Social Network Construction and Competitiveness in Global Value Chain

- Network Analysis Perspective on Chinese Aerospace Industry

Yihan Wang, Ekaterina Turkina, Ari van Assche

HEC Montréal, Canada

This article explores how the allocation of MNEs’ industrial specialization and geographic location in

Global Value Chain affects their position in horizontally and vertically integrated business network in the

context of emerging markets, thus enhances MNEs’ competitive advantage in the specific market. Based

on network data of Chinese aerospace industry, it exhibits that industrial specialization, geographic

location as well as social embeddedness all affect MNEs’ horizontal partnership network and vertical

supply chain network. Nonetheless, the degree of impact varies across multiple determinant attributes and

there exists a significant divergence between horizontal and vertical networks.

1. Introduction

Obtaining competitive advantages at global scale has been widely recognized as MNEs’ ultimate motive

of internationalisation, whilst analyzing the manifold cross-border business relations and activities

embedded in global networks has arisen as a new pattern to understand their MNE’s internationalisation

strategies in recent international business research (Andersson, Forsgren, & Holm, 2002, Cantwell, 2013,

Coviello, 2006, Ernst, 2002). This network perspective can be attributed to the fact that MNEs are

“internally differentiated inter-organisational networks” (Ghoshal & Bartlett, 1990) and that the

embeddedness in the industrial structure and relational dynamics strongly affect MNEs’ performance and

behavioral logic (Uzzi & Gillespie, 2002, Zaheer, Gulati, & Nohria, 2000). Trending the globalised

production, coordination and distribution activities of MNEs, such hierarchical distribution of functional

units and business relationships have been weaved into Global Value Chain, where MNEs could exert

their influence on global market based on transaction complexity, codification and supply-base

capabilities (Gereffi, Humphrey, & Sturgeon, 2005).

The competitive advantages of MNE’s functional units, which are spread over various phases of Global

Value Chain, are embedded in multiple complex business networks. However, in international business

research, only handful studies have attempted to bridge the relationships between MNE’s value chain

position and business network position with empirical evidence, leaving wide potential for further studies

considering the new trend of globalization and knowledge-driven economic growth. This study aims to

reconcile these overlapping notions from a network perspective taking the industrial and institutional

complexity into consideration. Furthermore, the research focus will be set on a complex industry in the

context of emerging economies.

The major contributions of this article are as followed. First of all, this article excavates the analytical

advantages of Social Network Analysis on social capital in business networks base on the in-field

relational data collected. Subsequently, it builds up the connection between new paradigm in

internationalisation based on social capital, divergence between globalization and regionalization in

various locations, industrial complexity in Global Value Chain, knowledge creation and diffusion in

global network, and the specific institutional constructs in emerging economies. Ultimately, it provides a

preliminary responds to how MNEs obtain their competitive advantage from business networks and how

their position in Global Value Chain impact on its allocation in business networks, specifically in context

of a complex industry in emerging market.

This paper is structured as follows: In the second chapter, in recent decades we will discuss the necessity

of bridging the gap between global value chain and MNEs’ business network from perspective of the

pattern of knowledge diffusion, social capital and strategic resources to overcome “liability of

outsidership” and new paradigms of internationalisation in emerging markets. The third chapter explains

the reason why aerospace industry and Chinese market is chosen for this study, bridging up theoretical

background with real business conducts. The fourth chapter constructs the empirical model of the study

and introduces value chain position, geographic location and institutional embeddedness as determinant

factors for MNEs’ position in horizontal partnership and vertical supply chain networks. In the last two

chapters, some preliminary findings will be discussed based on multinomial logit regression outcomes of

multiple determinant factors. Furthermore, future potential research directions will be discussed.

2. New Challenges to Internationalisation Theories

2.1 Knowledge Diffusion, Social Capital and Internationalisation

Mainstream international business research in the 20th Century argued that firms go abroad seeking for

strategic resources to acquire sustainable competitive advantages in terms of ownership, location and

internalization (Barney, 1991, Dunning, 1977, Dunning, 1988, Porter, 1985). In order to exploit foreign

market, they follow an incremental internationalisation strategy that commences with indirect export, then

continues with establishment of sales subsidiary and manufacturing plants (Johanson & Vahlne, 1977).

Nonetheless, drastic changes in technology and economic structure of global market in recent decades

fundamentally alters cross-border business. Factors of production such as raw material, labor and capital

goods are still fundamentally affecting manufacturing activities, but in the era of knowledge economy

they are not the first priority in foreign market exploration and exploitation. Intangible assets such as

business relations, organizational knowledge and innovation capability increasingly create values for firm

and lead to market success (Kaplan & Norton, 2004).

MNEs are as key agent of knowledge development and transfer. Their incremental investment and cross-

border transactions in R&D sector lead to worldwide knowledge diffusion as well as regional

specialization (Buckley & Casson, 1976, Cantwell, 2013). Alternatively, knowledge-based economic

growth is also reshaping the pattern of MNEs and determinants of competitive advantages. Firms learn in

networks, where they benefit from spill-over effect, rather than on point-to-point basis and learning in

networks turn out to be more effective and efficient than external market mechanisms (Gupta &

Govindarajan, 2000, Podolny & Page, 1998). Particularly, for firms specialized in industries that require

intense knowledge inflows and complex effort in R&D, cohesion and range of a network could contribute

to ease of knowledge transfer in an industrial network consisting of inter-related firms (Reagans &

McEvily, 2003).

Dunning (2001) recognized the limitation of his original eclectic OLI model of internationalisation and

suggested that the model should also take strategic asset-acquiring and non-equity alliances into

consideration. As network relationships strongly influences entry mode and sequence into foreign market

(Coviello, 2006), Johanson and Vahlne (2009) revised their “Old Uppsala Model” of sequential

internationalisation process, and proposed that MNEs exploit new business opportunities embedded

business network to overcome “liability of outsidership” in addition to “liability of foreignness”. From

perspective of transaction cost, effective connectivity to other influential firms could optimise contracting

and coordination costs (Zaheer, Gulati, & Nohria, 2000). The construction and the quality of inter-firm

linkage in various types of network, as well as shared goals and culture, trust, norms and identification all

determine the heterogeneity of individual firms, their influence on the others and behavioral logic (Inkpen

& Tsang, 2005, Uzzi, 1997). In this sense, resources accessed through social ties, that is, social capital

(Bourdieu, 1986), is regarded as strategic resource that determines firm’s competitive advantage in a

inimitable and unsubstitutable path-dependent process. (Barney, 1991, Gulati, 1999).

In international business research, MNE’s social capital accumulation, which is reflected by their

establishment and control over flows of strategic resources in world-wide business networks, strongly

affect their competitiveness in global market. (Cantwell, 2013) When a MNE enter, to certain degree will

they encounter liability of foreignness, that is, threats from entry barriers into foreign market, including

spatial distance, unfamiliarity with local environment and the possible host country and home country

transaction costs. (Chen, Griffith, & Hu, 2006, Zaheer, 1995). In order to grasp the enormous

entrepreneurial opportunities created by “insidership” in foreign market entry, it is necessary for MNEs to

identifying their local business partners and exploiting social ties with them through experiential learning,

trust and commitment building, and opportunity exploration and exploitation (Andersson, Forsgren, &

Holm, 2002, Ellis, 2000, Johanson & Vahlne, 2009).

In all, MNE’s market performance and competence developments potential is rooted in the long-term

relational commitment between different subsidiaries and interdependence in knowledge sharing and

technology development throughout the whole production process (Andersson, Forsgren, & Holm, 2002).

Social capital embedded in various business relationships and network enable and optimise knowledge

transfer among firms, consequently, fuels business development and sustainable growth of MNEs. In a

word, in addition to physical and human capital resources, social capital and knowledge in possession

should also be considered as determinant strategic resources that significantly boost MNE’s

competitiveness in global market (Winter, 1998).

2.2 Global Value Chain and Internationalisation

The hierarchical feature of organization, production procedure and relationships of MNEs are embedded

in the Global Value Chain, where MNEs obtain their indigenous competitive advantage throughout input-

and-output streams.(Porter, 1985) Cantwell and Mudambi (2005) suggested MNEs follow different

behavioral logics both qualitatively and quantitatively when they conduct mandate upon their foreign

subsidiaries as synergy of locational-, subsidiary- and group-specific factors in their internationalisation

strategy. They may either reduplicate same type of production or services into several locations, or select

the most prominent location with specialized competence for each value-added activities (Teece, 1985).

The entry approach of integration of MNEs are determined differently depending on the condition of

foreign market. Sturgeon, Van Biesebroeck, and Gereffi (2008) used the sample of global automotive

industry to illustrate the necessity to introduce Global Value Chain analysis in international business

studies. As Gereffi, Humphrey, and Sturgeon (2005) suggested, the essence of global value chain

framework is focused on the power that regulate coordination over inter-firm linkages embedded in

production networks. It captures the geographic characteristics, power distribution and institutions

structure among multiple players in value added activities fueled by complex information codification and

exchange, codification of knowledge based on resident in the supply base at both local and global scale,

which is exactly international business research attempt to understand.

Conventional internationalisation models emphasises the resource endowment and intrinsic capabilities of

MNEs as pre-condition of internationalisation. Nonetheless, sustainable competitive advantages are also

strongly affected by the external opportunities and threats (Barney, 1991). Global Value Chain

encompasses exogenous factors such as geographical characteristics, power distribution and institutional

structure in addition to the inherent advantages of MNEs, and fill up the divergence between endogenous

and exogenous determinants of competitive advantages. (Sturgeon, 2007). Since the whole production

process is increasingly fragmented and modularized, the allocation of multiple valued-added activities of

MNEs in different countries varies in accordance with the regional specialisation and level of

development (Cantwell, 2013) Iammarino and McCann (2013) characterized the value allocation in

Global Value Chain as the combination of “global flattening” and “local steepening”, suggesting that

industrial complexity leads to the decentralization of value-adding activities. High value-added products

are produced in a small range of specialized regions but sold globally, while low value-added products are

produced globally, especially in developing world. Industrial complexity and heterogeneity determines the

trade-off between globalization and localization, which implies for international business research that

rather than ambiguously characterise if cross-border business activities are more “globalized” or

“localized”, it is more beneficial to bear the complexity of heterogeneous industries in mind, and dissect

their value chain into simultaneously existing and inter-correlated discrete activities.

In conclusion, introduction of Global Value Chain in internationalisation strategy reflect the essence of

multiplex construction of network and the determinants of MNEs’ competitive advantages. In this study,

the value chain position of business units will be depicted by their intrinsic industrial specialisation

allocated over value chain phases and the geographic locations that enhance or constrain the performance.

H1: MNEs’ industrial specialization embedded in global value chain has a significant impact on their

positions in business networks.

H2: Business units specialized in primary activities have better position than those specialized in

supportive activities.

H3: For business units specialized in primary activities, those specialized in later phases in value chain

have better network position than those positioned in forward phases in value chain.

2.3 Emerging Economies and Internationalisation

Another challenge to conventional internationalisation theories come from the new patterns of market

entry strategies of MNEs from emerging markets that undergoing economic liberation and institutional

reform(Arnold & Quelch, 1998, Hoskisson, Eden, Lau, & Wright, 2000). On one hand, emerging

economies are home to more than 80% of world’s population1, which ensures not only high market

potential, but also provide an immense and promising pool of knowledge and local intelligence; on the

other hand, as a result of disadvantageous positions in global market, underdeveloped infrastructure and

institutional voids, most of MNEs from emerging economies do not own controlling power over strategic

resources, technological competitiveness and efficient connection with the most proliferate partners. In

order to “catch up” with their counterparts form developed economies, they usually adapt “leapfrog”

strategy through linkage, leverage, and learning (LLL) to accelerate their foreign expansion in contrast to

incremental internationalisation theory (Malhotra & Hinings, 2010, Yiu, Lau, & Bruton, 2007). In this

process, some firms pursue close connection with domestic partners and local governments embedded in

1 Source: European Central Bank: https://www.ecb.europa.eu/ecb/tasks/international/emerging/html/index.en.html

home country network at early stage as substitution for external markets before going abroad (Khanna &

Palepu, 1997), while others first “escape” for better institutional conditions abroad and grow large

internationally ahead of exploiting domestic market (Boisot & Meyer, 2008, Bonaglia, Goldstein, &

Mathews, 2007, Witt & Lewin, 2007). For MNEs from developed economies, in order to overcome

liability of foreignness and obtain first-mover advantage, they are keen on searching for business

partnership with local firms in emerging economies by acquiring their local knowledge regardless of

inferior competence (Ernst, 2002, Ernst & Kim, 2002, Mudambi & Santangelo, 2014). Internationalization

models based on the experience in developed economies cannot fully explain these phenomena, whilst the

immature but rapid changing institutional context entails high challenge and potential to international

strategy studies (Hoskisson, Eden, Lau, & Wright, 2000). In this sense, studying emerging economies not

only help to understand the specificity of the markets themselves, but it also contribute our knowledge of

internationalisation dynamics and evolution embedded in cross-border business networks.

H4: For foreign business units, those of developed economies origin have better position in business

networks than those are of emerging economies origin.

3 Outlook of Aerospace Industry and Chinese Market

3.1 Complexity and Demand Dynamism of Aerospace Industry

In this study, we specifically select aerospace industry as observation, because the heterogeneity and

complexity serves the purpose of studying heterogeneous complex networks well. The complexity of

products, manufacturing process, and relationships among various business units in aerospace industry

strongly affect the interaction between business network formation and global value chain pattern. Major

categories of aeronautical products, including passenger aircraft, aircraft carriers and engines, helicopters,

avionics equipment, flight simulator etc. belong to the high cost, complex products and systems (CoPS).

These products consist of large number of tailored-made and engineering intensive components, devices

and sub-systems, which require high degree of novel knowledge and technology. They are usually made in

one-off projects or small patches with specific focus on design, project management, systems engineering

and systems integration. In all, the complexity and cost structure of a product shape innovation processes,

organisational forms and industrial coordination (Hobday, 1998).

Aeronautical product manufacturing process reflects the hierarchical integration of wide range of inter-

related value-added sectors and knowledge exchange activities spread all over the world. The complex

manufacturing process of aeronautical products also relies on intensive and diversified R&D and requires

world-wide coordination and cooperation, which are strongly influenced by government support (Niosi &

Zhegu, 2005). In this sense, the complexity of production necessitate a broad array of organisations

including OEMs, multiple-tier suppliers, support service providers, airlines companies, research institutes

and universities simultaneously cooperating with each other. As a result, a complex aerospace industrial

network is constructed consisting of multiple types of players and differentiated within-and-cross-border

relationships. In complex industries, MNEs create markets in networks and exploit their advantages within

multi-firm projects. Their long-term success is notably determined by their capability to coordinate

business relationships among manufacturers, customers, suppliers and regulators deliver (Ernst, 2002,

Hobday, 1998).

Apart from the complexity in production, another point shat shape the unique Global Value Chain

governance pattern is the dynamisms of demand. Over the last decade, aerospace industry maintains long-

term above-average growth driven by global economic growth and technological innovation, in spite of

recent short-term market shocks, including financial crisis, oil price fluctuation and new security threats.

(Boeing, 2015). Increase in individual income-level and travel frequency contributes to increasing market

demand, which as result creates new market niche for aircraft OEMs. Technological innovation, lower oil

price and deregulation lowers market entry thresholds with considerable profit margin as well as

accelerate replacement cycle of aircrafts and related-equipment. These demand-driven factors not only

creates and amplifies the market potential, but also intensifies global competition between airlines, OEMs

and multiple-tier suppliers. On one hand, OEMs still carry out the majority manufacturing and assembly

and play the dominant role in controlling and coordinating the value chain top-down, on the other hand,

shifts in operational cost structure and technological advances impose drastic outsource of value-added

activities to third parties and decentralize their controlling power (Bales, Maull, & Radnor, 2004,

Williams, Maull, & Ellis, 2002).

3.2 Development of Aerospace Industry in China

From geographic perspective, although aerospace industry is still dominated by developed countries in

North America and Western Europe, emerging economies such as China, Russia and Brazil have settled

their roots in the industry and launched considerable challenge to their western competitors. The ongoing

interest in studying aerospace industry in emerging market is partly attributed to the gradually exploited

market potential on account of the large population base, increasing income level and demand for more

frequent demand of travel by air. More importantly, given the ongoing institutional transformation and

income increase, aerospace industry development also provide an opportunity to observe the interaction

between local institutional change and global industrial integration.

The emergence of Chinese aerospace industry distinctly reflects new opportunities and challenges arising

from emerging market in global aerospace industry. This progress is not only attributed to the substantial

financial and policy support from the government, but also to the active integration of China’s commercial

aviation manufacturers, suppliers and airlines in the global commercial aerospace market and supply

chain.

Characterized by world leading economic growth, constant attractiveness to FDI, large volume of

population with increasing income, strong governmental support, China appears to be world’s second

largest civil aviation market with robust growth rate (Roger Cliff, 2011). Increasing frequent travel by air

accelerating establishment of new air routes, delivery of new aircraft and construction of new airports.

Regardless of recent economic growth slow-down, the growth rate of passenger traffic and air cargo will

remain 1 to 2 percent above the economic growth rate in the next 20 years. Entrance of new airlines,

especially low cost carriers, stimulating domestic point-to-point travel, exploitation in less-developed

regions, liberation of international travelling all fuel long-term market growth. It is estimated that the

current value of Chinese civil aviation market is 950 billion US dollar and the total number of aircraft fleet

in 2034 will reach 7210, which triples that number in 2014.

Table Chinese Civil Aviation Statistics (2006-2014)    2006 2008 2010 2012 2014Number of Passengers Dispatched by Civil Aviation (Million Passenbers) 159.678 192.511 267.691 319.361 391.949

Domestic Routes 145.53 177.32 248.377 296.002 360.399International Routes 14.15 15.19 19.3143 23.3581 31.5498Regional Routes (HMT) 5.36 5 6.7237 8.3368 10.0524 

Passenger-Distance Dispatched by Civil Aviation (Passenger Kilometre) 2370.66 2882.8 4039 5025.74 6334.19

Domestic Routes 184.675 230.553 328.006 403.376 501.739International Routes 52.391 57.727 75.893 99.198 131.68Regional Routes (HMT) 7.581 7.182 9.818 12.388 14.966 

Number of Civil Aviation Routes (Line) 1336 1532 1880 2457 3142Domestic Routes 1068 1235 1578 2076 2652International Routes 268 297 302 381 490Regional Routes (HMT) 43 49 85 99 114 

Number of Civil Aviation Airports (Unit) 142 152 175 180 200Number of Civil Aircraft (Unit) 1614 1961 2405 3589 4168*HMT: Hong Kong, Macau, Taiwan Sources: National Bureau of Statistics of China

The stimulating market growth and enormous potential which is yet to exploit, accelerate manufacturer

and service providers in aerospace industry inflow into the Chinese market. With technologic advances,

market deregulation, and further integration to the global market provide local OEMs, multi-level

suppliers and firms specialized in supportive activities great opportunities to find their positions both at

home and abroad. Foreign MNEs are continuing exploiting business opportunities and competitive low-

cost manufacturing in China (Bales, Maull, & Radnor, 2004). At the same time, they have become

increasingly aware of the endogenous innovation capabilities of their domestic partners and the substantial

importance of acquiring local knowledge embedded in business networks.

Nonetheless, China’s ambition is more than being a gigantic market await to be partitioned by foreign

MNEs. With strong support from the government and better command of local knowledge, domestic firms

are determined to enforce their strength in safeguarding their home base. As one important sector of

“High-end Equipment Manufacturing”, aerospace industry is officially recognized by Chinese central

government as “Strategic Emerging Industry” that will serve as national economic pillar and forerunner of

economic growth (MoFC, 2012). Chinese central government provides special funds and enacts

preferential policies for domestic firms specialized in aerospace industry. In 2008, new domestic OEMs

are founded, seeing re-merge between AVIC I and AVIC II as the new Aviation Industry Corporation of

China, as well as the foundation of The Commercial Aircraft Corporation of China (COMAC). In 2015,

COMAC unveil its independently designed large commercial airliner at the same time, COMAC

announced the first delivery of its own regional jet ARJ21. Both models have attracted the attention in

international commercial aircraft market as low-cost substitutes for counterparts manufactured by Western

OEMs. This is a clear signal that, China is determined to develop a complete and independent aerospace

industry and challenge the existing global commercial aircraft market constitution.

In sum, studying Chinese aerospace industry not only helps to understand the development of Chinese

market itself, more importantly, it forecasts the future development direction of aerospace industry seeing

the drastic changes of complex business relationships among various business units in world-wide

production and services.

H5: For domestic business units, those located in high output efficiency regions have better position in

business networks than those are not located in those regions.

4. Research Methodology and Data Description

In previous paragraphs, we briefly discuss about the necessity to study social capital as competitive

advantage embedded in Global Value Chain, in addition, we explained why aerospace business networks

and value chain in China are selected as observations of the data. Previous scholars have been conducted

extensive research on each specific issue, but few of them attempted to synthesis these aspects together

with empirical evidence.

Social Network Analysis is about the structure of social relationships and position of interacting actors in

relations with each other (Borgatti, Everett, & Johnson, 2013). Having been profoundly elaborated both

theoretically and practically in sociology (Burt, 1992, Granovetter, 1973, Mitchell, 1969, Padgett &

Ansell, 1993, Uzzi, 1996), management science (Cowan & Jonard, 2004, Porter, 1998, Powell, Koput, &

Smith-Doerr, 1996) and statistic science studies (Albert & Barabási, 2002, Fortunato, 2010, Watts &

Strogatz, 1998), in recently decades Social Network Analysis is introduced to international business

studies (Coviello, 2006, Ghoshal & Bartlett, 1990, Nohria & Garcia‐Pont, 1991, Nohria & Ghoshal, 1997,

Porter, 1998, Zaheer, Gulati, & Nohria, 2000) as a comprehensive, powerful, and compatible analytical

method capturing both identical and structural characteristics of various business entities, including

individuals, organizations and communities(Burt, 1992, Coleman, 1994, Coviello, 2006, Kogut, 2000,

Kossinets & Watts, 2009, Zaheer, Gulati, & Nohria, 2000), in order to explain interconnectivity among

multinational enterprises as well as agglomeration phenomenon in cross-border business activities.

Reviewing the key concepts proposed in previous paragraphs, including multinational enterprises,

internationalisation and localization, knowledge diffusion, social capital, Global Value Chain, and

institutions in emerging economies, we may find that all these notions are characterized by inter-

connected heterogeneous players at various levels embedded in multiple positions an extensive

hierarchical structure. In a word, they could all be conceptualized as network in different forms.

Therefore, Social Network Analysis will be a justified tool that may help to understand the relations and

interactions between these elements, and build the bridge between social structure and content.

The main research question of this article is how firms’ allocations in Global Value Chain affect their

hierarchical position in business networks and how firms can achieve competitive advantages in terms of

social capital composition, control and coordination. Based on the motive and status of dyadic relations

among the units, two types of business networks are built up including partnership networks that consist

of horizontal linkages including strategic alliances, joint-venture, and joint R&D programs; as well as

supply chain networks that characterize the direct supplier-buyer relationships between multiple-tier

suppliers, OEMs and ultimate customers.

The business networks constructed in this research consists of the most influential functional units in

Chinese aerospace industry including firms, research institutes, and universities from home and abroad.

140 commercial aviation enterprises above designated size2 included in Civil Aviation Industrial Yearbook

2014 are selected as focal nodes (egos). Their first degree contacts with strategic alliance, joint venture,

co-research, suppliers and buyers are included as their alter nodes. This relational data are collected based

on publicly available secondary information since 2008 including units’ official websites, news reports,

bilateral contracts and protocols, and their financial reports if available. In our sample, 950 business

observed units including firms, governmental institutions, research institutes, universities and vocational

college, are included. The number of business units are evenly distributed in both networks, at the same

time, the geographic locations and functional sectors of these units are also recorded. Specifically to

mention, for incorporated units, that is, firms, formally registered in the national administrative system for

industry and commerce in mainland China (excluding Hong Kong, Macau, Taiwan due to regulatory

difference) are categorized as domestic firms, if not, that unit is regarded as foreign. For non-incorporated

units, such as research institutions, universities, governmental organizations and vocational colleges, their

domesticity is determined by the location of their major functional institute. For domestic firms, their

corporate information including official name in Chinese, address of registration, major shareholders, type

of incorporation and ownership, year of registration, and registered capital is mostly traced in the National

2 All state-owned enterprises and non-state-owned enterprises with an annual income over 20 million yuan (approximately 3 million US dollar) since 2011 (National Bureau of Statistics of China, NBSC)

Enterprise Credit Information Disclosure System (NECIDS) updated by the end of 2015. The NECIDS

entries of certain enterprises are modified, due to the fact that some have experienced significant

restructure process. In those cases, the corporate information will be combined with self-provided

information on their websites of financial reports as well as stock market information.

Table: Number of Business Units in Partnership and Supply Chain networks

Number of

Units

Partnership Supplier Chain Both

950 663 592 336

Table: Statistic Summary of Units Included in Chinese Aerospace Networks

Unit Total Domestic Foreign

Firm 753 396 357

Government 60 47 13

Research Institute 59 42 17

University or vocational college 78 65 13

4.1 Dependent Variable

Social capital represents the impact of reference on individual behavior assessment and acts as ultimate

arbiter of competitive success, whose benefits are secured by being a member of certain social network

and structures (Burt, 1992, Durlauf, 1999, Manski, 2000, Portes, 1998). As representation of positional

advantage in terms of structural dimension of social capital, centrality is specifically focused on this this

study. High level of centrality signifies prominent position to influence the others and predict positive

economic performance (Freeman, 1978, Wasserman & Faust., 1994).

At local level, how well a node is connected can be interpreted by its degree centrality, that is, the number

of direct connections with its neighbourhood (Nieminen, 1974). However, degree centrality cannot

measure the “uniqueness” a point is located at the very center of the whole network. Freeman (1978)

suggested a nodes’ centrality should be measure by its significance in the overall structure in the whole

network, and proposed that a node’s “global centrality” can be measured by the sum of geodesic distances

to reach all other nodes (closeness centrality) and the brokerage between other nodes’ geodesics

(betweenness centrality). Borgatti (2005) criticized on Freeman’s global centrality measurement

presuming flows only take place over shortest paths, since exchanges may re-occur at same nodes and

linkages over time. Taking other multiple simultaneously existing paths into account, Katz (1953) and

Bonacich (1972) proposed a set of algorithms (eigenvector centrality) to evaluate eigenvector values, that

is, a node’s proximity to other well connected nodes. Eigenvector centrality represent the sum of a node’s

connections to other nodes weighted by the centrality in terms of both degree and closeness.

In international business networks, eigenvector centrality is embodied in “flagships” that take strategic

leadership over key suppliers, key customers, selected competitors and the non-business infrastructure

(Rugman & D'Cruz, 1997). These flagships possesses prominence and power gained through individual

attributes and central position in order take control over dispersed resources and capabilities as authority

and coordinate transactions between other network members as hubs, based on long-term collaborative

relationships among major players in a business system (Dhanaraj & Parkhe, 2006, Ernst, 2002) .

Eigenvector centrality interprets both local and global connectedness of an MNE, while also imply the

cognitive and relational dimensions of social capital in international business network. (Jackson, 2008,

Scott, 1991, Wasserman & Faust., 1994). In this regards, eigenvector centrality serves as dependent

variable representing the structural position as well as identical and relational coherence of a firm in the

business networks in this study. In this research, each business unit’s eigenvector centrality is based on its

location in the global network of the industry as well as its impact on other neighbouring units, which

represent the competitive advantages in terms of network position allocation.

4.2 Independent Variables

This study mainly study the relationships between a firm’s specialization in Global Value Chain and the

potential network return it can achieve. Porter (1985) categorized value activities into two categories:

primary activities that engage physical creation and transfer of products, and support activities that

coordinate and sustain primary activities throughout the value chain. These activities are integrated in the

value chain through intersecting horizontal and vertical linkages. For horizontal integration, MNEs follow

the “host-market production” and determine their production capacity based on size and potential of

foreign market. In contrast, for vertical integration, they “seek-for-efficiency” to exploit the competitive

advantage of local production factors and maximize the output of them.(Bathelt & Glückler, 2012).

This research mainly focus on the value chain positions of incorporated business units in the form of

firms. Based on the industrial categorization of their major business sector and relevance to aerospace

industry, they are categorized in following three groups:

(1) Primary Group: Firms that are directly specialized in manufacturing process of aircraft components

and systems, raw material supply, and final aircraft assembly. In addition, ultimate customers of value

chain such as airlines companies, airports and air-craft leasing companies are also included in this

category.

According position in the supply chain, firms that are identified within this group are divided into four

sub-groups, that is, up-stream suppliers, down-stream suppliers, Original Equipment Manufacturer (OEM)

and customers.

(2) Supportive Group: Firms that are not directly specialized in aircraft manufacturing, but provide

direct supportive services for the manufacturing process including software development, logistics

support, managerial and IT consulting.

(3) Relevant group: Firms that are not directly specialized in aerospace industry, but have very close

relationship with business functions of the other two groups.

4.3 Control Variables

In addition to industrial heterogeneity across multiple value chain phases of aerospace industry, in the

context of emerging market, geographic location and institutional constraints of firms, duration of market

presence as well as embeddedness in various business networks also play a crucial role determining the

network positions of firms. In this research, firm’s geographic affiliation is determined by their location of

official registration. Those are registered in mainland China are regarded as domestic firms, while the

others are labeled as foreign. Foreign firms that are located in one of the “Advanced Economies” defined

by IMF World Economic Outlook (2016) are identified from Developed Economies (DE), others are

identified from Other Emerging Economies (EE). As for domestic firms, as geographic proximity and

knowledge-spill over contributes to the overall network position, firm’s location within significant

aerospace industrial cluster is taken into consideration, based on the calculation outcome of location

quotient of employment in aerospace industry provided by Civil Aviation Industrial Yearbook 2014.

Table: Summary of Firm Distribution in Partnership and Supply Chain Networks

Partnership Network Supply Chain Network

Firm 488 555

Country of OriginChina 279 280

Developed Economies 184 244

Other Emerging Economies 25 31

Value Chain PositionUpstream Supplier 125 155

Downstream Supplier 85 86

OEM 64 61

Customer 55 89

Support Service 67 91

Related Industry 92 73

In addition to geographic control, years of market presence is also considered as a crucial determinant of

firms’ position in business networks. For foreign companies entering emerging economies, liability of

foreignness will most likely become the early pains they have to endure with high transaction cost to adapt

heterogeneous institutional environment in local market (North, 1990, Williamson, 1985) and high risk of

failure or sunk cost (Hymer, 1976, Zaheer, 1995). For domestic firms, even if they are better acquainted

with local know-how, still liability of newness, in other words, their age of market presence and initial

size, significantly affect their life cycle in the market. On the other hand, for some long-existing SOEs,

they need to launch corporation reform as react to dynamic competitive condition, while for new ones,

they struggle to survive severe competition at their infancy (Freeman, Carroll, & Hannan, 1983). In this

model, the year of presence are calculated based on primary data on firm’s own statement online, as well

as secondary data recorded in NECIDS dataset. For domestic firms, the year of presence is based on the

foundation year of their major business establishment, which mostly applied for old SOEs that have gone

through multiple incorporate reforms. For foreign firms, year of presence is based on the date of formal

market entry through subsidiary establishment or joint-venture with domestic partners.

Last categories of control variables take the firm’s embeddedness into consideration. The embeddedness

represents the social and historical effect on economic life in terms of social structure, cognition, politics

and culture (Uzzi, 1997). In terms of formation of firms’ business networks, social embeddedness is

reflected in a path-dependent adaptation and reciprocal commitment between business partners

(Andersson, Forsgren, & Holm, 2002). In this study following variables are included as control for social

embeddedness:

(1) Coexistence in both partnership network and supply chain network. If a firm appears in both

business networks, it signifies a higher level of embeddedness of the integrated business network

considering the high complexity of the essence of business relationships.

(2) Core-periphery position of firm’s industrial specialization in business networks. We integrated

the inter-connection of various industrial specialization attribute into a core-periphery model based on

firms’ specialization information and their connection in both partnership and supply chain network. If a

firm’s specialisation is identified as “core”, it means that this business sector is identified as a crucial

phase in the whole value-adding progress and firm specialising in such sector have a more advantageous

position in business networks.

(3) Similarity in civil law legal system. If firms’ regional location applies civil law system, they are

identified as embedded in similar institutional context as the Chinese market.

(4) Identity as subsidiary. Subsidiaries serve as crucial intermediate in business network in cross-border

knowledge diffusion as well as network expansion whilst the brokerage position of subsidiaries also

signals the competitiveness in terms of building and controlling network resources. (Cantwell, 2013) In

this study, if the firm is a wholly owned subsidiary or joint-venture, which affiliates to another firm or

business group, it is identified as subsidiary, vice versa.

There are few issued to clarify ahead of the empirical study. First, due to the data availability and

sensibility, only non-military production and services of investigated units will be included. In addition,

the database build-up is primarily based on secondary data available to the public and the main purpose of

the research is not to exhaust all types of linkages in Chinese aerospace industrial networks, therefore,

only formal business linkages that are clearly stated in the original sources are included. The author realize

the important of informal linkages between units, especially in emerging economies. Nevertheless, to

include informal linkages, it requires extensive qualitative investigation such as survey and interviews,

which exceed the original purpose and resources allowed in this research. In this sense, the author suggest

further research could conduct ethnographic observation and survey to understand how informal

interpersonal relationships can affect the strategic position and performance of different types of units in

industrial networks.

5. Preliminary Multinomial Logistic Regression Analysis Results

The dependent and independent variables introduced in this study encompass both nominal variables

(network position, geographic location, and embeddedness) and continuous variables (eigenvector

centrality). Multinomial logistic regression analysis solve this problem of compatibility of variables. For

each category of nominal variable, one group of parameter will be selected as base group, while the

regression coefficients of other variables within the same category will be calculated based on logit

function. For category of value chain position, we select firms specialised in “related industry” that are

neither engaged in primary activities nor support activities as base. For geographic locations, domestic

firms that are not located in output-intense provinces is selected as based. Embeddedness control variables

are listed in their original form since unlike the previous two categories, they are not exclusive to each

other.

Table: Multinomial Logistic Regression Analysis of Value Chain Position and Geographic Location

on Firm’s Position in Partnership and Supply Chain Networks

Partnership Network Coef.

Std. Err. P>t  

Supply Chain Network Coef. Std. Err. P>t  

Position in Value Chain

Position in Value Chain

Upstream 0.005 0.005 0.326 Upstream 0.012 0.005 0.009 ***Downstream 0.017 0.005 0.002 *** Downstream 0.017 0.006 0.003 ***OEM 0.031 0.006 0.000 *** OEM 0.030 0.006 0.000 ***Customer 0.026 0.006 0.000 *** Customer 0.042 0.006 0.000 ***

Support 0.007 0.006 0.235 Support 0.024 0.005 0.000 ***Relate (base) Relate (base)   Region RegionDeveloped Economies 0.005 0.004 0.288

Developed Economies 0.009 0.004 0.029 **

Other Emerging Economies 0.016 0.008 0.045 **

Other Emerging Economies 0.002 0.007 0.749

Domestic Cluster 0.021 0.004 0.000 *** Domestic Cluster 0.008 0.004 0.051 *Domestic Non-Cluster (base)

Domestic Non-Cluster (base)

   Control Variables Control VariablesYear of Presence 0.000 0.000 0.012 ** Year of Presence 0.000 0.000 0.588Co-existence in both network 0.004 0.003 0.193

Co-existence in both network 0.013 0.003 0.000 ***

Core industry in Partnership 0.010 0.003 0.002 ***

Core industry in Supply Chain 0.002 0.003 0.541

Civil Law System -0.004 0.005 0.383 Civil Law System 0.000 0.004 0.936Subsidiary -0.007 0.003 0.048 ** Subsidiary -0.004 0.003 0.227_cons -0.004 0.007 0.551 *** _cons -0.010 0.006 0.128  

Note:*,** and *** represent significance at p=0.10, p=0.05, p=0.01, respectively

Based on the regression results, we can find support for the proposed hypothesis to certain degree.

Nonetheless, there is a clear divergence between partnership network and supply chain network. In supply

chain network, all 5 positions in value chain have significant impact on firms’ network position, H1 is

fully supported. While in partnership network, being upstream supplier or specialising in support activities

do not have significant influence on network position, thus H1 is only partly supported and it lacks

evidence to validate the comparison in H2 in terms of horizontal partnership.

In supply chain network, firms specialised in primary activities stringently follows the tier-based

hierarchy. Those allocated at the later stage of the value chain have better off network position in

comparison to those allocated at the forward stage. H3 is supported in supply chain network. However, the

regression of coefficient of firms specialised in support activities (0.24) lie between OEM and multiple-

tier suppliers. It is predicted that although firms specialised in support activities do not have the same level

of influence and control power in supply chain networks in comparison to OEMs and customers,

nonetheless, thanks to their role as knowledge distributor and relational coordinator, they may still have

better network position than suppliers in the supply chain network. Therefore, H2 is not fully supported in

supply chain network.

Regarding geographic locations, we can observe the divergence between partnership network and supply

chain network as well. Since neither of the two categories of foreign origin turn out to be significant at the

same time in either partnership network or supply chain network, H4 lacks empirical support in both

networks. Nonetheless, if we compare foreign firms with domestic ones, prominent findings can still be

concluded. In partnership network, origin of developed economies does not have significant impact on

firm’s network position, while firms from other emerging economies have better off position than

domestic firms located in output-intense regions but have less advantageous position than domestic firms

located in output-intense regions. In supply chain network, the impact of origin of other emerging

economies is not significant. Nonetheless, firms from developed economies have better off position than

both types of domestic firms. Regarding domestic firms, it turns out that the influence of location in high

output efficiency regions overwhelms the rest of the country in both partnership and supply chain

network, thus H5 is strongly supported in both types of networks.

Last but not least, from the regression result of embeddedness control variables, we may conclude that,

although social embeddedness also influence firms’ network position, nonetheless, the contributions of

different sorts of embeddedness attributes also diverge depending on the type of network. In partnership

network, longer duration of market presence, coreness of industry in the network and identity as

headquarter all significantly contribute to firms’ position in partnership network. While in supply chain

network, only co-existence in both types of network significantly contributes to the network position, In

addition, similarity in legal system does not significantly affect firms’ position in both types of networks.

6. Discussion and Conclusion

To interpret the practical implication of regression outcomes, following conclusions can be derived from

our empirical evidence:

1. In China, aerospace industry is strongly demand-driven, final customers occupy the most central

positions in supply chain and exert their influence back to the OEMs and suppliers.

2. In primary manufacturing sector, a few amount of OEM dominate the control and coordination power

in partnership network and supply chain. Comparatively, downstream supplier take a less advantageous

position in both networks, but in general they are better off than upstream suppliers which spread over the

peripheral space in the supply chain.

3. Firm specialized in support activities have a strong mitigation effect in the supply chain network

between OEMs and multiple-tier suppliers, but their impact in partnership network is not significant.

4. For foreign firms, country-of-origin effect do have a significant impact. Firms from developed

economies have a stronger position in supply chain network, because of their innovation capacity and

knowledge spill-over. But in partnership network, firms form other emerging economies have a

significantly better off position due to the political intervention. They benefit inter-governmental

reciprocal protocol assuring effective connection with the most competent domestic partners.

5. For domestic firms, location in provinces that have intensive output in aviation products significantly

contribute to the position in both partnership and supply chain networks.

6. Coreness of business sector in the networks, year of market presence and headquarter identity have a

significant positive effect on individual firms in partnership network, while embeddedness in both

horizontal partnership and vertical supply chain network contributes to firm’s position in supply chain

network.

7. Cognitive attributes such as similar legal system do not have a significant impact on the position in

partnership network and supply chain.

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