China s business network structure during institutional

China’s business network structureduring institutional transitions

Bing Ren & Kevin Y. Au & Thomas A. Birtch

# Springer Science + Business Media, LLC 2008

Abstract This study adopted a structural approach to examine the formation andcharacteristics of Chinese business networks via interlocking directorates during astage in China’s institutional transitions. Analyses of the network structures of 949listed companies revealed that: (1) Chinese business networks were smaller in scaleand lower in density than their Western counterparts; (2) no nationwide network witha dominant center existed; (3) interlock occurred mainly in the form of smallerbusiness groups which tended to be regionally fragmented; (4) ties were moreprevalent among industrial peers than with financial institutions; and (5) governmentownership was predominant. These findings therefore deepen our understandingabout the pattern and extent of business interlock in China. Not only do thesefindings provide substantive implications to the notion and dimensionality of guanxi,but they also offer inspiration to managers and policy makers by illuminating keycharacteristics of network structure. Laying these foundations shall pave the way forfuture research in the structure of Chinese business networks.

Keywords Business networks . Interlocking directorates . China .

Institutional transitions

Asia Pac J ManageDOI 10.1007/s10490-008-9106-3

This research was supported in part by the Natural Science Foundation of China (Fund code: 70602008).We thank Mike Peng (Editor-in-Chief) for his guidance, Yina Mao, Hao Liang and Liyang Pi for researchassistance, and Paul Miesing, Sunny Li Sun, and the two anonymous reviewers of the Asia Academy ofManagement conference in Tokyo for their helpful comments on earlier versions of this paper.

B. Ren (*)Department of Management, School of Business, Nankai University, Tianjin 300071, Chinae-mail: [email protected]

K. Y. AuDepartment of Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Konge-mail: [email protected]

T. A. BirtchCentre for Economics and Policy, University of Cambridge, Cambridge, UKe-mail: [email protected]

For nearly three decades, China has been undergoing an unparalleled institutionaltransitions—“fundamental and comprehensive changes introduced to the formal andinformal rules of the game that affect organizations as players” (Peng, 2003: 275). Atthe same time, Chinese business is widely acknowledged for its extensive use ofguanxi (connections) and network strategies. A number of studies have reported theprevalence and importance of business networks (Boxiot & Child, 1996; Peng &Heath, 1996; Peng & Luo, 2000; Park & Luo, 2001; Xin & Pearce, 1996). Yet, thesestudies primarily rely on survey data which does not fully reveal the underlyingconfiguration of China’s network economy. That is, prior research inhibits a morecomplete understanding of the structure and form of interorganizational networksamong Chinese firms at the national level.

A series of studies in the West and in East Asia have shown that to more fullyunderstand an economy and its capitalism necessitates an in depth examination ofthe structure of its business networks (Aguilera, 1998; Davis & Mizruchi, 1999;Gerlach, 1992; Stokman, Zieger, & Scott, 1985). Research has also shown that adeeper understanding of the network structure of an economy can emerge from anexamination of the extent of interlocking directorates (Pennings, 1980). Aninterlocking directorate occurs when a director sits on one or more boards (i.e.,holds multiple board seats). Since an interlocking network is comprised of tiesbetween large corporations, analyzing its structure can reveal important features,such as the size of the network, connections between businesses across regions andindustries, and the emergence of central, prominent businesses in an economy(Stokman et al., 1985).

Such an undertaking entails the systematic analysis of a significant amount ofdata that is typically unavailable from survey research. This may in part explain whyfew, if any, prior works are available for China. It is important for several reasons.First, the current literature by and large focuses on what a Chinese network economyis and how guanxi matters. Few studies systematically examine the overarchingstructure of guanxi or the network economy. Consequently, they do not offer thepotential insights that a more in depth structural analysis affords. Second, Chinesebusiness network and interlocking directorship studies are absent from mainstreamresearch. Hence, meaningful comparisons between China and other economies arebeing unduly restricted. Last, probing into the extent of interlocking businessnetwork formation during a state of institutional transitions could potentially shedlight on evolutionary aspects of business network formation, which is important tothe Chinese business research agenda.

Taking the first step toward filling the above gaps, this paper addresses thequestion: What network structures have Chinese firms formed via interlockingdirectorates? We begin by providing a brief overview of the phenomena commonlyobserved in the interlocking networks of other countries. This is followed byobservations specific to the Chinese context. Next, we present our methods andpreliminary data on the pattern of interlocks among Chinese firms. This discussion isextended to include a comparison between Chinese business network structures withthose of other economies. As the formation of business networks is closely related toinstitutional change in transition economies (Aguilera, 1998; Wong, 1996), we alsoprobe into how the formation of interlocking directorate networks are associatedwith China’s institutional transitions. To these ends, the present study adds to our

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understanding of China’s interlocking business network. A structural analysis onChina’s business network formation through interlocking directorates revealsdifferences and similarities between China and other economies and helps to pavethe way for future research, which is particularly important during a period ofmarked institutional transitions in China.

Understanding business network structures

A number of scholars have examined business network structures formed throughinterlocking directorates (Stokman et al., 1985; Scott, 1991a, 1992). A review of theliterature reveals three major business network models: the Continental European,the Anglo-American, and the Japanese. A more detailed review may be foundelsewhere (for example, Aguilera, 1998). Other variations have also been identified,for example, in East Asian countries. In Thailand, multinationals play a central rolein the economy and interlocking directorates are important to the governancefunction of the country’s business network (Peng, Au, & Wang, 2001). In Korea,large conglomerates are controlled by individuals and key family-owned holdingcompanies within the chaebol. The Korean government is also a key constituent inshaping the role of banks and trading companies (Biggart, 1990).

Earlier studies have also examined business networks in Hong Kong and Taiwan.In Hong Kong, dominant corporations formed business groups via interlockingdirectorships to enhance their power and status during the 1980s (Wong, 1996). Asthe environment changed, in particular Hong Kong’s accession to Chinese rule in1997, interlocking business networks were formed to reduce environmentaluncertainty (Au, Peng, & Wang, 2000). By contrast, most businesses in Taiwanconsist of family-controlled business groups which span multiple industries (Chang,2006; Chuang & Lin, 2008; Hamilton, 1991; Hamilton & Kao, 1990).

In the case of China, it has been argued that the system consists of “networkcapitalism” which “works through the implicit and fluid dynamic of relationship”(Boxiot & Child, 1996: 625). Where the Western market system is characterized byits primary reliance on legal contracts and property rights, “the networks of theemergent Chinese capitalism” are said to emphasize relational contracts and long-term trust relationships (Boxiot & Child, 1996: 625; Keister, 1999). Accordingly,guanxi and networks have been found to exert significant influence on business(Park & Luo, 2001; Peng & Heath, 1996; Peng & Luo, 2000; Xin & Pearce, 1996).

Moreover, business groups have been found to be an important interorganiza-tional form in the Chinese economy (Lu & Yao, 2006; Ma, Yao, & Xi, 2006).Chinese firms form diversified business groups to facilitate business operations(Keister, 1998; Yiu, Bruton, & Lu, 2005). They also share many network features.For example, firms may be tied to each other through ownership, capital, and traderelations (Keister, 2000). Similar to the Japanese keiretsu (Yoshikawa & McGuire,2008), the holding and trading companies in the business group are influential as aresult of their ability to provide capital and access to marketing channels (Keister,1998, 2000).

Keister (1998) did not study the structure of the networks but identifiedinterlocking directorates as a key feature of Chinese business groups during the

China’s business network structure during institutional transitions

late 1980s. Following on from this work, Ren and her associates examined thebusiness network structure formed by interlocking directorate ties in two regions inChina (Ren, Au, & Lam, 2001; Ren & Au, 2005; Ren, Au, & Peng, 2004). Thesestudies laid the foundation for such analysis in China and revealed the existence ofmarkedly different business networks in Shanghai and Guangdong (a Southernprovince of China started early in reform).

Analysis of interlocking directorate networks in China

The present study builds on earlier work. We selected 1999 as the focal year for ouranalyses because it currently represents the “mid-point” in China’s institutionaltransitions. China began its economic reform at the enterprise level in 1992. Sincethen reforms initiated by the Chinese government have included important changesto the economy (e.g., incentives and industrial structure) and institutions (e.g.,regulation and new establishments). Thus, the interorganizational relationships ornetwork patterns that have emerged offer potentially interesting insights into theimpact of China’s reformation.

Data

The China Securities Regulatory Commission (CSRC) requires public companies todisclose important information to satisfy listing requirements. Of relevance to thisstudy, we extracted our data (i.e., financial, governance and board composition,ownership, performance, and strategic information) for all companies listed on theShanghai and Shenzhen stock exchanges from COIN, a database created by theChina State Information Center.1

We compiled two data sets. The first, a comprehensive dataset, represented allpublicly listed companies in 1999. The data yielded a sample of 949 companies andenabled us to construct an overview of the national interlock network. The second,a comparative dataset, represented a subset of the first dataset and was comprised ofthe top 250 listed companies according to sales. This second dataset facilitated ourcomparison between China and other representative economies found in priorresearch.

Multiple directors were identified using Excel software. Prior to constructing theinterlock networks, we took some precautions to ensure the reliability of the data.First, we collected demographic information (age) and work experience aboutmultiple directors to confirm their identity.2 Next, we recoded the data to examinepotential mismatch. Lastly, the list of multiple directors was compiled and analyzedusing the UCINET software.

For our comprehensive sample, demographic data and variables of interest aresummarized in Table 1. Overall, the 949 listed firms had a mean size of 11.6(measured as the natural log of total assets); an average of 9.7 directors on their

1 The website of China State Information Center is http://www.sic.gov.cn/web/index.asp.2 Age information was obtained from the COIN dataset. Working experience was obtained from thewebsite of Sina.com which provides relevant information on public-listed stocks in China.

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http://www.sic.gov.cn/web/index.asp

boards; an average of being listed for 3.6 years; a mean size of shares listed of 32%;and on average, 45.4% of their shares were controlled by the largest shareholder. Morespecifically, 83.2% of the non-financial companies were state-controlled and 13.2%were non-state-controlled, including privately-controlled companies (7.3%), foreign-controlled companies (1.0%), and collectively-controlled companies (3.4%).3

Industries represented, included manufacturing (55%), wholesale and retail (9%),consolidated (9%), information and technology (7%), public utility and transporta-tion (4%), social service (3%), real estate (3%), and finance (1%), respectively.4

Network characteristics

We analyzed several network characteristics for each dataset, as summarized inTable 2. Similar to prior studies, these included density, multiplicity, and distance

3 Five ultimate controllers were identified from the data: the state, private (legal or individual), foreign,social community, and employee. In some cases it was not possible to identify the ultimate controller; i.e.,the sixth type of control is unidentified control. Control data was collected from the China Center forEconomic Research (CCER) database (see http://www.sinofin.com.cn).4 The industrial categorization method was released by China’s Securities Regulatory Commission(CSRC) in 2001 and is compatible with the international standard industrial code.

Table 1 Sample distribution based on industry and ultimate control (N=949).

Number of firms % of the full samplea

Industry distribution in the sampleAgriculture (A)b 19 2Mining (B) 11 1Manufacturing (C) 526 55Public utility (D) 34 4Construction (E) 18 2Transportation (F) 34 4Information and technology (G) 62 7Wholesale and retail (H) 85 9Financial institution (I) 6 1Real estate (J) 30 3Social service (K) 33 3Media (L) 10 1Consolidated (M)c 81 9Total 949 100Sample distribution based on ultimate controlTypes of ultimate control 13.2% of the sampled firms were non-state-controlled, including

privately-controlled companies (7.3%), foreign-controlledcompanies (1.0%), and collective-controlled companies (3.4%).

a The total number of firms in the 1999 full sample is 949.b China’s Securities Regulatory Commission (CSRC) in 2001 classifies Chinese firms into 13 primary (orbasic) industries in total, represented by the alphabetical letters ranging from A to M. This industrialcategorization is compatible to international standard industrial code. The 13 industries span over 80categories defined at the 2-digit level, while manufacturing industry (represented by letter C) has thelargest number of 2 digit subcategories with 33 categories in total. Letters in the bracket are representativeletters for each particular industry.c Consolidated industries refer to those highly diversified firms.


http://www.sinofin.com.cn

(Scott, 1992). Density reflects the ratio of the number of actual links in a populationto the number of possible links in the same population. Actors in a high-densitynetwork are likely to have a greater level of contact than those in a low-densitynetwork. Thus, information can be expected to flow more freely among members ofa high density network (Scott, 1992). Multiplicity measures the total number ofinterlocking directorates which two connected firms share in common (i.e., thestrength of ties between two connected firms). A higher multiplicity represents agreater tie strength. Distance measures the closeness between any two members ofthe network (Au et al., 2000). Operationally, it is measured by counting the numberof steps in a shortest path that a firm could reach another firm.

Table 2 The interlock business network in China and international comparison.

Variablesa China (mainland) Thailand HongKong

GreatBritain

UnitedStates

949 Top 250 Top 200 Top 200 Top 250 Top 250

Total numberof directors

8,811 2,521 1,511 1,628 2,682 3,108

Total number ofdirector seats

9,181 2,589 1,833 2,105 3,091 3,976

Total numberof multipledirectors

328 62 213 276 282 564

Proportionof multipledirectors

4% 2.46% 14% 17% 11% 18%

Cumulation ratio 1.04 1.03 1.21 1.29 1.15 1.28Number of director seats held by multiple directors2 90% 90.32% 69% 61% 69% 64%3 8% 9.68% 19% 25% 21% 24%4 1% 0% 8% 6% 6% 8%5 0.61% 0% 3% 5% 3% 3%>5 0.30% 0% 1% 3% 1% 1%Interorganizational relationshipsDensity 0.001 0.0024 0.042/0.015b 0.029 0.017 0.035Multiplicity1 91% 91% 86% 78% 94% 84%2 5% 3% 9% 10% 5% 13%3 3% 6% 3% 5% 2% 2%>3 1% 0% 3% 7% 0% 1%Distance1 5% 0.42% 2% 2% 2% 3%2 6% 0.27% 7% 10% 12% 22%>2 89% 0.31% 91% 88% 86% 75%

Analysis is based on 412 firms identified as having interlocking directorate ties in 1999. Thailand data areadapted from Peng et al. (2001), Hong Kong data are adapted from Au et al. (2000), and U.K. and U.S.data are adapted from Stokman et al. (1985).Multiple director Directors with two or more than two director seats, proportion of multiple directors totalnumber of multiple directors/total number of directors, cumulation ratio total number of director seats/totalnumber of directors, number of director seats held by a multiple director the number of companies that amultiple director serves, density the extent to which all possible relations are actually present.Operationally, it is the number of observed interlocks to all possible interlocks, multiplicity the numberof interlocking directors that any two firms have, distance a measure of the closeness between any twomembers of the network. Operationally, it is the shortest path by which a firm could reach another firm.a Definition of variables (Scott, 1991a).b Density data for Thailand have two different calculations: 0.042 is for MNEs (sample size is 102), and0.015 is for non-MNEs (sample size is 98). See Peng et al. (2001).

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Our comprehensive sample (n=949) yielded a total of 8,811 directors who held9,181 directorate seats. Three hundred and twenty-eight (4%) were classified asmultiple directors (i.e., held more than one board seat). Of these 90% sat on twoboards, 8% sat on three boards, and the remaining 2% sat on four or more boards.The data revealed that it was rare for directors to sit on more than three boards.

In terms of density, the comprehensive sample was 0.001, indicating that only0.1% of possible ties were formed among the companies. For multiplicity, 91% ofthe interlock ties (the total number of such ties was 378) were due to oneinterlocking directorate, indicating a multiplicity of 1. Five percent (19 interlock ties)had a multiplicity of 2, and 3% (12 interlock ties) had a multiplicity of 3. The threeremaining interlock ties were due to more than three interlocking directorates,representing a higher level of multiplicity. Lastly, for distance there were 7,101interlock ties,5 including 5% one-link ties, 6% two-link ties, and 89% three-link andabove three-link ties.

Two observations were immediately apparent. First, interlock networks weresparse and multiple directors were rare in China. Second, there was a surprisinglyhigh percentage with a multiplicity of 3, given the sparse network ties and lowcumulation ratio. This appears to reflect that a small group of companies shared alarge number of directors (by Chinese standards). Similar to the findings for pre-1997 networks in Hong Kong, networks in mainland China were also “separated butclose-knit islands in the middle of an ocean” (Au et al., 2000: 34). Strong social andeconomic ties with allies were preferred (Keister, 1999, 2000).

To explore for potential differences and similarities between Chinese interlockedbusiness networks and other representative economies, we compared our seconddataset (n= 250) with those found in prior studies of the United States (Stokman etal., 1985), Great Britain (Stokman et al., 1985), Hong Kong (Au et al., 2000), andThailand (Peng et al., 2001). The datasets in these representative economies werebased upon similar sample sizes ranging from the top 200 to 250 firms (rated bysales), thereby affording comparability for analyses.6 Our analysis involved re-analyzing the interlocking business networks formed in this smaller sub-grouping offirms (see Table 2, column 2).

For this comparative sample, 2.46% of the directors in China were multipledirectors versus 18% in the United States, 11% in Great Britain, 14% in Thailand,and 17% in Hong Kong. The cumulation ratio for China was 1.03. By comparison,Hong Kong had the highest ratio, at 1.29, followed by Thailand (1.21), the UnitedStates (1.15), and Great Britain (1.15). The results indicated that China had far fewermultiple directorships than in the other economies.

Moreover, we found for the comparative sample that the network density for Chinawas 0.0024, which represented less than a tenth of that found in other economies(0.042 for Thailand, 0.035 for the United States, 0.029 for Hong Kong, and 0.017 forGreat Britain). The top 250 companies in China also showed significant differences interms of the number of directorate seats held by multiple directors, multiplicity, and

5 The total number of one-link ties is 378, two-link ties is 419, and three-link ties is 6,304, respectively.6 The time periods of the samples used for comparison are somewhat different. However, since theseworks closely build upon each other, they represent the best available data for the purposes of comparison(Au et al., 2000).


distance. For the number of directorate seats held by multiple directors, 90% held twoseats, and almost 10% held three seats, but none held more than three seats. Thischaracteristic was significantly different than that found in the other four economies.Moreover, multiplicity was also significantly different with almost all Chinesecompanies having a multiplicity level of less than 4. It was interesting to note thatthe percentage of companies which had a multiplicity of 3 (6%) was much higher thanthat found in the other economies (e.g., United States: 2%; Hong Kong: 5%).

Interlocking business networks across regions and industries

We next explored the extent of interlock by region and industry. Our comprehensivesample spanned a total of 31 regions, including four municipalities, five autonomousregions, and twenty-two provinces. Table 3 provides a regional analysis of theinterlocks. The data revealed that each region had a different number of listedcompanies and, consequently, does not contribute equally to the formation ofinterlocking business networks. For example, Shanghai and Guangdong Provincetogether accounted for 25% of the total comprehensive sample. Another eightprovinces had a relatively lower proportion of listed firms, including Sichuan(5.6%), Shandong (5.2%), Jiangsu (5.1%), Hubei (4.9%), Liaoning (4.9%), Beijing(4.8%), Zhejiang (4.5%), and Fujian (3.9%).

Of the 412 firms that had interlocking directorates, 19.7% were from Shanghai and16.5% were from Guangdong Province. Hubei Province was also highly interlocked,and accounted for 6.3% of the total interlocked sample. A within-region ratio, definedas the total number of interlocked firms over the total number of firms within theregion, was calculated to evaluate the intensity of interlocks within a particular region.Ten provinces had interlocking directorates in over half of their firms. Due most likelyto a smaller number of listed firms, Fujian Province had the highest number involved,followed by the large cities of Shanghai and Tianjin (see Table 3, column 5).

Different industries may also vary in the extent of interlock. For example,industries such as finance, utility, and real estate have been found to be the mostintensely tied industries in many Western countries (Dooley, 1969; Pennings, 1980;Mintz & Schwartz, 1985; Aguilera, 1998; Davis & Mizruchi, 1999; Heinze, 2004).Table 4 illustrates the distribution of interlocks across industries in our sample of 412interlocked firms. One hundred and ninety-five companies (43.7%) belonged to themanufacturing industry. Other notable industries included wholesale and retail(10.9%), consolidated (9%), information and technology (8%), real estate (5.1%),public utility (4.6%), transportation (4.1%), and social service (3.9%).

When the total number of firms within each industry was taken into account, themost interlocked industry was the financial services industry, with all six firms beinginterlocked.7 This was followed by the real estate (70%), public utility (56%),wholesale and retail (53%), information (53%), transportation (50%), and media(50%) industries. These results closely resembled those found in Western countries,with the exception of the wholesale industry in China which is relatively moreconnected than its counterparts in the West (Stokman et al., 1985).

7 Since many financial institutions are not listed in China the results may not fully reflect the extent ofinterlocking directorship ties.

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Component analysis

To more fully understand how a business network is connected in China, weperformed component analysis to examine the number of components, distributionof N-memberships, and proportion of isolates.8 Component analysis revealed thatour comprehensive sample had formed 104 components, created by 412 interlockedcompanies, and contained 537 isolates. For the distribution of N-memberships, wefound a highly-skewed distribution for the 104 components. More specifically, 63components (61%) were two-member components and 24 (23%) were three-membercomponents. As N increases, the number of N-components becomes smaller [e.g.,

Table 3 Distribution of interlocks across regions.

Province Number ofinterlocked firms

% of totalinterlocked samplea

Number of firms(full sample within each region)

Withinregion ratiob

Shanghai 81 19.7 124 0.65Guangdong 68 16.5 116 0.59Hubei 26 6.3 47 0.55Sichuan 24 5.8 53 0.45Zhejiang 22 5.3 42 0.52Beijing 21 5.1 45 0.47Jiangsu 18 4.4 48 0.38Liaoning 15 3.6 46 0.33Hainan 14 3.4 21 0.67Jilin 13 3.2 28 0.46Fujian 12 2.9 37 0.32Shandong 9 2.2 49 0.18Tianjin 9 2.2 14 0.64Chongqing 9 2.2 21 0.43Heilongjiang 7 1.7 26 0.27Neimenggu 7 1.7 14 0.50Shanxi_shan 7 1.7 20 0.35Xinjiang 7 1.7 14 0.50Hunan 6 1.5 27 0.22Shanxi_jin 5 1.2 13 0.38Guangxi 4 1 12 0.33Guizhou 4 1 9 0.44Hebei 4 1 24 0.17Henan 4 1 22 0.18Ningxia 4 1 8 0.50Qinghai 4 1 7 0.57Gansu 2 0.5 11 0.18Jiangxi 2 0.5 12 0.17Yunnan 2 0.5 15 0.13Anhui 0 0 18 0Total 412 100 949 0.43

a The total interlocked sample size is 412.b Proportion of interlocked firms in 1 region to the total number of firms within the region.

8 A component is a subnetwork in which firms within it can reach all others through a direct or indirectpath (Scott, 1991a). Actors within a component are not attached to those outside it. Analyzing componentsis useful for assessing the pattern of the business network. For example, it can show whether the businesshas an integrated network center. A business can contain multiple independent components, which areunconnected with each other. It can contain only one large component in which all firms are connected toone another. A component can be described according to its size, defined as the N-membership (where N isnot less than two) or the number of its actors (Scott, 1992). Aside from components, there may also beisolates in the business which are not connected to others.


the number of four-member components was five (5%) and nine-membercomponents was one (1%)].

Overall, analyses indicated that only limited integration exists in the businessnetwork and that no single integrated or nationwide network center is apparent. It didhowever confirm the existence of several regional network centers. Similar findingshave emerged in North America (Bearden & Mintz, 1985; Mintz & Schwartz, 1985;Stokman et al., 1985), suggesting that interlocking directorships may be moreregionalized in nature (Moore, 1979; Kono, Palmer, Friendlan, & Zafonte, 1998),especially in cases like China and the United States which are geographically largeeconomies relative to many others. Thus, geographical size, may in part account fora sparse network structure in China. This finding may also reflect the comparativeease by which companies sharing a common region may interlock relative to thosewhich are more geographically dispersed.

Although integration was limited, we did find one example of a large componentwhich contained 116 member firms, a size much larger than the other componentsidentified. More specifically, firms in banking, wholesale, social service and utilityindustries occupied central positions in this large component. Geographically, theywere mainly located in Shanghai and Guangdong Province. The component alsocontained 16 firms located in the middle provinces of China, with Hubei Provincehaving ten member firms.

To find out how this largest component was connected we analyzed the cohesivesub-networks (cliques) found in the component. Cliques form a structure known as acircle, in which two or more cliques can be bridged together by an intermediate firm(Scott, 1991b).9 We identified six such circles as illustrated in Figure 1.

Table 4 Distribution of interlocks across industries.

Industry Number ofinterlocked firmsa

% of totalinterlocked sampleb

Within industryratioc

Manufacturing 195 (526) 47.3 0.37Wholesale and retail 45 (85) 10.9 0.53Consolidated 37 (81) 9 0.46Information and technology 33 (62) 8 0.53Real estate 21 (30) 5.1 0.70Public utility 19 (34) 4.6 0.56Transportation 17 (34) 4.1 0.50Social service 16 (33) 3.9 0.48Agriculture 8 (19) 1.9 0.42Construction 7 (18) 1.7 0.39Finance 6 (6) 1.5 1.00Media 5 (10) 1.2 0.50Mining 3 (11) 0.7 0.27Total 412 (949) 100 0.43

a Numbers in the bracket are the total number of firms within each particular industry category.b The total interlocked sample is 412.c Proportion of interlocked firms in 1 industry to the total number of firms within the industry.

9 In a full clique, the number of ties that are present is equal to the maximum number of ties that arepossible (Scott, 1992). For example, a three-member clique contains three ties, and a four-member cliquecontains six ties. In other words, in a full clique, all the firms have direct ties with the others.

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For the purposes of analysis, we defined circles according to their locationand industrial composition, as follows: circle 1 the Yangtze River Delta-SocialService Circle; circle 2 the Shanghai Pudong Bank Circle; circle 3 the ShanghaiDiversified Circle (reflecting its industrial diversification); circle 4 Hubei Wholesaleand Retail Circle; circle 5 Sichuan Industrial Cluster; and circle 6 GeographicalDiversified Chemical Industry Cluster (with all five of its firms distributed along fivedifferent coastal regions). Circles 1, 2, 3 and 4 occupied mediating positions, andthus represented the most central players in the component while circles 5 and 6 heldperipheral positions. Circle 4 represented the largest cluster in the component,containing a total of 19 firms. This was followed by circle 1 (15 firms) and circle 2(13 firms). Circle 5 was the smallest, containing only four firms. The three largestcircles are depicted in Figure 2, 3 and 4. We also report their ownership and bridgingfirms. As the figures illustrate, most circles are predominantly controlled by thestate. Only a limited number of cases are ultimately controlled by non-state firms.Table 5 summarizes features of the six circles in terms of their size, position in thelargest component, ownership, and industrial and geographic distribution.

Interlocked firms versus non-interlocked firms

To explore differences between interlocked versus non-interlocked firms we firstused component analysis to classify firms into three groups: those in the largestcomponent network, those in smaller component networks, and isolates. AnANOVA analysis was then performed to examine how the three groups differed interms of key factors identified in prior research (i.e., firm size, leverage ratio,ownership structure, and firm performance). This was essential for understandingabout potential key drivers in directorship interlock. In other words, we attempted toascertain why the business network was structured in a certain way, why some firmsmay prefer to stay in the largest component, why others act as central players, and

Hubei Wholesale and Retail Circle

Shanghai Pudong Bank Circle

Yangtze River Delta-Social Service Circle

Geographical Diversified Chemical Industry Cluster

Sichuan Industrial Cluster

Shanghai Diversified Circle

Figure 1 The largest component. This figure is extracted from the Netdraw of UCINET software


why still others are sparsely connected or even isolates? Table 6 summarizes thestatistical results.10

Firm size

In terms of demographic drivers of interlock, our analysis showed that firms in thelargest component had on average a significantly larger firm size, larger board, andlonger duration of listing. Prior research has suggested that a positive relationshipexists between size and interlocks. To a great extent this has been explained by “aprestige seeking and generating mechanism” (Stokman et al., 1985: 274). Assumingthat size means power in China, just like anywhere else, both small firms and largefirms are motivated to interlock. This suggests, first, that smaller firms are motivatedto interlock to increase status and reputation; and, second, that large firms aremotivated to interlock to maintain status and reputation.

By publicly associating with others that possess higher levels of status (throughformal positions in organizations), smaller firms can accumulate power and prestige.Evidence of widespread interlock between smaller components suggests that mostsmaller firms have yet to achieve the organizational capability required to join largercomponents. Perhaps as these smaller components evolve they may either becomelarger themselves or develop the capability to tie with larger networks. On the otherhand, larger firms interlock to maintain status and prestige. As analysis of the largestcomponent suggests only relatively larger firms are members, indicating aconcentration of economic power in the largest component.

Leverage ratio

The results also showed that firms in the largest component have a relatively higherleverage ratio. This result is similar to findings in the United States suggesting thatinterlock helps industrial corporations to acquire a greater level of financial capital. As theresource dependence perspective suggests, in order to obtain financial resources,industrial corporations employ interlocks to coopt financial officers, either by invitingthem to their boards or by sharing directors with financial institutions. However, differentfrom the Anglo-American case where cooptation takes place between industrial firms andfinancial counterparts (Stokman et al., 1985; Uzzi, 1997), cooptation in China is mainlybetween industrial peers rather than financial institutions.11

Ownership structure

Compared to the isolate group, the largest component and other component groupshad on average a smaller volume of largest shareholder holdings and a lower top-5shareholder concentration ratio. The largest component and other component groups

10 Definitions and measurements are delineated in Table 6.11 Another study conducted by the authors on the interorganizational loan guarantee ties suggested thatboard interlock is positively related to loan guarantee tie. Therefore, the positive relationship betweeninterlock and firm leverage ratio may be largely due to bank requests that industrial corporations providepeer guarantees (to mitigate risk) when they seek financial resources (i.e., the higher the interlock, thehigher the loan guarantee, and the higher the leverage ratio).

B. Ren et al.

also had a lower level of government ownership and publicly-listed shares. Analysis ofthe top 2 to 10 shareholder holdings however indicates a contrary pattern. That is, firmswith a higher level of top 2 to 10 largest shareholder holdings are more likely to stay inthe largest component and in the other component group.

Prior research suggests that ownership structure matters to directorship interlock.Owner-controlled and manager-controlled firms are found to be less interlocked.This has been explained by an unwillingness on the part of the dominant owner ormanagers to accept outside influence or control when the firms are interlocked(Dooley, 1969; Allen, 1974; Burt, 1983). Our results were similar. However,compared to the West, the pattern of interlock in China, as a whole, seems to beclosely related to the bargaining power of different shareholder groups, whether theybe the state, the largest shareholder, or any of the top ten shareholders (Meyer et al.,2002; Delios, Wu, & Zhou, 2006).

Firm performance

Both resource dependence and financial infiltration theories suggest that firmperformance may influence interlock. Resource dependence theory suggests thatfirms employ more interlocks because they want to secure resources whenperformance is declining (Lang & Lockhart, 1990). Moreover, infiltration theorysuggests that firms have more interlocks because financial institutions send more

DazhongTransport (K)

Shanghai Bashi (K)

Huchang Sp Steel (C)

Jiling (G)

Qiangsheng Holding (K)

DazhongS & T (M)

Jiaoyun (C)

Nanjing Zhongbei(K)

ShanghaiFeilo (G) Jiaoda

Nanyang (M)

Feilo Acoustics(M) Chlor Alkali

(C)

Tianjin Hi-tech (H)

Municipal Raw Water (D)

Shentong Metro (D)

Figure 2 Yangtze River Delta-Social Service Circle. Firms in the box are bridging firms. Letters in thebrackets represent industry category. C Manufacturing, D public utility, G information and technology, Hwholesale and retail, K social services, M consolidated. Firms in italics are non-state controlled


interlocking directorates to the firms with declining performance (Kotz, 1978; Norich,1980). Richardson in his study of the postwar Canadian economy noted that theextent of interlock was associated with the profitability of corporations involved(1987). Thus, interlock behavior may depend on organizational context.

By comparison, in most East Asian economies, firm involvement in businessgroup structures has been found to be related to the extent to which they seekinstitutional support or key resources, especially when formal institutions are notwell-developed (Khanna & Palepu, 1997). Other research has suggested that East

Pudong Development

Bank (I)

Jinqiao

(J)

Waigaoqiao

(J)Lujiazui

(J)

Shanggong

(C)

Zhangjiang

Hi-tech (M)

Fyita (C)

GD Enterprise

(M)

Unicom

Guomai

(L)

Shenzhen

Nanguang

(M)

Shenzhen

Tianma

(C)

Lianhua

Fibre (C)

Shanghai New

Asia (K)

Figure 4 Shanghai Pudong Bank Circle. Firms in the box are bridging firms. Letters in the brackets representindustry category. C Manufacturing, I finance, J real estate, K social services, L media, M consolidated.Firms in italics are non-state controlled

Wuhan Hanshang Group (H)

Shanghai No.1 Department Store (H)

Wuhan ZhongshangCommercial (H)

Wuhan Zhong bai (H)

Wuhan Department Store (H) Zhuzhou

Qingyun Development (K)

China Wanke (J)

Wuhan Cheng Cheng Investment in Culture (L)

Wuhan Linuo Industry (C)

Hubei Tianyi S & T (C)

Shanghai Shenda (C)

Harbin Hi-tech (C)

Wuhan Plastics (C)

Sino Pec Wuhan Phoenix (C)

Shenzhen Shenbao (C)

Shanghai East China Computer (G)

Shanghai Posts & Tele (G)

Shanghai Tunnel (E)SinoPec

Wuhan Petroleum (H)

Figure 3 Hubei Wholesale and Retail Circle. Firms in the box are bridging firms. Letters in the bracketsrepresent industry category. C Manufacturing, E construction, G information and technology, H wholesaleand retail, J real estate, K social services, L media. Firms in italics are non-state controlled

B. Ren et al.

Tab

le5

Descriptio

non

the6circlesin

thelargestcomponent.

CircleID

Size

Position

Ownership

Distributionof

industry

Distributionof

locatio

n

#of

firm

s#of

cliques

#of

bridging

firm

s

Yangtze

River

Delta-Social

Service

Circle(circle1)

158

7Central

mediatin

gGovernm

ent-controlled(13)

Socialservice(4)

Shanghai(13)

Employee

controlled(2)

Manufacturing

(3)

Jiangsu(1)

Con

solid

ated

(3)

Tianjin

(1)

IT(2)

Pub

licutility

(2)

Shang

haiPud

ongBankCircle

(circle2)

136

4Central

mediatin

gGovernm

ent-controlled(10)

Manufacturing

(4)

Shanghai(1)

Private-controlled(1)

Realestate

(3)

Guangdong

(3)

Not

identified(1)

Con

solid

ated

(3)

Finance

(1)

Service

(1)

Entertainment(1)

ShanghaiDiversified

Circle

(circle3)

52

1Central

mediatin

gGovernm

entcontrolled(4)

Consolid

ated

(1)

Shanghai(4)

Not

identified(1)

Textile

(1)

Hunan

(1)

Com

mercial

(1)

Utility

(1)

Electronic(1)

Hub

eiWholesale

and

RetailCircle(circle4)

1910

8Central

mediatin

gGovernm

ent-controlled(16)

Manufacturing

(7)

Hubei

(10)

Private-con

trolled(1)

Wholesale

andretail(6)

Shang

hai(5)

Not

identified(2)

IT(2)

Guangdo

ng(2)

Realestate

(1)

Heilongjiang

(1)

Service

(1)

Hun

an(1)

Architecture(1)

Propaganda(1)

Sichuan

Industrial

Cluster

(circle5)

42

2Peripheral

Governm

entcontrolled(3)

Construction(2)

Sichuan

(3)

Private

controlled(1)

Electricity

(1)

Shanghai(1)

Metal

refinery

(1)

GeographicalDiversified

Chemical

Indu

stry

Cluster

(circle6)

72

2Peripheral

Governm

entcontrolled(6)

Chemistry(2)

Shanghai(3)

Not

identified(1)

Textile

(2)

Liaoning(1)

Electricity

(1)

Tianjin

(1)

Industrial

others

(2)

Zhejiang

(1)

Shang

dong

(1)

Intotal

6330

24


Asian economies are mainly comprised of family businesses with concentrated andpyramidal ownership structures (Hamilton & Biggart, 1988). Thus, it is common forfirms to remain in a business group, or be involved in some affiliated structure.

Empirical studies are mixed in terms of how such affiliations relate to firmperformance. Interestingly, group affiliations have been found to be harmful tothe economic efficiency of affiliated firms’ in most economies (Chang, 2003 inKorea; Lincoln, Gerlach, & Ahmadjian, 1998 in Japan; Claessens, Fan, Joseph, &Lang, 2006 in East Asia economies, and Gaur & Delios, 2006 in India). Twopossible explanations exist for this negative relationship: (1) an entrenchment effectby the dominant family business group (Chang, 2003); and (2) the aim of businessgroups to maintain collective interests and maximize the group as a whole. Hence,in the case of the latter individual firms may sacrifice short-term gains in an effortto secure their long-term survival (Lincoln et al., 1998). This explanation is similarto that proposed by infiltration theory in the West. The difference for China,however, is that infiltration does not stem from a bank but rather the businessgroup.

To explore how different patterns of interlock may be associated with firmperformance, we collected performance data [i.e., return on asset (ROA)] for firms inthe three different groups. ANOVA tests revealed that both the largest component

Table 6 ANOVA tests at key factors for 3 groups of firms (the largest component, other components andisolates).

Variablesa The largestcomponent

Othercomponents

Isolates ANOVA tests

Mean SD Mean SD Mean SD F-value(significance)

Firm size 11.74 0.95 11.57 0.81 11.48 0.82 4.77**Board size 10.84 3.15 9.91 2.78 9.27 2.47 18.33***Days of listed 7.34 0.84 6.98 0.80 6.78 0.89 21.28***Leverage ratio 0.49 0.20 0.47 0.23 0.41 0.21 11.84***Largest shareholder holding 42.41 16.32 42.21 17.25 47.96 18.32 11.84***Top 5 concentration 58.27 14.88 58.95 14.00 61.15 14.11 3.38*Top 2–10 largest shareholder holding 19.16 12.84 19.97 13.52 15.62 13.89 10.69***Public listed share 0.28 0.17 0.31 0.13 0.33 0.14 5.88**State ownership 0.31 0.26 0.28 0.26 0.33 0.28 3.14*ROA 0.03 0.07 0.03 0.10 0.04 0.09 4.90**Valid N (listwise) 114 292 537

Two financial firms are excluded from the analysis.Firm size is measured as the logarithm of total assets, board size is measured as the total number ofdirectors on the board, days of listed is measured as the natural logarithm of total number of days forwhich the firm was listed till the end of 1999, leverage ratio proxy for firm financial dependence, and it ismeasured as the total debt to equity ratio, largest shareholder holding a proxy for largest shareholder’scontrol and is measured as the proportion of shares owned by the largest shareholder, top 2–10 largestshareholder holding measures nondominant shareholders’ control, operationalized as the aggregatedholding percentage of top 2 to 10 largest owners, top 5 concentration measures the aggregated holdingpercentage by the 5 largest shareholders, public-listed share is measured as the proportion of shares ownedby the individual investors in the stock market, state ownership is measured as the proportion of sharesowned by the state, ROA firms’ performance operationalized as net income to total asset.*P<0.05; **P<0.01; ***P<0.001.aMeasures of variables.

B. Ren et al.

group and other component groups had significantly lower ROAs than isolates. Thisresult is similar to that found in other research. First, directorship interlock has beenfound to be negatively associated with firm performance in most longitudinal studiesconducted in Western contexts (Boeker & Goodstein, 1991; Dooley, 1969; Lang &Lockhart, 1990; Mizruchi & Stearns, 1988). Second, in most East Asian contexts,group affiliation has been found to be negatively associated with affiliated firms’economic efficiency (Chang, 2003; Lincoln et al., 1998).

Discussion

Contributions

To the best of our knowledge, this study represents the first of its kind to investigatethe patterns of interlocking directorate networks for Chinese publicly-listedcompanies and the drivers that may influence interlock network formation duringa period of institutional transitions. The findings not only echo theories developed inthe Western context, but also suggest novel patterns of interlock may exist in thecontext of institutional transitions.

More specifically, the study contributes to our understanding in three importantways. First, we provide empirical evidence in relation to the structural aspects of theChinese economy. Although a large component was identified, overall most firmswere not connected after more than 10 years of transitions. This suggests that China’stransition to a market-oriented economy is still evolving, since firms and regions arenot as well-connected as their counterparts in more developed market economies(Meyer, 2008). An important caveat is that coastal regions and pillar industries wereplaying an active role in the development of business networks, suggesting that amarket-oriented economy was taking shape in certain regions and industries.

Second, and of considerable theoretical significance, in contrast to the typicalview that guanxi is pervasive in China, our study suggested that a guanxi-orientedsociety does not necessarily imply a densely interlocked business network. In fact, inour study of the entire population of listed firms we only found one example of alarge component (i.e., densely-connected guanxi interfirm interlock). The notion ofguanxi therefore appears to be more complex than prior research on guanxi wouldhave us believe and points to its possible multidimensionality (i.e., interorganiza-tional versus personal guanxi). This necessitates further research into both guanxiand network theory.

Third, our study provides a more in depth understanding about factors thatpotentially drive interlock. This has far reaching implications for both Western-basedresource dependency theory and Eastern-based theories that reflect the unique natureof transition economies such as China (Li & Peng, 2008). For example, wehighlighted the influential role that status and prestige play in interlock formation inChina, similar to that in the West. At the same time, we revealed how the interlockpositions of firms are contingent on ownership structures, which are quite different.Our findings related to the leverage ratio and firm performance are also closelyaligned with those in the West. However, they also reflect the unique characteristicsassociated with China’s transitional context. In this sense, we provide a foundation


from which the research agenda can be advanced and further in depth internationalcomparisons can be conducted.

Implications

The present study also offers several important implications to managers and policymakers. For managers, being able to identify their position relative to or thecharacteristics of an interorganizational network (e.g., a large and densely connectedcomponent, small component, or isolates, and industrial and regional distribution) isimportant to managing a firm’s external environment. Understanding these structuralelements provides important hints about why firms and other business partners areconnected and how these connections could potentially influence a firm’s operationsand performance. Moreover, an awareness of the pattern of interlock across differentregions or industries (e.g., the circle network) is essential to competitive positioning.For example, identifying horizontal clusters, such as in the case of the service-centeredcircle in Shanghai and the wholesale and retail-centered circle in Hubei, providesconsiderable insight into the different levels of influence that different actors within anetwork may wield. In the same vein, isolates also need to understand their positionrelative to others so that they can act accordingly within their competitive landscape.

Managers also need to be cognizant of the potential drivers that may influenceinterlock so that they can balance their strategies. Our study does not judge whetherinterlock is good or bad. Rather, it suggests that there are both potential advantagesand disadvantages to interlock. For example, highly interlocked firms, such as thosecontained in the largest component, have higher leverage ratios, which implies thatthey have been more influential in leveraging bank loans for business development.It may also imply that they have been able to obtain capital at a lower cost. However,interlock may also impose a greater amount of external control, for instance from adominant institutional player (e.g., supplier, shareholders, or even competitor),thereby affecting a firm’s ultimate decision-making autonomy. Our finding thathighly connected firms also have the lowest level of performance reflects possibleefficiency losses that may arise from entrenchment in a cohesive interlock network.

For policymakers, our study offers important insights into China’s institutionaltransitions and decentralization policy framework. Recent research suggests thatChinese firms lack national competitiveness due to their blind pursuit of size at theexpense of developing integrated coordination capabilities (Meyer, 2008). We foundthat most interlocks either occurred locally or were confined cross-regionally tointerlocks between coastal cities. Policymakers therefore need to consider how theirpolicies have influenced fragmentation (e.g., regional) and could potentially hindernational competitiveness and the development of nationally coordinated capabilities.More research is therefore vital to understanding how the structure of businessnetworks could influence the development of the Chinese economy and what stepscould be taken to correct any imbalance that has occurred during its transitions.

Limitations and future research directions

Several caveats warrant attention. First, although we do not refer to social cohesiontheory, it may shed further theoretical insight into explaining why firms interlock (e.g.,

B. Ren et al.

Domhoff, 1970; Useem, 1984). It may be the case that during institutional transitions,that local elites or dominant institutional constituents, such as local entrepreneurs andgovernment officials, have considerable bargaining power and self-interest incontrolling and coordinating local businesses. That is, local battles for power andcohesive interests may drive interlock patterns. To better explain how the socialcohesion mechanism functions, future research needs to be undertaken to analyzeboard composition and individual directors’ personal information (Palmer & Barber,2001). Second, this cross-sectional study provides a useful data point at an importanttime in China’s institutional transitions. However, business networks and institutionsin China continue to co-evolve. Further work is therefore required to explore, forexample, how network structures (e.g., density, interlock patterns) have evolved overtime (Peng, 2003). Lastly, given the above analyses, it is difficult to ascertain causalitybetween interlock and performance or to test the applicability of different theoreticalperspectives (e.g., resource dependence, infiltration, and entrenchment effects). Thus,future research should incorporate these important considerations.

Conclusion

Current business network studies seldom focus on economies undergoing institu-tional transitions. This paper has provided empirical evidence on the varieties ofinterfirm directorship interlock and analyzed the possible reasons for the patterns ofinterlocking networks that emerged. The findings reveal similarities to theinterlocking networks in other countries as well as differences specific to theChinese context. Overall, this research is an attempt to improve our understanding ofthe structural aspects of China’s transition economy (Stokman et al., 1985). In abroader sense, it also reflects an interest to integrate Western-based theories with theliteratures on guanxi and institutional transitions (Li & Peng, 2008). We thereforebelieve that this study will shed considerable light on the important role that acombination of structural and institutional analyses play in understanding businessnetworks in a dynamic transition economy such as China.

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Bing Ren (PhD, The Chinese University of Hong Kong) is an associate professor in the Department ofManagement, School of Business at Nankai University, China. Her main research areas include Chinesebusiness network studies, institutional transition and firm strategic choices, entrepreneurship and corporategovernance issues in China. Currently, she is undertaking two research projects funded by the naturalscience foundation of China. The first one relates to the intercorporate network of interlocking directoratesand its influence on firm strategies during China’s institutional transition. The second relates to alegitimacy perspective of new venture growth and creation in China.

Kevin Au (PhD, University of British Columbia) is an associate professor of management at the ChineseUniversity of Hong Kong, and serves as an associate director for the MBA programme and Centre forEntrepreneurship. His research interests span across micro and macro issues in global management, social


network, entrepreneurship, and research methodology. He serves on the editorial boards of Asia PacificJournal of Management and Journal of Organizational Behavior and conducts consulting projects forbusiness and government organizations. This is Professor Au’s fourth contribution to APJM.

Thomas A. Birtch is a Senior Research Fellow (Centre for Economics and Policy) at the University ofCambridge. He has held an academic appointment at The Chinese University of Hong Kong, visiteduniversities in Asia, Europe, and North America, and has affiliations with several research centres,including at MIT, Cambridge, and The Chinese University of Hong Kong. He has also held directorshipsand senior management positions in large scale organizations and government spanning three continentsand consulted in over 30 countries. His current research interests include the performance of individuals,organizations, and markets, the transferability of management practices, FDI and post-merger integrationstrategy, rewards and incentives, entrepreneurship and innovative organizations, and business in China.His recent publications appear in journals such as Human Relations, Management International Review,International Journal of Hospitality Management, International Journal of Human Resource Management,and Journal of International Management.

B. Ren et al.

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China s business network structure during institutional