635HOU et al: DYNAMIC MERGERS DRIVE INDUSTRIAL COMPETITIONJournal of Scientific & Industrial ResearchVol. 72, November 2013, pp. 635-647

*Author for correspondenceE-mail: hour@gdut.edu.cn.

Dynamic Mergers Drive Industrial Competition Evolution: A NetworkAnalysis Perspective

Rui Hou1*, Jianmei Yang2 and Canzhong Yao3

1School of Management, Guangdong University of Technology, Guangzhou 510520, China2School of Business Administration, South China University of Technology, Guangzhou 510641, China

3School of Economics and Commerce, South China University of Technology, Guangzhou 510641, China

Received 20 July 2012; revised 26 December 2012; accepted 13 May 2013

This paper presents a novel method to explore the relationship between dynamic mergers and evolution of industrialcompetition by introducing complex network tool. Taking the beer industry of China as an example, we establish Markets-Firmsbipartite time series networks, weighted Markets-Firms time series networks and industrial competition time series networks byusing the data from 1992 to 2009 respectively. Through analyzing the changes of topology index on these networks, we find thatdynamic mergers play a key important role in the evolution process of industrial competition. The results show that dynamicmergers promote the local fragmented submarkets to be consolidated into a global market for this industry, and they also show theevolution process that competitive relationship among rivals turns from local segmented markets to a global cross-marketgradually. In this paper, we provide a new view to observe the changes of industrial competition relationship driven by dynamicmergers.

Key words: dynamic merger; industry competition; complex networks; beer industry of China

IntroductionThe dynamic competition behaviors, such as quantity

competition, price competition, Ad service, productioninnovation and merger activity may cause the industrialcompetition relationship to evolve all the time. Amongthese competitive actions, merger activity, which wouldcause the disappearance of firms and the increase ofindustry concentration ratio, will bring a more thoroughchange on the structure of industrial competitionrelationship. Any firm can exert an influence to thecurrent industrial competition relationship by adopting themeans of merger action to attack against rivals forconsolidating their own market or invading rival’s market.Changes of the industrial competitiveness structure drivenby mergers, even small ones, will be perceived by rivalsand stimulate their counterattacks1. In order to makethe initiator to suffer the same loss, a subsequent mergeraction will be an ideal way of counter-attack.Consequently it may trigger dynamic mergers orsequential mergers while an industry merger wave will

be formed2-4. Dynamic mergers are the most effectiveand direct means to change the inter-firm competitiverelationships in an industry quickly. Therefore, it is veryvaluable to investigate the relationship between dynamicmergers and evolution of industrial competition for thefirms which are involved in competition in an industry, aswell as for the policy-makers who are responsible forformulating the industrial competition policy.

There are many literatures on studying therelationship between merger actions and inter-firmcompetition in industrial organization research field5-6.However, these studies mainly focused on how thecompetition structure in an oligopolistic market affectsmerger decisions and ends up with seeking a competitiveequilibrium. Strictly speaking, these studies belong tostatic analysis on the inter-firm competitive structureamong few rivals, which do not need to consider theinfluence of dynamic mergers upon the evolution ofindustrial competition relationship. Moreover, they did notconsider the complex competition context, in which alarge number of rivals compete with each other in manysubmarkets and attempt to implement cross-marketmergers. Although Kao & Menezes7 considered

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endogenous mergers under a multi-market competitioncontext, they failed to give a perspective to observe theevolution process of industrial competition relationshipcaused by dynamic mergers within a specific industry.

To elaborate this process, we run a case of the beerindustry of China and construct Market-Firm bipartitetime series networks, bipartite weighted time seriesnetworks and industrial competition relationship timeseries networks by introducing complex network tool.Why to introduce this method? It lacked of an effectiveresearch tool to deal with large-scale competitors intraditional industrial organization theory and strategicmanagement theory while there are many rivals in anindustry. Network analysis should be a feasible methodto resolve this problem. In fact, there are many scholarswho used network method to study the relationshipbetween competitive structure and competition action8-9.However, the scales of these networks are very small.This is, there are not enough competitors considered inthese networks. Since the small-world networks10 andscale-free networks11 were found, complex networktheories had been introduced into many natural scienceand social science research fields. Yang et al. first putforward the new conception of industrial competitioncomplex network12. Then, many empirical researchresults show that competitive relationship networks formany industries in China, such as, ceramic industry13,software industry14, automobile industry15, logisticsindustry16, follow the power law distribution. Recently,Yang et al. proposed a new complete theory frameworkto study the relationship between industry competitivestructure and firm competitive actions17,18. In thesestudies, competitive relationship between two rivals isdefined by market commonality and resource similaritytheory1. Obviously, according to this definition, there willbe a large number of competitors in the beer industryand their node degree of industrial competition networkalso follows the power law distribution. Based on suchreason, we introduce complex network to discuss therelationship between dynamic mergers and evolution ofindustrial competition.

In this paper, we analyze the evolution process ofindustrial competition structure driven by dynamicmergers and observe changes of topology index of thesenetworks. Since this evolution is caused by dynamic cross-market mergers, more focus is to be paid towardanalyzing on how do dynamic cross-market mergersaffect the evolution process of the entire industrialcompetition structure. Therefore, the contribution of this

paper will be manifested in two aspects. Firstly, wepresent a novel method to observe and describe therelationship between dynamic mergers and evolution ofindustrial competitiveness by introducing the complexnetwork tool, and this method perhaps open a door toanalyze the large-scale aggregative mergers formingmechanism, such as, industry merger waves. Secondly,we also reproduce the evolutionary process of the beerindustry competition in China and give a theoreticalexplanation in detail on its multi-market competitioncontext forming reasons from a network analysisperspective.

Dynamic mergers in beer industry of ChinaThe beer industry of China takes an obvious

characteristic of multi-market competition afterexperiencing a period of mergers and industryrestructuring. The multi-market here refers to multiplegeographic markets, not multiple product ones for thatthe beer products in China are homogeneous nearly whilethe feature of segmented geographic submarkets in thisindustry is obvious. The fragmentation of the beer industrymarket mainly dues to two reasons. The first one is thatall of the local governments established their own beerfacilities before reforming and opening policy. Theyadopted strict market protection policies and preventother rival’s products from entering their local segmentedmarkets. Thus, many closed local submarkets wereformed. The second one is the high transportation cost,which makes cross-market marketing become impossible,and all the beer products have to be sold in the localsubmarkets for lower marketing cost. At the same time,some beer firms seek to enter new potential submarketsfor expanding their market share, usually by taking themeans of mergers. Along with more and more cross-market mergers token place in this industry, a multi-market competition context is formed gradually.

Why do beer firms seek to implement cross-marketmergers? To answer this question, it is important tounderstand firm’s survival strategies and competitivetactics in this industry. After reviewing on thedevelopment of the beer industry of China, we can findthat two factors aggregate to the industry competition.The one is the excessive increasing beer firms while thewhole market capacity grows slowly, and the other oneis the stimulation coming from foreign rivals which enteredinto China’s beer markets in the last two decades. Forthese reasons, local beer firms have to think about theirown survival strategies. In China, occupying more market

637HOU et al: DYNAMIC MERGERS DRIVE INDUSTRIAL COMPETITION

share has become the last straw for all the beer firmswhose competitive strategies differ little from each other.How to get a larger market share? Generally, there aretwo ways, building new factories in other geographicmarkets or taking over the beer firms in those regions.From a practical view, the former way seems infeasibleas local governments will not allow rebuilding new firmsin their dominant regions if several ones have alreadyexisted. Thus, the latter one, mergers and acquisitions,will be the ideal way to expand their market share andenter to a new potential submarket.

With more and more industry restructuring activities,the competition becomes much fierce and some beerfirms begin to consolidate these fragmental submarketsby taking over target firms in those regions. Consequently,a group of well-known national brands formedrepresented by Tsingtao beer, Snow beer and Yanjingbeer, etc. At the same time, some local beer brandsrepresented by Pear River beer, King Star beer,Chongqing beer, Jingwei beer and Harbin beer, etc., arealso formed. According to the Competitive Dynamicstheory1, a competitive attack, by using the means ofmergers, may be motivate a counter-attack responsecoming from the rival, also by mergers. This may causea chain reaction. As a result, an industry merger wavemay occur19-21. Moreover, empirical studies have showedthat bank mergers as scale-free coagulation22. Actually,more than 330 merger activities have taken place in thisindustry during the last two decades. Therefore, theprocess of beer industry restructuring is the one that thebigger beer firms with national brands competed witheach other and rushed to take over the smaller beer firmsfor much more market share.

Dynamic mergers and evolution of industrialcompetitionIndustrial competition time series networks

Economic theory suggests that manufacturersprovided the same or similar products are treated ascompetitors, regardless of the size of their scales, whichmeans that there will be a large number of competitorsin one industry. Taking China’s ceramic industry as acase, Yang et al. developed a new method for industrialcompetition analysis and gave a novel discussion on therelationship between industrial competition structure andfirm’s competitive actions from complex networkperspective17,18. This method, attempting to analyzemultiple rivals in an industry, is very different from theexisting competitive analysis models which pay more

attention on oligopolistic markets. In this model, thecomplex network tool is introduced to explore therelationship between industrial competition structure andfirm’s competition actions. Here, nodes in networks areon behalf of rivals, and edge represents the competitiverelationship between two nodes. However, theseliteratures on industrial competition network analysisusually assume that networks are static, in other words,nodes and edges of these competition networks are fixedand unchanged all the time. They focus on the mutualcompetitive tactics and competition diffusion effect onthe static competitive networks13-18. However, industrialcompetition networks are not static but changingconstantly.

Among all the competition and confrontation means,mergers are different from the others. In the setting ofoutput competition or quality competition, the nodes inindustrial competition networks will not disappear, so doesthe edge. Thus, the industrial competition network showsas static. However, if competing by mergers, theindustrial competition networks would be changedbecause some firms involved in the merger activities willdisappear. As a result, the node, the edge and even thecompetitive tensions between rivals in industrialcompetition networks will change. Especially in a multi-market competition context, new competitive relationshipswill emerge due to the cross-market mergers and theindustrial competition network will be changedsignificantly.

Dynamic mergers bring a continuous change ofindustrial competition network all the time. Hence,industrial competition networks driven by dynamicmergers belong to time-dependent networks or dynamiccomplex networks23,24. If a network’s topologicalstructure index stays constant all the time, it should be atime-independent network. Otherwise, it should beregarded as a time-dependent network. Strictly speaking,industrial competition networks driven by dynamicmergers belongs to a kind of downsizing time-dependentnetworks for that whose number of node is decreasing,which is very different from the growing networks.Let industrial competition network within a certain periodbe a snapshot, then a set of time series networks areobservable if we string these snapshots together. Thereby,we could explore its evolution process intuitively byobserving the changing characteristics of these timeseries networks. In order to describe this processmentioned above, we established two kinds of time seriesnetworks in this paper. One is the Market-Firm bipartite

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time series networks and weighted bipartite time seriesnetworks which are used to describe the changes ofrelationship between markets and firms caused bydynamic cross-market mergers. The other one is theindustrial competition relationship time series networkswhich are used to display the changes of relationshipsamong rivals in the industry driven by dynamic cross-market mergers.

DataTo establish the time series networks mentioned

above, two kinds of data about the beer industry of Chinamust be collected yearly. One is the data reflectingcompetition properties, such as corporate brands,production capacity and market distribution. The otheris the data of dynamic merger activities including beerbrands involved the merger activities and the time ofmerger transaction. Since the earliest data about beerfirm mergers in China was recorded in 1993, we canconstruct the two kinds of time series networks mentionedabove by introducing the data from 1992 to 2009.Theresources of data are following. Firstly, officialpublications like the yearbook of China’s wine industry,the yearbook of China’s food industry and the investmentreport of China’s beer industry issued by consultingcompany. Secondly, the Internet including the website ofbeer industry, homepages of beer firms, online news andmedium reports. Noticeably, the data of productioncapacities of all the beer firms are ten thousand tons oraon:bove, and all the data we employed here are comefrom the reliable records. In order to test the reliabilityof the data, we have taken a mutual check between thedata gotten from the publications and the Internet.

Market-Firm bipartite time series networksNetwork-building rules

Bipartite network is the network that can reflect thelink relationship between two kinds of node. In this paper,we build a series of Market-Firm bipartite networks byusing the data of the beer industry of China for differentyears and find out the evolutionary laws of the relationshipbetween beer firms and markets they entered. By takingcross-market merger activities, some beer firms enteredmany potential local submarkets. The network-buildingrules are as follows:

Market node: Market node represents the independentlocal geographic submarket. In China, due to the historicaland administrative reasons, the local governmentsformulated some trade protection policies which

separated the beer industry markets into several localsubmarkets. According to the realistic situations, we cantake administrative provinces, municipalities orautonomous regions as local submarkets. Firm node: Firm node represent beer firm.

Edges: Edge represents a subordinate relationship.Here, more directly, it denotes that a firm exists in aspecific submarket. If firm N exist in the submarket M,then we can draw an edge between the firm N and thesubmarket M. Besides, if a firm Ni enters the submarketMm by taking over another the firm Nj, which exists insubmarket Mm, we should also draw an edge betweenfirm Ni and market Mm. Since this kind of network onlyconsidering whether there is a link relationship betweenthe two kinds of nodes, regardless of other linkinginformation, it gets another name as Market-Firm Booleanbipartite network. In other words, it lacks of more detailinformation between the Market node and Firm node,such as, a firm’s market share in a specific localsubmarket.

Based on this rules, one Market-Firm bipartitenetwork can be established by using one year’s data.Thus, we can get a series of Market-Firm bipartitenetworks from 1992 to 2009. Simply, the Market-Firmbipartite network of 1992 is marked by G1992. To showthe influence of dynamic mergers upon the evolutionprocess of Market-Firm bipartite networks, we draw theannual Market-Firm bipartite networks diagram fromG1992 to G2009. Actually, network has been in constantlychanging because of dynamic mergers every year. Inthe year of 2000, merger wave in the beer industry ofChina reached its first peak and 28 horizontal mergeractivities happened. After this year, an accelerated trendof horizontal mergers took place in this industry. Thus,2000 year is a turning point time during the industryrestructuring process. We choose a few typical networkdiagrams to show this evolution process. How manynetworks and which year’s network should be selecteddo not affect us to observe this process. Here, we extractthree bipartite network pictorial diagrams of G1992(starting point), G2000 (turning point) and G2009 (endingpoint), shown as in Fig.1.

In Fig.1, square nodes represent local submarkets inbeer industry, while circular nodes denote beer firms.The thickness of line/edge linked square nodes andcircular nodes are on behalf of firm’s production capacitiesin a specific submarket. From Fig.1, we can find thatrelationships between submarkets and firms in thisindustry experienced a dramatically change from 1992

639HOU et al: DYNAMIC MERGERS DRIVE INDUSTRIAL COMPETITION

to 2009. A fragmental network in 1992 has been evolvedinto a full connected network in 2009. We can also findthat the vast majority firms exist in the local submarketsin the earlier stage, but then some bigger firms began toenter into other local submarkets by means of cross-market mergers gradually. Finally, those bigger firmsalmost entered all local submarkets, thus the submarketsof the beer industry of China are to be unified into an

integrated one and a multi-market competition contextfor all beer firms is formed.

Changes of bipartite networks topological indexThere are two kinds of nodes in bipartite networks,

top node (namely, market node) and bottom node (namely,firm node). The degree of top node is defined as thenumber of edges linked to a specific top node, in otherwords, it is equal to the quantity of beer firms existing in

Fig. 1—Evolution process of Market-Firm bipartite networks

Fig. 2—changing curve of degree distribution index/weight distribution index of Top node (a)/(c), Bottom node (b)/(d)

(a) (b)

(c) (d)

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that submarket. Similarly, the degree of bottom node isdefined as the number of edges linked to a specificbottom node, namely, it is equal to the quantity ofsubmarkets in which a specific firm entered.

With more and more cross-market mergers, the localsubmarkets are integrated into a consolidated one and amulti-market competition context emerged gradually,which can be seen from the changes of quantity of topnode and bottom node in bipartite networks. In thesetime series networks of G1992-G2009, the degree of topnode started from the peak of 42 in 1992, decreased to32 in 2009 gradually. At the same time, the average degreeof nodes also decreased from 12.5 to 11.3. These changesindicate that the number of firms in each submarket keptdecreasing with the increase of cross-market mergers.For the bottom node, its degree started from 2 in 1992and then rose up to 20 in 2009, but the average degreeof nodes only grew from 1.0 in 1992 to 1.4 in 2009, whichindicates that only a few firms implemented many cross-market mergers while most of others only finished a few.

Both the top node degree and the bottom node degreeof all the bipartite networks from 1992 to 2009 followstretched exponential distribution. In the proposednetwork model, the degree distribution approximates astraight line in a log-log plot of the entire network. Let gbe an undirected and simply connected graph with | |n v= and, | |l ε= , where v and ε are the sets of thenodes and edges respectively. Define the degree of one

node as

1

n

i ijj

d a=

=∑ where aij indicates the adjacency

matrix. The distribution index of top node degree rangesfrom 1.161-1.422 with exponent R2 ≥0.92 (denotes thegoodness of fit), while that of the bottom node degreeranges from 0.290-0.348 with exponent R2≥0.93. Fig.2(a) describes the changes of stretched exponential indexof the top node degree distribution for those networksfrom 1992 to 2009, while Fig.2 (b) for the bottom node.The fact that the node degree follows stretchedexponential distribution shows that networks areheterogeneous. The variation of topology index indicatesthat these bipartite networks change all the time, and theonly driven factor is dynamics cross-market mergers inthis industry.Changes of weighted bipartite networks topological index

With the earlier development stage of the beerindustry of China, the main purpose of firm’s mergeractivity does not to reduce supply capacity, but to expandtheir production capacity because they want to occupy

much more market share by mergers. Cross-marketmerger activities are the quickest and the most effectiveway to achieve this goal. With increasing of cross-marketmergers, each beer firm’s supply capacity is on rising,as well as the total supply capacity of all firms in eachsubmarket.

Additionally, to describe the changes of beer firm’ssupply capacity and the total supply capacity of a specificsubmarket induced by dynamic mergers, similar toBoolean bipartite networks, we construct a seriesweighted Market-Firm bipartite networks, and the weightis equal to the production/supply capacity. In the weightedbipartite networks, the top node (market node) denotesthe total supply capacity of all firms in a specificsubmarket, while the bottom node (firm node) is the totalsupply capacity in all submarkets for a specific beer firm.

Let iw be the total weight of one top node i , we

have 1

1

n

i ijj

w w=

= ∑ whereindicates the weight between ith

top node and jth bottom node and n1 denotes the sets ofbottom nodes; While let iw be the total weight of one

bottom node i ,we similarity have 2

1

n

i ijj

w w=

= ∑ where ijwindicates the weight between ith top node and jth bottomnode and n2 denotes the sets of top nodes.

Calculating results show that the minimum weightof top node increased from 1 to 10 while the maximumalso increased steadily from 299 in 1992 to 663 in 2009.The average weight of top nodes had an increase from76 to 220, which indicates that the total supply capacityof each submarket grew all the time. The minimumweight of bottom node stayed at 1 while the maximumincreased dramatically from 80 in 1992 to 1105 in 2009.The average weight of bottom nodes grew from 6.2 to28.8, which shows that the average supply capacity forall firms grew rapidly in this period.

The weights of both top nodes and bottom nodesfollow power law distributions. Moreover, the power lawindex of top node weights (supply capacity of asubmarket) ranged from 1.091 to 1.291 with exponentR2≥0.97, and the power law index of bottom node weights(supply capacity of a firm) ranged from -1.549 to -0.818with exponent R2≥0.90. As shown in Fig.2 (c) and (d),the weighted Market-Firm bipartite networks have beenchanging all the time.

The node weights follow power law distributionsP(w)=w-y indicate that there is a big difference on supplycapacities among different firms, as well as the different

641HOU et al: DYNAMIC MERGERS DRIVE INDUSTRIAL COMPETITION

submarkets. This is, only a few firms and submarketsare equipped with large supply capacity, while the vastmajority of firms and submarkets do not. Anotherphenomenon is that, node weights of each networkfollows power law distribution, while their distributioncurves don’t overlap completely, which indicates that thecenter of weighted bipartite network is dynamic. In otherwordsÿeach network has a hub node and each nodedegree is different from others. How to explain thisphenomenon? At the earlier stage of beer industryrestructuring in China, supply capacities of all beer firmshad not much difference. However, some firms’ supplycapacities increased rapidly while others grew slowly oreven no change. As a result, the heterogeneity of supplycapacity both for each submarket and for each beer firmis increasing year by year.

Changes of market share of firms in submarketThe above discussion only concentrates on the

changing laws of supply capacity both beer firms andsubmarkets on macroscopic statistical level, but does notconsider the dynamic relationship between eachsubmarket size and each firm’s supply capacity onmicroscopic individual level. However, the latter is veryimportant for beer firm’s decision-making on cross-market mergers. With more and more cross-marketmergers, some beer firms begin to enter other submarketsand expand their market share, while others do not. Here,we take the typical beer firms and submarkets as anexample to discuss their dynamic relationship driven bycross-market mergers (Fig.3).

Fig.3 (a) describes the market share changes of threelarger beer firms, Tsingtao beer, Yanjing beer and Snowbeer, in Bejing submarket from 1992 to 2009. Of course,there were many other beer firms in this submarket and

we just chosen the largest three ones. From Fig.3 (a),we can find that Yanjing beer almost dominated Beijingsubmarket with a larger market share at the earlier stage,more than 40 percent, but its market share decreasedyear by year. The reason is that other firms, such as,Tsingtao beer firm and Snow beer firm entered thissubmarket by cross-market mergers and expanded theirproduction capabilities respectively in 1999 and 2003,which further occupied Yanjing beer firm’s inherentmarket and compressed its market space. Fig.3 (b)depicts the changes of submarket share of Tsingtao beerfirm in all the submarkets at the year of 1992, 2000 and2009 respectively. Here, horizontal axis represents thesubmarket numbers while vertical denotes the share ofsubmarket. Fig.3 (b) tells us that Tsingtao beer enteredalmost all of the submarkets in China’s beer industry bythe way of cross-market mergers in the last 20 years,while its market share also increased in manysubmarkets. Similarly, other beer firms have more or lesschanges of their market share in the submarkets as wellas Tsingtao beer firm.

Industrial competition time series networksNetwork-building rules

The Market-Firm bipartite networks can describethe connection information between market node andfirm node, but it is not able to display competitiverelationship among firms in industry intuitively. Therefore,it is necessary to convert bipartite networks into singleparticle networks, namely, industrial competition network,by projecting the firm nodes to the market nodes.

In fact, the projecting rule is to redefine thecompetitive relationship between two beer firms. Theprojecting rule may be expressed by the formula: ( )T

projA B Bδ= ∗

Fig. 3—market share changes of different firms in Beijing submarket (a) and market share changes of Tsingtao beer in all submarkets (b)(a) (b)

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Where ( )xδ represents the indicator function, projA denotes the projected matrix, and B represents thebipartite networks { | 1, 2 ; 1,2, };ijB b i j= = =L LHere, the definition of competitive relationship betweentwo firms is that they share the same beer market. If afirm exists or enters a local submarket by means ofmergers, then we can draw an edge between this firmand any firm in that submarket. According to the definitionof market commonality and resource similarity originatedb y C h e n

1, if two firms pursue the customers in the samemarket, there will be a competitive relationship betweenthem. Theoretically, it’s easy to do so if we can obtainenough data about market distributions information forall beer firms. However, it’s impossible and unnecessaryto collect such a large scale of data for all the beer firms.The reasons are given as follows. Firstly, we cannotdefine the boundary of beer submarkets accurately forthat beer submarkets used to be divided by administrativepolicy, which were not completely free competitivemarkets. Secondly, each firm’s markets are composedof focus ones (lager scale) and non-focus ones (smallscale), while the latter is insignificant to their rivals. So,we can define the competitive relationship by their focusmarkets. Simply, in this paper, we assume that all thebeer firms existing in one administrative region andsharing the same market are rivals. According to thisassumption, we can find that the industrial competitionrelationship network in one specific local submarket willbe a full connected graph while 1, ,ija i N j N= ∈ ∈ .

Based on the ideas mentioned above, we can get aset of time series industrial competition networks from1992 to 2009 by transforming those bipartite networks tosingle particle networks. We will explore the influenceof dynamic mergers upon the evolution of industrialcompetition relationship by analyzing the changes ofnetwork topology index.

Changes of networks topological indexChanges of the number of node and edge

Similarly, the industrial competition networks in 1992,2000 and 2009 are selected to observe the evolutionprocess of inter-firm competitive relationships in beerindustry driven by dynamic mergers intuitively (see Fig.4).From the evolution process, we can find some changinginformation on some specific beer firm’s competitivecomposition, which may be the main factor on triggeringthe firm’s merger motive.

In this kind of network, the node stands for beerfirm and the edge for competitive relationship betweentwo rivals. The quantity of nodes in these networks hadan annual decrease because some beer firms were takenover by others and disappeared, so does the change ofthe total number of edges in these networks. Fig.5 (a)and (b) shows the changes of quantity of nodes and edgesin the industrial competition networks from 1992 to 2009.

Changes of node’s degreeIn a local beer submarket, if ignoring the new

entrants, the average quantity of competitors, namelythe node degree in network, will decrease with theongoing of merger activities. While in a multi-marketcompetition setting, on the contrary, the average quantityof rivals will increase. The reason is that some beer firmsenter many potential submarkets by implementing cross-market mergers and they have to encounter more andmore new rivals in these submarkets, while in a singlemarket competition setting, firms only need to confrontthe local rivals.

Fig.5 (c) and (d) shows the changes of node degreeand average degree of these industrial competitionnetworks from 1992 to 2009. In 1992, the maximum nodedegree was 12 and the average degree was 2.1, whilethey grew to 128 and 7.6 respectively in 2009. FromFig.5 (c) and (d), we can see that firms began to facemore and more rivals after dynamic mergers. On average,the quantity of competitors is increase from 2.1 in 1992

Fig. 4—topology evolution process of industrial competition network

643HOU et al: DYNAMIC MERGERS DRIVE INDUSTRIAL COMPETITION

to 7.6 in 2009. These changes show that dynamicmergers have speed up the evolution process of industrialcompetition networks and accelerate the competitiveintensity among rivals in the beer industry of China.

Fig.6(a) depicts the distributions of node degree forthose industrial competition networks for the year of 1992,1996, 2000, 2004, 2008 and 2009. In this figure, K denotesthe node degree and P(k) indicates the cumulativeprobability of K. We can find that their cumulative nodedegree follow power law distribution approximate, a kindof spot mark presents one year’s distribution. It showsthat the changes of cumulative degree distributions varylittle while the curves are not overlapped completely,which indicates that the heterogeneity of industrialcompetition networks changed with the ongoing ofdynamic mergers.

Changes of degree distribution entropyThe variable, degree distribution entropy, is employed

to describe the homogeneity of node degree distributionin networks.

ikn

iikskH 2log

1)( ∑

=−=

H(x) represents the entropy, while ks denotes thenetworks structure and represents the node degree.According to its definition, the higher is the homogeneityof node degree distribution, the larger is the degreedistribution entropy, vice versa. By introducing thecalculating method of degree distribution entropy25, wegot those entropy values for each industrial competitionnetwork from 1992 to 2009 and their variations showedon Fig.6 (b).

From Fig. 6 (b) we can find that the entropy of nodedegree distribution for industrial competition network keptincrease until 2000 and then began to decreasecontinuously. Before 2000, the quantity of competitorswhich different beer firms faced were little different,but in the next years, the differences of rival amount foreach beer firm are obvious. This phenomenon can beexplained as follows. In the earlier stage of the beer

Fig. 5—changing curve of node’s number (a), edge’s number (b), maximum node degree (c), average node degree (d)

(a) (b)

(c) (d)

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industry in China, the quantities of beer firms in differentsubmarkets have an obvious difference, which meansthat some firms faced too many rivals while othersencountered only a few, which caused the entropy valueis high. However, at the later stage, with the increase ofcross-market dynamic mergers, some firms began toencounter more rivals and their competitive relationshipincreased gradually. Thus, a homogeneous degreedistribution is formed in the industrial competitionnetwork. Therefore, the entropy value became decreaseslowly.

Changes of clustering coefficientClustering coefficient here is the topology index

which is used to describe the transitivity of networks,namely the probability that a friend’s friend is also a friend.

)1(||2

−=

ii

jki kk

eC EeNvv jkki ∈∈ ,,

ik is defined  as the number of vertices i; Let AAgraph  ),( EVG =  formally consists of a set ofvertices V  and a set of edges  E  between them. AnAnedge  ije connects vertex  i  with vertex. j Here, in thispaper, the variable means that the probability of a rival’srival is also a rival. The larger the clustering coefficientis, the better the connectivity of network shows, viceversa.

From Fig.6 (c), we can find that dynamic mergerslead to the decrease of clustering coefficient in singleparticle networks while in Market-Firm bipartite networkswhose clustering coefficients almost reaches to the peak 1. Generally, there should be a reducing trend ofclustering coefficient, whose value is 0.993 in 1992 andreduced to 0.940 in 2009. The reason for this phenomenonis due to the competitive realities of the beer industry ofChina. In the case of fragmental submarket, accordingto the definition of competitive relationship, firms withinthe same submarket compete with each other. So, theindustrial competition network for each submarket is a

10 0 101 10 2 10310 -3

10 -2

10 -1

10 0

K

P(K)

199219962000200420082009

Fig. 6—node degree distribution (a) and changing curve of node degree structure entropy (b), clustering coefficient (c), number ofcommunity (d)

(c) (d)

(a) (b)

645HOU et al: DYNAMIC MERGERS DRIVE INDUSTRIAL COMPETITION

full connected graph. Since in the whole industry, only asmall amount of beer firms can enter other submarketsby cross-mergers. Thus, the average clusteringcoefficient for all isolated sub-networks is very high,nearly to its peak value 1. However, as more and morecross-market dynamic mergers took place, the segmentedsubmarkets began to consolidate into an integratedmarket, which caused the decrease of clusteringcoefficient. However, the decreasing does not mean thatcompletive tension among rivals becomes weaken, onthe contrary, it shows a more intense competition forthat more and more beer firms enters other potentialsubmarkets by cross-market mergers which make themhave to confront more new rivals than before. Insummary, the cross-market mergers accelerate thefragmented industrial competition networks in localsubmarkets to consolidate a united one for this industry.So, the decreasing clustering coefficient and theemergence of an integrated competition network haveverified the fact that the rivals in this industry haveexperienced a thorough transformation from localsubmarket competit ion to global multi-marketcompetition.

Changes of communitiesCommunity structure is one of the key properties of

complex networks and it plays an important role in theirtopology and function. Empirical studies have shown thatall the complex networks are composed of manycommunities. Community has an obvious characteristicthat the connections between nodes within communityare relatively close, but the connections between differentcommunities are relatively sparse. Based on the definitionand the detection algorithm of community structure incomplex networks, we got the communities of competitionnetworks on beer industry for each year. And we alsofound that the number of communities in industrialcompetition networks has reduced from 234 to 104quickly, from 1992 to 2009, which is shown in Fig.6 (d).

It is the reason that dynamic mergers make the localsegmented submarkets be a unified market in beerindustry. In the earlier stage, the administrativeinterventions from local governments lead to many localsegmented submarkets, small size of firms and fierceinter-firms competition. However, an integrated industrialcompetition network gradually emerged with the ongoingof the cross-market dynamic mergers. Along with thefrequent cross-market mergers, the isolated communitiesin the industrial competition network began to

consolidated, while the quantity of communitiesdecreased continuously. Furthermore, there were only afew beer firms expanded into other submarkets byimplementing a large number of cross-market mergers,while most other firms just only took a few ones. Thus,the non-uniform distribution of merger activities causedthat in some communities, there are too many rivals andhave complex competitive relationships while in othercommunities, there are only a small number ofcompetitors and have simple competitive relationships.All in all, the quantity of communities decreases, whiletheir numbers of competitive relationships in differentcommunities are different from each other. This resultexactly draws the outline of the evolution processtransiting from a local submarket competition to a globalmulti-market competition for the rivals in the beer industrybecause of cross-market dynamic mergers.

Theoretical explanationThe above case studies and statistical test results

show that dynamic mergers make the competitiverelationship of beer industry has been in a state of constantchange, and form a trend of industry concentration andoligopolistic power finally. Fundamentally, it is thesecompetitive interaction behaviors on firm-level (microlevel) that causes the evolution of competitive relationshipon industry-level (macro-level). Therefore, discussingdynamic merger motives from individual level andexploring the forming mechanism can help us tounderstand this phenomenon.

Actually, the evolution process and monopolydevelopment trend driven by dynamic mergers in beerindustry of China have no much difference from otherindustries in developed countries. This phenomenon canbe explained by the classic merger motive theories, whichincluding synergy efficiency theory, market power theoryand resource scarcity theory.

At the earlier stage of the beer industry of China,many independent and fragmental submarkets aredominated by a large number of firms which had smallersize, lower production efficiency and weaker competitiveability. In order to improve their efficiency, governmentsformulated industry restructuring policies by encouraginghorizontal mergers in the late 90s. Under these policiesbackground, some brand beer firms which have largersize and higher efficiency, for example, Tsingtao beer,Snow beer and Yanjing beer, began to carry out expansionstrategy by means of implementing cross-marketmergers. These firms generally thought that efficiency

J SCI IND RES VOL 72 NOVEMBER 2013646

improvements can only be derived from economy of scaleand scope brought out by mergers. This is also the focuscontent of traditional horizontal merger motivationtheories discussed26,27.

Market power theory emphasizes that horizontalmergers can reduce competitors and promote industryconcentration, which make firms get market power onproduct market and control or affect market price, so asto obtain a higher profit28,29. With the development ofhorizontal mergers, intra-industry competitors havedrastically reduced and some firms with larger size beganto expand their market share quickly, thus to won themarket power in this industry. Moreover, target beerfirms are usually regarded as a kind of resources whilethey are very rare. To obtain a bigger market share, beerfirms worried about the loss of the chance to get scarceresources and had to speed up the pace of horizontalmergers. Firms generally have a tendency of preemptingthe target firms by mergers and thus a merger wavemay be formed20. This is one of the important reasonsthat caused the beer industry of China to take amonopolistic competition trend.

Since local submarkets are segmental, almost all thehorizontal merger transactions are dealt in cross-marketcontext. Merger activities are regarded as a main meansto compete against with each other among these largerbeer firms. For example, if you attack my market A,then I will counterattack your market B, thus, thesecompetitive interactions will form a dynamic competitionprocess1. Obviously, many larger beer firms entered intoso many local and segmental submarkets through cross-market mergers, which promoted to form a multi-firmand multi-market competitive context. With more andmore cross-market mergers, the relationships betweenFirm and Market become more and more complex, sodid the competitive relationship among rivals in the beerindustry. Seeing from a network perspective, it is thesedynamic mergers which were implemented in manysubmarkets by a lot of firms that driving the evolution ofthese Firm-Market networks and competitive relationshipnetworks in the beer industry of China.

ConclusionsDynamic mergers have played an extremely

important role in the evolution of beer industrialcompetition in China. They can speed up thetransformation of beer industry market from some localand segmented submarkets into an integrated and unifiedone, and also bring the rapid evolution of competitive

landscape for beer firms from a local submarketcompetition to a global multi-market competition. In thispaper, we present a novel and intuitive method to describethis evolution process by introducing the complexnetwork tool. Taking the beer industry of China as anexample, we find some rules and features emerging inthe evolution process of industrial competition as follows.

First, both the Market-Firm bipartite networks andthe industrial competition networks driven by dynamicmergers belong to time-dependent networks. Both inMarket-Firm bipartite networks and in the industrialcompetition networks, node degree distributions neitheroverlap each other completely, nor does the node’s weightdistributions. We can find that the network topologycaused by dynamic mergers changes too much. Inindustrial competition networks, the disappearing rate offirm node caused by mergers is faster than the growthrate of new entrant firms, while the amounts of edges innetworks are growing rapidly. We can conclude that theindustrial competition network driven by dynamic mergersis a kind of network with increasing network intensityand decreasing node size. These results show that itwould be more valuable to study the relationship betweendynamic mergers and evolution of industrial competitionby using the time series data rather than the cross-sectiondata.

Second, the analytical results on the evolutionprogress of Market-Firm bipartite networks shows someuseful information. Firstly, dynamic mergers make a lotof smaller segmented local submarkets connect into somelarger markets, and finally form a unified one in thisindustry. Secondly, after entering some potentialsubmarkets by dynamic cross-market mergers, a fewbeer firms enlarged their supply capacities quickly whilethe supply capacities of different submarkets also becamelarger than before. Thirdly, the results of networkstatistical analysis show that beer firms, which pursue toentry potential submarkets and expand their market shareby mergers, tend to have the preference on selecting thetarget firms whose geographic location are closer andwhose markets are more attractive to themselves.

Third, four characteristics are displayed during theevolution process of industrial competition networks asfollows. Firstly, inter-firm competitive relationship hasexpanded from the local submarket to the global marketin the beer industry of China by dynamic mergers, whilethe industrial competition network becomes more andmore complex. Secondly, with the ongoing of dynamiccross-market mergers, the number of communities in

647HOU et al: DYNAMIC MERGERS DRIVE INDUSTRIAL COMPETITION

each industrial competition network begins to decreasewhile their size of communities becomes larger gradually.Thirdly, the industrial competition network experiencedthe changes from homogeneity to non-homogeneity andthen back to homogeneity. Fourthly, accompanied bydisappearance of firm nodes, the sizes of network scaleand the amount of competitive relationships in the industrydecreased continuously, while the intensity of competitionamong rivals increase.

AcknowledgmentThis work was supported in part by the National

Science Foundation of China under Grant 71103044,71273093, 71201060; Research Foundation for the MajorProject of the Human and Social Science Research Baseof Guangdong Province Higher Education under Grant10JDXM63005.

References1 Chen M J, Competitor analysis and interfirm rivalry toward a

theoretical integration, Acad Manage Rev, 21 (1996), 100-134.2 Motta M & Vasconcelos H, Efficiency gains and myopic antitrust

antitrust authority in a dynamic merger game, Int J Ind Organiz,23 (2005), 777-801.

3 Nilsson T & Sørgard L, Sequential horizontal mergers, Eur EconRev, 42(1998), 1683-1702.

4 Hou R, Yang J M & Yao C Z, A new conceptional model formerger waves: industry competition diffusion based on complexnetworks, in IEEE Int Conf on Networks and Digital Society(ICNDS)(China, Wenzhou), 30-31 May, 2010, 237-241.

5 Rodrigues V, Endogenous mergers and market structure, Int J IndOrganiz, 19(2001), 1245-1261.

6 Hackbarth D & Miao J J, The dynamics of mergers andacquisitions in oligopolistic industries, J Econ Dyn Control,36(2012), 585-609.

7 Kao T & Menezes F, Endogenous mergers under multi-marketcompetition, J Math Econ, 45(2009), 817-829.

8 D’Avenl R A, Competitive pressure systems: mapping andmanaging multimarket contact, MIT Sloan Manage Rev, 44(1)(2002), 39-49.

9 Gimeno J, Competition within and between networks: thecontingent effect of competitive embeddedness on allianceformation, Acad Manage J, 47(2004), 820-842.

10 Watts D J & Strogatz S H, Collective dynamics of small-worldnetworks, Nature, 393(1998), 440-442.

11 Barabasi A L & Albert R, Emergence of scaling in random networks,Science, 286 (1999), 509-512.

12 Yang J M, Lu L P & Xie W D, Analysis of the complexcompetitive relationship network in software enterprises inGuangzhou city, Proc of Second National Forum on ComplexDynamical Networks, (Beijing, China), 2005.

13 Yang J M, Huang X Z, Zhuang D, et al,The complex networkanalysis of competitive relationships between manufacturers inFoshan ceramic industry cluster, Proc of Int conf on Manage SciEng(ICMSE), (Lille, France), 5-7 Oct., 2006, 1020-1023

14 Yang J M, Wang S J, Lu L P, et al., Study of social network andcomplex competitive relationship network of software industryin Guangzhou, Chin J Manage, 3 (2006), 723-727.

15 Ou R Q & Yang J M, The community structure analysis of firm-product bipartite network—take Chinese auto-mobile industryas the example, Chin J Manage, 7 (2010) , 1403-1409.

16 Hou R, Yang J M & Yao C Z, Research on logistics industrycompetitive relationship model based on complex networks,Chin J Manageÿ7 (2010), 406-411.

17 Yang J M, Lu L P, Xie W D, et al., On competitive relationshipnetworks: a new method for industrial competition analysis,Physica A,382 (2007),704-714.

18 Yang J M, Wang W J & Chen G, A two-level complex networkmodel and its application, Physica A, 388 (2009), 2435-2449.

19 Town R J, Merger waves and the structure of merger andacquisition and time-series, J Appl Econ, 7(1992), 83-100.

20 Toxvaerd F, Strategic merger waves: a theory of musical chairs, JEcon Theory, 140(2008), 1-26.

21 Gärtner D & Halbheer D, Are there waves in merger activityafter all? Int J Ind Organiz, 27(2009), 708-718.

22 Pushkin D O & Aref H, Bank mergers as scale-free coagulation,Physica A, 336(2004), 571-58.

23 Marro J, Torres J J & Cortes J M, Complex behavior in a networkwith time-dependent connections and silent nodes, J Stat Mech,2(2008), 1-15.

24 Braha D & Yaneer B Y, Time-dependent complex networks:dynamic centrality, dynamic motifs, and cycles of socialinteraction (chapter 3). Understanding Complex System, 1st edn(Springer-Verlag,NJ), 2009, 39-50.

25 Tan Y J & Wu J, Network structure entropy and its applicationto scale-free networks, Sys Eng-theory & Practice, 6(2004), 1-3.

26 Jensen M C & Ruback R S, The market for corporate control:the scientific evidence, J financ econ, 11(1983), 5-50.

27 Berkovitch E & Narayanan M P, Motives for takeovers: anempirical investigation, J Financ Quant Anal, 28(1993),347- 362.

28 Kim E H & Singal V, Mergers and market power: evidence fromthe airline industry, Am Econ Rev, 83 (1993), 549-569.

29 Borenstein S, Airline mergers, airport dominance, and MarketPower, Am Econ Rev, 80(1990), 400-404.