How Social Network Structure Shapes Entrepreneurial Intentions?

Transcription

1 Journal of Global Entrepreneurship Research, Winter & Spring, 2011, Vol.1, No.1, pp.3-19 How Social Network Structure Shapes Entrepreneurial Intentions? Kim Klyver 1 Thomas Schøtt 2 Abstract Studies have been carried out on both entrepreneurial networks and entrepreneurial intentions. However, the crossfield between these two areas has been neglected. Earlier studies indicate that an individual s social circle or network of contacts provides information and resources which shape opportunity recognition and thereby influence the intention to become an entrepreneur. We have used data from GEM Denmark, (N=2001) in Logistic regression analyses support four hypotheses: Network density, contact with an entrepreneur, contact with business people and intention to become an entrepreneur. Furthermore, resources mediate the effects of contact with business people and contact with entrepreneurs. Keywords: Social Network, Entrepreneurial Intention, Individual Psychology, Global Entrepreneurship Monitor (GEM), Business Contacts Introduction ome people intend to become entrepreneurs whereas others do not have such intent. SHow is such intent formed? This study ascertains how an individual s intent is influenced by the social circle, the network surrounding the individual. For decades, sociologists have been interested in how people s social networks influence their status attainment (Granovetter, 1973). Overall, three propositions have emerged and have been formulated: (a) social networks affect the outcome of instrumental actions, (b) the nature of resources obtained from social networks is affected by people s original position, and (c) the nature of resources obtained from social networks is affected by the strength of ties (Lin, 1999). Interest in how social networks affect status attainment has also occupied the minds of entrepreneurship scholars (Aldrich & Zimmer, 1986; Greve, 1995; Jenssen & Greve, 2002). Entrepreneurship research shows that social networks among other things affect opportunity recognition (Singh, 2000), entrepreneurial orientation (Ripolles & Blesa, 2005) the vocational decision to become an entrepreneur (Davidsson & Honig, 2003) and growth (Lee &Tsang, 2001). Studies of networks and entrepreneurial intentions are substantial. Different entrepreneurial intention models have developed since the 1980s, and a theory of planned behaviour (Ajzen, 1991) and a theory of entrepreneurial event (Krueger, Reilly, & Carsrud, 2000) have become prominent. These models seek to explain how individuals intentions to start a business 1. Professor, University of Southern Denmark; Corresponding Author's kkl@sam.sdu.dk 2. Professor, University of Southern Denmark

2 develop and argue that intentions are the best predictor of any planned behaviour including starting a business. Entrepreneurial network literature also developed in the 1980s (Aldrich & Zimmer, 1986; Birley, 1985). Here it is argued that entrepreneurs obtain valuable resources from their networks which help them achieve their goals including the start of business. Literature Review he literature on entrepreneurial intention as well as the literature on entrepreneurial Tnetworks is both extensive, and time is ripe for research on the nexus between entrepreneurial intentions and entrepreneurial networks. This study, therefore, contributes significantly to combining foci on entrepreneurial intention and entrepreneurial network by formulating and testing several hypotheses about how networks shape entrepreneurial intentions. In a recent review, Hindle et al. (2009) called for more research into entrepreneurial intention that is less focused on cognition in its narrow sense and rather more focused on the broader and contextually embedded process of social cognition as conceived by Bandura (1977). This study aims to follow such broader and more embedded approach to entrepreneurial intentions. Based on a short review of existing knowledge on entrepreneurial intention and entrepreneurial networks, a section follows in which the main hypotheses are presented, while a section is devoted to the description of the data and methodology used. Then follows our analyses and tests of the hypotheses. The article concludes with discussion and interpretation of the results and conclusion. Theoretical Background: Two Core Literatures Entrepreneurial Intention In one of the earliest papers on entrepreneurial intention, Bird (1988) defines intentionality as a state of mind directing a person s attention (and therefore experience and action) toward a specific objective (goal) or path in order to achieve something (Bird, 1988, p. 442). The entrepreneurial intention approach which emerged in the 1980s drew heavily on Bandura s (1977) social learning theory. Much of the previous entrepreneurship literature had focused on how psychological traits, demographic and situational factors distinguish entrepreneurial individuals from non-entrepreneurial individuals. However, the results were disappointing for both explanatory power and predictive validity (Krueger et al., 2000). As a reaction, different entrepreneurial intention models developed. These models offer another way of predicting and understanding entrepreneurship. Bird argues that Entrepreneurs intentions guide their goal setting, communication, commitment, organization, and other kinds of work (Bird, 1988, p. 442). In addition, Krueger et al. (2000) indicate that intentions are the single best predictor of any planned behavior, including entrepreneurship (Krueger et al., p. 412). Two entrepreneurial intention models have received particular attention: the theory of entrepreneurial event (Shapero, 1982) and a theory of planned behavior (Ajzen, 1991). Representing the theory of entrepreneurial event, Shapero (1982) argues that entrepreneurial intentions depend on individuals perception of the desirability, feasibility and propensity of the entrepreneurial action. Individuals behavior is theorized to continue along the same path until something (job insecurity, job loss, receiving an inheritance, etc.) interrupts the inertia. This interruption makes individuals consider and evaluate other opportunities, including the start of a business. The model was developed specifically in order to explain entrepreneurial behavior. The theory of planned behavior was developed to explain planned behavior in general. Here it is argued that an individual s intention, whether entrepreneurial or other How Social Network Structure Shapes Entrepreneurial Intentions? 4

3 intentions, depends on the individuals attitudes, adherence to norms and perception of feasibility (Ajzen, 1991). The entrepreneurial event model has received empirical support (Krueger, 1993; Krueger et al., 2000; Shook et al., 2003; Segal et al., 2005). Krueger (1993) found in his study of 126 business students that desirability, feasibility and propensity to act explained more than half of the variance in the intentions toward entrepreneurship. The planned behavior model has also received empirical support (Kolvereid, 1996; Krueger et al.; Shook et al., 2003). Entrepreneurial Networks Like the literature on entrepreneurial intention, the literature on entrepreneurial networks emerged in the 1980s as a reaction to a deterministic approach adopted in many psychological studies of entrepreneurs. While the literature on entrepreneurial intention switched the focus of inquiry within the mind of the individual, the entrepreneurial network literature moved the focus away from the mind of the individual to the social context of the individual. Drawing on the resource perspective developed by Wernerfelt (1984), entrepreneurial network literature argues that entrepreneurs obtain non-redundant resources from their network which will make them perform better. The resources an entrepreneur obtains from the network are encompassing and they include elements such as information (Burt, 1992) advice (Christensen & Klyver, 2006) and legitimacy (Shane & Cable, 2002). Although scholars tend to agree on the importance of social networks for entrepreneurial outcome, different arguments have developed concerning which aspects of social networks enhance entrepreneurial outcome. Two prominent but contrasting arguments have come to be known as the closure argument and the structural holes argument. Some researchers (Brüderl & Preisendörfer, 1998; Anderson et al., 2005; Aldrich et al., 2002; Samuelsson, 2001) mostly follow the closure argument in line with Coleman (1988; 1990), arguing that a closed network of contacts around an entrepreneur, in which the contacts know each other, generates trust within the network. Trust between actors increase the likelihood that the entrepreneur may obtain sensitive information and emotional support. Furthermore, it is argued that a closed network forms a coalition enhancing the collective action compared to the performance of lesser interconnected individuals who act on their own (Aldrich & Zimmer, 1986). However, some researchers (Woodward, 1988; Renzulli et al., 2000; Singh, 2000) are more in line with the structural holes argument introduced by Burt (1992; 2000). Here it is argued that an entrepreneur with a network of contacts with holes or disconnects among them will obtain diverse and non-redundant information. As a synthesis to the two contrasting arguments, a third argument has developed. Here it is argued that the efficient structure of a network around an entrepreneur depends on the activities and challenges that the entrepreneur is facing. These activities and challenges change during the entrepreneurial process from when the entrepreneur has an intention to start a business and until when the entrepreneur is running a mature business. The most efficient structure of the network, therefore, depends on where in the entrepreneurial process, the entrepreneur is operating (Larson & Starr, 1993). The Nexus of Networking and Entrepreneurial Intention Whereas the literature on entrepreneurial intention and the literature on entrepreneurial networks are both extensive, studies on the nexus of entrepreneurial intention and network are scant. In the extensive body of entrepreneurship literature, there are only few investigations of how social networks influence entrepreneurial intentions. Some results have emerged from research on the role modelling. Here it is argued that individuals are more likely to develop entrepreneurial intentions if they have observed role models successfully perform the entrepreneurial act (Scherer, et al., 1989; Kolvereid, 1996; 5 Journal of Global Entrepreneurship Research, Winter & Spring 2011, Vol.1, No.1, pp. 3-19

4 Krueger, et al., 2000; Kirkwood, 2007). Some found a direct effect of role models on entrepreneurial intentions (Scherer et al., 1989; Hmieleski & Corbett, 2006), whereas others argued that role models impact indirectly through feasibility and desirability plus a propensity to act on opportunities (Krueger, 1993). Linan's and Santos' research (2007) appear to be the only study that deals specifically with how social networks impact the development of entrepreneurial intention, and this study actually deals only with cognitive aspects of social networks. They found that bonding social networks impact development of entrepreneurial intention indirectly through perceived desirability and perceived feasibility, while bridging social networks indirectly impact entrepreneurial intention through perceived feasibility. Thus, although a body of literature examines entrepreneurial intention and another body of literature entrepreneurial addresses networks, the crossfield between the two remains undeveloped. Prior research indicates that an individual s networking influences development of entrepreneurial intention. However, it is still unknown how social network structure influences entrepreneurial intention. Thus, in this study, we will investigate how the structure of the network shapes the development of entrepreneurial intention. Hypotheses on the Networks Affecting Entrepreneurial Intention Building on the above discussion on entrepreneurial intention and networks, we here have formulated seven plausible hypotheses regarding how social network structure influences development of entrepreneurial intention. We can distinguish six properties of an individual s network: its size, its density, its diversity, its age, its inclusion of contacts in business, and its inclusion of entrepreneurs who are the individual s potential peers and role-models. The size of the network or circle surrounding an individual is the property of the network that has been examined most often in the entrepreneurship literature. Size may influence development of entrepreneurial intentions through several mechanisms. First, individuals with large networks have better access to information and resources than the individuals with small networks. They are, therefore, more likely to recognize opportunities (Aldrich & Zimmer, 1986; Singh, 2000). When individuals recognize opportunities, then an intention to pursue it may emerge (Bhave, 1994). Furthermore, access to information and resources may also positively influence self-efficacy and perceived feasibility in regard to the start of a business, and entrepreneurial intention may emerge from increased self-efficacy (Krueger et al., 2000). We therefore hypothesize that people with large networks are more likely to develop entrepreneurial intention than people with small networks. So we set down our first hypothesis: Hypothesis 1: Size of the social network has a positive effect on the intention to become an entrepreneur. The density of the network of contacts surrounding an individual denotes the extent to which the contacts know one another. If the network is dense, so that the contacts will know each other, they are likely to trust each other, share norms, and form a coalition which will enhance their capability for collective action (Aldrich & Zimmer, 1986; Kim & Aldrich, 2005). An individual is likely to receive much social support if the network is dense (Marsden, 1987). When the individual s network is dense and capable of collective action, the individual is more likely to perceive that the start of a new business is feasible. The reasoning thus is that density enhances perception of feasibility and thereby promotes the intention to start a business. This is the closure argument. Another line of thinking is the structural holes argument. Holes or disconnects among an individual s contacts will allow the contacts to be more free from one another in their thinking and free to present their different thoughts to the individual who is free to selectively utilize their different thoughts, more free than if the How Social Network Structure Shapes Entrepreneurial Intentions? 6

5 contacts were densely interrelated so as to instill their common thoughts in the individual (Burt, 2000). Structural holes, or low density, thus increase the individual s access to nonredundant information and resources. Access to non-redundant information and resources increases people s likelihood of perceiving opportunities and an intention to pursue it may emerge from here. Further access to information and resources may also influence development of entrepreneurial intention indirectly through increased self-efficacy and perceived feasibility. The structural holes argument thus implies the hypothesis that density has a negative effect on the intention of becoming an entrepreneur. The closure argument, thus, favours the hypothesis that density has a positive effect on the intention whereas the structural holes argument favours the hypothesis that density has a negative effect on the intention. We have no good reason to prefer one argument over the other, so we state the hypothesis without direction. Hypothesis 2: Density of the social network has an effect on the intention to become an entrepreneur (without hypothesizing whether the effect is positive or negative). The diversity of the network of contacts refers to the extent to which the contacts differ from each other in one or more attributes, for example, gender, age, education, experience, job or values. Diversity among the contacts may promote entrepreneurial intention in several ways. Firstly, diverse contacts around an individual are likely to offer diverse information and resources that the individual may benefit from, and may specifically enable the individual to recognize opportunities (Aldrich & Zimmer, 1986; Singh, 2000). When the individual recognizes an opportunity then an intention to pursue it may emerge (Bhave, 1994). Furthermore, the individual s access to diverse information and resources may also enhance self-efficacy and perception of feasibility of starting a business, and thereby promote entrepreneurial intentions (Krueger et al., 2000). We therefore hypothesize that an individual with a diverse network is more likely to develop entrepreneurial intention than someone with more homogenous contacts. This hypothesis may be specified as follows: Hypothesis 3: Diversity of the social network positively affects the intention to become an entrepreneur. The age of an individual s network of contacts denotes the time that the individual has known the contacts. One individual may tend to keep contacts for many years, whereas another individual may tend toward a turnover of contacts (Marsden, 1987). The long-lasting contacts are likely to be strong ties, whereas recent contacts are more likely to be weak ties (Granovetter, 1973). Strong and weak ties tend to differ in the kinds of information and resources they convey. Strong ties are likely to provide emotional support and encouragement, whereas weak ties are likely to carry new information. New information is probably the important resource here. New information may enhance the perception of opportunities and thereby promote the intention to start a business. Through this sequence of effects, we hypothesize that an individual with a young network is more likely to get new information, perceive opportunities and form an entrepreneurial intention, whereas an individual with an older network is less likely to get new information and thereby less likely to develop an entrepreneurial intent. This hypothesis can be specified as follows: Hypothesis 4: Age of a social network has a negative effect on the intention to become an entrepreneur. The business contacts among the members of an individual s social circle are likely to promote the formation of intent to become an entrepreneur. An individual s network consists of people across several spheres of life. Some contacts take place in a context best described as a social context, a context of informality, sociability and solidarity, while other contacts take 7 Journal of Global Entrepreneurship Research, Winter & Spring 2011, Vol.1, No.1, pp. 3-19

6 place in a business context, and are more formal and based on exchange that is calculated, equitable, and mutually beneficial. Business contacts may influence development of entrepreneurial intentions, at a minimum, through two mechanisms. First, an individual with many business contacts is more likely to access non-redundant market information and discover opportunities (Birley, 1985; Evald et al., 2006). As a consequence of having opportunities in possession, it is more likely that an intention to start a business will emerge (Bhave, 1994). Second, access to non-redundant information and resources through business contacts may also positively influence self-efficacy and perceived feasibility regarding the start of a business, and entrepreneurial intentions may indirectly emerge through increased selfefficacy (Krueger et al., 2000). We therefore hypothesize that an individual with many business contacts is more likely to develop entrepreneurial intentions than an individual with few business relations. Hypothesis 5: Business contacts have a positive effect on the intention to become an entrepreneur. The contacts to entrepreneurs are especially likely to promote intention to become an entrepreneur. An individual s contacts may occupy many different positions on the labor market. People work in different industries and fulfil various roles either as blue collar or white collar workers. Some people in the network may even have experience with starting a business. A relation with someone in the network who recently has started a business is likely to develop an entrepreneurial intention through two mechanisms. First, if the individual has contacts who recently started a business, the individual is embedded in an entrepreneurial network, and is more likely to access useful information and resources. Therefore, the individual is likely to discover opportunities and develop an intention to start a business. Second, access to information and resources through contact with starters may also positively influence self-efficacy and perceived feasibility in regard to starting a business and entrepreneurial intentions may indirectly emerge through increased self-efficacy (Krueger et al., 2000). We therefore hypothesize that an individual embedded in a network including entrepreneurs is especially likely to develop an entrepreneurial intention. Hypothesis 6: Contacts to entrepreneurs have a positive effect on the intention to become an entrepreneur. As indicated in the above six hypotheses, the essential argument in entrepreneurial network literature is that entrepreneurs obtain non-redundant resources not already in possession that may help them achieve their goals. Thus, basically what we have argued is that those with certain social networks will more likely have the necessary competence and skills to develop entrepreneurial intentions. In entrepreneurship literature, competence and skills to start a business have been found to be among the strongest predictor of entrepreneurship (Koellinger et al., 2007). If networks provide entrepreneurs with resources, including competence and skills to start a business, the effect of social network are supposed to reduce or even disappear when controlling individuals competence and skills to start a business (Baron & Kenny, 1986). Thus, we are arguing that individuals competence and skills to start a business will mediate, reduce or eliminate the effects of social networks on individuals likelihood to develop entrepreneurial intentions. For each of the above hypotheses, we therefore argue that introduction of a variable capturing individuals competence and skills to start a business eliminates or reduces the network effect. Hypothesis 7: The effect of social networks on individuals likelihood to develop entrepreneurial intentions is mediated by individuals competence and skills to start a business. How Social Network Structure Shapes Entrepreneurial Intentions? 8

7 The seven hypotheses, each specifying a property of the social network that expectedly influences intention to become an entrepreneur or mediating these same relationships, will be tested here. The unit of analysis is an individual, an adult, in the population of adults. The dependent variable is the intention whether to become an entrepreneur or not and implies a dichotomy. The independent variables are properties of the social network and the intervening or mediating variable is the competence and skills to start a business. Methodology dult Population Survey in Global Entrepreneurship Monitor AThe Global Entrepreneurship Monitor (Minniti et al., 2006) is an international project that was launched in 1999 with 10 countries. Denmark has participated in this project since The project has generated an extensive database on a wide range of issues and factors germane to entrepreneurship worldwide. It was originally developed to investigate how entrepreneurial activity varies across the globe; what makes a country entrepreneurial; and how entrepreneurial activity affects a country s rate of economic growth and prosperity. Accordingly, GEM data so far have been analysed mostly on a macro level with the nation as the unit of analysis. However, it has been argued that the data set also has important value in relation to investigations of entrepreneurship at the individual level (Klyver, 2008). Every year, each participating nation completes a GEM Adult Population Survey comprising at least two thousand respondents, aged 18 to 64, who are asked a variety of questions regarding their engagement in and attitude towards entrepreneurship. The random selection makes for representativeness and thus for generalizability. These data can be used to analyse entrepreneurship at the individual level (Klyver, 2008). For this study the Danish Adult Population Survey in 2007 is used: 24,295 phone calls to 7,998 different households were made in order to get 2,001 respondents. The number of callattempts for the completed interviews ranges from 1 to 10, with an average of 2.4 calls. This gives a response rate of 25 % which is actually comparatively good in such surveys. In our data analysis, the N is a little less than 2,001 because of missing data. Operationalization of Entrepreneurial Intention and Networking Starting from an abstract concept and arriving at a measurement instrument that captures the concept has given rise to many discussions in social science (Cohen, 1989). This has also been the case with the two concepts used in this study entrepreneurial intentions and (entrepreneurs ) social networks. Many different instruments have been applied to measure entrepreneurial intentions (Krueger, 1993; Linan & Chen, 2009). Chandler and Lyon (2001) blame many studies in entrepreneurship using ad hoc research instruments. In a similar vein, Linan and Chen (2009) argue that inconsistent use of measurements explains much of the variation in results regarding entrepreneurial intentions. In fact, they identify different single-item (Krueger et al., 2000) and multi-item (Zhao et al., 2005) measurement approaches to entrepreneurial intentions. Although, there is a call for a more consistent use of measures and especially a multi-item measure (Linan & Chen, 2009) research into the use of single and multiple-items in management research suggests that single-item measures can be used, if the item reflects a homogenous construct as indicated by an alpha above 0.85 (Loo, 2002) which for instance is the case in Zhao et al. s (2005) study of entrepreneurial intentions. Thus, given our resource scarcity to conduct a large scale population survey, we have applied a single-item measure of 9 Journal of Global Entrepreneurship Research, Winter & Spring 2011, Vol.1, No.1, pp. 3-19

8 entrepreneurial intention. The chosen single-item measure is, however, widespread in previous studies and especially in larger national population surveys like ours, single-item measures are not rare (Kwong et al., 2009; Kwon & Arenius, 2010). Regarding social networks, a similar long discussion on measurement has taken place. Many different approaches have been used to track ego-centric social networks, but the namegenerator and the resource-generator are probably mostly used (Lin, 2001) particularly, the name-generator in entrepreneurship research (Greve & Salaff, 2003). With the use of the name-generator, researchers try to capture entrepreneurs social networks by asking whom they have discussed their business idea, start-up intention or start-up process with (Greve & Salaff, 2003). This approach has its strength in explaining variations after individuals have moved into the entrepreneurial process; however, before the entry, this approach has a clear weakness. It does not make sense to ask individuals with whom they have discussed their intention to start a business if they do not have an intention to start a business. Thus, when the variation in the dependent variable is whether or not individuals have moved into the entrepreneurial process, the question used to identify the social network suggested by the name-generator only makes sense to those who have moved into the process, not to those who have not. Another critique of the name-generator is its strong tie bias. Often only the five most important persons are identified although entrepreneurs social networks are larger than five (Greve, 1995). When research only focuses on a part of the social networks and, more specifically, on the most important part, the risk of overlooking weaker ties is likely. Furthermore, when meassurement is limited to the five closest contacts, density is overestimated, especially by the high density among family members. For this study, which focuses on very early stages of the entrepreneurial process and even before many enter it, we have chosen another measurement strategy than the name-generator. We measure social networks in a way that is meaningful for individuals development of entrepreneurial intentions and that makes sense to those who do not have such intentions either. Further, we try to avoid the strong tie bias. Some of the variables used in this study are the common GEM variables, whereas other variables have also been specifically added to the Danish 2007 Adult Population Survey for the purpose of this article. Dependent Variable Entrepreneurial intention: An individual s entrepreneurial intention was measured by the answer to the following question: Are you alone or with others, expecting to start a new business, including any type of self-employment, within the next three years? The answers were coded 0 for No and 1 for Yes. Independent Network Variables Size of network: The size of an individual s network was measured by the answer to the following question: When you think about your social circle, do you think it is larger or smaller than the social circle of an average Dane? The answers were coded 1 for smaller size, 2 for same size and 3 for larger than the social circle of the typical Dane. Density of network: The density of an individual s network was measured by the answer to the following question: When you think about the people in your social circle, do you think most people know each other or do you think most people in your social circle do not know each? The answers were coded 1 when the respondent answered that most people did not know each other, 3 when the respondent answered that most people knew each other, and 2 if the respondent answered in-between. Diversity of network: The diversity of an individual s network was measured by the answer to the following question: When you think about the people in your social circle, do you think most people are similar to or different from one another regarding their interests, knowledge and opinions? The answers were coded 1 if the respondent answered that most contacts are How Social Network Structure Shapes Entrepreneurial Intentions? 10

9 similar, 3 if the respondent answered that most contacts were different from one another and 2 if the respondent answered in-between. Age of the network: The age of an individual s network was measured by the answer to the following question: When you think about the people in your social circle you talk frequently with, have you known most of these people longer than 10 years or have you known most of them less than 10 years? The answers were coded 1 if the respondent has known most people less than ten years, 3 if the respondent has known most people longer than 10 years, and 2 if the respondent has known the same number of people longer than ten years and less than 10 years Business contacts: The business contacts in an individual s network were measured by the answer to the following question: Do you think you know fewer or more business people than the average Dane? The answers were coded 1 if the respondent knew fewer business people than the average Dane, 2 if the respondent knew a number of business people similar to the average Dane, and 3 if the respondent knew more business people than the average Dane. Contact with entrepreneur: The individual s contact with starters was measured by the answer to the following question: Do you personally know someone who started a business in the past two years? The answers were coded 0 for No and 1 for Yes. Control and Mediating Variables Gender: An individual s gender was coded 1 for male and 2 for female. Although the results from previous studies are still not thoroughly consistent, predominant emerging results indicate that female entrepreneurs have different social networks that male entrepreneurs (Shaw & Carter, 2005; Klyver & Terjesen, 2007). Age: A respondent s age was coded using two indicator variables one dummy for the age group between 30 and 49 years old, and another dummy for the age group between 50 and 64 years old, with a reference group of those between 18 and 30 years old. Previous literature shows that age affects how entrepreneurs use and activate their social networks (Greve & Salaff, 2003; Renzulli et al., 2000). Education level: A respondent s completion of a higher education was coded 0 for None, 1 for some secondary, 2 for secondary degree, 3 for post secondary, and 4 for graduate experience. Previous studies show that human capital and social capital are co-produced, and previous research reveals that especially education has an influential impact on the composition of individuals social network structures (Marsden, 1987; Boxman et al., 1991; Groot, van den Brink & van Praag, 2007). Competence and skills to start a business: A respondent s competence and skills to start a business was measured by Yes (coded 1) and No (coded 0) answers to the following question: Do you have the knowledge, skill and experience required to start a new business Results Empirical Findings: Testing for Network Effects The sample was first described by descriptive statistics for each variable followed by a correlation matrix. The hypotheses were then tested by logistic multiple regression analyses by controlling demographic variables. Description of the Sample and Bivariate Statistics Table 1 shows the frequency distribution of each variable. Before testing the hypotheses, we shall describe the sample, just by the frequency distribution of each variable. Notable is that 7 % of the sample (n=2001) intend to start a business within the next three years, whereas the remaining 93 % do not. 11 Journal of Global Entrepreneurship Research, Winter & Spring 2011, Vol.1, No.1, pp. 3-19

10 Table 2 shows the correlation matrix. The Spearman correlation among the dependent variable and independent variable show as expected a positive correlation between entrepreneurial intentions and business contacts (p<0.01) and between entrepreneurial intentions and contact with entrepreneur (p<0.01), a negative correlation between entrepreneurial intention and age of network (p<0.01) and between entrepreneurial intentions and density of networks (p<0.05). No significant correlations were found between the dependent variable and size of network and diversity of network. Females are less likely to develop entrepreneurial intentions (p<0.01), whereas individuals between 30 and 49 years old are more likely than younger adults (p<0.01) and individuals older than 50 years old are less likely than young adults (p<0.01) to develop entrepreneurial intentions. Those individuals with higher education are more likely to develop entrepreneurial intentions (p<0.01) and so are those individuals possessing the competence and skills to start a business (p<0.01). Table 1. Frequency Distribution of The Variables Variables Frequency Dependent variable Intention of individual Intending to start 7 % Not intending 93 % Independent variables Size of network Large 28 % Medium 45 % Small 28 % Density of network High 60 % Medium 11 % Low 29 % Diversity of network High 72 % Medium 3 % Low 25 % Age of network Old 69 % Medium 8 % Young 23 % Business contacts Many 37 % Medium 25 % Few 39 % Contact with entrepreneur Has contact 47 % No contact 54 % Control variables Gender of individual Female 56 % Male 44 % Age of individual Elderly 34 % Mid-age 51 % Young 16 % Higher education None 2 % Some secondary 9 % Secondary 10 % Post secondary 40 % Graduate experience 39 % Competence and skills to start a business Yes 40 % No 60 % Note: For each variable, the percentages total to 100%, apart from rounding. The correlations matrix in Table 2 also provides valuable information on the validity of the single-item measures used in the study. One way of validating single-item measures is to explore whether the single-item variable correlates as expected with other variables collected, except the dependent variable. If the single-item measure behave as expected, it is more How Social Network Structure Shapes Entrepreneurial Intentions? 12

11 likely that the item captures the construct as if the item behaved unpredictably according to previous research. Thus, if our social network measures correlate as expected from the previous research, validity to the measures is provided. Using Linan and Santos (2007) distinction between bonding and bridging, it may be expected that bonding social network variables (in this study density of network and age of network) are positively correlated with each other, than bridging social network variables (in this study size of network, diversity of network, business contacts, and contact to entrepreneur) are positive correlated with each other and that bridging social network variables are negatively correlated with bonding social network variables. From among the nine significant correlations in social network variables, eight of them correlate as expected. The only unexpected significant correlation is the positive correlation between size of network and density of network. Normally, one would expect larger networks to be less dense. What further adds to the validity of the single-item measures is that none of the correlations are strong. Thus measurements cannot be considered multiple indicators of the same concept. Rather, each measurement seems to be operationalization of a distinct concept or property of a network. Table 2. Correlation Matrix Mean SD 1. Intention Size of network Density of network -0.06* 0.08** Diversity of network ** Age of network -0.06** ** Business contacts 0.12** 0.08** ** Contact with entrepreneur 0.11** 0.10** -0.05* ** 0.19** Gender -0.08** 0.05* -0.06** ** -0.07** Age mid 0.06** -0.06** -0.07** ** 0.09** Age Old -0.12** ** ** Education 0.05* ** -0.07** 0.07** 0.06** 0.10** Knowledge and skills 0.20** * ** 0.19** Gender 1 9. Age mid Age Old ** Education 0.10** 0.14** 0.05* Knowledge and skills -0.25** ** 1 * p<0.05 ** p<0.01 Validity of the single-item measures can be further assessed by exploring their associations with demographic variables. As expected, among the significant associations older entrepreneurs are more likely to have large, dense and old networks. The fact that they are less likely to have many business contacts and less likely to know entrepreneurs is more questionable. The fact that those female entrepreneurs should have smaller network which are less dense compared to male entrepreneurs is also questionable. However, those female entrepreneurs have fewer business contacts and entrepreneurs in their networks fit well with the previous research. Thus, all in all, it may be concluded that although the single-item measures are far from perfection, most of the tests provided support reasons to believe in their ability to measure the concepts they represent. 13 Journal of Global Entrepreneurship Research, Winter & Spring 2011, Vol.1, No.1, pp. 3-19

12 Furthermore, the network measures and demographic measures are all modestly related or unrelated (with one exception, namely a correlation of 0.72 between the two dummies for the age of an individual, which is to be expected). This indicates that there is no multicollinearity among the variables that we would like to enter as regressor in a model for how they affect the intention. Multivariate Statistics: Entrepreneurial Intention Affected by Network Properties and Control Variables How each network property affects entrepreneurial intention, while taking into account other things, can be estimated by multiple regression of entrepreneurial intention upon many variables. Intention intending or not is a dichotamous dependent variable, so the appropriate model is a logistic regression. An effect is indicated by a significant regression coefficient B (a positive effect by a positive coefficient, and a negative effect by a negative B). The magnitude of a significant effect is indicated by the exponential value of the coefficient, exp(b), in Table 3. Our analytical strategy was first to include the control variables in model 1. In model 2, we added the network properties and finally in model 3 we added the mediator. The ommibus test of model coefficients is significant on a level for all models, indicating that there is an adequate fit between data and the model and that the model is therefore capable of predicting the dependent variable. Each step adding variables, from model 1 to model 3, also enhanced the model significance. As an approximation to the OLS R 2 (a measure of the explained variance), Nagelkerke R 2 was used. The Nagelkerke R 2 increases from being in model 1 to becoming in model 3. Table 3. Logistic Regression of Entrepreneurial Intention on Properties of The Network and Control Variables Model 1 Model 2 Model 3 B Exp(B) B Exp(B) B Exp(B) Gender -0.90*** *** * 0.59 Age of individual: mid * 0.52 Age of individual:elderly -1.59*** *** *** 0.16 Education 0.24* Size of network Density of network -0.31** ** 0.73 Diversity of network Age of network Contacts in business 0.50*** * 1.34 Contact with entrepreneur 0.48* Knowledge and skills 1.43*** 4.19 Constant -1.68*** ** ** 0.08 N Nagelkerke R Square Change in Nagelkerke R Square Note: Significant test for control variables are two-tailed. Significant tests for independent variables are one-tailed, except density of network which is two-tailed. * p<0.05 ** p<0.01 *** p<0.001 How Social Network Structure Shapes Entrepreneurial Intentions? 14

13 Model 1, including only the control variables, confirms the indications from the correlation matrix, showing negative effects of being female and older than 50 years old and a positive effect of higher education on individuals likelihood to develop entrepreneurial intentions. Hypothesis 1 to hypothesis 6 are tested in model 2. Hypothesis 1 states that the size of a network has a positive effect on intention. Therefore we use a one-tailed test of the coefficient. Model 2 in Table 3 shows, however, an insignificant effect of the size of network on entrepreneurial intentions. So the analysis fails to support hypothesis 1. Hypothesis 2 states that the density of a network has an effect on the network without specifying a direction. Therefore we use a two-tailed test of the coefficient. The coefficient is significant (p<0.01) and hypothesis 2 is, therefore, supported. The coefficient is negative; thus, supporting the structural holes argument (and disfavouring the closure argument). The statistical analysis thus reveals that the holes among the contacts, rather than dense interrelations among them, promote the intention to become an entrepreneur. In hypothesis 3 it is argued that entrepreneurial intention is positively correlated with network diversity. As the direction was specified, we used a one-tailed test of the coefficient. The coefficient is, however, insignificant when controlling other factors and hypothesis 3 is therefore not supported. In hypothesis 4, we used a one-tailed test to test whether the age of the network has a negative effect on entrepreneurial intention. The coefficient is insignificant when controlling other factors. So, hypothesis 4 is not accordingly supported. Hypothesis 5 states that contacts in business promotes entrepreneurial intention. Using a one-tailed test and controlling other factors, the analysis revealed a significant (p<0.001) and positive coefficient. Hypothesis 5 is therefore supported. Individuals with many business contacts are more likely to develop entrepreneurial intentions than the individuals with fewer business contacts. Hypothesis 6 states that contact with an entrepreneur promotes entrepreneurial intention. As the direction was specified, we used a one-tailed test of the coefficient. The coefficient was significant (p<0.05) and positive. Thus, hypothesis 6 is also supported. It may, therefore, be concluded that individuals being in contact with entrepreneurs are more likely to develop entrepreneurial intentions than the individuals without contact with entrepreneurs. In short, three of our six first hypotheses are supported, whereas the remaining 3 are not. Hypothesis 7 is tested in model 3. In hypothesis 7, we argue that individuals competence and skills to start a business mediate the network effects on entrepreneurial intentions. Thus, in order to support hypothesis 7, the effect of the significant network variables from model 2 should be reduced or eliminated when the mediating variable is added (Baron and Kenny, 1986). Introducing the mediator does not affect the regression coefficient between the density of network and entrepreneurial intentions. Thus, no mediating effect was discovered here. However, competence and skills to start a business mediate partly the effect of contacts in business and completely the effect of contact with entrepreneurs. In model 2, the B coefficient for contacts in business is 0.50 (p<0.001), but the B coefficient in model 3 is reduced to 0.29 (p<0.05), indicating a partial mediation. For contact with entrepreneur, the B coefficient in model 2 is 0.48 (p<0.05), but in model 3 the B coefficient is insignificant. Thus, hypothesis 7 is rejected in regards to the density of network, partially supported in respect of the contacts in business and completely supported in respect of contact with entrepreneurs. 15 Journal of Global Entrepreneurship Research, Winter & Spring 2011, Vol.1, No.1, pp. 3-19

14 Conclusion n this study, we have investigated how the social network structure shapes individuals Idevelopment of entrepreneurial intentions. A randomly selected sample of 2001 adults were used to test the seven-theory-based developed hypotheses on how the social network structure is expected to make individuals develop entrepreneurial intentions. The results were at some stage disappointing in the sense that only three out of the six hypotheses received support. On the other hand, the results are especially encouraging as they step into a virgin territory, at the nexus between the well-trodden fields of entrepreneurial networks and entrepreneurial intentions. The results, therefore, have to be seen as original and take us several steps toward understanding how social network structure shapes an intention to become an entrepreneur. It was found that individuals in a social network with a low density are more likely to develop entrepreneurial intentions. This is in line with the structural holes argument (Burt, 1992) and in contrast to the closure argument (Coleman, 1988). Accordingly, individuals with holes among their contacts are more likely to obtain access to non-redundant information and valuable resources. Through their access to non-redundant information and valuable resources, they are more likely to develop entrepreneurial intentions both indirectly through a higher likelihood of discovering opportunities and indirectly through a positive influence on self-efficacy and perceived feasibility in regard to the starting of a business The investigation has also revealed that individuals with relative large business networks are more likely to develop entrepreneurial intentions. Large business networks impact development of entrepreneurial intention both through a higher likelihood of discovering opportunities and through a positive influence on self-efficacy and perceived feasibility in regard to the starting of a business. The study further shows that individuals who are embedded in entrepreneurial networks containing entrepreneurs are more likely to develop entrepreneurial inten-tions. Here again the social network structure influences indirectly through increased likelihood of discovering opportunities and through increased self-efficacy and perceived feasibility in regard to the starting of a business. The remaining social network variables network size, network diversity and network age are not found to affect development of entrepreneurial intentions. Finally, the most original finding is that resources, more specifically competence and skills to start a business, mediate the effect of network supporting the old belief in entrepreneurial network literature that social network affects entrepreneurial outcome through access to resources. Although competence and skills to start a business completely mediates the effect of having contact with entrepreneurs, it only partly mediates having many business contacts and does not mediate the entire density of networks. This suggests that different network properties provide different kinds of resources. Knowing other entrepreneurs certainly seems to provide individuals with competence and skills to start a business, and so do business contacts, more faintly though. However, the density of networks does not seem to provide competence and skills to start a business, but might provide other resources valuable for the start-up process. The existing entrepreneurial network literature has already shown how social network structure impacts opportunity recognition (Singh, 2000), entrepreneurial orientation (Ripolles & Blesa, 2005), vocational decision to become an entrepreneur (Davidsson & Honig, 2003) and growth (Lee &Tsang, 2001). With the current study, we have now established that type of social network structure which plays an important role in developing the entrepreneurial intentions. This study fits with Linan's and Santos' (2007) previous study on how bonding and bridging social networks influence entrepreneurial intentions. This study, however, found that only bridging social networks represented by low dense network, business network size and entrepreneurial network play an important role in shaping individuals entrepreneurial How Social Network Structure Shapes Entrepreneurial Intentions? 16