Resilience through collaborative networks inemerging economies: evidence from Chinese
venture capital
Yuhong Zhoua1, Peter A. Gloorb, Stephanie L. WoernercaSouth China University of Technology, China. Massachusetts Institute of Technology,
Cambridge, MA, USA. yuhongz@mit.edu
bMassachusetts Institute of Technology, Cambridge, MA, USA. pgloor@mit.edu
cMassachusetts Institute of Technology, Cambridge, MA, USA. woerner@mit.edu

Abstract The high-speed growth of emerging economies attracts the attention of
global investors, but the uncertain institutional environment in emerging andtransitional economies makes investors uneasy. Using China’s venture capital(VC) data, this article examines the performance consequences of differences inownership between foreign and local investors, and network position establishedwhen VC firms (VCs) syndicate portfolio company investments. There is aphenomenon of separate institutional settings between China’s local VCs andforeign VCs in China, which makes ownership significantly affect investment
performance. The VCs’ positions in the collaborative networks can play a
mediating role; foreign VCs have better investment performance because of theirmore central-network position. Better-networked VCs can supplement or replaceformal institutions in transitional economies.

1 Introduction
Since the first venture capital firm was founded in the 1940s, global VC haspresented two characteristics. First, with a wealth of experience, VCs founded indeveloped countries are looking for investment opportunities on a global scale,and favor emerging economies that are growing. However, the unstable andimmature institutional environments of transitional economies present challenges
1

Corresponding author: Yuhong Zhou, email:yuhongz@mit.edu. We are grateful to two anonymous

reviewers and Pamela Siska at MIT WCC. We thank the Chinese Government Scholarship (CSC NO.
201606155079), the Humanities and Social Science Fund of Education Ministry of China (NO.13YJC630248)
and the Central Universities Fundamental Research Grant in China (NO.2015ZDXM01).

2

for foreign VCs (Ahlstrom & Bruton, 2006; Prijcker et al., 2012; Zhang &Pezeshkan,2016). Foreign VCs are unfamiliar with the specific problems oftransitional economies, for example, tax policy. Therefore, they are investing inemerging economies by registering offshore firms to avoid the negative impact
caused by unstable institutions (Fuller, 2010). Second, the governments are
speeding up the development of the domestic VCs in emerging economies andwelcome the foreign VCs (Bruton et al., 2009; Ahlstrom & Bruton, 2006). For
example, China's VC2 industry experienced rapid growth, especially in local VCs,
and related policies and regulations improved over time. For the first time, local
Chinese funds, mostly RMB-denominated funds, led over USD-denominatedforeign funds both concerning the number of funds and fundraising amount in2009. From then on, local Chinese investment became the dominant force in
China’s VCs. At the same time, foreign VCs in China still play an important role;
98.39 % of foreign VCs are from developed countries, and 53.77 % are from theUnited States 3 . To avoid the changes and constraints of the institutional
environment in China, foreign VCs often adopt a way of registering offshore firms
to participate in China’s VC investment, which results in a separate institutional
phenomenon between foreign-owner and Chinese-owner VCs (Fuller, 2010).
While the literature documents the conception of the separate institutional settings
between foreign VCs and Local VCs, the performance consequences of thisindependent ownership remain largely unknown. This paper investigates theimpact of this phenomenon on performance. Controlling for other known
determinants of VCs’ performance, we find that foreign VCs outperform local
ones.
How do foreign VCs achieve better investment performance in China than local
VCs? Social network theory has built a bridge between macro and micro levels ofsociological theory (Granovetter, 1973). More specifically, collaborative
innovation networks (COINs, Gloor, 2006) provide a potential solution. Under the
existing institutional environment, in addition to passively accepting the system,
enterprises can also expand their invisible collaborative relationship border,
facilitate trust formation between partners (Sorenson and Stuart, 2008; Meulemanet.al., 2016), reduce transaction costs, and thereby enhancing performance(Hochberg et al., 2007, 2015; Hain, 2016). Any VCs can be actively or passively
involved in the network environment. They will draw on their networks to seek
investment opportunities, share the risk, help the start-ups succeed, and improvetheir own performance (Hochberg et al., 2007, 2015; Abell & Nisar, 2007; Ewens,
2010; Zhou, 2014). Prior research has studied the performance effect of VC
networks, but the important intermediary role VC networks play between themacro system and microenterprises is still unknown. This paper examines theimportant mediating role of collaborative networks between ownership and2 Here, China’s VC refers to the VC which has invested in Chinese mainland start-ups. Foreign VC firm
is defined as the ownership of the firm is outside the Chinese mainland. A Chinese local VC firm is defined as
the ownership of the firm is in Chinese mainland.
3 Author is compiled from the data of CVsource database.

3

investment performance of VCs. Our paper suggests that, besides passively
accepting the system, VCs can increase resilience through collaborative networks
to thrive in uncertain institutional environments in transitional economies.

2 Theory and hypothesis

2.1 Ownership and investment performance
Prior research has examined the impact of ownership on enterprise performanceand shows that the performance of foreign owned enterprises is better than local
enterprises in emerging and transitional economies (Aydin et al., 2007; Lu, 2012).
Foreign owners have global managerial experience and can bring the latest andadvanced technology and management that improves enterprise efficiency (Aydin
et al., 2007). But few researchers have conducted empirical research on the impactof ownership on investment performance in the VC industry. In China, there is asignificant amount of foreign VCs, 98.39 % of them are from the developed
countries. There are many differences between local VCs and foreign VCs,
including registration, applicable law, sources, and exiting.
Fuller (2010) uses the grounded theory to analyze the institutional separation of
VCs in emerging economies. He believes that the government dominates one setof institutional arrangements while foreign VCs, through foreign direct
investment, link their Chinese activities to a set of more market-based institutions
located offshore. Most foreign VCs invest in Chinese start-ups by registering andexiting at offshore locations, while making substantial investments in the China
region (Zhu, 2015). Specific steps are as follows. The first step is registering alimited partnership at an offshore location at a tax haven (e.g., Virgin Islands,
Cayman Islands). The second step is setting up offices in China, and searching forinvestment projects. The third step, when foreign VCs decide to invest in a
Chinese start-up, they will ask Chinese start-ups to register a special purpose
company in the tax haven, and then invest in the special purpose company. In thelast step, the special purpose company raises money from other investors throughan initial public offering (IPO) of its shares on a stock market outside China andforeign VCs exit the company. Thus, foreign VCs do not have to pay the income
tax in China, practicing reasonable tax avoidance. However, local VCs must abideby the rules defined by the Chinese government and have been under a high tax
burden and other restrictions. For example, without the legal entity status of alimited partnership in China before June 2007, local VCs had to be structured ascorporate VCs and had to bear the burden of double taxation compared tocorporate VCs and venture investors (Jiang, 2011).

4

From the source of VC funds, foreign VC is mainly funded by pension funds,
insurance funds, banks, large corporations, governments, wealthy individuals andfamilies and so on. They have strong financial support-systems4. Chinese local VC
funds mainly come from the government fiscal budget and state-owned
institutions. In 2006, the government budget and state-owned institutions
accounted for 82.97 percent of the total, compared to 62.54 percent in 2010 (Feng,2012). When high-risk VC and scarce resources combine, it is prone togovernment rent-seeking which would lead to worse investment performance (Luet al.,2012; Hain et al.,2016). In July 2010, insurance funds were allowed for
private equity investment in China but had been prohibited in the business of VC
until 2014. Due to policy restriction, large capital sources are outside the reach of
the Chinese local VCs. Thus, Chinese local VC’s strength is much weaker thanforeign VC’s. The average total management funds of Chinese VCs was $202.93
million, compared to $4.5 billion of foreign VCs.
In addition, the exit channels of foreign VCs are more diverse than of local
VCs in China. The best exit way of VCs from start-ups is through an IPO(Hochberg et al., 2007, 2015). According to the data of CVsource database, only
2.60% of Local VCs exit via an IPO out of China’s mainland securities market,
compared to 22.3% foreign VCs exiting via an IPO in China mainland A-sharebourses, 30.54% of foreign VCs exit via NASDAQ, 20.10% via NYSE. Foreign
VCs can exit from the China security markets, as well as other global securities
markets. But for local VCs, it is difficult to exit out of China. Besides, M&A andtrade resale are also important exit channels for VCs. Foreign VC can takeadvantage of the global multi-level capital market. However, Local VCs have todepend on the immature capital markets in China.
With all the differences between the different ownership VCs, we find that
foreign VCs have distinct advantages. Thus, we hypothesize as follows:
Hypothesis 1: In China's VC market, VCs with different ownership havesignificantly different performance, and foreign VCs achieve better investment
performance.

2.2 The mediator effect of VC network position
The first Chinese local venture capital firm was founded in 1993, almost 50 years
later than in the U.S. With 98.39 percent of Chinese foreign VCs coming fromdeveloped countries, foreign VCs’ investment experience is much better thanChinese local VCs and they know the importance of the benefits of network
position in the collaborative networks. Foreign VCs leverage their advantages ofexperience and financial strength to attract local VCs to co-invest in syndicates(Hopp, 2010), and overcome entry barriers by cooperation with local VCs(Hochberg et al., 2010). Because more densely networked markets of VC
4

From Annual Statistics of China Venture Capital 2016

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experience fewer new entrants (Hochberg et al., 2010), we find that many foreign
VCs had established better positions in the network in the early 2000s, when most
Chinese local VCs did not pay much attention to it (Keil et al., 2010; Zhou &Song, 2014). Therefore, at the very beginning, many foreign VCs have become theincumbents in the Chinese VC networks and built their more central network
position by repeating interactions with other VCs to facilitate trust among them
(Meuleman et.al., 2016). Subsequently, foreign VCs used the externalities of the
network to prevent the entry of new entrants (Hochberg et al., 2010; Zhou &Song, 2014). Thus, they consolidated their central network positions.
Figure 1 shows the largest K-cores of China's VC networks from 2007 to 2012.
A k-core is a maximal group of actors, all of whom are connected to some number(k) of other members of the group5. If K is larger, the member is more central in
the network. Figure 1 illustrates that in the core part of China's VC networks,
foreign VCs are most numerous, joint VCs followed, very few local VCs are partof the core network.
From the above information, we state that VCs with different ownership havedifferent experience, , and motivation to set up their positions in the collaborative
networks. Thus, we hypothesize as follows:
Hypothesis 2: In the Chinese market, VCs with different ownership will havedifferent positions in the collaborative networks, and foreign VCs have more
central positions in the network.
Fig. 1 Largest K-cores of China's VC networks, 2007 -2012. The figure shows the largest K-coreof the network that were established when VCs syndicate portfolio company investments. A blue
dot is a China local VC, a red square is a foreign VC, a black triangle is a joint VC, a gray pointis a missing item.

12-core in 2007

12-core in 2008

11-core in 2009

11-core in 2010

10-core in 2011

9-core in 2012

VCs tend to syndicate their investments with other VCs, rather than invest
alone (Anne et al.,2016). Based on trust, they are bound by their current and pastco-investments in the collaborative networks with other VCs (Hochberg et al.,2007, 2010, 2015; Abell & Nisar, 2007; Ewens, 2010；Zhou & Song, 2012,5

http://faculty.ucr.edu/~hanneman/nettext/C11_Cliques.html#kcore

62014). Trusted networks can reduce transaction costs that exist in the market
without the need to increase transaction costs within the organization due to thesize of the firm (Carney, 1998). VCs are diseconomies of scale in the industry, so
it is an important way to reduce transaction costs through collaborative networks.
Local VCs are familiar with the local culture, politics, economy andinstitutional system, and are more likely to obtain information on investment
businesses and to withdraw from the local secondary securities market. On thecontrary, foreign VCs are not so familiar with the Chinese environment, and they
have the liability of being foreigners in China (Barnard, 2010; Geleilate etal.,2016). At the same time, foreign companies have the advantage of experience,
financial strength, and can more easily achieve favorable exits through an IPO on
a global scale. Therefore, VCs prefer to cooperate with other VCs to complement
each other. After fundraising, VCs have started an overlapping cycle ofinvestment processes, including searching for promising start-ups to invest in,post-investment management and value-added services, and then reaping the
rewards (Haislip, 2011). Syndication networks based on trust are a critical sourceof information and investment opportunities (Bygrave,1988). VCs invite others toco-invest in their promising deals for future returns. Through collaborative
networks, VCs can share human resources, professional skills, and specialized
knowledge to evaluate start-ups more accurately (Hopp, 2010). Regardless of theirfinancial strength, VCs can be combined into a larger pool of investment via VC
networks (Hochberg et al., 2007) and share many of the risks associated with newstart-ups with co-investing partners (Zhou & Song, 2012, 2014). This is morepractical for the underfunded local VCs. Because of the transitivity of thecollaborative network, VCs can transmit their own certification of start-ups
through the network (Large & Muegge, 2008). They can also help start-ups recruit
executives by networking (Carvalho, 2008), and transfer part of their social capitalthrough the network to start-ups (Luo et al.2014). All these opportunities, such as
selecting promising start-ups, adding value to start-ups and achieving better exit
rates, are main drivers of a VC’s performance. Therefore, we hypothesize asfollows:
Hypothesis 3: The network position of VCs is the mediator between VCs
ownership and VCs investment performance. VCs with different ownership getbetter investment performance through a dominant network position.

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3. Methodology

3.1 Sample and data
The data for our analysis comes from the CVsource database of China Venturegroup. Some of the missing data was complemented by the Z database 6 . We
concentrate solely on the VC investment rounds which had been done at Chinese
mainland start-ups before December 31, 20087. We distinguish between VC firms.
Our sample begins in 1989 and extends through 2008, with firm exit information
updated through 20168.We construct the multiple regression models of unbalanced
panel data and test the three hypotheses. The estimation data sets contain 1593
VCs that participated in 7,151 investment rounds involving 5,069 start-ups.
Excluding 455 missing investment data points, we finally collected 6,696 roundsof VC events and 108,416 connection relationships; 26.146% of the investment
rounds and 38.651% of sample start-ups involve syndicated funding.
In addition, the data used to construct the VC network dates from before
December 31, 2012. Networks are not static. Relationships may change, and entry
to and exit from the network may change each VC’s position in the network. To
capture this dynamic change, we use the co-investment relationship in the five
years before the investment happened to build the annual network (Hochberg etal., 2007; 2010; 2015). The actor in the network refers to the VC firm, and the tie
refers to the co-investment relationship between the VCs in each investment round(Hochberg et al., 2007, 2010; Abell & Nisar, 2007; Meuleman et al., 2009; Ewens,2010). In this paper, we construct a directed network and a non-directional
network for every year. The directed network is used to distinguish between theleading syndicate VC and VC invited to co-invest in the portfolio company. The
leading syndicate VC is the VC with the largest amount at this round ofinvestment. Since there were fewer investment rounds before 1996, the VC
network of 1996 consists of 50 investment rounds involving 29 VCs from 1989 to
1996. But from 1997 to 2012, we use the 5-year window, including the base year
to build the annual network.

6 CVsource database and Z database are the only professional private equity databases in China.
CVsource website is http://www.chinaventuregroup.com.cn/database/cvsource.shtml. Z database can berefered to http://www.pedata.cn/jsp/software/smt_win.jsp.
7 In order to focus on the study of Chinese venture capital, we only study the venture capital (VC)
events，and exclude the Angles, PE-Growth, PE-Buyout, PE-PIPE, Bridge / Mezzanine events.
8

Most VC funds are structured as closed-end, often 8-year in China. We close the sample period at theend of 2008 and use the following 8-year for measuring investment performance.

8

3.2 Variables

3.2.1 Dependent variable: investment performance
Most prior researchers use the exit rate as the investment performance of VCs(Hochberg et al., 2007, 2015; Abell & Nisar, 2007; Nahata, 2008). For this paper,
we also use the successful exit rate defined as the investment performance of VCs.
The successful exit rate of a VC firm in a year is measured as the fraction of
portfolio companies invested in the year that VCs successfully exit via IPO or asale to another company or receipt of follow-on funding.

3.2.2 Independent variable: the ownership of VCs
The ownership of VCs can be divided into local VCs, foreign VCs, and joint
VCs. As the proportion of joint ventures in Chinese VCs is very small and theaverage ratio is 9.94%, in the past 17 years, it will cause a high degree ofmultilinearity if the foreign capital and the Chinese capital ownership are
introduced into the model simultaneously. Therefore, we use the foreign
ownership (foreign) and joint venture ownership (CO) as a dummy variable todistinguish the three types of ownership of VCs.

3.2.3 Mediator: network position
For measuring the network position of VCs, we follow rules of Hochberg et al.
(2007), Abell and Nisar (2007) and use five different centralities to capture fivedifferent aspects, including degree, indegree, outdegree, betweenness andcloseness. To ensure comparability of networks with different size, we normalize
each measure (Freeman, 1979). To avoid strong multi-collinearity, we introduce
the five different centralities in different models.

3.2.4 Control variable
Our benchmark model mainly replicates Hochberg, Ljungqvist, and Lu (2007)
and Nahata’s (2008) model. We take the former research results of VCs’
performance as the control variable, including the investment experience of VCs,

9

investment risks, competition in the VC industry, and investment opportunities in
the market. We will build the panel data model and focus on the impact of VC
ownership and network position on investment performance.

3.3 Model specification
To test the hypothesis 1, we set the regression model as (1)kPerformanceit    Foreigni  coi    j Control jit   it(1)This model focuses on the impact of the ownership of VCs on investment
performance, which controls the investment experience, investment risk, industry
competition, and investment opportunities of VCs.
We introduce Baron and Kenny’s (1986) three-stage models to test hypothesis
2 and hypothesis 3. We use models (1), (2), (3) to test the mediator effect of the
network position of VCs between VCs’ ownership and VCs’ investment
performance.
j 1

kCentrality it     Foreigni  coi    i Control it   it

(2)

i 1

k

Performanceit    Foreigni  coi  Centralityit    j Control jit   it

(3)

j 1

4 Results and discussion
After the LM test and Hausman test, we choose the unbalanced panel model withrandom effects. We use the three-step regression method to demonstrate how VC
network position mediates the impact of ownership on investment performance.
The results are shown in Table 1 and Table 2.
Models 1 to 5 in Table 1 illustrate that the ownership of VC has a significant
impact on network position. Models 1 to 4 show that foreign and joint VCs areassociated with more central-network positions, including degree, outdegree,indegree and closeness. In Model 5, foreign ownership is not significant tobetweenness, but joint ownership is very significant, indicating that joint VCs
have the function of a “bridge” instead of foreign VCs in Chinese VC network.
These results support hypothesis 2, ownership of VCs significantly impacts VCs’
network position, and foreign VCs have more central position in the networks.
Model 6 in Table 2 shows that foreign VCs are associated with significantly
better investment performance, and Hypothesis 1 has been supported. In Models 7
to 11, we add the five measures for network position respectively to model 6. The
results show that foreign VC's significant positive impact on investment
performance either completely disappeared or weakened. Results in models 7, 9,

10

and 10 shows that the ownership is non-significant when we add network
positions to model 6, including degree, indegree and closeness. Thus, these three
network centralities are full mediators of the relationship between ownership andinvestment performance. The coefficients of ownership in models 8 and 11 aresmaller and weaker than in model 6, which explains that the network position
measured by outdegree and betweenness are partial mediators of the relationshipbetween ownership and investment performance. In the five mediator variables,
closeness has the biggest influence on investment performance. A one-standarddeviation increase in closeness is associated with a 13.898 percentage point
increase in successful exit rate. Degree and indegree come next. Outdegree andbetweenness have relatively small effects economically.
Table 1 The impact of VCs’ ownership on VCs’ network position. Standard errors inparentheses. We use ***, **, and * to denote significance at the 1%, 5% , and 10% level (twosided), respectively.
(1)
nrmdegree

Network position: centrality
(2)
(3)
(4)
nrmoutdeg
nrmindeg
ncloseness(5)
nbetween

0.1386***(0.024)0.1990***(0.029)0.0734***(0.015)0.1252***(0.018)0.0574***(0.012)0.0892***(0.014)8.8844***(2.305)11.0046***(3.054)-0.0413(0.107)
0.6319***(0.132)0.0053***(0.001)0.0007(0.008)0.0046***(0.001)0.0003(0.005)0.0029***(0.000)0.0018(0.004)0.0470(0.044)0.1063(0.478)0.0252***(0.004)0.0398(0.035)0.0142(0.011)-0.0038(0.017)-0.0119(0.022)0.0011(0.007)-0.0034(0.011)0.0272**(0.014)0.0045(0.005)-0.0037(0.009)-0.0071(0.011)

1.6278***(0.492)-0.2130(0.797)0.1707(0.998)0.0035(0.045)-0.0670(0.071)0.1374(0.090)-0.1633***(0.017)-0.0005(0.018)-0.1306***(0.011)0.0028(0.011)-0.0828***(0.008)0.0050(0.009)3.1704***(0.807)-0.3581(0.709)-0.2619***(0.069)-0.0272(0.073)mk_pe

0.0031***(0.001)0.0026***(0.000)0.0015***(0.000)-0.2303***(0.030)-0.0009(0.003)

Constant

1.0364***(0.118)0.7853***(0.075)0.5029***(0.059)11.5914**(5.612)2.0358***(0.491)

Observations
NO. of VC

868
319

868
319

868
319

868
319

868
319

VARIABLES
Ownership
foreigncoVCs’ experience
norounds
lnfirinvdays

Investment risk
lngrowdays
vcseries
avstage

Competition
lninvsize
lnvcinflows

Investment
Oportunities

11
Table 2 Mediator effect of VCs centrilities . Standard errors in parentheses. We use ***, **, and* to denote significance at the 1%, 5% , and 10% level (two-sided), respectively.
(6)
VARIABLES
Ownership
foreigncosu_rate(7)
su_rate0.1035**(0.046)
0.0585(0.058)0.0631(0.047)0.0111(0.059)

Network position
nrmdegree

Investment performance: exit rate
(8)
(9)
(10)
su_ratesu_ratesu_rate0.0874*(0.047)0.0452(0.058)0.0713(0.047)0.0282(0.058)0.0191(0.047)-0.0337(0.058)(11)
su_rate0.0923**(0.046)
0.0378(0.058)0.1237***(0.035)nrmoutdeg

0.0619*(0.037)

nrmindeg

0.1775***(0.054)

ncloseness0.0059***(0.001)

nbetween

0.0296**(0.012)

VCs’experience
norounds
lnfirinvdays

0.0003**(0.002)-0.0722(0.030)0.0002(0.002)0.0843***(0.030)0.0001(0.002)-0.0737**-0.0005(0.002)-0.0772**(0.030)(0.030)-0.0003(0.002)0.0777***(0.030)-0.0008(0.002)-0.0726**(0.030)Investment risk
lngrowdays

0.0550***(0.017)
0.0911***(0.022)-0.0465(0.032)

0.0539***(0.017)0.0827***(0.022)-0.0446(0.032)0.0520***(0.017)0.0909***(0.022)-0.0436(0.032)0.0504***(0.017)0.0903***(0.022)-0.0467(0.032)

lninvsize-0.0513*-0.0181-0.0435-0.0275

lnvcinflows(0.027)0.0204(0.024)(0.029)0.0213(0.024)(0.028)0.0291(0.024)

vcseries
avstage

0.0562***(0.017)0.0836***(0.022)-0.0525*(0.032)

0.0530***(0.017)
0.0923***(0.022)-0.0463(0.032)Competition-0.0430(0.028)0.0314(0.024)0.0912***(0.028)0.0136(0.024)(0.028)0.0232(0.024)

Investment opportunities
mk_pe

0.0002(0.001)0.0003(0.001)0.0003(0.001)0.0003(0.001)0.0004(0.001)0.0002(0.001)

Constant

0.8232***(0.223)616
252

0.6462***(0.227)614
252

0.7085***(0.228)614
252

0.6046***(0.230)614
252

1.0539***(0.230)614
252

0.7444***(0.227)614
252

Observations
NO. of VC

In summary, the results in Table 1 and 2 illustrate that the VC network position
is a mediator variable between the relationship of ownership and investment
performance and that foreign VCs have better investment performance because of
their advantages of network position. Hypothesis 3 is confirmed.

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5 Conclusions
With the full data of China's VCs, we examine the performance consequences ofenterprise’s ownership and collaborative networks’ position. To the best of ourknowledge, this is the first study to examine the relation between ownership,collaborative network, and performance among VCs. Our findings are as follows.
Due to the separate institutional environment in Chinese VC (Fuller, 2010),
different ownership of VCs leads to significantly different investment
performance. Foreign VCs have better investment performance in China than local
VCs. Their network centrality mediates performance and eliminates the effects ofbeing a foreign or a local VC. Foreign VCs’ more central network position is thereason why foreign VCs can have better investment performance. In certain
institutional environments, VCs can expand their advantage and make up for theirdeficiencies through enhancing their network relationships, so as to achieve better
investment performance.
In emerging economies, institutional theory is more powerful than traditional
economic theory (Ahlstrom & Bruton, 2006). For the first time, combining themacro-institutional environment, the meso-network position, and the microenterprise performance, this paper presents a way that micro-enterprises can adapt
to the unstable environment through better-networked position. VCs can enhancetheir collaborative network positions to achieve complementary advantages
instead of being completely passive in the face of the institutional environment.

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