China Journal of Accounting Studies, 2014 Vol. 2, No. 1, 53–73, http://dx.doi.org/10.1080/21697221.2014.887415 Directors’ social networks and ﬁrm efﬁciency: A structural embeddedness perspective Yunsen Chen* School of Accountancy, Central University of Finance and Economics, Beijing, People’s Republic of China The performance of ﬁrms is usually inﬂuenced by their structural embeddedness in social networks. This paper investigates the effects of structural hole positions of directors on their ﬁrms’ operating and investing efﬁciency, from a corporate ﬁnance perspective. We build an interlocking network of the directors in Chinese listed companies, and compute the network constraint index to represent the richness of structural holes in this network. The empirical results show that ﬁrms with more structural holes are more efﬁcient in both operating and investing activities (through the channel of reducing their ﬁrms’ under-investment problems), and these relations are more pronounced in competitive industries. Further tests ﬁnd that ﬁrms with more structural holes perform better over time. The results suggest that a ﬁrm’s ‘structural hole position’ plays an important role in ﬁrm efﬁciency. These ﬁndings provide new evidence for the emerging literature of social networks and corporate ﬁnance. Keywords: board network; investing efﬁciency; operating efﬁciency; social tie; structural hole 1. Introduction Research shows that the heterogeneity of ﬁrms’ internal resources can lead to different levels of operating and investing efﬁciency (Liu, Fu, & Qiu, 2011). However, this view arises mainly from the perspective of static resource heterogeneity, and ignores the pos- sibility of resource acquisition from the external environment, namely, a dynamic social network and its effects on ﬁrm efﬁciency. This paper focuses on directors’ social ties, and investigates the role of ﬁrms’ social networks in their operating and investing activ- ities. In the social network ﬁeld, the concept of ‘network’ involves two dimensions: network relation and network structure. Burt (1979) was the ﬁrst to study the connec- tions between ﬁrms’ directors, and to examine the effects of these social connections on ﬁrm efﬁciency from a structural perspective. In Structural holes: The social struc- ture of competition (1992), Burt systematically introduces the concept of the ‘structural hole’. He explains that there are some individuals who cannot contact each other directly, and describes these network contacts as holding disconnected positions. These disconnections can be regarded as holes in the overall network structure. In this kind of network, individuals who are in the core positions of structural holes can connect with different people, and thus have advantages in both information and control. Such advantages are helpful for enhancing a ﬁrm’s power in market competition. In *Email address: email@example.com Paper accepted by Kangtao Ye. © 2014 Accounting Society of China 54 Chen management studies, structural hole theory is used to explain various phenomena, especially strategic management (Liu et al., 2011; Qian, Yang, & Wu, 2010; Shipilov, Rowley, & Aharonson, 2006; Walker, Kogut, & Shan, 1997). From the perspective of resource dependence, a ﬁrm’s operating and investing activities can be signiﬁcantly inﬂuenced by the acquisition of social capital from its social network (Liu et al., 2011; Cai & Sevilir, 2012). From a sociological perspective, social networks comprise two components: network relations and network structures (Granovetter, 1992). Analyses of these two components are called the ‘relation embeddedness view’ and the ‘network structural hole position view’, respectively. The corporate ﬁnance literature mainly dis- cusses the existence and strength of network relations, that is, whether ﬁrm relations exist or how strong these relations are, as in the works of Engelberg et al. (2012), Fra- cassi and Tate (2012) and Larcker et al. (2013). However, the network structural hole position view focuses on the strategic position of individuals in a social network, and emphasises the roles of intermediates and bridges. Granovetter (2005) points out that under various conditions, the most important factor is not the network relation itself, but the structural position of bridges connecting different social networks. So according to the two dimensions of social network, related studies are divided into a ‘relation em- beddedness view’ and ‘network structural hole position’ view. Prior research, such as that of Cai and Sevilir (2012), Larcker et al. (2013) and Chen and Xie (2011, 2012), ﬁnds that a director’s network relations can help a ﬁrm to gain greater social capital, while a director’s network structures, such as the structural hole position in a network, can also improve a ﬁrm’sefﬁciency. The structural hole position carries the advantages of both information and control (Burt, 1992, p. 34). To my knowledge, there are few studies on the role of social network structural positioning in corporate ﬁnance and accounting, so this paper attempts to ﬁll this gap by investigating the relation between directors’ network hole positions and ﬁrms’ operating and investing efﬁciency in China. In social networks, organisations are embedded in an environment that constitutes various relations, and their resource acquisition channels depend mainly on the networks of other actors. Therefore, a ﬁrm can develop a ﬁrm-level network through its director-level network, that is, the network created by the directors’ or executives’ personal networks. The purpose of this paper is to explore the differences in efﬁciency among ﬁrms with various structural hole positions in their directors’ interlocking networks. A secondary purpose of the paper is to compute the network constraint index that represents the value and richness of structural hole positions. Based on data from all Chinese A-share listed companies between 2001 and 2011, the empirical results show that ﬁrms with more structural holes operate and invest more efﬁciently, and this effect is more pronounced in competitive industries. Further tests ﬁnd that ﬁrms with more structural holes are more likely to perform better over time, and that a ﬁrm’s structural hole position plays a key role in the ﬁrm’s operating and investing activities. This paper contributes to the accounting and ﬁnance literature in the following ways. First, there are few studies on ﬁrms’ network structural positions in the ﬁeld of corporate ﬁnance. In addition, existing studies on organisational and strategic manage- ment are usually based on small sample sizes, in which the real networks may be artiﬁ- cially severed. This paper is among the ﬁrst to employ a large sample of Chinese ﬁrms, and ﬁnds that a ﬁrm’s network position disparity inﬂuences ﬁrm efﬁciency. Second, although there are a few emerging studies involving crossover investigations of social networks and corporate ﬁnance, most of these studies are based on the ‘relation China Journal of Accounting Studies 55 embeddedness view’, which assumes that the directors’ power becomes stronger with more network relations. However, this assumption ignores the important role of network structural position. This paper adopts a ‘network structural hole position’ view, and extends social network analysis in the corporate ﬁnance literature. The remainder of this paper is organised as follows. Section 2 presents a literature review and develops the hypotheses. Section 3 lays out the research design, and Section 4 follows up with supporting empirical evidence. Section 5 provides some conclusions. 2. Literature review and hypothesis development 2.1. Research on structural hole position and corporate ﬁnance Several researchers focusing on international strategic management have found that net- work strategic positions among ﬁrms can inﬂuence their strategy and performance (Walker et al., 1997), market share (Baum, Rowley, Shipilov, & Chuang, 2005; Shipi- lov et al., 2006), patents (Ahuja, 2000), and knowledge transfer (Reagans and McEvily, 2003). Other scholars focus on the intra-ﬁrm structural hole position, and demonstrate that the CEO’s position in a ﬁrm affects its corporate governance efﬁciency (Mitchell, 2005). However, studies related to ﬁrms’ network positions remain very limited: Yao and Xi (2008) investigate the relations between the structural holes of executives’ con- sulting networks and their ﬁrms’ competition advantages, and ﬁnd out that there is a positive relation. Qian et al. (2010) collect integrated circuit industry data from Shenzhen City, and argue that ﬁrms located in the centre of their networks are more innovative, and thus beneﬁt more from innovation because of their network positions. Liu et al. (2011) illustrate that network resources gained from social networks are posi- tively correlated with a ﬁrm’s diversiﬁcation. They measure this correlation by adopting both centrality and structural hole measurement to represent ﬁrms’ network positions. Cai and Sevilir (2012), Chen and Xie (2011, 2012), Fracassi (2013) and Larcker et al. (2013) examine relationships between board network centrality and corporate governance activities such as investment, M&A, executive compensation and stock returns. It should be kept in mind that the measurement of network centrality is based only upon the strength of network relations, without distinguishing whether the relations are redundant. This kind of measurement also ignores network structural embeddedness, which is also the core component of a social network. In summary, studies that connect network structural positions and corporate activities are still lacking. 2.2. Structural hole position and ﬁrm behaviour Burt (1992, p. 19) points out that social networks contain some individuals who cannot directly contact others. These people hold disconnected positions in their social net- works, and such disconnections are called structural holes, as they are like holes in the entire network. Networks consist of various weak ties. Hence, they are full of structural holes. People in the core positions of structural holes, who do hold the connections, can obtain more information and resources, and those who build up connections with people who are formally disconnected enjoy both information and control advantages. The same situation applies within a ﬁrm’s network, in which various ﬁrms enjoy different advantages through holding different positions within their networks. As 56 Chen organisations are embedded in environments with diverse social networks, their speciﬁc resource access channels are obtained from the networks of individuals within those or- ganisations. In other words, the network relations of a ﬁrm need some kinds of instru- mentalities or intermediates. As Burt (1983) states, the structure of a director’s ties offers a non-market environment for a ﬁrm’s business transactions. Jackson (2008) refers to the analysis of such ties as ‘actor-based modelling’. Distinctions between indi- vidual-level network input (of board directors) and organisation-level network output (of the ﬁrm) are commonly made in social network research. For instance, Larcker et al. (2013) compute ﬁrms’ network centrality (in terms of degree, betweenness, close- ness and eigenvector) based on the interlocking directors. The extant research also examines the inﬂuence of ﬁrm networks on stock returns. Fracassi (2013) takes the social ties of board directors (from current employment, former employment, education, and other relations) as a basis for generating a ﬁrm-level social network, and then anal- yses the inﬂuence of ﬁrms’ social networks on the convergence of corporate policies. It is necessary to note that there are different types of structural holes in different posi- tions within the industrial chain. These ‘holes’ have distinctive roles in shaping corpo- rate behaviour. However, as most ﬁrms act as suppliers, producers and customers at the same time, it becomes difﬁcult to differentiate these types of networks in empirical studies. In addition, it is hard to determine whether a director’s social ties lie in the same areas of the industrial chain as those of the ﬁrm’s suppliers, producers and cus- tomers. Hence, in studying a ﬁrm’s structural hole position in terms of its directors’ network, this paper mainly focuses on the dimension of the producer role. So our paper mainly discusses networks of director-level relations among ﬁrms that are generated by interlocking. As shown in Figure 1, we assume that there are sub-networks A and B. In network A, ﬁrms A1, A2, A3, A4 and O1 do not directly contact each other. Instead, they build up relations indirectly through ﬁrm A5. That is to say, these ﬁve ﬁrms can only communicate with each other through ﬁrm A5, so that A5 occupies ten structural hole positions: A1-A2, A1-A3, A1-A4, A1-O1, A2-A3, A2-A4, A2-O1, A3-A4, A3-O1 and A4-O1. In this case, sub-network A is full of structural holes, and A5 can obtain resource advantages from its core position. However, in sub-network B, ﬁrms B1, B2, B3, B4 and O1 are connected with each other directly or can be connected through more than one ﬁrm’s help, which means without any ﬁrm in O1 A1 B3 B4 A5 A4 A2 B1 B2 A3 Figure 1. A ﬁrm’s structural hole position generated by the directors’ social networks. China Journal of Accounting Studies 57 sub-network B, other ﬁrms can also contact, so they form a non-hole network. In this kind of network, information and resources are similar and redundant among different ﬁrms, and the ﬁrms cannot control each other’s communicating channels. In both sub- networks A and B, ﬁrm O1 functions as the ‘bridge’, and controls more structural holes than ﬁrm A5. According to the deﬁnition of a structural hole, ﬁrms O1 and A5 undoubtedly control other ﬁrms’ communications. In addition, ﬁrm O1 further links two sub-networks, and is classiﬁed as a weak tie. In this type of network, ﬁrms that hold a key structural hole position can easily obtain more resources (Qian et al., 2010; Yao et al., 2008). In corporate activities, competition is a key factor in improving ﬁrm efﬁciency, and information access is one of the most important channels for winning competitions. Members that do not directly contact others cannot access information-sharing channels due to the existence of structural holes, so their information is heterogeneous and redundant. Thus, they may rely on a third member, who connects them as a bridge, to obtain beneﬁt from the third member’s information diversity and competitive advanta- ges. Burt (1992, p. 38) classiﬁes the beneﬁts of structural holes into two types: infor- mation advantages and control advantages. Information advantages may be further classiﬁed by whether they relate to access, timing or referrals. An access advantage can help a ﬁrm get more valuable information, reduce its information collecting costs, and improve its efﬁciency in collecting and transferring information. A timing advantage allows ﬁrms to acquire information earlier. A referrals advantage can help ﬁrms gain more opportunities and resources through introductions, communications and referrals. Having a control advantage means that the broker who builds a bridge that links a number of ﬁrms can decide whose beneﬁt should be taken care of ﬁrst. When a ﬁrm obtains a social network that is full of structural holes, it acts as a bridge in the ﬁrm-level network between directors who hold central positions in their networks. Such ﬁrms usually act as the mediators to contact other ﬁrms that used to be disconnected. Firms in the bridge position have more network centrality. They also hold more information transformation and business opportunities, or intermediary interests. We describe the beneﬁts that such bridging ﬁrms obtain from their operating and investing activities in the following sections. 2.3. Structural hole positions and the improvement of a ﬁrm’s operating efﬁciency: A control advantage perspective Firms’ operating activities usually face imperfect competition, mainly because different ﬁrms with various strategic network positions obtain their resources differently. If a ﬁrm wants to improve its operating efﬁciency, it must get beneﬁts from holding differ- ent positions in the industrial chain (such as ‘supplier-producer-seller’) and then reduce transaction costs at every stage of the chain (Yao & Xi, 2008). These constraints, which most ﬁrms generally face, are determined by the transaction networks between suppliers and customers. In this situation, a ﬁrm’s social network can make those transactions easier. Firms in the centre of a structural hole can use the control power embedded in their network to reduce their transaction costs (Stevenson & Radin, 2009). For instance, in the material purchasing stage, the ﬁrm can take advantage of its network control power to reduce purchasing costs and shorten the time needed to make purchases, thereby accelerating its material turnover rate. In the goods selling stage, a ﬁrm holding a structural hole position is more powerful in bargaining with customers in the market, which is helpful for increasing sales and improving its operating income. More 58 Chen importantly, ﬁrms with dominant structural hole positions can withdraw cash from cus- tomers more quickly, thus reducing transaction costs. These ﬁrms can accelerate the operating cycle, and thereby further improve operating efﬁciency. In a ﬁrm’s operating activities, the most inefﬁcient factor is the uncertainty of uncontrollable transactions (Yao & Xi, 2008). Firms with dominant structural hole positions can strengthen their control and reduce this uncertainty. However, a ﬁrm that is at the edge of the network typically faces a greater danger of being kicked out of the core network and lacking bargaining power over suppliers and customers (Burt, 1992). Therefore, these periphe- ral ﬁrms tend to suffer from greater uncertainty and larger transaction frictions, which ultimately lower their operating efﬁciency. In addition, ﬁrms in dominant structural hole positions can aggregate diversiﬁed information and generate collaborating opportunities (Lin, 2002, p. 61). Their multiple links for collaboration result in their interdependence among other ﬁrms, which increases trust while decreasing information asymmetry, and therefore improves the quality of collaboration. Hoskisson, Hitt, and Hill (1993) point out that intra-ﬁrm knowledge or information transformation can help diversiﬁed enterprises to reduce their operating costs. In summary, we have the ﬁrst hypothesis as follows. H1: The operating efﬁciency of ﬁrms occupying a dominant structural hole position in their directors’ network is greater than that of ﬁrms without such a position. 2.4. Structural hole positions and the improvement of a ﬁrm’s investing efﬁciency: An information advantage perspective The inﬂuence of a ﬁrm’s structural hole position on its investment activities is mainly effected through an information gathering advantage. Investment inefﬁciency can involve either over-investment or under-investment, which are mainly caused by manag- ers’ opportunistic incentives (Jensen, 1986; Stulz, 1990) or information asymmetry (Fazzari, Hubbard, & Petersen, 1988; Xu & Zhang, 2009) respectively. The ﬂow of information within the social network can signiﬁcantly change a ﬁrm’s investment activ- ity. Larcker et al. (2013) ﬁnd that a central position in a board’s network can help direc- tors get more relevant information, but members at the network’s edge positions can only rely on others to transfer information (Freeman, 1979). When it comes to invest- ment decision-making, the learning effect causes information and academic knowledge from different ﬁrms to disseminate among social network members (Westphal & Seidel, 2001; Zaheer & Bell, 2005). If a ﬁrm is in the key position within its network, it plays the role of ‘intermediary’ and ‘bridge’, and can therefore get more accurate and timely information related to investment activities (Granovetter, 1985). Speciﬁcally, the direc- tors of ﬁrms in a dominant structural hole position may serve as directors in other ﬁrms at the same time. This interlocking relationship helps these directors to observe the deci- sion-making processes of other ﬁrms as they conduct similar investing activities. These directors may communicate with other directors about similar investing experiences (Cai & Sevilir, 2012). Sometimes a director with few direct connections can still earn an information advantage through good communications with other directors who have more network relations (Bonacich, 1972). As a result, they can better understand the merits, growth prospects and risks related to their investment efﬁciency. More importantly, a ﬁrm has to command more heterogeneous information to obtain an excess proﬁt. Bridge ﬁrms always hold weak ties with other ﬁrms through which they can get more heterogeneous information from their networks (Granovetter, 1985) China Journal of Accounting Studies 59 and avoid convergent investment. The reason for a high value on weak ties is that these ties play the key role in connecting groups, organisations, and society, so that they can transfer non-redundant information and knowledge; while the strong ties in a group exist mainly to protect the inter-organisation relationship. Moreover, the access and referral beneﬁts of a structural hole position give an information advantage in the ‘tim- ing’ of competition, that is a ‘one-step ahead’ competitive advantage, to use the term proposed by Burt (1992, p. 115). In a competitive environment, good investment opportunities fade away so quickly that if a ﬁrm cannot respond rapidly enough, it lags behind other ﬁrms in seizing the opportunities, and the return on its investment declines (Chen & Xie, 2011). In this sense, speed in gathering information about good invest- ment projects is critical. If a ﬁrm’s directors occupy more structural hole positions, those directors can control more channels of information about good investment oppor- tunities, and can give timely advice to their boards and managers, thereby obtaining the ‘one-step ahead advantage’ in investment activities (Burt, 1992). This timing beneﬁtis the direct result of an information advantage in investment decision making. From this perspective, the second hypothesis emerges as follows. H2: Firms occupying a dominant structural hole position within the directors’ network outperform other ﬁrms in investing efﬁciency. The network effect is also inﬂuenced by each actor’s characteristics (Burt, 1992, p. 71), and the degree of industry competition is another core factor in a ﬁrm’s operat- ing and investing efﬁciency (Wang, 2011). Therefore, the effect of a structural hole depends partly on the industry competition that the ﬁrm confronts. In monopoly indus- tries, a ﬁrm can rely on its monopoly status to obtain advantages in operating and investing activities (Wang, 2011). In this circumstance, the inﬂuence of information acquisition and control advantage through the director’s network is less signiﬁcant. The ﬁrm would therefore take advantage of its monopoly position instead of its structural hole position during the negotiation process. However, in competitive industries, a stra- tegic network position can result in stronger structural autonomy, as most ﬁrms cannot gain excess proﬁts easily through the advantage of a direct monopoly position (Zaheer & Bell, 2005). This means that a ﬁrm in a strategic network position can obtain a lar- ger market share and make more efﬁcient investments. Compared with monopoly industries, ﬁrms in competitive industries rely more on structural hole positions to gain favourable resources. Hence, the third hypothesis is as follows. H3: The positive relation between a structural hole position and operating/investing efﬁciency is more pronounced for ﬁrms in more competitive industries than for ﬁrms in monopoly industries. Although most evidence from analytical and empirical studies supports the positive effect of a structural hole, some studies indicate negative effects. For example, Shipilov and Li (2008) ﬁnd that although a network can facilitate the gathering of information, the network may also restrict information for business partners if a lack of trust and poor resource-sharing prevents the ﬁrms in key network positions from concentrating on collaboration and performance improvement. The results of our empirical tests (e.g., Chen & Xie, 2011, 2012) indicate that at least within the Chinese context, the structural hole has a mainly positive effect. However, as the structural hole may bring about negative effects due to the ﬁrm’s outside-network environment, we distinguish market 60 Chen competition based on H1 from that based on H2. We are aware of the negative effect that a structural hole can cause, and hope to provide more in-depth evidence on this potential of structural holes in the future. 3. Research design 3.1. Measurement of structural hole position Having social capital can be redeﬁned from the structural hole perspective as having a position to act as a ‘bridge’ between network members, and therefore enjoying advanta- ges in both information and control through connecting those who cannot directly con- nect each other. In studying such social capital, the key question is how to measure the structural hole position of a member in a social network, as the presence or absence of structural holes determines the potential availability of information and control advanta- ges. Before measuring a structural hole, we must deﬁne a direct link between two ﬁrms (Freeman, 1979). By ‘direct link’, we mean a tie in which one or more directors/man- agers of a ﬁrm serve on the board of another ﬁrm in the same year. In that case, we assume that the two ﬁrms communicate with each other directly, and a network based on this direct link is referred to as the ﬁrm’s social network, which is generated from the directors’ interlocking ties. We compute structural hole positions based on the direc- tors’ interlocking networks in Chinese listed companies. In these companies, most of the CEOs also serve on the boards, so the directors’ networks usually include the cor- porate executives. This method of measuring individual-level networks (of board direc- tors) to represent the organisation-level networks (of ﬁrms) is common in social network studies (Jackson, 2008; Larcker et al., 2013). We assume (as Figure 2 illustrates) that ﬁrm A has four directors: I11, I12, I13 and O1. Firm B also has four directors: I21, I22, I23 and O2. Firm C has ﬁve directors: I31, I32, I33, O1 and O2. Director O1 serves in both ﬁrm A and ﬁrm C, and also links ﬁrms A and C directly through the path of 1 which means two actors connect to each other directly whereas path of 2 means two actors are linked indirectly through the help of a third actor. Similarly, director O2 serves in both ﬁrm B and ﬁrm C, and also links ﬁrms B and C directly through the path of 1. However, O2’s position is different from that of O1; ﬁrm A and ﬁrm B do not contact with each other directly without the communica- tion of O2, so there is a ‘hole’ in the social network among the three ﬁrms. In this case, ﬁrm C acts as a bridge, due to the interlocking status of directors O1 and O2. I31 I32 I13 Firm C O2 I12 I33 I23 O1 Firm A I11 I21 Firm B I22 Figure 2. Generation of ﬁrms’ network and structural hole positions, based on director-level networks. China Journal of Accounting Studies 61 Like Burt (1992, p. 54) and Zaheer and Bell (2005), we measure the structural hole using the following model: C ¼ p þ p p (1) ij ij iq iq where i means the ﬁrm being studied in the social network, j means the other ﬁrms in the network, excluding ﬁrm i, and q means a ﬁrm other than i or j, namely q 6¼ i; j.If ﬁrm i and ﬁrm j share a director, then they have a direct relationship through the path of 1. Thus, P represents the strength of the paths from ﬁrm i to ﬁrm j, which indicates ij direct relations between ﬁrm i and ﬁrm j. However, ∑P P represents the summed iq qj strength of the indirect network relations between ﬁrm i and ﬁrm j that pass through ﬁrm q, which indicates that ﬁrm i indirectly relates to ﬁrm j. The variable C represents ij constraints on ﬁrm i’s effort to communicate with ﬁrm j, namely the ‘network constraint index’. There are two indices used in measuring structural holes: the structural hole index (including factors of efﬁcient size, efﬁciency, constraints and rank), and the between- ness index. Both measures have their own advantages, but the network constraint index is more widely used (Liu et al., 2011). It is a comprehensive index that efﬁciently mea- sures the deﬁciency of structural holes. An increase in the value of the network con- straint index indicates a decrease of structural holes, which indicates that the ﬁrm concerned is at the edge of its network. A ﬁrm’s performance is usually negatively cor- related to its network constraint index (Burt, 2004). The largest value of this index is one, so researchers always use the difference between one and the constraint index to represent the richness of structural holes (Burt, 1992; Zaheer & Bell, 2005), calculated as follows: CI ¼ 1 C (2) i ij Using the new index, there is a positive relation between CI and the richness of struc- tural holes. 3.2. Research model and the deﬁnition of main variables After constructing the measurement of structural holes, the relation between a ﬁrm’s network position and its operating and investing efﬁciency is reﬂected in the following models: turnover =turnover adj ¼ a þ a CI þ Controls þ e (3) it 0 1 it it absINV =underINV =overINV ¼ b þ b CI þ Controls þ c (4) it it it it 0 1 Here, model (3) is used to test Hypothesis 1, that is, the relation between structural hole position and operating efﬁciency. The speed of asset turnover represents the efﬁciency in converting assets into sales and cash through the ‘supplying-producing- selling’ operating chain. In particular, the relative differences between particular peer ﬁrms can be captured by the heterogeneity of control power that each ﬁrm has in the operating chain (Li, 2007). We use both the raw value (turnover) and the industry- adjusted value (turnover_adj) of asset turnover to capture ﬁrm operating efﬁciency (Li, 2007). Our classiﬁcation of industries follows the CSRC’s 2001 standard of listed ﬁrms. This standard divides the manufacturing sector into ten groups using the 2- digit-SIC code, and other ﬁrms into 11 groups using the 1-digit -SIC code. 62 Chen Model (4) is used to test Hypothesis 2, that is, the relation between structural hole position and investing efﬁciency, which is estimated using the model in Richardson (2006): INV ¼ a þ a Q þ a Cash þ a ListY þ a Size þ a Lev þ a RET t 0 1 t1 2 t1 3 t1 4 t1 5 t1 6 t1 þ a INV þ e (5) 7 t1 We use the residual of Model (5) to represent investing efﬁciency, where INV is the amount of investment of a ﬁrm in year t,deﬁned as the change in ﬁxed assets, con- struction in progress, intangible assets and long-term investments, scaled by the average total assets; the construction of INV is based on balance sheet data, and we use data from cash ﬂow statements to construct INV in the robustness tests. The variable Q t-1 represents a ﬁrm’s growth opportunity at the end of year t–1, deﬁned as the sum of the year-end market value of its equity and the book value of its liabilities, scaled by total assets. Cash is a ﬁrm’s cash holdings, deﬁned as cash and cash equivalents, scaled t-1 by total assets at the end of year t–1. ListY is the listing age of the ﬁrm at the end of t-1 year t–1. Size and Lev are the logarithm of total assets and the leverage at the end t-1 t-1 of year t–1, respectively. RET is the cumulative monthly return, adjusted by the t-1 market return from May in year t–1 to April in year t. INV is the total investment in t-1 year t–1. To eliminate the industry and year effect, we regress the model for each industry-year separately. If the residual of the model is positive, it indicates over-invest- ment (overINV). If the residual of the model is negative, it indicates under-investment (underINV). Note that in the following analysis we multiply underINV by –1, which means that the larger the value of underINV, the more severe the under-investment. We use the absolute value of underINV and overINV to represent the magnitude of invest- ment efﬁciency. Following Biddle, Hilary, and Verdi (2009), Chen, Hope, Li, and Wang (2011), Chen (2011b), Chen and Xie (2011), and Li (2007), as ﬁrm’s corporate gover- nance can affect ﬁrm efﬁciency, we control for board size (number of board directors at the end of year t), the proportion of independent directors on the board at the end of year t (OUT), the natural log of the total compensation of the top three executives (COMP) and duality (DUAL – a dummy variable that equals 1 if the chairman and CEO are the same person, and 0 otherwise); ﬁrms’ ownership also affect ﬁrms’ perfor- mance, so we control for SOE (a dummy variable that equals 1 if the state controls the ﬁrm, and 0 otherwise), SHR1 (the percentage of shares held by the largest stockholder at the end of year t) and SEP (the separation of cash ﬂow rights and control rights at the end of year t); we also control for ﬁrms’ other characteristics, namely SIZE (the natural log of total assets at the end of year t), LEV (the ratio of total liabilities to total assets at the end of year t), ROA (return on assets in year t) and CATA (ratio of current assets to total assets at the end of year t). The deﬁnitions of these variables are shown in Table 1. The control variables used in various previous studies are different. One reason might be the differences in the targets of each study. To make our results robust, we add each of the corporate governance and ﬁrm characteristic variables into one or more of our models. For example, in Model (3) we add long-term investment ratio, accounts receivable turnover, and inventory turnover. In Model (4) we add operating cash ﬂow, management fee ratio and other receivables. These further analyses do not inﬂuence our main results. In Model (3) and Model (4), we expect CI to be positive, which means that the operating and investing efﬁciency of ﬁrms with a dominant China Journal of Accounting Studies 63 Table 1. Variable deﬁnitions. Variable name Deﬁnition Dependent variables Operating efﬁciency Operating efﬁciency, both the raw value (turnover) and industry- adjusted value (turnover_adj) of asset turnover of year t,deﬁned as sales divided by total assets. Investing efﬁciency Investment efﬁciency. For details regarding the computing process, please see Richardson (2006). As described before, we divide this variable into overINV and underINV as well. Independent and control variables CI Network constraint index. For detailed deﬁnition, please see 3.1, Computed using Pajek software. IHHI The Herﬁndahl index of industry concentration at the end of year t, calculated as the sum of squared sales, divided by the square of the sum of sales. BOARD The number of directors on a board at the end of year t. OUT The proportion of independent directors to all board directors at the end of year t. COMP The natural log of the total compensation of the three executives with the highest compensation. DUAL Equals 1 if the chairman and CEO are the same person, and 0 otherwise. SOE Equals 1 if the state controls the ﬁrm, and 0 otherwise. SHR1 The proportion of the shares held by the largest stockholders among all shares outstanding at the end of year t. SEP The separation of cash ﬂow rights and control rights at the end of year t. SIZE The natural log of total assets at the end of year t. LEV The ratio of total liabilities to total assets at the end of year t. ROA Return on assets in year t. ROE Return on equity in year t. CATA Ratio of current assets to total assets at the end of year t. IND/YEAR Industry and year dummy. The classiﬁcation of industries follows the CSRC’s 2001 standard of listed ﬁrms. This standard divides the manufacturing sector into 10 groups using the 2-digit-SIC code, and other ﬁrms into 11 groups using the 1-digit -SIC code structural hole position in their directors’ network is greater than that of ﬁrms not in such a position. 3.3. Data We collect data from 2001 to 2011 on Chinese A-share listed ﬁrms. After removing the ﬁnancial industries and the observations with missing data, the ﬁnal sample contains 10,415 ﬁrm-year observations. To maintain the integrity of the network, we include all A-share listed ﬁrms when computing the network constraint index of structural holes. Information on directors is manually collected (to determine whether directors with the same name are actually the same person), and the other data are from the CSMAR database (http://www.gtarsc. com), a ﬁnancial and stock trading database widely used in Chinese studies. The 64 Chen process of computing the networks generated by directors is as follows. First, we calculate the number of board seats of directors for each year, and also identify which directors have direct relations through sitting on the same board. If directors have such a relation, then their connecting path is marked as 1, that is, they have a direct network relationship. Second, we identify the indirect relations stemming from the direct network relations. Third, we integrate both the direct and indirect network relations for the whole board network. Next, we compute the network constraint index in the following way. First of all, we give every director a unique ID, and convert the data into a ‘ﬁrm-director’ one-mode matrix. After that, we import the matrix into Pajek using the ‘txt2pajek’ software (Fracassi, 2013; Fracassi & Tate, 2012), and compute the network constraint index. To eliminate the effect of extreme values, we winsorise all continuous variables at the 1% and 99% levels and build a cluster for the model at the ﬁrm level. 4. Main results 4.1. Descriptive analysis The statistical analyses of the main variables are shown in Table 2. The mean value of CI is 0.26, and the difference between the maximum and minimum value of CI is 0.789, which means that the disparity is large. This disparity facilitates our study. The means of turnover and turnover_adj are 0.676 and 0.102, respectively, and the mean value of investing efﬁciency is 0.063. 4.2. Results of multivariate regressions To better understand the issue, we multiply underINV by –1, so that the larger the und- erINV, the more severe the underinvestment. The results of Models (3) and (4) are shown in Table 3. The ﬁrst two columns illustrate the relation between structural hole position and ﬁrm’s operating efﬁciency. CI is positively correlated with both turnover and turn- over_adj at the 5% level, which also supports hypothesis 1. Columns 3 to 5 illustrate the relation between structural hole position and ﬁrm’s investing efﬁciency. This rela- tion is not signiﬁcant when the dependent variable is absINV, which means that there is no obvious correlation between a ﬁrm’s structural hole position and its overall invest- ment efﬁciency. However, if we divide the investing inefﬁciency into over-investment and under-investment, the coefﬁcient on CI is negatively signiﬁcant when the depen- dent variable is underINV, and insigniﬁcant when the dependent variable is overINV. This ﬁnding shows that the ﬁrm’ structural hole position can help gain information and opportunities, which further helps reduce under-investment. A core network structural position endows a ﬁrm with an information advantage, especially for investing activi- ties. When dependent variable is turnover or turnover_adj, the coefﬁcients on SOE, ROA and LEV are signiﬁcantly positive, and the coefﬁcients on BOARD, DUAL and OUT are insigniﬁcant; when dependent variable is absINV, the coefﬁcient on SIZE is signiﬁcantly negative, and the coefﬁcients on BOARD, DUAL and OUT are also insig- niﬁcant. The results show that ﬁrms’ corporate governance are not related to ﬁrm efﬁ- ciency. The reason may be the convergence of ﬁrms’ corporate governance mechanisms (Chen, 2011a). China Journal of Accounting Studies 65 Table 2. Descriptive statistics. Variables Obs. Mean Median Max. Min. Std. CI 10415 0.262 0.000 0.789 0.000 0.295 turnover 10415 0.676 0.557 2.246 0.054 0.485 turnover_adj 10415 0.102 0.000 2.027 -0.730 0.462 absINV 10415 0.063 0.045 0.278 0.001 0.061 overINV 4952 0.059 0.046 0.148 0.000 0.047 underINV 5463 -0.059 -0.044 0.000 -0.208 0.053 BOARD 10415 9.306 9.000 15.000 5.000 2.017 OUT 10415 0.310 0.333 0.556 0.000 0.121 COMP 10415 13.277 13.356 15.283 10.913 0.951 DUAL 10415 0.163 0.000 1.000 0.000 0.369 SOE 10415 0.553 1.000 1.000 0.000 0.497 SHR1 10415 38.516 36.550 100.000 0.160 16.148 SEP 10415 5.539 0.000 53.424 0.000 8.064 SIZE 10415 21.333 21.198 24.196 19.126 1.105 LEV 10415 0.488 0.485 1.172 0.074 0.227 ROA 10415 0.030 0.035 0.169 -0.243 0.069 CATA 10415 0.548 0.558 0.971 0.078 0.216 Table 3. Relation of structural hole position and operating/investing efﬁciency. turnover turnover_adj absINV (–1) underINV overINV ** ** ** CI 0.059 0.062 0.002 –0.005 0.007 (2.17) (2.15) (0.67) (–2.17) (1.40) ** BOARD 0.004 0.003 –0.001 –0.001 –0.000 (0.73) (0.54) (–1.33) (–2.14) (–0.46) OUT –0.110 –0.141 0.026 0.007 0.017 (–0.74) (–0.91) (1.49) (0.52) (1.19) *** *** ** *** COMP 0.096 0.094 –0.003 –0.003 –0.001 (7.27) (6.44) (–2.51) (–3.33) (–0.71) DUAL –0.011 –0.016 –0.000 0.001 –0.001 (–0.46) (–0.68) (–0.08) (0.20) (–0.52) *** *** *** *** * SOE 0.091 0.088 –0.006 –0.007 –0.003 (4.13) (3.68) (–2.78) (–3.13) (–1.91) *** *** *** *** * SHR1 0.002 0.002 0.000 0.000 0.000 (3.37) (3.40) (2.68) (2.82) (1.68) SEP 0.001 0.001 –0.000 –0.000 –0.000 (1.13) (0.97) (–0.59) (–0.32) (–0.94) *** *** SIZE 0.015 0.020 –0.004 –0.003 –0.003 (1.31) (1.61) (–3.33) (–2.95) (–1.57) *** *** ** LEV 0.249 0.257 0.001 0.009 –0.002 (5.09) (4.81) (0.17) (2.46) (–0.51) *** *** ** * ROA 1.286 1.308 –0.024 0.022 –0.031 (11.07) (10.65) (–1.62) (2.11) (–1.72) *** *** *** *** *** CATA 0.535 0.581 –0.045 –0.068 –0.024 (9.40) (9.29) (–8.23) (–15.85) (–5.81) *** *** *** *** ** Cons –1.678 –2.159 0.211 0.209 0.135 (–6.67) (–8.01) (8.38) (9.57) (2.04) Ind/Year yes yes yes yes yes R _adj 0.319 0.177 0.063 0.106 0.038 F-Value 30.94 13.89 8.81 27.34 3.24 Obs. 10415 10415 10415 4952 5463 *** ** * Note: , , and indicate 0.01, 0.05, and 0.10 signiﬁcance levels, respectively. The results are clustered at the ﬁrm level. 66 Chen Table 4 shows the results from the subsamples of monopoly and competitive industries. As in Table 2, IHHI is the Herﬁndahl index of industry concentration at the end of year t, calculated as the sum of squared sales, divided by the square of the sum of sales. In order to generate a partition variable, we deﬁne HHI as a dummy variable, which equals 1 if the value of IHHI is greater than the median value of all industries, and 0 if otherwise. Hence HHI with the value of 1 represents the monopoly industries. The ﬁrst two columns illustrate the relation between a structural hole position and oper- ating efﬁciency in various industries (the dependent value is turnover_adj). We can see that the coefﬁcient on structural hole position is signiﬁcantly positive in competitive industries, but is insigniﬁcant in monopoly industries. This ﬁnding suggests that in competitive industries, having a structural hole position enhances operating efﬁciency. The last two columns describe the relations between a structural hole position and investing efﬁciency in different industries (the dependent value is underINV). Similarly, in competitive industries the coefﬁcient of CI is signiﬁcant, and there is no obvious relation in monopoly industries. These results support Hypothesis 3, that the positive Table 4. Product market competition, structural hole position and operating/investing efﬁciency. HHI=0 HHI=1 HHI=0 HHI=1 turnover_adj turnover_adj underINV underINV *** * CI 0.071 0.020 –0.004 –0.006 (3.42) (0.67) (–1.74) (–1.31) BOARD 0.005 0.000 –0.000 –0.001 (1.64) (0.10) (–0.64) (–1.60) OUT –0.201 –0.038 0.009 0.002 (–1.77) (–0.24) (0.66) (0.09) *** *** COMP 0.082 0.111 –0.000 0.001 (9.85) (8.84) (–0.25) (0.47) DUAL –0.023 –0.017 –0.003 0.000 (–1.33) (–0.68) (–1.53) (0.11) *** *** *** SOE 0.090 0.088 –0.006 –0.000 (6.88) (4.30) (–3.55) (–0.09) *** *** ** SHR1 0.002 0.002 –0.000 0.000 (6.09) (3.67) (–0.47) (1.96) ** ** SEP 0.001 0.001 0.000 –0.000 (2.04) (1.10) (1.32) (–2.53) *** *** ** SIZE 0.029 0.003 –0.003 –0.003 (4.45) (0.32) (–2.99) (–2.44) *** *** *** ** LEV 0.249 0.151 0.011 0.014 (8.46) (3.76) (2.72) (2.16) *** *** *** *** ROA 1.450 0.963 0.058 0.058 (15.14) (7.16) (4.78) (2.82) *** *** *** *** CATA 0.400 0.488 –0.035 –0.036 (14.07) (13.45) (–9.70) (–6.59) *** *** *** *** Cons –2.174 –1.928 0.108 0.112 (–15.29) (–9.66) (6.22) (3.87) Ind/Year yes yes yes yes R _adj 0.161 0.154 0.033 0.044 F-Value 66.89 22.91 10.05 4.69 Obs. 7634 2781 3675 1277 *** ** * Note: , , and indicate 0.01, 0.05, and 0.10 signiﬁcance levels, respectively. The results are clustered at the ﬁrm level. China Journal of Accounting Studies 67 relation between a structural hole position and operating efﬁciency (or investing efﬁciency) is more pronounced for ﬁrms in competitive industries. 4.3. Additional analysis Burt (1992, p. 34) argues that the information and control advantages obtained from a structural hole position can further improve a ﬁrm’s performance over time. To test whether this is true in the network of ﬁrm directors, we investigate whether ﬁrms located centrally in a structural hole position improve their performance in years t+1 and t+2. We use both ROA and ROE as our proxies for performance. As shown in Table 5, the relation between CI and performance in year t+1 and year t+2 are both positive at the 5% and 10% levels. Moreover, in untabulated analysis we use the Table 5. Structural hole position and future performance. Year t+1 Year t+2 ROA ROE ROA ROE ** ** * * CI 0.0054 0.0158 0.0046 0.0148 (2.30) (1.99) (1.82) (1.80) BOARD 0.0002 0.0005 0.0000 0.0005 (0.66) (0.43) (0.03) (0.41) OUT 0.0095 0.0736 –0.0250 0.0375 (0.73) (1.64) (–1.39) (0.78) *** *** *** *** COMP 0.0117 0.0360 0.0127 0.0347 (11.88) (10.78) (11.14) (9.95) ** * * ** DUAL –0.0040 –0.0130 –0.0044 –0.0139 (–2.05) (–1.95) (–1.78) (–2.00) *** *** *** *** SOE –0.0061 –0.0185 –0.0068 –0.0163 (–3.91) (–3.51) (–3.89) (–2.94) *** *** *** *** SHR1 0.0004 0.0010 0.0004 0.0009 (8.19) (6.91) (7.90) (5.90) *** *** SEP –0.0002 –0.0008 –0.0001 –0.0002 (–2.78) (–2.89) (–0.74) (–0.80) *** ** *** SIZE –0.0023 0.0056 –0.0032 0.0034 (–3.01) (2.05) (–3.38) (1.17) *** *** *** *** LEV –0.0240 –0.0899 –0.0278 –0.0797 (–7.01) (–6.71) (–5.09) (–5.60) *** *** CATA 0.0031 0.0623 –0.0016 0.0492 (0.83) (4.81) (–0.43) (3.59) *** *** ROA 0.3756 0.2042 (33.95) (10.83) *** *** ROE 0.2364 0.1011 (19.32) (7.92) *** *** *** *** Cons –0.0945 –0.6084 –0.0573 –0.5881 (–5.62) (–10.32) (–2.75) (–9.79) Ind/Year yes yes yes yes R _adj 0.2659 0.1359 0.1535 0.0877 F-Value 85.23 35.39 39.83 19.92 Obs. 8978 8586 8093 7702 *** ** * Note: , , and indicate 0.01, 0.05, and 0.10 signiﬁcance levels, respectively. The results are clustered at the ﬁrm level. 68 Chen change in ROA and ROE to measure a ﬁrm’s performance, and ﬁnd that ROA changes and CI are positively correlated at the 5% level in year t+1. In addition, ROE changes and CI are positively correlated at the 10% level in year t+1. However, these correlations are not signiﬁcant in year t+2. These additional tests strongly support our main results. 4.4. Robustness tests The following tests are performed to corroborate our results. First, we use both the industry-adjusted ROA/ROE and market-adjusted yearly return (RET) as performance measures to examine the relationship between structural hole and future performance. Table 6 shows that the coefﬁcients are all positively Table 6. Robustness test (1). Year t+1 Year t+2 ROA_adj ROE_adj RET ROA_adj ROE_adj RET ** ** *** * *** CI 0.005 0.015 0.485 0.004 0.014 0.084 (2.27) (1.96) (19.04) (1.54) (1.70) (3.76) BOARD 0.000 0.001 0.004 0.000 0.000 0.005 (0.59) (0.42) (0.91) (0.35) (0.33) (1.33) ** OUT 0.007 0.067 –0.313 –0.023 0.034 –0.125 (0.53) (1.51) (–2.07) (–1.56) (0.73) (–0.94) *** *** *** *** *** COMP 0.012 0.036 –0.037 0.012 0.035 –0.012 (11.98) (10.99) (–3.33) (11.93) (10.18) (–1.25) ** ** * *** ** DUAL –0.004 –0.013 0.044 –0.006 –0.015 0.022 (–2.27) (–2.05) (1.94) (–2.86) (–2.12) (1.12) *** *** *** *** *** * SOE –0.006 –0.018 –0.068 –0.007 –0.016 –0.029 (–3.96) (–3.46) (–3.83) (–4.25) (–2.87) (–1.87) *** *** *** *** *** SHR1 0.000 0.001 0.002 0.000 0.001 0.001 (8.23) (6.87) (3.41) (8.11) (5.86) (1.20) ** *** ** SEP –0.000 –0.001 –0.002 –0.000 –0.000 –0.000 (–2.42) (–2.71) (–2.26) (–0.60) (–0.69) (–0.37) *** ** *** *** *** SIZE –0.002 0.005 –0.070 –0.002 0.003 –0.041 (–2.77) (2.04) (–7.59) (–2.69) (1.19) (–5.02) *** *** ** *** *** ** LEV –0.024 –0.088 0.092 –0.023 –0.077 0.086 (–7.02) (–6.68) (2.03) (–6.11) (–5.51) (2.16) *** * *** CATA 0.004 0.059 –0.081 –0.002 0.047 –0.007 (0.94) (4.66) (–1.87) (–0.51) (3.46) (–0.19) *** *** ROA 0.371 0.195 (33.47) (16.06) *** *** ROE 0.231 0.017 0.099 0.042 (19.14) (0.45) (7.89) (1.29) *** *** *** *** *** *** Cons –0.134 –0.670 2.512 –0.118 –0.621 0.939 (–7.97) (–11.53) (12.89) (–6.93) (–10.48) (5.43) Ind/Year yes yes yes yes yes yes R _adj 0.237 0.120 0.553 0.110 0.067 0.697 F-Value 73.01 30.74 295.24 83.30 14.96 499.02 Obs. 8978 8586 8630 8093 7702 7842 *** ** * Note: , , and, indicate 0.01, 0.05, and 0.10 signiﬁcance levels, respectively. The results are clustered at the ﬁrm level. China Journal of Accounting Studies 69 signiﬁcant, whether using ROA_adj, ROE_adj, or RET as the performance measures in year t+1. However in year t+2, the relation between CI and ROA_adj is no longer signiﬁcant. The signiﬁcance levels when using ROE_adj or RET decrease, consistent with the main test results. Second, we use the models developed by Biddle et al. (2009) and Chen and Xie (2011) to measure the investment efﬁciency. Biddle et al. (2009) argue that researchers can regress investment on a ﬁrm’s growth directly to compute the investment efﬁciency. The model for this is INV ¼ c þ c Growth þ d, where Growth is measured as t t1 t1 0 1 the sales growth in year t-1. Chen and Xie (2011) further investigate the nonlinear effect of sales growth, and change the model into INV ¼ k þ k Neg þ t 0 1 t1 k Neg Growth þ k Growth þ v, where Neg is the dummy variable, to 2 t1 3 t1 t1 determine whether the sales growth is negative or positive. The variable absINV_B is the absolute value of investment efﬁciency generated from the residual of the Biddle et al. (2009) model, and absINV_C refers to the residual of the Chen and Xie (2011) model. Both results are demonstrated in Table 7. The relations between CI and Table 7. Robustness test (2). Investment inefﬁciency absINV_B absINV_C ** CI –0.008 –0.008 ** (–2.19) (–2.17) *** BOARD –0.002 –0.001 (–3.64) (–1.59) OUT –0.002 0.017 (–0.11) (0.84) COMP 0.001 –0.001 (0.56) (–0.47) DUAL 0.004 0.004 (1.16) (1.36) *** *** SOE –0.009 –0.009 (–3.56) (–3.85) SHR1 –0.000 –0.000 (–1.83) (–0.82) SEP –0.000 –0.000 (–0.71) (–0.42) *** *** SIZE –0.005 –0.005 (–4.53) (–4.02) *** *** LEV 0.048 0.043 (8.20) (8.33) ** *** ROA 0.044 0.070 (2.33) (4.14) *** *** CATA –0.028 –0.033 (–4.54) (–5.70) *** *** Cons 0.267 0.278 (11.30) (11.94) Ind/Year yes yes R _adj 0.042 0.061 F-Value 16.82 17.46 Obs. 9120 9120 *** ** * Note: , , and indicate 0.01, 0.05, and 0.10 signiﬁcance levels, respectively. The results are clustered at the ﬁrm level. 70 Chen absINV_B or absINV_C are both negatively signiﬁcant at the 5% level, which means that our results for the network’s role on investment efﬁciency are stable. Our paper faces a potential endogeneity problem that must be dealt with. The ﬁrms with core structural hole positions are systematically different from ﬁrms without a core structural hole, which could cause these ﬁrms to select directors with more network relations and therefore achieve higher operating and investing efﬁciency. Under these conditions, a positive correlation between the structural hole position and ﬁrm efﬁ- ciency might not be the result of the network structure. First of all, we regress the ﬁrm- level ﬁxed effects for Models (2) and (3). The main results remain the same; in addi- tion, like Larcker et al. (2013), we run the regression using two subsamples: the ﬁrst sub-sample in which the ﬁrm directors do not change, and the second sub-sample in which the ﬁrm’s independent directors do not change. This regression ensures that the results are caused by network structure rather than changes in directors. The untabulat- ed results show that the positive relation between CI and operating efﬁciency is more signiﬁcant than that in Table 3. Also, the negative relation between CI and under- investment is close to but not signiﬁcant. As Larcker et al. (2013) claim, researchers cannot remove all of the endogeneity problems. We can only use the limited methods available to alleviate these problems. 5. Conclusion A growing literature is focusing on the relation between social networks and corporate ﬁnance (e.g., Cai and Sevilir, 2012; Fracassi and Tate, 2012; Larcker et al., 2013). According to the social network theory, there are two components of networks: network relations and network structures. The existing literature mainly investigates the network relations of ﬁrms, but the structural embeddedness view argues that it is the structural position of networks rather than simply the network relations themselves that enable actors to acquire resource advantages. As ﬁrms are embedded in various social net- works, these networks are important aspects of economic organisations, so the networks speciﬁcally generated by interlocking relations between directors need to be measured and analysed. The structural hole, as ﬁrst analysed by Burt (1992) is a highly crucial network structure. The different structural hole positions generated by ﬁrm directors’ networks can bring both control and information advantages to a ﬁrm. Such network positions can inﬂuence a ﬁrms’ operating and investing activities, and its future performance. To investigate directors’ social networks, this paper ﬁrst constructs the network of directors for Chinese A-share listed ﬁrms between 2001 and 2011. We then compute the ﬁrms’ structural hole positions using Pajek, which is one of the most widely used software tools in social network studies. Finally, we investigate the effects of ﬁrms’ structural hole positions on their operating and investment efﬁciency. Our results indicate that ﬁrms with more structural holes operate and invest more efﬁciently, and these effects seem even more pronounced in competitive industries. Further tests show that ﬁrms with more structural holes also perform better over time. Our results show that the main activities of ﬁrms, such as operations and investments, can be profoundly inﬂuenced by the structural hole positions of their directors’ interlocking networks. Therefore, we suggest that network structure should be taken into consideration when analysing ﬁrms’ investment decisions, which would effectively expand the existing interdisciplinary research on ‘social network and corporate ﬁnance’. In addition, as one type of social network of China, namely Chinese China Journal of Accounting Studies 71 ‘Guanxi’, structural holes is not paid enough attention. While there is widespread con- ﬁrmation of the importance of structural holes in generating power and productivity in Western economies, evidence is weak and disconﬁrming for East Asia especially China (Chai & Rhee, 2009). As the study of structural holes in Chinese ﬁrms can relate to Confucian capitalism and the ‘East Asian Model’ of the ﬁrm, we expect more and more related studies in this ﬁeld. Acknowledgements I am grateful for the helpful comments and suggestions from Professors Jason Xiao, Kangtao Ye, Xi Wu, Huilong Liu, Oliver Li, Wei Luo and participants at the 2012 Annual Conference of China Journal of Accounting Studies and the 11th International Symposium on Empirical Accounting Research in China. I would also like to thank two anonymous referees and the edi- tors for their insightful comments. This work was supported by the National Natural Science Foundation of China (71202126), the Research Fund for the Doctoral Program of Higher Educa- tion (20120016120003), grants from the Beijing Municipal Commission of Education ‘Pilot Reform of Accounting Discipline Clustering’, grants from the Beijing Municipal Commission of Education ‘Joint Construction Project’ and the ‘Project 211’ Fund from Central University of Finance and Economics. Notes 1. If one director serves on more than one board, then all of the ﬁrms on which that director serves can obtain network advantages from the director’s joint position, which is described in social network theory as an undirected graph. This paper only studies the directors’ positions in the current year, so that if one director serves on two different boards in different years, it would not show as a direct link according to our deﬁnition. Generally, interdisciplin- ary research on social networks and corporate ﬁnance identiﬁes networks based on the directors’ serving behaviour in the current year’s position (Larcker et al., 2013). However, for research into the learning effect or contagion effect of interlocking director relations, the directors’ positions in different years are often included to test the spread of corporate policies (Bizjak, Lemmon, & Whitby, 2009; Fracassi & Tate, 2012). 2. We do not include the non-listed ﬁrms, because data for non-listed ﬁrms are hard to obtain. 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China Journal of Accounting Studies
– Taylor & Francis
Published: Jan 2, 2014
Keywords: board network; investing efficiency; operating efficiency; social tie; structural hole