post-merger cultural integration from a social network perspective: a computational modeling...

Comput Math Organ Theory (2013) 19:516–537DOI 10.1007/s10588-012-9139-5

M A N U S C R I P T

Post-merger cultural integration from a social networkperspective: a computational modeling approach

Junichi Yamanoi · Hiroki Sayama

Published online: 26 September 2012© Springer Science+Business Media, LLC 2012

Abstract Although cultural integration, or sharing a common corporate culture, iscrucial for the success of mergers, previous studies have been limited to firm-levelanalyses. From a social network perspective, this study explores how cultural inte-gration emerges from the patterns of social interactions among individuals. Usingan agent-based model, we investigate the impact of network structures within andbetween two merging firms on post-merger cultural integration and organizationaldysfunctions—individual turnover, interpersonal conflict and organizational commu-nication ineffectiveness—that arise from insufficient cultural integration. The sim-ulation results demonstrate that the highest level of cultural integration is achievedwhen social ties are more centralized within each merging firm and the social tiesbetween the merging firms are less concentrated on central individuals. Additionally,the results show that within-firm and between-firm network structures significantlyaffect individual turnover, interpersonal conflict and organizational communicationineffectiveness, and that these three outcome measurements do not vary in tandem.

Keywords Mergers · Cultural integration · Social networks · Agent-based models ·Adaptive networks

How and why does the network of social ties between individuals in merging firms in-fluence post-merger cultural integration and its consequences? Management scholars

J. Yamanoi (�)Faculty of Policy Studies, Chuo University, 742-1-11551 Higashinakano, Hiachioji, Tokyo 1930393,Japane-mail: [email protected]

H. SayamaDepartments of Bioengineering & Systems Science and Industrial Engineering, BinghamtonUniversity, State University of New York, P.O. Box 6000, Binghamton, NY 13902-6000, USAe-mail: [email protected]

mailto:[email protected]

mailto:[email protected]

Post-merger cultural integration and social networks 517

acknowledge that post-merger cultural integration is critical for achieving synergiesbetween merging firms (Haleblian et al. 2009). For example, the failure of the mergerbetween Daimler and Chrysler is attributed to interpersonal conflict among top exec-utives due to the clash of their American and European cultures (Finkelstein 2002).Likewise, AOL and Time Warner failed to achieve synergy between the communica-tion and media businesses partially due to interpersonal miscommunication derivedfrom the differences between AOL’s information age culture and Time Warner’s oldtraditional culture (Bradley and Sullivan 2005). Corporate cultures reside in individ-ual perceptions and are transmitted among individuals through interpersonal commu-nication (Nahavandi and Malekzadeh 1988). However, the structures of individualsocial networks within and between merging firms have rarely been investigated inprevious studies, which have exclusively adopted the firm or top executive levels ofanalyses. Such analyses limitedly clarify how cultural integration actually proceedsamong individuals in merging firms. A meta-analysis of studies of post-merger per-formance, by King et al. (2004), demonstrates that none of the antecedents of post-merger performance proposed by previous studies has significant explanatory power.This result would, at least partially, arise from the lack of focus on the individual-levelcultural integration process.

Our study will therefore adopt an individual level of analysis by examining the ef-fect of the structures of individual social networks within and between merging firmson post-merger cultural integration. Individuals in a merged firm are rarely embeddedin the same structure of social networks. We contend that the extent to which individ-uals in a merging firm are exposed to the corporate culture of the other merging firmvaries depending on the structures of individual social networks. Since corporate cul-tures are transmitted through interpersonal communication, we infer that post-mergercultural integration is affected by the network structures within and between mergingfirms.

Additionally, we claim that the structures of individual social networks influenceorganizational dysfunctions that arise from a lack of cultural integration. Previousstudies propose that the lack of cultural integration lowers post-merger financial per-formance due to various organizational dysfunctions, such as employee and executiveturnover, interpersonal conflict, and organizational communication ineffectiveness. Itis theoretically inferred that the degree of these organizational dysfunctions dependson the structures of individual social networks because the organizational dysfunc-tions arise from individual contact with a different corporate culture. Thus, the fre-quency and pattern of individual contact would have a major impact on the culturalintegration of a merged firm.

Toward the end of this study, we rely on an agent-based model, a type of computa-tional model focusing on the behaviors of adaptive actors making up a social systemand influencing one another through their interactions (Macy and Willer 2002). In ouragent-based model of cultural integration, individuals in merging firms possess cul-tural elements, which are defined as elements of a vector in multidimensional culturalspace, and exchange them with their neighbors in a network. Since this agent-basedmodel grants us control over the concentration levels of social ties within and be-tween two merging firms, we can observe the impact of within- and between-firmnetwork structures on cultural integration and its consequences. Computational mod-eling is valuable when the theory in question seeks to explain phenomena that are

518 J. Yamanoi, H. Sayama

challenging to study through empirical methods because of their time and data de-mands (Davis et al. 2007). In an empirical sense, it is virtually impossible to collectobservations of individual-level social networks in merging firms that are sufficientto conduct traditional hypothesis testing. Thus, the computational modeling approachis well suited to examine our research question.

1 Theoretical background

1.1 Conventional wisdom about corporate cultures in mergers

Although management scholars have defined it in numerous ways, a corporate cul-ture is generally considered to include the cognitive representations of values, norms,beliefs, and assumptions in a firm (Schein 1985; O’Reilly and Chatman 1996). Eventhough a firm’s corporate culture resides within the individuals of that firm, they donot necessarily have the same understanding of its corporate cultural dimensions; in-dividuals within a firm imperfectly share the firm’s values and beliefs (Earley andMosakowski 2000). The aggregation of a firm’s corporate culture at the individuallevel (i.e., beliefs, values, and assumptions conceived by an individual of the firm) isregarded as the corporate culture at the firm level (O’Reilly et al. 1991).

Corporate cultures are shared and transmitted by individuals over time. Individu-als in a firm gradually absorb the elements of a corporate culture and adapt them-selves to it through the process of socialization (Van Mannen and Schein 1979).Organizational socialization occurs among individuals via contact with others; in-dividuals generally prefer to have contact with those having similar attitudes andvalues (Darr and Kurtzberg 2000). Frequent and close contact smoothly transmitsinformation, knowledge, beliefs, and values among individuals (Granovetter 1973;Wasserman and Faust 1994).

In the setting of mergers, management scholars have recognized that corporatecultures have a major impact on the performance of merged organizations (Stahland Voigt 2008). Mergers and acquisitions are defined as combining two or moreorganizations into one.1 Although corporate mergers are commonplace as a meansfor diversifying business portfolios and achieving corporate growth (Nahavandi andMalekzadeh 1988), post-merger performance does not necessarily attain an expectedlevel due to the cultural dissonance between merging firms because corporate cul-tures provide individuals with unspoken rules and implicit assumptions in firms.O’Reilly and Chatman (1996) argue that a corporate culture functions as a socialcontrol mechanism because it prescribes “what should be done” and “how it shouldbe done” in a firm. Therefore, dissonance between the corporate cultures of mergingfirms inevitably leads to conflicts in decision making and difficulty in communica-tion among top executives, middle managers, and shop floor workers (e.g., Caloriet al. 1997; Chatterjee et al. 1992; Larsson and Lubatkin 2001; Ramaswamy 1997;

1In this study, we do not strictly distinguish between mergers and acquisitions because the results ofcomputational simulations are not affected by the possession of the control right of a merged organization.For the sake of clarity, we use “mergers” to represent “mergers and acquisitions.”


Weber and Camerer 2003; Weber and Schweiger 1992; Zollo and Singh 2004); suchconsequences eventually lower firm performance. For instance, a meta-analysis ofcultural differences in mergers conducted by Stahl and Voigt (2008) has illustratedthat differences in organizational cultures have a significant negative impact on post-merger performance. Likewise, as a laboratory experiment on the impact of groupmergers on work performance, Weber and Camerer (2003) have demonstrated thatthe work performance of a merged group dropped immediately after the merger dueto cultural difference among individuals in the group.

Using diverse approaches, ranging from laboratory studies through qualitativeanalysis, management scholars have reached a consensus that integration of cor-porate cultures is critical for post-merger performance (e.g., Chatterjee et al. 1992;David and Singh 1994; Jemison and Haspeslagh 1991; Weber and Camerer 2003;Zollo and Singh 2004). Cultural integration is the process in which individuals fromdifferent organizations gradually adopt the same corporate culture. As cultural in-tegration in a merged firm proceeds, individuals gradually adopt a shared identity,possess positive attitudes toward the merged firm, and eventually trust each other,thereby achieving synergy between two merging firms (Birkinshaw et al. 2000).

Management scholars have yet to investigate two issues. First, even thoughcorporate cultures reside within individuals and transfer among them, few stud-ies have examined how individual social ties in a merged firm influence the cul-tural integration process. In reality, it is uncommon that all individuals in merg-ing firms have connected with one another (Allatta and Singh 2011). For exam-ple, top executives in merging firms would have built strong relationships witheach other through meetings for the merger of their firms; however, shop floorworkers in one firm do not know those in the other firm because they usuallyhave limited opportunities to meet and know one another. Therefore, individualsare never structurally equivalent in the social network of the merged firm. So-cial influence on individuals occurs via ties with others and its magnitude is par-tially determined by individual network positions and structures (Granovetter 1973;Wasserman and Faust 1994). Correspondingly, from a social network perspective, itcan be inferred that the individual ties and network structure in the merged firm wouldhave a significant influence on cultural integration.

Second, the impact of individual social ties on the cause-and-effect relationshipbetween cultural integration and post-merger performance remains unclear. Previ-ous studies have focused primarily on firm-level financial performance, such asaccounting-based performance and market-based value, as the consequences of cul-tural dissonance. In actuality, paths from cultural dissonance to detrimental financialperformance are divergent and diverse. Cultural dissonance generates various dys-functions within organizations, such as employee and executive turnover (Lubatkinet al. 1999; Rafferty and Restubog 2009), miscommunication and interpersonal con-flicts among individuals (Weber and Camerer 2003), and the failure of synergy re-alization (Birkinshaw et al. 2000), resulting in unsatisfactory financial performance.Organizational dysfunctions mediate the relationship between post-merger culturalintegration and financial performance.

Greater cultural difference results in greater turnover within the merged firm. Em-ployees tend to feel great anxiety and stress about the changes derived from merg-ers with culturally different firms, thereby leading to employee turnover (Buono


and Bowditch 1989; Griffeth et al. 2000; Schweiger and DeNisi 1991). Top ex-ecutives who feel they are misfits in the corporate culture of a merged firm tendto depart (Lubatkin et al. 1999). Previous studies report that the turnover of exec-utives and employees caused by mergers decreases post-merger performance dueto the drop in productivity and employee morale (e.g., Buono and Bowditch 1989;Schweiger and DeNisi 1991).

Likewise, greater cultural difference generates interpersonal conflicts among in-dividuals in a merged firm. Interpersonal conflict is generally defined as “percep-tions by the parties involved that they hold discrepant views or have interpersonalincompatibilities” (Jehn 1995: 257). Since corporate cultures function as assump-tions of thoughts, individuals shaped by disparate corporate cultures tend to havedifficulty in communicating their ideas with each other (Barry and Crant 2000;Weber and Camerer 2003). Therefore, cultural difference between an individual fromone merging firm and those from the other would generate miscommunication be-tween them. A meta-analysis of interpersonal conflict and team effectiveness demon-strates that interpersonal conflict has a negative impact on team performance andteam member satisfaction because interpersonal conflict increases tension and antag-onism among team members, thereby distracting them from performing tasks (DeDreu and Weingart 2003). Additionally, individual-level conflict would result in theimpediment of overall communication within the merged firm, or ineffective organi-zational communication, because the effectiveness of communication depends on theextent to which communication within the dyadic interpersonal relationship is highin shared meaning (Barry and Crant 2000). Ineffective communication in a mergedfirm would lead to the failure of synergies, a typical cause of unsatisfactory post-merger performance. Top executives fail to coordinate projects and then individualsand teams cannot cooperatively proceed with projects (Birkinshaw et al. 2000; Jemi-son and Haspeslagh 1991).

Such organizational dysfunctions arise from communication among individualswith different corporate cultures; therefore, the relationship between cultural disso-nance and various organizational dysfunctions would depend on the frequency andpattern of individual communication. Thus, a limited focus on individual social tieswithin a merged firm has only partially revealed the relationships between culturalintegration and post-merger performance.

1.2 Roles of social networks in cultural integration in mergers

Provided that corporate cultures are perceived and transmitted among individualsthrough social interactions with others, the cultural integration process in a mergedfirm would depend on network ties and structures because these aspects are as-sociated with individual social influence (Granovetter 1973; Wasserman and Faust1994). Social influence occurs when an individual’s thoughts or behavior are af-fected by others. Individual influence on others would be asymmetric due to theavailability of resources derived from such relationships because network ties andstructures determine resources, such as information, knowledge, advice, social sup-port, or friendship, that individuals can access (Burt 1992; Kilduff and Brass 2010).


Fig. 1 Research focus. The smallest circles represent individuals in organizations, while the bold linesstand for ties. The ties are directed in our computational model, but their directions are omitted in thisfigure for clarity

In addition, the structures of networks determine the speed and pattern of disper-sion and concentration within the networks (Rogers 2003; Watts and Strogatz 1998;Wasserman and Faust 1994).

In the context of mergers, two types of networks would have an impact on thecultural integration process: networks within merging firms and networks betweenmerging firms. The network within a merging firm is the collection of ties connect-ing individuals within the same merging firm. These networks determine the locationof social influence within a merging firm. In the cultural integration process, centralindividuals in the within-firm network have considerable opportunities to share el-ements of corporate cultures with others. For example, if a merging firm is highlycentralized, that is, its top executives have direct contact with most employees, thoseexecutives can then communicate with them, wielding direct influence in order topromote cultural integration.

On the other hand, the network between merging firms is the collection of tiesconnecting individuals in two different merging firms. In the merger process, notall individuals in a merging firm have contact with their counterparts in the otherfirm. As described above, only a small circle of individuals in merging firms—suchas top management teams or project teams composed of middle managers—worktogether for mergers and have frequent interpersonal interactions with each other.As a result, the top executives or the middle managers involved in the project teamsfor mergers are more likely to be exposed to the other firm’s corporate culture. Thus,some individuals in a merging firm would gain more exposure to the corporate cultureof the other merging firm.

To summarize, it is theoretically expected that social networks within and betweentwo merging firms have a significant impact on the cultural integration process (Fig. 1illustrates the research focus of this study). Two types of network structures would


have a significant impact on cultural integration in mergers: Concentration withinmerging firms (i.e., how concentrated within-firm ties are on a small number of indi-viduals in each firm) and concentration between merging firms (i.e., how concentratedthe between-firm ties are on central individuals of each merging firm). The pattern oftie-concentration among actors determines the speed and scope of resource diffu-sion and concentration in a network (Bearman et al. 2004; Burt 1992; Rogers 2003;Wasserman and Faust 1994).2 Therefore, in the merger context, these two concentra-tion measures reflect which individuals in a merging firm are exposed to the corporateculture of the other merging firm and how fast and widely the corporate cultures aretransmitted among individuals in the merging firms.

2 A computational model of cultural integration in mergers

Our goal is to find network structures that promote or impede post-merger culturalintegration. In so doing, we developed a simulation model of cultural adoption anddiffusion among individuals in a merged firm. The individuals are connected throughsocial ties and their perceived elements of the corporate culture are transmitted viathose ties. We stress that using computational modeling is appropriate because themodel of this study encompasses complex interactions among many individual ac-tors, each being situated in a unique local social network context. Typical analyticaltreatments that assume randomness and thereby describe dynamics of average proper-ties in a low-dimensional state space would not capture what we aim to study, i.e., theeffects of specific social network topologies on corporate merger. Therefore, a com-putational approach to gain numerical answers is more suitable.

2.1 Setting culture and individuals in a merged firm

Our computer simulation model is an agent-based model operating on a dynamic net-work structure, where individuals (nodes) exchange elements of a corporate culturewith others who are connected to it through social ties (links). In this model, we settwo merging firms (A & B) that engage in the merger process. Each merging firmpossesses individuals. In our simulations, we set the number of individuals in eachfirm to 50. Therefore, the total number of individuals in the merged firm is 100. Al-though the number of individuals in the firms is by far smaller than that of publiclytraded firms, we found that this parameter has a negligible impact on the simulationresults when network density is kept at the same level.

Individuals in a merging firm conceive its corporate culture. We model a corpo-rate culture as a vector in a multi-dimensional continuous cultural space. The culturalspace is composed of several cultural dimensions; each dimension represents an el-ement of a corporate culture. We set 10 cultural dimensions for the cultural space;this number is founded on previous empirical studies of corporate culture. For ex-ample, O’Reilly et al. (1991), who investigated eight large US public accounting

2In this study, we also use within-firm concentration and between-firm concentration as synonyms ofconcentration within merging firms and concentration between merging firms, respectively.


firms, found eight dimensions of organizational cultures: innovation, attention to de-tail, outcome orientation, aggressiveness, supportiveness, emphasis on rewards, teamorientation, and decisiveness. Likewise, Chatterjee et al. (1992) measured culturaldistance (the degree of cultural difference between two firms) perceived by the topmanagement teams of acquired firms across seven dimensions of organizational cul-tures: innovation and action orientation, risk-taking, lateral integration, top manage-ment contact, autonomy and decision making, performance orientation, and rewardorientation. Therefore, it can be concluded that setting 10 dimensions as elements ofcorporate cultures would be a more conservative approach.

In our model, we characterize the distance between two cultures by the Euclideandistance between two vectors in the cultural space. The average cultural differencebetween the two merging firms is characterized as the average cultural distance be-tween two individuals—one in Firm A and the other in Firm B. If the value of thismeasurement is larger, the corporate culture that individuals perceive in Firm A is,on average far different from that in Firm B. In our computational model, we setthe initial individual cultural vectors as follows: First, two cultural “center” vectorswere created for the two merging firms, and these center vectors were separated by3.0 (in an arbitrary unit) in the cultural space. Then individual cultural vectors werecreated for individuals in each firm by adding a small random number drawn from anormal distribution with a mean of 0 and a standard deviation of 0.1 (in the same unitused above) to each component of the cultural center vector of that firm. This set-ting creates an initial condition where the average between-firm cultural difference isapproximately seven times larger than the average within-firm cultural difference.

2.2 Cultural integration process

Individuals in our model are connected to each other through directed social ties.A tie going from one individual to another works as a conduit that can transmit, fromthe origin node to the destination node, information and knowledge that include theelements of their corporate cultures. Each tie has a weight associated with it, calledtie strength in the social network literature (Granovetter 1973; Wasserman and Faust1994). The range of possible tie strength values is bounded between 0 and 1. Corpo-rate cultures diffuse among individuals through their ties. The algorithm for simulat-ing the dynamics of cultural diffusion, and subsequent social network changes, is asfollows.

One iteration in a simulation consists of simulations of individual actions for allindividuals in a sequential order (therefore there are always 100 individual actionssimulated in each iteration). When it is its turn to take an action, an individual firstselects an information source. For 99 % of the time, the individual chooses the in-formation source from its local in-neighbors, that is, the nodes from which directedties are coming to the individual. The probability for a neighbor to be selected as theinformation source is proportional to the strength of the tie that connects the neighborto the individual; this represents that individuals tend to listen more often to otherswhom they trust more or with whom they have stronger connections. Otherwise (witha 1 % chance), the individual chooses as the information source any individual in theconnected component in which the individual belongs. If there is no existing tie from


Fig. 2 Update of tie strengthcaused by cultural acceptance orrejection

the randomly selected source to the individual, a new tie with a very weak strength(0.01) will be created between them. This represents an informal, incidental commu-nication, like a “water-cooler” conversation within an organization.

Once the information source is selected, the individual receives the source’s cul-tural vector and then measures the distance between the received cultural vector andits own cultural vector. With a probability that decreases monotonically with increas-ing cultural distance, the individual accepts the received culture. The probability ofacceptance, PA, is mathematically represented as PA(d) = (1/2)d/dc , where d is thedistance between the two cultural vectors and dc is the characteristic cultural distanceat which PA becomes 50 %. We used dc = 0.5 for our simulations. If the individualaccepts the received cultural vector, it adopts the mean of the two vectors (i.e., thesum of the two vectors divided by 2) as its new cultural vector, and the strength ofthe tie from the source to the individual is increased by the following formula:

Snew = logistic(logit(Scurrent) + 1

)

Here Scurrent and Snew are the current and updated tie strengths, respectively (thisformula guarantees that the tie strength is always constrained between 0 and 1). Onthe other hand, if the individual rejects the received cultural vector, its own vectorwill not change, and the tie strength is decreased by the following formula:

Snew = logistic(logit(Scurrent) − 1

)

The mechanism of the update of tie strength caused by cultural acceptance orrejection is illustrated in Fig. 2. If the tie strength falls below 0.01, the tie is consideredinsignificant and is removed from the social network.

2.3 Initial social network structures

We set the network structures within and between merging firms so that there aresubstantially more within-firm ties than between-firm ties at the beginning of eachsimulation. The number of ties within each merging firm is 490. Since the number ofindividuals in each firm is 50, the network density of the firm is 490/(50 ∗ 49) = 0.2.The number of ties from one merging firm to the other (that is, A → B or B → A)


is 50 for each direction. All tie strengths of those connections are initialized usingrandom numbers drawn from a uniform distribution between 0 and 1.

In our computational experiments, we set two experimental parameters that con-trol topological characteristics of the initial social network among individuals. Oneis what we call the within-firm concentration, denoted by variable w. This parameterdetermines the probability for each individual to be selected as an information sourceof a within-firm tie. It is mathematically defined as

Pw(i) ∼ (i/n)w (i = 1,2, . . . , n),

where i is the ID number of the individual within a firm, n the firm size (n = 50in our simulations), and Pw(i) the probability for individual i to be selected as aninformation source when within-firm ties are initially created. The operator, “∼”,represents a proportional relationship. When w = 0, within-firm ties are uniformlydistributed within the firm so that the organizational structure of the firm is “flat”. Forlarger w values, the within-firm information sources are more concentrated on a smallnumber of individuals with greater ID numbers, which represent a highly centralizedorganizational structure of the firm, such as that with a one-man CEO. In our model,w values 1, 3, 5, 10, 20, and 30.

The other experimental parameter is what we call the between-firm concentration,denoted by variable b. This parameter determines the probability for each individualto be selected as either an origin or a destination of a between-firm tie. It is mathe-matically defined as

Pb(i) ∼ cbi (i = 1,2, . . . , n),

where i and n are the same as in the previous formula, ci the within-firm closenesscentrality of individual i, and Pb(i) the probability for individual i to be selected as aconnecting person, either as origin or destination, when between-firm ties are created,which is done only after all the within-firm ties have been created. The operator, “∼”,represents a proportional relationship. When b = 0, between-firm ties randomly con-nect individuals across firms, regardless of their social positions. For larger b values,the between-firm ties are more concentrated on a small number of individuals withhigher centralities that represent the formation of top-level (only) inter-firm commu-nication channels. In our model, b values 0.1, 0.5, 1, 3, and 5. Figure 3 illustratesimages of within-firm and between-firm concentrations.

Note that the above two parameters affect only the initial social network structure.As cultural integration progresses, the network topologies will change dynamicallyin our simulations.

2.4 Measuring post-merger cultural distance and organizational dysfunctions

As a primary dependent variable of our computational experiments, we measure theaverage cultural distance between individuals who used to belong to different pre-merger firms and who still remain in the largest connected component of the socialnetwork. If the average cultural distance decreases from its initial value, cultural in-tegration proceeds among individuals in the merged firm.

Likewise, we use three measures of the consequences of cultural integration:turnover, interpersonal conflict, and organizational communication ineffectiveness.


Fig. 3 Illustrations of within-firm and between-firm concentrations. The directions of ties are omitted inthis figure for clarity

All the measures should influence overall firm performance. Turnover is measuredby the number of individuals in the simulations who do not stay in the largest con-nected component of the social network. In our model, if an individual terminates allties with his neighbors, he is considered to have left the merged firm.

Interpersonal conflict is calculated as cultural distances multiplied by tie strengthsbetween individuals. This quantity is summed up for every pair of individuals withinthe largest connected component. Since tie strength can be considered to repre-sent communication frequency (Granovetter 1973), individuals who are strongly tiedto neighbors with different perceptions of corporate culture would often encountergreater communication conflict in the workplace.

On the other hand, organizational communication ineffectiveness is calculated bythe cultural distances multiplied by the edge betweenness of the social ties betweenindividuals. This quantity is, again, summed up for all the pairs of individuals withinthe largest connected component. Edge betweenness is defined as the number ofgeodesics (shortest paths) going through an edge (Wasserman and Faust 1994). Ifa tie with high edge betweenness is filled with cultural conflict, most communica-tion between individuals in a firm would be conflicted. As a result, information andknowledge transfer in the firm would be delayed or impeded.


2.5 Simulation procedures

We implemented the simulation model and analysis tools by using Python version2.7.2 with the NetworkX 1.5 network modeling module (Hagberg et al. 2008). Theprogram codes of the model are available from the authors upon request. We set 200time steps in one simulation.3 Following caveats of computer simulations in man-agement studies (Harrison et al. 2007), we ran 50 simulations for each experimentalcondition and conducted regression analysis of the generated simulation results.

3 Findings

Table 1 illustrates the means (over 50 runs) of the simulated values of cultural dis-tance, turnover, interpersonal conflict, and organizational communication ineffective-ness, for varying within-firm and between-firm concentrations. Using the data, Fig. 4plots the results of within-firm and between-firm concentrations. A brief observationof Table 1 and Fig. 4 indicates that the values of cultural distance and organizationaldysfunctions vary depending on within-firm and between-firm concentrations. If theconcentrations have no impact on cultural integration, there will be no variance inthe values of cultural distance and organizational dysfunctions. Therefore, from thesimulation results, we can conclude that within-firm and between-firm concentrationshave an impact on cultural integration and organizational dysfunctions.

Table 2 shows the regression of simulated data, which clarify the effects of within-firm and between-firm concentrations on cultural distance and organizational dys-functions. All regression coefficients in the models are standardized. In Model 1,cultural distance is regressed on within-firm and between-firm concentrations. Thecoefficient of within-firm concentration is negative and statistically significant (β =−0.31, p < 0.01), while that of the between-firm concentration is positive and sta-tistically significant (β = 0.10, p < 0.01). These results imply that within-firm con-centration of merging firms decreases post-merger cultural distance (promotes cul-tural integration), but between-firm concentration increases it (impedes cultural in-tegration). In Model 2, we added the interaction term of within-firm and between-firm concentration on cultural distance to Model 1. The coefficient of the interactionterm is positive and statistically significant (β = 0.37, p < 0.01). Adding the in-teraction term to the model increases R2 by 0.04, which is statistically significant(F = 70.46, p < 0.01). Therefore, considering the results of Model 1, these outputsof Model 2 indicate that within-firm concentration promotes more cultural integrationwhen between-firm concentration is lower.

In Model 3, turnover was regressed on within- and between-firm concentrations.The coefficient of the within-firm concentration is positive and statistically signifi-cant (β = 0.27, p < 0.01), whereas the coefficient of the between-firm concentrationis negative and statistically significant (β = −0.07, p < 0.01). Therefore, more indi-viduals leave the merged firm when within-firm concentration is higher or between-firm concentration is lower. Following Model 3, we tested for the interaction term of

3In order to check whether the simulation results do not depend on the number of time steps, we alsoanalyzed data on 100 time steps and found similar results on 200 time steps.


Fig

.4C

ultu

rald

ista

nce

and

orga

niza

tiona

ldys

func

tions

byw

ithin

-firm

and

betw

een-

firm

conc

entr

atio

ns


Tabl

e1

Sum

mar

yof

sim

ulat

ion

resu

lts

Bet

wee

n-fir

mco

ncen

trat

ion

=0.

1B

etw

een-

firm

conc

entr

atio

n=

0.5

Bet

wee

n-fir

mco

ncen

trat

ion

=1.

0B

etw

een-

firm

conc

entr

atio

n=

3.0

Bet

wee

n-fir

mco

ncen

trat

ion

=5.

0

With

in-fi

rmco

ncen

trat

ion

=1.

0C

ultu

rald

ista

nce

2.64

22.

577

2.65

42.

538

2.50

5T

urno

ver

00

00

0In

terp

erso

nalc

onfli

ct19

.873

18.7

3918

.033

17.3

8225

.193

Org

aniz

atio

nalc

omm

unic

atio

nin

effe

ctiv

enes

s0.

013

0.01

20.

013

0.01

20.

012

With

in-fi

rmco

ncen

trat

ion

=5.

0C

ultu

rald

ista

nce

2.41

02.

271

2.20

32.

063

1.90

1T

urno

ver

0.04

00

00.

02In

terp

erso

nalc

onfli

ct17

.891

19.8

4421

.417

18.6

1228

.969

Org

aniz

atio

nalc

omm

unic

atio

nin

effe

ctiv

enes

s0.

014

0.01

30.

012

0.01

20.

011

With

in-fi

rmco

ncen

trat

ion

=10

.0C

ultu

rald

ista

nce

2.13

61.

887

1.35

81.

487

1.55

7T

urno

ver

00

00.

020.

02In

terp

erso

nalc

onfli

ct16

.610

19.1

9118

.461

15.1

199.

905

Org

aniz

atio

nalc

omm

unic

atio

nin

effe

ctiv

enes

s0.

015

0.01

30.

009

0.01

00.

010

With

in-fi

rmco

ncen

trat

ion

=20

.0C

ultu

rald

ista

nce

1.43

61.

281

1.32

61.

719

1.65

4T

urno

ver

0.10

0.20

0.10

0.16

0.14

Inte

rper

sona

lcon

flict

8.94

99.

357

9.24

89.

992

4.85

2O

rgan

izat

iona

lcom

mun

icat

ion

inef

fect

iven

ess

0.01

40.

012

0.01

20.

016

0.01

5

With

in-fi

rmco

ncen

trat

ion

=30

.0C

ultu

rald

ista

nce

1.21

81.

352

1.33

71.

474

1.90

7T

urno

ver

0.48

0.24

0.92

0.34

0.24

Inte

rper

sona

lcon

flict

10.4

287.

385

6.87

45.

919

8.38

4O

rgan

izat

iona

lcom

mun

icat

ion

inef

fect

iven

ess

0.01

60.

017

0.01

60.

017

0.02

2


Tabl

e2

Est

imat

ion

for

the

impa

ctof

with

in-fi

rman

dbe

twee

n-fir

mco

ncen

trat

ions

.n=

1,50

0fo

rea

chm

odel

.t-v

alue

sar

ein

pare

nthe

ses

Cul

tura

ldis

tanc

eT

urno

ver

Inte

rper

sona

lcon

flict

Org

aniz

atio

nalc

omm

unic

atio

nin

effe

ctiv

enes

s

Mod

el1

Mod

el2

Mod

el3

Mod

el4

Mod

el5

Mod

el6

Mod

el7

Mod

el8

With

in-fi

rmco

ncen

trat

ion

−0.3

1**

(−12

.83)

−0.5

0**

(−15

.30)

0.27

**

(10.

70)

0.36

**

(10.

62)

−0.2

7**

(−10

.79)

−0.2

6**

(−7.

65)

0.28

**

(11.

26)

0.10

**

(3.0

7)

Bet

wee

n-fir

mco

ncen

trat

ion

0.10

**

(4.2

8)−0

.15**

(−3.

83)

−0.0

7**

(−2.

67)

0.06

(1.5

3)−0

.03

(−1.

10)

−0.0

2(−

0.49

)0.

13**

(5.1

0)−0

.11**

(−2.

73)

With

in-fi

rm×

Bet

wee

n-fir

m0.

37**

(8.3

9)−0

.19**

(−4.

09)

−0.0

1(−

0.25

)0.

34**

(7.6

4)

Con

stan

t(4

9.11

)**(4

6.86

)**(−

1.37

)(−

3.36

)**(2

4.07

)**(2

0.15

)**(2

4.11

)**(2

4.83

)**

R2

0.11

0.15

0.08

0.09

0.07

0.07

0.09

0.13

F(d

f)

91.5

0(2

,149

7)**

87.3

1(3

,149

6)**

60.7

9(2

,149

7)**

46.5

3(3

,149

6)**

58.7

7(3

,149

7)**

39.1

8(3

,149

7)**

76.3

9(2

,149

7)**

72.3

6(3

,149

6)**

�R

20.

04**

0.01

**0.

000.

04**

**fo

rp

<0.

01


within-firm and between-firm concentrations in Model 4. The coefficient of the in-teraction term is significantly negative (β = −0.19, p < 0.01) and the increase in R2

(0.01) is also statistically significant at the 1 percent level (F = 16.74, p < 0.01). Ac-cordingly, within-firm and between-firm concentrations interactively prevent turnoverof individuals.

In Model 5, we tested the effects of within- and between-firm concentrations oninterpersonal conflict. The coefficient of within-firm concentration is negative andstatistically significant (β = −0.27, p < 0.01); however, that of the between-firmconcentration is not significant (β = −0.03, n.s.). According to this result, interper-sonal conflict is lower when the corresponding within-firm concentration is higher.In Model 6, we added the interaction of within-firm and between-firm concentra-tions. The coefficient of the interaction is not statistically significant (β = −0.01,n.s.). From this result, it can be inferred that within- and between-firm concentrationsinfluence the level of interpersonal conflict independently.

Finally, Model 7 tests the effects of within- and between-firm concentrations on or-ganizational communication ineffectiveness. The coefficients of the within-firm con-centration and the between-firm concentration are positive and statistically significant(β = 0.28, p < 0.01; β = 0.13, p < 0.01, respectively). This result indicates thatorganizational communication ineffectiveness increases when within-firm concen-tration or between-firm concentration is higher. Additionally, Model 8 includes thewithin- and between-firm concentrations’ interaction. The coefficient of the interac-tion is positive and statistically significant (β = 0.34, p < 0.01). The interaction termsignificantly improves the explanatory power of the statistical model (�R2 = 0.04,F = 58.43, p < 0.01). Therefore, it can be concluded that organizational communi-cation ineffectiveness decreases when both within-firm and between-firm concentra-tions are lower. All of the statistical conclusions derived above can be better under-stood via visual observation in Fig. 4.

4 Discussion

The main finding of this study is that concentrations within and between merg-ing firms have an impact on post-merger cultural integration and its consequences.Regarding cultural integration, we found that within-firm concentration in mergingfirms decreases post-merger cultural distance; however, between-firm concentrationincreases post-merger cultural distance. Additionally, when between-firm concentra-tion becomes lower, within-firm concentration promotes more cultural integration.These results indicate that, in a merging firm with high within-firm concentration,central individuals have ties to numerous individuals and can spread their culturalelements widely throughout the merging firm. At the same time, central individu-als may strongly hold its own corporate culture by engaging in frequent contact withother central individuals within the same firm. Central individuals would form a smallcluster sticking to the corporate culture of the merging firm. Accordingly, when thecentral individuals of a merging firm are connected with their counterparts of theother merging firm, they would resist each other’s culture, which prevents culturalintegration. On the other hand, peripheral individuals in a merging firm have limited


opportunities to expose themselves to cultural elements from others in their mergingfirm. Therefore, when peripheral individuals have social ties with individuals in theother merging firm, they can quickly share elements of the common corporate cul-ture, thereby promoting cultural integration. By definition, central individuals havemultiple ties with numerous peripheral individuals; as a result, central individuals areopen to the common corporate culture through social ties with peripheral individualswho share the common corporate culture of a merged firm. Once the central individ-uals of one merging firm surely share elements of the corporate culture with those inthe other merging firm, they can spread widely the common corporate cultures amongindividuals in a merged firm due to the concentration feature of network centrality.

As for organizational dysfunctions, simulated results imply that within-firm andbetween-firm concentrations have different effects on turnover, interpersonal con-flict, and organizational communication ineffectiveness. Turnover occurs more fre-quently when within-firm concentration is higher or when between-firm concentra-tion is lower. The existence of central individuals in a merged firm tends to dropindividuals who do not share the same corporate culture. This finding is consistentwith previous empirical work (e.g., Krackhardt and Porter 1985, 1986; Moynihanand Pandey 2008), which indicates that a lack of social ties with neighbors havingsimilar values increases individual turnover intention. Since social ties are concen-trated on a few individuals in a high within-concentration firm, most individuals havefew ties with neighbors; therefore, individuals are more likely to leave merged firmswith high within-firm concentration. On the other hand, higher between-firm con-centration decreases individual turnover in a merged firm. In the context of higherbetween firm concentration, peripheral individuals in a merging firm have limitedchances to be exposed to the corporate culture of the other merging firm due to socialties with the other firm’s individuals, resulting in peripheral individuals being morelikely to stay in the merged firm. Based on the findings of cultural distance, the ef-fects of within- and between-firm concentrations on cultural integration are partiallyderived from those on turnover by dismissing individuals who do not fit the commoncorporate culture in a merged firm.

The simulation results depict a complex picture of interpersonal conflict and or-ganizational communication ineffectiveness. In terms of within-firm concentration,higher within-firm concentration tends to reduce interpersonal conflict; however, itgenerates organizational communication ineffectiveness in a merged firm. As within-firm concentration increases, interpersonal conflict simply decreases because cen-tral individuals communicate more widely about their cultural elements with others,and peripheral individuals are less exposed to the different corporate culture. In con-trast, within-firm concentration raises organizational communication ineffectivenessbecause communication in a merged firm relies on a few individuals. If those keyindividuals do not share the same elements of the corporate culture, organizationalcommunication ineffectiveness inevitably arises. The result that between-firm con-centration increases organizational communication ineffectiveness endorses this in-ference. When central individuals are connected between merging firms, the overallcommunication in the merged firm relies heavily on social ties between a few cen-tral individuals, which can cause organizational communication ineffectiveness in themerged firm. Collectively, from these results, it can be inferred that within-firm and


between-firm concentration have different effects on interpersonal conflict at the localand global network levels.

Investigation of these organizational dysfunctions caused by cultural distance pro-vides insight into merger failures because the negative impact of organizational dys-functions on post-merger financial performance depends partially on the character-istics of firms. For example, executive and employee turnover might have a largerimpact on financial performance of firms in knowledge-intensive industries thanin capital-intensive industries. Since executives and employees in the knowledge-intensive industries could possibly possess knowledge that would be crucial for firms,their leaving would likely cause critical damage to firms. Likewise, organizationalcommunication ineffectiveness may be important for firms in which functional de-partments are highly interdependent in order to achieve tasks and which requiredetailed coordination; however, interpersonal conflict would be even more criticalfor firms in which departments can operate independently and need close coopera-tion within teams at the site level. Since these organizational dysfunctions would notchange in tandem depending on cultural integration, the effective network structuresin increasing post-merger financial performance would vary based on the character-istics of firms. Post-merger organizational dysfunctions can potentially explain howcultural distance and integration affect post-merger financial performance.

This simulation study provides several theoretical contributions. The first contri-bution is that we successfully introduced a social network perspective into the field ofcultural integration in mergers. According to a meta-analysis of mergers and acquisi-tions conducted by King et al. (2004), none of the factors that were empirically identi-fied by previous studies significantly explains variances in post-merger performance.King and his co-authors suggest that management researchers search for novel an-tecedents and moderators of post-merger performance. Against a backdrop of theirsuggestion, our computational model presented evidence that the network ties andstructure of individuals in a merged firm have a significant impact on cultural inte-gration and organizational dysfunctions that influence post-merger performance. Weinsist that social network theory would cast new light on merger studies.

As the second contribution, we successfully crafted a computational model of cul-tural integration from a social network perspective. By explicitly incorporating socialnetworks within and between two merging firms, our model is a significant expan-sion of the existing computational models of cultural contagion (e.g., Carley 1991;Mark 2003). Our computational model would work as a template of the cultural inte-gration model, which would be applicable to other studies in management and relateddisciplines. In particular, our model is unique compared to other social network basedmodels, because it represents the co-evolution of node states and link topologies thatdevelop over the same time scales.

Finally, this research also contributes to theoretical network science by presentinga new organizational instance of “adaptive networks”—a novel class of complex net-works in which individuals and their ties co-evolve at similar time scales (Gross andBlasius 2008; Gross and Sayama 2009). Historically, network science has either con-sidered dynamics on a network with a fixed topology, or dynamics of a network withno individual node dynamics considered, but not both. Just in the last few years, how-ever, network science scholars have started considering the combination of these two


dynamics. Our work adds to this growing literature a new form of such adaptive net-works because node properties (i.e., cultures in individual actors) change graduallydue to the influence coming through social ties, while the ties themselves strengthenor weaken by the similarity or dissimilarity of node properties across them (i.e., cul-tural dissonance). Following pioneering studies, such as Braha and Bar-Yam (2006)and Braha et al. (2011), our work presents one of the few early instances of adaptivenetwork modeling applied to management and organizational sciences.

This study provides some practical implications for practitioners. First, the simu-lation results reveal that an effective cultural integration process is contingent uponnetwork ties and structures. Although existing studies of mergers repeatedly empha-size the importance of cultural integration for post-merger performance, they haveprovided few suggestions as to how individuals should be connected to achievehigher post-merger performance. Our computational model expresses precise mes-sages about the ideal network ties and structure in a merged firm for higher post-merger performance. Our simulation results indicate that social ties of top execu-tives between merging firms (i.e., low between-firm concentration) could not promotecultural integration when the merging firms are highly centralized; however, the so-cial ties of middle managers or frontline workers between merging firms (i.e., lowbetween-firm concentration) might achieve higher cultural integration.

Second, our model can predict cultural integration levels and post-merger per-formance depend on the network ties and structures of merging firms. Predictionsregarding these measures would be beneficial for practitioners in contriving practicesof connecting individuals in order to achieve higher post-merger performance. Inparticular, our model indicates that three post-merger organizational dysfunctions donot necessarily vary in tandem. Therefore, practitioners engaging in mergers shouldconsider the most influential factor in their firm’s performance and set appropriatenetwork ties and structures to achieve the goal in the merger process.

We note that this study still has some limitations, which illuminate directions forfuture studies. First, our current model does not consider asymmetries between merg-ing firms; two firms are identical in many aspects, such as size, network density andcultural strength (i.e., standard deviations of initial culture distribution within a firm).We made this symmetric assumption so that our study could be focused on the ef-fects of social network structure on cultural integration. In the real business world,however, it is unlikely for merging firms to be so symmetric. We plan to incorporatevarious asymmetries of merging firms in future models to investigate their impactson cultural integration.

Second, our model does not consider different roles or functions of individuals. Inactual organizations, for example, individuals who work on similar tasks might formsubcultures within a firm, and they might have greater interpersonal conflict withothers who have different subcultures. Also, individuals coming from different firmsmight be able to develop personal connections more easily if they work on similartasks in their respective firms. Such function-related influence could also be includedin our future models.

Third, our model does not reflect the differences in the functions of cultural trans-mission between formal and informal ties. Social network researchers have reportedthat informal ties more smoothly transmit affects between individuals than do for-mal ties (Kilduff and Brass 2010). Therefore, even though two sets of merging firms


have the same within- and between-firm network structures, the differences in theproportions of formal and informal ties may have a significant impact on culturalintegration and its performance. Since the intent of our model is to clarify the pureimpact of within- and between-firm network structures on cultural integration and itsperformance, we used a simple model. However, to draw a more complete picture ofcorporate merger, formal and informal ties should be considered in future research.

Finally, in our model, corporate cultures are represented by vectors. We acknowl-edge that this treatment of corporate cultures makes our model so simple that it maynot reflect all the aspects of post-merger cultural integration. However, this simplic-ity is a necessary evil of computational modeling to illuminate the aspect in question,within- and between-firm network structures. Our current model is a stepping stoneto a deeper understanding of cultural integration; we will continue to advance it byincluding more diverse factors to further approach to the reality.

In summary, this study is the first attempt to investigate cultural integration in amerged firm from a network perspective through computational modeling. Our com-putational simulations indicated that network ties and structure of the individuals in amerged firm matter to the patterns of cultural integration and its consequences. Over-all, our findings will surely broaden the theoretical and practical horizons of culturalintegration in mergers and social networks.

References

Allatta JT, Singh H (2011) Evolving communication patterns in response to an acquisition event. StrategManag J 32(10):1099–1118

Barry B, Crant MJ (2000) Dyadic communication relationships in organizations: an attribution/expectancyapproach. Organ Sci 11(6):648–664

Bearman PS, Moody J, Stovel K (2004) Chains of affection: the structure of adolescent romantic andsexual networks. Am J Sociol 110(1):44–91

Birkinshaw JM, Bresman H, Hakanson L (2000) Managing the post-acquisition integration process: howthe human integration and task integration processes interact to foster value creation. J Manag Stud37(3):395–426

Bradley S, Sullivan EE (2005) AOL Time Warner, Inc. Harvard Business Publishing, BostonBraha D, Bar-Yam Y (2006) From centrality to temporary fame: dynamic centrality in complex networks.

Complexity 12(2):59–63Braha D, Stacey B, Bar-Yam Y (2011) Corporate competition: a self-organized network. Soc Netw 33:219–

230Buono A, Bowditch JL (1989) The human side of mergers and acquisitions: managing collisions between

people, cultures and organizations. Jossey-Bass, San FranciscoBurt RS (1992) Structural holes: the social structure of competition. Harvard University Press, CambridgeCalori R, Lubatkin M, Very P, Veiga J (1997) Modeling the origins of nationally-bound administrative

heritages: a historical institutional analysis of French and British firms. Organ Sci 8(6):681–696Carley K (1991) A theory of group stability. Am Sociol Rev 56:331–354Chatterjee L, Lubatkin M, Schweiger D, Weber Y (1992) Cultural differences and shareholder value in

related mergers: linking equity and human capital. Strateg Manag J 13(5):319–334Darr ED, Kurtzberg TR (2000) An investigation of partner similarity dimensions on knowledge transfer.

Organ Behav Hum Decis Process 82:28–44David K, Singh H (1994) Sources of acquisition cultural risk. In: Krogh G, Sinatra A, Singh H (eds) The

management of corporate acquisitions. MacMillan & Co., Basingstoke, pp 251–292Davis JP, Eisenhardt KM, Bingham CB (2007) Developing theory through simulation methods. Acad

Manag Rev 32(2):480–499De Dreu CKW, Weingart LR (2003) Task versus relationship conflict, team performance, and team member

satisfaction: a meta-analysis. J Appl Psychol 88(4):741–749


Earley CP, Mosakowski E (2000) Creating hybrid team cultures: an empirical test of transnational teamfunctioning. Acad Manag J 43(1):26–49

Finkelstein S (2002) The DaimlerChrysler merger. Tuck School of Business, Dartmouth CollegeGranovetter M (1973) The strength of weak ties. Am J Sociol 78(6):1360–1380Griffeth RW, Hom PW, Gaertner S (2000) A meta-analysis of antecedents and correlates of employee

turnover: update, moderator tests, and research implications for the next millennium. J Manag26(3):463–488

Gross T, Blasius B (2008) Adaptive coevolutionary networks: a review. J R Soc Interface 5:259–271Gross T, Sayama H (eds) (2009) Adaptive networks: theory, models and applications. Springer, New YorkHagberg AA, Schult DA, Swart PJ (2008) Exploring network structure, dynamics, and function using

NetworkX. In: Varoquaux G, Vaught T, Millman J (eds) Proceedings of the 7th Python in scienceconference, pp 11–15

Haleblian J, Devers CE, McNamara G, Carpenter MA, Davison RB (2009) Taking stock of what we knowabout mergers and acquisitions: a review and research agenda. J Manag 35(3):469–502

Harrison JR, Lin Z, Carroll GR, Carley KM (2007) Simulation modeling in organizational and manage-ment research. Acad Manag Rev 32(4):1229–1245

Jehn KA (1995) A multimethod examination of the benefits and detriments of intragroup conflict. AdmSci Q 40:256–282

Jemison DB, Haspeslagh PC (1991) Managing acquisitions: creating value through corporate renewal.Free Press, New York

Kilduff M, Brass DJ (2010) Organizational social network research: core ideas and key debates. AcadManag Ann 4:317–357

King DR, Dalton DR, Daily CM, Covin JG (2004) Meta-analysis of post-acquisition performance: indica-tions of unidentified moderators. Strateg Manag J 25:187–200

Krackhardt D, Porter LW (1985) When friends leave: a structural analysis of the relationship betweenturnover and stayers’ attitudes. Adm Sci Q 30(2):242–261

Krackhardt D, Porter LW (1986) The snowball effect: turnover embedded in communication networks. JAppl Psychol 71(1):50–55

Larsson R, Lubatkin M (2001) Achieving acculturation in mergers and acquisitions: an international casesurvey. Hum Relat 54(12):1573–1607

Lubatkin M, Schweiger D, Weber Y (1999) Top management turnover in related M&A’s: an additional testof the theory of relative standing. J Manag 25(1):55–73

Macy MW, Willer R (2002) From factors to actors: computational sociology and agent-based modeling.Annu Rev Sociol 28:143–166

Mark NP (2003) Culture and competition: homophily and distancing explanations for cultural niches. AmSociol Rev 68(3):319–345

Moynihan DP, Pandey SK (2008) The ties that bind: social networks, person-organization value fit, andturnover intention. J Public Adm Res Theory 18:205–227

Nahavandi A, Malekzadeh AR (1988) Acculturation in mergers and acquisitions. Acad Manag Rev13(1):79–90

O’Reilly CA, Chatman JA (1996) Culture as social control: corporations, cults, and commitment. ResOrgan Behav 18:157–200

O’Reilly CA, Chatman J, Caldwell DF (1991) People and organizational culture: a profile comparisonapproach to assessing person-organization fit. Acad Manag J 34(3):487–516

Rafferty AE, Restubog SLD (2009) The impact of change process and context on change reactions andturnover. J Manag 36(5):1309–1338

Ramaswamy K (1997) The performance impact of strategic similarity in horizontal mergers: evidencefrom the US banking industry. Acad Manag J 40(3):697–715

Rogers EM (2003) Diffusion of innovations. Free Press, New YorkSchein EH (1985) Organizational culture and leadership. Jossey-Bass, San FranciscoSchweiger DM, DeNisi AS (1991) Communication with employees following a merger: a longitudinal

field experiment. Acad Manag J 34(1):110–135Stahl GK, Voigt A (2008) Do cultural differences matter in mergers and acquisitions? A tentative model

and examination. Organ Sci 19(1):160–176Van Mannen J, Schein EH (1979) Toward a theory of organizational socialization. Res Organ Behav 1:209–

264Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University

Press, Cambridge


Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(4):440–442Weber RA, Camerer CF (2003) Cultural conflict and merger failure: an experimental approach. Manag Sci

49(4):400–415Weber Y, Schweiger DM (1992) Top management culture conflict in mergers and acquisitions: a lesson

from anthropology. Int J Confl Manag 3:1–17Zollo M, Singh H (2004) Deliberate learning in corporate acquisitions: post-acquisition strategies and

integration capability in US bank mergers. Strateg Manag J 25:1233–1256

Junichi Yamanoi is an Associate Professor in the Faculty of Policy Studies at Chuo University. He earnedhis PhD in Business Administration from the University of Connecticut and MA and BA from Waseda Uni-versity. His research interests include competitive dynamics, multipoint competition, and social networktheory.

Hiroki Sayama is an Associate Professor in the Departments of Bioengineering & Systems Science andIndustrial Engineering, and the Director of the Collective Dynamics of Complex Systems (CoCo) ResearchGroup at Binghamton University, State University of New York. He received his BSc, MSc and DSc inInformation Science, all from the University of Tokyo. He did his post-doc work at the New EnglandComplex Systems Institute (NECSI), Cambridge, Massachusetts. He is currently an affiliate of NECSI. Hisresearch interests include complex dynamical networks, human and social dynamics, collective behaviors,artificial life/chemistry, and interactive systems, among others.

post-merger cultural integration from a social network perspective: a computational modeling...

Documents