Large-Scale Collaboration Network Structure and Firm Innovation:
An Empirical Research
Yan Zhao, Xiang-jie Zheng
School of Management, Shanghai University, Shanghai, China
(xiangjie5065@163.com)
Abstract - Researchers have argued that alliance
network structure can influence firm innovation. We
construct alliance networks of 10 high-technology industries
in China to analyze their structure characteristics. We use
negative binomial regression to study the impact of alliance
network structure on the innovation capability of firms. The
empirical results show that firms embedded in alliance
networks with higher reach will have greater innovative
output, and the impact of clustering on the innovation
capability of firms is not significant, but the alliance
networks that exhibit both high reach and high clustering
will obviously improve the innovation capability of firms.
These conclusions will provide new scientific basis for firms
to develop alliance activities and for relevant government
departments to make industrial policies.
Keywords - alliance firms, network structure, innovation
capability, patent
I. INTRODUCTION
Innovation can promote economic development and
improve production efficiency, so it arouses widespread
concern in academia. Network researchers have argued
that cooperation among actors affect their behaviors and
creative output. Chen and Guan (2009) constructed patent
collaborate networks for 9 innovative countries, and their
empirical results showed that both short path length and
higher small world quotient are correlated with increased
innovation [1]. Uzzi and Spiro (2005) analyzed collaborate
networks of the creative artists who made Broadway
Musicals from 1945 to 1989, and argued that the largescale structure of the artists’ collaboration network
significantly influenced their creativity [2]. Guimera et al.
(2005) constructed scientific research collaborate
networks for 4 subjects (ecology, astronomy, economics
and social psychology), and found that dense network
promoted outputs of scientific research [3].
Strategic alliances help firms to absorb external
resources and knowledge, optimize the allocation of
resources, and improve the competitiveness of enterprises.
Therefore, collaborative networks among firms also have
potential impact on innovation. Giuseppe Soda (2011)
examined alliance networks structures of global auto
industry, and reported that network position significantly
influenced firm’s innovation performance [4]. Schilling
and Phelps (2007) analyzed the alliance networks of 11
American high-technology industries, and proposed that
interfirm collaboration networks also influence firm
innovation [5]. These raise the following questions: Does
the large-scale interfirm collaboration network in China
influence innovation capability of firms in the network? If
so, what structural properties will enhance firm’s
knowledge creation?
To address these questions, we focus our study on
two important structure indexes: clustering coefficient and
reach, and analyze their impact on firm innovation. Dense
local clustering can promote mutual trust among nodes,
accelerate the flow of information, and improve
knowledge transmission capacity. In addition, higher
reach can ensure that all kinds of non redundant
information resources can be absorbed by firms in the
network (Burt et al.2001) [6]. Therefore, we argue that the
alliance networks that exhibit both high reach and high
clustering will obviously improve the innovation
capability of firms.
We organize this paper as follows. First, the main
ideas of alliance network structure and firm innovation
are presented, thereafter we provide the hypothesis. Then
our research methodology is presented. Finally, we
discuss our results and give conclusions.
II. NETWORK STRUCTURE AND INNOVATION
When firms form and maintain alliances with each
other, they are weaving relationship networks. Firms
embedded in these networks can gain access to
knowledge and technologies from their partners, and
increase the creative output (Gomes-Casseres et al. 2006,
Owen-Smith and Powell 2004) [7] [8]. So we argue that
alliance network structure influence firm’s innovation
capability and clustering and reach play important roles in
firm’s knowledge creation.
In interfirm knowledge networks, knowledge and
information will be exchanged more intensely or
frequently
when
they
have
similarities
or
complementarities. Technology similarity or geography
proximity will lead to a high degree of clustering (Baum
2003) [9], and this will enhance transmission capability of
knowledge information. On the one hand, clustering can
accelerate the flow of knowledge and information, deepen
collective understanding, and ensure the accuracy of
knowledge and information; on the other hand, firms that
are embedded in networks are able to obtain richer and
more complete information. Each firm has its distinct
knowledge and advantages. Therefore, exchanging
heterogeneous information among firms is an effective
way to recombine their knowledge and advantages.
Network size and average path length also have
important influences on information diffusion and
recombination. If a firm can reach more firms, it can gain
much more knowledge and information from other firms.
Probability, speed and completeness of knowledge
transfer are related to path length directly, the shorter
average path length, the more quickly and more complete
information transfer (Watts 1999) [10]. Therefore, firms
that are embedded in alliance networks with more nodes
and have shorter average path length are able to gain more
information quickly, and have less information distortion
risk.
Hence, our hypothesis is:
Firms that are embedded in alliance networks with
higher reach and higher clustering will exhibit higher
innovation capability than firms in networks that do not
have these characteristics.
III. MODELS
To test our hypothesis, based on three-year windows
(i.e., 2000-2002, 2001-2003 …, 2006-2008), we use the
alliance relationship data from SDC Platinum database to
construct an unbalanced panel of Chinese firms in 10
high-technology industries for the period 2000-2008,
resulting in 7 snapshots of network structure in each
industry, for a total of 70 alliance network snapshots.
Each network snapshot is constructed as an undirected
binary adjacency matrix, and network structure indicators
can be calculated by the programming or statistical
software. Component structure for 2000-2002 is depicted
in figure 1. A summary of network size and structural
statistics for each industry (2000-2008) is presented in
table 1.
Fig.1. Network in year 2000-2002
TABLE 1
ALLIANCE NETWORK SIZE AND STRUCTURE STATISTICS
FOR EACH INDUSTRY (2000-2008)
Semiconductors
Measuring and
controlling
Aerospace
Chemicals
Computers
Household
audiovisual
equipment
Automotive
Petroleum refining
Telecommunicatio
n equipment
Pharmaceuticals
130
Average network size
（average number
of nodes in each
snapshot）
42.4
15
5.2
100%
22
210
97
8
71.2
33.7
70%
10.7%
27.3%
43
15
80%
195
55
71.3
17.5
26.1%
75%
145
45.9
43.2%
150
49.6
25.2%
Average number
106.3
36
47.6%
Industry
Number
of firms
Average
percent in
main
component
18.5%
A. Dependent Variable: Patents
Patent is an effective and steady index to measure
firm’s knowledge creation (Trajtenberg 1987) [11]. Firms
in high-technology industries often use patent to manifest
their knowledge creation achievement, so patentsi t, the
number of patent applications and successfully approved
for firm i in year t, represents their innovation capability.
Patent data are totally extracted from CNIPR, and are
checked by CD-ROM patent database for their accuracy.
B. Independent Variables
1) Clustering coefficient: Clustering coefficient
measures the percentage of a firm’s alliance partners that
are also partnered with each other. This variable can be
calculated as
(1)
clustering  3  N  (i) / N3 (i)
Where N△ (i) is the number of triangles in the graph,
and a triangle is a set of three nodes, each of which is
connected to both of the others. N3 (i) is the number of
connected triples, and a connected triple is a set of three
nodes in which at least one is connected to both the
others. The factor of three in the numerator ensures that
the measure can range from 0 to 1, with larger values
indicating higher clustering.
2) Reach: We take into account both the number of
firms and path length to calculate reach [12] . This measure
is calculated as
n m
1
(2)
reach  [ ] / n
i 1 j 1 d ij
Where n is the number of firms that can be reached
by any path from a given firm, and dij is the path length
(minimum distance or geodesic) it takes to reach them.
This variable can range from 0 to n, with larger values
indicating higher connectedness.
3) Clustering*reach: The interaction term,
clustering*reach will be measured to predict the impact of
the combination of clustering and reach on member firm
innovation.
C. Control Variables
1) Presample5, the sum of patents obtained by a firm
in the 5 years prior to its entry into the sample, are used to
control for unobserved heterogeneity in firm patenting.
2) Partner presample5, the sum of patents obtained
by all direct alliance partners in the 5 years prior to its
entry into the sample, are used to represent alliance
partners’ knowledge stock.
3) Betweenness centrality (BC), the centrality of a
focal firm in a network, is calculated as the fraction of the
shortest paths among other firms that pass through the
focal firm, and it indicates the firms' control power in the
network. We employ Freeman’s (1979) measure of
betweenness centrality to operationalize Centrality [13].
4) Network density, the ratio of existing links in the
network to the number of possible pairwise combinations
of firms, and we can calculate it as
(3)
density  l /[n(n  1) / 2]
Where l is the number of existing links, n is the
number of the nodes. This variable can range from 0 to 1,
with larger values indicating increasing density.
D. Model Specification
Patents, the dependent variable in this study, is a
count variable and takes on only nonnegative integer, so
we use negative binomial regression to construct the
models (i.e., sample is negative binomial distribution.). It
often takes firms 1 or 2 years to realize the innovation
benefits of interfirm alliances. Thus we consider the
lagged effect, and employ 1-year and 2-year lags to
ensure the robustness of our findings.
knowledge, and therefore higher clustering is unable to
improve the innovation capability of firms.
TABLE 2
PANEL NEGATIVE BINOMIAL REGRESSION MODELS WITH
RANDOM EFFECTS (n=2025)
presample5
partnerpresample5
density
BC
clustering
reach
clustering*
reach
constant
log
likelihood
presample5
partnerpresample5
density
BC
clustering
reach
clustering*
reach
constant
log
likelihood
presample5
partnerpresample5
density
BC
clustering
reach
clustering*
reach
constant
log
likelihood
1
0.001***(0.000)
patentsi t+0
2
0.001***(0.000)
3
0.001***(0.000)
0.001***(0.000)
0.001***(0.000)
0.001***(0.000)
-0.784(0.568)
0.005**(0.001)
-0.957(0.605)
0.001(0.003)
-0.142(0.252)
0.065***(0.022)
-1.608*(0.606)
0.004(0.003)
-1.103**(0.398)
-0.200**(0.091)
0.464***(0.151)
0.405***(0.085)
0.416*(0.222)
1.045***(0.299)
-4624.089
-4619.446
-4614.521
4
0.001***(0.000)
patentsi t+1
5
0.001***(0.000)
6
0.001***(0.000)
0.001(0.001)
0.001(0.001)
0.001(0.001)
0.214(0.601)
0.006**(0.002)
0.206(0.655)
0.001(0.003)
-0.258(0.242)
0.076***(0.022)
-0.515(0.716)
0.004(0.003)
-1.188**(0.383)
-0.194**(0.093)
0.467***(0.151)
0.357***(0.086)
0.448*(0.216)
1.067***(0.292)
-4845.656
-4839.172
-4834.126
7
0.001***(0.000)
patentsi t+2
8
0.001***(0.000)
9
0.001***(0.000)
-0.001(0.001)
-0.001(0.001)
-0.001(0.001)
1.551*(0.683)
0.005**(0.002)
1.603*(0.710)
0.004*(0.002)
-0.171(0.237)
0.007(0.024)
1.000(0.782)
0.007**(0.003)
-0.915**(0.382)
-0.223**(0.099)
0.367***(0.086)
0.489*(0.213)
0.381**(0.154)
-4960.305
-4959.948
1.000***(0.296)
-4956.735
IV. RESULTS AND DISCUSSION
We use panel negative binomial regression models
with random effects to estimate after Hausman test. Table
2 describes the results of the negative binomial regression
model. Models (1, 2, 3), models(4, 5, 6)and models(7, 8,
9) respectively report the regression results using 0-year
lag、1-year lag and 2-year lag between the independent
variables and firm patenting.
It is known that higher clustering enhances mutual
understanding and mutual trust between each other,
makes information exchange smoother, and ensures
information more accurate. However, the impact of
clustering on the innovation capability of firms doesn’t
exhibit statistical significance. One explanation for this is
that when clustering is close to a certain high level, firms
may gain more redundant information and repeated
Reach has significantly positive influences upon
patentsi t+0 and patentsi t+1, which demonstrates that firms
embedded in alliance networks with higher reach will
have greater innovative output. Because higher reach
endows firms with convenient access to other firms, and
obtain more abundant knowledge information to
recombine. However, significant impact disappears in 2year lag. One explanation for this is that the benefit of
firms from marginal increment of useful information
decreases over time, so it is unable to greatly enhance
firm’s innovation capability.
Clustering*reach has significantly positive impacts
on patent output (patentsi t+0, patentsi t+1, patentsi t+2 ), thus
our hypothesis is supported. Firms embedded in alliance
networks that exhibit both high reach and high clustering
can exchange knowledge information more expediently,
more quickly, and more efficiently, so they can prompt
knowledge creation.
Based on the data of 10 high-technology industries in
China, we explore the impact of alliance network
structure on the innovation capability of firms. The impact
of clustering on the innovation capability of firms is not
significant, but firms embedded in alliance networks with
higher reach will have greater innovative output.
Therefore, network structure attribute should be
considered adequately when firms develop alliance
activities and when relevant government departments
make industrial policies. Excessive scattered alliance
networks will cause information exchange inconvenience,
and affect the knowledge transfer speed, while dense local
clustering means more repeated and redundant
knowledge. In this case, it’s hard to get abundant and
novel knowledge even if firms spend high cost to engage
in collaborative alliances. The two conditions both have
adverse effects on firm innovation. Hence, relevant
alliance policy makers should take the two aspects into
consideration together. Before joining an alliance, firms
must consider network structure attribute of the alliance
network where they will be embedded, thus to get more
diversified information.
Interfirm Collaboration relationships have significant
impact on innovation performance of firms, and our
results suggest that alliance networks may be an important
mechanism of knowledge spillover. The cohesion and
connectivity of alliance networks can enhance knowledge
creation, and to some extent, our results have enriched the
small-world network theory (Cowan and Jonard 2003) [14].
The limitations of this study are mainly manifested
in the following aspects. Firstly, our results are likely to
be limited to industries that frequently employ alliances.
In addition, different knowledge characteristics are
capable of influencing the process of knowledge
integration and creation (Zander 1995) [15], which we
don’t consider when we study knowledge transfer. Thus it
is important for us to investigate the impact of these
factors on knowledge creation and they are worthy of
future development.
V. ACKNOWLEDGMENT
The authors are grateful for the suggestions of the
colleagues in the Center for Global Innovation and
Chinese Entrepreneurship, Shanghai University, for their
help in data processing and broad recommendations. This
research is supported by China National Science
Foundation “Cluster and Alliance: Chinese Empirical
Study of Firms’ Embeddedness and Dynamic Decisions in
Complex Innovation Network” (No. 71003069).
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