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Journal of Small Business Management 2012 50(1), pp. 63–86

The Impact of Human and Social Capital on Entrepreneurs’ Knowledge of Finance Alternatives jsbm_344 63..86

by Arnout Seghers, Sophie Manigart, and Tom Vanacker

Building upon prior research that demonstrates how the limited knowledge of finance alternatives of entrepreneurs may cause suboptimal finance decisions, this paper examines how entrepreneurs’ human and social capital influence their knowledge of finance alternatives. For this purpose, we use survey data from 103 Belgian start-ups. Results demonstrate that entrepreneurs with a business education and entrepreneurs with experience in accountancy or finance have a broader knowledge of finance alternatives. Having a strong network in the financial community is further positively associated with the knowledge of finance alternatives. However, generic human capital, including higher education, industry experience, and management experience, is almost not related with the knowledge of finance alternatives.

Introduction Finance is one of the necessary resources required for new ventures to form and subsequently operate (Ang 1992; Gilbert, McDougall, and Audretsch 2006; Van Auken 2001). Finance decisions are hence key decisions made by entrepreneurs, which bear significant implications for the operations, risk of failure, performance, and future growth potential of ventures (Cassar 2004; Van Auken 2001). Traditional finance theory

resorts to the framework of perfect capital markets (Modigliani and Miller 1958). This framework assumes that information is free and directly available to all entrepreneurs, which allows entrepreneurs to make comprehensive finance decisions with wealth maximization as their ultimate goal (Brealey and Myers 2000). Moreover, in this perspective, the supply and demand for finance are in equilibrium, which implies that all valuecreating projects will find sufficient finance. Contrary to this image portrayed

Arnout Seghers is Research and Teaching Assistant in the Department of Accounting and Corporate Finance at Ghent University, Gent, Belgium. Sophie Manigart is Full Professor at Vlerick Leuven Gent Management School and at Ghent University, Gent, Belgium. Tom Vanacker is Assistant Professor at Ghent University, Gent, Belgium. Address correspondence to: Sophie Manigart, Accounting and Corporate Finance, Ghent University, Kuiperskaai 55 E, Gent 9000, Belgium. E-mail: [email protected].

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in traditional finance theory, entrepreneurial ventures are often confronted with finance constraints and are not able to raise sufficient outside finance necessary to conduct all their value-creating investment projects (Heshmati 2001; Himmelberg and Petersen 1994; Hubbard 1998; Ullah and Taylor 2007). As a result, the growth of entrepreneurial ventures is often constrained by internal finance (Carpenter and Petersen 2002; Heshmati 2001). Scholars studying finance constraints within ventures have largely stressed supply-side arguments, thereby putting the decision-making process of investors in the foreground (Mason and Harrison 2002; Wright and Robbie 1998). Within this perspective, prior research mainly focused on the role of information asymmetries and transaction costs in explaining why investors may refrain from investing in value-creating entrepreneurial ventures (Berger and Udell 1998). Nevertheless, finance decisions may also be driven by demand-side factors, and more specifically by the characteristics of entrepreneurs. Research on demand-side arguments, which puts the decisionmaking process of entrepreneurs in the foreground, is more limited but growing rapidly (Bates 1997; Cassar 2004; Coleman 2000; Scherr, Sugrue, and Ward 1993; Van Auken 2005). Entrepreneurs are the driving force of important decisions, and entrepreneurial characteristics may hence play a key role in explaining finance decisions (Cassar 2004). For example, though traditional finance research assumes that value maximization is the overarching firm objective, entrepreneurs may want to pursue other goals (Kotey and Meredith 1997; LeCornu et al. 1996; Sadler-Smith et al. 2003) that may affect their finance choices. An important goal for many entrepreneurs, for instance, is to retain full control over their ventures, which might preclude them from raising equity from new shareholders such as venture

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capital investors or business angels (Ang 1992; Manigart and Struyf 1997; Norton 1991; Sapienza, Korsgaard, and Forbes 2003). Furthermore, some entrepreneurs with conservative personal values adopt reactive strategies emphasizing risk avoidance (Kotey and Meredith 1997), and hence such entrepreneurs may refrain from the use of financial debt. This paper focuses on another entrepreneurial characteristic that may impact the finance decisions of entrepreneurs, namely their knowledge of finance alternatives. Traditional finance theories assume that all parties have full information, hence that entrepreneurs are fully aware of the existence of all potential finance alternatives and their respective advantages and disadvantages (Brealey and Myers 2000). However, the assumption that information is free and widely available may not hold in the context of new and small ventures (Gibson 1992; Holmes and Kent 1991; Van Auken 2005). Indeed, entrepreneurs in these new and small ventures may have limited access to information and skills that are necessary to understand the process of finance acquisition (Gibson 1992; Van Auken 2005). This is empirically supported by Van Auken (2001), who shows that entrepreneurs generally have a limited knowledge of finance alternatives that hamper their ability to price and negotiate investments. Moreover, learning theory posits that individuals who are not knowledgeable of a certain topic will not actively search to expand their knowledge (Cohen and Levinthal 1990). Though information on finance alternatives may be abundant for individuals actively searching for it, an initial knowledge gap will lead to a restricted set of finance alternatives considered (Van Auken 2001). Overall, a good knowledge of finance alternatives is the basis for making good financial decisions (Gibson 1992). Hence, even for entrepreneurs who do not need or would be unable to raise certain finance

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alternatives given the characteristics of their ventures, it may be important to be knowledgeable about all finance alternatives in order to make wellinformed choices. After all, how can entrepreneurs know that they will be unable to raise particular finance alternatives when they are not knowledgeable about these alternatives or are misinformed about the characteristics of these alternatives? The goal of this study is to expand this stream of research by exploring why some entrepreneurs have a higher knowledge of finance alternatives compared with others. This is relevant, given that a limited understanding of finance alternatives is likely to be an obstacle to the development of successful capital acquisition strategies (Gibson 1992; Van Auken 2001, 2005). In this study, we focus on the association between entrepreneurs’ human and social capital and their knowledge of finance alternatives. We propose and show that higher levels of specific human and social capital— that is, more experience in accountancy or finance, business education, and knowledgeable networks in the financial community—are associated with a broader knowledge of finance alternatives. However, more generic human capital is almost not related to the knowledge of finance alternatives. This offers an explanation as to why some entrepreneurs are confronted with a stronger knowledge gap, potentially leading to suboptimal finance decisions. In the following section, the theoretical arguments and hypotheses on the impact of human and social capital on an entrepreneur’s knowledge of finance alternatives will be developed. Next, the empirical strategy used to test the hypotheses will be explained; the data and variables employed in this study are further described. Thereafter, the empirical findings will be presented, followed by concluding remarks and avenues for future research.

Theoretical Development Numerous scholars have highlighted the importance of taking entrepreneurial characteristics into account to more fully understand finance decisions in young and small ventures (Ang 1991, 1992; LeCornu et al. 1996; McMahon et al. 1993). We focus on the relation between entrepreneurs’ human and social capital and their knowledge of finance alternatives. This is important as prior research demonstrates how entrepreneurs’ knowledge of finance alternatives influences their finance behavior (Van Auken 2001). Though traditional finance theories generally assume that decision-makers are fully aware of all finance alternatives and their characteristics, entrepreneurship scholars argue that not all entrepreneurs have an equally broad understanding of the finance options that are available, which leads to a knowledge gap (Gibson 1992; Holmes and Kent 1991; Van Auken 2001). Entrepreneurs often lack detailed knowledge on finance alternatives, thereby limiting the set of finance options that they consider (Van Auken 2001). This limited knowledge of finance alternatives further hampers entrepreneurs when negotiating and pricing investments and may result in entrepreneurs being unsuccessful in raising capital or raising inappropriate levels and combinations of capital (Van Auken 2001, 2005). We draw upon human and social capital theory to theorize about the relationship between entrepreneurs’ characteristics and their knowledge of finance alternatives. The conceptual framework behind the hypotheses is summarized in Figure 1. Prior research demonstrates how an entrepreneur’s human capital and finance strategies are linked. First, human capital is positively related with the wealth of entrepreneurs. Hence, entrepreneurs with more human capital can use more of their personal funds to mitigate their venture’s finance

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Figure 1 Conceptual Framework HUMAN CAPITAL

Generic human capital Specific human capital

H1

H2

KNOWLEDGE OF FINANCE ALTERNATIVES

Van Auken (2001)

FINANCE BEHAVIOR

H3 SOCIAL CAPITAL

constraints (Holtz-Eakin, Joulfaian, and Rosen 1994; Lindh and Ohlsson 1996). Second, the human capital of entrepreneurs serves as a quality signal to external investors. This is especially valuable in an environment with high levels of information asymmetry, as it increases the probability that investors will provide financial resources to uncertain entrepreneurial ventures (Hallen 2008). Both effects explain why ventures established by entrepreneurs with higher human capital generally have less binding capital constraints (Astebro and Bernhardt 2005; Hsu 2007). We argue that the human capital of entrepreneurs may not only be associated with their personal wealth or quality signals but also with their knowledge of finance alternatives. Human capital theory states that entrepreneurs with a greater human capital stock will outperform their less endowed counterparts (Becker 1975). The accumulated knowledge of entrepreneurs is likely to provide them with superior cognitive abilities that make them more productive and efficient in a

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range of start-up activities including financial decision-making (Alsos and Kolvereid 1998; Van Auken 2005; Westhead, Ucbasaran, and Wright 2005). Entrepreneurs with more human capital are therefore expected to be more knowledgeable about finance alternatives and as such generate finance strategies that are more elaborated and fine-tuned to their business and personal needs (Van Auken 2005). Following prior studies, we distinguish between generic and specific human capital (Becker 1975; Colombo and Grilli 2005; Dimov and Shepherd 2005; Gimeno et al. 1997). The distinction between generic and specific human capital is context specific and as such may differ from prior studies that do not focus on financial decision-making (Dimov and Shepherd 2005). In the current research context, generic human capital refers to the general knowledge acquired by entrepreneurs through both formal education and professional experience, which is broad in application and not directly related to the acquisition of

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financial resources. In our context, specific human capital relates to education and experience, which is directly applicable to financial decision-making (Dimov and Shepherd 2005). We propose that entrepreneurs with higher levels of generic human capital will experience a lower knowledge gap of finance alternatives compared with their peers with lower levels of generic human capital. More specifically, we expect a positive association between the level of education of entrepreneurs and their knowledge of finance alternatives. Higher education contributes to the ability of entrepreneurs to analyze information, to develop skills to acquire knowledge independently, and to use knowledge in order to solve unforeseen problems (Dochy, Segers, and Sluijsmans 1999). Although higher education is generally broad in its application and may not directly provide knowledge related to financial decision-making, the arguments that were just given may explain why we expect educational attainment to be positively related with higher knowledge of finance alternatives. This would provide an additional explanation for the relation between education and the amount of finance raised (Robinson and Sexton 1994). Next to education, we propose a positive association between entrepreneurial experience and knowledge of finance alternatives. There is a considerable amount of evidence that novice entrepre-

neurs differ from entrepreneurs with prior founding experience in many important ways (Westhead and Wright 1998; Wright, Robbie, and Ennew 1997). Entrepreneurs with higher levels of prior founding experience and prior work experience may have learned from their experience and as such may exhibit more elaborate strategies for starting new ventures related to business planning, financing of the venture, and interactions with external environments among others (Alsos and Kolvereid 1998; MacMillan 1986). Prior research further indicates how less experienced or novice entrepreneurs conduct search routines that are narrower in terms of the amount of information collected compared with experienced entrepreneurs that may draw upon their previous business ownership experience (Westhead, Ucbasaran, and Wright 2005). This implies that entrepreneurs with more previous work and founding experience may search for more information on finance alternatives and their characteristics. The expertise and broader search process, which may lead to a broader knowledge of finance alternatives, may explain why experienced entrepreneurs raise start-up finance more easily and in larger amounts (Starr and Bygrave 1991).1 This leads to our first hypothesis: H1: Entrepreneurs with higher levels of generic human capital have a greater knowledge of finance alternatives

1

The literature on experienced founders segments experienced founders into “portfolio” founders, that is, those who start ventures in a parallel fashion, and “serial” founders who start ventures sequentially (Alsos and Kolvereid 1998; Westhead, Ucbasaran, and Wright 2005; Westhead and Wright 1998; Wright, Robbie, and Ennew 1997). Though these authors document differences between types of experienced founders, following Hsu (2007), we do not focus on the distinction between both types of entrepreneurs, as we are interested in contrasting experienced entrepreneurs with novice entrepreneurs. Furthermore, 30 percent of our respondents are experienced entrepreneurs, with the vast majority (90 percent) of them being portfolio entrepreneurs and only 10 percent of the experienced entrepreneurs being serial entrepreneurs. The limited number of serial entrepreneurs prevents us further from doing more refined statistical analyses.

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than entrepreneurs with lower levels of generic human capital. Arguments related to absorptive capacity indicate that entrepreneurs will be more likely to learn when they perform tasks that are more related to their prior experience and knowledge base (Cohen and Levinthal 1990). Hence, the specific human capital of entrepreneurs may turn out to be more valuable than their generic human capital. Davidsson and Honig (2003), for instance, show how specific human capital, which is of immediate relevance to the task at hand, is positively associated with entrepreneurial discovery and the successful exploitation of such discoveries. The same authors also show how the effect of more generic human capital indicators was weaker and less consistent. Entrepreneurs with a business education may not only have developed general problem-solving skills (Dochy, Segers, and Sluijsmans 1999) but may possess more relevant knowledge within the finance domain compared with entrepreneurs with higher nonbusiness education or compared with entrepreneurs with less education (Dimov and Shepherd 2005). Even if the knowledge on finance alternatives acquired during their business studies might be outdated when needed, their specific knowledge may trigger them to actively search for up-todate information and enable them to more easily acquire other finance-related knowledge (Cohen and Levinthal 1990). The knowledge of finance alternatives may also benefit from specific experience accumulated by entrepreneurs in the accountancy or finance domain. It is expected that individuals, who possess more finance-related experience, will be more aware about the value that different types of finance providers may bring to their ventures (Dimov and Shepherd 2005). Direct experience with capital acquisition is expected to further provide entrepreneurs with deeper knowledge of

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multiple finance alternatives and their characteristics (Van Auken 2005). This leads to our second hypothesis: H2: Entrepreneurs with higher levels of specific human capital have a greater knowledge of finance alternatives than entrepreneurs with lower levels of specific human capital. The above hypotheses are nontrivial, however. When entrepreneurs accumulate experience in only one particular area, this may increase the risk of entering competency traps (Levinthal and March 1993). More experienced entrepreneurs may, for instance, become increasingly trapped in current modes of thought, thereby not recognizing that situations have changed and that these changing situations require new approaches (Shepherd, Zacharakis, and Baron 2003; Starr and Bygrave 1991). Moreover, transferring routines from prior experiences to new ones may be similar to transferring old lessons to new environments where they do not fit (Haleblian and Finkelstein 1999). As such, experienced entrepreneurs may fail to develop a more elaborate knowledge of a broad range of finance alternatives. Next to human capital, entrepreneurs can also learn about finance alternatives through their social capital (Hsu 2007). The central proposition in social capital theory refers to the ability of actors to extract benefits, for example, information, from their social structures, networks, and memberships (Granovetter 1985; Lin, Ensel, and Vaughn 1981). A high level of social capital of the entrepreneur in the form of relationships between individuals is useful in obtaining information that would otherwise be unavailable or costly to locate (Granovetter 1985). Relationships with relevant individuals and organizations provide an advantage to entrepreneurs through access to private information (Podolny 1994). We claim that knowledgeable

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relationships in the financial community, established before start-up, may also reduce information problems experienced by entrepreneurs, as they enable information transfer to entrepreneurs about potential finance alternatives and investor characteristics (Van Auken 2005). For example, entrepreneurs that have relationships with bankers are able to discuss their specific financial needs with them (Ang 1992; Wright, Robbie, and Ennew 1997), allowing entrepreneurs to gain a deeper understanding of finance alternatives. Relationships hence reduce information asymmetries on the demand side of the market (Behr and Güttler 2007). This leads to our final hypothesis: H3: Entrepreneurs with more ties in the financial community have a greater knowledge of finance alternatives than entrepreneurs with fewer ties in the financial community.

Research Method Data Collection Strategy A random sample of 450 Flemish ventures founded between April 2008 and September 2008 were selected from the records of business incorporation as provided by the Flemish government.2 Given the homogeneous sample frame, nonmeasured variance in terms of geographical location and venture age is reduced. Moreover, survivorship and recollection biases are limited by sampling ventures close to the period of formation (Cassar 2004). Between mid-November 2008 and mid-January 2009, all ventures were telephoned in order to identify whether or not they fulfilled the conditions of our research. Following Chandler and Hanks (1998), we exclude 118 subsidiaries or companies that merely changed their legal form from further study, as the 2

focus of this research is on real start-ups. Furthermore, 44 start-ups indicated they were not interested in participating to our research. This resulted in a sample of 288 independent start-ups, which were mailed a questionnaire. Several possibilities to complete and return the questionnaire were offered, including e-mail, fax, post, and web survey. A total of 125 questionnaires were returned after telephone recalls (response rate of 43 percent). Comparing characteristics of early and late respondents, including management experience, experience in the same industry and level of education, with Mann–Whitney tests, showed no significant differences between the two groups. This indicates that the sample is unlikely to suffer from a nonresponse bias. The majority of respondents (84 percent) completed the questionnaire using the web survey. Since some questionnaires were incomplete, the number of responses used throughout this study further decreases to 103. Some basic characteristics of our sample firms are summarized in Table 1. Almost all ventures (100 out of 103) are independent companies set up under the initiative of the founding entrepreneur. Three ventures are spin-outs of a corporate company. Almost one-third of all ventures are active in professional, scientific, and technical activities (32.0 percent) and another third in the wholesale and retail trade industry (31.1 percent). Other important industries are hotels and restaurants (8.7 percent), construction (7.8 percent), and information and communication companies (7.8 percent). Almost half of the ventures (44.7 percent) were founded with less than €20,000 start-up capital, whereas 11 percent of the ventures were founded with more than €250,000 start-up capital. This shows that the initial size of the ventures in the sample varies widely.

Flanders is a region in Belgium.

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Table 1 Characteristics of Sample Firms (n = 103) Characteristic Origin of the company Independent company Corporate spin out Industry Professional, scientific, and technical activities Wholesale and retail trade Hotels and restaurants Construction Information and communication Other Amount of start-up capital Less than €20,000 €20,000–€50,000 €50,001–€100,000 €100,001–€250,000 Greater than €250,000

The questionnaire was developed based on previous research (Van Auken 2001) and pretested through personal interviews with entrepreneurs. These pretests allowed us to optimize the questionnaire by including finance alternatives specific to the Flemish research setting and to make the questionnaire comprehensible for entrepreneurs. The questionnaire was organized in three main sections. The first section collected information about the venture, including the amount of start-up finance raised and the industry in which the venture operates. The second section asked respondents to what degree they are familiar with various finance alternatives. The final section of the questionnaire asked details on entrepreneurs’ education, prior experiences, and ties with finance experts.

Variables Dependent Variables. A list of finance alternatives was composed based on the

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Frequency

Percent

100 3

97.1 2.9

33 32 9 8 8 13

32.0 31.1 8.7 7.8 7.8 12.6

46 21 13 12 11

44.7 20.4 12.6 11.6 10.7

finance sources listed by Van Auken (2001) and government programs aimed at start-ups specific for the Flemish region. The knowledge of the respondent with respect to the different finance alternatives was measured on a sevenpoint Likert scale ranging from -3 = unaware of the existence of a particular finance alternative to 3 = very extensive knowledge, with 0 indicating average knowledge. Hence, negative values on the scale represent belowaverage knowledge, and positive values represent above-average knowledge of finance alternatives. The disadvantage of self-reported data is that entrepreneurs could be influenced by their perceptions of what seems to be a desirable response rather than indicating their actual knowledge of finance alternatives. Nevertheless, if this would be the case, we would expect the average entrepreneur to indicate that he or she is more knowledgeable than average. This was not the case. On the contrary, as described further,

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many entrepreneurs even indicated that they are unaware of the existence of particular finance alternatives. To further motivate our respondents to give accurate data, we also promised strict confidentiality. Consistent with prior studies in entrepreneurial finance (Carter and Van Auken 2005; Winborg and Landström 2001), we conducted an exploratory factor analysis to identify groups of finance alternatives. Factor analysis is a data reduction technique that reduces a list of measures to their essence, that is, a smaller set of factors that capture patterns in the data (Hair et al. 1998). This has two major advantages. First, factor analysis enabled us to identify groups of finance alternatives that are highly related to each other. This allows us to draw conclusions that are more generalizable. Second, if we would study the knowledge of entrepreneurs on each individual finance alternative, we would have to test specific models for each of the 14 finance alternatives studied. Testing a large number of models on related dependent variables increases the probability of finding significant relationships that are merely due to chance.3 Table 2 presents the results of the factor analysis. The Kaiser–Meyer–Olkin measure is 0.868 and Bartlett’s test is 0.000, implying that a factor analysis is meaningful (Hair et al. 1998). Only factors with an eigenvalue larger than 1 are included in further analyses. This procedure yields three factors, capturing 69 percent of the total variance after varimax rotation. The factors are broadly consistent with those identified by Van

Auken (2001). Factor one captures the knowledge of five traditional and commonly used finance alternatives: loans, credit lines, trade credit, leasing, and friends and family financing (Cronbach’s alpha = 0.875). Factor two (advanced finance alternatives for the start-up phase) captures the knowledge of four specific finance alternatives targeted toward start-ups (Cronbach’s alpha = 0.742). Besides Business Angels financing, three specific government measures (IWT-subsidy, Vinnof, and ARKimedes) are included. Factor three captures the knowledge of five advanced finance alternatives targeted toward growth-oriented ventures: public and private equity, bonds, factoring, and venture capital (Cronbach’s alpha = 0.887). Given the high Cronbach’s alpha reliability coefficients of the three factors, these factors are used as variables in the multivariate analyses. The variables were calculated by summing the values for the items that compose the respective factors and dividing it by the number of items. The knowledge of finance alternatives is limited to very limited: the three aggregated variables, as well as many of the individual finance alternatives, have negative mean values. Unsurprisingly, the best known finance methods are common finance alternatives (-0.10). Within the group of common finance alternatives, some finance options even have a positive average knowledge including bank loans (0.14) and leasing (0.11). The knowledge of advanced finance alternatives for the start-up phase (-2.41) and growth phase (-1.31) is

3

We thank one of our reviewers for pointing out that the use of factor analysis may also have limitations. For instance, we lose more fine-grained information on the individual finance alternatives. In order to test for the robustness of our findings, we also ran regressions using each individual finance alternative as a dependent variable. These additional regressions largely confirm the main conclusions drawn from using the broader groups of finance alternatives as identified by the factor analysis. These more fine-grained analyses are available from the authors upon request.

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Table 2 Descriptive Statistics and Rotated Orthogonal Factor Analysis for Knowledge of Finance Alternatives (n = 103) Finance alternatives

Common finance alternatives Loans Credit lines Trade credit Leasing Friends and family financing Advanced finance alternatives for the start-up phase Vinnof IWT-subsidy ARKimedes Business angels Advanced finance alternatives for the growth phase Public stock Private stock Bonds Factoring Venture capital

Mean

Factor 1

2

3

0.89 0.88 0.68 0.72 0.64

0.03 0.11 0.17 0.13 0.10

0.18 0.17 0.43 0.24 0.28

-0.10 0.14 -0.15 -0.37 0.11 -0.39

1.00 1.09 1.33 1.38 1.04 1.26

-2.41

0.83

-2.76 -2.43 -2.59 -1.99 -1.31

0.79 1.23 0.91 1.37 1.13

-0.03 0.12 0.13 0.36

0.82 0.78 0.76 0.58

0.15 -0.01 0.21 0.39

-1.35 -1.05 -0.93 -1.65 -1.68

1.24 1.36 1.21 1.58 1.47

0.22 0.19 0.31 0.46 0.44

0.12 0.10 0.17 0.22 0.44

0.88 0.85 0.68 0.61 0.60

lower. The advanced finance alternatives for the start-up phase are the least known by the entrepreneurs. The average knowledge of finance alternatives like Vinnof and ARKimedes are all below -2.50, where -3 indicates that entrepreneurs are unaware of the existence of these finance alternatives. Independent Variables. Descriptive statistics and correlations between dependent, independent, and control variables are presented in Table 3. The key independent variables measure the human and social capital of the founding entrepreneur. Scholars generally distinguish between specific and generic human capital on the basis of whether education

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Standard Deviation

and experience in a particular domain provide skills that are directly relevant for carrying out specific activities (Becker 1975; Dimov and Shepherd 2005; Gimeno et al. 1997). In this study, the generic human capital of the founding entrepreneur relates to overall education and practical experience with a scope of application that is typically broader and not limited to financial decision-making, whereas specific human capital relates to education and experience that is directly relevant for finance decisions in the entrepreneurial ventures. Higher education can be considered more general in its contribution to human capital (Dimov and Shepherd 2005). Higher education in humanities or

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Dependent Variables 1 Knowledge of Common Financing Alternatives 2 Knowledge of Advanced Financing Alternatives for the Start-Up Phase 3 Knowledge of Advanced Financing Alternatives for the Growth Phase Independent Variables Human Capital Generic Human Capital 4 Higher Education (Dummy) 5 Number of Years of Work Experience Gained by Founders in the Same Industry 6 Number of Years of Work Experience Gained by Founders in Other Industries 7 Founder with a Prior Management Position in a Large or Medium Company (i.e., Number of Employees Greater Than 100) (Dummy) 8 Founder with a Previous Self-Employment Experience (Dummy) 9 Founder with Previous Start-Up Experience (Dummy)

Variables

Experience Self-Employment Experience Start-Up

1.00 1.00

0.00 0.00

0.29

0.34

0.20

0.00

1.00

6.19

0.00 20.00

Experience Other Industry Management Experience

0.73 9.07

0.00 1.00 0.00 40.00

1.40 -1.31

-3.00

Growth Advanced

Higher Education Experience Same Industry

2.00 -2.41

-3.00

Start-Up Advanced

Mean

2.00 -0.10

Max

-2.80

Min

Common

Abbreviation

0.46

0.48

0.40

6.78

0.45 7.78

1.13

0.83

1.00

Standard Deviation 2

3

4

1.000

5

6

0.255

-0.049 0.036 0.062 -0.089

1.000

7

8

9

0.132 0.153 -0.165 0.668 1.000

0.108 0.122 -0.261 1.000

0.322 0.028

0.060 -0.311 1.000

0.042 0.135 0.088 -0.068

0.018 0.100 0.189

0.176 0.004 0.063

0.258 0.190 0.320 1.000 -0.158 0.026 0.034 -0.051

0.676 0.537 1.000

0.400 1.000

1.000

1

Correlation

Table 3 Descriptive Statistics and Correlations of the Dependent, Independent, and Control Variables (n = 103)a

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JOURNAL OF SMALL BUSINESS MANAGEMENT Abbreviation

Min

2.28 0.47 0.47

0.27 0.30

5

0.149 0.242

0.086

0.044

7

8

0.024

0.126 -0.152

0.003 0.068 0.147 -0.022 0.088 -0.117 -0.160 0.097

0.170

0.132

0.057 0.033 0.162 0.072 -0.044 -0.011

6

0.036 -0.139 -0.123

0.219 -0.228 0.066 -0.107

4

Correlation

0.026 0.066

0.043

0.044

0.157 0.066

9

0.366 -0.001 0.144 0.058 -0.121 -0.078 -0.247 0.106 0.093 -0.111 -0.204 -0.210 -0.108 -0.117 0.114 -0.183 -0.039 -0.015 0.217 0.355 0.272 0.232 0.168 -0.007 0.324 -0.053 -0.211

0.204 0.166

0.190

0.153 -0.013 0.105 0.074

0.149

13.14

0.204

0.474 0.338

3

0.216

0.248

0.45

0.200 0.188

2

0.145

0.395 0.327

1

0.48 4.82

Max Mean Standard Deviation

Specific Human Capital 10 Business Education (Dummy) Business Education 0.00 1.00 0.35 11 Number of Years of Work Experience Experience in 0.00 40.00 1.27 Gained by Founders in the Industry of Accountancy or Accountancy or Finance Finance Social Capital 12 Relationships in the Financial Community Relationships in -1.00 1.00 0.34 Financial Community Control Variables 13 Targeted Number of Employees after 5 Number of 0.00 90.00 5.16 Years Employees 14 Financial Planning (Dummy) Financial Planning 0.00 1.00 0.92 15 Shares 100% Retained by the External 0.00 1.00 0.90 Entrepreneurial Team (Dummy) Shareholders 16 Ln (Level of Start-Up Capital) Start-Up Capital 2.56 15.42 10.14 17 Industry: Wholesale and Retail (Dummy) Industry Sales 0.00 1.00 0.31 18 Industry: Professional, Scientific and Industry Activities 0.00 1.00 0.32 Technical Activities (Dummy)

Variables

Table 3 Continued

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Abbreviation

Min

Max

Correlation coefficients significant at p < .05 are shown in bold.

a

Specific Human Capital 10 Business Education (Dummy) Business Education 0.00 1.00 11 Number of Years of Work Experience Experience in 0.00 40.00 Gained by Founders in the Industry Accountancy or of Accountancy or Finance Finance Social Capital 12 Relationships in the Financial Relationships in -1.00 1.00 Community Financial Community Control Variables 13 Targeted Number of Employees after Number of 0.00 90.00 5 Years Employees 14 Financial Planning (Dummy) Financial Planning 0.00 1.00 15 Shares 100% Retained by the External 0.00 1.00 Entrepreneurial Team (Dummy) Shareholders 16 Ln (Level of Start-Up Capital) Start-up Capital 2.56 15.42 17 Industry: Wholesale and Retail Industry Sales 0.00 1.00 (Dummy) 18 Industry: Professional, Scientific and Industry Activities 0.00 1.00 Technical Activities (Dummy)

Variables

0.48 4.82

0.45

13.14 0.27 0.30 2.28 0.47 0.47

0.34

5.16 0.92 0.90 10.14 0.31 0.32

Standard Deviation

0.35 1.27

Mean

Table 3 Continued

0.151

1.000

12

1.000

13

0.091

15

0.088 0.059 0.195 0.078

1.000 0.150 1.000

14

1.000 0.122

16

1.000

17

18

0.174 -0.058 -0.034 0.014 -0.062 -0.461 1.000

0.065 0.104 0.074 -0.097

0.062 -0.064 0.064 0.032 0.176 -0.212

0.038 -0.064

0.022

1.000

11

0.223 0.231 -0.052 -0.015

0.137 0.034

0.033

0.059

1.000 0.264

10

Correlation

science for instance generally does not provide skills directly related to financial decision-making by entrepreneurs. We include a dummy variable higher education, which is equal to 1 if the entrepreneur has a university-level or equivalent degree and 0 otherwise. Almost threequarters of the entrepreneurs have a university-level or equivalent education. Similarly, prior work experience is very broad and includes multiple tasks that may not directly relate to financial decision-making but may nevertheless enhance an entrepreneur’s information processing and decision-making skills. The number of years of work experience by the entrepreneur in the same industry and the number of years of work experience in other industries proxy for entrepreneurs’ general human capital. Before starting their venture, entrepreneurs have on average nine years of work experience in the same industry as their start-up venture and six years in another industry. We further include management experience, which is measured by a dummy variable equal to 1 if the entrepreneur previously held a management position in a company employing more than 100 people and 0 otherwise. About one in five entrepreneurs have previous management experience. Self-employment experience is measured by a dummy variable equal to 1 if the entrepreneur has prior self-employment experience and 0 otherwise. One-third of the entrepreneurs have prior self-employment experience. Finally, start-up experience is measured by a dummy variable equal to 1 if the entrepreneur has prior start-up experience and 0 otherwise. Although entrepreneurs that have prior start-up experience may have searched for finance in the past, prior start-up experience is much broader in its application during new business formation (Alsos and Kolvereid 1998). Moreover, it is not necessarily the case that entrepreneurs with prior start-up expe-

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rience have experience directly related to financial decision-making, since entrepreneurs may have restricted themselves to personal sources of finance in the past (Van Auken and Neeley 1996) or cofounders may have focused on the finance aspect in previous businesses due to functional specialization (Maurer and Ebers 2006). Hence, previous start-up experience is considered to contribute to an entrepreneur’s generic human capital but not to the specific human capital. Close to 30 percent of the entrepreneurs have start-up experience, which is in line with findings from other studies (Birley and Westhead 1993; Hsu 2007). Contrary to generic human capital, which is broad in its application, specific human capital relates to the entrepreneur’s education and experience that is directly valuable for financial decisionmaking. Business education is likely to equip entrepreneurs with a toolset for financial decision-making and hence contributes more to specific human capital as opposed to general human capital (Dimov and Shepherd 2005). It is measured by a dummy variable, which equals 1 if the entrepreneur has a degree in business and 0 otherwise. Approximately 35 percent of all entrepreneurs have a degree in the field of business. Moreover, prior experience in accountancy or finance domains is likely to be particularly relevant for financial decisionmaking. We therefore include the number of years of work experience in accountancy and/or finance as another measure of specific human capital. About 16 percent of the entrepreneurs have previous work experience in the field of accountancy or finance. These entrepreneurs have on average eight years of experience in the field of accountancy or finance. The social capital variable is measured with a six-item five-point Likert scale ranging from -2 = strongly disagree to +2 = strongly agree, about network ties

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between the entrepreneur and finance experts, adapted from the items of Shane and Cable (2002). A finance expert is defined as an individual with correct and reliable information about finance alternatives. The items are: “Prior to the company’s start-up, I had a professional relationship with at least one finance expert”; “Prior to the company’s start-up, at least one finance expert was someone with whom I had engaged in informal social activity (for example, playing tennis, going to the movies)”; “Prior to the company’s start-up, at least one finance expert was a personal friend”; “Someone whom I trust to discuss important confidential matters knew at least one finance expert”; “A third party whose judgment I trust can bring me in contact with a finance expert”; and “Through my network of contacts, I could obtain information from a finance expert.” An exploratory factor analysis is undertaken in order to identify whether all items were measuring the same construct. The Kaiser–Meyer–Olkin measure is 0.819 and Bartlett’s test is 0.000, implying that a factor analysis is meaningful (Hair et al. 1998). Only one factor with an eigenvalue larger than 1 was extracted. As a result, the six items that were just given are measures for the same construct (Cronbach’s alpha = 0.863). The social capital variable is calculated by taking the average of the values for the six items. Entrepreneurs report an average level of social capital of 0.34. Control Variables. Entrepreneurs with growth ambitions may have more thoroughly prepared the start-up of their venture and hence have acquired a better knowledge of finance alternatives.

We therefore control for the expected growth rate, which is measured as the target number of employees (in full time equivalents) five years after start-up. The average employment target equals approximately five employees with a maximum of 90 employees.4 In order to further control for preparation, a dummy variable measures whether or not the entrepreneur performed formal financial planning before start-up. Almost all entrepreneurs (92 percent) indicate that they performed formal financial planning before start-up. We also distinguish between start-ups with and without external shareholders, with a dummy variable equal to 1 if there are external shareholders and 0 otherwise. If external shareholders are involved, the knowledge base is likely to be broader. Only 10 percent of the startups have external shareholders. In order to account for the initial size of the venture, we include the natural logarithm of start-up capital as a control. Entrepreneurs setting up larger start-ups may have a higher knowledge of finance alternatives. Finally, we control for industry effects. We created two industry dummy variables, “Wholesale and retail” and “Professional, scientific and technical activities.” More than 60 percent of the start-ups are active in these two industries. Correlations across explanatory variables are generally low and do not indicate the existence of multicollinearity problems.

Results Main Findings As the dependent variables are censored, the multivariate relationships

4

We used targeted turnover after five years as an alternative proxy for the expected growth rate. Unfortunately, only 78 respondents filled in the targeted turnover after five years. The correlation between the targeted number of employees and the targeted turnover after five years is 0.348. Using this variable rather than the targeted number of employees leads to qualitatively similar results.

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between the dependent variables and independent variables are analyzed with Tobit regressions. Table 4 panel “Common” contains the regression models with the knowledge of common finance methods as dependent variable, panel “Start-up Advanced” contains the models with the knowledge of advanced finance methods for the start-up phase as dependent variable, and panel “Growth Advanced” contains the models with the knowledge of advanced finance methods for the growth phase as dependent variable. Three models are reported in each panel. The first model in each panel only includes the control variables. The second model includes the human capital variables next to the control variables from the first model. The third model in each panel is the full model, including control variables, human capital, and social capital variables. As the significance and the sign of the coefficients are broadly consistent in the three models within each panel, the discussion of the results will focus on the full models. Adding human capital variables significantly improves all model fits, whereas adding the social capital variable significantly improves the models explaining the knowledge of common finance methods and of advanced finance methods for the growth phase. The coefficients of the control variables show that entrepreneurs with higher growth aspirations have a significantly higher knowledge of all finance alternatives. A higher level of start-up capital is associated with a higher knowledge of common finance techniques (p < 0.01). Interestingly, entrepreneurs of companies active in the industry of “Wholesale and retail” have a significantly lower knowledge of common finance methods (p < .05) and advanced finance methods for the growth phase (p < .05). Entrepreneurs of companies active in the industry of “Professional,

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scientific and technical activities” have a significantly higher knowledge of advanced finance methods for the start-up phase (p < 0.1). In order to test the first hypothesis, we examine the impact of generic human capital variables on the knowledge of finance alternatives. Neither higher education nor experience in the same industry has an impact on the knowledge of finance alternatives. Experience in other industries has a positive impact on the knowledge of common finance alternatives (p < 0.01) and advanced finance alternatives for the growth phase (p < 0.1). Entrepreneurs with previous start-up experience, however, have a lower knowledge of common finance alternatives (p < .05). Experience as a self-employed and overall management experience have no impact on an entrepreneur’s knowledge of finance alternatives. Overall, support for H1 is weak. The impact of specific human capital on the knowledge of finance alternatives is more pronounced. Specific human capital leads to a significantly higher knowledge of finance alternatives, especially of common finance alternatives and of advanced finance alternatives for the growth phase. More specifically, both business education and experience in accountancy or finance lead to significantly higher knowledge of common finance alternatives (p < .05) and advanced finance alternatives for the growth phase (p < 0.01). Overall, these results strongly support H2. The social capital of entrepreneurs is significantly associated with their knowledge of finance alternatives in several model specifications. Specifically, entrepreneurs who have network ties with finance experts have a greater knowledge of common finance alternatives (p < 0.001) and advanced finance alternatives for the growth phase (p < .05). These findings provide strong support for H3.

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0.462* 0.065*

0.480* 0.054*

0.192 0.000 -120.000

0.143 -0.003 0.039** 0.025 0.079 -0.493*

0.159 -0.003 0.032* -0.057 0.141 -0.444†

0.095 0.000 -134.300

0.013* 0.357 0.300 0.129** -0.436* 0.030

0.013† 0.251 0.415 0.128** -0.273 0.234

0.011 0.441 0.336 0.166*** -0.149 0.499*

0.670*** 0.236 0.000 -113.400

-2.608***

-2.480***

-2.657***

Where †p < .1; *p < .05; **p < .01; ***p < .001 (conservative two-tailed tests).

Constant Control Variables Number of Employees Financial Planning (Dummy) External Shareholders (Dummy) Start-up Capital Industry Sales (Dummy) Industry Activities (Dummy) Independent Variables Human Capital Generic Human Capital Higher Education (dummy) Experience Same Industry Experience Other Industry Management Experience (Dummy) Experience Self-Employment (Dummy) Experience Start-Up (Dummy) Specific Human Capital Business Education (Dummy) Experience in Accountancy or Finance Social Capital Relationships in Financial Community McFadden’s Pseudo R2 Prob > [chi symbol]2 Log likelihood

Model 3

Model 2

Model 1

Common

0.062 0.012 -123.400

0.020* -0.002 0.535 -0.007 -0.188 0.868**

-3.556***

Model 1

0.114 0.007 -116.500

0.420 0.040

0.381 -0.007 -0.007 -0.030 0.635† -0.061

0.017† -0.063 0.248 -0.077 -0.129 0.748*

-3.082***

Model 2

0.402 0.040

0.371 -0.006 -0.003 -0.015 0.584 -0.065

0.018† 0.010 0.184 -0.074 -0.189 0.665†

-3.177***

Model 3

0.299 0.118 0.009 -116.000

Start-Up Advanced

Table 4 Multivariate Tobit Regression Models (n = 103)

0.082 0.000 -148.700

0.022* 0.768† 0.719* 0.062 -0.373 0.601*

-3.499***

Model 1

0.177 0.000 -133.200

0.862*** 0.051*

0.237 0.015 0.020 0.145 0.117 -0.143

0.019** 0.427 0.663† 0.012 -0.390† 0.221

-3.315***

Model 2

0.857*** 0.051**

0.225 0.016 0.027† 0.204 0.061 -0.174

0.019** 0.510 0.573† 0.013 -0.515* 0.053

-3.431***

Model 3

0.539* 0.197 0.000 -130.000

Growth Advanced

Robustness Tests Additional models were tested in order to provide evidence with respect to the robustness of our main findings.5 First, many entrepreneurs may not need the wide range of finance alternatives included in our study. Hence, one could argue that the existence of a knowledge gap is likely to be less problematic for entrepreneurs who do not require large amounts of finance. Entrepreneurs with higher growth aspirations are known to realize higher growth rates (Wiklund and Shepherd 2003) and hence are more likely to need multiple sources of finance besides owner funds (Vanacker and Manigart 2010). Nonparametric tests comparing entrepreneurs with high versus low growth aspirations (median split) show no significant differences in the knowledge of finance alternatives between these two types of entrepreneurs: the knowledge of finance alternatives of entrepreneurs with high growth aspirations is equally limited compared with that of entrepreneurs with limited growth aspirations. Hence, entrepreneurs who will likely need to raise more finance from more diverse sources to fulfill their growth aspirations start with an equally low knowledge of finance alternatives. Furthermore, in the subsample of entrepreneurs with high growth ambitions, specific human capital in the form of business education and years of experience in accountancy or finance is associated with an enhanced knowledge of common finance alternatives and advanced finance alternatives for the growth phase. Relationships in the financial community are also positively associated with the knowledge of advanced finance alternatives for the growth phase. This indicates that the results from a subsample of entrepreneurs with high growth aspirations are

broadly similar to those provided for the entire sample including all entrepreneurs. Specific human capital and social capital of entrepreneurs with high growth aspirations are associated with higher knowledge of finance alternatives, providing support for H2 and H3. High growth-oriented entrepreneurs with high levels of generic human capital do not necessarily report higher knowledge of finance alternatives, hence providing only weak support for H1. Second, additional control variables were added to the regression models, including the gender of the founding entrepreneur, number of founders, and founder’s age. The main results remain qualitatively similar when these additional control variables are included, but this reduces the sample size. Female entrepreneurs exhibit different finance behaviors compared with their male counterparts (Coleman 2000; Greene et al. 2001; Neeley and Van Auken 2010). Our findings show that female entrepreneurs are less knowledgeable of advanced finance alternatives for the growth phase, potentially partly explaining the difference in finance behavior. The larger the number of founders, the greater the tangible and knowledgebased resources they will have available (Colombo and Grilli 2005). Nevertheless, the number of founders is negatively associated with the knowledge of advanced finance alternatives for the growth phase. Finally, the age of the founding entrepreneur is added. Though the age of the entrepreneur may serve as a proxy for generic human capital, we included more detailed measures (including their educational background and work experiences) in our regressions, which, when combined, should reflect the age of entrepreneurs. The age of the founding entrepreneur is negatively

5

These additional tests are not reported due to space considerations but available from the authors upon simple request.

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associated with the knowledge of advanced finance alternatives for the start-up phase. Finally, we implicitly assumed throughout this paper that the knowledge of finance alternatives will influence finance behavior. Van Auken (2001) indeed demonstrates that limited knowledge of finance alternatives hampers the ability of entrepreneurs to price and negotiate investments. We study new ventures, and no data are available yet on the finance decisions in these ventures after start-up. Nevertheless, nonparametric tests (further supported with the regressions in Table 4) indicate that entrepreneurs who raise more start-up finance have a higher knowledge of common finance alternatives. This provides at least some explicit evidence that within our research context the amount of start-up capital raised is positively associated with the knowledge of finance alternatives, which is of significant interest given the evidence on the undercapitalization of many private ventures (Holtz-Eakin, Joulfaian, and Rosen 1994).

Discussion and Conclusion It is widely acknowledged that financial resource acquisition is a key process in the start-up and growth of new businesses. Traditional finance theories generally emphasize wealth maximization as an overarching goal, rational behavior of all actors, and prefect information (Brealey and Myers 2000). In such a theoretical setting, financial decision-making is straightforward: all value creating investment projects will find sufficient finance, and finance decisions will not influence firm value (Modigliani and Miller 1958). In practice, however, the process of acquiring sufficient and adequate finance is fraught with difficulties, especially for small and new ventures. Scholars have argued that information asymmetries between investors and entrepreneurs may constrain the

supply of finance toward young and small entrepreneurial ventures (Berger and Udell 1998). Theories building on the existence of information asymmetries typically assume that (potential) investors are informationally constrained, which is expected to influence their investment decisions (Wright and Robbie 1998). This paper highlights a second information asymmetry problem, namely the fact that entrepreneurs do not have full information of finance alternatives. This knowledge gap may lead entrepreneurs to select only these finance alternatives they are familiar with, potentially leading to suboptimal finance structures (Gibson 1992; Van Auken 2001, 2005). The main contribution of this paper lies in the finding that entrepreneurs with higher levels of specific human capital and higher levels of social capital experience lower knowledge gaps. Consistent with prior research, we find that specific human capital is more valuable than generic human capital (Davidsson and Honig 2003). Specific human capital, that is, business education and previous experience in accountancy or finance, increases an entrepreneur’s knowledge of finance alternatives. Generic human capital in the form of higher education or general experience has a more modest and less consistent impact. The social capital of entrepreneurs at start-up, and more specifically their ties to finance experts, is positively associated with their knowledge of finance alternatives. Overall, we contribute to a further socializing of the finance acquisition process in entrepreneurial ventures (Shane and Cable 2002) by demonstrating the key role of entrepreneurial characteristics on entrepreneurs’ knowledge of finance alternatives in start-ups. This is critical as a good knowledge of finance alternatives is the basis for making good financial decisions (Gibson 1992). We have shown that entrepreneurs’ knowledge of finance alternatives in general is rather limited. Even the know-

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ledge of commonly used finance methods is limited. More complex finance options, specifically targeted toward growthoriented ventures, are even less known. The advanced finance methods targeted at start-ups is the least known category. Though the specific human capital and social capital of entrepreneurs explain differences in their knowledge of common finance alternatives and of advanced finance alternatives for the growth phase, these entrepreneurial characteristics are not associated with their knowledge of advanced finance alternatives for the start-up phase. This may be driven by the fact that there is little variation in the knowledge of the latter, as witnessed by the low standard deviation of this variable. Almost all entrepreneurs reported that they either are unaware of these finance alternatives, or that their knowledge thereof is very limited. Hence, the government programs specifically targeted toward start-ups are badly known by all entrepreneurs, even those with high levels of human and social capital. This finding is broadly consistent with previous findings of Van Auken (2001) for U.S. entrepreneurs and Audet, Berger-Douce, and St-Jean (2007) for Canadian entrepreneurs. Two potential explanations have been advanced for this general lack of knowledge of government programs (Audet, BergerDouce, and St-Jean 2007; Stevenson and Lundström 2002). First, many entrepreneurs do not seem to understand the utility or relevance of the government programs. Second, entrepreneurs may not need or may not be eligible for several of these government programs. We demonstrate how some entrepreneurs are simply unaware of the existence of government programs targeted specifically toward start-ups. When entrepreneurs are unaware of government programs, they are both unable to judge their utility and unable to judge whether or not their ventures are eligible for such programs.

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A methodological strength of this study is that all social and human capital variables are measured at start-up, hence eliminating survival and recall biases. It would be interesting to add a longitudinal dimension to the current research. This would allow understanding how the initial knowledge gap influences subsequent finance and growth processes. Is the knowledge gap of an entrepreneur at start-up a major hindrance in the development of the start-up, or is the entrepreneur able to overcome this liability through subsequent learning and experience? Moreover, the importance of a knowledge gap may be more or less critical depending upon the growth ambitions of entrepreneurs and their intention to remain independent. For instance, it might well be that a limited knowledge of finance alternatives will constrain high growth-oriented entrepreneurs more than low growth-oriented entrepreneurs who have a lower need for resources. These are important avenues for future research. The study suggests implications for policymakers and for entrepreneurs. The role of business education is highlighted. Strengthening lifelong education for entrepreneurs on business in general and on financial matters in particular is warranted. Furthermore, when new policy initiatives are developed, frequent and clear communication with the target group and their advisors is key. This study suggests that well-designed initiatives often fail to capture the attention of their target group. Entrepreneurs should understand that finance is a key resource for their business; failure to understand the finance alternatives and their characteristics may seriously hamper the development of their ventures. Most entrepreneurs, however, have a limited knowledge of finance options, even if they have a broad business experience. They may enhance their understanding of finance through training. Furthermore, they

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should understand that links to financial experts are valuable in reducing the knowledge gap. If they do not have ties with finance experts yet, they should actively seek to establish them. Moreover, when entrepreneurs already have links to experts, they should actively tap their knowledge.

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