HUMAN CAPITAL, COMPETITIVE INTENSITY AND ENTREPRENEUR’S PROPENSITY TO EXPLOIT SOCIAL NETWORKS IN RESOURCE ACQUISITION
Zhang Jing, National
University of Singapore
Soh
Pek-hooi, National University of Singapore
Wong
Poh-kam, National University of Singapore
ABSTRACT
INTRODUCTION
CONCEPTUAL BACKGROUND
HYPOTHESIS DEVELOPMENT
METHODOLOGY
RESULTS
DISCUSSION
NOTES
CONTACT
REFERENCES
FIGURE 1
TABLE 1
TABLE 2
Literatures have confirmed the importance of social networks in facilitating the entrepreneurial resource acquisition. However, rarely have scholars investigated why not all entrepreneurs approach initial resources from their networks. We suggest that entrepreneurs’ human capital may influence their propensity to use networks. Testing hypotheses with a sample of 378 high-tech new ventures located in Singapore and Beijing, we argue that while high occupational or educational prestige may increase the accessibility of resources from networks and in turn the frequency of using networks, better social skills may reduce their dependence on existing networks. Such relationships are positively moderated by competitive intensity.
This research examines how human capital of entrepreneurs influences the entrepreneurs’ propensity to exploit their social network ties to acquire initial resources at the early stage of new venture creation. New venture creation is characterized by heightened uncertainty and information asymmetry problems, which hamper the resource owners’ ability to properly evaluate the viability of the new venture (Venkataraman, 1997; Shane & Stuart, 2002). Network ties are therefore seen as an important mechanism to overcome these problems and to facilitate cooperative exchange (Birley, 1985; Aldrich & Zimmer, 1986; Starr & MacMillan, 1990; Shane & Cable, 2002). Yet, in practice, not all entrepreneurs use their network ties in all situations. In this study we explain an entrepreneur’s propensity to exploit social networks as a function of his/her human capital, and how such relationship is contingent on market competitive intensity.
In entrepreneurship research, most studies treat human capital (work and educational experience) and social capital (network ties) of entrepreneurs in parallel as resource endowment at the start of the resource acquisition process (Brush, Greene & Hart, 2001). However, we suggest that entrepreneurs’ human capital may influence their propensity to use networks, because human capital is the accumulation of their efforts in developing networks and social skills (Baron & Markman, 2000). Our results imply that while high occupational or educational prestige may increase the resource accessibility from networks and in turn the frequency of using networks, better social skills may reduce the dependence on existing networks, because entrepreneurs with better social skills may be more willing and able to seek more resources in the market place. Moreover, such relationships are moderated by competitive intensity. We investigated this research question in the context of approaching three types of resource owners, i.e., key management team members, investors and lead users. We found that in approaching different resource owners, entrepreneurs engaged social networks at different level.
Scholars have found that compared with market approaches, such as attending public events, advertising, or making cold calls, social networks (interpersonal relationships) are faster and cheaper channels through which entrepreneurs gain access to various resources at the early stage of new venture creation. Network ties can provide emotional support, a kind of intangible resource, for entrepreneurial risk-taking (Bruderl & Presendorfer, 1998), and more importantly, entrepreneurs access their networks for information and advice (Birley, 1985; Aldrich & Zimmer, 1986), reputation (Stuart, Hoang & Hybels, 1999; Shane & Cable, 2002), or cheap or even free goods (Starr & MacMillan, 1990). By providing these means, social networks of entrepreneurs facilitate the process of recognizing and realizing business opportunities.
Despite these benefits, entrepreneurs do not always rely on social networks in acquiring initial resources in practice. Perhaps the most common reason is that entrepreneurs have only limited time and energy to devote to creating ties, thus the quantity and the range of expertise of existing personal contacts are limited and may not meet the specific resource needs of new ventures (Podolny & Baron, 1997). Hence, the propensity of entrepreneurs to use social networks may be due to the differences across individuals in the resource supply of their social networks (Lin, 1999). Moreover, entrepreneurs’ social skills—specific competencies that help them interact effectively with others—may influence their ability and willingness to mobilize social networks (Baron & Markman, 2000). Yet relatively few studies have explored the determinants of the propensity to exploit social networks (Hoang & Antoncic, 2002).
Some entrepreneurship studies have focused on the demographic features of entrepreneurs. Many sociologists argued, “Social groups (gender and race) have different access to social capital because of their advantaged or disadvantaged structural positions and social networks (Lin, 1999: 483).” In entrepreneurship research, Aldrich and Zimmer (1986) found that immigrant entrepreneurs in many cases formed ethnic networks to share capital or business in order to overcome hostility in the host countries. However, demographic features per se cannot explain the phenomenon exclusively. For instance, Aldrich and Zimmer (1986) also mentioned that although Chinese and Japanese immigrants to the U.S. established densely connected communities to develop small businesses, black migrants from the Southern to the Northern cities in the U.S. after World War I did not unite as closely as their Asian counterparts.
In this study, we investigate this research question from the perspective of human capital of entrepreneurs. Human capital includes the natural qualities—charm, health, intelligence, and looks—combined with the skills people have acquired through work and educational experience (Burt, 1992). In contrast, social capital “refers primarily to resources accessed in social networks” (Lin, 1999, p. 471). Some sociologists have proposed that social capital helps produce human capital (Bourdieu, 1986; Coleman, 1990). Well-connected parents and social ties can indeed enhance the opportunities for individuals to obtain better education, training, skill and knowledge credentials. On the other hand, it is conceivable that human capital induces social capital. Better educated and better trained individuals tend to move in social circles and clubs rich in resources (Lin, 1999). However, to our best knowledge, such relationship has not been sufficiently recognized in entrepreneurship research. Many studies treated the two types of capital in parallel as personal resource endowment at the start of the resource acquisition process (Brush et al., 2001). One exception is the study of Cooper, Folta and Woo (1995). Using the U.S. sample of entrepreneurs, they found that management experience and educational level were positively correlated with the use of personal ties for information search to create a new venture. However, no further studies have continued this investigation, for example, whether their findings also fit the context outside the U.S., or the context of resource acquisition rather than information search. In this study, we make endeavor to enhance our understanding of the influence of entrepreneurs’ human capital on their propensity to use social networks.
Our general proposition is that human capital of entrepreneurs affects their propensity to exploit social networks, including direct ties and indirect ties. We define direct tie as a prior relationship between entrepreneurs and resource owners (Larson, 1992), and indirect tie as a relationship between entrepreneurs and resource owners who are not directly linked but are connected by a common third party with whom both parties have direct ties (Burt, 1987). We discuss this proposition in the context of approaching three types of resource owners throughout the whole paper. Above all, we expect that the types of resource owners per se may influence the frequency of using networks.
Three Types of Resource Owners
Three types of resource owners are most crucial for a new venture: key management team members, investors and lead users (Kazanjian, 1988; Lichtenstein & Brush, 2001). Key management team members refer to those chief executives who would join the venture after the founding team is formed. Investors refer to those institutions or individuals who would invest money or in-kind resources in the first-round external funding. Lead users refer to first few customers who would sign a sales contract.
To approach the three types of resource owners, entrepreneurs may need private information about resource owners at different levels, such as their reputation and bargaining power. Social networks may be engaged at different levels, because of the mutual knowledge built up in direct ties and the role of private information source in indirect ties. Literatures have found that when entrepreneurs make partner recruitment decision, the most important criterion would be the mutual compelling interests among team members or their common aspirations to start a venture (Kamm & Nurick, 1993). Similarly, interpersonal attraction theory explains the formation of the entrepreneurial team as people being drawn to others who have similar beliefs, interests, and chemistry (Bird, 1988). Therefore, we expect that network ties may be used frequently in approaching key management team members.
By comparison, investors are infrequently identified through the networks of entrepreneurs. This can be explained by extant literature on the comparison of venture capitalists (VCs) and informal investors. VCs mainly rely on information provided by other VCs in evaluating new business proposals (Fiet, 1995; Bygrave, 1987). Moreover, since most entrepreneurs lack work experience in the VC industry, VCs become “unfamiliar” partners to them (MacMillan, Kulow & Khoylian, 1988). Thus, not many social networks may be engaged in searching for VCs. In contrast, other studies have shown that informal investors, such as families or personal friends, are major sources for financial aids, especially in seed funding stage for high-tech new ventures (e.g., Roberts, 1991). Combining the arguments of the two lines of research, we expect that both social networks and market approaches are employed in seeking investors.
Compared with the first two types of resource owners, lead users might be an even more distant group to which entrepreneurs could gain access conveniently among their contacts. The applications of a new venture’s technology may reach many industries, which often go beyond the expertise range of the entrepreneur’s networks. Even if entrepreneurs happen to know some potential customers in relevant industries, the reputation of these customers may not be prominent enough to subsequently create the bandwagon effect in attracting majority customers (Stuart et. al., 1999). In order to approach numerous and reputable companies as lead users, entrepreneurs have to use market approaches frequently. Thus, we predict the first hypothesis:
Hypothesis 1: Among the three types of resource owners, the founding teams will be most likely to use ties when approaching key management team members, and least likely to use ties when approaching lead users; investors in between.
Impact of Human Capital on the Propensity to Use Social Networks
The level of entrepreneurs’ human capital may increase their propensity to use networks. First, prestigious entrepreneurs in high social positions may obtain more chances to find people also in high positions from their networks. Those contacts can either provide resource benefits, i.e., supplying their own resources in direct tie cases, or provide information benefits, i.e., recommending other potential resource owners in indirect tie cases (Seibert, Kraimer, & Liden, 2001). According to social resources theory (e.g., Lin, 1999), the advantages that networks could convey depend on what resources the contacts possess. In a hierarchical social structure, a top position has greater access to and control of resources not only because more resources are attached to the position intrinsically, but also because the higher position has greater access to position elsewhere in the ranking (Lin, Ensel, & Vaughn, 1981). Hence, entrepreneurs in high position will have more chances to get to know other prestigious people who control more resources and possess more information or links about the potential resource owners. Second, scholars in information search and decision-making theories argue that information sources will be used more frequently if they bring more marginal benefits (e.g., Stigler, 1961). Therefore, we predict that the level of human capital will be positively correlated with the propensity to leverage their networks as resource owners.
We test the proposition from two primary aspects of human capital, work and educational experience, since education and job training are the most important investment in human capital (Becker, 1993). Studies have found that people with different occupational position or educational level possess remarkably different networks. For example, Corroll and Teo (1996) reported that compared with non-managers, managers have wider organizational membership networks—they belong to more clubs, societies, and the like; and educational level of entrepreneurs is also positively correlated with the network size. All of the argument above predicts the following two hypotheses:
Hypothesis 2a: The founding teams with higher prior occupational status will be more likely to use ties when approaching resource owners.
Hypothesis 2b: The founding teams with higher educational level will be more likely to use ties when approaching resource owners.
Entrepreneurs’ human capital may influence their propensity to employ social networks through work or educational experience that determines the level of their social skills. Social skill literature suggested that entrepreneurs with high level of social skills—specific competencies that help them interact effectively with others—are able to read other persons accurately (Ferris, Witt & Hochwarter, 2001), to make a good first impression on them (Leary, 1995) and to persuade or influence them (Argyle, 1969). Therefore, they are likely to have built up a broader network prior to creating new ventures (Baron & Markman, 2000).
This stream of research has further identified that the development of social skills arises mainly from work or educational experience involving management or marketing functions. First, compared with personality, which is relatively stable and enduring, social skills are trainable (Baron & Markman, 2000). People who take non-technical positions such as marketing and management are given more opportunities to master their social skills than those who are in technical positions. Non-technical positions tend to expose the individuals to persuade, explain and influence others to accomplish certain tasks or to buy their products and services (Argyle, 1969). Furthermore, people with better social skills are more likely to obtain specific positions, since job specification by employers filters the people who are too introversive from the position requiring more communication skills. For instance, Barrick and Mount (1991) found that extroversion was a valid predictor for two job functions involving social interaction, namely management and sales. Hence, people who have taken a non-technical job, such as general managers or sales/marketing staff, may own higher level of social skills than their technical colleagues. Hence, we predict that founding teams with management or marketing experience are more likely to possess and leverage extensive networks than other lacking such experience. Regarding educational experience, because a student’s discipline determines his/her job function to a great extent, that is, a business graduate is more likely to take up administration or marketing function than a engineering or science graduate, we expect that the founding teams of which their members have obtained a business degree have broader networks and greater intent to use ties than others. This lead to the following hypotheses:
Hypothesis 3a: Compared with founding teams which members purely had technical work experiences, the teams which members had non-technical work experience will be more likely to use ties when approaching resource owners.
Hypothesis 3b: Compared with founding teams which members purely had engineering/science degree, the teams which members had business degree will be more likely to use ties when approaching resource owners.
The Moderating Effects of Competitive Intensity
Competitive intensity refers to the extent of rivalry among industry competitors, the threat of potential entry and substitutes. According to Porter’s framework of industry competition (1980), the strength of the competitive forces in an industry determines the return of investment and thus the degree to which this inflow of investment occurs. When the number of competitors increases, more firms may be prone to competing with each other for the similar resources for survival and sustainable growth. The competition may inevitably increase the bargaining power of the suppliers (resource owners) and influence the price, delivery time and quality of the input. For high-tech new ventures, their dependence on the external resource is more serious than that of the incumbent firms because they need specially trained personnel who might not be easily identified or available in the market. Moreover, they need large amount of initial investments and the pool of potential lead users may be scarce (Roberts, 1991). According to resource dependence theory, firms pursue network strategies to mitigate the adverse impact of external forces and secure resources (Pfeffer & Salancik, 1978). Thus, entrepreneurs in more competitive environment will be more likely to seek resources from their network ties than others, since the information benefits from networks may facilitate the identification of appropriate resources at affordable prices (Starr & MacMillian, 1990). Therefore, although entrepreneurs will employ their social networks when the contacts are accessible, such propensity is particularly significant for those in highly competition environment. This discussion leads to the following hypotheses:
Hypothesis 4a: The influence of prior occupational status of the founding teams on the usage of ties will be stronger when competitive intensity is higher.
Hypothesis 4b: The influence of educational level of the founding teams on the usage of ties will be stronger when competitive intensity is higher.
Hypothesis 4c: The influence of non-technical work experience of the founding teams on the usage of ties will be stronger when competitive intensity is higher.
Hypothesis 4d: The influence of business degree of the founding teams on the usage of ties will be stronger when competitive intensity is higher.
In summary, when examining the influence of entrepreneurs’ human capital on their propensity to use social networks in approaching initial resource owners, we consider three questions: (1) do networks get involved in approaching potential key management team members, investors and lead users at the same level? (2) How does human capital of entrepreneurs influence the propensity to use social networks? And (3) how is the impact of human capital moderated by competitive intensity? Figure 1 depicts the three questions by contingency framework.
Sample and Data Collection
The data sets we analyze consist of a sample of 128 high tech start-ups located in Singapore and a sample of 250 high tech start-ups located in Beijing, China. The following criteria were used in defining the universe from which the firms were sampled: (1) the firms must be independent start-ups where the founding entrepreneurs maintain significant control; (2) the firms must be operating in high-tech industries, including IT hardware, software, Telecom, Biotech or Life science, and other high-tech manufacturing (mainly Manufacturing of electronics or electrical machinery & apparatus and Manufacturing of precise instruments); and (3) the firms must be less than 8 years old, in order to assure that the initial resource acquisition processes were still fresh in entrepreneurs’ minds (Shane & Cable, 2002).
In Singapore, the sampling frame was constructed from the following independent sources: (1) a listing of local university spin-offs provided by the two local comprehensive universities; (2) a listing of tenant firms located in the three Science Parks provided by the National Science & Technology Board of Singapore (NSTB); (3) a listing of firms that have obtained venture capital funding in 2001 and 2002 provided by the Economic Development Board of Singapore (EDB); (4) a listing of start-up firms in IT and telecommunications provided by the Infocomm Development Authority of Singapore (IDA); and (5) a listing of biotech firms provided by EDB. A total of 460 unique firms were identified after removing duplicate cases and cross-checking to ensure that they meet our selection criteria. We collected data through on-site interviews. We assured confidentiality to all respondents to encourage candid responses (Xin & Pearce, 1996). Our data collection efforts yielded 128 completed questionnaires after four stages of work. The participation rate is about 30% (128 of 460), which is satisfactory compared with the average response rate of 10% by mailing questionnaire survey in Singapore (Wong, Soh, Neo & Goren, 1993). We found no significant differences between early and late respondents, which indicated that non-response bias was not a major problem (Oppenheim, 1966).
In Beijing, the sampling frame was identified from 6 high-tech incubators and science parks in Beijing. The management authorities of these incubators and science parks helped us identify 523 firms that met our criteria. We collected data through on-site interviews. A total of 250 entrepreneurs completed the surveys, yielding a response rate of about 48% (250 of 523). As in Singapore, tests for non-response bias were performed. Results showed that there were no significant differences for the descriptors between the respondents and the non-respondents.
There are two sections in the questionnaire. In the first section, principal founders were asked to identify up to three most early resource owners they approached, including key management team members, investors and lead-users respectively.1 They were asked to recall by which means they approached these resource owners case by case—direct ties, indirect ties, or others (such as cold call, public events and advertisement). In the second section, respondents were asked to report profiles of up to four most active founding team members, including their educational, work and entrepreneurial experience, and to assess the competitive intensity upon starting up their companies.
Measures
Level of the usage of networks. The dependent variable was the ratio of the number of cases that founding teams employed direct ties or indirect ties when approaching resource owners over the number of all cases for one particular type of resources. For instance, the respondent in firm X reported how he found the first two potential key team members (who may or may not join the firm after the invitation)—one was his colleague, the other was recommended by his friend, then the level of the usage of networks in approaching key team member by firm X is 1. Similarly, if firm X reported 3 cases about investors, where 1 of them used relationships, while the other 2 were through cold calls, then the level of the usage of networks in approaching investors by firm X was about 0.33. If firm X did not report any case about lead users, then the level of using networks in approaching lead users was a missing datum. Therefore, firm X produced 3 dependent variables, YKM=1, YIV=0.33, and YLU was a missing datum.
Type of resource owner. To test hypothesis 1, we constructed two dummy variables: key team member (dummy) and investor (dummy). The lead user group became the base group.
Human capital. “Average appointment level” was the average value of the appointment levels of a firm’s founders with equal weight. For each founder, the value was “2” if he/she worked in the position as upper level administer, such as CEO, CTO, COO in prior employment; “1” for middle level position, such as manager of a division/department; and “0” for lower positions below division managers. “Average educational level” was calculated as the average value of the highest educational degrees of a firm’s founders with equal weight. For each founder, the value was “5” for a Doctor degree; “4” for a Master degree; “3” for a Bachelor degree; “2” for “diploma or A level” and “1” for lower levels below A level.2
The value of “Job function (dummy)” was “1” if any of a firm’s founders had work experience in management or marketing and “0” otherwise. The value of “Discipline in formal education (dummy)” was “1” if any of a firm’s founders obtained any degree, including their highest and lower degrees, in “Business” discipline and “0” otherwise.
Competitive intensity. This scale was composed of three questions: (1)
“Do you agree that there were a lot of real competitors?” (2) “Do you
agree that there were a lot of potential competitors?” and (3) “Do you
agree that there were a lot of substitute products or services?” “1”
means strongly disagree” and “5” means strongly agree. The construct
had high level of reliability as indicated by their Cronbach’s alpha of
.
Control variables. We controlled for several characteristics of founding teams that might influence the likelihood of using network ties by entrepreneurs. Firstly, we controlled for “entrepreneurial experience.” Research has found that experienced entrepreneurs learn to trust themselves more, and rely less on input from other people when they search for business opportunities (Kaish & Gilad, 1991). The value of this dummy variable was “1” if any of the team members had created companies before and “0” otherwise.
Secondly, we controlled for team diversity, since some researchers have suggested that function-rounded teams were more likely to get to know variety of people in different types of business circles (Ancona & Caldwell, 1992). It took the average value of “job function diversity,” “discipline diversity” and “diversity of work experience in different industries” with equal weight. “Job function diversity” was measured by the ratio of the number of job functions (general management, sales/marketing, finance, technical, human resource, others) taken by founding team members in their last jobs divided by the team size. In similar way, we measured the later two items. “Discipline” included science, engineering, business and others. Regarding “work experience in different industries,” we asked “has he/she worked in other industries than that of the new venture?” The value was “1” for “Yes” and “0” for “No.”
We also controlled for industry by employing a series of dummy variables for hardware, software, telecom, and biotech. “Others” was the base group. Finally, we used a dummy variable to control for country. Since the institution environment for new venture creation in China was less mature than that in Singapore, more usage of network ties was expected in China than in Singapore (Nee, 1992; Wong, 1995; Bian & Ang, 1997).
Table 1 shows the descriptive statistics and correlations for all variables. The correlation matrix suggests that the collinearity among the main variables is low. However, country dummy is an exception, in that it is highly correlated with “average appointment level,” “average educational level” and “competitive intensity.” To examine its robustness, we tested the hypotheses by entering all variables into the regressions, and then retested the hypotheses by deleting country dummy from each of the models. All of these models showed consistent results. This indicates that our models are robust. So we reported the results of full model in Table 2.
Table 2 presents the results from Tobit regression, since a large portion of our dependant variable equals 1 (60% in the pooled sample, 73% in the sample of key team member, 61% in the sample of investor, 47% in the sample of lead users) (Long, 1997).
Hypothesis 1 predicted that networks would be most likely to be used in approaching key team members, and least likely to be used in approaching lead users, and investors were in between. Model 1 and 2 test this hypothesis, based on pooled sample that includes the cases of all three types of resource owners. Since each firm reported one dependent variable for each type of resource owners respectively, the support for Hypothesis 1 would be indicated if our results showed significant and positive coefficients of Key team member (dummy) and Investor (dummy). Our findings reveal two strongly significant and positive coefficients, which support Hypothesis 1. Moreover, model 2 shows that the result remains when the moderating effects of competitive intensity is included.
Model 3 to Model 8 were designed to test Hypothesis 2, 3 and 4 in approaching the three types of resource owners respectively. Model 3, 5 and 7 test Hypothesis 2a, 2b, 3a and 3b, which predicted the main effects of human capital. The results show that in approaching key team members, none of the four human capital variables have significant impacts on the propensity of using networks. In contrast, in approaching investors and lead users, human capital variables influenced entrepreneurs’ behavior in similar pattern—while educational background did not show significant impacts, work experience demonstrated significant influences. In summary, (1) the average occupational status was positively correlated to the probability of using ties, and (2) compared with others, the teams that were composed of one or more members who had work experience in management or marketing were less likely to use ties. Hence, Hypothesis 2a was supported in approaching investors and lead users; Hypothesis 2b was not supported; in testing Hypothesis 3a, significant results in opposite direction were however found in approaching investors and lead users; Hypothesis 3b was not supported.
Model 4, 6 and 8 test Hypothesis 4, which predicted the moderating effects of competitive intensity. The results were consistent among the scenarios in approaching the three types of resource owners—competitive intensity did moderate the influence of both work experience variables (Jaccard, Turrisi, & Wan, 1990). In other words, when the competitive intensity was higher, the founding teams with high occupational status in prior jobs were especially likely to seek resources from their networks, and the founding teams with work experience in management or marketing were especially unlikely to search for resources from their networks. In summary, in the scenario of approaching key team members, investors and lead users, Hypothesis 4a and 4c were supported, while Hypothesis 4b and 4d were not supported.
With respect to our control variables, first, team diversity showed positive influence in approaching key team members and lead users. Second, compared with other industry sectors, biotech industry was the section where more social networks were engaged in approaching investors and lead users. Last, entrepreneurs in China significantly used more networks in approaching investors and lead users than their counterparts in Singapore, while it was not the case in approaching key team members.
In this study, we examined how entrepreneurs human capital influences their propensity to employ network to acquire initial resources. We investigated this research question in the context of approaching three types of resource owners, and found that in comparison with searching for key team members or investors, entrepreneurs were significantly unlikely to resort to their networks in searching for lead users. Moreover, we noticed that in 44% of our cases entrepreneurs approached lead users by using market mechanisms. The finding supported Sarasvathy’s (2001) argument that it takes whatever means the entrepreneurs already possess to achieve their dreams of building a new venture.
With respect to the influence of human capital of entrepreneurs, our sample showed two interesting findings. First, we did not find significant main effects of human capital variables in the cases of approaching key team members, while we did find such effects in the cases of approaching investors and lead users. One possible explanation is the lack of variance in the dependent variable for key team members (for 73% of the firms, YKM = 1) (Long, 1997). Second, while work experience influenced the propensity to use networks in approaching investors and lead users, educational background did not show significant impacts. The reasons may be (1) networks built in schools/ universities may be less diversified than those built in work places; (2) engineering or science graduates may not have lower social skills than business graduates, because increasingly many universities encourage students from all disciplines to attend social science classes to improve their communication and other social skills; and (3) many engineering or science graduates get promoted to management positions after years of work. On the whole, differences in network features that stem from school education may decline after years of employment. This finding may indicate that time spent in employment is more valuable than time spent in schools, in terms of preparing networks for creating new business. In other words, it suggests that professional networks, which are built through business interaction involving colleagues, customers or suppliers, are more helpful for the development of new ventures, than academic networks (Birley, 1985; Dubini & Aldrich, 1991).
One unexpected result of this study deserves more attention. The literature has suggested that teams with management or marketing experience will exploit more social networks than teams with only technology experience because they may have better social skills and broader networks (Baron & Markman, 2000; Lin, 1999). However, we found that they were less likely to use social networks. One explanation is that they prefer market methods consciously, since they realize the risks or bias in using social networks (Portes, 1998; Adler & Kwon, 2002). For example, if entrepreneurs choose majority of the key team members from families or close friends, such cohesive teams will unwittingly fall into “groupthink,” that is, they fail to examine their own assumptions or consider alternative courses of action, thus making poor decisions (Janis, 1972). Another reason is that, entrepreneurs with better social skills have more capability of adjusting to a wide range of social situations and feel comfortable with individuals from diverse backgrounds (Kilduff & Day, 1994), thus they may be more willing to move out of their limited existing networks and approach strangers in the market place that supplies varying resources.
Furthermore, our study found that competitive intensity positively moderates the influence of human capital. This implies that fierce competition in the market constrains entrepreneurs’ choice of resource owners in free market, so that the entrepreneurs with extensive social networks will have salient advantages in turning to their networks for help as alternative. In other words, it suggests that the information and resource benefits provided by rich social networks are more significant when the public information about where the entrepreneurs could find qualified resource owners becomes more ambiguous (Hoang & Antoncic, 2002).
Empirically, our results provide some managerial implications for nascent entrepreneurs. First, since pursuing the three types of resources owners needs public or private information to different extents, entrepreneurs should not constrain their information search within their limited network contacts, especially when they seek for lead users. Second, individuals with intention to start a new venture should attempt to accumulate social capital from professional ties during their employments. Third, since a heterogeneous founding team is useful in bringing in broader networks, entrepreneurs should choose co-founders with varied work experiences.
1. The cases we
collected included both the successful cases in which resources were
acquired and the failure cases in which entrepreneurs approached the
resource owners but were rejected.
2. For China data, the value was “2” for “high school level” and “1”
for “primary school level or lower.”
CONTACT: Zhang Jing, National University of Singapore, NUS Entrepreneurship Center, 14 Prince George’s Park, Singapore 118412; (T) +65 98444683; (F) +65 67750216; fbap9513@nus.edu.sg
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