The Comparison of International Strategic Alliance Network
between Chinese and American Pharmaceutical Industry
Chih-Sheng Hsu, National Taiwan University, Taiwan
ABSTRACT
Strategic collaboration has become an important and worldwide mechanism for pharmaceutical firms
to succeed in drug discovery, development, and commercialization under the pressure of mass resources
needed in R&D and increasingly intense competition in the global drug market. How collaboration network
of pharmaceutical industry evolve in China versus United States? This article contributes to the literature
on the networks of international strategic alliance by comparing geographic distance, economic distance,
mean number of partner, density of network and the type of international strategic alliances in China and
the United States. Taking both static and dynamic view of network and focusing on the pharmaceutical
industry, we propose not only the framework of different motivations for alliances, but also the different
forms of alliance network between Chinese and American firms. Additionally, we advance and examine the
hypotheses using 20 years of pharmaceutical industry data and find some new outcomes.
Keywords: International Strategic Alliance, Network, Pharmaceutical Industry
INTRODUCTION
Chinese firms, which are different from firms in western countries, might have different
motivations and partner selection for strategic alliance. Although strategic motives and partner selection
criteria have become popular research themes in the examination of ISAs, few researchers have
conducted a detailed analysis of Chinese firms. How has the alliance network of the pharmaceutical
industry evolved in both China and the United States? This is one of the most interesting questions in
global business research.
The purpose of this study is to compare the strategic alliance of pharmaceutical firms from China
with those in the United States in order to understand the different motivations of alliance and how it
affects partner selection. We are most interested in understanding the differences in (1) cross-national or
domestic alliances; (2) the economic distance between partners; (3) numbers of partner per alliance; (4)
average number of partners (per firm); (5) density of alliance network; and (6) types of alliance network
between Chinese firms and American firms in pharmaceutical industry. This study also examines their
dynamic evolution.
This research is unique in several ways. First, unlike prior research on alliance network in any
industry, we focus explicitly on the pharmaceutical industry. As a result, our analysis yields important
insights. Second, few studies of strategic alliance network have distinguished Chinese from American
firms. Our research is designed to address the explicit differences between the two. Third, most of the
recent research on alliance networks investigates their static patterns. In this paper we integrate this
research stream with the dynamic evolution by empirically analyzing the longitudinal data. Fourth,
Chinese firms generally have more simultaneous cross-national partners than American firms do.
Nevertheless, we find that the result is the opposite. The study thus provides academic researchers,
Journal of International Management Studies, Volume 6, Number 3, October 2011
109
managers in pharmaceutical companies, and governments with a more precise understanding of the
differences between the alliance networks created in China and those in the United States from both static
and dynamic perspectives.
CONCEPTUAL BACKGROUND AND PROPOSITIONS
1.Different Motivations for Strategic Alliances between Chinese Firms and Firms from the U.S.
The major motivations for strategic alliance differ between Chinese firms and those from the U.S.
Many businesses in China are young or have recently been privatized, and their resource endowments are
unlikely to be strong. They may have specific resource endowments but may need additional resources to
be competitive. Such a need is a primary reason for strategic alliances. Therefore, Chinese firms may use
alliance as a means of acquiring the tangible and intangible resources to develop their capability to
compete in their domestic and international markets (Hitt et al., 2000). In contrast, firms from the U.S.
have richer resource and experience. They are motivated to form strategic alliances in several ways. First,
through interaction with other firms in the same country, a firm can combine its own knowledge with
external knowledge, thereby generating collective competencies or new knowledge. Additionally, for
cross-national alliance, they can extend the scope of current operations, achieve scale economies, and
reduce entry costs, labor costs and risks in new foreign markets.
2.Partner Selection for International Strategic Alliances
The specific motive for alliance formation is likely to have an impact on the partner selection
process, as firms are likely to place different value on the resources or capabilities of a potential partner
based on this initial motive. For instance, if a Chinese firm’s main motivation for forming an alliance is to
learn advanced technology, selecting an international partner with the technology that is needs may be
most important. Conversely, if a American firm’s main motivation for alliance is international expansion
and entry into an emerging market, selecting a local partner may be of higher value. Therefore, partner
selection in strategic alliances will differ with the strategic motives for alliance formation.
(1) Cross-national or Domestic Alliances
The formation of cross-border or domestic alliances often follows directly from the motivation for
alliance. For Chinese firms, most of which lack the resources and critical technologies or skills for
survivorship, cross-national alliance with firms from developed countries are most often used, especially
for high-tech industry (such as the pharmaceutical industry) and new business start-ups.
However, as several traditional internationalization theories suggest, geographic distance has had a
major impact on the target country selected for alliance (Ojala, 2007) because the costs of trade rise with
geographic distance (Chang, 2004). In general, the geographic distance of a cross-national alliance is
greater than a domestic alliance, and firms from the U.S. are tend to make alliances with domestic firms
to leverage complementary resources rather than search a foreign partner with higher cost of trade, culture
distance and behavior uncertainty. Therefore:
Hypothesis 1a: Chinese firms are more likely to make cross-national alliances; however, American
firms are more likely to make domestic alliances.
Hypothesis 1b: In a dynamic perspective, over the past 20 years, Chinese firms continue make more
proportion on cross-national alliances, and American firms continue make more
proportion on domestic alliances.
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Journal of International Management Studies, Volume 6, Number 3, October 2011
(2) Economic Distance
Economic distance is a measure of economic disparity between two countries (Ghemawat, 2001).
The economic distance between two countries often reflects differences in factor costs (such as wages)
and in technological capability, both important factors leading to the conflict and affecting the process of
international alliance and performance (Tsang & Yip, 2007).
Johnson (2008) suggested that it is easier for a multinational enterprise to deal with host countries
that are close in economic distance from their home country. There are several reasons. First, countries
close in economic development have similar market segments that can afford to consume similar types of
goods and services. Thus, knowledge about market demand transfers easily from home to host country.
Second, countries that are similar in economic development have similar physical infrastructure, such as
airports, roadways, railways, and seaports. Thus, firms serving a host country with an infrastructure
similar to the home country will enjoy efficiencies in its operations, thus lowering costs. Third, firms
develop competencies or knowledge-based resources that are related to their markets (Madhok, 1997).
These resources can be best leveraged in countries that are similar in economic development because the
skills learned in one market can be replicated in or adapted to the new markets. Besides, Dunning (1998)
argued that firms entering countries that are widely different economically from their home country need
to adjust to the new market conditions, thus reducing their likelihood of success. According to these
articles on foreign direct investment, we may know that economic distance between partners seems will
be negatively associated with successful alliance. Indeed, American firms tend to engage in cross-national
alliances with partners that are in economic proximity to their countries; however, Chinese firms are still
likely to make cross-national alliances with partners that are economically similar to China in order to
learn the technologies or skills that are not possessed by partners that are in economic proximity to their
countries. Thus:
Hypothesis 2a: Chinese firms are more likely to make cross-national alliances with partners that are
great in economic distance from their countries; however, American firms are more
likely to make cross-national alliances with partners that are close in economic distance.
Hypothesis 2b: In a dynamic perspective, over the past 20 years, in terms of the cross-national alliance,
Chinese firms continue make more proportion on cross-national alliances with partners
that are great in economic distance from their countries, and American firms continue
make more proportion on alliances with partners that are short in economic distance.
3. Scale of Alliance Network
(1) Number of Partner (per Alliance)
Most studies on international strategic alliance have focused on those formed between two firms,
where the underlying assumption has been that a strategic alliance involves only two-partner firms.
Although this structure dominates, many firms enter into partnerships of three or more firms.
Franko (1971) theorized that as the number of parent firms in an international alliance increases,
individual roles become more complex, thus leading to higher failure rates. Elaborating on this theory,
Park and Russo (1996) examined the impact of the number of partners on performance and found a
positive relationship. Hu and Chen (1996), however, claimed the relationship between alliance
performance and number of partners was curvilinear; increasing the number of partners (up to five) is
positively associated with alliance performance. Thereafter, increasing the number of partners tends to
decrease alliance performance (Beamish, 2004).
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In effect, although some researchers have found a positive impact of the number of partners (over
two) on returns in the formation of alliance, there is evidence that more partners in one alliance is not
good. Such a view underlies Esteban’s (2007) findings. First, a higher number of partner augments
coordination and motivation costs (Oxley, 1997). Each new partner requires additional efforts so the total
amount of relational investments needed to make the alliance work is higher. As a consequence, partners
have fewer incentives to invest in the relationship, which may diminish the functioning of the alliance.
Second, a higher number of partners make agreement more difficult. A new partner not only increases the
required amount of relational investments, it also reduces the chances to profit from these investments,
given that it is more difficult to define new projects which satisfy the requirements of all the partners. A
final factor limiting the value created by these alliances is that each new partner makes it more difficult to
put the reciprocity mechanism into practice (Parkhe, 1993). For all of these reasons, multi-partner
alliances (over two) are seen as less stable, less successful and not as long-lasting as dyadic agreements
(Oxley, 1997; Park & Russo, 1996).
Hypothesis 3: Two-partner strategic alliance is the most stable for both Chinese and American firms is two.
(2) Average Number of Partner (per Firm)
According to resource-based theory, firms earn returns because they possess sustainable
competitive advantages (Amit & Shoemaker, 1993) that come from tangible and intangible resources
(Beamish, 2004). Resource-based theory predicts that firms earn greater returns when they have more
partners of alliance. First, they benefit from the environmental scanning mechanisms and managerial
expertise of all partners, thus allowing them to identify and neutralize a greater number of potential
opportunities and threats. Second, having more partners expresses more heterogeneous resources as
partners draw from distinctly different resource pools, thus increasing the potential for synergy from
integration, and this heterogeneity or diversity is increased when partners come from different countries.
In other words, resource heterogeneity refers to different, complementary capabilities that help the
organization create value (Roller & Sinclair-Desgagne, 1996).
Recall our earlier argument in which Chinese firms lack or need more capital and technology
(resources and capabilities), and they can obtain the resources and capabilities that they lack from their
partners, thus enhancing their own competencies and their competitive advantages through numerous
alliances. Thus, if a Chinese firm’s main motivation for forming a cross-national alliance is to receive
more resources or to get more capital and learn more advanced technology, they are likely to engage more
gradually in cross-national alliance. The converse holds for American firms, since they already have
comparatively more resources and capabilities, and their dominant motivation for cross-national alliances
is to extend the scope of current operations, achieving scale economies, reducing entry costs, labor costs
and risks in foreign markets. For this reason, we consider that American firms need less cross-national
alliance than do Chinese firms.
Hypothesis 4: Chinese firms have more simultaneous cross-national partners than American firms.
(3) Density of Alliance Network
The interconnectedness of nodes in a network — the ratio of existing ties between team members
relative to the maximum possible number of such ties — is the density of the network’s structure. Density
is perhaps the most common way to index network structure; it reflects the level of interrelatedness, or
reticulation, among all possible social ties (Balkundi, 2006). Density describes the linkages among the
points in a graph. The more points that are connected, the denser the graph is (Scott, 2000).
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As the last hypothesis suggests, Chinese firms have more cross-national partners, so they should
have denser networks. The main reason is that the external networks can be looked upon as strategic
resources influencing the firm’s future capability and expected performance. The resources and
capabilities owned by the Chinese firms depend upon network density, the commonality of knowledge
between firms, and the learning capability of firms. Industries with greater network density have a higher
learning effect.
Hypothesis 5: In the pharmaceutical industry, the density of alliance networks made by Chinese firms
is higher than of that made by American firms.
4. Alliance Network Types
Gomes-Casseres (1988) identified three types of alliance: supply based, learning-based, and
market-based. Supply-based alliances are organized along the supply line and involve resource transfer
beyond simple exchange relationship (finance, design, management skills and technology may flow
between the partners). Its main objective is to reduce transaction costs and enhance the possibility for
innovation. Learning-based alliances enable both creation and transfer of tacit knowledge across
organizational boundaries. Market-based alliances are motivated by a need to reduce competition (Nielsen,
2003). There are two types of alliance networks: technical and business. The former are characterized by
alliance with technology transfer, including R&D alliance, licensing, manufacturing outsourcing and the
like; the latter have no technology transfer, including funding, supply, or marketing agreement.
Firms in a developing country need current technology to compete in global markets (Svetlicic and
Rojec 1994). These firms often lack the knowledge and capabilities to develop or employ sophisticated
manufacturing or product technologies (Luo 1999); thus, they seek alliance partners with technological
capabilities (Hitt, 2004; Zahra et al. 2000). Gaining access to technology is important to Chinese firms
because it takes a long time to internally develop the know-how to create new technology and use it
independently. Conversely, in order to extend the scope of current operations, achieve scale economies,
reduce entry costs, labor costs and risks in developing markets, American firms frequently license their
technology to other firms in a developing country or make an outsourcing agreement with them to reduce
costs. In other words, both Chinese and American firms make more technical alliances and shape
technical alliance networks than form business alliance networks based on business alliance.
Hypothesis 6a: In terms of cross-national alliance, both Chinese and American firms are more likely to
make technical alliances.
Hypothesis 6b: In a dynamic perspective, over the past 20 years, both Chinese and American firms
continue make more proportion on technical alliances.
DATA AND METHODOLOGY
1. Data collection
In this article, we focus on the strategic alliances of pharmaceutical firms (SIC code including 2833,
2834, 2835 and 2836) from China and United States from 1989 to 2008. Our data comes from the SDC
database. SDC database collects information about global merger and acquisition, venture capital,
corporate restructuring, corporate governance, new issue, security trading and global finance. It is one of
the most accurate and comprehensive databases available on strategic alliances.
Through the SDC database, we obtained the following information, which is available for strategic
alliance: (1) announced alliance date; (2) participants in alliance; (3) participant alliance; (4) alliance type.
Journal of International Management Studies, Volume 6, Number 3, October 2011
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To identify the economic level of each participant nations, we examined the divisions of the world’s
economies based roughly on classifications used by the United Nations and The Economist. Developed
countries, such as United States, Germany and Japan, have mature economies with substantial per capita
GDPs and international trade and investments. The developing countries, including transition economies
and emerging markets, such as China and India, have economies that have grown extensively over the
past two decades. A total of 2,312 alliances over 20-years period (276 Chinese firms and 2,036 American
firms) have been identified in this research.
2. Measurement
(1) Cross-national or Domestic Alliances
Cross-national participants were identified via the participant nation for each alliances. We created
a dummy variable of 1 if the alliance participants come from different countries (to represent
cross-national alliance) and 0 if the alliance participants come from the same country (to represent
domestic alliance). Afterwards, we count the number and percentage of these alliances made by Chinese
and American pharmaceutical firms.
(2) Economic Distance
Based on the United Nations and the Economist, each participant nation was classified as a
developing- or developed country. Within a alliance, participants from different country categories were
recoded 1 to represent a great economic distance between participants and the same country category
were coded 0 otherwise. Then, we count the number and percentage of these alliances made by Chinese
and American pharmaceutical firms.
(3) Number of Partners (per Alliance)
The number of partners (per alliance) for each alliance was counted by the number of participants.
In our data, we find the number of partners ranged from 2 to 7. We also count the number and percentage,
so that we can compare two kinds of alliances made by Chinese firms and American firms.
(4) Average Number of Partners (per Firm)
From a network point of view, two points that are connected by a line are said to be adjacent.
Adjacency is the graph theoretical expression of the fact that two agents represented by points are directly
related or connected. Those points to which a particular point is adjacent are termed its neighborhood, and
the total number of other points in its neighborhood is termed its degree (degree of connection). Thus, the
degree of a point is a numerical measure of the size of its neighborhood (Scott, 2000). Following Scott
(2002), we use UCINET software to measure the mean degree of points to indicate the average number of
partner (per firm).
(5) Density of Alliance Network
For undirected graphs, the density of a graph is defined as the number of lines in a graph, expressed
as a proportion of the maximum possible number of lines. The formula for the density is L / [n(n-1)/2],
where L is the number of lines present. The simplest and most straightforward way to measure the density
of a large network from sample data would be to estimate it from the mean degree of the cases included in
the sample. The density of the graph can be estimated by calculating [(n*mean degree)/2]/[n(n-1)/2],
which reduces to [(n*mean degree)]/[n(n-1)] (Scott, 2000). Based on Scott (2000), we also measure the
density of alliance network for Chinese and American alliances by UCINET software.
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Journal of International Management Studies, Volume 6, Number 3, October 2011
(6) Alliance Network Types
To measure types of alliance network, we divide all types of alliance into technical and business.
R&D alliance, licensing, manufacturing outsourcing are examples of technical alliance; funding, supply,
marketing agreement are types of business alliance. However, some alliances are both technical and
business. Then, we count the number and percentage of these types for Chinese and American alliances.
3. Analytical Approach
To test H1 ~ H3 and H6, we conduct statistics of the number and percentage of variables during
1989-2008 and divisions of period, respectively. In addition, we compute Chi-squares between opposing
variables to express the significance. We also use t-test to analysis the outcome and its significance
between Chinese and American alliances. To test H4 and H5, we compute the average number of partners
(per firm) and density of alliance network using UCINET software, and draw the graphs of network
structure of Chinese and American alliance networks.
FINDINGS
Table 1 presents the findings of H1a and H1b. During the total period (1989-2008), there is a larger
percentage for cross-national alliance (85.87%) than domestic alliance (14.13%) in China. However, in
the United States, there is a smaller percentage of cross-national (44.84%) than domestic alliance
(55.16%). From a lens of dynamic perspective, for each period of time, the percentage of cross-national
alliance is greater than domestic alliance for China, while, the opposite is found for United States. In
addition, we have a significant Chi-square within both China and United States and a significant negative
effect on T-test which means that Chinese firms tend to make cross-national alliances, while American
firms tend to make domestic alliances. Thus, H1a and H1b are supported.
In table 2, we find that the percentage of great economic distance alliance is larger (86.50%) for
China. In contrast, for the United States, the short economic distance alliance is more. Still, a significant
Chi-square within both countries is found. The T-test between them is significant and positive. In other
words, both firms are likely to establish cross-national alliances with American firms. The same result is
found on dynamic analysis. Therefore, the findings provide strong support for H2a.and H2b.
Table 3 reports the tests for H3. The overwhelming majority of strategic alliances firms from both
countries involve only two-partner firms. There is a significant Chi-square within both countries as well
as significant and positive T-test. For this reason, H3 is also supported.
Table 4 indicates that the average degree of points for alliances from Chinese firms is 1.515 which is
smaller than alliances from American firms with 2.007. That is to say, H4 is rejected. As far as the density of
alliance network, it is 0.0035 for China and 0.0018 for the U.S. Thus, H5 is supported. In addition, the table
shows the graph of alliance network structure of both countries, and we can compare the different of number
of partner (per firm) and the density of alliance network between those two graphs.
In table 5, we examine the number and percentage of three alliance network types, judging from the
static result (1989-2008), although technical alliances is in the majority for both countries and there is a
significant Chi-square within both of them, however, the value of T-test between them is not significant,
therefore, H6a is not fully supported. In a dynamic perspective, only one significant and positive result of
T-test is during 1989-1993, however, during that period, the percentage of “both technical and business
alliance network” is the largest for Chinese firms (65.71%), while the percentage of “technical alliance
network” is the largest for American firms (47.25%). Therefore, H6b is not fully supported too.
Journal of International Management Studies, Volume 6, Number 3, October 2011
115
I
II
III
IV
Total
I
II
III
IV
Total
Period
1989-1993
1994-1998
1999-2003
2004-2008
1989-2008
N
T-test
Table 1: Findings of H1: Cross-national and Domestic Alliance
Chinese firms’ Alliance
American firms’ Alliance
Cross
Domestic
Chi2
Cross
Domestic
Chi2
N
%
N
%
N
%
N
%
35 87.50% 5
12.50% 22.500*** 364 45.90% 429 54.10%
5.328**
129 90.21% 14
9.79%
92.483*** 480 44.78% 592 55.22%
11.701**
38 74.51% 13 25.49% 12.255***
46 40.00% 69
60.00%
4.600**
35 83.33% 7
16.67% 18.667***
23 41.07% 33
58.93%
1.786
237 85.87% 39 14.13% 142.043*** 913 44.84% 1123 55.16% 21.660***
2,312
-8.450***
Period
1989-1993
1994-1998
1999-2003
2004-2008
1989-2008
N
T-test
Table 2: Findings of H 2: Economic Distance
Chinese firms’ Alliance
American firms’ Alliance
Great ED
Shirt ED
Chi2
Great ED
Shirt ED
Chi2
N
%
N
%
N
%
N
%
30
85.71%
5 14.29% 17.857*** 12 3.30% 352 96.70% 317.582***
112
86.82%
17 13.18% 69.961*** 35 9.04% 445 92.71% 350.208***
34
89.47%
4 10.53% 23.684*** 4 8.70% 42 91.30%
31.391***
29
82.86%
6 17.14% 15.114*** 7 30.43% 16 69.57%
3.522
205
86.50%
32 13,50% 1.263*** 58 6.35% 855 93.65%
6.957***
1,150
32.667***
Period
I
1989-1993
II
1994-1998
III 1999-2003
IV 2004-2008
Total 1989-2008
N
T-test
116
Table 3: Findings of H 3: Number of Partner (per Alliance)
Chinese firms’ Alliance
American firms’ Alliance
2
N
%
N
%
Chi
Chi 2
2
29
82.86%
246
95.05%
3
5
14.29%
16
4.40%
39.314***
954.396***
4
1
2.86%
1
0.27%
5
0
0.00%
1
0.27%
2
110
85.27%
436
90.83%
3
17
13.18%
38
7.92%
255.217***
1116.383***
4
1
0.78%
1
0.21%
5
1
0.78%
5
1.04%
2
29
76.32%
45
97.83%
3
9
23.68%
0
0.00%
10.526***
42.087***
4
0
0.00%
1
2.17%
5
0
0.00%
0
0.00%
2
34
97.14%
22
95.65%
3
0
0.00%
1
4.35%
31.114***
19.174***
4
1
2.86%
0
0.00%
5
0
0.00%
0
0.00%
2
202
85.23%
849
92.99%
3
31
13.08%
55
6.02%
468.063***
2.258***
4
3
1.27%
3
0.33%
5
1
0.42%
6
0.66%
1,150
3.924***
Journal of International Management Studies, Volume 6, Number 3, October 2011
Table 4: Findings of H 4: Average Number of Partners (per Firm) and H5: Density of Alliance Network
Chinese firms’ Alliance
American firms’ Alliance
Figure
Standard Deviation
Figure
Standard Deviation
H4
Ave Num.
1.515
0.871
2.007
2.407
H5
Density
0.0035
0.0605
0.0018
0.0442
Network Structure
Period
I
1989-1993
II
1994-1998
III
1999-2003
IV
2004-2008
Total 1989-2008
N
T-test
Table 5 Findings of H6: Alliance Network Types
Chinese firms’ Alliance
American firms’ Alliance
2
N
%
N
%
Chi
Chi 2
Technical
11
31.43%
172
47.25%
Business
1
2.86%
20.800***
33
9.07%
97.159***
Both
23
65.71%
159
43.68%
Technical
80
62.02%
277
57.71%
Business
1
0.78%
73.442***
16
3.33%
219.712***
Both
48
37.21%
187
38.96%
Technical
33
86.84%
28
60.87%
Business
0
0.00%
20.632***
2
4.35%
22.087***
Both
5
13.16%
16
34.78%
Technical
30
85.71%
20
86.96%
Business
1
2.86%
43.600***
0
0.00%
12.565***
Both
4
11.43%
3
13.04%
Technical
154
64.98%
497
54.44%
Business
3
1.27% 144.329***
51
5.59%
3.449***
Both
80
33.76%
365
39.98%
1,150
-0.157
DISCUSSION
Contributions
This article makes two sets of theoretical and empirical contributions. Theoretically, we argue that
for strategic alliance, partner selection and alliance network for Chinese and American firms are
considerably different, and these differences come from different motivations for alliance. Empirically,
we propose several hypotheses about international strategic alliance and alliance network, including
topics of cross-national or domestic alliances, economic distance, number of partner (per alliance),
average number of partner (per firm), average number of partner (per firm), density of alliance network
Journal of International Management Studies, Volume 6, Number 3, October 2011
117
and alliance network types in China versus the United States. We take both static and dynamic view of
network and focusing on pharmaceutical industry. In general, we find that almost our hypotheses are fully
or partly supported, while only H4 is rejected. For H4, we propose that since Chinese firms lack resources
and capabilities, and they aspire to obtain the heterogeneity resources and technologies that they do not
have from their partners, and thus enhance their own competencies and their competitive advantages
through numerous alliances. Thus, we previously argue that Chinese firms have more cross-national
partners at the same time than American firms.
There are two possible reasons why Chinese firms have less cross-national partners than American
firms simultaneously. The first reason is that the first, pharmaceutical industry is one of the world’s most
technologically dynamic. The links to basic science are tight. In the extremely competitive and fast
technology change environment, pharmaceutical firms have to make more international alliances,
particularly technical alliance, to accommodate the change of technical environment, even if big
pharmaceutical firms with plenty resources and professional technique in the U.S. Second, due to
American pharmaceutical firms have more resources and capabilities, and so many firms in Chinese firms
prefer to cooperate with them, so they have more opportunities to establish cross-national alliances than
Chinese firms with fewer resources and a poorer technique.
Limitations and Future Research Directions
Due to data constraints, one potential limitation is our sampling completeness, although the SDC
database collects almost alliance information over the world, however, and not all alliances are
announced and published in this database, especially those of countries beyond the United States. Our
findings with significant outcomes provide a useful baseline for future work. Because we use the special
network data, we could not examine the effect of each partner’s size, type, and international experience,
so perhaps these constructs may lead to other interesting findings or to different results. However, these
issues need to be clarified and explored. Fine-grained future research will provide additional insights into
the issue of international alliance networks.
CONCLUSION
Strategic alliance network is a key driver of industry development and value creation in the
pharmaceutical industry, but the partner selection and the type of network might be greatly different between
Chinese firms and those in the United States. In this study, we propose not only the different motivations for
alliances, but also the different forms of alliance network within Chinese and American firms through the
network perspective. Additionally, we examine the hypotheses using 20 years of pharmaceutical industry
data and find some new outcomes. We invite more researchers to join us in comparing the differences
between China and the U.S. In conclusion, we argue that the concepts and findings of this study take into
consideration of specific experiences of managers, directors, and firms in China and the U.S.
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