Venture capitalists’ managerial involvement in entrepreneurships: Is too much of a good thing bad?
April M. Knill Department of Finance Florida State University 821 Academic Way, 143 RBB Tallahassee, FL 32306-1110 Phone: (850) 644-2047 Fax: (850) 644-4225
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ABSTRACT Venture capitalists are investors that provide valuable hand-holding for the companies in which they invest. A venture capitalist chooses the level of involvement with his portfolio companies. Involvement spans from a very relaxed, limited communication ‘laisse faire’ approach to a very involved, almost stifling, ‘hands on’ approach. If venture capitalist involvement is valuable, is more involvement better, or is too much of a good thing bad? The answer to this question lies in the nature of the relationship between venture capitalist managerial involvement and portfolio company performance; specifically, whether it is linear or nonlinear. Using data from SDC Platinum VentureXpert, I find that there exists a nonlinear relationship between the level of VC involvement for both PC performance and outcome. Results suggest that extreme levels of VC involvement should be avoided.
JEL Classification: G2; G3; L26 Keywords: Venture capital; Entrepreneurship; Involvement; Control; Portfolio Companies
1 Electronic copy available at: http://ssrn.com/abstract=1337110
1. Executive Summary Surveys of venture capitalists (VCs) have shown different management styles (e.g., see Macmillan, Kulow and Khoylian, 1988). On one end of the spectrum, the VC exudes a ‘laisse faire’ attitude, or acts as a silent manager. Under this style, there is a lack of direct involvement in the operations of the entrepreneurships, or portfolio companies (PCs), in which it invests. One might say that this style is equivalent to the financing often achieved through angels.1 This style arguably lacks one of the main values touted by VCs - guidance (Hellmann, 2000; Hellmann and Puri, 2000; 2002; Renucci, 2000; Sapienza et al., 1996; Ehrlich and De Noble, 1994; Sapienza, 1992). Managerial involvement provided by venture capitalists is desirable to entrepreneurs who excel in the service provided by their company (e.g., technology expertise for a high tech company entrepreneur), but lack the business savvy to succeed in the critical early years (Gorman and Sahlman, 1989; Kaplan and Stromberg, 2001). At the other end of the spectrum, the VC exudes a very ‘hands on’ approach, or is a co-manager. One could argue that this style has the potential to be stifling and that it can translate into interference (Burkart, Gromb and Panunzi, 1997). At the very least, it is the opposite of the autonomy many entrepreneurs seek out in starting a business. At most it could be stifling; squelching innovativeness, productivity, etc. Though extant literature has certainly established both the value of PC managerial guidance and the spectrum of involvement possible by the VC, the nature of the relationship has not yet been examined. Given the implication by extant literature that managerial guidance is valuable, in abstract, one could hypothesize that more involvement is better, and VCs that “co-manage” should maximize the value of their investment: the PC. This need not be the case, however, and one could certainly envision a world where VC involvement was actually intrusive and counter-productive. The question then to be examined is whether or not the relationship between VC involvement and entrepreneurial success is linear.
2 Electronic copy available at: http://ssrn.com/abstract=1337110
VCs could certainly benefit from this knowledge because involvement is expensive; its opportunity cost is foregone investment opportunities due to finite resources. Since VCs need to expend resources doing due diligence on future investments (to maximize their overall portfolio value), it would be valuable to know if there are decreasing scaled to VC involvement. To that end, this paper examines the involvement strategies of VCs to discern which levels result in optimal performance/outcome for the entrepreneurs. I find that the relationship between VC involvement and PC performance/outcome is, in fact, nonlinear. Too much of a good thing can indeed be bad in this case. Analysis done at different levels of each proxy of VC involvement suggests that average VC involvement (versus either extreme) maximizes the positive impact on firm performance.
2. Introduction VCs do not have unlimited resources. They must divide their time amongst their existing investments (PCs) and their new investments (i.e., perform due diligence). If the involvement style of the VC is closer to a silent manager (co-manager), the time spent on new investments will be more (less). This amounts to a cost of involvement for the VC, which could ultimately impact the PC. If these costs become substantive, the performance and outcome of these fledgling firms could be negatively impacted, ultimately delaying the exit of these firms. Conversely, however, some entrepreneurs may find less VC involvement as a positive factor. Indeed, from the PCs perspective, VC involvement may be considered stifling. The self-selection process of entrepreneurs suggests that entrepreneurs seek out situations where they are not managed.2 Such individuals often flourish when autonomous environments exist. Although too little involvement (i.e., management) is still a risk since some of these individuals lack important business savvy, excessive involvement can be just as damaging.
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During the contract-writing phase of venture capital, certain involvement variables are established such as cash-flow and voting rights. The essence of the involvement rights is formed at this stage of the VC/PC relationship (Kaplan and Strömberg, 2003). It is this process that will set into motion the general level of involvement the VC will exude (see Bergemann and Hege, 1998 for a model of inter-temperal risk-sharing in the VC/PC relationship). As such, in order to maximize profit, VCs must know how much involvement they should undertake with their portfolio companies before they set up the contract. Knowing the relationship between involvement and PC performance and outcome can help VCs make this important decision. This paper attempts to provide information valuable in this decision. There have been several studies that highlight the mentoring benefit of VCs, but very few that examine involvement from a strategic point of view. The two papers that are most similar to this paper are Gompers (1995) and Macmillan, Kulow and Khoylian (1989). Gompers (1995) establishes that monitoring and staging of venture capitalist investments can be explained using agency theory. In other words, the paper establishes what factors predict VC monitoring and staging of investments (e.g., the level of information asymmetry). Macmillan, Kulow and Khoylian (1989) is arguably the most comparable paper in extant literature. The authors use a survey to examine the activities of VCs that constitute involvement and the relative impact of these activities. Though they do touch on the relationship between VC involvement and PC performance, they do not examine the entrepreneurial outcome. The work of Macmillan, Kulow and Khoylian was a valuable first step in looking at the range of VC involvement that exists out there. The paper is silent, however, on any strategy involved in establishing a certain level of involvement with the PC, leaving room for future research. In fact, they write, “Because tests…did not significantly explain why the three distinct types of venture capitalist involvement emerged, it appears that venture capitalists exhibited different involvement levels solely because they elected to do so.” Further, as is pointed out in Zacharakis and Meyer (1998), surveys of VCs are often “biased and somewhat misleading”. This paper intends to look at involvement as a VC strategy to maximize his performance when the cost of
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involvement is lost time to perform due diligence on future investments. Further, this paper uses the full VentureXpert database to avoid any bias that survey analysis might introduce. The rest of the paper is structured as follows. Section 3 of the paper describes the empirical method used in the study. The data used is described in Section 4. Section 5 describes the results and Section 6 provides a description of robustness tests. Section 7 suggests some limitations of the study and opportunities for future research and Section 8 concludes the paper.
3. Empirical motivation and method The relationship between VC involvement and PC performance can be motivated by the model set forth in Cumming and MacIntosh (2001). This model demonstrates the objective function of a venture capitalist who is trying to maximize the value of its investments given the costs of its investments. The model is represented mathematically as:
Max PVA
,
,
,
,
,
(1)
e,T
where PVA is the projected value added, PCOST is projected cost, and the VC maximizes using effort (represented in the equation as e) and total investment duration (represented in the equation as T). Cumming and MacIntosh used e to represent anything a VC can do to add value to a PC. They interpret PCOST as all of the direct and overhead costs associated with creating value and the opportunity cost of using the capital elsewhere. Cumming and MacIntosh account for all expected future costs and efforts and include a time interval since VCs periodically reassess the value of continuing their effort level. In their model, x(T) and z(T) are vectors that represent other factors that may randomly generate shifts in the PVA and PC functions.
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In this paper, e is used to represent the value of managerial involvement and PCOST is represented by the cost of this involvement including both the direct and overhead costs associated with the involvement (i.e., phone calls, business trips, etc.) and the opportunity cost associated with spending the time on this versus due diligence on other investment. Translating the model of Cumming and MacIntosh (2001) into an empirically viable equation, this paper looks at how different levels of involvement affect both the financial performance and outcome of the entrepreneurship.
3.1. Entrepreneurial performance I examine the relationship between the level of involvement a VC may choose with their PCs and PC performance in the following robust OLS regression: Performancejt = α + β0 Involvementit + β1Xi,j,t + β2Costi,t + β3Zk,t+ε,
(2)
where Performance is the financial performance level for PCj at time t as measured by the level of sales.3 Standard errors are robust due to clustering around the PC. Involvementit (equivalent to e in equation 1) is a proxy for the level of involvement VCi establishes with its PCs at time t. Proxies include the average VC investment per round, the percent of the VC portfolio comprised by the PC investment, geographic proximity (i.e., distance from VC), and the average amount of time between rounds. Proxies are regressed separately. Xi,j,t is a vector of investment characteristics such as Portfolio size/mgr, which controls for the number of investments per VC manager, IT industry, an indicator variable that describes whether the PC is in the IT industry or not, Early stage, an indicator variable that describes whether the PC is in the early stage or not, Years since last investment, which controls for the time since the VC’s last investment in the PC, and Corporate VC, which is an indicator variable describing whether the VC is a corporate VC or not. Costi is a proxy for the cost of involvement with a specific PC; the IPO value at time t (i.e., the value of foregone investments that go public) derives this proxy. Zk,t is a vector of economic variables including Market return, GDP growth, and Domestic credit in country k at time t. Market return is the annual return
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on the local market index. GDP growth is the percentage growth in gross domestic capital. Domestic credit is the amount of credit extended to the private sector scaled by the value of GDP. If there is a significant impact of VC involvement on PC performance, we would expect to see a statistically significant coefficient on Involvement, β0. To discern whether there exists an optimal level of involvement for the VC with regard to the specific proxies for VC involvement (i.e., whether the relationship is nonlinear), I add to equation (2) a quadratic term of the involvement proxy. This enables me to examine whether there is a possible maximum or minimum for each proxy. The regression is as follows: Performancejt = α + δ0 Involvementit + δ1 Involvement2it + δ2Xi,j.t + δ3Costi,t + δ4Zk,t+ε
(3)
All variables are defined as they were in equation (2). If there is indeed an optimal level (i.e., nonlinearity), we would expect to see statistical significance in the coefficient on the squared involvement variable, δ1, with a sign that is opposite to that of involvement, δ0. 3.2. Entrepreneurial outcome To examine whether VC involvement has any impact on the current status of the PC, I use a multinomial logit model to regress the following: Pr(CurrentStatusj) = Ψ(α + γ0 Involvementit+ γ2Xi,j,t + γ3Costi,t + γ4 Zk,t)
(4)
where CurrentStatus is the current standing of the entrepreneurial (PC) company (i.e., defunct, private, subsidiary, or public).4 Ψ is the cumulative logistic probability distribution function. All other variables are as they were previously defined. Once again a squared involvement term is added to check for the existence of an optimal level of involvement; this term is included in some but not all of the specifications.
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4. Data Data is collected from Thomson Reuters VentureXpert, The data is collected for all portfolio companies in the dataset. The time frame included is 01/01/1962 through 03/31/2010.5 4.1. VC and company-specific information Specifics about the VC/PC investment relationship are also obtained. They are: 1) Portfolio size per manager, 2) Years since last investment, 3) IT industry, 4) Early stage, and 5) Corporate VC. Portfolio size per manager accounts for the number of companies that each VC manager must oversee, which has a direct implication on the costs of involvement. Years since last investment is included to control for the length of time since the VC last invested. It is more likely that a firm would have exited the venture capital cycle if the last investment occurred less recently.6 Dummy variables for the riskiest sectors of the industry and stage diversification dimensions are included to control for the level of risk undertaken by the VC (IT industry and Early Stage), which could impact both PC performance and outcome. Gompers and Lerner (1999a) explain that investment at certain stages entails more risk, and Knill (2009) explains that investment in these stages, accordingly, offers more opportunity (for diversification) than others. Along those same lines, Manigart et al. (2002) find that VCs require a higher return for early stage investments. Similarly, there are some industries that are riskier than others. Corporate VC is a dummy variable that indicates whether a VC is corporate or not. It is included to control for VC fund characteristics and follows Cumming and Johan (2009).7 4.2. Market conditions Macroeconomic variables such as Market return, GDP growth, and Domestic credit are included to control for the general state of the VC industry as well as the market in general. Market return is included to control for public market conditions. GDP growth is included to control for the general state of the economy. These variables will likely pick up the counter-cyclical nature of the venture capital
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industry (Groshen and Potter, 2003). Domestic credit is included to control for bank funding, which is a considerable factor in entrepreneurial fundraising. 4.3. VC involvement proxies Venture capitalists can maintain involvement over their investment by disbursing investment monies in smaller quantities across time (i.e., instead of in one lump sum).8 Indeed, Sahlman (1990) finds that this is a very important means of establishing continual monitoring of the PCs.9 As such, the average VC round investment may be an important proxy for involvement with entrepreneurs. The percent of the VC’s portfolio is a measure of how much the VC is relying on this PC to perform well. At the extreme – let’s say 100% – the VC would likely be motivated to be quite involved; he would have the incentive to manage in a more co-managerial manner. At the other extreme, a small percent – let’s say 5% – the VC would have an incentive to be involved with the PC at a reduced level. Given resource constraints and the more diversified approach of the VC in this scenario, a more laisse faire managerial role would be taken. Given the differences in these logical approaches, this serves as an important proxy for involvement in the paper.10 To address the fact that the level of management involvement may be affected by the VC’s ability to travel to/from the PC, I include distance as a measure of involvement The inclusion of this proxy follows the findings of Lerner (1995), who finds that the oversight of distant businesses is more costly than that of local firms. Lastly, I include a proxy for the average term between rounds. Sahlman (1990) establishes this as a means of control over the portfolio companies in which VC’s invest. Gompers and Lerner (1999b, p. 131) state that “Major reviews of progress and extensive due diligence are confined to the time of refinancing… [they] are designed to limit opportunistic behavior by entrepreneurs between evaluations”. Extending this time between financings (and the reviews/due diligence between the rounds), in effect
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delaying (or even thwarting) the receipt of financing, could be perceived as a means of involvement. This is thus included as a proxy for involvement. Table 1 includes summary statistics for all of the variables used in the analyses. PCs have, on average, $10.55 million in sales. The average VC investment per round is $9.41 million. A single portfolio company investment accounts for 3.19% of a VC’s portfolio. VCs do not limit themselves to investments in the same city, or even state, as is evidenced from that average distance between the VC and PC of 1,570 kms (approximately 975 miles). Given the state of technology and the ease of commuting from location to location, this is not necessarily inconsistent with VCs’ general reputation of being involved. The average length of a time between rounds is 1.8 years. Approximately 7% of the VCs in our sample are corporate VCs. The average VC is fairly conservative, as evidence by the average VC not investing in either IT or early stage investments (i.e., 46% in IT industry and only 6% in early stage PCs). [Insert Table 1 here]
4.4. Correlation Table 2 contains the pairwise correlations of the variables used in each analysis separately. There are no concerns with regard to multicollinearity. All pairwise correlations are below levels that would suggest resulting empirical difficulties.
5. Results 5.1. Establishing nonlinearity To establish the exact relationship between the proxy for PC performance – Sales – and the proxies for VC involvement, following Cohen and Cohen (1983), a two-way graph that allows for quadratic relationships (i.e., nonlinear) is created. In doing so, it is obvious that all proxies for VC
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involvement possess a curvilinear, or nonlinear, relationship with Sales. Figure 1 displays the graphs for each involvement proxy. All of the proxies show concave relationships, though the curves vary in their slope. Distance, in particular, is fairly flat. This makes sense given the average distance between the VC and PC and the ease with which VCs can communicate with or visit the PC.
[Insert Figure 1]
5.2. Firm performance Since the results of the scatter/quadratic line plot suggest that there is in fact a nonlinear relationship between VC involvement and PC performance, regression analysis using a quadratic term may be used to further analyze the relationship. Table 2 displays the results from the base specification. The first thing to note is that the Pseudo R-squared increases with the inclusion of the squared term in most specifications, suggesting that its inclusion helps to explain changes in the dependent variable; i.e., the nonlinear relationship. Second, the joint statistical significance of the linear and quadratic term suggests that the relationship is, in fact, curvilinear (i.e., nonlinear). Specifically, the relationship is concave (i.e., where involvement>0 and involvement2
