VENTURE CAPITAL, APRIL–SEPTEMBER
2004, VOL. 6, NO. 2/3, 105 – 123
Selection and support strategies in venture capital financing: high-tech or low-tech, hands-off or hands-on? CAROLA JUNGWIRTH and PETRA MOOG (Final version accepted 16 March 2004)
The advantage of specialization in venture capital financing makes the presence of generalist investors perplexing. In order to understand their function, the authors investigate the knowledge resource bases of both generalist and specialist venture capital funds, the types of enterprises they select and their corresponding support strategies. Arguing that differences in strategy can be attributed to differences in knowledge, the authors hypothesize that specialists select high-tech projects; generalists, on the other hand, select low-tech projects. Specialists support ‘hands-off’; generalist support ‘hands-on’. These hypotheses are tested with a dataset of 103 venture capitalists in Austria, Germany and Switzerland. The empirical results from OLS-regressions show a close relationship between knowledge and selection as well as support strategies. These results invite further research on differences in venture capitalists’ strategies as they relate to differences in knowledge.
Keywords: venture capital financing; knowledge; specialist; generalist; portfolio selection; hands-off-support; hands-on-support
Introduction Venture capitalists are intermediaries with strong advantages in financing risky investments (Ruhnka and Young 1991, Amit et al. 1998). Venture capitalists, specializing in one industry, control risk at lower cost compared to other players in the market (e.g. banking houses), because they accumulate specific know-how, experience and access to networks and information. Norton and Tenenbaum (1993) provide evidence that controlling portfolio risk through specialization in certain industries is a superior strategy for venture capitalists. However, practical experience shows that the market encompasses not only ‘specialists’ but also nonspecialists, or generalists. The existence of such generalists is puzzling Dr Carola Jungwirth and Dr Petra Moog are Assistant-Professors at the Institute for Strategy and Business Economics, University of Zurich, Plattenstrasse 14, CH-8032 Zurich. Their research interests lie in combining institutional and personnel economic models with empirical data mainly in the area of entrepreneurship research. Contact details: University of Zurich, Department of Economics, University of Zurich, isu, Plattenstr. 14, CH-8032 Zu¨rich; e-mail:
[email protected],
[email protected] Venture Capital ISSN 1369-1066 print/ISSN 1464-5343 online # 2004 Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080/1369106042000224703
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because it is unclear what advantages they have in the market. Explaining their function is the contribution that the authors of this study intend to make. The literature on generalists is not extensive. Nevertheless, three strands of literature are helpful in formulating the research focus of this paper more precisely. The first strand points out the advantages of specialization for venture capitalists. Sahlman (1990) and Amit et al. (1998) refer to the monitoring advantages of specialization that allow the venture capitalist to very strictly govern the relationship to the portfolio enterprise. Norton and Tenenbaum (1993) compare specialization vs. diversification as instruments for risk handling. They argue that venture capitalists need costly information in either case. Therefore, they must build up a costly stock of information and knowledge that can be justified by economies of scale only if the focus is on one industry. The results of De Clercq et al. (1999) indicate that venture capitalists learn from experience that specialization pays off. The longer venture capitalists stay in the market, the more they tend to specialize in industries. However, these studies do not consider the issue of non-specialists within the population of venture capitalists. Another strand of literature deals with the heterogeneity of venture capitalists. Elango et al. (1995) explicitly try to find out in what ways venture capitalists differ. They differentiate between the stage of investment, support intensity, firm structure and geographical differences, and relate these factors to special features within the relationship between venture capitalist and portfolio enterprise. Cumming (2001) investigates the determinants of portfolio size. Manigart et al. (2002) study differences in expected profits. Both authors refer to the type of venture capitalist (dependent or independent), to syndication behaviour (lead investor or not) and to the type of portfolio enterprise venture capitalists attend (high-tech or low-tech financing stage). In some earlier articles, Bygrave (1987, 1988) analysed the syndication behaviour of venture capitalists who focus on high-tech or low-tech investments. However, none of these papers addresses differences in knowledge as a central theme. A third strand of literature deals with selection and support strategies that venture capitalists pursue. Murray and Lott (1995) and Lockett et al. (2002) investigate the British venture capitalists’ bias against high-tech investments that are perceived as more risky than the low-tech alternatives. Fiet (1995a, 1995b) analyses what type of information venture capitalists use when they select portfolio enterprises. He shows that the more professional a venture capitalist is, the more he or she relies on professional informants and vice versa. MacMillan et al. (1988) investigate the venture capitalists’ involvement in their investments. They identify three distinct levels of involvement (laissez-faire, moderate and close tracker) but cannot identify differences in characteristics of venture capitalists who chose the different involvement levels. Moreover, no significant differences in the performance of the portfolio enterprises were found. Hellmann (2000) and Hellmann and Puri (2002) show that venture capitalists also serve as coaches for their portfolio enterprises, but the intensity and the way they support their portfolio enterprises differ from venture capitalist to venture capitalist. A central finding from Barney et al. (1996) is that not only do
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venture capitalists differ in their support intensity but also that portfolio enterprises differ regarding the amount of support they wish to receive. However, none of these authors offers an extensive explanation as to why venture capitalists differ in their support and selection strategies. This study attempts to identify differences in selection and support strategies of different types of venture capitalists, namely specialists and generalists. The starting point is the insight that the strategies an individual can choose are limited by the knowledge he or she owns. Therefore, specialists are defined here as venture capitalists owning industry-specific knowledge while generalists are defined as venture capitalists who lack such knowledge. This definition differs from that of other authors (e.g. Norton and Tenenbaum 1993, Murray and Lott 1995, Lockett et al. 2002), who refer to industry focus. The second section of the paper develops the framework of the study. In order to analyse the connection between knowledge and strategy, agency and knowledge transfer costs are introduced. These costs determine the strategies both the generalists and the specialists can choose. Extreme or ideal types of venture capitalists, portfolio enterprises and support are defined in order to clarify the expected relationships between knowledge and strategy. However, the variables used to test the hypotheses are ratios, because the data yields no ideal types but rather tendencies toward one type or the other. In the third section two hypotheses are derived. It is expected that generalists will select low-tech enterprises, while specialists will select high-tech enterprises. Furthermore, it is expected that generalists will support their portfolio enterprises more intensively (hands-on). Because a trade-off exists between the time venture capitalists can support or select (Gifford 1997, 1998), specialists will allocate their time more on careful selection and will be hands-off rather than support intensively. Data and methodology are presented in section four. The data were collected from venture capitalists associated in the German Venture Capital Association eV (BVK), the Swiss Private Equity and Corporate Finance Association (SECA) and the Austrian Private Equity and Venture Capital Organization (AVCO) in February, March and May 2003. Hypotheses are tested via OLS regressions. An empirical analysis yielded evidence that the hypotheses are sustainable. Framework In this section, a framework based on theoretical and empirical findings in the literature is developed. The framework allows comparison of generalists and specialists concerning their portfolio decisions (selection of portfolio enterprises and provision of support). Information cost incurred attending a portfolio enterprise
Jensen and Meckling (1995) analyse the effect of different kinds of knowledge—namely general and specific—on the allocation of decision
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rights within an organizational structure. They characterize specific knowledge as costly to transfer among agents, and general knowledge as relatively inexpensive to transfer. Defining general knowledge, Jensen and Meckling (1995: 7 – 8) refer to prices or management ratios. Defining specific knowledge, they refer to idiosyncratic and scientific knowledge, as well as to knowledge produced by assembling and analysing particular circumstances (through time and/or across circumstances such as location, income, education and age).1 In the framework of the present study, specific knowledge is defined as special know-how concerning a certain industry, where the venture capitalist enjoyed an education and collected practical experience.2 General knowledge is defined as the standard venture capitalists’ basic know-how in founding and financing new enterprises. Thus, general knowledge is simply common knowledge in the market where new enterprises are financed.3 The Jensen and Meckling analysis focuses on a conflict that occurs whenever the principal must decide whether to delegate a decision directly to an agent. If the principal delegates a decision to an agent, agency costs must be considered because different interests between principals and agents make it probable that the agent will pursue self interests on behalf of the principal. To protect against the agent’s moral hazard, the principal has to monitor the agent and bear the damages caused by different interests. Agency costs are assumed to be high if the agent possesses the relevant specific knowledge to decide on a certain issue but the principal does not. From the venture capitalist’s point of view, he or she has invested capital in the portfolio enterprise but cannot be sure that the enterprise’s management works as hard as possible to maximize returns on the investment. If a task is fulfilled by the principal rather than delegated, the principal must consider knowledge transfer costs. They arise because the principal has to acquire information about time and place (idiosyncratic knowledge), and specific skills, know-how and experience, in order to be able to decide correctly. We define these knowledge transfer costs explicitly as the costs of deciding to perform a task oneself. The costs are low if the principal possesses the relevant specific knowledge or if ‘only’ general knowledge is needed to decide. In any case, the principal has to choose between making decisions and bearing knowledge transfer costs in acquiring the relevant knowledge or delegating decisions to an agent and bearing the agency costs. In reality, a mix exists between agency costs and knowledge transfer costs. Given the information asymmetries that exist between venture capitalists and portfolio enterprises, it is safe to say that the venture capitalist’s knowledge directly influences the costs of selecting a certain enterprise as well as the costs of providing a certain amount of support. Defining ideal types
The use of ideal types allows a selective search for informative correlations between types of venture capitalists and selected portfolio enterprises and the support provided. First, we differentiate between two types of venture capitalists on the basis of their knowledge. One type is the ‘generalist’ who
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owns general knowledge concerning the process of founding and financing a new enterprise. This know-how is constitutive of the generalist’s role as an intermediary between outside investors and portfolio enterprises. However, generalists do not own deeper insights into a particular industry. Trying to succeed in a specific industry would incur nearly prohibitive knowledge transfer and agency costs, because the generalist has neither the knowledge to decide alone, nor the knowledge to assess how an agent performs. The other type of venture capitalist is the ‘specialist’ who also owns founding and financing skills, but in addition has knowledge concerning special industries, processes and products, such as biotechnology. Compared to other competitors, his or her specialization lowers knowledge transfer and agency costs. As mentioned earlier, this definition of generalists and specialists differs from those of other authors, some of which refer to the numbers of industries (Norton and Tenebaum 1993) or the kind of enterprises that a venture capitalist invests in (Murray and Lott 1995, Lockett et al. 2002). Our definition expects that specialization in one or two industries correlates with the knowledge resource base of a venture capitalist. It also expects that selection of high-tech or low-tech investments results from endowment with knowledge. Second, we differentiate between two types of portfolio enterprises-tobe—namely ‘high-tech’ and ‘low-tech’—that can be selected into the venture capitalist’s portfolio. In a similar vein to other categorizations of this type (Baruch 1997, Murray and Marriott 1998, Lockett et al. 2002), the assumed level of education and experience in the firm’s workforce, the expenditure on research and development (R&D) and the area of the firm’s activity work are used as differentiation criteria. Low-techs are founding projects that do not need substantial R&D activities. They are not too complex but based on a clever idea like a coffee-to-go-chainconcept. Low-techs are characterized by the low knowledge transfer costs incurred to understand them. High-techs have substantial R&D-activities, a high proportion of employees with university degrees and the ‘area of activity is advanced technology, on the cutting edge of technology developments’ (Baruch 1997: 187). All that makes the production, the product itself, as well as its marketability hard to assess. Years of scientific education and specific experience are needed to become familiar with them. Business ideas in bio- or gene-technology are typical examples of ‘high-techs’. Third, we differentiate between two ways of support that a venture capitalist can deliver: hands-on and the hands-off support. ‘Hands-on’ means that the venture capitalist is deeply involved in the business process of the portfolio enterprise and supports it intensively. MacMillan et al. (1988: 27) identified empirically a so-called close tracker involvement ‘in which venture capitalists exhibited more involvement than the entrepreneur in a majority of the identified activities.’ The venture capitalist provides help on an operative level and makes strategically important decisions and therefore he or she must acquire the relevant knowledge. Extreme hands-on support could be defined as deciding everything for oneself, so in pure form, hands-on support would
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incur only knowledge transfer cost. Realistically, however, hands-on support means that the venture capitalist decides a lot in the portfolio enterprise, but agency problems still exist. Hands-on support could be a general solution to mitigate agency problems but it is expensive in terms of venture capitalist management resources. Gifford (1997, 1998) points out the trade off between allocating attention on improving current projects and on evaluating new projects for possible funding. She shows that reducing support in order to have more time to select new venture can be efficient from a venture capitalist’s point of view. That explains that despite agency problems a ‘hands-off’ support is also common in practice. If a venture capitalist chooses the ‘hands-off’ support, time and energy are focused on the selection of enterprises that are controlled afterwards on the basis of aggregated data.4 A venture capitalist does so because fishing for the best business ideas and management teams out of the pool of enterprises-to-be increases the probability of success. If the ideas as well as the founding teams are really good, support is no longer the critical factor. Barney et al. (1996) as well as Sweeting and Wong (1997) show that portfolio enterprises differ in their need for support. Therefore, it is part of the hands-off strategy to select portfolio enterprises that are not reliant on the venture capitalist management resources.5 It is enough to monitor through hands-off methods to avoid damages caused by moral hazard. Overall, in embarking on a hands-off strategy, the venture capitalist minimizes knowledge transfer costs but accepts higher agency costs. In a pure form, ‘hands-off’ entails only agency costs. Hypotheses Considering which kind of venture capitalist will invest in which kind of portfolio enterprise, we expect that generalists will match with low-techs and specialists will match with high-techs. The generalist’s rationale is that because relevant knowledge is lacking, he or she is neither able to decide among high-techs nor assess the business idea as well as the high-tech’s management. Therefore, supporting a high-tech investment in a hands-on way causes prohibitive knowledge transfer costs, and to support it in a hands-off way causes prohibitive agency costs. The specialist, however, would give away important competitive advantages in selecting a low-tech investment because it does not exploit his or her additional specific skills. First and most importantly, expected profits for low-tech investments are lower than for high-tech investments.6 Second, there are many more serious competitors on the market for low-tech investments than for high-tech investments. Having access to alternative financial sources increases the contracting power of the portfolio enterprise. Consequently, the venture capitalist has to accept a sharing rule that is inferior compared to the market for ‘high-tech’ investments. The next issue is the kind of support intensity used by each of the two sets: specialists and high-techs, and generalists and low-techs. At first
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glance, the specialist seems to have advantages in hands-on support because owning the relevant knowledge allows him to minimize agency costs without taking into account high knowledge transfer costs when supporting the portfolio enterprise in a hands-on way. However, there are two serious problems in accepting this argument. First, having special knowledge about an industry does not necessarily mean that every business idea in this field is easily understandable for a specialist. Though the costs might be lower compared to costs for a generalist or any other investor, even the specialist has to bear considerable additional knowledge transfer costs to make decisions within the high-tech portfolio enterprise.7 Second, the founder acting as manager of the portfolio enterprise has a crucial interest in maintaining information asymmetry. Otherwise, another manager could replace them before they can extract full returns from their idea.8 Finally, providing hands-on support seems too expensive because high knowledge transfer and agency costs are expected to arise. A specialist who pursues the hands-off strategy, however, will be effective. The specialist has information advantages compared to other competitors (e.g. private investors or banks). Overall, specific knowledge allows a superior assessment of whether an innovative project will be promising and successful or not and enables the project to be monitored at lower agency costs. Therefore, we assume that hands-off is the superior strategy for a specialist. One could argue that a generalist could realize profits in ‘specializing’ in the selection and control process. However, the market situation is different for the generalist. Supporting a good business idea with money is attractive not only for venture capitalists but also for commercial banks and other private investors. In the case of ‘easy to understand’ business ideas, the comparative advantages of venture capitalists are thus eroded, because all other players in the market can provide the same service (provision of capital) at the same cost. However, pursuing a hands-on strategy in this case is equivalent to providing an additional service, namely management consulting. Compared to commercial banks and other private investors, the generalist has advantages because he or she can provide professional support in establishing an enterprise as a complement to an innovative business idea. By owning equity, the generalist can offer consulting activities at lower costs than a management consultancy, because profits are the result of an increase in value of the enterprise. Finally, because general knowledge suffices to assess a low-tech the generalist can embark on a hands-on strategy to lower knowledge transfer costs, which allows minimizing agency costs at the same time. That is the generalist’s core advantage compared to the other competitors in the market. Based on these considerations, we derive the following hypotheses: H1: Specialists are more likely select high-techs; generalists are more likely to select low-techs. H2: Specialists are more likely to choose the hands-off strategy; generalists are more likely to choose the hands-on strategy.
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Database and methodology The data
To test these hypotheses we generated a dataset based on addresses from the German Venture Capital Association (BVK), the Austrian Private Equity and Venture Capital Organization (AVCO) and the Swiss Private Equity and Corporate Finance Association (SECA). All three organizations have full and associate members. Focusing the survey on the data of the full members provides a population of venture capitalists in Austria, Germany and Switzerland comprising 276 companies. However, as we called the venture capitalists before sending out the questionnaire we discovered that some venture capitalists refused to participate or no longer existed. Therefore, we sent the questionnaire to an adjusted gross sample of 217 venture companies. This generated 103 well-answered cases, a response rate of 47.5% (Table 1).9 The returns are representative of the population of venture capitalists in Austria, Germany and Switzerland, as shown by a comparison of our data with the data of BVK (http://www.bvk-ev.de/index.php/aid/50, 9 April 2003). We compared average data concerning the portfolio volume and the number of portfolio investments, the industry or investment stage orientation, and the geographical activities.10 We developed a standardized questionnaire collecting extensive data on the venture capitalists’ firm structure and investment focus, their endowment with knowledge, and their behaviour toward the portfolio enterprises as well as the type of enterprises they attend. (1) Firm structure and investment focus: we asked for data that reflects the size of the company (measured in numbers of employees, number of portfolio companies and the volume of the portfolio), the spatial focus of the company (regional, nationwide or international) and the governance structure (being an independent business or not). Concerning the investment focus of the venture capitalist, we asked to what degree the venture capitalist was specialized in an industry (measured by the number of industries a venture capitalist is investing in, ranging from one industry - being more of a specialist - up to seven and more industries and thus being more a generalist or all-rounder, as the venture capitalists called themselves).12 We required these data to control for differences across venture capitalists. (2) Knowledge to which the venture capital firm has access: the survey comprised information about education (the kinds of studies they had pursued and the kinds of university degrees they had obtained) and experiences of the employees and founders of the venture capital companies (industries in which they had obtained working experience). (3) Behaviour of venture capitalists toward their portfolio enterprises and the types of enterprises they finance: we asked the venture capitalists how they work with their portfolio companies (hands-off or hands-
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on) and in which fields they provide support to their companies (management, marketing, personnel, etc.). Another question referred to the time that venture capitalists spent either in selecting new businesses or in supporting their portfolio enterprises (measured in percentage of their overall working time spent in the one or the other activity). Based on this information, we differentiated between a hands-on and a hands-off strategy. In order to understand what kinds of portfolio enterprises they financed we gave some examples for high-tech and low-tech businesses and asked venture capitalists to categorize their own investments as high-tech or low-tech. They did this by reporting how many and what sort of high-tech and low tech investments they had in their portfolio. Methodology
In order to test our assumption that venture capitalists’ knowledge is associated with their degree of specialization in one or two industries we looked for a correlation.11 We want to deliver evidence that a venture capitalist who owns a high amount of specific knowledge is likely to focus in one or two industries. As our data show, the two indicators correlate significantly on a medium scale with a correlation coefficient of 7 0.582 and p = 0.000. The negative correlation shows that the more specific knowledge a venture capitalist obtains, the less he will spread investments across different industries and the more he will focus on one or two industries. Our first hypothesis tests the relationship between the degree of specific knowledge that a venture capitalist possesses and the investments made in high-tech or low-tech ideas. f ðinvestÞ ¼ a þ bðknowledgeÞ þ e If our argumentation holds, specialists have strong reasons to select more high-tech projects and generalists to select more low-tech projects. Therefore, we test the impact of the specificity of knowledge on investments in high-tech ideas. Based on these results, we also draw conclusions regarding the relationship of general knowledge and investments in low-tech ideas. Our data on investments are employed in an ordinary least square analysis (OLS regression) because we expect the method to deliver a robust estimate. Our second hypothesis explores the link between the sort of knowledge a venture capitalist holds and his or her chosen support strategy, i.e. handson or hands-off. f ðsupportÞ ¼ a þ bðknowledgeÞ þ e Here, we will present two regression results. That allows us to back up the effect of being a specialist or a generalist in the support provided in two ways. First, the question arises as to how the venture capitalist’s knowledge
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effects directly his or her hands-off or hands-on behaviour. Second, how much time does a venture capitalist allocate on the selection of new ventures and on support of current ventures? When analysing the literature concerning hands-off or hands-on support, we found this different allocation of time was an important distinction between the two support strategies. Of course, venture capitalists never only support or only select. Therefore, this behaviour is measured as a proportion of selection and support time. If this ratio is greater than 1, the venture capitalist supports by hands-off rather than hands-on behaviour. The variable is metric. To test the second hypothesis that way, all control variables c.p. two other OLS regressions are applied. Operationalization
The next step was to operationalize our dependent, independent and control variables. Dependent variables: Our first hypothesis deals with the selection of hightech or low-tech projects. We evaluate the selection strategy by focusing on the percentage of all investments in high-tech investments measured by the variable HIGHTECH. To test the second hypothesis concerning the relationship between knowledge and support, we created two different dependent variables. OFFON identifies a hands-off or hands-on strategy. We created this variable as an index-variable. We asked venture capitalists to score the strength of influence on their portfolio enterprises. We asked them to do this for three different financing stages. By constructing the index-variable OFFON, we merged the dimensions to one variable ranging from 1 (hands-off) up 15 (hands-on) (Diekmann 2001). As mentioned before, we assume that a trade-off exists between time for selection and time for support. If this is true, an intensive selection process is an important difference between hands-off and hands-on support. Therefore, we created a relative variable that measures the ratio of time spent in selecting and time spent in supporting portfolio enterprises (SELECNURT). Independent variable: We refer to the venture capitalist’s knowledge as the key-explanatory variable. Venture capitalists’ knowledge is categorized on the basis of the information obtained on the education and experience of the firm’s employees and founders. If they have only a degree and/or experience in business administration or law, they are categorized as having ‘general knowledge’. If they have degrees or experience in science or technology, they have ‘specific knowledge’. In fact, most of the companies have a knowledge mix which is specified by a percentage grading of specific and general knowledge. For instance, having two ‘master of business administration’ and one lawyer and one technical scientist within the team yielded 75% in general knowledge and 25% in specific knowledge. The next step was to build a ratio of these portions (specific knowledge divided by general knowledge) to create the variable HCSPECGEN which measures
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the ratio of specialized and general knowledge found in the venture capital firm. This variable ranges from 0.01 for a very low portion of specific knowledge in a company up to 100 if a company is working only with specific knowledge. The higher the ratio then the higher is the share of specific knowledge within the venture capital company. As soon as the numerator (percentage of specific knowledge) becomes larger than the denominator (percentage of general knowledge) we categorized a venture company as specialist. 41.8% of our venture capitalists are specialists in this knowledge-based sense, while 58.2% are below the line and are therefore defined as generalists. This is our main independent variable. We assume that it determines the strategic position of a certain venture capitalist within the venture capital market, because it is the determining factor to select a high-tech or low-tech project and to support it in either a hands-off or hands-on way after the investment. Control variables: In the empirical analysis, we need to control for differences across the venture capital companies and their effects on the dependent variables that are not emphasized in our theoretical approach (Wooldridge 2003). Therefore, in order to control for further influences resulting from other results of former studies or facts, several control variables are included in the empirical test. As Kannianen and Keuschnigg (2003) and Cumming (2001) showed, a trade-off exists between portfolio size and support intensity. Therefore, we control at first the influence of the portfolio size, operationalized by the variable PORTSIZE. It measures the number of investments a venture capitalist has in their portfolio, which are 12 on average. Another coherence can be assumed between financing-stage and strategies. We know, for example, from Lockett et al. (2002) that earlystage investors tend to choose high-techs and late-stage investors tend to choose low-techs. Elango et al. (1995) assume that early-stage venture capitalists support their portfolio enterprises more intensively, but they cannot support this assumption with data. The variable (FINSTAG) differentiates early-stage investors (negative values) and late-stage investors (positive values) and measures the potential influence of investment stage on selection and support strategy. On the basis of previous research we expect an early stage investor to have a higher ratio of high-tech investments and to support them rather hands-off. Further, we use the dummy variable VCSTATUS to insert information about the governance status of venture capitalists into our model. Being independent or dependent (like a corporate venture capitalist) could cause different strategic choices as Cumming (2001) and Manigart et al. (2002) have already pointed out. We expect independent venture capitalists to be more profit-orientated. Corporate venture capitalists or institutions allocating public venture capital are backed by corporate or public capital. Therefore, they are not forced to provide as high returns on investment as independent venture capitalists. We expect an independent venture capitalist to have a higher ratio of high-tech investments and to support hands-off. We also control for the co-operation behaviour of venture capitalists with the dummy variable VCCOOP. A total of 87% of venture capitalists in our
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sample co-operate and most of them syndicate. Unfortunately, we did not ask what proportion of their investments actually is syndicated. A study of Deloitte & Touche (2002: 16) concerning the German venture capital market reports that 88% have syndicated in the past and 77% were syndicating at the time of the survey. This result is in line with our data. The proportion of investments that actually are syndicated is 37% of investment volume and a 30% share of the overall portfolio. Even these proportions could fit our data. Trying to estimate how syndication influences selection and support strategies overall we assume that syndication is diluting our results. It makes specific knowledge available for generalists and so the link between knowledge and strategy becomes weaker (Jungwirth and Moog 2004). We controlled for co-operation because of Bygrave’s (1987, 1988) results that showed that venture capitalists who are specialized in high-techs syndicate more intensively than the others. However, Lockett and Wright (2001) found that syndication behaviour does not differ significantly between venture capitalists. Therefore, investing in high-techs and choosing a hands-off strategy could correspond with co-operating. A follow-up study is dedicated to this complex and ambiguous syndication problem (Jungwirth and Moog 2004). Finally, results in the literature (e.g. Lerner 1995) suggest that the distance to the portfolio enterprise is an important determinant of support intensity. The smaller the distance is, the more hands-on the support can be expected. Therefore, we controlled for geographical aspects. The regression encompasses whether a venture capitalist invests more regionally (dummy variable REGION) or nationwide (dummy variable NATION). In cases of regional or nation-wide investment, international investments stand as the reference category for these dummies. Table 2 reports the descriptive statistics as well as the meaning and measurement for the dependent, the independent and the control variables. Empirical results Descriptive results
Descriptive results (t-tests) show that almost 18% of the specialists put their investments solely in high-tech projects compared with only 2% by generalists. Specialists also tend to support rather hands-off than hands-on. Specialists invest more time (as a percentage of all working time) in selecting new investments; this is significantly more than double the working time of generalists. However, the data also reveal that support time does not differ between specialists and generalists. Therefore, we analysed the question ‘What sort of support do you provide to your portfolio enterprises?’ via a factor analysis. Correlations show that specialists are significantly more active in strategic questions but generalists more in daily business. This affirms statements of practitioners that hands-off support is nearly as time-consuming as hands-on support.13 Results from testing the first hypothesis are presented in table 3, regression 1. Results presenting the relationships from testing the second
SELECTION & SUPPORT STRATEGIES IN VC FINANCING
Table 1.
Dataset and returns. Sent outs
Germany Austria Switzerland S
117
Returns
Return rate (%)
79 13 11 103
47.30 65.00 36.70 47.47
167 20 30 217
Own data (2004).
Table 2. Variable
Definition of variables and descriptive statistics (n = 103). Mean Meaning and measurement
Dependent variables HIGHTECH OFFON
0.41 5.5
SELECNURT
0.84
Independent variable HCSPECGEN
0.89
Ratio of specific to general knowledge a venture capitalist obtains, metric
Control variables PORTSIZE FINSTAG
12.0 0.27
VCSTATUS VCCOOP REGION NATION INTERNATION
0.51 0.87 0.32 0.33 0.35
Number of investments in portfolio companies; metric Index-variable measuring the degree of working on an early or late stage; negative numbers (early stage), positive numbers (late stage); quasi-metric VC is dependent or independent (bivariate 0/1) Cooperation with other VC (bivariate 0/1) VC acting on a regional focus (bivariate 0/1) VC acting on a national focus (bivariate 0/1) VC acting on an international focus, reference category (bivariate 0/1)
Investments in high-tech ideas, metric Index-variable measuring how strong the influence on portfolio companies is; 0 (hands-off) – 15 (hands-on); quasi-metric Ratio of time spent in selecting and in supporting portfolio enterprises, metric
Own data (2004).
Table 3. Regression 1: high-techs or low-techs? Variables Beta
t-value
HCSPECGEN PORTSIZE FINSTAG VCSTATUS VCCOOP REGION NATION
2.050 0.637 7 1.948 0.884 0.119 7 2.080 7 1.238
0.260* 0.011 7 8.571* 6.323 1.308 7 18.37* 7 10.45
Own data (2003). F = 2.446*; R2 = 17.3; n = 89. ***significant on a 0% level; **significant on a 1% level; *significant on a 5% level, + significant on a 10% level.
hypothesis are found in tables 4 and 5, regression 2 and 3. The first column always provides the results concerning the beta-coefficients, the second column the t-values. Our findings are broadly consistent if we look at the three dependent variables. Overall, results show that venture capitalists having a high level
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Table 4. Regression 2: hands-off or hands-on? Variables Beta HCSPECGEN PORTSIZE FINSTAG VCSTATUS VCCOOP REGION NATION
7 0.018 7 0.002 + 7 0.276 0.323 0.925 7 1.220 + 7 0.384 +
t-value 7 1.853 7 1.728 7 0.836 0.590 1.083 7 1.815 7 0.584
Own data (2003). F = 1.964 + ; R2 = 13.8; n = 93. ***significant on a 0% level; **significant on a 1% level; *significant on a 5% level, + significant on a 10% level.
Table 5. Regression 3: selection time or support time? Variables Beta
t-value
HCSPECGEN PORTSIZE FINSTAG VCSTATUS VCCOOP REGION NATION
2.088 0.392 1.343 0.141 0.162 7 1.825 7 1.389
0.009* 0.000 0.196 0.034 0.061 7 0.542+ 7 0.404
Own data (2003). F = 1.662 + ; R2 = 10.0; n = 93. ***significant on a 0% level; **significant on a 1% level; *significant on a 5% level, + significant on a 10% level.
of specific knowledge are more likely to select high-techs than low-techs and are more likely to choose hands-off support than hands-on support. The independent variable always has the predicted signs and is always significant. Results from OLS-Regressions
Regression 1: Testing our first hypothesis concerning the selection strategy, we show that venture capitalists having a high level of specific knowledge focus on high-tech projects. If a venture capitalist has one more unit of specific knowledge in relation to general knowledge, the investments in high-techs increases about 26% on a 5% significance level, all other variables c.p. The regression results also show that the investment stage has an influence on investing in high-tech ideas: as expected, the more that a venture capitalist invests at a later stage the less he invests in high-tech ideas. This goes along with our thoughts that the portfolio enterprise is transformed from an intangible business idea in early stage to a tangible asset in a later stage. The less tangible an enterprise is, the more knowledge the venture capitalist must have to assess the quality of the idea as well as its market opportunities. This coherence between investing in a certain financing stage and being a specialist or a generalist can be seen in our data.14 In addition, our data show that if a venture capitalist acts more with a regional focus the less he or she invests in high-tech ideas compared to
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international investing companies. The number of portfolio enterprises, the status of a venture capitalist, the co-operation strategy as well as the national focus do not have a significant influence on the high-tech investments. Support is found for the first hypothesis, since the estimated coefficient on knowledge is positive (as predicted) and significant. Finally, we observe a strong relation between knowledge and selection strategy. For a venture capitalist with highly specific knowledge, it seems to make sense to invest in high-tech ideas. We interpret this as realizing a competitive advantage caused by a knowledge head start. Regression 2: Testing our second hypothesis concerning the support strategy, we are able to show that venture capitalists having a high level of specific knowledge focus more on a hands-off strategy than generalists, and vice versa. As the regression coefficients in table 4 show, one unit of specific knowledge more in the venture capital company decreases the hands-on activities by 0.02 units on a significant level. Therefore, we can show that specialist venture capitalists do focus more on a hands-off attendance while generalists do focus more on a hands-on strategy, just as our theoretical discussion predicted. For only for two of the control variables can significant influences be observed. Considering the trade-off between portfolio size and support intensity (Kanniainen and Keuschnigg 2003), the first result seems to be plausible. It reflects the fact that the larger the portfolio size is, the less a venture capitalist is working hands-on. The other result is surprising at first glance: venture capitalists with a regional focus act less hands-on than those with an international focus. One possible explanation here could be that six venture capitalists acting regional have more than 100 (one has 1373) enterprises in their portfolio. These are governmental venture capitalists who distribute subsidized money across newly founded enterprises. Therefore, regional focus could also represent portfolio size in this case and therefore shows a negative relation with hands-on strategy. All other control variables do not show any influence on the hands-off or hands-on strategy. This regression result points to strong and significant effects of specific knowledge on the hands-off strategy; increasing the specific knowledge results in less hands-on activity of a venture capitalist. We will now present regression results for selection or support time a venture capitalist spends with his or her portfolio companies because they allow us to assure the effect of specific knowledge on support strategy. Regression 3: If a venture capitalist has more specific knowledge in relation to general knowledge, the ratio of selecting time to support time increases at about 0.09 units at a 5% significance level. Thus, a specialist invests more time in selecting when specific knowledge increases, all other variables c.p. The estimated coefficient of the knowledge variable is positive and highly significant. This finding again provides support for our second hypothesis: the more specific knowledge a venture capitalist possesses, the more the venture capitalist follows the hands-off strategy. Looking at the control variables only, again the regional focus is affected.
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The more regionally a venture capitalist acts, the stronger the ratio of time spent in selecting new enterprises decreases with respect to time spent in supporting current enterprises on a highly significant level compared to internationally acting venture capitalists. This corresponds to the first result that acting regionally corresponds with low-tech investments that are expected to get more support. However, it contradicts the second result, which showed that the more regionally a venture capitalist acts the more hands-off he or she supports. However, not to invest time on the selection process and not to support would fit the poor image of governmental venture capitalists. In sum, we are able to show that venture capitalists with more specific knowledge support more hands-off than generalists. Conclusion If Jensen and Meckling’s (1995: 5) statement that ‘the opportunity set confronting an individual or a firm is a function of the individual’s knowledge’ is true, it must be possible to draw conclusions from the venture capitalists’ knowledge to the strategies they embark on. Therefore, we used an agency theoretical framework that focuses on knowledge transfer and agency costs as the central theme. These costs served as a link between general and specific knowledge and strategies and allowed us to derive strategies that we tested with our own unique dataset. We could show that differences in the venture capitalists’ knowledge involve systematic differences in the selected investment projects as well as in provided support intensity. Generally, venture capitalists seem to behave consistently according to economic predictions. Thus, our results are important from a theoretical perspective. However, even for practitioners the results of this paper could be helpful. Venture capitalists who ask if their chosen strategy fits their capabilities compared to the other players in the market could decide to acquire additional knowledge or to choose another type of portfolio. Even entrepreneurs asking for venture capital might want to ascertain whether a venture capitalist really is a ‘good match’. Analysing the know-how of venture capitalists allows entrepreneurs to form expectations as to whether a venture capitalist can cover needs concerning the intensity and quality of support. Overall, the study helps to understand the venture capital market better. However, much work remains to be done. Working out the relationship between knowledge and selection and support strategy is just one aspect of dealing with the heterogeneity of venture capitalists. Future investigations should concern generalists’ governance status, network strategies and differences in criteria for selecting portfolio enterprises. Further data is needed on managers working at the interface between consultancies, banking houses and venture capital firms. Acknowledgements The authors gratefully acknowledge financial support from the Richard Bu¨chner-Foundation, Zurich. They are very indebted to Bruno S. Frey,
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Eric Lehmann, Colin Mason, Urs Meister, William McKinley and two anonymous reviewers for their helpful comments. They are also grateful for research assistance provided by Jeanine Hotz and Dominique Leu. Notes 1. Whether or not education is specific knowledge is discussed differently by V. Hayek (1945: 521) and Jensen and Meckling (1995: 7). V. Hayek argues that scientific knowledge can be bought from the market, while Jensen and Meckling claim that purchased knowledge such as advice or a book does not enable per se to decide oneself. In order for that to happen, knowledge has to be internalized and cannot be bought. Therefore, it is specific. 2. The venture capitalist Dotzler (2002: 7) describes that type of knowledge as follows: ‘An ideal background which would enable one to evaluate products and technologies would be technical training and work experience in an operating company in engineering, science, or clinical affairs. Someone who has worked in business development might also have the facility for evaluating companies in this technical dimension. Education in engineering, science, or medicine is helpful’. 3. Jensen and Meckling (1995: 7) only refer to prices and quantities as examples of general knowledge. This classification seems too narrow. Most types of knowledge would be specific. However, we think that within a group (in our case the group of venture capitalists) a certain basic knowledge exists that is shared by all group members. 4. For a detailed description of a hands-off support, see the case study of Sweeting and Wong (1997: 134 – 146). 5. Sweeting and Wong (1997: 125) worked out this coherence very thoroughly: ‘Our research supports the view, that over time, and by a process of feedback learning from post-investment performance monitoring, investees are selected that are compatible with this particular [hands-off, added by the authors] approach’. 6. See Manigart et al. (2002), Lockett et al. (2002) and Murray and Marriott (1998). It has to be noticed that the cited papers differentiated between early stage investments ( = high tech) and later stage investments ( = management buy out = low tech) but did not categorize the single project as high-tech or low-tech. However, the logic is straightforward: High-techs need to make high R&D expenditure and so have to raise external finance from an early stage. Lowtechs have to establish first and can require venture capital if fast expansion calls for it. 7. Often, for example, the newly founded enterprises are spin-offs from universities where researchers worked for years on a business idea they want to bring to the market. 8. It is a common strategy of venture capitalists to replace founders by a professional management when the business idea has transformed into a tangible asset. See Neher (1999) and Hellmann and Puri (2002). 9. We checked whether all venture capitalist companies in the database of the three associations had an e-mail access or not, because we wanted to carry out an online survey (sending the survey via e-mail and getting it back again by e-mail). All companies did have e-mail access. Two weeks after sending out the questionnaire, we reminded them by phone and e-mail to encourage venture capitalists who had not answered yet to do so. We offered them to send the questionnaire again by e-mail, by mail or by fax to increase the response rate. Only 10% of all responses did not return by e-mail. Offering the other two possibilities to send back the questionnaire, we could ensure that there was no response bias concerning the online survey technique (for more information on this kind of survey technique, see Isfan and Moog 2003). 10. We did this, too, to check for a potential non-response bias (Abraham et al. 2002). We can say that the structural and demographic data of our respondents do not differ with regard to the research population data, so there is no non-response bias concerning a special kind of group of venture capitalist, refusing to participate or refusing to fill in the survey. 11. We aggregated industries in seven fields, usually named by venture capitalist as typical investment fields, namely bio-technology, computer software, medicine, electrical engineering, computer hardware, communication technology and internet/e-commerce. 12. We are looking for this correlation because it seems idle to explain the relation of the degree of specialization with the amount of specific knowledge and vice versa.
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13. The question remains why specialists can allocate more time on selecting given that they allocate nearly the same time on support. Asking the venture capitalists how they spend their time in attending the portfolio enterprises, we offered the categories selection time, support time and miscellaneous. Generalists just spend more time with miscellaneous. 14. We tested for potential endogeneity problems concerning the variables. No multi-collinearity could be fount testing the partial correlations.
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