior experiences and early decisions about functional structures are explored. Subsequent executives and structures bear strong resemblance to founding executives and structures through a process of imprinting, which relies on path dependency mechanisms. It can therefore be concluded that founding teams that begin with broadly experienced team members are more likely to attract broadly experienced executives, and firms that begin with a range of functional structures are more likely to develop more complete functional structures over time. The outcomes of this research showed that the initial conditions (founding team composition) do matter and not all firms professionalize and grow easily.

(8)

5

2.2 Life-cycle theory

The first organizational life-cycle model was introduced by Chandler in 1962. The stages of this life-cycle model changed, and so did firm’s strategies and structures. Over time new organizational life cycle models were developed and varied widely in a number of features, including the actual number of stages (Jawahar and McLaughlin, 2001). Miller and Friesen (1980) suggested a rough sequential ordering of the stages: birth, growth, maturity, revival and death. Based on the data used in the study of Miller and Friesen (1980), Drazin and Kazanjian (1990) reanalysed the life cycle and found support for a similar model consisting out of four stages. The life-cycle model including four different stages is used in a number of studies (Baird & Meshoulam, 1988; Milliman, Von Glinow and Nathan, 1991). In general, the life cycle of a firm consists out of four identifiable, but overlapping phases of (1) start-up, (2) emerging growth, (3) maturity, and (4) revival (Drazin and Kazanjian, 1990).

Organizations are likely to have diverse needs in different stages of the organizational life cycle, because of the threats and opportunities in the external and internal environment (Jawahar and McLaughlin, 2001). This was also found by Dodge and Robbins (1992) whom analysed 364 small business case reports to identify the frequency of major problem categories over the life cycle. The results showed that firms contend with different problems in various stages of the life cycle. In the early stages of the organizational life-cycle small firms were more concerned with the obstacles to the attainment of capital requirements than in later stages. Kazanjian (1988) also found that organizational problems increased or decreased in importance over time, some were simply more dominant than others. The results show that strategic positioning is important for both the first and last stage, while the people (managerial) factor ranks first in the first two stages of the organizational life cycle. In the last (fourth) stage firms are looking to develop second-generation or completely new products to spur further growth, and organizational problems arise.

(9)

6 Similar results were found by Miller and Shamsie (2001) in their study on the effect of the management team on firm performance. New management teams start by learning and experimentation. They make great efforts and find out more about market opportunities and organizational strengths by experimenting broadly and thus product lines and strategies can change extremely. Managers are aware of their markets and aware of changing environments. However, after a longer period in the later stages of the firm the management team becomes more complacent. The management team assumes much, questions little and is unwilling to learn which results into a losing touch with their markets.

Miller and Friesen (1980) also found that the importance of the management team decreased over time by studying the influence of CEO succession on the organizational development of the firm. The results showed that in the early stage of the firm’s life the CEO was of major importance, however in later stages the importance of the same CEO for the organization decreases. Therefore, CEO succession seems necessary to break the organizational momentum.

Hvide (2008) even goes a step further by studying the effect of the death of the founder across a variety of performance measures, such as sales, asset growth, survival, firm growth, and profitability. The results of his study showed that firms where the founder dies only slightly inferior to the firms where the founder stays alive. Firms where the founder dies have 5 percentage points lower probability of surviving in the first 4 years of operations than firms where the founder stays alive. For 6-year survival, there is no difference at all between the two groups of founders. The effect on asset growth and sales of founder death seem therefore negligible. His results therefore suggest that the entrepreneur’s importance to the nascent firm is quite limited; once firms are set up, the founder seems to be substitutable. As pointed out by Kaplan, Sensoy and Stromberg (2009), for early firms it is reasonable to think of the founder as the critical resource, however after some time non-human assets become more important.

(10)

7

3. Hypotheses generation

This section deals with the predictions about the direct relationship between the investment focus of the investor on investee growth and the moderating role of the stage of investment.

Previous empirical and anecdotal evidence suggests that private equity investors tend to resolve trade-offs differently. Gompers and Lerner (2001) described how Don Valentine, founder Sequoia capital, assessed the size and growth of the market, while Arthur Rock, well-known American investor, put more emphasis on the quality and integrity of the founding team. This trade-off is better known in the investment literature as the jockey (human capital) versus the horse (non-human capital).

3.1 Jockey view

“You can have a good idea and poor management and lose every time. You can have a poor idea and good management and win every time (Gladstone and Gladstone, 2002).”

The quote above comes from Gladstone and Gladstone (2002) whom argued that the investee company can have a good ‘horse’, but a poor ‘jockey’ and lose every time, while a poor ‘horse’ and a good ‘jockey’ can lead to outstanding results. However, what exactly can be considered as the jockey?

(11)

8 Mason and Harrison (1999) studied the deal rejection factors of PE investors in the UK. They found that the most common rejection factors for investors are associated with the entrepreneur/management team. These results were also found by Feeney, Haines and Riding (1999), however not in the UK but in Canada. Deal breaking criteria were the knowledge of the management and the management team in general, while the most important deal making criteria is the experience of the management team. More recently, Sudek, Mitteness and Baucus (2008), Bernstein, Korteweg and Laws (2017), and Gompers et al. (2017) came to a similar conclusion regarding the prominent position of the management team. The results of Sudek, Mitteness and Baucus (2008) showed that the entrepreneurs matters most for American PE investors when deciding whether a deal should proceed to due diligence. 64% of 885 American institutional venture capitalists mentioned the management team as the most crucial factor for a successful investment (Gompers et al., 2017) and the information about human assets is causally important for the funding of early-stage firms and hence for entrepreneurial success (Bernstein, Korteweg and Laws, 2017).

Remarkable about the aforementioned studies is that the majority is focused on the USA and that the studies are more explanatory in nature. Muzayka, Birley and Leleux (1996) also discuss this in their study and argue that these results are therefore not valid and applicable for the European private equity sector. The results are not applicable for the European PE investors since the investment cycle and the average amount invested in the investee company differ from American investors (Basta, 2017). Therefore, an investigation relating the trade-offs made by PE investors in Europe has been undertaken by Muzayka, Birley and Leleux (1996). However, despite the differences between USA en European investors the results of their study also proved that the management team was the most important for PE investors. All five management criteria were ranked among the first seven criteria. The management team is found to be of utmost importance because investors spend a substantial amount of time with management time and therefore the so-called “chemistry” between the investor and the investee must be present. There needs to be a fit between both entities and the investee must have the willingness and ability to cooperate with the investor (Bernstein, Korteweg and Laws, 2017; Mason and Harrison, 1999; Muzayka, Birley and Leleux, 1996). Furthermore, the findings of Muzayka, Birley and Leleux (1996), and Kollmann and Kuckertz (2010) show that the management team influences venture’s future success and can therefore be considered as the most important factor for making an investment decision.

Based on the arguments above mentioned, the following hypotheses is provided:

(12)

9

3.2 Jockey view and early stage investment

Support for the jockey view is found in several studies (Bernstein, Korteweg and Laws, 2017; Fiet, 1995; Gompers et al., 2017; MacMillan, Siegel and SubbaNarasimha, 1985; Mason and Harrison, 1999; Muzayka, Birley and Leleux, 1996; Sudek, Mitteness and Baucus, 2008). However, more interesting is the fact that these studies focus on the early life of the investee company. MacMillan, Siegel and SubbaNarasimha (1985) focus on the investment criteria used by private equity investors to evaluate new venture proposals. Bernstein, Korteweg and Laws (2017) include early-stage investors in their study, and Mason and Harrison focus on investee companies in their seed, start-up and early stages. Furthermore, Gompers et al. (2017) found that the management team was the most important factor for 64% of early-stage PE investors.

Similar results were found by Fiet (1995) who studied the venture capital market in the USA. According to his study the agency risk was considered as more important by VCs that specialized in early-stage investing, while market risk was found to be more important by PE investors specialized in late-stage. The agency risk concerns the relationship between the investor and the investee company and emphasizes the importance of the management team and the so-called “chemistry” between both entities. It is therefore that the most experienced and successful investors react most strongly to information about the founding team (Bernstein, Korteweg and Laws, 2017).

The importance of the jockey in the early life of the firm can be explained by the path dependence theory. According to this theory the conditions in which a firm is born are most important for firm performance (Geroski, Mata and Portugal, 2010). The conditions are determined by external and internal factors, and human capital is found to be one of the most important (Eesly, Hsu and Roberts, 2013). The human capital factors seem to have relatively long-lived effects on firm survival (Geroski, Mata and Portugal, 2010). According to Eesly, Hsu and Robert (2013) the founding team indirectly influences firm survival through the decisions that are made relating the team composition. The founding team composition shapes firm behaviour and organizational structure, and as a result firm growth and survival. Burton and Beckman (2008) even found support for a direct relationship between the founding team and firm growth through a process of path dependence.

Based on the arguments above mentioned, the following hypothesis is derived:

(13)

10

3.3 Horse view

“I like opportunities that are addressing markets so big that even the management team can’t get in its way (Valentine, 2015).”

The quote above comes from Don Valentine and according to him even a subpar management team can win in a market that is really big. That is also the main reason why Don Valentine invested in Cisco, a tech company. Cisco was turned down by many other PE investors because the management team was considered weak, but the market in which it operated had a huge market potential according to Don Valentine (2015). The market in which a company is active can be considered as the horse (Kaplan, Sensoy and Stromberg, 2009), however what else can be considered as the horse?

The definitions of the horse are very divergent, but also have similarities. MacMillan, Siegel and SubbaNarasimha (1985) simply defined the horse as the products of an organization. This definition is partly used by Gompers et al. (2017), however they also included the technology and the business plan. A somewhat broader definition is provided by Kaplan, Sensoy and Stromberg (2017), whom define the horse as the business and as earlier mentioned the market the firm operates in. A different definition is provided by Sudek, Mitteness and Baucus (2008) whom consider the horse as the opportunity. The overarching similarity are the business-related characteristics that do not include the human capital of a firm.

(14)

11 The most important criteria for a proposal to be forwarded to the next stage is the industry in which the firm is active, it needs to be profitable. Furthermore, it had to fit within the lending guidelines of venture firm for stage and size of the investment.

In 1999 the Australian private equity sector has been studied by Shepherd. In his study Shepherd (1999) investigated the relation between the investment criteria of the investor and investee firm survival. The results of the study show that the survival rate was highest if the venture had a long lead time (i.e. an extended period of monopoly), low competitive rivalry and high industry related competence. The lead time of a venture is especially important because it provides time to learn new tasks, to develop an informal structure, and to develop organizational inertia and stability that will encourage customer trust.

The importance of stability of a business for PE investors is also described by Kaplan, Sensoy and Stromberg (2009). In their study they analysed the early business plan to initial public offering (IPO) to public company for 50 PE-financed companies. The results show that firms typically maintain or broaden their offerings within their initial market segment, sell to similar customers and compete against similar competitors. It can therefore be considered that the business plan of an organization is the most important criteria for PE investors (Hall and Hofer, 1993). The stability of an organization allows ventures to increase its growth and thus firm survival.

(15)

12

3.4 Horse view and late stage investment

Gompers et al. (2017) made a distinction between early and late stage investors and showed that the business-related factors, such as business model, technology, market, and industry, were important for 60 percent of the late stage investors, while it only was important for 47 percent of the early stage investors. The business model was also ranked as one of the most important factors that contributed to successful investments, while this was not the case for early-stage investors. Similar results were presented by Carter and Van Auken (1992) whom found that late-stage investors were more concerned with uniqueness of the product and other non-human capital related factors. Late-stage investors also considered the market risk as more important than the agency risk, which is more important for early-stage investors. The market risk represents the possibility for an investor to experience losses due to factors that affect the overall performance of the markets in which the investee company is involved. In contradiction to the agency risk which highlights the importance of human capital the market risk does not emphasize a prominent role for human capital (Carter and van Auken, 1992).

The importance of non-human assets in the later stage of the organizational development, can be also be explained by the study of Rajan (2012). Rajan (2012) argues from a financial point of view and suggests that the human capital is important in the early life of the firm, but needs to be replaceable in later stage so that outside investors can obtain control rights. The various stages in which a firm can be located are also described in the life-cycle theory. This theory states that the firm requires different resources and capabilities in different organizational stages (Miller and Friesen, 1980).

In the initial stages of the organizational life cycle model managers start by learning and experimentation. They make great efforts and find out more about market opportunities and organizational strengths by experimenting broadly and thus product lines and strategies can change (Eesly, Hsu and Roberts, 2013). However, after a longer period in the later stages of the firm the management becomes more complacent, assumes much, questions little and is unwilling to learn (Miller and Shamsie, 2001). This will result into a losing touch with their markets and eventually firm growth (Miller and Shamsie, 2001). Hvide (2008) studies the effect of founder death on firm growth and performance. And the results of this study showed that firms where the founder dies have 5 percentage points lower probability of surviving in the first 4 years of operation than firms where the founder stays alive. Furthermore, in firms where the founder is non-essential it can be expected to have easier access to credit and be less exposed to bottlenecks due to limited capacity of the founder.

Based on the argument above mentioned, the following hypothesis is derived:

(16)

13

4. Method

An investigation was conducted on the influences of the investment stage and the investor focus on the growth of the investee firm, multiple databases have been used. The first database is composed by the NVP and consists out of 95% of all the investments made by Dutch Private Equity investors in Dutch firms from 1948 till September 2017. However, the data that have been used in this study cover the 2008 till 2016 period, which results into a total of 1361 investments made by 198 PE investors. 2008 was the first included year in this study because in this year the global financial crisis affected lending from European banks to SMEs negatively and other ways of financing like private equity became more important (Roosenboom, 2014). The effect of the financial crisis was also noticeable in the database of the NVP (2017). In the 9 years from 2008 till august 2017 just as much investments (49,5%) took place as from the 60 years before that. This shows that the two periods are not comparable and therefore only the most recent period was studied. Furthermore, an updated picture on the private equity sector will enable SMEs to develop better proposals and negotiate more effectively (Pintado, Lema, and Van Auken, 2007). The year 2017 will not be taken into account, since no data is available yet for this year. The year 2016 will only be used if the divestment took place in this year. If the investment took place in 2016 it is not possible to measure the firm’s growth and will therefore also be excluded from this study. The second source of data is the website of the PE investors. The investors represent themselves among other things through their website and they often mention their investment focus. The focus describes what PE investors value most when making an investment decision (Kaplan, Sensoy and Stromberg, 2009). However, there are 24 PE investors, responsible for 85 investments, that do not have a website which makes it impossible to observe the focus of the investor. The website does not exist anymore because of bankruptcy or a dissolved fund. There is also a number of 29 PE investors, responsible for 192 investments, that do not have a clear focus written on their website or a deliberate choice which corresponds with the horse or jockey view. These investors highlight for example both the management team and the market the firm operates in or do not have a focus at all on their website. Therefore, the sample decreased to 1088 investments made by 149 PE investors.

(17)

14 In the remaining sample also some ‘large’ investee companies are included that do not fit in the definition of SMEs as proposed by the European Commission (2016). That means an employee number above 250 and a balance sheet total of more than 43 million. Therefore, another 25 investee companies and 5 PE investors will be excluded from this study.

A detailed presentation of the sample selection is presented in table 1. TABLE 1: SAMPLE SELECTION

The resulting sample consists out of 72 private equity investors with a clear focus written on their website and 193 investments made in SMEs from 2008-2015. These investors have made a total of 8499 investments with an average of 45 investments per investor. 21 of these investors have a specific industry focus. The industries Life Sciences (7), Computer and Consumer Electronics (6), and the Business and Industrial Products/Services (5) supply the largest share in this sample. There are also 11 funds with a geographic focus on a specific region/province in the Netherlands. Zuid-Holland (3) and Noord-Holland (3) represent the largest part of the sample. The majority of PE investors exclusively invests in later-stage ventures (49) instead of exclusively in early-stage ventures (12).

The investee companies are on average 14 years before an PE investor participates in the firm. The industries Business and Industrial Products/Services (97), Computer and Consumer Electronics (44), Life Sciences (30) and Consumer Goods and Retail (16) represent the largest share in which the investee companies are active in.

Investments made by PE investors in Dutch investee firms from 2008-2017

226 PE investors 1684 investments

LESS:

-Investments in 2016 and 2017 -PE investors with no website

-PE investors with no clear investment focus -Investee companies without data relating growth -Investee companies that do not fit the SME classification 28 PE investors 24 PE investors 29 PE investors 68 PE investors 5 PE investors 323 investments 85 investments 192 investments 871 investments 25 investments

(18)

15

4.1 Measurement of investor focus

(19)

16 TABLE 2: DEFINITIONS OF THE JOCKEY AND THE HORSE

I will give two examples of the assessment of the jockey or horse view. 1. Investment focus of H2 Equity Partners:

`We focus on investments in mid-sized companies and differentiate ourselves through our strategic and operational value-added approach.’

Clearly described is the focus on mid-sized companies which corresponds with the definition of the horse view as discussed by Bernstein, Korteweg and Laws (2017) and Kaplan, Sensoy and Stromberg (2009). It can therefore be concluded that H2 Equity Partners values the horse more than the jockey.

2. Investment focus of Newion Investments:

‘Our funds provide growth capital for ambitious entrepreneurs with a strong ambition to grow and succeed.’

The focus of Newion Investments is in line with the jockey view as described by MacMillan et al. (1985) and Sudek, Mitteness and Baucus (2008). It can therefore be concluded that Newion Investments values the jockey considerably more than the horse.

The differences in investor focus will be represented by a dummy variable, 1 representing jockey view, and 0 representing the horse view.

Author Jockey Horse

Bernstein, Korteweg and Laws (2017) Gompers et al. (2017)

Kaplan, Sensoy and Stromberg (2009) MacMillan et al. (1985)

Sudek, Mitteness and Baucus (2008)

Founding Team Management Team Management Team Entrepreneur Entrepreneur & New Venture Team

The business and the innovation Technology and the business plan The business and the market the firm operates in

(20)

17

4.2 Measurement of the investment stage

In the database of the NVP (2017) the investment stage is provided for each singular investment. However, the number of different investment stages is enormous and will therefore be divided into two categories: early and late stage. This segmentation of investments is also used in earlier studies (Moseika, 2012; Davila, Foster and Gupta, 2003; Gompers et al., 2017; Pintado et al., 2007). According to Allen and Song (2003) the seed and start-up phase can be classified as early-stage investments and the expansion, bridge, mezzanine etc. phases can be classified as late-stage investments.

This classification corresponds with the definition as provided by INVEST Europe (formerly named European Private Equity & Venture Capital Association). According to INVEST EUROPE the early stage investments are made for launch (seed) and early development (start-up), while later stage investments are used for expansion. Several stages can be distinguished in the expansion stage: growth, rescue/turnaround, buyouts and replacement (INVEST Europe, 2016).

The investments stages that are applicable to the sample size are: buyout (34), early stage (31), expansion (1), growth (80), later stage (6), Management buy-in (3), management-buy-out (14), Replacement (1), Secondary (1), Seed (2), Start-up (9), Turnaround (6).

An overview of the distinction between early-and late-stage investments is presented in table 3. TABLE 3: EARLY AND LATE STAGE INVESTMENTS

Early-stage investment Late-stage investment

Seed Start-Up Early-stage 2 9 31 Buy-out MBO MBI Expansion Growth Replacement Later-stage Secondary Turnaround 34 14 3 1 80 1 6 1 6

(21)

18 The majority of investments takes place in the late stage. This is also found in a study focused on Venture Capital in Spain by Pintado et al. (2007). A significantly higher percentage of VC firms invested in later-stage companies than in early-later-stage companies because of the low level of risk. The sample is also representative for the European private equity market. The three stages with the highest scores (Early-stage, Buy-out and Growth) are also the three major parts of all the European private equity investments over a period of 4 years (2012-2016) (Invest Europe, 2016).

The differences in stage of investment will be represented by a dummy variable, 1 representing an early-stage investment, and 0 representing a late-early-stage investment.

4.3 Measurement of firm growth

The interest of PE investors lies in the growth of firm value, which determines the rentability of the venture (Huergo and Jaumandreu, 2004). To measure the rentability accounting-based measures can be used, for example: Return-on-Investment (ROI) or economic value added (Huergo and Jaumandreu, 2004). However, the data necessary to calculate these measures is not as available for SMEs as for public companies. This lack of data is due to the private nature of SMEs (Davila, Foster and Gupta, 2003). Another important measure for investors is firm growth which can be measured by several attributes such as turnover/sales, employment, assets, market shares and profits (Zhou and de Wit, 2009). Among these measures, sales and employment growth are most used as indicators for growth (Wu, 2009). However, since sales growth is not available for all the companies in the dataset only employment growth will be used to maximize the sample for the empirical analysis. Furthermore, it is found that sales and the number of employees are nearly linearly correlated, and it can therefore be expected that growth of both variables is also highly correlated (Engel and Keilbach, 2006).

Growth in employment is easy to obtain and measure. Furthermore, it reflects both short-term and long-term changes in a firm (Zhou and de Wit, 2009). These indicator is also considered as being more objective compared to other indicators such as market share. It also allows to relate the findings of this thesis to other studies that also used employment (Engel and Keilbach, 2006).

(22)

19

4.4 Control variables

Previous research has shown that several other variables can influence the performance of the investee company. To control for cofounding effect, control variables on both investee firm (sector, firm age) as PE investor (experience, geographic focus, industry focus) are included in this study.

4.4.1 Sector dummies

Sector dummies are a commonly used control variable and it has been proved that sector differences matter in empirical results (Zhou and de Wit, 2009). For instance, a firm in the labour-intensive sector might be more likely to engage in employment growth when compared to a less labour-intensive one. Therefore, the sectors are divided into labour-intensive and less-labour intensive. According to Scott (2006) the Agriculture, Business and Industrial Services/Products, Construction, Consumer Goods and Retail, Life Sciences, and Transportation industries can be considered labour-intensive.

Therefore, the labour-intensive industries will be represented by a dummy variable being 1 and the less-labour intensive industries will be represented by a dummy variable being 0.

4.4.2 Investee firm age

(23)

20

4.4.3 Experience of the PE investor

Krohmer (2007) argues that more experienced investors have a deeper understanding of the private equity market and can take advantage of this knowledge. Furthermore, these experienced investors build up a network over years. Krohmer (2007) also argues that usually young and inexperienced investors over-hold loss-making investments and invest a relative higher share of fund’s portfolio capital into them, signifying that the investors’ efforts levels are low. Therefore, it is essential when running the regression analysis to include the differences in PE investor experience.

The experience of the PE investor can be measured according to the total number of prior investments (Nooteboom and Stam, 2008). In this study they took the average number of investments made by private equity investors. Therefore, a PE investor will be considered as experienced if it has 73 or more investments made and inexperienced if it has less than 73 investments made over the years. This is represented by a dummy variable, 1 representing an experienced PE investor, and 0 representing an inexperienced PE investor.

4.4.4 Geographic focus

Gupta and Sapienza (1992) show that PE investors specialized in a geographic area perform better than PE investors that do not focus on a geographic region. The network of the PE investor in a specific region can increase the growth of the investee company (Gupta and Sapienza, 1992).

According to the European Union (2016) the Netherlands are divided into four different regions, being: region 1 (North Netherlands): Groningen, Friesland, Drenthe, region 2 (East Netherlands): Overijssel, Gelderland, Flevoland, region 3: Utrecht, North-Holland, South-Holland and Zeeland, and region 4 (South Netherlands): North Brabant, Limburg. If all the investee companies of one specific PE investor are located in one single region it can be considered as an investor with a geographic focus. This will be represented by a dummy variable, 1 representing an PE investor with a geographic focus, and 0 representing an PE investor without a geographic focus.

4.4.5 Industry focus

(24)

21

4.5 Measurement of dependent and independent variables

In table 4 the different variables with corresponding proxies are presented. Also, the measurements of the variables are described.

TABLE 4: DEPENDENT, INDEPENDENT AND CONTROL VARIABLES

VARIABLE PROXY MEASUREMENT

DEPENDENT VARIABLE Growth of the investee firm (SME)

GIF Score determination based on 1 growth

indicator: number of employees INDEPENDENT VARIABLE

Explanatory variable

Investor Focus IV Score determination based on the

jockey view (1) or horse view (0) Moderating variable

Early/Late stage investing ELI Score determination based on early (1) or late stage (0)

Control variables

Industry Industry Score determination based on

labour-intensive (1) or less-labour labour-intensive (0) Firm age of investee firm Firm age Score determination based on young

(0-4 years) (1) or old (5+) (0) Experience of PE investor Experience Score determination based on

experience (>73 investments) (1) or no experience (<72 investments) (0)

Geographic Focus Geo. focus Score determination based on

geographic focus (all investments in specific region) (1) or no focus (not all investments in specific region) (0) Industry Focus Ind. focus Score determination based on industry

focus (all investments in specific industry) (1) or no focus (not all investments in a specific industry) (0).

4.6 Statistical model

I estimate on the following econometric models by means of ordinary least squares (OLS): GIF-score = ß1(Jockey) + ß2(Early-stage investing) + ß1(Jockey)*ß2(Early-stage investing) + ß3(Industry) + ß4(Firm age) + ß5(Experience) + ß6(Geo.focus) + ß7(Ind.focus) + ε;

GIF-score = ß1(Horse) + ß2(Late-stage investing) + ß1(Horse)*ß2(Late-stage investing) + ß3(Industry) + ß4(Firm age) + ß5(Experience) + ß6(Geo.focus) + ß7(Ind.focus) + ε;

(25)

22

5. Results

This section presents the descriptive statistics and the correlation matrix, and discusses OLS results. Furthermore, a robustness check is executed to show the reliability of econometric results.

5.1 Descriptive statistics

Table 5 presents the descriptive statistics of the investigated dependent, independent and control variables used in this study. The statistics show that the average investee firm grows by 50 percent during the investment period. The majority of Dutch PE investors is focused on the horse (53%) and invests in late-stage companies (59%). This is different from the explorative study of Gompers et al. (2017) who found that 64% of the PE investors focus on the jockey and 84% mainly invest in early-stage companies. These differences highlight the importance of a study focused on the Netherlands instead of the American private equity sector. Most of the Dutch investors have a low level of experience gathered through earlier investments and do not focus on a specific industry or geographic region. The average investee firm is 12 years old at the time of the investment. Furthermore, the majority of investee firms is active in a labour-intensive industry.

TABLE 5: DESCRIPTIVE STATISTICS

Mean S.D. Min. Max.

GIF (%) 50.52 164 -238 1171 IV -Jockey -Horse .473 .527 .501 .501 0 0 1 1 ELI -Early -Late .234 .766 .425 .425 0 0 1 1 INDUSTRY .697 .460 0 1

FIRM AGE (YEARS) 12.3 17.3 0 107

EXPERIENCE .165 .372 0 1

INDUSTRY FOCUS .282 .451 0 1

(26)

23

5.2 Correlation analysis

Table 6 displays the correlation coefficients between the different variables. The jockey view of the investor is positive and significant related to the growth of the investee firm (r = .227, p = .000). This is an indication for the confirmation of the first hypothesis (H1a).

The highest correlation coefficients between control variables is found between the investment stage and the age of the firm (r = -.207, p = .000, r = .207, p = .000). This significant correlation between the investment stage and the development of the firm is also described by Pintado, Lema and Van Auken (2007).

The general rule regarding the correlation coefficients, as proposed by Valentine (1969), suggests that a coefficient greater than 0.7 between two independent variables can bring up multicollinearity issues, the situation in which two or more independent variables in a regression model are strongly related. As shown in the table below none of the correlation coefficients exceeds the 0.7 threshold.

TABLE 6: CORRELATION COEFFICIENTS

1 2 (Jockey) 2 (Horse 3 (Early) 3 (Late) 4 5 6 7 8 1. GIF - 2. IV -Jockey -Horse .227** -.227** - -1.000** - 3. ELI -Early -Late .006 -.006 .004 -.004 -.004 .004 - -1.000** - 4. Industry -.123 -.163* .163* .009 -.009 - 5. Firm age -.127 .041 -.041 -.207** .207** .184 - 6. Experience .036 .009 -.009 .044 -.044 .044 -.124 - 7. Geo. focus -.0.10 -.060 .060 .054 -.054 -.118 -.207 .178** - 8. Ind. Focus .109 -.026 .026 .045 -.045 -.201* -.171 -.278 -.163 - *p<.05 (two-tailed), **p<.01 (two-tailed)

5.3 Regression analysis

(27)

24 Table 7 provides the results of the regression analysis. Model 1 is a controls-only model that explains 6.1% (adj.r² = 0.061) of the variance in investee firm growth and is significant (F = 3.449, p = .005). However, no significant relationships are found between the growth of the investee firm and the industry in which the investee firm operates (ß = 16.514, p = .414), the age of the investee firm at the time of the investment (ß = -.292, p = .679), experience of the PE investor (ß = 48.790, p = .204), the industry focus of the PE investor (ß = 31.761, p = .693), and the geographic focus of the PE investor (ß = 6.241, p = .831).

Model 2 and 3 are used to test hypotheses 1a and 1b. Model 2 explains less variation in investee growth (adj.r² = .117) than model 3 (adj.r² = .124) because the control variables are included in model 3. Both model 2 (F = 9.333, p = .000) and model 3 (F = 4.338, p = .001) are significant. Building on earlier studies (Muzayka, Birley and Leleux, 1996) it is the jockey who fundamentally determines firm growth and is therefore considered as the crucial factor when making an investment decision. This hypothesis (H1a) is confirmed in both model 2 (ß = 91.250, p = .000), and model 3 (ß= 92.592, p= .000).

The interaction term between the focus on the jockey and investing in an early stage is based on the path dependence theory. This theory argues that the initial management team shapes future behaviour, organizational structure, and as result firm growth (Eesly, Hsu and Roberts, 2013). However, the interaction term of early stage investing based on the management team is negative and insignificant in both model 2 (ß = -27.683, p = .599), and model 3 (ß = -41.129, p = .484) and can therefore not be confirmed.

The test statistics of hypotheses 2a and 2b are represented in model 4 and 5. Model 4 (without control variables) explains 9,2% of the variation in investee growth (adj.r² = .092) and is significant (F = 7.323, p = .000). Similar amounts of variation in investee growth (adj.r² = .109) are explained in model 6 (F = 3.965, p = .000). In contradiction to the first hypothesis (H1a) the third hypothesis (H2a) proposes that investors should bet on the horse because of the unstableness of the jockey (likely to change or fail) (Kaplan, Sensoy and Strömberg, 2009). However, this is not confirmed in both model 4 (ß = 21.957, p = .521) and model 5 (ß = 1.178, p = .980).

(28)

25 Therefore, it is expected that the business (horse) leads to higher firm growth when investing in a late-stage. However, the statistical outcome of model 5 shows a significant (p = .023), but negative coefficient (ß = -100.167) relating this hypothesis (H2b).

Another remarkable finding that is not found during the theoretical framing is the single effect of late-stage investing on investee firm growth. In model 4 the coefficient is positive and significant (B = 91.250, p = .000), as well as in model 5 (B = 86.786, p = .004). This indicates that late-stage investing leads to higher investee firm growth than early-stage investing.

TABLE 7: REGRESSION ANALYSIS

Variables Model 1 Model 2 Model 3 Model 4 Model 5

Independent variables Early Stage - 21.957 (33.690) 12.123 (40.238) - - Late Stage - - - 91.250 (19.877)** 86.786 (29.646)** Jockey view (H1a) - 91.250 (19.593)** 92.592 (24.368)** - - Horse view (H2a) - - - 21.957 (34.177) 1.178 (45.992) Early*Jockey (H1b) - -27.683 (52.555) -41.129 (58.633) - - Late*Horse (H2b) - - - -100.167 (43.780)* -87.097 (52.466) Control variables -Industry 16.514 (20.174) - -4. 537 (21.930) - -3.156 (27.151) -Firm Age -.292 (.704) - -.894 (.702) - -.879 (.727) -Experience 48.790 (38.241) - 38.896 (37.948) - 49.120 (37.657) -Industry Focus 31.761 (24.642) - 48.261 (24.876) - 48.148 (23.647) -Geographic Focus 6.241 (29.131) - -3.467 (29.811) - 8.051 (29.735) Observations 188 188 188 188 188 F-test 3.449** 9.333** 4.335** 7.323** 3.965** Adjusted R2 .061 .117 .124 .092 .112

**p<0.01 (one-tailed), *p<0.05 (one tailed)

(29)

26

5.4 Robustness check

The financial crisis changed the financial landscape drastically, for i.e. SMEs, and therefore the first year included in this study is the start of the crisis in the Netherlands. However, the period from 2008 till 2017 is quite long and therefore the results are not likely to be influenced by the crisis. Therefore, the regression is re-run from 2011.

As shown in table 8 the results remain unchanged in model 2 and 4 which excluded the first 4 years from 2008.

TABLE 8: ROBUSTNESS CHECK

Variables Model 1 Model 2 Model 3 Model 4

Independent variables Early Stage 12.123 (40.238) 17.163 (44.746) - Late Stage - - 86.786 (29.646)** 112.147 (36.485)** Jockey view 92.592 (24.368)** 99.038 (27.910)** - - Horse view - - 1.178 (45.992) 14.538 (52.652) Early*Jockey -41.129 (58.633) -82.530 (66.832) - - Late*Horse - -87.097 (52.466) -118.689 (60.700) Control variables -Industry -4. 537 (21.930) -18.359 (21.900) -3.156 (27.151) 1.539 (30.773) -Firm Age -.894 (.702) -864 (.704) -.879 (.727) -.883 (.729) -Experience 38.896 (37.948) 38.644 (41.837) 49.120 (37.657) 50.048 (41.384) -Industry Focus 48.261 (24.876) 27.459 (29.703) 48.148 (23.647) 47.447 (30.048) -Geographic Focus -3.467 (29.811) -10.287 (32.994) 8.051 (29.735) 10.147 (32.311) Observations 188 131 188 131 F-test 4.335** 4.042** 3.965** 3.770** Adjusted R2 .124 .131 .112 .121

(30)

27

6. Conclusion

This study examines the question whether it is better for private equity investors to bet on the so-called ‘jockey’ or ‘horse’ and the influential role of the investment stage. The ‘jockey’ refers in this discussion to the factor of human capital and the ‘horse’ refers to non-human capital factors (Bernstein, Korteweg and Laws, 2017). This discussion is becoming more relevant because the financial crisis forced SMEs to search for alternative ways of financing including the increasing importance of private equity (Roosenboom, 2004). Furthermore, the majority of studies relating this discussion are explorative in nature, focused on the American private equity sector and do not include the investment stage as an influential factor. Therefore, this study tries to contribute to the debate by empirically testing the relationship between the investor focus and the investment stage on investee firm growth in the Netherlands.

To examine the role of the investment stage of investment and the focus of the investor on investee firm growth, data is derived from 72 investment companies responsible for 188 investments over the period 2008-2017. To measure the different variables hand-collected data from different databases has been examined.

6.1 Findings

The vast majority of Dutch investors invest in late-stage firms, do not have a geographic- or industry focus and mainly focus on the horse when making an investment decision. However, it is the jockey-focused investor that invested in companies that grew considerably more than the horse jockey-focused investor. This highlights the importance of the management team/founder for the firm, and thus its relevance as criterion for making investment decisions. As suggested earlier by Muzyka, Birly and Leleux (1996) the American private equity investors clearly focus first on human capital. This is consistent with the results of this thesis leading me to conclude that PE investors in both communities share a common model of what constitutes a good opportunity.

(31)

28

6.2 Theoretical implications

This study theoretically conforms to the growing body of literature regarding the discussion between the jockey or the horse. Specifically, this study investigates the relationship between the focus of the investor and the growth of the investee firm and the moderating effect of the investment stage. The influence of the investment stage can be considered as the gap in the literature, since this has not been studied before (especially not in the Netherlands) as theoretical block to tease out which view (path-dependency vs. life-cycle) prevails in private equity markets for entrepreneurial finance. Furthermore, the theories proposed complementary outcomes.

According to the path-dependence theory the jockey is one of the most important initial conditions that shapes the organization and future performance: however, this effect decreases over time as suggested by the life-cycle theory, and therefore different resources and capabilities would be necessary. However, this could not be confirmed by the statistical outcomes of the different models.

6.3 Practical implications

The results also have practical implications for policymakers, entrepreneurial firms seeking funding, PE investors, consultants, and support agencies that provide capital-acquisition assistance. A greater understanding of PE investment decision-making can assist in the development of government policies that promote private equity investing. New government policies could promote SMEs to publish more information on the management team(s) that were responsible for the firm. The more comprehensive information on the management team can enable PE investors to pick the most valuable investment opportunities. This also allows PE investors to devote less time and resources in the selection of portfolio companies, and dedicate more effort in value-addition activities. Entrepreneurial firms seeking funding should worry less about packaging the financial aspects, but build a good management with strong leadership. Furthermore, entrepreneurs should not confine themselves to national boundaries when seeking funding. Better insight into the focus of the investor and the effect on investee firm growth can enable consultants and support agencies to improve the advice they give to investee and investor firms.

6.4 Limitations and future research

(32)

(33)

30

7. References

Arthur, W.B. 1989. Competing Technologies, Increasing Returns, and Lock-In by Historical Events. The

Economic Journal, 99: 116-131.

Balakrishan, J.D. (1999) Decision Processes in Discrimination: Fundamental Misrepresentations of Signal Detection Theory. Journal of Experimental Psychology: Human Perception & Performance, 25: 1189-1206.

Baird, L. & Meshoulam, I. (1988) Managing Two Fits of Strategic Human Resource Management. The

Academy of Management Review, 13 (1): 116-128.

Basta, V. (2017) Venture Investing in the US and Europe are Totally Different Industries. Retrieved from: https://techcrunch.com/2017/06/07/venture-investing-in-the-us-and-europe-are-totally-different-industries/

Bernstein, S., Korteweg, A. & Laws, K. (2017) Attracting Early-Stage Investors: Evidence from a Randomized Field Experiment. The Journal of Finance, 72(2): 509-538.

Berger, A. & Udell, G. (1998) The Economics of Small Business Finance: The Roles of Private Equity and Debt Markets in the Financial Growth Cycle. Journal of Banking & Finance, 22(6/8): 613-673.

Burton, D.M. & Beckman, C.M. (2008) Founding the Future: Path Dependence in the Evolution of Top Management Teams from Founding to IPO. Organization Science, 19(1): 3-24.

Carter, R.B. & Van Auken, H. (2007) Capital Acquisition Attitudes: Gender and Experience. The Journal

of Entrepreneurial Finance, 12(2): 55-74.

Chandler, A.D. (1962) Strategy and Structure: Chapters in the History of the Industrial Enterprise. United States: Cambridge: M.I.T. Press.

Cooper, A.C., Gimeno-Gascon, F.J. & Woo, C.Y. (1994) Initial Human and Financial Capital as Predictors of New Venture Performance, Journal of Business Venturing, 9(5): 371-395.

David, P.A. (1985) Clio and the Economics of QWETRTY, American Economic Review, 75:332-337. Davila, A., Foster, G. & Gupta, M. (2003) Venture Capital Financing and the Growth of Startup Firms,

Journal of Business Venturing, 18(6): 689-708.

Delmar, F. & Davidsson, P. (1998) A Taxonomy of High-Growth Firms, Frontiers of Entrepreneurship

Research, 18:189-216.

Dodge, H.R. & Robbins, J.E. (1992) An Empirical Investigation of the Organizational Life-Cycle Model for Small Business Development and Survival. Journal of Small Business Development, 30:27-37. Drazin, R. & Kazanjian, R.K. (1990) A Stage-Contingent Model of Design and Growth for Technology Based New Ventures, Journal of Business Venturing, 5(3):137-150.

Eesly, C.E., Hsu, D.H. & Roberts, E.B. (2013) The Contingent Effects of Top Management Teams on Venture Performance: Aligning Founding Team Composition with Innovation Strategy and

Commercialization Environment, 35(12):1798-1817.

(34)

31 Engel, D. & Keilbach, M. (2006) Firm-Level Implications of Early Stage Venture Capital Investment – An Empirical Investigation, Journal of Empirical Finance, 14:150-167.

European Commission. (2018, January 16) What is an SME? Retrieved from:

http://ec.europa.eu/growth/smes/business-friendly-environment/sme-definition_nl

Feeney, L., Haines, G.H. & Riding, A.L. (1999) Private Investors’ Investment Criteria: Insights from Qualitative Data, Venture Capital, 1(2):121-145.

Fiet, J.O. (1995) Reliance Upon Informants in the Venture Capital Industry, Journal of Business

Venturing, 10(3):195-223.

Garud, R. & Karnoe, P. (2001) Path Dependence and Creation, London: Lawrence Erlbaum.

Geroski, P. & Gugler, K. (2004) Corporate Growth Convergence in Europe, Oxford Economic Papers, 56(4): 597-620.

Geroski, P.A., Mata, J. & Portugal, P. (2010) Founding Conditions and the Survival of New Firms,

Strategic Management Journal, 31(5): 510-529.

Gladstone, D. & Gladstone, L. (2002) Venture Capital Handbook. New Jersey: Prentice Hall. Gompers, P.A., Gornall, W., Kaplan, S.N. & Strebulaev, I.A. (2017) How do Venture Capitalists make Decisions?, Stanford University Graduate School of Business Research Paper, no. 16-33

Gompers, P. & Lerner, J. (2001) The Money of Invention. Boston, MA: Harvard Business School Press. Gupta, A.K & Sapienza, H.J. (1992) Determinants of Venture Capital Firms’ Preferences Regarding the Industry Diversity and Geographic Scope of their Investments, Journal of Business Venturing,7:347-362.

Haar, N.E., Starr, J. & MacMillan, L.C. (1988) Informal Risk Capital Investors: Investment Patterns on the East Coast of the U.S.A., Journal of Business Venturing, 3:11-29.

Hall, J. & Hofer, C.W. (1993) Venture Capitalists’ Decision Criteria in New Venture Evaluation, Journal

of Business Venturing, 8:25-42.

Haines, Jr. G., Madill, J. & Riding, A. (2003) Informal investment in Canada: Financing Small Business Growth, Journal of Small Business and Entrepreneurship, 16(3/4):13-40.

Harrison, R.T. & Mason, C.M. (2002) Backing the Horse or the Jockey? Agency Costs, Information and the Evaluation of Risk by Business Angels, paper presented at the Babson-Kaufmann entrepreneurship conference, www.babson.edu/entrep/fer/BABSON2002, 15 October.

Huergo, E. & Jaumandreu, J. (2004) Firms’ Age, Process Innovation and Productivity Growth,

International Journal of Industrial Organization, 22(4):541-559.

Hvide, H.K. (2008) Are Founders Non-Substitutable? Evidence from Nascent Firms where a Founder Dies. Working paper, University of Aberdeen, Business School.

INVEST Europe. (2016) Private Equity Investments, retrieved from: http://ec.europa.eu/eurostat/cache/metadata/DE/tin00141_esms.htm

(35)

32 Kaplan, S., Sensoy, B. & Stromberg, P. (2009) Should Investors Bet on the Jockey or the Horse?

Evidence from the Evolution of Firms from Early Business Plans to Public Companies, Journal of

Finance, 64(1):75-115.

Kazanjian, R.K. (1988) Relation of Dominant Problems to Stages of Growth in Technology-Based New Ventures, The Academy of Management Journal, 31(2):257-279.

Kimberly, J. (1979) Issues in the Creation of Organizations: Initiation, Innovation, and Institutionalization, The Academy of Management Journal, 22(3):437-457.

Krohmer, P. (2007) The Liquidation Dilemma of Money Losing Investments – The Impact of Investment Experience and Window Dressing of Private Equity and Venture Capital Funds. Working Paper Series, pp.1-36.

Koenker, R.W. & Basset, G. (1978) Regression Quantiles, Econometrica, 46(1):33-50.

Kollmann, T. & Kuckertz, A. (2010) Evaluation Uncertainty of Venture Capitalists’ Investment Criteria,

Journal of Business Research, 63(7):121-151.

MacMillan, I.C., Siegel, R. & SubbaNarasimha, P.N. (1985) Criteria Used by Venture Capitalists to Evaluate New Venture Proposals, Journal of Business Venturing, 1(1):119-128.

MacMillan, I.C., Zemann, L. & SubbaNarashima, P.N. (1987) Criteria Distinguishing Successful from Unsuccessful Ventures in the Venture Screening Process, Journal of Business Venturing, 2(2):123-137. Mason, C.M. & Harrison, R.T. (1996) Informal Venture Capital: A Study of the Investment Process and Post-Investment Experience, Entrepreneurship and Regional Development, 14(2):35-51.

Mason, C.M. & Harrison, R.T. (1999) Public Policy and the Development of the Informal Venture Capital Market: UK Experience and Lessons for Europe, Industrial Policy in Europe, pp. 199-223. Miller, D. & Friesen, P.H. (1980) Momentum and Revolution in Organisational Adaption, Academy of

Management Journal, 23:591-614

Miller, D. & Shamsie, J. (2001) Learning Across the Life Cycle: Experimentation and Performance among the Hollywood Studio Heads, Strategic Management Journal, 22:725-745.

Milliman, J., Von Glinow, M.A. & Nathan, M. (1991) Organizational Life Cycles and Strategic

International Human Resource Management in Multinational Companies: Implications for Congruence Theory, Academy of Management, 16(2):318-339.

Mitteness, C.R., Baucus, M.S. & Sudek. (2011) Betting on the Horse or the Jockey: The Impact of Stage of the Race and Experience, Frontiers of Entrepreneurship Research, 31(1): art.2.

Moseika, E. (2012) The Effect of the Investment Committees’ Size and Composition on the Limited Partners’ Governance Levels (Doctoral dissertation). Retrieved from:

http://arno.uvt.nl/show.cgi?fid=127648.

Muzayka, D., Birley, S. & Leleux, B. (1996) Trade-offs in the Investment Decisions of European Venture Capitalists, Journal of Business Venturing, 11(4):273-287.

National Survey of Small Business Finances (1996) Retrieved from: https://www.federalreserve.gov/pubs/oss/oss3/nssbf93/method93.pdf

(36)

33 North, D. (1990) Institutions, Institutional Change and Economic Performance, Cambridge: Cambridge University Press.

NVP (2017) Overview of Portfolio Businesses. Retrieved from:

http://www.nvp.nl/pagina/overzicht_portefeuillebedrijven/#!lang=en

Pintado, T.R., De Lema, D.G.P. & Van Auken, H. (2007) Venture Capital in Spain by Stage of Development, Journal of Small Business Management, 45(1):68-88.

Rajan, R.G. (2012) Presidential Address: The Corporation in Finance, The Journal of Finance, 67(4):1173-1217.

Romanelli, E. (1989) Environments and Strategies of Organization Start-Up: Effects on Early Survival,

Administrative Science Quarterly, 34(3):369-387.

Roosenboom, P.G.J. (2014) Het Externe Financieringsaanbod voor het Nederlandse MKB, MKB in

Beeld, pp. 63-69.

Scott, A.J. (2006) Geography and Economy, Oxford: Oxford University Press.

Shepherd, D.A. (1999) Venture Capitalists’ Introspection: A Comparison of ‘In Use’ and ‘Espoused’ decision policies, Journal of Small Business Management, 37(2):76-87.

Sudek, R., Mitteness, C. & Baucus, M. (2008) Betting on the Horse or the Jockey: The Impact of Expertise on Angel Investing, Academy of Management Best Paper Proceedings.

Sydow, J., Schreyögg, G. & Koch, J. (2009) Organizational Path Dependence: Opening the Black Box, The

Academy of Management Review, 34(4):689-709.

Tyebee, T.T. & Bruno, A.V. (1984) A Model of Venture Capitalist Investment Activity, Management

Science, 30:1051-1066.

Valentine, D. (2015) A Dozen Things that I’ve Learned about Venture Capital and Business. Retrieved from: https://25iq.com/2015/02/21/a-dozen-things-ive-learned-about-venture-capital-and-business/ Valentine, T.J. (1969) A Note on Multicollinearity, Australian Economic Paper, 8(12):91-106.

Van Osnabrugge, M. (1998) Do Serial and Non-Serial Investors Behave Differently? An Empirical and Theoretical Analysis, Entrepreneurship: Theory & Practice, 22(4):23-42.

Wu, D. (2009) Measuring Performance in Small and Medium Enterprises in the Information & Communication Technology Industries (Doctoral dissertation). Retrieved from:

https://researchbank.rmit.edu.au/eserv/rmit:6859/Wu.pdf

Zacharakis, A. & Meyer, G.D. (1998) A Lack of Insight: Do Venture Capitalists Really Understand their own Decision Processes? Journal of Business Venturing, 13:57-76.

Zhou, H. & De Wit, G. (2009) Determinants and Dimensions of Firm Growth, SCALES EIM Research

(37)

34

Appendix A

Variables Model 1 Model 2

Should Private Equity Investors Bet on the Jockey or the Horse? An empirical analysis on the influence of the investment stage on SMEs’ growth