antecedents of m&a success: the role of strategic … · 2017-09-09 · strategic management...

Strategic Management JournalStrat. Mgmt. J., 35: 269–291 (2014)

Published online EarlyView 26 April 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/smj.2091

Received 9 February 2011 ; Final revision received 16 November 2012

ANTECEDENTS OF M&A SUCCESS: THE ROLE OFSTRATEGIC COMPLEMENTARITY, CULTURAL FIT,AND DEGREE AND SPEED OF INTEGRATION

FLORIAN BAUER1 and KURT MATZLER2*1 MCI Management Center Innsbruck, Innsbruck, Austria2 Department of Strategic Management, Marketing and Tourism, University ofInnsbruck, Innsbruck, Austria

In this paper, we develop a comprehensive model of M&A success. We integrate fundamentalconstructs of different schools and discuss their interdependencies with M&A success. Ourtheoretical framework was tested empirically across a sample of 106 SME transactions in themachinery, electronic, and logistic industries in the German-speaking part of Central Europe.The results of our study support the demand for an integrative perspective and theory on M&A.M&A success is a function of strategic complementarity, cultural fit, and the degree of integration.Strategic complementarity also positively influences cultural fit and the degree of integration.Cultural fit positively influences M&A success, but surprisingly has a negative impact on thespeed and degree of integration. The degree of integration is positively related to speed ofintegration. Copyright 2013 John Wiley & Sons, Ltd.

INTRODUCTION

Next to strategic alliances and joint ventures,mergers and acquisitions (M&A) are an importantsource of external growth and corporate devel-opment. For more than a hundred years, theyhave represented an essential part in strategicmanagement practice and research (Cartwright,2006). Although the number of transactions hasdeclined through the current financial and eco-nomic crisis, there is strong evidence that anew M&A wave has already started. However,it is not the big transactions—which are quiteprominent in the media—that create the enor-mous volume in the market for corporate con-trol (e.g., US$1.78 trillion in 2011, according to

Keywords: M&A; strategic complementarity; culturalcompatibility; post-merger integration; M&A success*Correspondence to: Kurt Matzler, Department of StrategicManagement, Marketing and Tourism, University of Innsbruck,Universitatsstrasse 15, A-6020 Innsbruck, Austria.E-mail: [email protected]

Copyright 2013 John Wiley & Sons, Ltd.

Bloomberg), but it is rather the small and mediumsized enterprise (SME) transactions from whichthis amount mainly derives (Jansen, 2008). Eventhough SMEs play an important role for the Euro-pean economy—e.g., they represent 99 percentof all European companies (Avram and Kuhne,2008), and the last M&A wave was mainly SMEdriven (Jansen, 2008; Salvato, Lassin, and Wik-lund, 2007)—they are broadly ignored in cur-rent research. Success rates of M&As are poorin general, and it is regularly reported that onaverage 40–60 percent of M&As fail in creatingvalue (Bagchi and Rao, 1992; Bower, 2001); someauthors even speak of failure rates between of 70and 90 percent (Christensen et al., 2011).

Since the 1970s, the amount of research inM&A has increased enormously, and the M&Aphenomenon has been studied through severaltheoretical lenses (Barkema and Schijven, 2008;Birkinshaw, Bresman, and Hakanson, 2000; Lars-son and Finkelstein, 1999). With the growingbody of research and literature, four schools

270 F. Bauer and K. Matzler

of thought, which reduce the complexity ofthe research field, have become well established(Haspeslagh and Jemison, 1991; Schewe, Lohre,and Ortwein, 2007). Firstly, the financial eco-nomic school—as the most prominent stream inliterature (Cartwright and Cooper, 2001; Stahland Voigt, 2008)—analyzes the performance andwealth effects of M&As on the basis of stockmarket-based measures (Dixon Wilcox, Chang,and Grover, 2001; Haspeslagh and Jemison, 1991).In so-called event studies, the share prices of buyerand bidder companies are investigated in a certaintime frame around the announcement day (Aktas,de Bodt, and Cousin, 2007; Haleblian and Finkel-stein, 1999). Secondly, scholars of the strategicmanagement school have studied the effect of pre-merger relatedness, perceived similarity, or com-plementarity on performance (Cartwright, 2006;Chatterjee, 2009; Haspeslagh and Jemison, 1991;Larsson and Finkelstein, 1999). Thirdly, the orga-nization behavior school investigates the effects oftransactions on organizations, organizational cul-ture, and individuals (Birkinshaw et al., 2000;Haspeslagh and Jemison, 1991) or the impactof organization-related variables, e.g., acquisitionexperience (Haleblian and Finkelstein, 1999; Hale-blian, Kim, and Rajagopalan, 2006) on perfor-mance. Studies on international M&As look atthe impact of cultural distance on M&A suc-cess. Barkema, Bell, and Pennings (1996), forinstance, taking an organizational learning per-spective, study the impact of cultural distance andexpansion experience on the longevity of interna-tional market entries via acquisitions. The organi-zation behavior school picks up premerger issues(e.g., cultural fit or compatibility) as well as post-merger issues (e.g., degree of integration). Lastly,the process school (or perspective) has derivedfrom the strategic management and the organi-zation behavior school (Haspeslagh and Jemison,1991). Following the arguments of scholars in thisfield, M&A performance is fostered by the M&Aprocess (Haspeslagh and Jemison, 1991; Jemisonand Sitkin, 1986). An effective and efficient inte-gration process is decisive for the postmerger inte-gration phase and, therefore, for the success of atransaction (Birkinshaw et al., 2000).

These schools of thought are not mutuallyexclusive. However, most scholars usually fol-low an isolated perspective (Larsson and Finkel-stein, 1999; Schewe et al., 2007). Even thoughthe call for an integrative perspective is not

new (Buono and Bowditch, 2003; Cartwright andCooper, 2001), very little research has been donein developing a holistic understanding of M&Asand their performance (Larsson and Finkelstein,1999; Sarkar et al., 2001). Next to the specializa-tion on the schools of thought, M&A research isstrongly fragmented concerning the scope of anal-ysis (Cartwright and Schoenberg, 2006). Next tothe focus on premerger issues, there is a growingperception of the importance of the postmerger orintegration phase (Cartwright, 2006; Larsson andFinkelstein, 1999; Stahl and Voigt, 2008). Eventhough this development should be favored, thereis clear evidence that an isolated perspective ona single phase disregards the interdependencies ofthe M&A process (Cartwright, 2006; Cartwrightand Cooper, 2001; Haspeslagh and Jemison, 1991;Larsson and Finkelstein, 1999). As prior workclearly shows, M&A success depends on pre-merger issues as well as on postmerger issues(Barkema and Schijven, 2008; Bower, 2001; Stahland Voigt, 2008). However, the fragmentation con-cerning schools of thought and the isolation ofcertain phases “has been at the cost of a moreholistic understanding of what determines theirperformance and what consequences they bring”(Cartwright, 2006). Against the fragmented andspecialized background of M&As, the aim of thispaper is to (1) develop an integrative and moreholistic research model that connects central con-structs of three schools of thought (strategic man-agement, organizational behavior, and process),and (2) generate a deeper understanding of theinterdependencies of the M&A phases and theireffects on M&A performance. Due to the impor-tance of SMEs for the Central European economyand the deficit of attention in the current litera-ture, we test our research model with a sampleof SME transactions. Our paper is organized asfollows: After presenting the theoretical frame-work, we test a complex network of antecedents ofM&A success derived from three different schoolsof thought. The model tests the interdependenciesof strategic complementarity, cultural fit, speed ofintegration, degree of integration, and M&A suc-cess. The results shed light on the mechanismsthrough which the constructs influence success,and it becomes evident that taking an integrativeperspective is an important step forward in M&Aresearch. A better understanding of how and whythese constructs explain M&A success also yieldsimportant implications for managers.

Copyright 2013 John Wiley & Sons, Ltd. Strat. Mgmt. J., 35: 269–291 (2014)DOI: 10.1002/smj

Antecedents of M&A Success 271

M&A Issue Premerger phase Postmerger phase M&A successSchool of thoughtFinancial economic school

Wealth effects for shareholders; Timeframe: around theannouncement day.

M&A performancemeasures:

Stock market-based (e.g.,CAPM, CAR)Accounting-basedSurvey based

Prominent literature:Mandelker (1974), Hassan et al. (2007),Jensen and Ruback (1983), Jensen (1988)Strategic management school Strategic Fit:

RelatednessSimilarityComplementarity

Prominent literature:Singh and Montgomery (1987), Kim and Finkelstein (2009),Larsson and Finkelstein (1999) and Shelton (1988)

Organizational behavior school Cultural Fit:Cultural DistanceSimilarityCompatibility

Integration:Cultural IntegrationTask IntegrationDegree of Integration

Prominent literature:Chatterjee et al. (1992), Datta (1991),Appelbaum et al. (2000), Shrivastava (1986)

Process (perspective) school Process:Speed of integrationOther topics e.g.communication, etc.,

Prominent literature:Angwin (2004), Ellis, Reus and Lamont (2009),Homburg and Bucerius (2006) and Jemison and Sitkin (1986)

Figure 1. Research foci in M&A literature

DEVELOPING A MORE HOLISTICPERSPECTIVE ON MERGERS ANDACQUISITIONS

Our study focuses on SMEs, which are usuallynot listed on the stock market or publicly tradedand which have lower valuation rules concerningless stringent requirements for transparency aswell as for financial disclosures. Therefore, thefinancial economic school cannot be taken intoconsideration. The remaining three schools ofthought have different scopes of analysis andheterogeneous research foci as shown in Figure 1.Even though some researchers have begun tosynthesize issues of different research streams(Larsson and Finkelstein, 1999), there is still alack of a broader and more holistic perspective(Bower, 2001; Cartwright and Schoenberg, 2006;Larsson and Finkelstein, 1999). We argue thatM&A success depends on the central constructsof the schools of thought and derives frompremerger issues (strategic complementarity andcultural fit) as well as from postmerger issues(degree of integration and speed of integration). Inthe following section, we develop the theoreticalframework and the hypotheses of our researchmodel.

Strategic complementarity

A very prominent research stream in strategicmanagement literature presumes the strategic fitas decisive for M&A success (Homburg andBucerius, 2006; King et al., 2004; Seth, 1990).The central argument is that a high fit enlarges

market power and productivity (Cartwright, 2006).Representatives of the market-based view usuallydetermine “fit” with branch codes and argue thatbranch relatedness has a positive effect on suc-cess via economies of scale and the reductionof redundancies (Capasso and Meglio, 2005). Todate, there is no clear and valid empirical evidenceof a positive relationship between branch related-ness and M&A success (Stimpert and Duhaime,1997). The main reason for the inconclusiveresults lies in the discrepancy of branch relat-edness and perceived internal fit (Robins andWiersema, 1995). Researchers using the resource-based perspective operationalize the construct offit with product market, resource, and/or supplychain-related similarity (Pehrsson, 2006; Stimpertand Duhaime, 1997). Scholars regularly arguethat similarity is an indicator for the synergypotential of a transaction (Meyer and Altenborg,2008). Even though a higher similarity (indepen-dent from its measurement) seems to provide forbetter results (Capron, Mitchell, and Swaminathan,2001; Prabhu, Chandy, and Ellis, 2005; Swami-nathan, Murshed, and Hulland, 2008; Tanriverdiand Venkatraman, 2005), there are no overall con-sistent findings. Despite the dominant logic thatstrategic similarity fosters value creation, there arefundamental arguments that complementary differ-ences are more crucial for M&A success. Comple-mentarity, as a relatively new concept in the M&Aliterature, has a promising denotation (King et al.,2004; Larsson and Finkelstein, 1999).

In the similarity literature, the strategic fitconcept (related or similar businesses sharesimilarities in management styles, organizational



culture, and administrative processes) enablescompanies to effectively leverage resources andcapabilities (Palich, Cardinal, and Miller, 2000).Therefore, researchers from this stream arguethat strategic fit improves M&A performance.Even though literature shows a tendency thatsimilarity has a positive impact on M&A success,empirical evidence remains mixed. Anotherresearch stream argues that complementary dif-ferences offer valuable resource redeployment(Larsson and Finkelstein, 1999). Here, thevalue creation mechanisms are quite different.While similarity is seen as an indicator forefficiency-based synergies (scale and scope), com-plementarity provides firms with both efficiencysynergies and value created from those differencesthat are mutually supportive (enhancement-basedsynergies). Studies following this dominant logicgive clear empirical evidence that complementar-ities are a significant factor for M&A success.Thus, as Kim and Finkelstein (2009) note, com-plementarities offer merging firms a “wider arrayof business opportunities to develop competenciesthat either firm could not create alone” (p. 618,see also Capron and Mitchell, 1998; Harrisonet al., 1991; King, Slotegraaf, and Kesner, 2008).Additionally, Larsson and Finkelstein (1999)point out that complementarity increases M&Asuccess by boosting synergy realization. Theydraw more attention to “economies of fitness”than to “economies of sameness.”

Complementarity has been studied in terms oftop management team complementarity (Krish-nan, Miller, and Judge, 1997), technologicalcomplementarity (Makri, Hitt, and Lane, 2010),strategic and market complementarity (Kim andFinkelstein, 2009), or product complementar-ity (Wang and Zajac, 2007). Most empiricalevidence on complementarity has been gainedfrom large transactions with publicly tradedcompanies. Interestingly, even though SMEs arequite different from large companies in termsof ownership profiles, innovativeness, and riskattitudes (Weitzel and McCarthy, 2011), there isonly poor empirical evidence concerning com-plementarity. From a resource-based perspective,complementary differences are the basis forresource redeployment and exploitation (Kimand Finkelstein, 2009; Sarkar et al., 2001).Complementarity is defined as differentcharacteristics that are independent and mutuallysupportive (Tanriverdi and Venkatraman, 2005;

Wang and Zajac, 2007). Through the interactionof complementary characteristics, value creationdoes not only derive from cost savings, value isalso created by a growing turnover and marketshare (Helfat, 1997). So far, studies have givenclear evidence that strategic complementarityhas a positive impact on M&A success throughenhancement-based synergies (Jemison and Sitkin,1986; Kim and Finkelstein, 2009; Sarkar et al.,2001; Tanriverdi and Venkatraman, 2005; Wangand Zajac, 2007). Krishnan et al. (1997) stud-ied top management team complementarity ofpublicly traded companies. Makri et al. (2010)tested technology complementarity with a sampleof 96 high technology M&As. In their sampleof publicly traded U.S. acquirers, they measuredtechnology complementarity as the overlap inpatents in the same subcategory, but in a differentclass. Kim and Finkelstein (2009) analyzedstrategic and market complementarity in the U.S.commercial banking industry. All acquiring com-panies were publicly traded in the United States.Market complementarity was operationalized withthe Mahalanobis distance (MD) between the loanportfolio of the acquirer and the target markets;strategic complementarity was measured withthe MD in the loan portfolio between target andacquirer. Tanriverdi and Venkatraman (2005)operationalized complementarity as a secondorder construct consisting of internal product,external product, customer, risk and investment,and alliance management knowledge relatedness.They used a multibusiness sample from Fortune1000. King et al. (2008) examined publicly tradedhigh technology firms with a minimum marketcapitalization of the target of US$10 million.They analyzed resource complements (R&D andmarketing), operationalizing it with secondarydata. In their study on “alliance or acquisition,”Wang and Zajac (2007) focus on product related-ness (in terms of similarity and complementarity)and use the categorization level of the four-digitNAICS for measurement with data from thelargest U.S. firms from 1991 to 2000. Our studydiffers in two respects from previous work.First, we apply the concept of complementarityin a different empirical context. To the best ofour knowledge, this is the first study explicitlyexamining SME transactions with this concept.M&A behavior and success within SMEs arequite different from large deals, which are thefocus of most studies. SMEs differ from big firms



in terms of ownership profiles, innovativeness,and risk attitudes (Johnson, 2007). Weitzel andMcCarthy (2011) come to the conclusion thatSME M&As are quite different from larger deals.Thus, they point out that M&A theory has tobe adapted to SME transactions (Weitzel andMcCarthy, 2011). Second, to the best of ourknowledge, our study is the first one in whichthe concept of complementarity is broadened ina way that product market, resource-related, andvalue chain issues are considered and measuredfrom a managerial perspective. We thereforepropose:

Hypothesis 1 (H1): The greater the strategiccomplementarity, the greater the M&A success .

There is an interdependency of culture andits more tangible characteristics such as strategy(Rowlinson, 1995). Rowlinson (1995) argues that“culture can be seen as the meanings attachedto more tangible aspects of organizations, suchas strategies, structures, and labor managementpractices” (p. 123). If the strategies of the twocompanies involved are complementary, they aremutually supportive. Hence, the cultures of thetwo organizations are likely to be compatible.Therefore, we argue that strategic complementarityindicates a higher cultural fit (Stahl and Voigt,2008). This leads us to the following hypothesis:

Hypothesis 2 (H2): The greater the strategiccomplementarity, the greater the cultural fit .

Next to the positive relation of strategy andorganizational culture, strategic complementarityhas an impact on the integration process. The fit ofstrategic characteristics fosters the speed of inte-gration (Bragado, 1992). Homburg and Bucerius(2006) state that internal relatedness has a mod-erating effect on speed of integration. We assumea positive relationship between strategic comple-mentarity and integration speed for two reasons.First, with a high degree of strategic comple-mentarity, the need for postmerger modification islower, and, secondly, with a high degree of com-plementary characteristics, mutual support can begenerated faster than with low complementarity(Cording, Christmann, and King, 2008; Homburgand Bucerius, 2006). Thus, we propose:

Hypothesis 3 (H3): The greater the strategiccomplementarity, the greater the speed of inte-gration .

The underlying assumption of our next hypothe-sis is that, in the postmerger phase, complementaryfirms can use their common potential better thannoncomplementary firms (Pablo, 1994). Larssonand Finkelstein (1999) point out that complemen-tarities increase M&A success by boosting synergyrealization. Value creation takes place in the post-merger integration phase (Haspeslagh and Jemi-son, 1991; Larsson and Finkelstein, 1999). There-fore, we conclude that firms with complementarycharacteristics try to reach a high degree of integra-tion to benefit from both synergies and potentials(Ellis et al., 2009; Larsson and Lubatkin, 2001;Zollo and Singh, 2004):

Hypothesis 4 (H4): The greater the strategiccomplementarity, the greater the degree of inte-gration .

Cultural fit

Cultural incompatibility or misfit is one of themost cited reasons for the low success rates ofM&As (Bijlsma-Frankema, 2001; Cartwright andSchoenberg, 2006; Lodorfos and Boateng, 2006;Nguyen and Kleiner, 2003). This enormous impactoriginates from the influence of organizational cul-ture on nearly all organizational practices, direc-tives, leadership styles, and administration pro-cesses (Chatterjee et al., 1992). Literature arguesthat national and organizational culture are sep-arate constructs with “different attitudinal andbehavioral correlates and, possibly, different impli-cations for the postmerger integration process”(Stahl and Voigt, 2008). In a recent literaturereview (Teerikangas and Very, 2006) and in ameta-analytic study (Stahl and Voigt, 2008), it wasfound that differences in national culture have aless negative impact on sociocultural integration,synergy realization, and shareholder value thancultural differences on an organizational level. Inthis paper, therefore, we focus on the organiza-tional dimension of culture, defined as beliefs,values, and assumptions shared by the membersof an organization (Schein, 1985).1 The cultural

1 Companies in our sample are from Austria, Germany, andSwitzerland. Whereas there are cultural differences between



fit is relevant for the realization of the synergiesand potentials (Cartwright, 2006; Cartwright andCooper, 2001; Datta, 1991). Although it seemsobvious that cultural similarity fosters integrationand success, there is empirical evidence that cul-tural differences have a strong positive impacton synergy and potential realization and, there-fore, on value creation (Cartwright and Cooper,2001; Schraeder and Self, 2003; Teerikangas andVery, 2006). For this reason, we rely on culturalfit—which allows similarities as well as differ-ences that mutually support each other—ratherthan on cultural similarity. As mentioned above,cultural fit has a strong impact on the realizationof potentials and synergies and on M&A success(Chatterjee et al., 1992; Schraeder and Self, 2003;Teerikangas and Very, 2006). This leads us to thefollowing hypothesis:

Hypothesis 5 (H5): The greater the cultural fit,the greater the M&A success .

As organizational culture influences organiza-tional practices, directives, leadership styles, andadministrative processes (Chatterjee et al., 1992),a high cultural fit goes along with lower organi-zational resistance in the postmerger integrationphase (Bijlsma-Frankema, 2004). On the otherhand, cultural misfit would lead to less accep-tance of the other organization, and to the desireto remain in the former culture. This circumstancewould cause the necessity of a longer changeperiod as employees would have to become famil-iar with the other culture and, therefore, accept it(Homburg and Bucerius, 2006; Olie, 1994). On theone hand, in the case of high organizational cul-tural misfit, a high speed of integration would havedetrimental consequences (Homburg and Bucerius,2006). One negative effect that could occur isingroup/outgroup bias (Elsass and Veiga, 1994).On the other hand, with a high cultural compatibil-ity, employees are more likely to abandon their for-mer culture and to accept the other culture faster.Therefore, we argue that with a higher cultural fit,a faster integration is possible (Bijlsma-Frankema,2004; Bragado, 1992; Jemison and Sitkin, 1986).Thus:

these countries (Hofstede, 1991), they still belong to the sameculture cluster with similar cultural profiles (Schneider andBarsoux, 1997).

Hypothesis 6 (H6): The greater the cultural fit,the greater the speed of integration .

As the degree of integration goes in one linewith the scope of interaction and coordinationbetween the two merging firms (Shrivastava,1986), the above-mentioned relation of cultureand organizational practices, directives, leadershipstyles, and administrative processes allows ahigher degree of integration. The risk of a culturalor organizational clash with a high degree ofintegration is lower with compatible organizationalcultures than with incompatible ones (Aguilera andDencker, 2004; Lodorfos and Boateng, 2006; Olie,1994). Furthermore, we argue that firms with ahigh cultural fit seek a deeper integration, as theycould better leverage efficiency- and enhancement-based synergies. Therefore, we propose that a highcultural fit fosters the ambition of a high degree ofintegration (Slangen, 2006; Teerikangas and Very,2006). Thus, we propose:

Hypothesis 7 (H7): The greater the cultural fit,the greater the degree of integration .

Degree of integration

The postmerger integration phase is often citedto be decisive for M&A (Haspeslagh and Jemi-son, 1991; Stahl and Voigt, 2008). In the post-merger integration phase, well-established opera-tional sequences and patterns are partially or com-pletely changed and, throughout the new com-pany, harmonized (Buono and Bowditch, 2003;Haspeslagh and Jemison, 1991). Therefore, thisphase is—due to employee resistance and a cul-tural clash—very risky. But without any integra-tion, resource redeployment and exploitation, aswell as the elimination of redundant resources,are not feasible (Cording et al., 2008; Homburgand Bucerius, 2006; Karim, 2006; Pablo, 1994).Apart from this argument, it must be stated thatthe degree of integration is a mixed blessing. Onthe one hand, there is a positive effect on synergyand potential realization and therefore on success(Larsson and Finkelstein, 1999), while on the otherhand, a high degree of integration leads to morechange and more coordination costs (Pablo, 1994;Slangen, 2006; Teerikangas and Very, 2006). Nev-ertheless, there is empirical evidence that showsthat at least some degree of integration is deci-sive for M&A success (Chatterjee et al., 1992;



Singh and Montgomery, 1987; Zollo and Singh,2004). We argue that a high degree of integrationleads to better resource redeployment and exploita-tion (King et al., 2004) and, therefore, to valuecreation (which derives from operative efficiencyand market power; Meyer and Altenborg, 2008).Therefore, we propose:

Hypothesis 8 (H8): The greater the degree ofintegration, the greater the M&A success .

Due to the fact that a high degree of integrationmeans a high degree of change on differentorganizational levels (sociocultural, production,marketing, and system integration), it is ques-tionable whether integration can be reachedat the same time on all organizational levels(Birkinshaw et al., 2000). The harmonization ofaccounting systems could possibly be realizedquickly, but a complete sociocultural integrationwould last much longer (Teerikangas and Very,2006). Even though it is typically argued thata fast integration is beneficial for the M&Aoutcome, a few—mainly qualitative—studiesaddress the positive effects of a slow integration(Inkpen, 2000; Olie, 1994). Beyond the effects ofintegration speed, the determinants that influencespeed are broadly neglected in academic literature.We argue that a high degree of integration causesenormous changes in the organizations that leadto the necessity of higher efforts for coordinationand interaction (Larsson and Finkelstein, 1999).Bragado (1992) argues that employees need timeto study and understand the other organization.Thus, the demand for studying and understandingis higher with a high degree of integration.Following these arguments, we claim that:

Hypothesis 9 (H9): The greater the degree ofintegration, the slower the speed of integration .

Speed of integration

Even though speed of integration seems to be akey driver of M&A transaction success, it is anunderresearched topic and has been addressed byonly a handful of academic studies (Angwin, 2004;Gerpott, 1995; Homburg and Bucerius, 2005,2006; Inkpen, McClelland, and Rockwood, 2000;Olie, 1994; Ranft and Lord, 2002). The speed of

integration describes the time period from the clos-ing of the deal to the desired degree of integration(Homburg and Bucerius, 2006). It is argued thatspeed of integration can lead to faster exploitationof synergies and to faster returns on investment(Angwin, 2004). From a behavioral psychologyperspective, speed of integration can reduce uncer-tainty among employees (Angwin, 2004; Homburgand Bucerius, 2005). A faster integration also min-imizes time spent in a suboptimal condition andtakes advantage of the momentum in the earlyenthusiasm phase after the deal (Angwin, 2004).Homburg and Bucerius (2005, 2006) empiricallytest the role of integration speed in marketingand sales, arguing that speed lowers customer’suncertainty and should therefore increase market-related performance (i.e., market share and cus-tomer loyalty), which in turn influence M&A per-formance. They find a strong relationship betweenspeed of integration and performance, however,which is moderated by internal and external relat-edness. As their study is limited to marketingand sales integration, the results are not necessar-ily generalizable to the integration of other func-tions. We follow the argument that faster inte-gration leads to less uncertainty, lower resistanceof employees, faster and more effective synergyand potential realization, and competitor’s inabil-ity to profit from the internal organizational changephase (Angwin, 2004; Cannella and Hambrick,1993; Cording et al., 2008; Homburg and Bucerius,2005). This leads us to the following hypothesis:

Hypothesis 10 (H10): The greater the speed ofintegration, the greater the M&A success .

As control variables, we use type of transaction,relative size, industry growth, and institutionaldistance. We chose these particular controls dueto their potential impact on the M&A processand success. Acquisition experience has been usedas a control variable in previous M&A studies(Uhlenbruck, Hitt, and Semadeni, 2006; Wrightet al., 2002) as it influences M&A outcomes.As in our sample of SME transactions whereonly 10 percent of the companies made morethan two transactions within one year, and only13 percent made more than four transaction inthe last ten years, we could not use acquisitionexperience as a control variable. Figure 2 showsthe hypotheses of our study.



Strategic management school

Organizational behavior school

Process (perspective) school

School of thought

M&A issue Premerger phase Postmerger phase M&A success

H1: +

H3: + H4: + H5: + H8: + H10: +

H6: + H9: -

H7: +

Strategiccomplementarity

Culturalcompatibility

Degree ofintegration

Speed ofintegration

M&A success

H2: +

Figure 2. Research model

METHODOLOGY

Sample and data

We used mail and internet survey methodologyfor data collection, which was conducted in sum-mer 2010. As already mentioned, we focused onSME transactions that took place between Jan-uary 2005 and April 2008. We chose this periodto guarantee that the integration process wouldeither be in a final stage or already completed(Ellis et al., 2009; Homburg and Bucerius, 2005;Zollo and Meier, 2008) and that the capacityfor recollection would still be sufficient (Krish-nan et al., 1997; Reus and Lamont, 2009). Inour survey, we focused on the German-speakingpart of Central Europe (Austria, Germany, andSwitzerland) and transactions in the machinery,electronic, and logistic industries. As respondents,we chose managers from the acquiring firms, asthey are most knowledgeable about the intentionsof transaction and postmerger integration (Elliset al., 2009; Walsh, 1988). We would have pre-ferred to interview managers from buying andcorresponding target firms, but due to manage-rial turnover, in most cases it was impossible toidentify former target executives (Homburg andBucerius, 2006). The transaction details were iden-tified with the Zephyr database. In sum, we couldidentify 976 transactions from which 524 wererelevant (the others were simply financial trans-actions, transactions without contact details, ortransactions where the firm was going bankrupt).To test our survey questionnaire, we conducted a

two-step pretest in spring 2010 (Churchill, 1995).Concerning the design and structure of our ques-tionnaire, we followed the recommendations ofDillman (2000). After sending out the question-naire, we received 41 complete answers. After atwo-week period, we conducted follow-up tele-phone calls and sent reminder e-mails. In the sec-ond wave, we gathered 55 questionnaires, and in afinal wave an additional 10 questionnaires werereceived. In sum, 106 completed questionnaireswere collected, which represents a response rateof 20.23 percent. Nonresponse bias was tested bycomparing the respondents of our three surveywaves (Armstrong and Overton, 1977). The resultsof the test indicate that nonresponse bias is nota problem. Item nonresponse bias was tested byusing Berdie and Anderson’s item response-rateindex (1976). The maximum number of missingvalues of one item was 5.6 percent and the relationof values to missing values is 104–1. Therefore,we conclude that item nonresponse bias is not aproblem.

Measurement development

In developing the measurement models, we fol-lowed the advice of King and colleagues that“future M&A researchers would be well advisedto build on past research models and not sim-ply create new models” (King et al., 2004). Forthis reason, we selected applicable and already-tested measurement models from a literaturereview.



Strategic complementarity

The construct of complementarity has been mostlystudied in more narrow terms as top manage-ment team, technological, strategic, and marketor product complementarity. In our study, wewanted to broaden the concept of complemen-tarity in a way that product market, resource-related, and value chain issues are considered.For assessing strategic complementarity, we usedthe constructs and items developed by Pehrsson(2006). We changed the wording in a way that wecould assess complementarity instead of similar-ity. Strategic complementarity is a second orderconstruct consisting of product market (measuredwith six items) and resource-related dimensions(measured with six items) as well as value chainattributes (measured with four items). Strategiccomplementarity is assessed on a 5-point scale.

Cultural compatibility

Up to now, there has been no consistent conceptualagreement on how to measure cultural fit orcompatibility (Teerikangas and Very, 2006). Aftercomparing different measurement models usedin the literature, we decided to use the scalesdeveloped by Jons and colleagues (Jons, Froese,and Pak, 2007; Jons, Hodapp, and Weiss, 2005).Cultural fit is a second order construct, whichconsists of four dimensions: strategy (measuredwith four items), structure (measured with twoitems), leadership (measured with five items),and interrelationship (measured with four items).Cultural fit is assessed on a 5-point scale.

Degree of integration

The degree of integration, as one of the centralconstructs of the postmerger phase, is assessedon different organizational levels. To display thedifferent levels of integration, we adopted the mea-surement model of Cording et al. (2008), whichagain is a second order construct. The four dimen-sions are: sociocultural integration (measured withthree items), integration of production (measuredwith two items), marketing integration (measuredwith three items), and system integration (mea-sured with three items). The degree of integrationis assessed on a 7-point scale, ranging from 1 = nointegration at all to 7 = complete integration .

Speed of integration

As with the degree of integration, there are differ-ent speeds for the different organizational levels.The desired degree of integration is not reachedfor all levels at the same time (Olie, 1994;Ranft and Lord, 2002). Therefore, our measure-ment model consists of the same levels as thedegree of integration. Following Cording et al.(2008), we assess speed of integration with fouritems and measure it on a 5-point scale rangingfrom 1 = longer than 24 months to 5 = shorter than7 months .

M&A success

M&A success and performance—as the depen-dent variable of our study—is a broadly discussedissue in the M&A literature. Its measurementreaches from stock market and accounting-basedmeasures to the assessment of key informants. Bynow, there is no consensus about the nature ofthe relation of M&A and firm success (Larssonand Finkelstein, 1999). Research based on stockmarket and accounting-based measures (so calledquantitative objective indicators) usually focuseson short-term periods around the announcementday and, therefore, ignores the importance of theintegration phase. Furthermore, most performancemeasures are one-dimensional stock market-based,and therefore “potentially relevant dimensionsof firm performance” are ignored (King et al.,2004). Even though one would use a multiple setof stock market and accounting-based measures,the problem of interpretation still remains. If astudy sample involves companies from more thanone country, due to different valuation rules, thedata would be difficult to compare. For instance,Weetman and Gray (1991) found that accountingprinciples differ from country to country, leadingto quantitative differences in profits reported.Basu, Hwang, and Jan (1998) find that differ-ences in accounting measurement rules affectthe predictability of earnings, and Leuz, Nanda,and Wysocki (2003) show that earnings man-agement varies systematically across institutionalclusters.

For these reasons, we decided to measure M&Aperformance from a managerial perspective.Managers from the acquiring firms tend to havean enormous knowledge about the transaction andthe integration phase (Datta, 1991; Homburg and



Bucerius, 2005), and, furthermore, many studiesgive clear evidence that their rating correlates(highly and significantly) with objective successmeasures (Datta, 1991; Homburg and Bucerius,2005). Further validation of the success measurewith the PDI database developed by Homburgand Bucerius (2005) was not feasible due to themissing disclosure requirement of most of thecompanies in our sample. As already mentioned,M&A success should consist of multiple mea-sures. With self-reported data, researchers suggestat least two success dimensions, one objectiveand one subjective (Datta, 1991; Reinartz, Krafft,and Hoyer, 2004). For this reason, we decidedto assess M&A success with the measurementmodel developed by Becker consisting of thetwo dimensions—each measured with four items(Becker, 2005). The objective dimension wasassessed using a 7-point scale ranging from1 = strong negative development to 7 = strongpositive development ; the subjective dimen-sion was measured on a 7-point scale rangingfrom 1 = completely disagree to 7 = completelyagree.

Control variables

The controls type of transaction , relative size, andindustry growth were measured as single items.The institutional distance (Kostova, 1999; Kos-tova, Roth, and Dacin, 2008) influences strategiesand behavior and survival rates of multinationalenterprises (Gaur and Lu, 2007; Xu and Shenkar,2002). Gaur and Lu (2007), for instance, arguethat institutional distance may present opportuni-ties of institutional arbitrage but also necessitateslearning about new environments and has perfor-mance effects for foreign subsidiaries dependingon ownership positions. To assess institutionaldistance, we used secondary data. We chose thedata from the global competitive index developedby the World Economic Forum, which consistsof three factors (basic requirements, efficiencyenhancers, and innovation and sophistication).The World Competitiveness Yearbooks have beenwidely used by researchers to measure differencesin institutional environments (Delios and Beamish,1999; Gaur and Lu, 2007). Distance was assessedas the deltas of buying country index and targetcounty index. The three deltas were bundled intoone construct.

RESULTS

Descriptive data and research approach

In Table 1, we show the descriptive data of ourresearch. We show the position of the respondents,the seat of the buying company, the type oftransaction, the type of industry, the averagegrowth three years prior to the transaction, therelative size, and the actual annual turnover of theconsolidated business.)

We used structural equation modeling for test-ing our research model. We decided to apply avariance-based approach (with SmartPLS; Ringle,Wende, and Will, 2005), instead of a covariance-based approach for four reasons: (1) PLS is bettersuited for more complex models (Haenlein andKaplan, 2004); (2) sample size requirements arelower (Fornell and Bookstein, 1982; Haenlein andKaplan, 2004; Tenenhaus et al., 2005); (3) weonly use reflective measurement models; there-fore, the accuracy of the results can be comparedwith the accuracy of covariance-based approaches(Vilares, Almeida, and Coelho, 2010); and (4) ourobjective is to explain M&A success—PLS opti-mizes the dependent construct locally, and notthe whole structural model—therefore, PLS hasa higher predictive character. Even though higherorder constructs reduce the accuracy, we appliedthem to increase the possibility of generalization(Gorsuch, 1983). For assessing the second orderconstructs, we chose the hierarchical componentsapproach suggested by Lohmoller (1989) insteadof the often-used two-step approach (Agarwal andKarahanna, 2000) for the reason that the two-stepapproach employs two independent approxima-tions. Therefore, several underlying assumptionsthat are inherent (Fornell and Yi, 1992) can lead toconfusing interpretations (Burt, 1973). The hybridapproach, even though it is a promising perspec-tive for higher order constructs, is not applicablefor our study due to missing guidelines for assess-ment and reporting. Before evaluating our researchmodel in two steps (assessment of the measure-ment models and assessment of the structuralmodel) following the guidelines of Hulland (1999)and the guidelines for assessing higher order con-structs employed by Wetzels et al. (2009), wetested our data for a potential common methodbias.



Table 1. Sample description

Sample description

Buyer and target country Industry % Relative size %

Austria n Machinery 36.8 <25% 48Switzerland 2 Electronics 28.3 25–49% 33Germany 14 Logistics 17 50–74% 11Austria 5 Others 16 75–100% 5Others 2 Missing 1.9 >100% 2Germany n Missing 1Switzerland 17Germany 28 Average growth % Annual revenues of consolidated business in ¤ %Austria 9Others 9 > −15% 3 <25 million 6Switzerland n −10% to −5% — 25–49 million 30Switzerland 14 −5% to 0% 7 50–99 million 28Germany 5 0% to 5% 45 100–249 million 19Austria — 5% to 10% 38 250–499 million —Others 1 11% to 20% 3 500–1,000 million 7

21% to 30% 2 >1,000 million 5Missing 2 Missing 5

Common method bias

We applied two statistical analyses to assess apotential common method bias. First, we con-ducted a Harman’s single factor test (Podsakoffand Organ, 1986). The results indicate no com-mon method bias problem. In addition, we usedthe stricter ad hoc approach suggested by Pod-sakoff et al. (2003) and followed the guidelinesfor assessment in PLS developed by Liang et al.(2007). The ratio of substantive variance to methodvariance is 46 to 1 (for details, see AppendixTable A1). We therefore conclude that commonmethod bias is not a problem in our data.

Assessing the measurement models

Before assessing the structural model, we evalu-ated each second order construct individually. Theevaluation shows that all manifest indicators of thefirst order constructs have clear loadings above 0.7,therefore, indicator reliability is given. Constructreliability, assessed with composite reliability andCronbach’s alpha, is fulfilled for all first and sec-ond order constructs as all values are clearly above0.7. Construct validity is given due to averagevariance extracted (AVE) values above 0.5. Dis-criminant validity for the second order constructswas assessed with cross loadings and the Fornelland Larcker (1981) criterion (see also Appendix

Table A2). All second order constructs are con-vergent and valid. Table 2 shows the evaluationcriteria for all second order constructs.

Speed of integration is the only first order con-struct in the structural model. After the deletionof two items, the measurement model was suf-ficiently reliable and valid. Before assessing thestructural model, we evaluated discriminant valid-ity using cross loadings and the Fornell-Larckercriterion for all items and constructs used in ourresearch model. The cross loadings criterion is ful-filled. Table 3 shows the assessment of the Fornell-Larcker criterion.

As the data shows, discriminant validity isgiven. Therefore we could start with the evaluationof the structural model and the hypotheses testing.

Assessing the structural model

Figure 3 represents the estimations obtained fromPLS analysis. The R2 value of 0.575 of thedependent construct indicates a substantial amountof variance for M&A. The Stone-Geisser criterionshows that the empirical data reconstructs thetheoretical model in a substantive way (all valuesabove 0).

The goodness of fit index (GoF) developedby Tenenhaus and colleagues is relevant for theassessment of the model fit (Tenenhaus et al.,2005). The GoF value of 0.505 indicates a



Table 2. Overview of second order constructs

Strategiccomplementarity

Culturalcompatibility


M&Asuccess Recommended value

Composite reliability 0.959 0.959 0.951 0.939 >0.6Cronbach’s alpha 0.953 0.954 0.943 0.912 >0.6Average variance extracted 0.593 0.611 0.640 0.744 >0.5Cross loadings OK OK OK OK —Fornell-Larcker criterion OK OK OK OK —Product market-related issues 0.963*** — — — Loadings of first order constructsResource-related issues 0.921*** — — —Value chain issues 0.876*** — — —Dimension strategy — 0.936*** — —Dimension structure — 0.809*** — —Dimension leadership — 0.858*** — —Dimension interrelationship — 0.929*** — —Sociocultural integration — — 0.823*** —Production integration — — 0.859*** —Marketing integration — — 0.915*** —System integration — — 0.841*** —Objective success — — — 0.956***Subjective success — — — 0.952***

*p < 0.05; **p < 0.01; ***p < 0.001.n.s. not significant.

substantial fit for our research model (Wetzelset al., 2009).

Hypotheses testing

Our data show strong support for H1. Thepath coefficient is very strong with a value of0.368***,2 whereas the effect size f2 with 0.11,as well as the prediction relevance q2 = 0.063,are medium. Therefore, we conclude a strongpositive effect from strategic complementarity toM&A success. The data shows clear evidencefor H2 as well. Strategic complementarity hasa strong impact on cultural compatibility (pathcoefficient = 0.781***). The influence of strategiccomplementarity on speed of integration (H3)can only be verified on a 0.1 significance level.Although the path is 0.164+, it must be statedthat the f2 and q2 values are quite low. Ourproposed relation from strategic complementarityto the degree of integration (H4) finds strongsupport in the empirical data. The path forH4 is 0.652***, the effect size is 0.24, andthe q2 = 0.133. This leads us to the conclusionthat there is a strong interrelation between thetwo constructs. H5 states a positive relationship

2 ***p < 0.001; **p < 0.01; *p < 0.05; +p < 0.1.

between strategic complementarity and the degreeof integration. The path is positive (0.296) andsignificant at a 0.001 level. F2 and q2 are at amedium level. We find no empirical support forH6, which suggests a positive relationship betweenstrategic fit and speed of integration. Instead, thepath is strongly negative (−0.351***), and theeffect size as well as the predictive relevanceare medium. Therefore, we conclude that culturalfit lowers the speed of integration. This is asurprising finding and could be attributed to thefact that organizational cultures are compatibleand speed is attributable to other factors. Totest this assumption, we analyzed “cultural fit”regarding mean values, median, and standarddeviation. The range of the mean values is between3.1 and 3.52 (on a scale from 1 to 7, witha standard deviation between 0.92 and 1.2) forall items; the median value is between 3 and4. Therefore, it can be concluded that a highcultural fit is not responsible for the negativerelationship between cultural fit and speed ofintegration. Another—although surprising—resultis the negative path from cultural fit to thedegree of integration (H7; −0.109*). Our resultsdo not support the vast body of research in thisarea (Aguilera and Dencker, 2004; Homburg andBucerius, 2006; Lodorfos and Boateng, 2006).Our data show that a high degree of cultural fit



Tabl

e3.

Forn

ell-

Lar

cker

crite

rion

Forn

ell-

Lar

cker

crite

rion

AV

EA

BC

DE

FG

HI

JK

LM

N

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41

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arke

ting

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grat

ion

0.93

80.

356

1C

Dim

ensi

onst

rate

gy0.

688

0.37

70.

106

1D

Dim

ensi

onle

ader

ship

0.82

80.

315

0.07

00.

462

1E

Dim

ensi

onst

ruct

ure

0.87

20.

262

0.07

10.

583

0.35

21

FD

imen

sion

inte

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atio

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755

0.37

80.

094

0.73

00.

512

0.46

21

GPr

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tion

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grat

ion

0.93

80.

365

0.60

30.

045

0.09

10.

005

0.08

91

HO

bjec

tive

succ

ess

0.84

40.

277

0.15

90.

316

0.33

80.

102

0.32

60.

243

1I

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late

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0.71

10.

661

0.20

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398

0.50

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257

0.42

60.

292

0.45

01

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0.67

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468

0.14

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490

0.52

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373

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091

0.39

10.

666

1K

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tura

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atio

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811

0.18

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403

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128

0.41

60.

146

0.13

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129

1L

Subj

ectiv

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793

0.31

20.

135

0.36

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476

0.14

70.

374

0.21

20.

673

0.53

60.

375

0.09

61

MSy

stem

inte

grat

ion

0.83

20.

274

0.37

40.

117

0.17

60.

084

0.18

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460

0.22

60.

287

0.13

80.

340

0.30

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leads to a lower degree of integration. However,our results affirm Puranam et al.’s study, whoshowed that a “common ground” (we assumethat cultural compatibility is an indicator forcommon ground) makes a formal integrationthrough informal coordination mechanisms moreor less obsolete (Puranam, Singh, and Chaudhuri,2009). We find strong empirical evidence for H8,suggesting a positive relationship between thedegree of integration and M&A success. The pathis positive and significant (0.211***) and f2 andq2 show a medium effect (0.08 and 0.038). Theproposed negative relation from the degree ofintegration to speed of integration is not supported(H9). The path is 0.200*, the effect size is 0.056,and q2 is 0.024. This leads us to the conclusionthat firms tend to integrate as fast as possible ifthe desired degree of integration is high. Finally,H10 must also be rejected. There is no empiricalevidence for a positive relationship between thespeed of integration and M&A success.

The controls have an influence, to some extent,on the latent variables of the model. Interest-ingly, relative size has a strong negative influenceon cultural fit (−0.103***). This result indicatesthat cultural fit depends on relative size. TakingPuranam et al.’s (2009) findings into consideration,it seems that the potential for a common grounddecreases with an increase of relative size. Firmsin fast-growing industries do not tend to integrateas deeply as firms in slow-growing or negative-growth industries (−0.116***). Even though insti-tutional distance between the countries is rathersmall, it has at least some impact on our model.The less the target country has developed com-pared to the buyer country, the higher the needfor integration (coefficient 0.079*; f2 = 0.010).Furthermore, it seems that M&As with targetsfrom less-developed countries are more successful(coefficient 0.084*; f2 = 0.014). In the followingsection, we discuss the results of our study.

DISCUSSION

Limitations

As with all retrospective survey data, our empir-ical data is faced with the problem of decreasingcapacity of recollection. This problem is inher-ent with all M&A research based on surveys dueto the fact that it takes three to five years sub-sequent to a transaction to measure success. The



Product-marketrelated issues

CR=0.900AVE=0.694

R2=0.767

CR=0.936AVE=0.711

R2=0.928

CR=0.924AVE=0.671

R2=0.848

CR=0.959AVE=0.593

R2=--

CR=0.916AVE=0.688

R2=0.876

CR=0.932AVE=0.872

R2=0.655 CR=0.959AVE=0.613

R2=0.611

CR=0.951AVE=0.640

R2=0.318

CR=0.824AVE=0.621

R2=0.083

CR=0.939AVE=0.744

R2=0.575

CR=0.937AVE=0.832

R2=0.708

CR=0.956AVE=0.844

R2=0.914

CR=0.939AVE=0.793

R2=0.906

CR=0.978AVE=0.938

R2=0.838

CR=0.930AVE=0.938

R2=0.738

CR=0.928AVE=0.811

R2=0.678

CR=0.951AVE=0.828

R2=0.737

CR=0.925AVE=0.755

R2=0.929

StrategicComplementarity

Resource relatedissues

Value chainissues

Dimensionstrategy

Dimensionstructure

Dimensionleadership

Dimensioninterrelationship

Culturalfit

Socioculturalintegration

Productionintegration

Marketingintegration

Sytemintegration


Objective measure

M&A success

Subjective measure

Speed ofintegration

0.963***

0.921***

0.876***

0.936***

0.809***

0.858***

0.929***

0.841***

0.915***

0.859***

0.823***

0.956***

0.952***

0.368***

-0.109*

-0.351*** 0.2*

0.652*** 0.296*** 0.211***0.164+0.781*** -0.02n.s.

Figure 3. Results of PLS analysis

main reason for this delayed measurement is theduration of the integration process (Ellis et al.,2009). Therefore, survey-based research in M&Ais always in the area of conflict between reli-able measurement and capacity of recollection.To work against this effect, we operationalizedthe items in the premerger phase with only a 5-point scale instead of a 7-point scale. Anotherretrospective survey-inherent problem is that infor-mants tend to make more positive assessmentsin the long term (Golden, 1992). However, wefind empirical evidence in our data that showsthat the success rates are as low as they are reg-ularly reported. Therefore, we conclude that thediscussed assessment effect is not a substantialproblem in our data. Furthermore, a longitudinalresearch design seems to be more recommendableas a cross-sectional design. However, longitudinalstudies in the M&A context are problematic due tomanagerial turnover in the postmerger phase, andthe problem of practicability (due to the lack ofwillingness of managers to participate in such astudy). Furthermore, the effects of M&A on suc-cess depend on integration. Literature points out

that it takes three to five years to measure M&Asuccess in a sufficient way (Ellis et al., 2009;Homburg and Bucerius, 2005; Zollo and Meier,2008). Therefore, a longitudinal design is verydifficult and problematic to implement. A finallimitation is the number of observations and thestatistical power. We assume that at least the rela-tion between strategic complementarity and speedof integration could become significantly strongerwith a larger number of observations. Previouswork in the M&A literature has studied the impactof acquisition experience on M&A success. Theresults are mixed (King et al., 2004), ranging fromuniformly positive (Barkema et al., 1996; Fowlerand Schmidt, 1989), U-shaped (Haleblian andFinkelstein, 1999), inverted U-shaped (Hayward,2002), to negative (Kusewitt, 1985; Uhlenbrucket al., 2006), or not significant (Zollo and Singh,2004). Muehlfeld, Rao Sahib, and Van Witteloos-tuijn (2012) find that learning from acquisitionfailures and success is highly context-specific andoutcome-dependent. In their comprehensive litera-ture review, Haleblian et al. (2009) conclude that,



although a positive relationship between acquisi-tion experience and performance seems intuitive,results of empirical studies are mixed, suggestingmoderating influences. Acquisition experience isoften used as a control variable in M&A stud-ies (Uhlenbruck et al., 2006; Wright et al., 2002).Acquisition experience is usually measured by thenumber of acquisitions the firm has made in thepast (Haleblian and Finkelstein, 1999). However,studies on experiential learning show that severalacquisitions are required to experience a learningeffect (Laamanen and Keil, 2008) and that “corpo-rate forgetting frequently makes experience gainedfrom acquisitions more than five years in the pastunavailable” (p. 667). As in our sample, there werevery few companies with acquisition experience,and we could not control for the effect of acquisi-tion experience.

Implications

Theoretical implications

First, the results of our study underline the rel-evance of an integrative perspective on M&A.There is clear empirical evidence that shows thatit is not one single success factor that makesM&A work, but rather the interdependencies ofseveral constructs that determine M&A success.Therefore, our study is in line with other inte-grative research (Larsson and Finkelstein, 1999).Secondly, our study supports the notion that com-plementarity is a promising area in M&A research(King et al., 2004; Larsson and Finkelstein, 1999).We show empirical evidence that proves thatstrategic complementarity is decisive for post-merger integration and M&A success. To ourknowledge, this is the first time strategic com-plementarity has been operationalized in such abroad way that market-related, resource-related,and value chain characteristics are included in asecond order construct. Up to now, a broader per-spective on strategic issues of the premerger phasecould only be found in the literature on similar-ity (Pehrsson, 2006; Stimpert and Duhaime, 1997).Our results indicate that there is clear demand forconceptual work on the construct of complemen-tarity. Furthermore, the results of strategic com-plementarity, in combination with cultural fit, giveclear evidence that it is not only economies ofsameness that foster value creation in M&As but,moreover, it is economies of fitness that make

M&As work. Future research should highlight themechanisms of how these economies of fitness fos-ter value creation. Third, our results support theprevious research of Puranam et al. (2009). Highcultural fit as an indicator for common groundleads to lower “formal” organizational integration.We go along with Puranam and colleagues, assum-ing that this effect occurs due to informal coor-dination mechanisms. We found further evidencethat this effect decreases with the relative size ofthe target. Future research should focus on howthis effect works. Fourth, even though we foundno empirical evidence on the relation of speed tosuccess, we assume that conceptual work on theconstruct speed as well as on the interdependen-cies with other constructs of different phases isnecessary.

When determining the optimal mode of an eco-nomic organization, managers face two interrelatedproblems (Leiblein, 2003): Identify and assemblea bundle of resources that create value, and decideon how to capture value through the governanceof this bundle. From a transaction cost perspec-tive, a higher level of uncertainty leads to a higherneed of monitoring and control, and cultural differ-ences can make “managerial digestability” (Van-haverbeke, Duysters, and Noorderhaven, 2002) aserious problem. Hence, monitoring and controlties between the acquirer and the target may bean important moderator of the cultural fit–M&Asuccess relationship and should be the subject offuture studies.

Our empirical results on institutional distancesupport those of Gaur and Lu (2007) indicatingthat, with high regulative distance, the effortfor control increases (in their study in terms ofownership). Our study gives empirical evidencethat the need for integration—and, therefore, thetransfer of organizational practices—is higher ifthe target country is less developed than the buyercountry. On the other hand, this need decreaseswhen the target country is more developed thanthe buyer country.

Managerial implications

A first managerial implication arises from theholistic perspective of our research. Managersshould focus on premerger issues as well as onpostmerger issues. Even though the integrationphase is cited to be most decisive for M&A



success, managers should consider the interde-pendencies in this phase and characteristics ofthe whole M&A process. Secondly, we statedthat cultural fit—through informal coordinationmechanisms—leads to a lower demand for formalintegration. However, managers should not auto-matically rely on this mechanism for two reasons:(1) this effect decreases with an increase of therelative size of the target, and (2) informal coor-dination mechanisms cannot guarantee the desiredintegration and the proposed synergy realization(from a manager’s perspective). Therefore, man-agers should be aware of the role of integra-tion. Finally, we state that, even though we didnot find empirical evidence on the relationshipbetween speed and success, managers should beaware of this topic. It seems that among manymanagers there is an implicit assumption thatM&A success strongly depends on speed of inte-gration as this managerial statement shows, forinstance: “There are three things that matter themost here, and they are speed, speed, speed”(Chase, 1998). Some consulting firms and somecompanies (e.g., GE Capital) strictly follow the100-day rule: Within 100 days, the integration hasto be completed (Ashkenas, DeMonaco, and Fran-cis, 1998). It is argued that speed leads to afaster exploitation of synergies and returns oninvestment, reduces uncertainty among employ-ees, minimizes time spent in a suboptimal condi-tion, and takes advantage of the momentum in thedirect aftermath of a deal (Angwin, 2004; Hom-burg and Bucerius, 2006). There are, however,also negative effects of speed. A slower integra-tion might minimize conflicts between partners,enhance trust building, and reduce disruption ofexisting resources and process in both firms (Hom-burg and Bucerius, 2006). Hence, speed comes at acost, and there may be situations in which the costsof speed outweigh the benefits. Setting the rightspeed of integration requires a holistic understand-ing of all processes surrounding the focal deal.Therefore, speed should not be chosen intuitively.

Overall, this study has shown that M&A suc-cess is a function of the interplay among strategiccomplementarity, cultural fit, and integration, andit furthermore supports the need for an integrativeperspective and theory on M&A. Success dependson decisions in different M&A phases. To success-fully manage M&As, managers need to be awareof these complex relationships—there are no sim-ple solutions to complex problems. We hope that

this study stimulates further research to develop amore holistic understanding of M&A success.

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APPENDIX

Table A1

Common method bias

Substantive factor Common method factor

Construct Item Loading R1 Sig. Loading R12 Loading R2 Sig. Loading R22

Value chain attributes VCA1 0.827 *** 0.684 0.299 + 0.089VCA2 0.831 *** 0.691 0.113 n.s. 0.013VCA3 0.891 *** 0.794 −0.070 n.s. 0.005VCA4 0.779 *** 0.607 −0.359 * 0.129

Product market-related issues PMR1 0.857 *** 0.734 −0.104 n.s. 0.011PMR2 0.896 *** 0.804 −0.204 + 0.042PMR3 0.739 *** 0.546 0.106 n.s. 0.011PMR4 0.849 *** 0.720 0.083 n.s. 0.007PMR5 0.866 *** 0.750 0.074 n.s. 0.005PMR6 0.844 *** 0.712 0.069 n.s. 0.005

Resource-related issues RRI1 0.758 *** 0.574 0.138 n.s. 0.019RRI2 0.764 *** 0.584 0.268 * 0.072RRI3 0.844 *** 0.713 −0.088 n.s. 0.008RRI4 0.847 *** 0.718 −0.221 n.s. 0.049RRI5 0.814 *** 0.662 −0.290 n.s. 0.084RRI6 0.881 *** 0.777 0.213 n.s. 0.045

Dimension leadership CDL1 0.826 *** 0.682 0.030 n.s. 0.001CDL2 0.864 *** 0.746 −0.153 n.s. 0.023CDL3 0.889 *** 0.790 −0.162 n.s. 0.026CDL4 0.729 *** 0.531 0.223 n.s. 0.050CDL5 0.830 *** 0.689 0.108 n.s. 0.012



Table A1 Continued

Common method bias

Substantive factor Common method factor

Construct Item Loading R1 Sig. Loading R12 Loading R2 Sig. Loading R22

Dimension structure CDST1 0.944 *** 0.891 0.053 n.s. 0.003CDST2 0.923 *** 0.852 0.055 n.s. 0.003

Dimension strategy CDS1 0.901 *** 0.812 0.040 n.s. 0.002CDS2 0.906 *** 0.821 0.114 + 0.013CDS3 0.915 *** 0.837 −0.150 * 0.023CDS4 0.909 *** 0.826 −0.120 n.s. 0.014

Dimension interrelationship CDI1 0.802 *** 0.643 −0.004 n.s. 0.000CDI2 0.898 *** 0.806 0.084 n.s. 0.007CDI3 0.849 *** 0.721 −0.098 n.s. 0.010CDI4 0.921 *** 0.848 0.101 n.s. 0.010

Integration of production IOP1 0.923 *** 0.852 −0.054 n.s. 0.003IOP2 0.941 *** 0.885 0.052 n.s. 0.003

Marketing integration IMA1 0.958 *** 0.918 −0.010 n.s. 0.000IMA2 0.971 *** 0.943 −0.038 n.s. 0.001IMA3 0.975 *** 0.951 0.047 * 0.002

Sociocultural integration SCI1 0.898 *** 0.806 −0.089 n.s. 0.008SCI2 0.892 *** 0.796 0.147 n.s. 0.022SCI3 0.913 *** 0.834 −0.057 n.s. 0.003

System-integration SYI1 0.891 *** 0.794 −0.057 n.s. 0.003SYI2 0.911 *** 0.830 0.012 n.s. 0.000SYI3 0.933 *** 0.870 0.041 n.s. 0.002

Speed of integration SIOP 0.941 *** 0.885 0.054 n.s. 0.003SIMA 0.851 *** 0.724 −0.063 n.s. 0.004

Objective success OBJ1 0.952 *** 0.906 0.018 n.s. 0.000OBJ2 0.941 *** 0.885 0.020 n.s. 0.000OBJ3 0.943 *** 0.889 0.031 n.s. 0.001OBJ4 0.832 *** 0.692 −0.079 n.s. 0.006

Subjective success SUB1 0.921 *** 0.848 0.036 n.s. 0.001SUB2 0.861 *** 0.741 0.056 n.s. 0.003SUB3 0.871 *** 0.759 −0.103 n.s. 0.011SUB4 0.906 *** 0.821 0.009 n.s. 0.000

Sum 45.619 40.198 0.121 0.867Mean 0.877 0.773 0.002 0.017

Ratio: 46 1

+p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001.



Tabl

eA

2

Cro

sslo

adin

gs

Loa

ding

san

dcr

oss

load

ings

OB

JSU

BSp

eed

IOP

IMA

SCI

SYI

VC

APM

RR

RI

CD

LC

DST

CD

SC

DI

OB

J10.

952

0.76

50.

073

0.41

80.

482

0.39

70.

470

0.51

70.

631

0.59

50.

557

0.32

40.

543

0.54

8O

BJ2

0.94

10.

771

−0.0

100.

359

0.42

50.

345

0.45

30.

485

0.61

70.

616

0.55

70.

325

0.57

00.

570

OB

J30.

943

0.79

30.

042

0.33

50.

442

0.34

40.

478

0.52

40.

668

0.59

90.

541

0.32

30.

538

0.54

4O

BJ4

0.83

20.

680

0.04

80.

354

0.46

50.

318

0.33

70.

399

0.54

40.

478

0.39

70.

193

0.48

10.

426

SUB

10.

757

0.92

1−0

.004

0.38

30.

407

0.27

10.

504

0.52

80.

684

0.60

80.

544

0.41

60.

675

0.52

3SU

B2

0.69

40.

861

−0.1

190.

417

0.55

30.

355

0.49

10.

537

0.67

70.

525

0.48

40.

248

0.57

80.

491

SUB

30.

786

0.87

1−0

.154

0.20

10.

312

0.23

00.

462

0.41

20.

562

0.49

30.

534

0.32

10.

545

0.57

3SU

B4

0.67

90.

906

−0.1

290.

309

0.37

40.

249

0.49

90.

516

0.68

80.

552

0.58

60.

376

0.65

80.

592

SIO

P−0

.022

−0.1

610.

941

0.27

30.

212

−0.0

18−0

.006

0.02

60.

005

−0.1

21−0

.141

−0.1

55−0

.177

−0.1

88SI

MA

0.13

0−0

.012

0.85

10.

193

0.26

10.

018

0.04

00.

167

0.08

1−0

.048

−0.0

43−0

.117

−0.0

09−0

.054

IOP

10.

343

0.26

50.

261

0.92

30.

652

0.61

30.

465

0.51

50.

353

0.35

40.

338

0.30

60.

175

0.30

2IO

P2

0.39

80.

410

0.23

80.

941

0.78

80.

573

0.66

30.

592

0.48

20.

350

0.27

30.

197

0.31

10.

272

IMA

10.

459

0.43

50.

198

0.73

30.

958

0.63

70.

625

0.55

90.

528

0.29

70.

184

0.05

20.

284

0.28

4IM

A2

0.43

40.

422

0.28

70.

757

0.97

10.

612

0.64

70.

604

0.51

20.

252

0.19

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0.26

20.

271

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537

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254

0.76

50.

975

0.62

40.

697

0.59

30.

530

0.32

60.

243

0.10

00.

329

0.31

3SC

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299

0.18

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0.51

60.

549

0.89

80.

450

0.33

50.

255

0.28

20.

273

0.08

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190

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484

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0.61

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593

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569

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90.

403

0.38

70.

365

0.23

50.

294

0.39

1SC

I30.

245

0.18

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0.58

30.

597

0.91

30.

549

0.38

40.

312

0.29

80.

271

0.16

20.

187

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430

0.46

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0.49

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621

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20.

891

0.45

70.

495

0.29

70.

236

0.20

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342

0.31

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410

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0.47

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553

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10.

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485

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323

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369

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101

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40.

263

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114

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396

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0.79

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610

0.56

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515

0.60

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613

0.66

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0.45

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0.36

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0.26

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0.61

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RR

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595

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323

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591

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574

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442

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188

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261

0.55

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658

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70.

580

0.56

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antecedents of m&a success: the role of strategic … · 2017-09-09 · strategic management...

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