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Come together? The organizational dynamics of post-merger cultural integration
Simulation Modelling Practice and Theory 10 (2002) 349–368 www.elsevier.com/locate/simpat
Come together? The organizational dynamics of post-merger cultural integration Glenn R. Carroll b
a,*
, J. Richard Harrison
b
a Stanford University, 518 Memorial Way, Stanford CA 94305, USA University of Texas at Dallas, PO Box 8311327, Richardson TX 75083, USA
Received 1 January 2002; received in revised form 11 June 2002; accepted 11 June 2002
Abstract Cultural integration of two organizations following an acquisition depends on the compatibility of the contents of their respective cultures as well as the demographic flows of persons into and out of the new entity. Conducting simulations using an established formal demographic model of the enculturation process, we find that negative growth promotes cultural integration while positive growth impedes it, and that cultural integration proceeds more rapidly when the acquiring firm is large relative to the acquired firm. We also find that cultural recovery for merged firms experiencing either positive or negative growth is slower than for firms with zero growth. Ó 2002 Elsevier Science B.V. All rights reserved. Keywords: Organisations; Organisational culture; Demography; Merger; Cultural integration
1. Introduction Corporate mergers and acquisitions often occur because of the strategic or financial imperatives of one or both partner firms. For example, the recently announced merger attempt between computer makers Hewlett-Packard and Compaq is claimed to be ‘‘creating an absolute powerhouse in the market house’’ [16]. Similarly, when Daimler-Benz and Chrysler merged in 1999 the CEOÕs of both companies cited as motivation the increasing role of scale economies in a globalizing industry as well as complementarities between the companiesÕ products [14]. And, the merger of
*
Corresponding author. E-mail address: [email protected] (G.R. Carroll).
1569-190X/02/$ - see front matter Ó 2002 Elsevier Science B.V. All rights reserved. PII: S 1 5 6 9 - 1 9 0 X ( 0 2 ) 0 0 0 9 0 - 4
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hospitals between prestigious Stanford University and the University of California at San Francisco was supposed to ‘‘not only offer superior patient care but would provide financial stability that would ensure the medical centersÕ survival in a brutally competitive health care industry. Together, Stanford and UCSF would bargain more aggressively with insurance companies and suppliers, boosting revenues and saving money’’ [5]. Indeed, a recent academic review suggests enhanced efficiency is the most common general motivation for mergers and acquisitions [9]. Despite these market-based motivations, the success or failure of a merged corporation often depends on its ability to integrate effectively the two previously independent organizations and to operate as a coherent entity. Although integration may be thwarted by any number of factors, extant cultural differences between the organizations involved in a merger often prove problematic. For instance, the business press opined about CompaqÕs earlier merger with Digital Equipment Corporation that: ‘‘The two cultures clashed as CompaqÕs high-volume, high-speed approach ran into DigitalÕs low-volume system in which sales of big computers took time. . . The company was so distracted by the merger that it lost its long standing crown as the largest seller of PCs to rival Dell’’ [12]. Likewise, the dismal performance of Daimler-Chrysler has been attributed to the difficulties of combining a ‘‘hierarchical bureaucratic’’ culture (Daimler-Benz) with a ‘‘free wheeling creative’’ culture (Chrysler) [14]. And, after losing $176 million in 28 months, the Stanford-UCSF hospital merger was reversed because ‘‘the two hospitals had radically different cultures, which made the merger impossible in the end’’ [13]. Moreover, the increasing prevalence of cross-border mergers and acquisitions suggests that post-merger cultural conflict may be on the rise as national cultural differences may reinforce organizational differences. Certainly, the disappointing performance of recent high-profile cross-border mergers such as Daimler-Benz with Chrysler suggests this possibility. Already some are predicting a similar fate for the European units involved in the proposed HP-Compaq merger [4]. Analyses such as these, which assess the compatibility or fit between two cultures to explain outcomes, focus on what we call the content of culture. Cultural content may, in fact, be behind a broader set of reasons often offered for post-acquisition success and failure. For instance, a recent detailed economic analysis of two large mergers concludes that ‘‘in both cases, post-acquisition difficulties resulted because managers of the acquiring company did not understand deeply the target company. (For example), despite the fact that (the acquirer) Cooper Industries had operations in (the target) Cameron Iron WorkÕs industry (the petroleum equipment business), CooperÕs management did not understand that its expertise in manufacturing technology and internal control would not translate into success for Cameron. As Cameron managers described it, Cooper did not understand that ÔCameronÕ was not a manufacturing business. It was a service business with a manufacturing component’’ [10]. An objective post-hoc analysis of this merger might regard its difficulties as arising from noncomplementary lines of business, which is true superficially, but ignores the fact that the lack of understanding something so basic likely reflects an impenetrable or deeply ingrained cultural difference.
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Clearly, content-based cultural assessment is the dominant way that many observers, including social scientists, analyze the cultural aspect of mergers and acquisitions. In fact, this way of thinking is so engrained that some progressive firms such as Cisco Systems now use such analysis proactively: they systematically analyze cultural fit in assessing potential merger targets. Although it is not widely recognized, the post-merger integration of two cultures is a function of organizational demography as well as cultural content. To see the potential impact of demography, imagine a hypothetical merger between two organizations with dissimilar and highly incompatible cultures. In the first merger, all members of both organizations possess lifetime job security; they cannot be terminated and they are in general expected to remain in their positions until retirement. In the second merger, organizational membership is fluid: members come and go with high frequency. In general, we think that cultural integration should be easier to achieve in the second merger. Why? In our view, the cultural differences in the first merger will likely persist because they can be overcome only by transforming previously enculturated individuals. By contrast, the second merger can wash out extreme cultural differences through the departure of alienated individuals and their replacement with fresh ones more susceptible to enculturation. Underlying this conjecture is the presumption that it is easier to socialize new organizational members than it is to resocialize existing members. Nonetheless, it should be clear from even this stylized scenario that post-merger cultural integration involves personnel demographic flows and associated socialization processes as much as it does the contents of the cultures being merged. This article addresses the issue of cultural integration following mergers in an abstract general way. It does so by focusing on demographic factors related to integration and examining their implications for content-based factors such as cultural compatibility or fit. The analysis is conducted with a computer simulation, using a well-established formal mathematical model of cultural change in organizations over time [1,6–8]. This model explicitly links the flows of individuals into and out of an organization with the intensity and heterogeneity of the culture found within an organization. Previous investigation of the model, via computer simulation techniques, shows that the model generates empirical predictions broadly consistent with many known patterns of culture across different structural forms of organization. The model has also been used to explore linkages between organizational culture and demography and organizational outcomes such as performance and survival. The model shows high portability, across both types of organizations and cultural research problems.
2. A dynamic model of organizational culture In using the model, we assume initially that an individualÕs propensity to embrace the values and norms of a particular organizational culture can be meaningfully represented by a single measure, an enculturation score, indicating the degree to which an employee fits managementÕs cultural ideal. This embodies a very specific
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conception of organizational culture, focusing on cultural fitness rather than on the constellation of underlying cultural content dimensions that determines fitness. In a sense, we are assuming that an organizationÕs management possesses a cultural utility function that it uses to assign a cultural fitness score to each individual in the organization. The cultural transmission model consists of three components and a set of embedded parameters. For each time period, the components: (1) determine the persons hired to the organization; (2) specify the change in the enculturation level of each person within the organization; and (3) identify the persons departing from the organization. The parameters of the model control the growth rate of the organization, the recruitment rate to vacancies, the selectiveness of the recruitment process with respect to cultural criteria, the intensity of socialization, the natural decay rate of socialization, and the turnover rate. 2.1. Hiring component The number of persons hired in a time period is denoted by NH ðtÞ, and the number of vacancies by NV ðtÞ. Using these variables, organizational hiring is then modeled as NH ðtÞ ¼
NVX ðt1Þ
Hj ðtÞ;
ð1Þ
j¼1
where Hj ðtÞ ¼
1 0
if position j is filled in period t; otherwise;
and NV ðt 1Þ ¼ fNV ðt 2Þ NH ðt 1Þ þ NDðt 1Þ þ GR½N ðt 1Þg;
ð2Þ
and GR is the organizational growth rate (associated with stochastic changes in the number of positions), NDðtÞ represents the number of persons departing the organization at time t, N ðtÞ is the number of members of the organization at time t, and RR is a stochastic rate of recruitment to vacant positions used to find values of Hj ðtÞ. Individual hiring is conceived as drawing individuals from a pool of candidates. The pool has a distribution of values on the desired characteristics, and the distribution is known for the pool. Because cultural criteria are subtle and not readily observable in many instances, the choice of any particular individual is somewhat random. In fact, the characteristics of the pool are determined by the selectiveness of the hiring policies of the organization [2]. The candidate pool is more or less centered on the desired characteristics, and more or less noise is tolerated in the information used in the selection process. We denote the selectivity of the recruitment pool SEL by its mean enculturation level. Hiring on the basis of cultural criteria is simulated by randomly drawing values of Ci from parameterized distributions. The parameters of the distribution are defined
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by the hiring policies of the organization. At each time period, therefore, NH ðtÞ persons with a variety of fits with the management-desired culture of the organization are hired. 2.2. Socialization component Any individualÕs change over time with respect to socialization is a combination of the pulls from three sources: management, peers (fellow employees), and decay. The three forces are allowed to vary in their relative strengths. Expected change in socialization is modeled as a function of the individualÕs distance from a target for each source (one, the maximum value of Ci , for management; the group enculturation mean for peers; and zero, the minimum of Ci , for decay) multiplied by a parameter. The model also introduces an error term to allow for noise in the process. Harrison and CarrollÕs [6] model posits the function for socialization change intensity as SMR½1 Ci ðt 1Þ þ SNR C Ci ðt 1Þ þ SDR½0 Ci ðt 1Þ þ e; SIi ðtÞ ¼ SMR þ SNR þ SDR ð3Þ where e is an error term and SMR, SNR, and SDR are parameters representing the pulls toward ideal socialization (from management), mean socialization level (from peer pressure), and zero socialization (from decay), respectively. In effect, the denominator normalizes the function SIi ðtÞ to ensure that an individualÕs Ci score remains between 0 and 1. The error e is constructed to be normally distributed with mean zero and adjustable variance. Individuals can be more or less susceptible to socialization, whatever its source. Susceptibility is greatest at the time of entry into the organization and then declines with tenure [11]. Newcomers are unfamiliar with an organizationÕs culture and, consequently, are more open to social influence as they adapt to their new environment. Over time, as they adjust their cultural orientations in response to organizational socialization, they gain familiarity with the culture and become increasingly resistant to further change. We simulate susceptibility to socialization forces with the following equation: SUi ðtÞ ¼ TA1 þ exp f TA2 TA3½ui ðt 1Þg;
ð4Þ
where ui is individual iÕs tenure with the organization. With the parameter values used here (TA1 ¼ 0:02; TA2 ¼ 0:60; TA3 ¼ 0:30 ), susceptibility begins with a value less than unity and declines exponentially with tenure toward a nonzero asymptote. It is important for the value of the function to remain between 0 and 1 because it will be used below as a multiplier. In this specification, TA1 is associated with the asymptotic level of susceptibility, TA2 with the level of susceptibility at entry (tenure equals zero), and TA3 with the speed of the decline in susceptibility with increasing tenure from the entry level to the asymptotic level.
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The socialization function is completed by taking an individualÕs prior enculturation level Ci ðt 1Þ and adding to it the effect of socialization-change intensity SI i ðtÞ multiplied by its influence SUi ðtÞ. That is, Ci ðtÞ ¼ Ci ðt 1Þ þ ½SUi ðtÞ½SIi ðtÞ:
ð5Þ
Distributional measures of the Ci scores characterize the organizational culture at any particular point in time. In particular, dispersion measures such as the standard deviation of enculturation across all individuals indicate cultural heterogeneity. 2.3. Turnover component Turnover might be connected to organizational culture for at least two reasons. First, individuals who do not embrace the culture might be motivated to leave voluntarily [2]. Second, those who do not fit in, or those who fail to change, might be forced to leave involuntarily. In both cases, the issue may be thought of as one of alienation [15], related to the distance between an individualÕs embodiment of the culture, Ci , and the management ideal of one. We formalize the alienation process 3 with the term AR½1 Ci ðt 1Þ , where AR is a parameter allowing greater or less sensitivity to alienation as a cause of turnover. The value of this expression increases rapidly as Ci approaches zero, but in general the effect of alienation on turnover in the model is much smaller than the effect of other (noncultural) factors. Allowing all other reasons for leaving an organization [3] to be captured in an adjustable base-turnover factor (associated with the parameter ER), the number of persons departing the organization in time period t is then given by NDðtÞ ¼
NX ðt1Þ
Di ðtÞ;
ð6Þ
i¼1
where Di ðtÞ ¼
1 0
individual i leaves in period t; otherwise:
The stochastic rate of departure for individual i, used to find Di ðtÞ, is 3
RDi ðtÞ ¼ ER þ AR½1 Ci ðt 1Þ ;
ð7Þ
where both ER and AR are parameters of the rate.
3. Research design In using the model to study cultural integration, we begin by considering two separate organizations with different cultural ideals. We designate one of the organizations as the acquiring firm––its cultural ideal dominates after the merger––and the other the acquired, or target, organization. We posit an incompatible relationship between the cultural ideals of the two organizations. So, following the merger, we
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reset the enculturation scores of all individuals in the target organization to reflect this: they are assigned new scores in random fashion distributed around a pre-set mean and standard deviation. We then treat the combined post-merger organization as a single organization and observe how its culture evolves over time. We pay special attention to the process of cultural integration in the first year (12 elapsed months) following the merger. In particular, we examine two outcomes: (1) the extent of cultural integration, indicated by the standard deviation of enculturation for the merged organization; and (2) the extent of cultural ‘‘recovery’’ for the merger organization, indicated by the distance of the mean enculturation level of the postmerger organization from the cultural mean of the acquiring organization at the time of the merger. 3.1. Experimental design We wanted to simulate two merging organizations with different cultures. Since the model we use specifies cultural fit according to some arbitrary organizationally defined scale, comparing across organizations requires positing a transformation rule between scales. Rather than tackle this issue generally, we focused on the condition of greatest substantive interest––high difference in content (a sort of ‘‘inverse’’ relationship between scales where a high score on one implies a low score on the other). We designed the experiments to allow for the content differences of the two organizations to be at the same fairly high level for all experiments; thus, content differences are high but controlled. Simulation experiments started by allowing each pre-merger organization to develop strong cultures independently for 120 simulation periods (ten simulation years), using hiring and management enculturation strategies to achieve these outcomes. In these runs, the mean enculturation levels varied from 0.79 to 0.90 after ten simulation years, depending on cultural strategy. However, because enculturation is assumed to be based on different criteria in different organizations, the values are not comparable across organizations. Thus, we used a rule to reassign all values of one organization to the scale of the other. Our goal was to make the culture of the target unit ‘‘incompatible’’ or very different from the acquiring organization (in terms of content). So, we reset the mean enculturation level of the target unit at 0.2 (with random assignment of enculturation scores to individuals based on a normal distribution); the tenures of the individuals in the target organization were preserved. We then combined the two organizations and let the new ‘‘combined’’ organization follow the previously set hiring practices, management enculturation practices, strength of alienation, and turnover rates of the acquiring organization before the merger. (These variations are best viewed as background conditions.) Of particular substantive interest here are variations related to the growth rates of the two organizations and their relative sizes before the merger. For these experiments, we attempt to identify the conditions under which the post-merger organization quickly develops a strong culture, or fails to do so in a reasonable time interval, and the conditions under which the post-merger organization moves more or less quickly toward the pre-merger cultural mean of the acquiring organization.
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Background conditions of the acquiring organization. The simulation experiments vary two characteristics of the acquiring organization, which also apply to the combined organization. First, the hiring and management enculturation strategies are varied. Three sets of conditions are used to produce a ‘‘strong’’ culture: (1) low cultural selectivity in hiring and strong management socialization; (2) moderate hiring selectivity and moderate management socialization; and (3) strong hiring selectivity and weak management socialization. Peer socialization is complementary to management socialization: when management socialization is strong, peer socialization is relatively weak, and vice versa. (Other possible combinations, such as strong hiring selectivity and strong management socialization, will produce even stronger cultures.) We term this combined set of conditions ‘‘cultural strategy,’’ and code them in analysis of the simulation data as 0, 1, or 2, corresponding to the above conditions (1), (2), and (3), respectively. We also vary the alienation level by setting the AR parameter to either 0.15 or 0.6 for weak or strong alienation effects, coded in the data analysis as 0 or 1. Conditions of interest: Growth rates. Before the merger, the growth rate for both organizations is set to zero. After the merger, we simulate three conditions: (1) negative growth, a combined stochastic growth rate of )0.04 per simulation period (month); (2) no growth, a combined growth rate of zero; and (3) positive growth, a combined stochastic growth rate of 0.04 per period. Conditions of interest: Relative sizes. Finally, we vary the relative sizes of the two organizations at the time of merger. Each organization is simulated with merger sizes of 50, 200, or 800, generating nine possible size combinations. In the analysis, we create the size ratio by dividing the merger size of the acquiring organization by the merger size of the acquired organization (target), so the size ratio varies from 0.0625 to 16. We also analyzed the simulation output data using dummy variables for each size setting and using logs of size and their interactions; the findings did not differ substantively from the analysis using the size ratio, so we report only the size ratio findings. All of these settings are consistent with previous research using our model. The settings for the variations used in the simulation are given in Table 1. 3.2. Simulation trials Each simulation begins by running the two separate organizations for 120 periods with zero growth, using the Harrison and Carroll procedure [6]. Then the cultural mean for the acquired organization is reset by generating new cultural scores from a normal distribution with a mean of 0.2 and a standard deviation of 0.1 (with a minimum of zero), the two organizations are merged, and the evolution of the merged organization using the pre-merger background conditions of the acquiring organization is observed for an additional 12 periods. The simulation is run for 162 different conditions, corresponding to the combinations of three possible cultural strategy settings, two alienation settings, three growth settings, and nine size settings. Each condition is simulated 10 times and the results are averaged. The total number of observations of 162 conditions for 12 periods is 1944.
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Table 1 Settings used in simulation experiments Variations Cultural strategies: Weak management socialization Cultural strategy variable ¼ 0
Moderate management socialization Cultural strategy variable ¼ 1
Strong management socialization Cultural strategy variable ¼ 2
Parameter/value setting SEL ¼ 0:8 RR ¼ 0:223 SMR ¼ 0:18 SNR ¼ 0:8 SEL ¼ 0:5 RR ¼ 0:693 SMR ¼ 0:49 SNR ¼ 0:49 SEL ¼ 0:2 RR ¼ 1:61 SMR ¼ 0:8 SNR ¼ 0:18
Alienation: Weak alienation, alienation variable ¼ 0 Strong alienation, alienation variable ¼ 1
AR ¼ 0:15 AR ¼ 0:6
Growth: Negative growth No growth Positive growth
GR ¼ 0:04 GR ¼ 0 GR ¼ 0:04
Size: Starting acquirer size Starting acquiree (target) size
N ðAQRÞ ¼ 50, 200, or 800 N ðAQEÞ ¼ 50, 200, or 800
Constant settings
Parameter
Standard deviation of hiring pool Socialization decay Standard deviation of error term in socialization-change intensity Socialization susceptibility
rðSELÞ ¼ 0:15 SDR ¼ 0:02 rðeÞ ¼ 0:1
Base turnover rate
TA1 ¼ 0:02 TA2 ¼ 0:6 TA3 ¼ 0:3 ER ¼ 0:01
3.3. Outcomes The first outcome we examine is cultural heterogeneity, measured as the standard deviation of the enculturation levels of the individuals in the organization. Given the general structure of the simulation experiments, the lower the cultural heterogeneity, the higher the level of cultural integration of the merged organization. The second outcome of interest is cultural distance, the difference between the enculturation mean of the acquiring organization at the time of the merger and the enculturation mean of the post-merger organization; since the cultural distance is in
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Fig. 1. Cultural heterogeneity over time following a merger.
general negative (the enculturation mean of the merged organization is usually below that of the acquiring organization at the time of merger), the higher the cultural difference, the greater the cultural recovery of the merged organization. The values of these two outcome variables, aggregated across all simulation conditions for the 12-month period following the merger, are shown in Figs. 1 and 2. Since both variables demonstrate a logarithmic shape with respect to time, we use the log of time as a variable in the subsequent regression analyses.
4. Findings Table 2 shows the descriptive statistics and correlations for the simulation output. The starting conditions for the two dependent variables, heterogeneity at merger and distance at merger, corresponding to the dependent variables cultural heterogeneity and cultural distance, respectively, are included in Table 2 since we use them as control variables in the regressions. Each represents the variableÕs value at the time of merger for the associated simulation condition. Cultural strategy, coded 1, 2, or 3 as mentioned above, is treated as an interval variable; analyses using dummy variables for the three cultural strategy conditions yielded essentially identical results, so we chose to report the interval findings for simplicity of interpretation. The many correlations of 0 and )0.5 are ‘‘structural’’ in that they are the consequence of the design structure of the simulation experiments. We used linear regression analysis to examine the effects of simulation conditions on the outcomes of interest. In the regressions, we included variables for all the
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Fig. 2. Cultural distance over time following a merger.
simulation condition variations and control variables for the starting conditions. Since we used dummy variables for the growth conditions, we excluded the dummy for zero growth; the effects of negative growth and positive growth are the effects of these conditions relative to zero growth. We also tested all possible interactions between simulation conditions, but none substantially improved the models, so these results are not reported. Apparently, the different simulation conditions exert effects on cultural change following mergers that are largely independent of one another. The regressions are estimated using simulation output data for the first twelve months following the merger. The time period variable (log month) controls for the time trend toward cultural integration and cultural recovery; since it is uncorrelated with any of the simulation condition variables, it essentially removes the time path effects and permits a focus on the effects of the simulation conditions on cultural change in the merger organization in the first year. The first regression addresses cultural integration, measured by cultural heterogeneity. This measure decreases over time, since the two firms have ‘‘incompatible’’ cultures at the time of the merger (see Fig. 1). Lower cultural heterogeneity corresponds to stronger cultural integration; variables with negative estimates promote cultural integration, and those with positive estimates hinder it. The regression estimates are given in Table 3. As Table 3 shows, cultural integration increases over time following the merger––the log month effect is negative. Cultural integration is promoted by alienation, less growth, and a larger size ratio. Conceptually, alienation promotes integration by
360
Variable
Mean
1. 2. 3. 4. 5. 6. 7. 8. 9.
Cultural heterogeneity 0.200 Cultural distance )0.192 Log month 1.666 Cultural strategy 1.000 Alienation 0.500 Negative growth 0.333 Zero growth 0.333 Positive growth 0.333 Size ratio (acquirer/ 3.063 target) 10. Heterogeneity at 0.303 merger 11. Distance at merger 0.327 1
1. 2. 3. 4. 5. 6. 7. 8. 9.
Cultural heterogeneity Cultural distance Log month Cultural strategy Alienation Negative growth Zero growth Positive growth Size ratio (acquirer/ target) 10. Heterogeneity at merger 11. Distance at merger a
N ¼ 1944.
Standard deviation
Minimum
Maximum
0.064 0.141 0.724 0.817 0.500 0.472 0.472 0.472 4.794
0.039 )0.638 0.000 0.000 0.000 0.000 0.000 0.000 0.063
0.356 0.020 2.485 2.000 1.000 1.000 1.000 1.000 16.000
0.067
0.140
0.368
0.190
0.662
0.034
2
3
4
5
6
7
8
9
10
)0.65 )0.55 0.35 )0.30 )0.01 )0.02 0.03 )0.49
0.39 )0.16 0.25 )0.01 0.03 )0.01 0.59
0.00 0.00 0.00 )0.00 )0.00 0.00
0.00 0.00 )0.00 )0.00 0.00
)0.00 )0.00 )0.00 0.00
)0.50 )0.50 0.00
)0.50 0.00
0.00
0.28
)0.04
0.00
0.19
0.06
0.63
)0.32
)0.32
)0.34
)0.36
0.80
0.00
)0.10
)0.04
0.00
)0.00
0.00
0.74
)0.05
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Table 2 Descriptive statistics and correlationsa
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Table 3 Regression analysis for cultural heterogeneitya Independent variable Constant Log month Cultural strategy Alienation Negative growthc Positive growthc Size ratio (acquirer/target) Cultural heterogeneity at merger Adjusted R2 ¼ 0:770
Estimateb
0.251 )0.492
0.025
)0.039
)0.013
0.004
)0.006
0.152
Standard error 0.005 0.001 0.001 0.001 0.002 0.002 0.000 0.016
a
N ¼ 1944. p < 0:05,
p < 0:01,
p < 0:001. c Omitted growth category is zero growth. b
inducing less culturally ‘‘fit’’ employees to leave the organization, reducing the cultural variance. Negative growth has a similar effect; less fit employees, who are more likely to exit the organization because of alienation, are not replaced during organizational decline. On the other hand, positive growth increases cultural heterogeneity in the short run by adding less socialized employees to the organization. The size ratio effect shows that cultural integration proceeds more rapidly when the acquiring firm is large relative to the acquired firm–the firm has a lower proportion of people to assimilate into the culture. Finally, the cultural strategy effect indicates that a strategy of greater selectivity in hiring and less attention to management socialization efforts is more effective for cultural integration than a strategy placing less emphasis on hiring selectivity and more on socialization programs by management. The control variable shows an obvious effect: the less integrated the cultures are at the time of the merger, the less integrated they will be in subsequent time periods. To understand further the relationships in the simulation data, we also calculated for the conditions of interest what might be called ‘‘controlled’’ predicted effects from the regression estimates. To construct these effects, we first calculated the predicted values for each observation in the output file, using the regression estimates in Table 3. We then separated the observations by each condition of interest (i.e., zero growth, negative growth, and positive growth) and by month. We then computed the controlled predicted values by computing the mean predicted value by month for each condition of interest. These values allow us to see the effect of the condition of interest by month when combined with the effects of other variables set at their mean values for all observations within the condition. Fig. 3 graphs the controlled predicted effects of growth on cultural heterogeneity. Cultural heterogeneity is greatest for positive growth, but is almost identical for zero and negative growth in the aggregate. Fig. 4 presents the controlled predicted effects of the size ratio on cultural heterogeneity, using the same procedure. The unmarked bottom line in the figure is for the
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Fig. 3. Cultural heterogeneity over time by growth condition.
Fig. 4. Cultural heterogeneity over time by size ratio (acquirer/target).
largest size ratio, 16. Notice that the path for the smallest size ratio, 0.0625, lies below those for size ratios of 0.25 and 1, and then the paths become successively lower as the size ratio increases further. What produces this anomaly for the smallest size ratio?
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Fig. 5. Cultural heterogeneity in month 12 by size ratio and growth condition.
A clue lies in the effects of growth for the smallest size ratio; for this condition, cultural heterogeneity is actually substantially lower for zero and positive growth relative to negative growth, in contrast to the pattern in Fig. 3. To show this effect, we plotted the growth conditions as a function of the size ratio for month 12; the result is given in Fig. 5. The mechanism underlying this effect is not now understood, and will be a subject of further research. Table 4 shows the regression analysis for the cultural difference between the firmÕs current enculturation mean and the enculturation mean for the acquiring firm at the time of the merger. By design, the enculturation mean for the target firm is below
Table 4 Regression analysis for cultural distancea Independent variable
Estimateb
Standard error
Constant Log month Cultural strategy Alienation Negative growthc Positive growthc Size ratio (acquirer/target) Cultural distance at time of merger Adjusted R2 ¼ 0:881
)0.138 0.076
)0.138
0.079
)0.008
)0.008
)0.001
0.611
0.005 0.002 0.001 0.002 0.003 0.003 0.000 0.009
a
N ¼ 1944. p < 0:05,
p < 0:01,
p < 0:001. c Omitted growth category is zero growth. b
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that of the acquirer, so this measure starts out negative and moves upward over time (see Fig. 2). Consequently, positive estimates in Table 4 signify factors that facilitate cultural recovery from the merger. The findings in Table 4 are consistent with those of Table 3 in many respects. Cultural strategy, alienation level, and time, which contribute to cultural integration, also contribute to cultural recovery. However, both the size ratio and growth have different effects on cultural distance relative to cultural heterogeneity. The growth effect on cultural distance in Table 4 is nonlinear. Cultural recovery for merged firms experiencing either positive or negative growth is slower than for firms with zero growth. Fig. 6 shows graphically the controlled predicted effects of growth on cultural distance over time. Zero growth firms again appear to recover fastest. Growing firms are likely to hire more new employees who have poorer fits to the firmÕs culture (in the aggregate across all cultural strategies), while declining firms are likely to lose culturally fit employees as well as some who are less fit. From the estimates in Table 4, larger size ratios apparently inhibit cultural recovery (the negative coefficient). However, this effect appears to be a consequence of the simulation design and the way the cultural distance measure is constructed. When an acquiring firm is large relative to its target, the post-merger firm experiences only a small decrease in its enculturation mean relative to the pre-merger mean of the acquirer, so it has less of a cultural deficit to make up; but when an acquiring firm is small relative to its target, the merged firm experiences a larger decrease in its enculturation mean and has a much greater opportunity for cultural improvement. The estimated effect of the control variable (cultural distance at time of merger) rein-
Fig. 6. Cultural distance over time by growth condition.
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Fig. 7. Cultural distance over time by size ratio (acquirer/target).
forces this interpretation; a firm with a ‘‘larger’’ (more negative) cultural distance at the time of the merger exhibits greater recovery, although it still faces a cultural handicap over time compared to a firm that experiences less of a decrease in its enculturation mean when it acquires a target firm. Indeed, one way to think about this effect is that (given the simulation design) the size ratio contains a strong ‘‘built-in’’ counter-effect from cultural distance at time of merger. This can be seen from the high correlation of 0.59 between the two variables (Table 1); it can also be seen from looking at the means of the control variable by size ratio: when the size ratio is 0.0625, the mean cultural distance at time of merger is 0.615; when the size ratio is 0.25, the mean is )0.523; when it is 1.0, the mean is )0.328; when it is 4.0, the mean is )0.131; and when it is 16.0, the mean is )0.039. In fact, the counter effect is so strong that the controlled predicted effects of the size ratio on cultural distance appear inverted when compared to the estimate in Table 4. As Fig. 7 shows, once these and other effects are taken into account, the smallest size ratio is associated with the lowest cultural distance values and the remainder of the size ratios are monotonically ordered in their effects. (The unmarked top line in the figure is for the largest size ratio, 16.)
5. Discussion It is widely recognized that achieving cultural integration in a newly merged organization is difficult and sometimes even impossible. Prior cultural differences between the merged entities often persist for months and years, possibly generating conflict and rendering the organization less effective. Indeed, the widespread inability of
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mergers to accomplish their goals frequently gets blamed on the failure to integrate the cultures of the merging organizations. Cultural integration following a merger is almost always analyzed in terms of the contents of the organizational cultures involved. The basic question raised is, ‘‘Are the cultures compatible or consistent?’’ Analyses then focus on how the various elements of the cultures might be reconciled, compromised or emboldened in an organizationÕs operation. Both popular and scholarly assessments tend to use this mode of reasoning. While clearly insightful, the content-based approach to studying cultural integration following a merger also has limitations. First, these analyses are often conducted at a high level of abstraction and it is difficult to know how much of an organizationÕs culture is captured by simple characterizations such as ‘‘hierarchical’’ or ‘‘free-wheeling.’’ Second, there is no developed theory guiding the assessments of cultural elements as compatible or not; the assessments tend to be intuitive and ad hoc (or post-hoc) in most instances. Third, since many cultural phenomena are tacit in nature, judging how people will react to their alteration is a daunting task for social science. It seems to us that such reactions likely depend on the particular phenomena involved as well as the process by which their alteration is attempted. An alternative yet still general way to assess cultural integration following a merger is to examine the demographic systems underlying the organizational cultures. When the demographics of two merger partners show fluid movement of individuals into and out of the organizations, then it seems that cultural integration might be achieved more quickly than if the two partners show stagnant demographics with little movement in and out. The primary assumption behind such a conjecture is that it is easier to recruit and socialize ‘‘new’’ individuals to the cultural ideals of the merged organization than it is to re-socialize and re-train incumbents already inculcated in the ways of one or the other partner. Of course, the demographic approach and the content-based approach complement each other. However, one has received all the attention and the other has been neglected. So, we focused here on the neglected demographic approach. Using a formal model of organizational culture, we set up the research problem by assuming that the content differences among merging organizations are very strong. We then investigated how quickly cultural integration might be achieved under a variety of commonly seen demographic conditions. We concentrated on variations in organizational growth rate and in the size ratio of the acquiring organization relative to the acquisition target, conditions readily observable to a number of real world cases. We used simulation methods to control other factors in the integration process. In terms of outcomes, our simulation experiments looked at both cultural heterogeneity, measured as the standard deviation of the enculturation levels of the individuals in the merged organization, and cultural distance, defined as the difference between the enculturation mean of the acquiring organization at the time of the merger and the enculturation mean of the post-merger organization. The findings show that post-merger cultural heterogeneity decreases more quickly in organizations with no growth or negative growth (decline) as opposed to those
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with positive growth. We also find that mergers involving equal sized partners experience higher cultural heterogeneity than do those with unequal sized partners. Perhaps surprisingly, this finding holds true even for cases where the target is larger than the acquiring firm. For cultural distance, the simulations show that post-merger firms experiencing either positive or negative growth recover more slowly than do firms with zero growth. Larger size ratios (of acquirer/target) apparently inhibit cultural recovery, but this effect appears to be a methodological artifact of our procedures. We regard these general findings as provocative and worthy of additional investigation. As with any simulation, the findings are not themselves empirical discoveries but instead disciplined theoretical conjectures. Given the complexity of organizational demographic systems, we think such investigations are warranted and potentially very insightful. However, it should also be obvious that our efforts here represent only a first step towards understanding how demographic processes affect post-merger cultural integration and recovery.
References [1] G.R. Carroll, J.R. Harrison, Organizational demography and culture: insights from a formal model, Administrative Science Quarterly 43 (1998) 637–667. [2] J. Chatman, Matching people and organizations: selection and socialization in public accounting firms, Administrative Science Quarterly 36 (1991) 459–484. [3] J. Chatman, K. Jehn, Assessing the relationship between industry characteristics and organizational culture: how different can you be? Academy of Management Journal 37 (1994) 522–553. [4] K.J. Delaney, D. Woodruff, In Europe H-P and Compaq Face Tougher Merger Task, Wall Street Journal, September 6 (2001). [5] B. Feder, Ill-Fated Merger Costs California UniversitiesÕ Medical Care Centers $176 Million, San Jose Mercury News, December 14 (2000). [6] J.R. Harrison, G.R. Carroll, Keeping the faith: a model of cultural transmission in formal organizations, Administrative Science Quarterly 36 (1991) 552–582. [7] J.R. Harrison, G.R. Carroll, Modeling culture in organizations: formulation and extension to ecological issues, in: A. Lomi, E. Larsen (Eds.), Dynamics of Organizations: Computational Modeling and Organization Theories, AAAI Press/MIT Press, Menlo Park, CA, 2001, pp. 37–62. [8] J.R. Harrison, G.R. Carroll, Modeling organizational culture: demography and influence networks, in: C.L. Cooper, S. Cartwright, P.C. Earley (Eds.), International Handbook of Organizational Culture and Climate, Wiley, Chichester, UK, 2001, pp. 185–216. [9] B. Holmstrom, S.N. Kaplan, Corporate Governance and Merger Activity in the U.S.: Making Sense of the 80s and 90s, University of Chicago Graduate School of Business, Unpublished ms. [10] S.N. Kaplan, M.L. Mitchell, K.H. Wruck, A Clinical Exploration of Value Creation and Destruction in Acquisitions: Organizational Design, Incentives and Internal Capital Markets, University of Chicago Graduate School of Business, Unpublished ms. [11] M.R. Louis, Surprise and sense making: what newcomers experience in entering unfamiliar organizational settings, Administrative Science Quarterly 25 (1980) 226–251. [12] G. McWilliams, Computer Megamerger: Will Bigger be Better? Wall Street Journal, September 5 (2001). [13] A. Pyati, UCSF/Stanford: Marriage was Rough; Divorce is Expensive, San Francisco Business Times, April 21 (2000) p. 25. [14] B. Vlasic, B.A. Stertz, Taken for a Ride: How Daimler-Benz Drove off with Chrysler, William Morrow, New York, 2000.
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[15] J.P. Wanous, Organizational Entry, Addison-Wesley, Reading, MA, 1980. [16] M. Williams, H-PÕs Deal for Compaq Has Doubters as Value of Plan Falls to $20.52 Billion, Wall Street Journal, September 5 (2001).
Come together? The organizational dynamics of post-merger cultural integration Glenn R. Carroll b
a,*
, J. Richard Harrison
b
a Stanford University, 518 Memorial Way, Stanford CA 94305, USA University of Texas at Dallas, PO Box 8311327, Richardson TX 75083, USA
Received 1 January 2002; received in revised form 11 June 2002; accepted 11 June 2002
Abstract Cultural integration of two organizations following an acquisition depends on the compatibility of the contents of their respective cultures as well as the demographic flows of persons into and out of the new entity. Conducting simulations using an established formal demographic model of the enculturation process, we find that negative growth promotes cultural integration while positive growth impedes it, and that cultural integration proceeds more rapidly when the acquiring firm is large relative to the acquired firm. We also find that cultural recovery for merged firms experiencing either positive or negative growth is slower than for firms with zero growth. Ó 2002 Elsevier Science B.V. All rights reserved. Keywords: Organisations; Organisational culture; Demography; Merger; Cultural integration
1. Introduction Corporate mergers and acquisitions often occur because of the strategic or financial imperatives of one or both partner firms. For example, the recently announced merger attempt between computer makers Hewlett-Packard and Compaq is claimed to be ‘‘creating an absolute powerhouse in the market house’’ [16]. Similarly, when Daimler-Benz and Chrysler merged in 1999 the CEOÕs of both companies cited as motivation the increasing role of scale economies in a globalizing industry as well as complementarities between the companiesÕ products [14]. And, the merger of
*
Corresponding author. E-mail address: [email protected] (G.R. Carroll).
1569-190X/02/$ - see front matter Ó 2002 Elsevier Science B.V. All rights reserved. PII: S 1 5 6 9 - 1 9 0 X ( 0 2 ) 0 0 0 9 0 - 4
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hospitals between prestigious Stanford University and the University of California at San Francisco was supposed to ‘‘not only offer superior patient care but would provide financial stability that would ensure the medical centersÕ survival in a brutally competitive health care industry. Together, Stanford and UCSF would bargain more aggressively with insurance companies and suppliers, boosting revenues and saving money’’ [5]. Indeed, a recent academic review suggests enhanced efficiency is the most common general motivation for mergers and acquisitions [9]. Despite these market-based motivations, the success or failure of a merged corporation often depends on its ability to integrate effectively the two previously independent organizations and to operate as a coherent entity. Although integration may be thwarted by any number of factors, extant cultural differences between the organizations involved in a merger often prove problematic. For instance, the business press opined about CompaqÕs earlier merger with Digital Equipment Corporation that: ‘‘The two cultures clashed as CompaqÕs high-volume, high-speed approach ran into DigitalÕs low-volume system in which sales of big computers took time. . . The company was so distracted by the merger that it lost its long standing crown as the largest seller of PCs to rival Dell’’ [12]. Likewise, the dismal performance of Daimler-Chrysler has been attributed to the difficulties of combining a ‘‘hierarchical bureaucratic’’ culture (Daimler-Benz) with a ‘‘free wheeling creative’’ culture (Chrysler) [14]. And, after losing $176 million in 28 months, the Stanford-UCSF hospital merger was reversed because ‘‘the two hospitals had radically different cultures, which made the merger impossible in the end’’ [13]. Moreover, the increasing prevalence of cross-border mergers and acquisitions suggests that post-merger cultural conflict may be on the rise as national cultural differences may reinforce organizational differences. Certainly, the disappointing performance of recent high-profile cross-border mergers such as Daimler-Benz with Chrysler suggests this possibility. Already some are predicting a similar fate for the European units involved in the proposed HP-Compaq merger [4]. Analyses such as these, which assess the compatibility or fit between two cultures to explain outcomes, focus on what we call the content of culture. Cultural content may, in fact, be behind a broader set of reasons often offered for post-acquisition success and failure. For instance, a recent detailed economic analysis of two large mergers concludes that ‘‘in both cases, post-acquisition difficulties resulted because managers of the acquiring company did not understand deeply the target company. (For example), despite the fact that (the acquirer) Cooper Industries had operations in (the target) Cameron Iron WorkÕs industry (the petroleum equipment business), CooperÕs management did not understand that its expertise in manufacturing technology and internal control would not translate into success for Cameron. As Cameron managers described it, Cooper did not understand that ÔCameronÕ was not a manufacturing business. It was a service business with a manufacturing component’’ [10]. An objective post-hoc analysis of this merger might regard its difficulties as arising from noncomplementary lines of business, which is true superficially, but ignores the fact that the lack of understanding something so basic likely reflects an impenetrable or deeply ingrained cultural difference.
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Clearly, content-based cultural assessment is the dominant way that many observers, including social scientists, analyze the cultural aspect of mergers and acquisitions. In fact, this way of thinking is so engrained that some progressive firms such as Cisco Systems now use such analysis proactively: they systematically analyze cultural fit in assessing potential merger targets. Although it is not widely recognized, the post-merger integration of two cultures is a function of organizational demography as well as cultural content. To see the potential impact of demography, imagine a hypothetical merger between two organizations with dissimilar and highly incompatible cultures. In the first merger, all members of both organizations possess lifetime job security; they cannot be terminated and they are in general expected to remain in their positions until retirement. In the second merger, organizational membership is fluid: members come and go with high frequency. In general, we think that cultural integration should be easier to achieve in the second merger. Why? In our view, the cultural differences in the first merger will likely persist because they can be overcome only by transforming previously enculturated individuals. By contrast, the second merger can wash out extreme cultural differences through the departure of alienated individuals and their replacement with fresh ones more susceptible to enculturation. Underlying this conjecture is the presumption that it is easier to socialize new organizational members than it is to resocialize existing members. Nonetheless, it should be clear from even this stylized scenario that post-merger cultural integration involves personnel demographic flows and associated socialization processes as much as it does the contents of the cultures being merged. This article addresses the issue of cultural integration following mergers in an abstract general way. It does so by focusing on demographic factors related to integration and examining their implications for content-based factors such as cultural compatibility or fit. The analysis is conducted with a computer simulation, using a well-established formal mathematical model of cultural change in organizations over time [1,6–8]. This model explicitly links the flows of individuals into and out of an organization with the intensity and heterogeneity of the culture found within an organization. Previous investigation of the model, via computer simulation techniques, shows that the model generates empirical predictions broadly consistent with many known patterns of culture across different structural forms of organization. The model has also been used to explore linkages between organizational culture and demography and organizational outcomes such as performance and survival. The model shows high portability, across both types of organizations and cultural research problems.
2. A dynamic model of organizational culture In using the model, we assume initially that an individualÕs propensity to embrace the values and norms of a particular organizational culture can be meaningfully represented by a single measure, an enculturation score, indicating the degree to which an employee fits managementÕs cultural ideal. This embodies a very specific
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conception of organizational culture, focusing on cultural fitness rather than on the constellation of underlying cultural content dimensions that determines fitness. In a sense, we are assuming that an organizationÕs management possesses a cultural utility function that it uses to assign a cultural fitness score to each individual in the organization. The cultural transmission model consists of three components and a set of embedded parameters. For each time period, the components: (1) determine the persons hired to the organization; (2) specify the change in the enculturation level of each person within the organization; and (3) identify the persons departing from the organization. The parameters of the model control the growth rate of the organization, the recruitment rate to vacancies, the selectiveness of the recruitment process with respect to cultural criteria, the intensity of socialization, the natural decay rate of socialization, and the turnover rate. 2.1. Hiring component The number of persons hired in a time period is denoted by NH ðtÞ, and the number of vacancies by NV ðtÞ. Using these variables, organizational hiring is then modeled as NH ðtÞ ¼
NVX ðt1Þ
Hj ðtÞ;
ð1Þ
j¼1
where Hj ðtÞ ¼
1 0
if position j is filled in period t; otherwise;
and NV ðt 1Þ ¼ fNV ðt 2Þ NH ðt 1Þ þ NDðt 1Þ þ GR½N ðt 1Þg;
ð2Þ
and GR is the organizational growth rate (associated with stochastic changes in the number of positions), NDðtÞ represents the number of persons departing the organization at time t, N ðtÞ is the number of members of the organization at time t, and RR is a stochastic rate of recruitment to vacant positions used to find values of Hj ðtÞ. Individual hiring is conceived as drawing individuals from a pool of candidates. The pool has a distribution of values on the desired characteristics, and the distribution is known for the pool. Because cultural criteria are subtle and not readily observable in many instances, the choice of any particular individual is somewhat random. In fact, the characteristics of the pool are determined by the selectiveness of the hiring policies of the organization [2]. The candidate pool is more or less centered on the desired characteristics, and more or less noise is tolerated in the information used in the selection process. We denote the selectivity of the recruitment pool SEL by its mean enculturation level. Hiring on the basis of cultural criteria is simulated by randomly drawing values of Ci from parameterized distributions. The parameters of the distribution are defined
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by the hiring policies of the organization. At each time period, therefore, NH ðtÞ persons with a variety of fits with the management-desired culture of the organization are hired. 2.2. Socialization component Any individualÕs change over time with respect to socialization is a combination of the pulls from three sources: management, peers (fellow employees), and decay. The three forces are allowed to vary in their relative strengths. Expected change in socialization is modeled as a function of the individualÕs distance from a target for each source (one, the maximum value of Ci , for management; the group enculturation mean for peers; and zero, the minimum of Ci , for decay) multiplied by a parameter. The model also introduces an error term to allow for noise in the process. Harrison and CarrollÕs [6] model posits the function for socialization change intensity as SMR½1 Ci ðt 1Þ þ SNR C Ci ðt 1Þ þ SDR½0 Ci ðt 1Þ þ e; SIi ðtÞ ¼ SMR þ SNR þ SDR ð3Þ where e is an error term and SMR, SNR, and SDR are parameters representing the pulls toward ideal socialization (from management), mean socialization level (from peer pressure), and zero socialization (from decay), respectively. In effect, the denominator normalizes the function SIi ðtÞ to ensure that an individualÕs Ci score remains between 0 and 1. The error e is constructed to be normally distributed with mean zero and adjustable variance. Individuals can be more or less susceptible to socialization, whatever its source. Susceptibility is greatest at the time of entry into the organization and then declines with tenure [11]. Newcomers are unfamiliar with an organizationÕs culture and, consequently, are more open to social influence as they adapt to their new environment. Over time, as they adjust their cultural orientations in response to organizational socialization, they gain familiarity with the culture and become increasingly resistant to further change. We simulate susceptibility to socialization forces with the following equation: SUi ðtÞ ¼ TA1 þ exp f TA2 TA3½ui ðt 1Þg;
ð4Þ
where ui is individual iÕs tenure with the organization. With the parameter values used here (TA1 ¼ 0:02; TA2 ¼ 0:60; TA3 ¼ 0:30 ), susceptibility begins with a value less than unity and declines exponentially with tenure toward a nonzero asymptote. It is important for the value of the function to remain between 0 and 1 because it will be used below as a multiplier. In this specification, TA1 is associated with the asymptotic level of susceptibility, TA2 with the level of susceptibility at entry (tenure equals zero), and TA3 with the speed of the decline in susceptibility with increasing tenure from the entry level to the asymptotic level.
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The socialization function is completed by taking an individualÕs prior enculturation level Ci ðt 1Þ and adding to it the effect of socialization-change intensity SI i ðtÞ multiplied by its influence SUi ðtÞ. That is, Ci ðtÞ ¼ Ci ðt 1Þ þ ½SUi ðtÞ½SIi ðtÞ:
ð5Þ
Distributional measures of the Ci scores characterize the organizational culture at any particular point in time. In particular, dispersion measures such as the standard deviation of enculturation across all individuals indicate cultural heterogeneity. 2.3. Turnover component Turnover might be connected to organizational culture for at least two reasons. First, individuals who do not embrace the culture might be motivated to leave voluntarily [2]. Second, those who do not fit in, or those who fail to change, might be forced to leave involuntarily. In both cases, the issue may be thought of as one of alienation [15], related to the distance between an individualÕs embodiment of the culture, Ci , and the management ideal of one. We formalize the alienation process 3 with the term AR½1 Ci ðt 1Þ , where AR is a parameter allowing greater or less sensitivity to alienation as a cause of turnover. The value of this expression increases rapidly as Ci approaches zero, but in general the effect of alienation on turnover in the model is much smaller than the effect of other (noncultural) factors. Allowing all other reasons for leaving an organization [3] to be captured in an adjustable base-turnover factor (associated with the parameter ER), the number of persons departing the organization in time period t is then given by NDðtÞ ¼
NX ðt1Þ
Di ðtÞ;
ð6Þ
i¼1
where Di ðtÞ ¼
1 0
individual i leaves in period t; otherwise:
The stochastic rate of departure for individual i, used to find Di ðtÞ, is 3
RDi ðtÞ ¼ ER þ AR½1 Ci ðt 1Þ ;
ð7Þ
where both ER and AR are parameters of the rate.
3. Research design In using the model to study cultural integration, we begin by considering two separate organizations with different cultural ideals. We designate one of the organizations as the acquiring firm––its cultural ideal dominates after the merger––and the other the acquired, or target, organization. We posit an incompatible relationship between the cultural ideals of the two organizations. So, following the merger, we
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reset the enculturation scores of all individuals in the target organization to reflect this: they are assigned new scores in random fashion distributed around a pre-set mean and standard deviation. We then treat the combined post-merger organization as a single organization and observe how its culture evolves over time. We pay special attention to the process of cultural integration in the first year (12 elapsed months) following the merger. In particular, we examine two outcomes: (1) the extent of cultural integration, indicated by the standard deviation of enculturation for the merged organization; and (2) the extent of cultural ‘‘recovery’’ for the merger organization, indicated by the distance of the mean enculturation level of the postmerger organization from the cultural mean of the acquiring organization at the time of the merger. 3.1. Experimental design We wanted to simulate two merging organizations with different cultures. Since the model we use specifies cultural fit according to some arbitrary organizationally defined scale, comparing across organizations requires positing a transformation rule between scales. Rather than tackle this issue generally, we focused on the condition of greatest substantive interest––high difference in content (a sort of ‘‘inverse’’ relationship between scales where a high score on one implies a low score on the other). We designed the experiments to allow for the content differences of the two organizations to be at the same fairly high level for all experiments; thus, content differences are high but controlled. Simulation experiments started by allowing each pre-merger organization to develop strong cultures independently for 120 simulation periods (ten simulation years), using hiring and management enculturation strategies to achieve these outcomes. In these runs, the mean enculturation levels varied from 0.79 to 0.90 after ten simulation years, depending on cultural strategy. However, because enculturation is assumed to be based on different criteria in different organizations, the values are not comparable across organizations. Thus, we used a rule to reassign all values of one organization to the scale of the other. Our goal was to make the culture of the target unit ‘‘incompatible’’ or very different from the acquiring organization (in terms of content). So, we reset the mean enculturation level of the target unit at 0.2 (with random assignment of enculturation scores to individuals based on a normal distribution); the tenures of the individuals in the target organization were preserved. We then combined the two organizations and let the new ‘‘combined’’ organization follow the previously set hiring practices, management enculturation practices, strength of alienation, and turnover rates of the acquiring organization before the merger. (These variations are best viewed as background conditions.) Of particular substantive interest here are variations related to the growth rates of the two organizations and their relative sizes before the merger. For these experiments, we attempt to identify the conditions under which the post-merger organization quickly develops a strong culture, or fails to do so in a reasonable time interval, and the conditions under which the post-merger organization moves more or less quickly toward the pre-merger cultural mean of the acquiring organization.
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Background conditions of the acquiring organization. The simulation experiments vary two characteristics of the acquiring organization, which also apply to the combined organization. First, the hiring and management enculturation strategies are varied. Three sets of conditions are used to produce a ‘‘strong’’ culture: (1) low cultural selectivity in hiring and strong management socialization; (2) moderate hiring selectivity and moderate management socialization; and (3) strong hiring selectivity and weak management socialization. Peer socialization is complementary to management socialization: when management socialization is strong, peer socialization is relatively weak, and vice versa. (Other possible combinations, such as strong hiring selectivity and strong management socialization, will produce even stronger cultures.) We term this combined set of conditions ‘‘cultural strategy,’’ and code them in analysis of the simulation data as 0, 1, or 2, corresponding to the above conditions (1), (2), and (3), respectively. We also vary the alienation level by setting the AR parameter to either 0.15 or 0.6 for weak or strong alienation effects, coded in the data analysis as 0 or 1. Conditions of interest: Growth rates. Before the merger, the growth rate for both organizations is set to zero. After the merger, we simulate three conditions: (1) negative growth, a combined stochastic growth rate of )0.04 per simulation period (month); (2) no growth, a combined growth rate of zero; and (3) positive growth, a combined stochastic growth rate of 0.04 per period. Conditions of interest: Relative sizes. Finally, we vary the relative sizes of the two organizations at the time of merger. Each organization is simulated with merger sizes of 50, 200, or 800, generating nine possible size combinations. In the analysis, we create the size ratio by dividing the merger size of the acquiring organization by the merger size of the acquired organization (target), so the size ratio varies from 0.0625 to 16. We also analyzed the simulation output data using dummy variables for each size setting and using logs of size and their interactions; the findings did not differ substantively from the analysis using the size ratio, so we report only the size ratio findings. All of these settings are consistent with previous research using our model. The settings for the variations used in the simulation are given in Table 1. 3.2. Simulation trials Each simulation begins by running the two separate organizations for 120 periods with zero growth, using the Harrison and Carroll procedure [6]. Then the cultural mean for the acquired organization is reset by generating new cultural scores from a normal distribution with a mean of 0.2 and a standard deviation of 0.1 (with a minimum of zero), the two organizations are merged, and the evolution of the merged organization using the pre-merger background conditions of the acquiring organization is observed for an additional 12 periods. The simulation is run for 162 different conditions, corresponding to the combinations of three possible cultural strategy settings, two alienation settings, three growth settings, and nine size settings. Each condition is simulated 10 times and the results are averaged. The total number of observations of 162 conditions for 12 periods is 1944.
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Table 1 Settings used in simulation experiments Variations Cultural strategies: Weak management socialization Cultural strategy variable ¼ 0
Moderate management socialization Cultural strategy variable ¼ 1
Strong management socialization Cultural strategy variable ¼ 2
Parameter/value setting SEL ¼ 0:8 RR ¼ 0:223 SMR ¼ 0:18 SNR ¼ 0:8 SEL ¼ 0:5 RR ¼ 0:693 SMR ¼ 0:49 SNR ¼ 0:49 SEL ¼ 0:2 RR ¼ 1:61 SMR ¼ 0:8 SNR ¼ 0:18
Alienation: Weak alienation, alienation variable ¼ 0 Strong alienation, alienation variable ¼ 1
AR ¼ 0:15 AR ¼ 0:6
Growth: Negative growth No growth Positive growth
GR ¼ 0:04 GR ¼ 0 GR ¼ 0:04
Size: Starting acquirer size Starting acquiree (target) size
N ðAQRÞ ¼ 50, 200, or 800 N ðAQEÞ ¼ 50, 200, or 800
Constant settings
Parameter
Standard deviation of hiring pool Socialization decay Standard deviation of error term in socialization-change intensity Socialization susceptibility
rðSELÞ ¼ 0:15 SDR ¼ 0:02 rðeÞ ¼ 0:1
Base turnover rate
TA1 ¼ 0:02 TA2 ¼ 0:6 TA3 ¼ 0:3 ER ¼ 0:01
3.3. Outcomes The first outcome we examine is cultural heterogeneity, measured as the standard deviation of the enculturation levels of the individuals in the organization. Given the general structure of the simulation experiments, the lower the cultural heterogeneity, the higher the level of cultural integration of the merged organization. The second outcome of interest is cultural distance, the difference between the enculturation mean of the acquiring organization at the time of the merger and the enculturation mean of the post-merger organization; since the cultural distance is in
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Fig. 1. Cultural heterogeneity over time following a merger.
general negative (the enculturation mean of the merged organization is usually below that of the acquiring organization at the time of merger), the higher the cultural difference, the greater the cultural recovery of the merged organization. The values of these two outcome variables, aggregated across all simulation conditions for the 12-month period following the merger, are shown in Figs. 1 and 2. Since both variables demonstrate a logarithmic shape with respect to time, we use the log of time as a variable in the subsequent regression analyses.
4. Findings Table 2 shows the descriptive statistics and correlations for the simulation output. The starting conditions for the two dependent variables, heterogeneity at merger and distance at merger, corresponding to the dependent variables cultural heterogeneity and cultural distance, respectively, are included in Table 2 since we use them as control variables in the regressions. Each represents the variableÕs value at the time of merger for the associated simulation condition. Cultural strategy, coded 1, 2, or 3 as mentioned above, is treated as an interval variable; analyses using dummy variables for the three cultural strategy conditions yielded essentially identical results, so we chose to report the interval findings for simplicity of interpretation. The many correlations of 0 and )0.5 are ‘‘structural’’ in that they are the consequence of the design structure of the simulation experiments. We used linear regression analysis to examine the effects of simulation conditions on the outcomes of interest. In the regressions, we included variables for all the
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Fig. 2. Cultural distance over time following a merger.
simulation condition variations and control variables for the starting conditions. Since we used dummy variables for the growth conditions, we excluded the dummy for zero growth; the effects of negative growth and positive growth are the effects of these conditions relative to zero growth. We also tested all possible interactions between simulation conditions, but none substantially improved the models, so these results are not reported. Apparently, the different simulation conditions exert effects on cultural change following mergers that are largely independent of one another. The regressions are estimated using simulation output data for the first twelve months following the merger. The time period variable (log month) controls for the time trend toward cultural integration and cultural recovery; since it is uncorrelated with any of the simulation condition variables, it essentially removes the time path effects and permits a focus on the effects of the simulation conditions on cultural change in the merger organization in the first year. The first regression addresses cultural integration, measured by cultural heterogeneity. This measure decreases over time, since the two firms have ‘‘incompatible’’ cultures at the time of the merger (see Fig. 1). Lower cultural heterogeneity corresponds to stronger cultural integration; variables with negative estimates promote cultural integration, and those with positive estimates hinder it. The regression estimates are given in Table 3. As Table 3 shows, cultural integration increases over time following the merger––the log month effect is negative. Cultural integration is promoted by alienation, less growth, and a larger size ratio. Conceptually, alienation promotes integration by
360
Variable
Mean
1. 2. 3. 4. 5. 6. 7. 8. 9.
Cultural heterogeneity 0.200 Cultural distance )0.192 Log month 1.666 Cultural strategy 1.000 Alienation 0.500 Negative growth 0.333 Zero growth 0.333 Positive growth 0.333 Size ratio (acquirer/ 3.063 target) 10. Heterogeneity at 0.303 merger 11. Distance at merger 0.327 1
1. 2. 3. 4. 5. 6. 7. 8. 9.
Cultural heterogeneity Cultural distance Log month Cultural strategy Alienation Negative growth Zero growth Positive growth Size ratio (acquirer/ target) 10. Heterogeneity at merger 11. Distance at merger a
N ¼ 1944.
Standard deviation
Minimum
Maximum
0.064 0.141 0.724 0.817 0.500 0.472 0.472 0.472 4.794
0.039 )0.638 0.000 0.000 0.000 0.000 0.000 0.000 0.063
0.356 0.020 2.485 2.000 1.000 1.000 1.000 1.000 16.000
0.067
0.140
0.368
0.190
0.662
0.034
2
3
4
5
6
7
8
9
10
)0.65 )0.55 0.35 )0.30 )0.01 )0.02 0.03 )0.49
0.39 )0.16 0.25 )0.01 0.03 )0.01 0.59
0.00 0.00 0.00 )0.00 )0.00 0.00
0.00 0.00 )0.00 )0.00 0.00
)0.00 )0.00 )0.00 0.00
)0.50 )0.50 0.00
)0.50 0.00
0.00
0.28
)0.04
0.00
0.19
0.06
0.63
)0.32
)0.32
)0.34
)0.36
0.80
0.00
)0.10
)0.04
0.00
)0.00
0.00
0.74
)0.05
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Table 2 Descriptive statistics and correlationsa
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Table 3 Regression analysis for cultural heterogeneitya Independent variable Constant Log month Cultural strategy Alienation Negative growthc Positive growthc Size ratio (acquirer/target) Cultural heterogeneity at merger Adjusted R2 ¼ 0:770
Estimateb
0.251 )0.492
0.025
)0.039
)0.013
0.004
)0.006
0.152
Standard error 0.005 0.001 0.001 0.001 0.002 0.002 0.000 0.016
a
N ¼ 1944. p < 0:05,
p < 0:01,
p < 0:001. c Omitted growth category is zero growth. b
inducing less culturally ‘‘fit’’ employees to leave the organization, reducing the cultural variance. Negative growth has a similar effect; less fit employees, who are more likely to exit the organization because of alienation, are not replaced during organizational decline. On the other hand, positive growth increases cultural heterogeneity in the short run by adding less socialized employees to the organization. The size ratio effect shows that cultural integration proceeds more rapidly when the acquiring firm is large relative to the acquired firm–the firm has a lower proportion of people to assimilate into the culture. Finally, the cultural strategy effect indicates that a strategy of greater selectivity in hiring and less attention to management socialization efforts is more effective for cultural integration than a strategy placing less emphasis on hiring selectivity and more on socialization programs by management. The control variable shows an obvious effect: the less integrated the cultures are at the time of the merger, the less integrated they will be in subsequent time periods. To understand further the relationships in the simulation data, we also calculated for the conditions of interest what might be called ‘‘controlled’’ predicted effects from the regression estimates. To construct these effects, we first calculated the predicted values for each observation in the output file, using the regression estimates in Table 3. We then separated the observations by each condition of interest (i.e., zero growth, negative growth, and positive growth) and by month. We then computed the controlled predicted values by computing the mean predicted value by month for each condition of interest. These values allow us to see the effect of the condition of interest by month when combined with the effects of other variables set at their mean values for all observations within the condition. Fig. 3 graphs the controlled predicted effects of growth on cultural heterogeneity. Cultural heterogeneity is greatest for positive growth, but is almost identical for zero and negative growth in the aggregate. Fig. 4 presents the controlled predicted effects of the size ratio on cultural heterogeneity, using the same procedure. The unmarked bottom line in the figure is for the
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Fig. 3. Cultural heterogeneity over time by growth condition.
Fig. 4. Cultural heterogeneity over time by size ratio (acquirer/target).
largest size ratio, 16. Notice that the path for the smallest size ratio, 0.0625, lies below those for size ratios of 0.25 and 1, and then the paths become successively lower as the size ratio increases further. What produces this anomaly for the smallest size ratio?
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Fig. 5. Cultural heterogeneity in month 12 by size ratio and growth condition.
A clue lies in the effects of growth for the smallest size ratio; for this condition, cultural heterogeneity is actually substantially lower for zero and positive growth relative to negative growth, in contrast to the pattern in Fig. 3. To show this effect, we plotted the growth conditions as a function of the size ratio for month 12; the result is given in Fig. 5. The mechanism underlying this effect is not now understood, and will be a subject of further research. Table 4 shows the regression analysis for the cultural difference between the firmÕs current enculturation mean and the enculturation mean for the acquiring firm at the time of the merger. By design, the enculturation mean for the target firm is below
Table 4 Regression analysis for cultural distancea Independent variable
Estimateb
Standard error
Constant Log month Cultural strategy Alienation Negative growthc Positive growthc Size ratio (acquirer/target) Cultural distance at time of merger Adjusted R2 ¼ 0:881
)0.138 0.076
)0.138
0.079
)0.008
)0.008
)0.001
0.611
0.005 0.002 0.001 0.002 0.003 0.003 0.000 0.009
a
N ¼ 1944. p < 0:05,
p < 0:01,
p < 0:001. c Omitted growth category is zero growth. b
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that of the acquirer, so this measure starts out negative and moves upward over time (see Fig. 2). Consequently, positive estimates in Table 4 signify factors that facilitate cultural recovery from the merger. The findings in Table 4 are consistent with those of Table 3 in many respects. Cultural strategy, alienation level, and time, which contribute to cultural integration, also contribute to cultural recovery. However, both the size ratio and growth have different effects on cultural distance relative to cultural heterogeneity. The growth effect on cultural distance in Table 4 is nonlinear. Cultural recovery for merged firms experiencing either positive or negative growth is slower than for firms with zero growth. Fig. 6 shows graphically the controlled predicted effects of growth on cultural distance over time. Zero growth firms again appear to recover fastest. Growing firms are likely to hire more new employees who have poorer fits to the firmÕs culture (in the aggregate across all cultural strategies), while declining firms are likely to lose culturally fit employees as well as some who are less fit. From the estimates in Table 4, larger size ratios apparently inhibit cultural recovery (the negative coefficient). However, this effect appears to be a consequence of the simulation design and the way the cultural distance measure is constructed. When an acquiring firm is large relative to its target, the post-merger firm experiences only a small decrease in its enculturation mean relative to the pre-merger mean of the acquirer, so it has less of a cultural deficit to make up; but when an acquiring firm is small relative to its target, the merged firm experiences a larger decrease in its enculturation mean and has a much greater opportunity for cultural improvement. The estimated effect of the control variable (cultural distance at time of merger) rein-
Fig. 6. Cultural distance over time by growth condition.
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Fig. 7. Cultural distance over time by size ratio (acquirer/target).
forces this interpretation; a firm with a ‘‘larger’’ (more negative) cultural distance at the time of the merger exhibits greater recovery, although it still faces a cultural handicap over time compared to a firm that experiences less of a decrease in its enculturation mean when it acquires a target firm. Indeed, one way to think about this effect is that (given the simulation design) the size ratio contains a strong ‘‘built-in’’ counter-effect from cultural distance at time of merger. This can be seen from the high correlation of 0.59 between the two variables (Table 1); it can also be seen from looking at the means of the control variable by size ratio: when the size ratio is 0.0625, the mean cultural distance at time of merger is 0.615; when the size ratio is 0.25, the mean is )0.523; when it is 1.0, the mean is )0.328; when it is 4.0, the mean is )0.131; and when it is 16.0, the mean is )0.039. In fact, the counter effect is so strong that the controlled predicted effects of the size ratio on cultural distance appear inverted when compared to the estimate in Table 4. As Fig. 7 shows, once these and other effects are taken into account, the smallest size ratio is associated with the lowest cultural distance values and the remainder of the size ratios are monotonically ordered in their effects. (The unmarked top line in the figure is for the largest size ratio, 16.)
5. Discussion It is widely recognized that achieving cultural integration in a newly merged organization is difficult and sometimes even impossible. Prior cultural differences between the merged entities often persist for months and years, possibly generating conflict and rendering the organization less effective. Indeed, the widespread inability of
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mergers to accomplish their goals frequently gets blamed on the failure to integrate the cultures of the merging organizations. Cultural integration following a merger is almost always analyzed in terms of the contents of the organizational cultures involved. The basic question raised is, ‘‘Are the cultures compatible or consistent?’’ Analyses then focus on how the various elements of the cultures might be reconciled, compromised or emboldened in an organizationÕs operation. Both popular and scholarly assessments tend to use this mode of reasoning. While clearly insightful, the content-based approach to studying cultural integration following a merger also has limitations. First, these analyses are often conducted at a high level of abstraction and it is difficult to know how much of an organizationÕs culture is captured by simple characterizations such as ‘‘hierarchical’’ or ‘‘free-wheeling.’’ Second, there is no developed theory guiding the assessments of cultural elements as compatible or not; the assessments tend to be intuitive and ad hoc (or post-hoc) in most instances. Third, since many cultural phenomena are tacit in nature, judging how people will react to their alteration is a daunting task for social science. It seems to us that such reactions likely depend on the particular phenomena involved as well as the process by which their alteration is attempted. An alternative yet still general way to assess cultural integration following a merger is to examine the demographic systems underlying the organizational cultures. When the demographics of two merger partners show fluid movement of individuals into and out of the organizations, then it seems that cultural integration might be achieved more quickly than if the two partners show stagnant demographics with little movement in and out. The primary assumption behind such a conjecture is that it is easier to recruit and socialize ‘‘new’’ individuals to the cultural ideals of the merged organization than it is to re-socialize and re-train incumbents already inculcated in the ways of one or the other partner. Of course, the demographic approach and the content-based approach complement each other. However, one has received all the attention and the other has been neglected. So, we focused here on the neglected demographic approach. Using a formal model of organizational culture, we set up the research problem by assuming that the content differences among merging organizations are very strong. We then investigated how quickly cultural integration might be achieved under a variety of commonly seen demographic conditions. We concentrated on variations in organizational growth rate and in the size ratio of the acquiring organization relative to the acquisition target, conditions readily observable to a number of real world cases. We used simulation methods to control other factors in the integration process. In terms of outcomes, our simulation experiments looked at both cultural heterogeneity, measured as the standard deviation of the enculturation levels of the individuals in the merged organization, and cultural distance, defined as the difference between the enculturation mean of the acquiring organization at the time of the merger and the enculturation mean of the post-merger organization. The findings show that post-merger cultural heterogeneity decreases more quickly in organizations with no growth or negative growth (decline) as opposed to those
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with positive growth. We also find that mergers involving equal sized partners experience higher cultural heterogeneity than do those with unequal sized partners. Perhaps surprisingly, this finding holds true even for cases where the target is larger than the acquiring firm. For cultural distance, the simulations show that post-merger firms experiencing either positive or negative growth recover more slowly than do firms with zero growth. Larger size ratios (of acquirer/target) apparently inhibit cultural recovery, but this effect appears to be a methodological artifact of our procedures. We regard these general findings as provocative and worthy of additional investigation. As with any simulation, the findings are not themselves empirical discoveries but instead disciplined theoretical conjectures. Given the complexity of organizational demographic systems, we think such investigations are warranted and potentially very insightful. However, it should also be obvious that our efforts here represent only a first step towards understanding how demographic processes affect post-merger cultural integration and recovery.
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