COMPLEMENTARITY, SIMILARITY, AND VALUE CREATIONIN MERGERS AND ACQUISITIONS

AKBAR ZAHEERCarlson School of Management 3-365

University of MinnesotaMinneapolis, MN 55455

Telephone: (612) 626-8389Fax: (612) 626-1316

e-mail: azaheer@csom.umn.edu

XAVIER CASTAÑERHEC School of Management (Paris)

1 Rue de la Libération78351 Jouy-en-Josas

FranceTelephone: 33.1.39.67.94.45

Fax: 33.1.39.67.70.84e-mail: castaner@hec.fr

DAVID SOUDERCarlson School of Management 3-365

University of MinnesotaMinneapolis, MN 55455

Telephone: (612) 625-6723Fax: (612) 626-1316

e-mail: dsouder@csom.umn.edu

Version: January, 2005

Acknowledgements: We are grateful for research access and financial support from the member companies of the M&A Consortium project of the Strategic Management Research Center (SMRC), Carlson School of Management, University of Minnesota (U of M), without which this project would not have been possible. We also thank Keith Bahde, Phil Bromiley, Pierre Dussauge, Mehmet Genç, Maggie Schomaker, Harbir Singh, Sri Zaheer, Maurizio Zollo and seminar participants at Cornell University and Tilburg University for their comments on presentations and earlier versions of this paper. We are also grateful to members of the M&A Consortium, who in addition to the U of M faculty and doctoral students acknowledged above, included George Meredith and Mary Nichols, for their involvement, participation, and suggestions along the way. We thank Sharon Hansen for both administrative and research support and the SMRC for financial support. All errors are ours.

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COMPLEMENTARITY, SIMILARITY, AND VALUE CREATIONIN MERGERS AND ACQUISITIONS

Abstract

Although M&A researchers associate relatedness with acquisition value creation, there is

little research that distinguishes between the different sources of synergy that are inherent in

relatedness. We argue for distinguishing between three dimensions of relatedness – business

similarity, product complementarity, and geographic complementarity – because each implies a

different source of synergistic value in M&As. Furthermore, realizing value from these

synergies requires integration between the acquiring and target firms, with the appropriate degree

of integration depending in part on which dimension of relatedness is producing synergistic

value. Empirical validation for our arguments comes from 88 M&As. We find that acquisition

performance is higher when each dimension of relatedness is appropriately matched with the

degree of integration. Specifically, business similarity and product complementarity are

associated with negative performance when integration is low and become more valuable as the

degree of integration increases, although surprisingly, product complementarity is not a

significant source of value even at the highest level of integration. In contrast, geographic

complementarity is associated with higher performance under low integration conditions, while

high integration is detrimental to performance.

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Prior research has established the importance of relatedness between acquiring and target

firms as a potential source of synergy – ultimately leading to value creation – in mergers and

acquisitions (M&As) (Seth, 1990; Singh & Montgomery, 1987). The degree of relatedness is

often conceptualized as the ‘closeness’ or similarity between the target and acquirer’s products,

customers, or resources (Chatterjee, 1986; Lubatkin, 1987). However, scholars have long

observed that it is not exclusively similarity that is the source of value creation in acquisitions,

but also the potential to combine attributes of the two firms in ways that are complementary

(e.g., Penrose, 1959). While the concept has been widely discussed, few empirical tests

unpacking the components of relatedness have been conducted. It is important to do so because

including both similarity and complementarity allows us to more comprehensively understand

the M&A phenomenon and because the logic of value creation in M&As differs noticeably for

each type of relatedness.

At the same time, the literature on post-merger integration has argued that the appropriate

degree of integration of the two previously independent organizations is crucial to acquisition

success (Haspeslagh & Jemison, 1991). High integration is necessary if value creation depends

on high degrees of interdependence between activities or resources of the two hitherto

independent organizations. However, if value creation does not require a high degree of

interdependence, then integrating the two organizations to a high degree may in fact be

detrimental to acquisition performance, since integration involves high coordination and other

costs. Consequently, matching the degree of integration with the type of relatedness is likely to

significantly explain acquisition success since the logic of value creation differs among the

different types of relatedness.

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Within the overall rubric of relatedness, we distinguish between three sources of in

M&As: business similarity, product complementarity, and geographic complementarity. We

draw on theory to posit that these three types of relatedness cover a wide range of value sources,

but importantly do not all imply the same degree of integration as a necessary condition for

attaining value. By unpacking the sources of value creation, establishing the degree of

integration as a contingent condition for the actual attainment of value, and demonstrating

empirically that correctly matching the degree of integration with the type of relatedness creates

value while inappropriate matching reduces acquisition performance, we advance the literature

on M&As and more broadly, provide a possible explanation for the much-touted failure of

M&As to create value.

Empirical validation for our arguments comes from a study of 88 recent acquisitions,

with data collected through a mailed questionnaire. The results support our contention that

M&A performance is improved when appropriate fit is achieved between the type of relatedness

and the degree of integration. Specifically, we find strong support for the idea that business

similarity needs high integration to yield value, and is related to poor performance when

integration is kept low. Product complementarity is also associated with negative performance at

low levels of integration, with meaningful improvements in performance as integration increases.

Contrary to expectations, however, the positive value of product complementarity is tenuous at

best, even in high integration scenarios. For geographic complementarity, we find unambiguous

support for our postulates that its benefits are achieved at low degrees of integration, while

increasing integration is associated with lower performance. In sum, by a fine-grained

disaggregation of the concept of relatedness in M&As and a more precise articulation and

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demonstration of the conditions under which it yields value, we present a deeper understanding

of the nature of relatedness and its relationship with acquisition performance.

THEORY AND HYPOTHESES

Disaggregating Relatedness

Much of the acquisitions literature has attributed the source of value creation to

relatedness between the acquirer and the target organizations (Lubatkin, 1987; Singh &

Montgomery, 1987). Often construed as similarity, relatedness has been studied broadly, in

areas such as products, distribution channels, customers, and technologies (Chatterjee, 1986;

Seth, 1990). Yet similarity of attributes is not the only source of relatedness that leads to value

creation in M&As. Differences in attributes between target and acquirer firms can also be

sources of value if the differences are synergistic (Harrison, Hitt, Hoskisson, & Ireland, 1991),

thus constituting complementarity across the two firms.

Broadly speaking, relatedness refers to the degree to which two independent firms are

synergistic. In this paper, we use the term synergy to represent the antecedent potential for value

creation through a merger or acquisition, as opposed to the post-merger realization of value,

which we refer to as value creation. For example, potential complementarity refers to the mere

existence of potentially mutually reinforcing attributes across the two organizations. When these

are appropriately combined through managerial action, complementarity can be realized and

value created.

We identify three distinct types of relatedness in M&As: business similarity, product

complementarity, and geographic complementarity. Business similarity describes the extent to

which attributes such as the product-market portfolio or the internal operations of the acquirer

resemble the same attributes from the target. Value is created from similarity in M&As through

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the exploitation of scale economies and the possible exercise of market power (Capron, 1999).

Product complementarity refers to the target’s ability to extend the acquirer’s domain into

additional product lines or technologies that are in some way related to its existing ones,

allowing them to ‘round out’ their product offerings or technological bases. An acquirer may

seek to combine its products with the target’s to form a single offering that better meets market

preferences (Ansoff, 1965; Larsson & Finkelstein, 1999) or combine their complementary

technologies to create new products (Galunic & Rodan, 1998). Finally, geographic

complementarity arises from the addition of non-overlapping geographies in which the acquiring

firm is able to achieve cost savings by expanding its footprint into new geographic markets

(domestically or internationally).

Having defined the three different dimensions of relatedness (business similarity, product

and geographic complementarity), the issue is one of establishing the conditions under which

these potential sources of value creation yield up actual value. The most crucial contingency that

we identify to establish out these conditions is that of acquisition integration, to which we turn

next.

The Integration Challenge

While prior M&A research acknowledges similarity and complementarity as different

concepts, it has also implied that the processes by which value is created from these two concepts in

M&As are not differentiated. For example, in their pioneering work, Larsson and Finkelstein (1999)

define ‘combination potential’ to include both similarity and complementarity, and find that this single

construct is associated with both a higher degree of integration and value realization. The position we

take in this paper is that the value creation processes for each type of complementarity are

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fundamentally distinct both from each other and from the value creation processes for business

similarity. The distinctions between the effectiveness of each of the value creation processes revolve

around the degree to which the target firm is integrated into the acquirer organization in the post-

acquisition integration phase.

The M&A literature points to the challenges of post-acquisition integration, or the degree to

which different functions of the two previously separate organizations are brought under a single

hierarchical structure. In particular, choosing and implementing the appropriate integration approach

is posited to lead to acquisition success (Haspeslagh & Jemison, 1991). Combining this stream of

research with the studies of relatedness, we contend and argue below that the realization of value in

M&As results largely from matching each of the dimensions of relatedness with the appropriate

degree of post-merger integration.

In the context of M&As, only after the transaction is completed and the integration process

gets under way is it possible to begin extracting value from the dimensions of relatedness. Given the

complexity of an acquisition, an acquiring firm decides to purchase the target under conditions of

uncertainty and incomplete information; it may anticipate the potential for realizing value but cannot

know whether it is achievable until after the fact (Barney, 1988). Moreover, to realize the benefits of

relatedness, the acquiring firm needs to determine precisely how the similar and complementary

components should be combined across the two hitherto independent firms (Haspeslagh & Jemison,

1991).

For many acquiring firms, determining and implementing the appropriate integration

approach is a critical decision that varies across acquisitions. The degree of integration between

the target and its acquirer reflects a tradeoff between autonomy granted to the target, to preserve

the attributes which provided the original rationale for the acquisition, and assimilation with the

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acquirer to exploit those attributes (Haspeslagh & Jemison, 1991). Prior research underscores

the difficulty in bringing together two firms’ knowledge bases, creating social connections, and

building a shared identity (Ghoshal & Gratton, 2002). Under low integration conditions the

operations and the structure of the target remain largely independent of the acquirer. On the

other hand, high integration implies that many or most of the activities and resources of the two

organizations are managed and organized in a unified fashion.

Given the well-established importance of appropriate integration to acquisition success in

broad terms, we develop our hypotheses by drawing on the M&A literature and applying them to

the specific circumstances of business similarity as well as product and geographic

complementarity. Specifically, as we argue below, the synergy inherent in business similarity

and product complementarity demands a high degree of integration for their potential value to be

realized, while the synergy of geographic complementarity requires the opposite – a low degree

of integration.

Business Similarity

Business similarity is a potential source of value creation in M&As because of the

efficiencies made possible through the consolidation of similar elements of the two firms’

operations. The rationale is that similarities allow for the straightforward elimination of

overlapping or redundant activities (Capron, 1999), or an increase in overall turnover which can

generate economies of scale. Another source of value may arise from the greater market power

of the resulting larger firm in both input and output markets. Many of the large and well-known

bank mergers, such as that between Chase Manhattan and Chemical Bank, have relied heavily on

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the logic of business similarity, as similar products, customers, and distribution channels enabled

the consolidation of back-office operations and the rationalization of branches.

It is important to note, however, that for business similarity to achieve its promise in an

M&A, the two organizations must structurally and operationally integrate the parts of their

respective businesses that are similar. Similarity between the businesses only results in greater

efficiency when redundancies are eliminated and scale economies actually begin to get realized.

Put differently, the actual creation of value, or the realization of the benefits of similarity

requires an appropriate management effort to bring together under the same organizational

structure the elements that are similar from the two businesses. Such a major reorganization

implies that the two organizations are required to become highly integrated. Only then can scale

economies – in any part of the value chain – flow through into positive gains from the

acquisition.

Conversely, when business similarity is high but the degree of integration is low, the

acquiring organization may have identified the potential economies of scale but is unable to

realize them despite incurring the costs of acquisition, resulting in poor acquisition performance.

Therefore, we argue that the degree of integration represents a contingency factor that moderates

the ability for the potential synergies from business similarity to generate positive acquisition

performance. Extending this logic further, we expect that business similarity will have a

negative relationship with acquisition performance when integration is kept low, and will show a

positive relationship with acquisition performance when firms integrate their operations to a high

degree. Formally,

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H1A: Integration positively moderates the relationship between business similarity and

acquisition performance

H1B: Business similarity has a positive relationship with acquisition performance when the

degree of integration is high.

H1C: Business similarity has a negative relationship with acquisition performance when

the degree of integration is low.

Product Complementarity

Product complementarity is a source of synergy when firms seek to extend their domains

into additional product lines or technologies that are partially related to their existing ones. Such

extension allows them to ‘round out’ their product offerings or technological bases in ways that

are potentially valuable due to economies of scope (Teece, 1980). The value of complementarity

in M&As has a long history in the field of strategy, from Penrose (1959) to Ansoff (1965) to

more recent work of scholars such as Harrison et al (1991). In the recent organizational

economics literature, complementarity has also attracted considerable attention. Milgrom and

Roberts (1995), in their much-cited work, define complementarity as being present when having

more of one organizational attribute increases the returns from having another. The core of

Milgrom and Roberts’ concept of complementarity constitutes the notion of mutual

reinforcement between distinct organizational elements.

We adapt this notion to the M&A context and define product complementarity in M&As

as the potential value arising from the mutually-reinforcing combination of a target firm attribute

with a different attribute from the acquirer firm, where attributes could include products or their

underlying technologies. For example, bundling and cross selling involve the marketing of

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products that are different – but related – to the same customer segments, thereby achieving

revenue enhancement from targeting the customer bases of each erstwhile firm. In this way,

product complementarity may enable the combined firm to better serve existing customers as

well as attract new ones that see value in such integrated solutions of compatible products. One

firm that has pursued this logic is Cisco Systems, which acquired a series of companies with high

product complementarity and developed an integrated offering because its customers valued the

more reliable systems integration that a full-line supplier could provide (Finkelstein, Choi, &

Tran, 1998). Furthermore, product complementarity may also lead to cost savings arising from

better utilization of the sales forces and distribution systems of each organization.

We argue that economies of scope – which give rise to value from product

complementarity – are realized through a high level of integration between the relevant activities

of the post-acquisition organization. Complementarity theory supports this view by arguing that

potential value is realized provided that firms tightly couple the potentially complementary

elements (Milgrom & Roberts, 1990; 1995). In essence, the theory implies that the means by

which value is created is through careful meshing of reciprocally interdependent firm activities.

Applied to the M&A context, the potential value of product complementarity comes from the

successful coordination and combination of a range of diverse activities that were previously part

of two separate organizations.

For example, with cross selling, sales initiatives need to be developed that highlight the

benefits of combining two products that were previously sold separately, and salespeople need to

be educated about the attributes of unfamiliar products. In the case of integrated offerings,

products have to be redesigned and interfaces ought to be developed, production needs to be

coordinated, and new marketing programs created. In such cases, design and production

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activities that were previously distinct should be combined into single functions. To combine

complementary technologies in a meaningful way, research labs need to be brought together and

tacit knowledge exchanged. Moreover, while coordination mechanisms such as cross-

organization task forces may be set up, they may not achieve the deep level of mutual

understanding and the emergence of a common organizational language (Teece, 1982).

Research has demonstrated that physical proximity is a requirement for the successful transfer of

tacit scientific knowledge (Allen, 1977). Using the example of Cisco Systems again, the

company ensured that every acquisition target was physically co-located with one of its existing

locations (Finkelstein et al., 1998). Thus, the processes that give rise to value from product

complementarity require a high level of post-acquisition integration.

On the other hand, if the potential for product complementarity is a motive for the

acquisition, the failure to integrate precludes the ability to realize this value since the level of

coordination and required combination across the previously independent firms will be

insufficient to result in a tightly-coupled activity set. Put differently, failure to integrate the two

organizations when product complementarity is present is likely to be detrimental to performance

because the opportunity loss from the product complementarity means that the acquisition

underperforms relative to the price paid for it, given that a premium which is partly based on

synergies is usually paid (Sirower, 1997). Thus, the degree of integration again represents a

contingency factor that moderates the ability for the potential synergies from product

complementarity to generate positive acquisition performance. We further expect that product

complementarity will have a negative relationship with acquisition performance when integration

is kept low, but conversely be associated with positive acquisition performance when the degree

of integration is high. Stated as formal hypotheses:

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H2A: Integration positively moderates the relationship between product complementarity and

acquisition performance

H2B: Product complementarity has a positive relationship with acquisition performance when

the degree of integration is high.

H2C: Product complementarity has a negative relationship with acquisition performance

when the degree of integration is low.

Geographic Complementarity

In M&As, complementary geographies expand the ‘footprint’ of the combined firm, due

to the non-overlapping geographic scope of the acquirer and target, opening up new

opportunities for the combined firm that neither predecessor could pursue independently. For

complementarity to exist, the target firm must provide access to a geographic region that offers

specific benefits to the acquiring firm. Geographic complementarity does not exist either in

cases where the geographic presence of the two firms is sufficiently similar that the markets

could have been served without the merger, on in cases where the geographic presence of the

target firm lacks strategic importance for the acquirer. Thus, the benefits of geographic

complementarity are attributable in part to the specific regions in which the target firm has a

presence; an otherwise identical firm might not provide this potential source of synergy if its

locations were not complementary to the acquirer. It is therefore the complementary matching

between the geographic regions of the two firms that leads to value.

This definition of geographic complementarity begs the question of how an acquiring

firm can realize value from an attribute that is essentially exogenous. We submit that the value

of geographic complementarity arises from the opportunity for the acquiring firm to capture

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network effects. Following Liebowitz and Margolis (1994), we intentionally use this generic

terminology (rather than the common reference to externalities) to avoid the suggestion that such

benefits must come from market failure. Network effects are valuable when an existing entity

benefits from an increase in the number of participants in the network. Languages and technical

standards are two classic examples of network effects in general (Liebowitz & Margolis, 1994).

Applied specifically to M&As, a firm benefits from network effects by acquiring another firm

that does not merely enlarge its domain, but actually increases the value of the existing

enterprise.

We have identified at least two ways in which geographic complementarity can provide

network effects, both from supply and demand sides. The first, supply side network effects,

leads to scale economies when a firm possesses an indivisible resource that is not fully utilized,

such as a brand name, an executive management team, or a minimum-efficient scale plant. As

Penrose (1959) famously observed, an efficiency motive may cause the firm to seek avenues to

more fully utilize such resources, and thus lead to the firm’s growth. Geographic

complementarity may represent such an avenue. For example, consider a firm with a footprint in

much of the U.S., but not all of it. To promote its brand, it may find it more expedient to

purchase national media advertisements rather than negotiate locally in its current locations. By

acquiring another firm with geographic complementarity – i.e., a presence in the missing region

– the cost of advertising would remain unchanged and could be amortized across a greater

number of units.

Alternatively, demand side network effects may result from a more expansive geographic

presence that makes the products of the combined firm better able to meet the needs of

customers, particularly those who either frequently travel or are themselves located between the

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footprints of the respective antecedent firms. In better meeting this customer need than its

competition, the combined firm may be able to either command premium pricing, or attract a

disproportionate share of multi-market customers at market price levels. Both cases represent

synergies attributable to geographic complementarity, since the value to customers derives from

the specific geographies that are now jointly served. For instance, cellular phone customers in

the U.S. who travel frequently are more likely to choose a service provider that allows a

nationwide ‘local region’ for billing purposes than a regional-only service provider.

Unlike the realization of scale and scope economies from business similarity and product

complementarity, respectively, network effects can be achieved without a high degree of

integration. By their very nature, network effects are achieved through connection rather than

integration. Such a connection may have even existed prior to the acquisition; in the case of

merging phone companies, what changes is not the ability to place calls across regions, but

simply the demand and supply side network effects accruing to a single firm. More generally,

when a firm pursues a strategy to capture for itself network effects – as in the case of franchising

– a common objective is to achieve such benefits without incurring the added cost of a highly

integrated organization. The M&A application we are proposing here suggests that firms can

benefit from making acquisitions that provide geographic complementarity through supply side

network effects, or scale, from the more efficient use of corporate level resources, and demand

side network effects through providing greater value to customers. Consolidation of business

level activities is neither implied nor, in most cases, feasible. Whereas combining firms in

overlapping geographies may realize synergies by reducing operational redundancy, the target’s

non-overlapping geographies would be irrelevant – rather than complementary – if eliminating

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such geographical presence were possible. Thus, network effects are best achieved in low

integration scenarios.

Furthermore, not only is integration unnecessary to create value through geographic

complementarity, but adverse outcomes may also result if integration takes place. A high level of

integration may threaten the autonomy of the target’s local operations and undercut the viability

of leveraging the target’s market knowledge and customer ties. The same reasoning applies to

selling the target’s products in the acquirer’s geographical markets. Integrating the operations of

both companies across geographic markets may jeopardize acquisition value stemming from

geographic complementarity because any integration is costly in terms of time and resources, and

is wasteful and inefficient if unnecessary to achieve results. Thus, a high a degree of integration

implies that the likelihood of generating value from geographic complementarity is lower and

can even turn into value reduction. This reasoning suggests the following hypotheses:

H3A: Integration negatively moderates the relationship between geographic complementarity

and acquisition performance

H3B: Geographic complementarity has a negative relationship with acquisition performance

when the degree of integration is high.

H3C: Geographic complementarity has a positive relationship with acquisition

performance when the degree of integration is low.

METHODS

Data

Our research used mailed surveys to collect quantitative data regarding M&As, with

acquisitions from a variety of industries included to increase external validity. From the

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Securities Data Corporation (SDC) database, we identified all M&As in the Midwest region of

the United States that occurred in the period 1995-2002. The purpose of this time window was

to allow sufficient time for the acquisition to demonstrate performance after it was completed,

without exacerbating concerns about the effect of retrospective bias on the part of respondents

(Huber & Power, 1985). An initial total of 812 M&As were identified from the database. We

excluded from this set stock buy-backs and acquisitions that were initiated but never completed.

After these exclusions, our final sampling frame included 585 firms that engaged in at least one

M&A during the period.

After a reminder post card, a phone call, and a second round of questionnaire mailing, we

received responses on 96 M&As from 68 companies. Eight surveys were excluded because of

incomplete data on the variables of interest, leaving us with a final sample of 88 M&As. To

control for firm-specific characteristics, dummy variables were used for seven firms that

provided data on more than one M&A.

The individuals who completed the questionnaires are senior executives who were

personally involved in the process leading up to the acquisition decision, as well as the post-

merger integration. Titles for these individuals fall along four lines of responsibility: board

members, presidents/chief executives (of either the acquiring division or the entire corporation),

finance executives, and corporate development executives.

Testing for Non-Response Bias

The participation rate of 11.6% (i.e., 68 firms responding out of a total of 585 firms in the

sampling frame) raises the issue of non-response bias. To assess this, we conducted a series of

tests, both at the level of the M&A and at the level of the responding firm.

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First, at the level of the firm, we drew a random sample of 68 non-responding firms from

the sampling frame and tested the extent to which they differed systematically from the

responding group of firms in sales and number of employees. We found no statistically

significant difference between responding and non-responding firms in either sales (t = .54) or

number of employees (t = .77). Because a majority of firms in both the responding group and the

non-responding sample are privately held – and therefore not required to divulge financial

information – we could not test for differences along other dimensions, such as profitability or

abnormal returns based on event study methodology.

Second, we employed a technique discussed by Armstrong and Overton (1977) to assess

whether there were statistically significant differences in the characteristics of different waves of

responses to the questionnaire. For this procedure, we used the M&A event as the unit of

analysis, and tested whether for differences along four dimensions: sales, number of employees,

acquisition size and acquisition performance. Three waves of responses were defined based on

the number of reminders sent by our research team before the participant’s survey was

completed. As summarized in Table 1, we found no significant differences between any of the

waves along any of the four dimensions.

Insert Table 1 About Here

Measurement

We developed scales for our key variables based on prior research and relevant theory.

We pre-tested a preliminary set of measures with managers from local companies who had

expertise with M&As and incorporated their feedback into a revised set of instruments that

comprised the final questionnaire. Table 2 reports the details of the scales we used in our

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analysis, including the specific questionnaire items that make up each scale, as well as the

Cronbach alpha reliability values for each. The reliability values of all of the measures meet

Nunnally’s (1978) criterion of acceptability of .70 or above, and factor analysis confirmed the

unidimensionality of all scales. Table 3 reports descriptive statistics and zero-order correlations

among constructs. We discuss the specific scales below.

Insert Tables 2 and 3 About Here

Given that our central contention is that business similarity, product complementarity,

and geographic complementarity are three distinct types of relatedness that have different

characteristics, it is important to demonstrate in the data that each type is distinguishable from

the others. This can be achieved by examining the cross-correlations for the three types of

relatedness. If business similarity and product complementarity were not distinct types of

relatedness, the correlation between them would be expected to be high. As shown in Table 3,

the opposite is true; the correlation between business similarity and product complementarity is

-.06. Likewise, if product and geographic complementarity merely represented a single

construct, they would also be expected to have a high correlation. However, Table 3 shows a

relatively low correlation of .17 between these conceptions of complementarity, providing

support for our argument that they represent distinct sources of synergy in M&As. The third

dyadic comparison, between business similarity and geographic complementarity, shows a

higher correlation of .44. As described below, however, there is little resemblance between the

survey questions on which these two constructs are based, suggesting that this correlation exists

between the constructs themselves, rather than providing evidence of a lack of discriminant

validity.

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The questionnaire-generated data for each acquisition raises the possibility of common

method variance, specifically through single source bias. As recommended by Podsakoff and

Organ (1986), we used Harman’s one-factor test to assess the possibility that systematic bias

resulted from our requisite reliance on a single informant for each M&A. This test assumes that

the manifestation of single source bias would occur through the presence of a single factor that

explains a significant percentage of the variance across the variables of interest in our model.

We conducted a factor analysis of the variables in our study, concluding that single source bias is

not a significant concern in the data, since three distinct factors can be identified.

Acquisition performance is the dependent variable. It is reported on a five-point scale in

response to the question, “Overall, how would you rate today the performance of the

acquisition?” The scale runs from 1 = Very poor to 5 = Very good. Even though this is a single-

item scale, we believe that it provides an accurate assessment of acquisition performance,

especially given the level of authority and functional responsibilities of the individuals who

completed the questionnaires. This approach is further recommended by its ability to focus on

the performance of the acquisition alone, rather than that of the acquirer or the combined entity.

Isolating the performance of an acquisition is often a challenge, given that accounting measures

for the target firm are often consolidated into the acquiring firm’s overall results after the merger,

therefore requiring inferences to be drawn about the performance of the acquisition itself from

the combined firm’s overall performance.

We have four explanatory variables. First, business similarity measures the degree to

which the target firm and its acquirer were alike as independent entities prior to the acquisition,

considering product lines and operating activities. Six items are included in the scale, which has

a Cronbach alpha of .71. Second, product complementarity measures the extent to which, prior

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to the acquisition, the two firms had product lines and technology that supplemented each other.

A total of three items comprise this scale, which has a Cronbach alpha of .80. Third, geographic

complementarity captures the degree to which the acquisition expanded the footprint of the

combined organization, compared to the two independent firms prior to the acquisition. This

two-item scale has a Cronbach alpha of .73. Finally, degree of integration measures the extent to

which the target firm was integrated into the structure of the acquirer along four dimensions:

strategy formulation, marketing, R&D, and operations. This four-item scale has a Cronbach

alpha of .76.

Our hypotheses predict a moderating effect of the degree of integration on each

dimension of relatedness. We test these with interaction terms, which are computed in the

customary manner by first mean-centering the values on all independent variables to generate

deviation scores for each observation, and then multiplying each deviation score for the relevant

type of complementarity by the corresponding deviation score for the degree of integration

(Aiken & West, 1991; Jaccard, Turrisi, & Wan, 1990).

Control Variables

We include as control variables several measures that have been linked to acquisition

performance in prior research. First, acquisition size has been shown to have a negative

influence on performance (Copeland, Koller, & Murrin, 1994). To control for the size of the

acquisition, the model includes the natural log of the acquisition price (in millions). Seven firms

in the sample declined to provide the acquisition price; for these firms, we used mean

substitution.

21

Second, prior research has generally demonstrated a significant effect of acquisition

experience (Lubatkin, 1983), in both linear and curvilinear models (Haleblian & Finkelstein,

1999). We control for this factor by including the natural log of the number of prior acquisitions

undertaken by the acquiring firm in the five years prior to the administration of the

questionnaire.1 A third control measure is years since acquisition. We include this measure

because our data include acquisitions that occurred over a multi-year period.

Fourth, several scholars have argued that implementation capabilities vary at the firm

level and are influential for acquisition performance (Haspeslagh & Jemison, 1991; Zollo, 1998).

We control for quality of implementation by including in our model a seven-item scale that

captures the acquirer’s post-hoc assessment of the implementation of the acquisition. The scale

incorporates a broad range of activities, including functions (such as sales) and systems (such as

financial reporting). The scale has a Cronbach alpha of .82. By controlling for the quality of

implementation, our analyses capture the pure benefits of complementarity, since the impact of

the implementation process is partialled out.

Prior research has found a positive effect on acquisition performance when the target and

acquiring firms have parallel managerial approaches (e.g., Datta, 1991). While this is not

properly considered a source of synergy or a traditional conception of relatedness in M&As, we

acknowledge that it is nonetheless an important criterion in acquisition success. Thus, our final

control variable, management similarity, reflects the degree to which the target and its acquirer

were alike prior to the acquisition in terms of managerial approach. Three items are included in

the scale, which has a Cronbach alpha of .70.

1 The natural log is used because the variable acquisition experience is not normally distributed. Because of the possibility of a firm having zero prior acquisitions, for which the natural log is not defined, we added a unity constant to each firm’s reported acquisitions before taking its natural log.

22

Model

We analyzed our data using ordinary least squares regression, with results reported in

Table 4. As shown, we first construct a base case, in which only the control variables are used to

explain acquisition performance (Model 1). By comparing the change in R2 from this base case

to a series of models that include our hypothesized independent variables, we demonstrate that

the variables of interest have incremental value in explaining acquisition performance. Model 2

introduces the main effects. The interaction terms are added in Model 3, which is used to test

our hypotheses.

Insert Table 4 About Here

As shown by the F-statistics presented in Table 4, all of the models are statistically

significant. The change in R2 is also highly significant for both models 2 and 3, suggesting that

our independent variables add meaningful explanatory power with regard to acquisition

performance. For our full analysis, the adjusted R2 of .34 in model 3 indicates that our model

contributes substantially to an understanding of acquisition performance, although other factors

are also at play. All of the variance inflation factors are well below 10, indicating that

multicollinearity is not a concern.2

RESULTS

The empirical evidence strongly supports our contention that a high degree of integration

is beneficial for realizing value from business similarity but a low degree of integration is

2 Results from these diagnostic tests are available from the authors upon request.

23

preferable for realizing value from geographic complementarity. For product complementarity,

however, the data suggest a more nuanced relationship with acquisition performance than theory

would suggest.

Regression results are reported in Table 4, but the statistical nature of interaction terms

requires further analysis to test the hypotheses above. We present these analyses in Tables 5 and

6. In Table 5, we present a differentiation of the full regression equation at various degrees of

integration. The algebraic notation for these calculations is shown below, using product

complementarity as an example (Aiken & West, 1991; Jaccard et al., 1990):

Acquisition Performance---------------------------------------- = bPC + bPC*DI * Degree of Integration (DI)Product Complementarity (PC)

Based on the data presented in the unstandardized column of Table 4, Model 3, the coefficient

values can be substituted as follows to obtain point estimates at different values for degree of

integration:

Acquisition Performance--------------------------------- = -.230 + .159 * Degree of IntegrationProduct Complementarity

The same procedure is conducted with regard to business similarity and geographic

complementarity. In Table 5, we present calculations of these values at five specific levels of

integration: the minimum, mean, and maximum values, as well as the values that are plus and

minus one standard deviation from the mean. Because the variables were mean-centered prior to

the computation of the interaction terms, the calculations are performed using centered values.

24

For help in interpretation, we also show the corresponding raw scores in the heading of Table 5.

Standard errors are calculated based on a formula presented by Jaccard et al. (1990):

SEPC*DI = [VarPC + (DI2 * VarPC*DI ) + (2 * DI * Cov (PC, PC*DI))] 1/2

Finally, we calculate t-statistics by taking the point estimate divided by the standard error, and

assess significance based on the overall degrees of freedom for Model 3.

Insert Tables 5 and 6 About Here

For H1A, H2A, and H3A, we simply predict the direction in which the degree of

integration moderates the relationship between acquisition performance and each of the three

types of relatedness – i.e., in a positive direction for business similarity and product

complementarity, and in a negative direction for geographic complementarity. These hypotheses

are tested by t-tests of the differences in means for each type of relatedness at high and low

degrees of integration, respectively. For ease of presentation, we have summarized these results

in Table 6, conservatively defining high integration as one standard deviation above the mean,

and low integration as one standard deviation below the mean. Table 6 shows that there is strong

statistical support for the hypothesized directional relationships for all three types of relatedness.

As predicted in H1A, higher integration moderates the relationship between business similarity

and acquisition performance in a positive direction (difference = .720, t = 3.29, p<.01). Higher

integration is also a positive moderator between product complementarity and acquisition

performance, as predicted in H2A (difference = .337, t = 2.48, p<.05). For geographic

25

complementarity, however, higher integration is a negative moderator on acquisition

performance, as hypothesized in H3A (difference = -.536, t = -3.70, p<.001).

Our remaining hypotheses made more specific predictions about the relationships

between the types of relatedness and acquisition performance in high and low integration

scenarios. These more specific predictions can be evaluated by returning to Table 5 and

examining the maximum and minimum levels of integration. Support is generated for both H1B

(b = .553, t = 1.99, p=.05) and H1C (b = -.807, t = -2.42, p<.05), providing evidence that the

relationship between business similarity and acquisition performance is significantly positive at

high degrees of integration, and significantly negative at low degrees of integration.

Interestingly, H2B is not supported (b = .045, t = .25, ns), as product complementarity does not

have a significantly positive relationship with acquisition performance even at the maximum

degree of integration. On the other hand, H2C is strongly supported, as product

complementarity has a significant negative relationship with acquisition performance at low

degrees of integration (b = -.591, t = -2.67, p<.01).

We expected that the benefits of geographic complementarity could be achieved with a

low degree of integration (H3C), while a high degree of integration would counteract these

benefits (H3B), and the data are consistent with this effect. When the degree of integration is at

its lowest point, geographic complementarity is significantly associated with gains in acquisition

performance (b = .504, t = 2.22, p<.05). As the degree of integration increases, however, the

value of geographic complementarity diminishes, and becomes significantly negative at high

degrees of integration (b = -.508, t = -2.71, p<.01). Overall, these results provide strong support

for our thesis that not only is the relationship between relatedness and acquisition performance

26

moderated by the degree of integration, but also that the three dimensions of relatedness interact

with the degree of integration in different ways.

The control variables for quality of implementation and management similarity are

important features of our analyses. Controlling for implementation quality – which is, as

expected, positively related to acquisition performance – enhances the validity of our results

since it is clearly a sine qua non for achieving acquisition success. Likewise, the control for

management similarity conforms to a priori expectations, exerting an generally positive effect on

performance, although not at a level of statistical significance. Because of its inclusion in our

model, we are able to rule out the possibility that our results on the dimensions of relatedness are

produced by the underlying compatibility of the two management teams rather than the attributes

of relatedness between the two predecessor firms.

DISCUSSION

We advanced the thesis that business similarity and product and geographic

complementarity are three distinct sources of value in M&As, exerting different effects on

acquisition performance as the degree of integration increases. Our position is novel in its

application to M&As, but has grounding in recent theoretical developments. The elimination of

redundancies to achieve scale economies by consolidating similar activities requires that the

post-acquisition firm be highly integrated. Further, the tight coupling required to achieve

product complementarity implies that success should follow from a high degree of integration,

while pursuing network effects through geographic complementarity suggests that success is

more likely when integration is limited. As these contingency arguments challenge conventional

wisdom about value creation in M&As, we speculate that the well-known failure to create value

27

in acquisitions may be partially attributable to inappropriate matching between the kind of

relatedness and degree of integration.

Our disaggregation of relatedness into three elements – similarity and product and

geographic complementarity – argues for different relationships with acquisition performance as

the degree of integration changes. Our results are consistent with our expectations. We observe

a positive relationship between similarity and acquisition performance in the high integration

condition, and a strong negative relationship between acquisition performance and business

similarity in low integration conditions. The strong negative relationship with acquisition

performance when integration is low was observed for product complementarity as well.

Surprisingly, however, the relationship between product complementarity and acquisition

performance at high integration levels is tenuous at best, as no corresponding positive effect is

detected in our sample. With geographic complementarity, we obtain unambiguous support for

our expectations that its value is realized through a limited degree of integration, and may be

smothered by substantial integration.

We now consider the specific results in more detail. Our results for business similarity

are largely consistent with our expectations and reading of the existing literature. In essence, the

large body of research that finds relatedness has a positive effect on M&A value (e.g., Singh &

Montgomery, 1987), suggests implicitly that operational and product-market similarity drives

value creation. Our finding that similarity helps acquisition performance more when integration

is high fits well with such reasoning, since it is necessary to merge operations to enjoy the scale

economies from bringing together similar operations. Our results also show that not integrating

similar business operations is associated with negative acquisition performance. Thus, the

findings suggest a contingent relationship between business similarity and degree of integration

28

from acts of both commission and omission. Consequently, not only do our results reinforce

extant research which finds that greater relatedness, as shown by business similarity between

acquirer and target firms, exerts a positive effect on the performance of acquisitions (Datta,

Narayanan, & Pinches, 1992; Healy, Palepu, & Ruback, 1992), but they also add evidence that

the managerial action needed to elicit this value is a high degree of integration. Moreover,

failing to integrate appears to negate any potential synergies from business similarity, resulting in

poor acquisition performance.

Our findings with regard to product complementarity are less straightforward. While the

evidence supports the notion that extracting value from product complementarity is enhanced by

a high degree of integration, this by itself this is insufficient to produce high acquisition

performance. This result either casts doubt on the contention that product complementarity can

be a source of the synergistic benefits accruing from relatedness, or it indicates that a further

contingency beyond the level of integration is necessary to understand how value is created from

product complementarity.

We speculate that the latter explanation is more likely, and offers a direction for future

research. The realization of value from potentially complementary attributes of the merging

organizations may be offset by several factors; these include the limited exposure that acquiring

firms may have had with the target’s potentially complementary components, a lack of

familiarity with the challenges of absorbing and combining these non-similar components

(Cohen & Levinthal, 1990), and their limited experience in engaging in complementarity-driven

acquisitions (Haleblian & Finkelstein, 1999). Strategy scholars have referred to such difficulties

in fully understanding new businesses and industries as arising from unfamiliar ‘dominant logic’

(Prahalad & Bettis, 1986).

29

Even without considering the challenges posed by the nature of M&As, theorists have

noted that it is not easy for firms to successfully combine complementary elements (Whittington

& Pettigrew, 2003). Siggelkow (2002) explains that this occurs because the tight coupling

required by complementary attributes means that problems in any part of the system produce a

cascading and deleterious effect on the system as a whole. Given that complementarity is

difficult to sustain even in stable organizations, a complementary fit may be even more difficult

to attain when combining two hitherto independent firms. At a minimum, the work of these

theorists in combination with the unexpected empirical evidence presented here suggests that the

synergies of product complementarity are more difficult to realize than is generally assumed by

managers, or for that matter, by much of the extant literature on M&As.

An implication of our unambiguous finding that the benefits of geographic

complementarity can be best achieved without a high degree of integration is that it is

advantageous to keep integration to a minimum when the geographic footprint is being

expanded, since conversely, maximally integrating complementary geographies incurs

unnecessary coordination costs and may diminish the preexisting level of local responsiveness.

These findings further suggest that geographic complementarity is a double-edged sword, which

can hurt acquisition performance if geographic operations are erroneously integrated, but has the

potential to add value when minimal integration is fostered.

Putting it all together, one prescription for practicing managers is that the synergistic

value from different types of relatedness may not be additive. An acquirer might identify

economies of scale worth x resulting from business similarity, and separately but simultaneously

identify network effects worth y from geographic complementarity. The problem arises if the

acquirer anticipates the realization of x plus y when calculating the price it is willing to pay for

30

the target firm, as this ignores the possibility that the managerial actions necessary to realize x

may be opposite to the managerial actions necessary to realize y. In this study, we find evidence

of this problem; high levels of integration are associated with realizing x while low levels of

integration are associated with realizing y. As these are mutually exclusive conditions, it may

prove impossible for the acquiring firm to achieve the full additive value of both x and y, despite

the fact that both offer realistic synergistic potential.

Given the empirical support for our thesis, this study challenges the conventional wisdom

that automatically extols the virtues of potential complementarity as a source of value creation in

M&As. Our results stress that it is only possible to generate value from relatedness by a series of

appropriate managerial actions in the post acquisition phase – in particular by correctly matching

the degree of integration with the type of relatedness. As noted above, our work suggests a

potential answer to the poor record of M&A success (Jensen & Ruback, 1983; Sirower, 1997):

M&As may be undertaken with a sanguine view of the difficulties inherent in achieving

synergies, as acquirers focus on the potential benefits of relatedness without fully considering

either the difficulties of achieving them or the possibly harmful effects of disrupting the

independent tightly-coupled systems of the predecessor firms. Without a corresponding road

map for the type of integration that is required, synergies are unlikely to be realized in M&As.

At the same time, creating an advance road map for integration is a necessary, but hardly

sufficient condition for acquisition success. It is the first step on a process that is likely to be

highly sensitive to the dynamic and complex nature of M&As.

31

Limitations and Directions for Future Research

The most obvious limitation of our research is its reliance on a single source of data (the

mailed questionnaire), which serves as a basis for both the dependent and the independent

variables. However, as mentioned earlier, it is rare to find large sample studies with the richness

of detail that is necessary to investigate deeper sources of value creation in M&As, such as

complementarity.

Our theory and results suggest that relatedness is a more complex and nuanced concept

than it appears in extant research on M&As. When we drilled down into its component types,

we confirmed complex patterns of contingent relationships. Clearly the area is open for much

further investigation, by identifying more of the conditions that may serve as contingencies

influencing the relationship between types of complementarity and acquisition performance.

One such condition may be the antecedent processes, such as the ease of the negotiation process

leading up to the acquisition which, in turn, might be affected by the extent to which the acquirer

and target firm managements were acquainted with each other before the acquisition.

Further, since the concept of relatedness is fundamentally dependent on knowledge

sharing, transfer, and creation, one set of factors that could either serve as antecedent conditions

or as additional contingencies relate to the acquiring firm’s absorptive capacity (Cohen &

Levinthal, 1990) and its knowledge management capabilities and routines (Zollo, 1998).

In addition, the notion of integration itself may be unpacked; for example, certain

integration processes may be more effective than others, as well as other implementation

processes such as coordination and interaction (Haspeslagh & Jemison, 1991). Such hybrid

approaches may allow the benefits of complementarity to be realized without the corresponding

32

challenges affiliated with a high degree of integration. This would suggest testing the

moderating role of hybrid integration approaches in addition to full integration in future research.

Finally, our results raise the thorny issue of how to approach integration when a target

provides, say, both product and geographic complementarity to the acquirer. It is unclear if a

hybrid approach to integration would be successful, or if attempting to realize value

simultaneously from two types of relatedness that require opposing approaches to integration

might lead to conflict and poor results. Analyzing this problem will be a productive path for

future research.

CONCLUSION

Our main contribution is in advancing the position that acquisition performance will be

best understood if researchers unpack the concept of relatedness into business similarity and

product and geographic complementarity, three dimensions of relatedness that do not exert

parallel effects on acquisition performance and require different managerial actions for potential

synergies to be realized. At a theoretical level, this helps explain the well-known disconnect

between the conventional wisdom among managers that M&As are a powerful source of value

creation, and the consistent empirical evidence that finds that more M&As are failures than

successes. In practice, our findings alert managers to the crucial importance of selecting an

integration approach that is appropriately matched to the precise source of value; for example,

the degree of integration that allows geographic complementarity to flourish is detrimental to

product complementarity, and vice versa.

33

Table 1. Test Results for Non-Response BiasComparison of Comparison of Comparison of

Wave 1 and Wave 2 Wave 1 and Wave 3 Wave 2 and Wave 3Variable T-statistic T-statistic T-statisticSales 1.86 0.64 -1.12Number of Employees 1.12 0.32 -0.69Acquisition Size 0.18 -1.68 -1.54Acquisition Performance 0.26 0.76 0.35

N (Wave 1) = 52 N (Wave 1) = 52 N (Wave 2) = 16N (Wave 2) = 16 N (Wave 3) = 22 N (Wave 3) = 22

Natural logs were used for all figures except Acquisition Performance.

** Significant at the 0.01 level (2-tailed).* Significant at the 0.05 level (2-tailed).

34

Table 2. Questionnaire Items Used in Constructs.

Acquisition Performance Scale: 1= Very poor, 2 = Poor, 3 = Average, 4 = Good, 5 = Very good

1 item Overall, how would you rate today the performance of the acquisition?

Business SimilarityCronbach alpha reliability = .71

Scale: 1= Not at all, 2 = To a limited extent, 3 = To some extent, 4 = To a considerable extent, 5 = To a great extent

6 itemsPrior to the acquisition, to what extent were your firm and the target similar in the following areas?

1. Customer characteristics2. Distribution channels3. Products4. Technology5. Operations6. Degree of diversification

Product ComplementarityCronbach alpha reliability = .80

Scale: 1= Not at all, 2 = To a limited extent, 3 = To some extent, 4 = To a considerable extent, 5 = To a great extent

3 itemsPrior to the acquisition, to what extent did you expect the target to complement (i.e., round out) your firm or vice versa, in the following?

1. Products2. Product portfolio3. Technology portfolio

Geographic ComplementarityCronbach alpha reliability = .73

Scale: 1= Not at all, 2 = To a limited extent, 3 = To some extent, 4 = To a considerable extent, 5 = To a great extent

2 itemsPrior to the acquisition, to what extent did you expect the target to complement (i.e., round out) your firm or vice versa, in the following?

1. Geographic scope2. Distribution channels

Acquisition Experience1 item How many acquisitions has your firm completed in the last five years?Quality of ImplementationCronbach alpha reliability = .82

Scale: 1= Very poor, 2 = Poor, 3 = Average, 4 = Good, 5 = Very good

7 itemsHow would you rate today the implementation of the acquisition on the following?

1. Financial integration2. HR/personnel systems integration3. IT systems (email, software …) integration4. Sales integration or cross-selling5. Cultural integration6. Technology integration7. Operational integration

Management SimilarityCronbach alpha reliability = .70

Scale: 1= Not at all, 2 = To a limited extent, 3 = To some extent, 4 = To a considerable extent, 5 = To a great extent

3 itemsPrior to the acquisition, to what extent were your firm and the target similar in the following areas?

1. Managerial skills2. Management style3. Business strategy

Degree of IntegrationCronbach alpha reliability = .76

Scale: 1= Not at all, 2 = To a limited extent, 3 = To some extent, 4 = To a considerable extent, 5 = To a great extent

4 itemsTo what extent did you integrate the following functions?

1. Strategy formulation2. Marketing3. R&D4. Operations

35

Table 3. Descriptive Statistics and Zero-Order Correlations

Variable Min Max Mean SD 1 2 3 4 5 6 7 8 9 10

1. Acquisition Performance 1.00 5.00 3.62 1.01 1.00

2. Business Similarity 1.00 4.50 3.01 0.72 0.15 1.00

3. Product Complementarity 1.00 5.00 3.15 1.09 -0.18 † -0.06 1.00

4. Geographic Complementarity 1.00 5.00 2.97 1.18 -0.07 0.44 ** 0.17 1.00

5. Degree of Integration 1.00 5.00 3.27 1.06 0.16 0.30 ** 0.11 0.09 1.00

6. Acquisition Size (ln) -0.51 7.72 2.93 2.01 -0.10 0.19 † 0.05 -0.09 0.16 1.00

7. Acquisition Experience (ln) 0.00 3.71 1.90 0.80 0.04 -0.01 0.11 0.31 ** 0.02 0.07 1.00

8. Years since Acquisition 1.92 8.50 4.75 1.70 -0.05 -0.09 -0.06 0.15 0.07 -0.24 * 0.12 1.00

9. Quality of Implementation 1.14 4.71 3.36 0.73 0.43 ** 0.29 ** -0.04 -0.01 0.41 ** 0.11 -0.01 -0.05 1.00

10. Management Similarity 1.00 4.67 2.77 0.81 0.31 ** 0.33 ** -0.04 0.24 ** 0.05 0.13 -0.01 -0.02 0.22 * 1.00

SD = Standard DeviationN = 88

** Correlation is significant at the 0.01 level (2-tailed).* Correlation is significant at the 0.05 level (2-tailed).† Correlation is significant at the 0.10 level (2-tailed).

36

Table 4. Determinants of Acquisition Performance (a)

Model 1 Model 2 Model 3Variables b b T-stat b b T-stat b b T-statConstant Term 1.649 2.414 * 1.604 2.056 * 1.230 1.648Control Variables:

Acquisition Size (ln) -0.084 -0.167 -1.251 -0.076 -0.150 -1.125 -0.060 -0.120 -0.941Acquisition Experience (ln) 0.085 0.067 0.508 0.101 0.080 0.597 0.049 0.038 0.301Years since Acquisition -0.090 -0.150 -1.325 -0.101 -0.170 -1.471 -0.084 -0.141 -1.285Quality of Implementation 0.552 0.398 3.774 ** 0.558 0.403 3.491 ** 0.635 0.458 4.099 **Management Similarity 0.203 0.161 1.436 0.193 0.153 1.311 0.206 0.164 1.474

Business Similarity 0.083 0.060 0.462 -0.035 -0.025 -0.199Product Complementarity -0.247 -0.267 -2.347 * -0.230 -0.248 -2.293 *

Geographic Complementarity -0.122 -0.143 -1.068 -0.070 -0.083 -0.629Degree of Integration 0.009 0.009 0.078 -0.006 -0.007 -0.059Interaction Terms:

Business Similarity x Degree of Integration 0.340 0.295 2.726 **Product Complementarity x Degree of Integration 0.159 0.193 1.831 †

Geographic Complementarity x Degree of Integration -0.253 -0.346 -2.836 **

F 2.900 ** 2.848 ** 3.276 **Degrees of Freedom 12, 74 16, 70 19, 67F change 2.150 † 3.761 *R2 0.320 0.394 0.482Change in R2 (b) 0.074 0.088 **Adjusted R2 0.210 0.256 0.335

N = 88(a) Columns labeled "b" present unstandardized variables; Columns lableled "b" present standardized variables;

Results for dummy variables are calculated but not shown(b) Change in R2 is from the preceding model; The change in R2 for Model 3 is significant compared to both prior models

** Significant at the 0.01 level (2-tailed).* Significant at the 0.05 level (2-tailed).† Significant at the 0.10 level (2-tailed).

37

Table 5. Interpretation of Interaction Coefficients (a)

Dependent Variable = Acquisition Performance

Value for Degree of Integration

Minimum Minus 1 SD Mean Plus 1 SD MaximumRaw Score: 1.00 2.21 3.27 4.33 5.00

Centered Score: -2.27 -1.06 0.00 1.06 1.73

Business Similarity

Point Estimate -0.806 -0.395 -0.035 0.325 0.553

Standard Error 0.333 0.219 0.175 0.219 0.277

T-value -2.424 -1.804 -0.199 1.486 1.993

Level of Significance (b) * † †

Product Complementarity

Point Estimate -0.591 -0.399 -0.230 -0.061 0.045

Standard Error 0.221 0.136 0.100 0.136 0.181

T-value -2.669 -2.930 -2.300 -0.452 0.249

Level of Significance (b) ** ** *

Geographic Complementarity

Point Estimate 0.504 0.198 -0.070 -0.338 -0.508

Standard Error 0.227 0.145 0.112 0.145 0.188

T-value 2.221 1.366 -0.625 -2.331 -2.706

Level of Significance (b) * * **

(a) Uses unstandardized coefficients(b) Degrees of Freedom = 67

SD = Standard Deviation** Significant at the 0.01 level (2-tailed).

* Significant at the 0.05 level (2-tailed).† Significant at the 0.10 level (2-tailed).

38

Table 6. Mean Differences in High and Low Integration (a)

Dependent Variable = Acquisition Performance

Point EstimatesLow Integration High Integration Standard

(Minus 1 SD) (Plus 1 SD) Difference Error (b) T-value (c)

Business Similarity -0.395 0.325 0.720 0.219 3.290 **

Product Complementarity -0.399 -0.061 0.337 0.136 2.478 *

Geographic Complementarity 0.198 -0.338 -0.536 0.145 -3.697 **

(a) Uses unstandardized coefficients(b) As shown in Table 5, the standard error at 1 standard deviation below the mean is equal to the standard

error at 1 standard deviation above the mean(c) Degrees of freedom = 67

SD = Standard Deviation** Significant at the 0.01 level (2-tailed).* Significant at the 0.05 level (2-tailed).† Significant at the 0.10 level (2-tailed).

39

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