Entrepreneurship of Small & Medium Business in Industrial Cluster

Entrepreneurship of Small & Medium Business in Industrial Cluster
of Zhejiang Province: A Sociological Perspective of Social Capital
Zhao Guangzhou1 He Mao1,2
1 School of Management & Economics, Kunming University of Science and Technology, Kunming,
650093, P.R.China
2 School of Management, Zhejiang University, Hangzhou, 310058, P.R.China
：
Abstract This paper aims to explain the role of local context in the development of start-ups’ social
networks, according to a sociological perspective of entrepreneurship, which considers social capital as
a main factor of success for the growth of young firms. This research problem is dealt with in
connection with the more consolidated theoretical studies of clustering phenomena in Zhejiang Province,
which have attributed to social networks a central role in explaining the concentration of the
entrepreneurial process in restricted geographical areas. The results of our analysis show that a strong
incidence of the local factor can help start-ups. A strong impact of local context, in fact, has a positive
influence on the industrial cluster in Zhejaing Province ,which brings the immediate advantage to reduce
the costs of control and to develop trust in the business relations.
Key words: Social Capital, Industrial Cluster, Entrepreneurship
1. Introduction
Since the 1980s, with the economic system transition and social structure transformation in the
People’s Republic of China (PRC), between the crevices of traditional system, a new type of economy
appeared in the market economy, which is different from public economy. According to 2003
Development Report of China’s Private Economy, there are nearly three million registered small &
medium business(SMB) investors in the PRC, possessing nearly 1,000 billion yuan registered assets,
employing more than 18 million people, and operating about 1.5 million enterprises of different types
(Statistical Yearbook of China, 2004). Take Zhejiang Province, where private economy is the most
developed nationwide as an example, in 2000 alone, nearly 50,000 private SMBs were “born” (ibid).
This paper aims to explain the role and the influence of local context of Zhejiang Province in the
development of startups’ social networks, according to a sociological perspective of entrepreneurship,
which considers social capital as a main factor of success for the growth of young firms (Aldrich, 1999).
This research problem is dealt with in connection with the more consolidated theoretical studies of
clustering phenomena, which have attributed to social networks a central role in explaining the
concentration of the entrepreneurial process in restricted geographical areas.
2. Theoretical background
2.1 The Social Capital: An Overview
In the last fifteen years the concept of social capital has been growing more and more popular in a
vast range of social disciplines (Bourdieau, 1983 ;Coleman, 1990). An increasing number of different
researchers, such as for instance economists and sociologists, have used this concept to answer a wide
range of questions related to their own specific fields of research, in accordance with the idea that social
phenomena can influence economic activities(Burt, 1992; Moran & Ghoshal, 1996).Social capital can
take different forms, primarily trust, norms, and networks.
Moving to key elements of social capital one of these is “embeddedness” (Granovetter, 1985),
which is determined by specific types of social structure and that for firms is the personal ties and
networks of relations between and among firms that differentiates them, explains performance and
economic development processes more generally. In this paper, we follow this definition of social
capital, which seems to be neutral to the external vs. internal characteristic distinction, according to Lin
(2001): “resources embedded in a social structure which are accessed and/or mobilized in purposive
actions”. It is a multidimensional definition, which considers three different elements of social capital:
resources embedded in a social structure, accessibility to such social resources by individuals, and use or
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mobilization of such social resources by individuals in purposive actions. According to this definition,
the social capital can be seen as a factor that is able to influence in a positive way the action of a single
actor, of a collective group and of a global organization.
2.2 The Social Capital Approach to The Field of Entrepreneurship
During the 1980s, a social network perspective was suggested to explain why some people are
more successful in starting and developing businesses and in general the entrepreneurship process
(Aldrich & Zimmer, 1986). This approach suggests that a start-up’s growth process is contingent on the
nature and structure of his social relationships, which also provides the resources and support required
for entrepreneurship. Indeed, a lot of varied empirical evidence regarding this topic has strongly denied
the image of start-ups as atomistic actors, showing how these economic units are more and more
embedded in large social and professional networks with other organizational actors (Yli-Renko, 2001).
The start-up’s social networks have been called the most significant resource of the firm
(Yli-Renko, 2001) and especially social encounters between the single entrepreneur, with whom the
firm at this stage of growth is normally identified, and his or her network contacts are often the main
strategic elements that are able to improve new venture development. In deed, the larger network
structure in which entrepreneurs are embedded constitutes a significant portion of their opportunity
structure (Aldrich, 1999).
In this context social capital is defined as start-ups’relations and contact with other different units;
such contacts to the extent that they provide the means for identifying opportunities or obtaining
resources or to the extent that they facilitate the utilization of other resources, are potential sources of
competitive advantage. It is normal to think that the importance of social capital in the entrepreneurship
has been attributed to the fact that they provide access to resources and emotional support (Lin, 2001).
2.3 The Social Capital Approach to The Industrial Cluster Phenomenon
In the literature regarding clusters, great attention has been especially attributed to the
entrepreneurship issue according to a social perspective. In fact, the importance of the social capital
construct during the start-up process would seem to reflect the publication of numerous studies based
both on industrial districts and on clusters (Porter, 2000), which have shown that social networks are the
most important factors in the creation of a local system of firms and that social capital plays a leading
role in the development of co-located economic activities (Saxenian, 1994). According to social capital
theory, we consider that industrial clusters are different from these traditional explanations in that there
is a belief that such clusters reflect not simply economic responses to the pattern of profitable
opportunities and complementarities, but also a peculiar level of embeddedness and social integration
(Gordon, McCann, 2000).
In this perspective, the specific characteristic of a cluster is the strong link between social and
economic elements, so that the firms located there would not represent the whole of the production unit.
Sociological analyses focus on how cultural similarities, community cohesiveness, interdependence
among local firms, repeated interaction, and familiarity allow firms to trust that their counterparts will
not act opportunistically. This trust can facilitate the smooth functioning or fragmented clusters made up
of many participants.In this sense, the industrial cluster is always seen as a privileged place for the
creation of social networks interfirms because of the presence of trust and informality in the economic
transactions of co-located actors that are facilitated by their proximity.
3. The data set and variables
For empirical analysis, this paper makes use of a data set which is from 361 queationaries on
Zhejiang private enterprises. To model the determinants of self-employment choice using maximum
likelihood logit analysis, we essentially assume a logistic distribution for the probability that optimal
self-employment is greater than zero. The dichotomous dependent variable is defined to be equal to 1 if
an individual reported “self-employed” as his or her employment status or claimed to be currently
“trying to start a business,” either alone or with others. The independent variables are described below.
3.1 Social network variables
Measures of the various elements of social networks were constructed from direct the Wisconsin
Entrepreneurial Climate Study (WECS) survey instruments. One set of instruments asked respondents to
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list specific individuals residing in Wisconsin to whom they would speak if considering a major career
change (such as changing jobs or becoming self-employed). The number of individuals named becomes
the variable Friends Network in this paper. The WECS separately ascertained for each respondent the
number of adult family members living in Wisconsin; this quantity becomes the variable Family
Network. In addition, the WECS ascertained the number of actual, discouraged, failed, and nascent
entrepreneurs with which the respondents had social contact. Actual entrepreneurs refer to individuals
who are currently self-employed, discouraged entrepreneurs refer to individuals who attempted
self-employment without having started a business, failed entrepreneurs are those who started but could
not maintain a business, and nascent entrepreneurs are those who are currently seeking entrepreneurism
for the first time. These variables will allow investigation of the extent to which social contact with
entrepreneurs (or aspirants) with different experiences influences self-employment.
3.2 Demographic variables
This study incorporates several demographic variables essential for control purposes and typically
encountered in self-employment research. A series of dummy variables describes whether an individual
is male, white, formally educated beyond high school, and married or single. Other dummies capture
whether a respondent is new to his or her current county (equal to 1 if the person has lived in the county
for five or fewer years) and whether he or she previously gave up on or experienced the failure of a
business. Continuous variables measure the respondent’s age and the number of his or her children.
Table1 Descriptive Statistics
N=295
Mean
Dependent Variable
Self-Employed
0.07
Social Network Variables
Family Network
6.04
Friends Network
6.33
Failed Entrepreneurs Known
0.33
Discouraged Entrepreneurs Known
0.31
Nascent Entrepreneurs Known
0.21
Actual Entrepreneurs Known
0.22
Total Entrepreneurs Known
1.06
Entrepreneurs Cheat
1.97
We Rarely Meet Entrepreneurs Socially
2.54
Bankers Help New Firms Get Started
2.30
Demographic Variables
Male
0.50
Age
36.79
Educated Beyond High School
0.51
Newcomer to County
0.48
Married
0.64
Children
1.17
Previously Experienced Business Failure
0.76
S. D.
0.25
3.73
6.38
0.72
0.72
0.55
0.58
1.84
0.73
0.61
0.64
0.50
11.13
0.50
0.50
0.48
1.34
0.46
4. Empirical results
After removing observations with unusable or missing data, a sample of 295 individuals remained.
Of these, 7.1% claimed to be actual or nascent entrepreneurs, a rate consistent with self-employment
rates in the aggregate. Table 1 presents descriptive statistics for the variables used in this study.
To investigate the cluster entrepreneurship of SMB in Zhejiang Province, as posed formally in
Table II, logit models were estimated to analyze the probability of self-employment as a function of the
social network, and demographic control variables.
Results from the model (Table 2) suggest that several factors relating to social networks may be
significant. With respect to issues of social network size or composition, individuals appear more likely
to choose self-employment when their network of family members is larger and when there is a greater
concentration of failed entrepreneurs (relative to actual entrepreneurs) in their network. Other significant
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results of this type relate to the Likert-scale variables in the model. Greater agreement with the
proposition that one’s male friends have started new firms is associated with a lesser likelihood of
self-employment; the companion instrument relating to female friends has a positive coefficient estimate
but is statistically significant only at the 11.4 percent level. Greater agreement with the proposition that
one’s associates believe successful entrepreneurs cheat others is associated with a lesser likelihood of
self-employment, while greater agreement that bankers and other investors help new firms get started is
associated with a greater propensity to choose self-employment.
As pertains to the influence of family, friends, and entrepreneurs, the effects visible in the basic
model emerge as significant principally for men: a larger network of family members and a greater
concentration of failed entrepreneurs in the social network retain their positive, statistically significant
coefficients for men only. The Likert-type variables which were significant in the basic model take on
different signs and significance levels across gender as well. A more positive opinion of entrepreneurial
support available from bankers and other investors increases self-employment by men but not women.
The logit models generate mostly intuitive results pertaining to the Demographic Variables. Results
pertaining to the Demographic Variables indicate that males, whites, older individuals, and those with
previous experience with an unsuccessful enterprise are significantly more likely to have chosen
self-employment than those in the omitted categories. These results are in general agreement with
previous findings in the self-employment literature.
Table2 Logit analysis of probability of self-employment
Explanatory Variable
Coefficient Standard Error
Intercept
210.18***
2.43
Social Network Variables
Family Network
0.10**
0.05
Friends Network
0.08
0.02
Failed-Actual Entrepreneurs Ratio
0.01*
0.00
Discouraged-Actual Entrepreneurs Ratio
20.03
0.01
Nascent-Actual Entrepreneurs Ratio
20.04
0.07
We Rarely Meet Entrepreneurs Socially
0.06
0.33
Men Friends Have Started New Firms
21.16***
0.36
Women Friends Have Started New Firms
0.69
.44
Entrepreneurs Cheat
20.53*
0.29
Men Would Start Businesses with Financial Assistance
0.54
0.47
Women Would Start Businesses with Financial Assistance
-0.51
0.45
Bankers Help New Firms Get Started
0.58*
0.31
Demographic Variables
Male
0.30
0.39
Age
0.07***
0.02
Educated Beyond High School
20.01
0.40
Newcomer to County
20.40
0.41
Married
0.14
0.43
Children
0.09
0.16
Previously Experienced Business Failure
0.77*
0.44
log-likelihood
2151.81
chi-square
81.03***
Number of Observations
295
*** Significant at the 1 percent level.
** Significant at the 5 percent level.
* Significant at the 10 percent level.
5. Conclusion
These results certainly allow us to accept the hypothesis that a strong connection with the local
context is always able to impact positively on the development of cluster entrepreneurship of SMB in
Zhejaing Province.In the wake of the empirical analysis, three essential findings emerge.
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First, with respect to social network composition, a larger network of family members and a greater
concentration of failed entrepreneurs in one’s network appear to increase the likelihood of
self-employment. Such results are consistent with previous findings which suggested that the most
influential source of “material” social support, as distinguished from “emotional” support, was the
family. The significance of the concentration of failed entrepreneurs in social networks is surprising in
that no other type of entrepreneur appears significant, but the result is consistent with the proposition
that individuals with entrepreneurial experience may provide more beneficial information than nascent
or discouraged entrepreneurs.
While the size of the network of friends appears relatively unimportant, the empirical analysis
yields a noteworthy result relating to the presence of individuals in the social network who have started
new firms. On the question of whether men and women in their social networks have started new firms,
male and female respondents exhibited no significant difference of opinion when the mean responses
were analyzed. However, greater agreement with this proposition as it pertained to male friends was
associated with a lesser probability of self-employment, a result that held as significant for both men and
women.
Finally, there appear to be visible differences by gender with respect to self-employment and the
influence of certain aspects of the broader social environment—in particular, cynicism about the source
of entrepreneurial success and the support of bankers and investors. In general, when members of a
respondent’s social network express a stronger belief that successful entrepreneurs gain success by
cheating others, the individual in question is significantly less likely to have chosen self-employment. A
more positive opinion of the support available from bankers and other investors is associated with a
greater propensity for self-employment, a result which emerges as robust for men only. These last
results may have an implication for the efficiency of entrepreneurial policy.
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