Availableonlineatwww.sciencedirect.comresearoSCIENCIRECTpolicyELSEVIERResearch Policy 35 (2006) 642654www.elsevier.com/locate/respolMergers and acquisitions: Their effect on the innovativeperformance of companies in high-tech industriesMyriam Cloodt a,*, John Hagedoorn b,1, Hans Van Kranenburg c.2^ECIS and Organisation Science and Marketing (OSM),Departmentof Technology Management(TM),TUleTechnischeUniversiteitEindhoven,PO.Box513.5600MBEindhoven,TheNetherlandsbMERITand Department of Organization and Strategy,Faculty of Economics and Business Administration,Maastricht UniversityP.O.Box616,6200MDMaastricht,TheNetherlands Radboud University Nijmegen, Nijmegen School of Managemen, P0.Box 9108,6500 HK Njmegen,The NetherlandsReceived 6 October 2004; received in revised form 6 July 2005; accepted 22 February 2006Available online 2 May 2006AbstractThis study examines the post-M&A innovative performance of acquiring firms in four major high-tech sectors. Non-technologicalM&As appear to have a negative impact on the acquiring firm's post-M&A innovative performance. With respect to technologicalM&As,a large relative size of the acquired knowledge basereduces the innovative performance of the acquiring firm.The absolutesize of the acquired knowledge base only has a positive effect during the first couple of years after which the effect turns around andwe see a negative effect on the innovative performance of the acquiring firm.The relatedness between the acquired and acquiringfirms' knowledge bases has a curvilinear impact on the acquiring firm's innovative performance. This indicates that companiesshould target M&A'partners' that are neither too unrelated nor too similar in terms of their knowledge base2006ElsevierB.V.AllrightsreservedKeywords: M&As; Innovative performance; High-tech industries1.Introductiona preliminary explanation why M&As continue to beapopulargrowthstrategyof manycompanies(WorldContributions based on the resource-based view ofInvestmentReport,2000).In that context, itis stressedthe firm (Barney,1986,1991:Wernerfelt,1984),inthatopportunities fororganizational learning increasecombination with related work that stresses the impor-when a firm is exposed to new and diverse ideas basedtanceof organizational learningand innovation (Conneron differences in technological capabilities between theand Prahalad,1996;Grant,1996;Levitt and Marchacquiring and the acquired firm(see also Ghoshal,1987:1988:Nonaka,1991),provide someusefulinsights andHitt et al., 1996). Acquiring diverse external knowledgebases and making proper use of this newknowledge arefoundtoberelevantcontributions to a firm's post-M&ACorresponding author.Tel.:+31402475242;fax:+3140 2468054.innovativeperformanceE-mail addresses: m.m.a.h.cloodt@tm.tue.nl (M. Cloodt),Our current study is clearly linked to recent researchj.hagedoorn@os.unimaas.nl (J.Hagedoorn)that has already made some progress in analyzing criti-h.vankranenburg@fm.ru.nl (H.VanKranenburg)cal success factors that have a significant influence on a1 Tel.: +31 43 3883823; fax: +31 43 3884893.firm'spost-M&Ainnovativeperformance.Forinstance2Tel.: +31 243612028; fax: +3124 3611933.0048-7333/$ - see front matter 2006 Elsevier B.V. All rights reserved.doi:10.1016/jrespol.2006.02.007
Research Policy 35 (2006) 642–654 Mergers and acquisitions: Their effect on the innovative performance of companies in high-tech industries Myriam Cloodt a,∗, John Hagedoorn b,1, Hans Van Kranenburg c,2 a ECIS and Organisation Science and Marketing (OSM), Department of Technology Management (TM), TU/e Technische Universiteit Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands b MERIT and Department of Organization and Strategy, Faculty of Economics and Business Administration, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands c Radboud University Nijmegen, Nijmegen School of Management, P.O. Box 9108, 6500 HK Nijmegen, The Netherlands Received 6 October 2004; received in revised form 6 July 2005; accepted 22 February 2006 Available online 2 May 2006 Abstract This study examines the post-M&A innovative performance of acquiring firms in four major high-tech sectors. Non-technological M&As appear to have a negative impact on the acquiring firm’s post-M&A innovative performance. With respect to technological M&As, a large relative size of the acquired knowledge base reduces the innovative performance of the acquiring firm. The absolute size of the acquired knowledge base only has a positive effect during the first couple of years after which the effect turns around and we see a negative effect on the innovative performance of the acquiring firm. The relatedness between the acquired and acquiring firms’ knowledge bases has a curvilinear impact on the acquiring firm’s innovative performance. This indicates that companies should target M&A ‘partners’ that are neither too unrelated nor too similar in terms of their knowledge base. © 2006 Elsevier B.V. All rights reserved. Keywords: M&As; Innovative performance; High-tech industries 1. Introduction Contributions based on the resource-based view of the firm (Barney, 1986, 1991; Wernerfelt, 1984), in combination with related work that stresses the importance of organizational learning and innovation (Conner and Prahalad, 1996; Grant, 1996; Levitt and March, 1988; Nonaka, 1991), provide some useful insights and ∗ Corresponding author. Tel.: +31 40 2475242; fax: +31 40 2468054. E-mail addresses: m.m.a.h.cloodt@tm.tue.nl (M. Cloodt), j.hagedoorn@os.unimaas.nl (J. Hagedoorn), h.vankranenburg@fm.ru.nl (H. Van Kranenburg). 1 Tel.: +31 43 3883823; fax: +31 43 3884893. 2 Tel.: +31 24 3612028; fax: +31 24 3611933. a preliminary explanation why M&As continue to be a popular growth strategy of many companies (World Investment Report, 2000). In that context, it is stressed that opportunities for organizational learning increase when a firm is exposed to new and diverse ideas based on differences in technological capabilities between the acquiring and the acquired firm (see also Ghoshal, 1987; Hitt et al., 1996). Acquiring diverse external knowledge bases and making proper use of this new knowledge are found to be relevant contributions to a firm’s post-M&A innovative performance. Our current study is clearly linked to recent research that has already made some progress in analyzing critical success factors that have a significant influence on a firm’s post-M&A innovative performance. For instance, 0048-7333/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.respol.2006.02.007
643M. Cloodt et al. / Research Policy 35 (2006) 642-654Ahuja and Katila (2001) studied the impact of the abso-lar knowledge sources (Bierly and Chakrabarti, 1996)luteand relative sizeof acquiredknowledgebaseson theTherefore, it is the firm's ability to acquire, transferinnovativeperformanceoffirmsinthechemicalsindus-and integrate the acquired firm's knowledge base intotry.Our study is an extended replication of the analysistheknowledge base of the acquiring firm that creates abyAhuja and Katila (2001).Contraryto the applied sci-sustainablecompetitiveadvantage(Barney,1986).How-ences,replication studies arenot very popular in mostofever, we do realize that not all acquisitions are under-thesocialsciences,withthepossibleexceptionofappliedtaken for technological reasons with the soleintent toeconometrics and some fields in psychology.Hubbardlearn(Hamel,1991).M&AsmightalsobemotivatedandVetter(1992.1997)andFuess(1996)foundthatby market-entry and market-structure related consider-leadingjournalsineconomics,managementandfinanceations,or bythedesireto expand the firm'sproductpublish relativelyfew extended replication studies.Fromrangeinternationally(BerkovitchandNarayanan,1993Chakrabarti et al.,1994; Hagedoorn and Sadowski.a purely methodological point of view, this comes asquite surprisingbecause so far themanagement litera-1999:Trautwein,1990).Theseconsiderationsmotivateturehas generated a slightlyconfusing body of literature,firms to undertake non-technological acquisitions thatto say the least.For many basic questions in the litera-areless likelytoprovidetechnological knowledgetotheture,the reader will find contradictory findings,differentacquiring firm.3If M&As involvenooronlyafewtechnological com-measurements,unclearinternationalimplications.andthe use of a range of partlyoverlapping constructs.ponents, they are expected to have little or no effectTheAhujaandKatila(2001)studyconcentrated onon the innovation routines of the acquiring firm.How-the effect of M&As in a single medium-tech industrialever,if M&As create a disruption of the establishedcontext: the chemicals sector.Their contribution invitesroutines, thereby consuming significant managerial timesubsequent researchby others to consider a wider rangeand energy, they can havea negative impact on the post-of industries,in particular high-tech industries.OurM&Ainnovativeperformance(Ahuja andKatila,2001;HaspeslaghandJemison,1991;Hittetal.,1996).Instudy extends their work by analyzing the post-M&Ainnovativeperformancebymeans of a large sampleofparticular,acquisitions motivated by non-technologicalfirms operating infourhigh-tech sectors (OECD,1997):incentives,suchasshort-termprofitgrowth.canrequireaerospace and defense,computers and officemachin-so much managerial attention that this leads to a lowerery,pharmaceuticals,andelectronicsandcommunicamanagerial commitmenttolong-term investmentsininnovation(Hittetal.,1996).In summary,weexpecttions.Thesehigh-tech sectors are selectedfor twomainthat non-technological M&As either contribute little toreasons.First,these industries areprimarilyknowledgedriven industries (OECD, 1997).Technological learningthe innovative output of the acquiring firm, or that thereis expected tobe akey determinant in creating and sus-might be a negative impacton the post-M&Ainnovativetaining acompetitive advantagefor manyof the sampleperformance.Hence,firms in these industries (Bierly and Chakrabarti, 1996)Hypothesis 1.Non-technological acquisitions willSecondfor each of these industries.we can measurehave either a negative or a non-significant effect on theinnovativeperformance through the same indicator,ie.post-M&A innovative performance ofthe acquiringfirm.patents. It is well known that particularly in these indus-tries,patents playa significantrole in indicating impor-Thepossiblepositive impactoftechnological M&Astantaspects of innovative performance (Hagedoorn andoninnovativeperformancedependsonanumberoffac-Cloodt,2003;OECD,1997).Aswillbedemonstratedtors. A first critical dimension in the technological uni-below.ourextendedreplicationstudyisabletoconfirmfication of two firms concernsthe size of theacquiredsome findings of the Ahuja and Katila (2001) study butknowledgebases(Ahuja andKatila,2001).Theeffectitalsogeneratessomeimportantnewinsightrelatedtothe specific role of knowledge depreciation and time-constrained knowledge transfer through M&As in a3 As indicated by one of the referees, we focus in our paper on scalenumber of high-tech industries.and scope effects intechnological knowledgebutfirms are of coursealso concerned with scale and scope effects in products and industries.If we envisage the role of the firm as transforming technologies into2.Theoryand hypothesesproducts,non-technological acquisitions can expand sales and marketshares of products having a positive effect ontheeconomicperfor-According to theresource-based theory of the firmmance ofthe acquiringfirm.The same argument holdsfor undertakingand theknowledge-based view,differences in innova-a very closely related technological acquisition that may raise markettive performance between firms are a result of dissimi-power and patent exploitation
M. Cloodt et al. / Research Policy 35 (2006) 642–654 643 Ahuja and Katila (2001) studied the impact of the absolute and relative size of acquired knowledge bases on the innovative performance of firms in the chemicals industry. Our study is an extended replication of the analysis by Ahuja and Katila (2001). Contrary to the applied sciences, replication studies are not very popular in most of the social sciences, with the possible exception of applied econometrics and some fields in psychology. Hubbard and Vetter (1992, 1997) and Fuess (1996) found that leading journals in economics, management and finance publish relatively few extended replication studies. From a purely methodological point of view, this comes as quite surprising because so far the management literature has generated a slightly confusing body of literature, to say the least. For many basic questions in the literature, the reader will find contradictory findings, different measurements, unclear international implications, and the use of a range of partly overlapping constructs. The Ahuja and Katila (2001) study concentrated on the effect of M&As in a single medium-tech industrial context: the chemicals sector. Their contribution invites subsequent research by others to consider a wider range of industries, in particular high-tech industries. Our study extends their work by analyzing the post-M&A innovative performance by means of a large sample of firms operating in four high-tech sectors (OECD, 1997): aerospace and defense, computers and office machinery, pharmaceuticals, and electronics and communications. These high-tech sectors are selected for two main reasons. First, these industries are primarily knowledgedriven industries (OECD, 1997). Technological learning is expected to be a key determinant in creating and sustaining a competitive advantage for many of the sample firms in these industries (Bierly and Chakrabarti, 1996). Second, for each of these industries, we can measure innovative performance through the same indicator, i.e. patents. It is well known that particularly in these industries, patents play a significant role in indicating important aspects of innovative performance (Hagedoorn and Cloodt, 2003; OECD, 1997). As will be demonstrated below, our extended replication study is able to confirm some findings of the Ahuja and Katila (2001) study but it also generates some important new insight related to the specific role of knowledge depreciation and timeconstrained knowledge transfer through M&As in a number of high-tech industries. 2. Theory and hypotheses According to the resource-based theory of the firm and the knowledge-based view, differences in innovative performance between firms are a result of dissimilar knowledge sources (Bierly and Chakrabarti, 1996). Therefore, it is the firm’s ability to acquire, transfer and integrate the acquired firm’s knowledge base into the knowledge base of the acquiring firm that creates a sustainable competitive advantage (Barney, 1986). However, we do realize that not all acquisitions are undertaken for technological reasons with the sole intent to learn (Hamel, 1991). M&As might also be motivated by market-entry and market-structure related considerations, or by the desire to expand the firm’s product range internationally (Berkovitch and Narayanan, 1993; Chakrabarti et al., 1994; Hagedoorn and Sadowski, 1999; Trautwein, 1990). These considerations motivate firms to undertake non-technological acquisitions that are less likely to provide technological knowledge to the acquiring firm.3 If M&As involve no or only a few technological components, they are expected to have little or no effect on the innovation routines of the acquiring firm. However, if M&As create a disruption of the established routines, thereby consuming significant managerial time and energy, they can have a negative impact on the postM&A innovative performance (Ahuja and Katila, 2001; Haspeslagh and Jemison, 1991; Hitt et al., 1996). In particular, acquisitions motivated by non-technological incentives, such as short-term profit growth, can require so much managerial attention that this leads to a lower managerial commitment to long-term investments in innovation (Hitt et al., 1996). In summary, we expect that non-technological M&As either contribute little to the innovative output of the acquiring firm, or that there might be a negative impact on the post-M&A innovative performance. Hence, Hypothesis 1. Non-technological acquisitions will have either a negative or a non-significant effect on the post-M&A innovative performance of the acquiring firm. The possible positive impact of technological M&As on innovative performance depends on a number of factors. A first critical dimension in the technological uni- fication of two firms concerns the size of the acquired knowledge bases (Ahuja and Katila, 2001). The effect 3 As indicated by one of the referees, we focus in our paper on scale and scope effects in technological knowledge but firms are of course also concerned with scale and scope effects in products and industries. If we envisage the role of the firm as transforming technologies into products, non-technological acquisitions can expand sales and market shares of products having a positive effect on the economic performance of the acquiring firm. The same argument holds for undertaking a very closely related technological acquisition that may raise market power and patent exploitation
644M.Cloodt et al. / Research Policy 35 (2006)642-654of M&As depends on whether targets have a similar orThe integration of a knowledge base that is of a rela-preferablylargerR&Dinput and otherinnovative activi-tivelylargesizecandisruptexistinginnovativeactivitiesties.The unification of twoknowledgebases can provideand render the different integration stages more com-opportunities for synergies in future R&D, while reduc-plex,moretimeconsuming andfull ofrisks (Capron anding redundant or duplicate R&D efforts and provide aMitchell,2000;Chakrabartietal.,1994;HaspeslaghandJemison,1991).Duetosuchproblems,integratingarela-largerresearchbaseto financecosts (Cassiman et al.,2005:Hall, 1990).tivelylargeknowledgebaserequires additional resourcesAnother positive effect of the increased size ofto be devoted to integration activities, leaving fewerknowledge bases, is found in the potential for aggre-resourcesfor theactual innovative endeavor (Ahujaandgation (Grant, 1996).The transfer ofknowledge fromKatila, 2001).Thus,we expect that with the integrationthe acquired firm to the acquiring firm involves bothof a relatively large knowledge base, fewer resourcestransmission and receipt (Grant, 1996). Receipt canwill be available for innovative activities, which has abe analyzed in terms of a firm's absorptive capacity.negative impacton theacquirer's post-M&A innovativewhich plays a dual role in improving innovative per-performance.formance (Cohen and Levinthal, 1989, 1990).When aHypothesis 3.There is a negative relationship betweenfirm increases its internal knowledge base by acquiringknowledge,it can usethisknowledgeto generatenewthe relative size of the acquired knowledge base and theinnovations.In addition,the expansion of theinternalpost-M&Ainnovativeperformanceoftheacquiringfirm.knowledge base also increases thefirm's ability to rec-ognize thevalueof new information,to assimilate it andAthird importantfactorin themerger of two firmsto exploit itforcommercial ends(Cohen and Levinthal,is their relatedness in terms of particular fields of tech-1989).nology that the acquiring firm shares with the acquiredHence,byundertakingM&As,firmsarenotonly con-firm(Cassiman et al.,2005;Hagedoorn and Duysters,fronted with the internally created knowledge base of2002).WhiletherelatednessofM&Asintermsofthe acquired firm. By taking over the acquired firm'sproduct-markets concerns the industry-aspect,the tech-knowledgebase,thefirmwillbeableto view somenologicalrelatednessrefersto firm-specific aspects suchissues from a different perspective and recognize theas technological disciplines and engineering capabili-value of new external knowledge,which can help theties.The positive effect of relatedness in technologicalknowledge on the success of M&As is found by severalacquirer develop a richer knowledge base (Ahuja andKatila,2001;Levinthal and March,1993:VermeulenstudiesthatemphasizetheeffectsofeconomiesofscaleandBarkema,2001).Several studies mention the advanandscopeofR&D,suchasashorterinnovationlead-timetages of creating a richer or broader knowledge base,and the possibility to engage in larger combined projectssuch as increased strategic flexibility, sustainable com+(Gerpott,1995:HagedoornandDuysters,2002)petitive advantage, and increased performance (BierlyFrom an organizational learning perspective,this pos-andChakrabarti,1996;HendersonandCockburn,1994;itive effect lies in theabilityto better evaluateand utilizeReed andDeFillippi, 1990).We expectthatthe acquisi-related externally acquiredknowledge than unrelatedtion ofexternally availableknowledgeleads to increasedexternally acquired knowledge (Cohen and Levinthal,economies of scale and scope and a broaderknowledge1990).This is based on the idea that a firm's absorptivebase, both having a positive effect on innovative perfor-capacity depends mainly on its level of knowledge in aspecific field (Cohen and Levinthal,1990; Duysters andmance.Hagedoorn,2000:Moweryet al.,1996).Iftheknowl-Hypothesis 2.There is a positive relationship betweenedge base of the acquirer is not sufficiently adapted tothe absolute size of the acquired knowledge base andtheacquiredknowledge,theabsorptionprocessbecomesvery difficult (Duysters and Hagedoorn, 2000). There-thepost-M&A innovativeperformance of theacquiringfirm.fore, we argue that unrelated technologies often requirearadical change inthe way oforganizing research(KogutThe challengefor companies is not just to acquireand Zander,1992)which can easilybecounterproduc-knowledgebases butalso tointegratethem in ordertotive(Ahuja andKatila,2001;Dosi,1988).improvethepost-M&Ainnovativeperformance(AhujaHowever,technological knowledge and engineeringand Katila,2001:Child et al.,2001:Haspeslagh andcapabilities that are too similar to the already existingJemison,1991).This integration processforms the sec-knowledge of the acquiring company will contributelittle to the post-M&A innovative performance. Someond critical dimension in the unification of two firms
644 M. Cloodt et al. / Research Policy 35 (2006) 642–654 of M&As depends on whether targets have a similar or preferably larger R&D input and other innovative activities. The unification of two knowledge bases can provide opportunities for synergies in future R&D, while reducing redundant or duplicate R&D efforts and provide a larger research base to finance costs (Cassiman et al., 2005; Hall, 1990). Another positive effect of the increased size of knowledge bases, is found in the potential for aggregation (Grant, 1996). The transfer of knowledge from the acquired firm to the acquiring firm involves both transmission and receipt (Grant, 1996). Receipt can be analyzed in terms of a firm’s absorptive capacity, which plays a dual role in improving innovative performance (Cohen and Levinthal, 1989, 1990). When a firm increases its internal knowledge base by acquiring knowledge, it can use this knowledge to generate new innovations. In addition, the expansion of the internal knowledge base also increases the firm’s ability to recognize the value of new information, to assimilate it and to exploit it for commercial ends (Cohen and Levinthal, 1989). Hence, by undertaking M&As, firms are not only confronted with the internally created knowledge base of the acquired firm. By taking over the acquired firm’s knowledge base, the firm will be able to view some issues from a different perspective and recognize the value of new external knowledge, which can help the acquirer develop a richer knowledge base (Ahuja and Katila, 2001; Levinthal and March, 1993; Vermeulen and Barkema, 2001). Several studies mention the advantages of creating a richer or broader knowledge base, such as increased strategic flexibility, sustainable competitive advantage, and increased performance (Bierly and Chakrabarti, 1996; Henderson and Cockburn, 1994; Reed and DeFillippi, 1990). We expect that the acquisition of externally available knowledge leads to increased economies of scale and scope and a broader knowledge base, both having a positive effect on innovative performance. Hypothesis 2. There is a positive relationship between the absolute size of the acquired knowledge base and the post-M&A innovative performance of the acquiring firm. The challenge for companies is not just to acquire knowledge bases but also to integrate them in order to improve the post-M&A innovative performance (Ahuja and Katila, 2001; Child et al., 2001; Haspeslagh and Jemison, 1991). This integration process forms the second critical dimension in the unification of two firms. The integration of a knowledge base that is of a relatively large size can disrupt existing innovative activities and render the different integration stages more complex, more time consuming and full of risks (Capron and Mitchell, 2000; Chakrabarti et al., 1994; Haspeslagh and Jemison, 1991). Due to such problems, integrating a relatively large knowledge base requires additional resources to be devoted to integration activities, leaving fewer resources for the actual innovative endeavor (Ahuja and Katila, 2001). Thus, we expect that with the integration of a relatively large knowledge base, fewer resources will be available for innovative activities, which has a negative impact on the acquirer’s post-M&A innovative performance. Hypothesis 3. There is a negative relationship between the relative size of the acquired knowledge base and the post-M&A innovative performance of the acquiring firm. A third important factor in the merger of two firms is their relatedness in terms of particular fields of technology that the acquiring firm shares with the acquired firm (Cassiman et al., 2005; Hagedoorn and Duysters, 2002). While the relatedness of M&As in terms of product–markets concerns the industry-aspect, the technological relatedness refers to firm-specific aspects such as technological disciplines and engineering capabilities. The positive effect of relatedness in technological knowledge on the success of M&As is found by several studies that emphasize the effects of economies of scale and scope of R&D, such as a shorter innovation lead-time and the possibility to engage in larger combined projects (Gerpott, 1995; Hagedoorn and Duysters, 2002). From an organizational learning perspective, this positive effect lies in the ability to better evaluate and utilize related externally acquired knowledge than unrelated externally acquired knowledge (Cohen and Levinthal, 1990). This is based on the idea that a firm’s absorptive capacity depends mainly on its level of knowledge in a specific field (Cohen and Levinthal, 1990; Duysters and Hagedoorn, 2000; Mowery et al., 1996). If the knowledge base of the acquirer is not sufficiently adapted to the acquired knowledge, the absorption process becomes very difficult (Duysters and Hagedoorn, 2000). Therefore, we argue that unrelated technologies often require a radical change in the way of organizing research (Kogut and Zander, 1992) which can easily be counterproductive (Ahuja and Katila, 2001; Dosi, 1988). However, technological knowledge and engineering capabilities that are too similar to the already existing knowledge of the acquiring company will contribute little to the post-M&A innovative performance. Some
645M. Cloodt et al. / Research Policy 35 (2006) 642-654degree of differentiation in technological capabilitiesthe summed coefficients can also be computed with thisbetween thefirms may enrich the acquiring firm'sknowl-model specification (Gujarati,1988)To control for unobserved heterogeneity,we gatherededgebaseandcreateopportunitiesforlearning(Ghoshal1987:Hittetal..1996).Thisenrichmentof the acquiringpre-sampleinformation oftheunobserveddifferences inknowledge stocksbetweenthesamplefirms.Weincludefirm'sknowledgebase and aproper use oftheexternalknowledge are relevantcontributions to a firm's innova-unobserved heterogeneity as an additional covariate intiveperformance(Cohen and Levinthal,1989;Grilichesthe model (Xir-1). However, possible unobserved firm1990;Pakes and Griliches,1984).In other words,weeffects can lead to serial correlation among the residu-expect that one has to strive for moderate relatednessalsofobservationsfromthesamefirm.Toaddressthisbetweenknowledgebases.On theone hand,theacquiredissue of unobserved heterogeneity we used the general-knowledge has to show enough overlap to facilitate theized estimating equations (GEE)estimation procedureabsorptionprocess.Ontheotherhand,thecombinationto estimate all models.This procedure provides a directof knowledgebasesrequires enoughdiversitytomakeapproach to modeling longitudinal count data with seriala substantial contribution to the post-M&A innovativecorrelation (Liang and Zeger, 1986)performance.Hence,3.2. Sample and dataHypothesis 4.The technological relatedness of theThe hypotheses are tested on a relatively large inter-acquired knowledge base will be curvilinearly (inversenational sample of companies covering four high-techU-shaped)related tothepost-M&Ainnovativeperfor-industries:aerospace and defense (SIC-codes 372 andmance of the acquiring firm.376),computersand officemachinery(SIC-code357)pharmaceuticals(SIC-code 283)and electronics and3.Methodscommunications(SIC-code36).Our sample consistsof347companiesof which21(6.05%)operateinthe3.1. Modelaerospace and defense industry,76 (21.9%)arefoundincomputersandofficemachinery,77(22.19%)areThis study uses a panel dataset model that combinesactiveinpharmaceuticals,and 173(49.86%)operate intimeseriesandcross-sectionstoanalyzeourdataandtestheelectronicsandcommunicationssector.Thesamplethehypotheses.FollowingAhujaandKatila(2001),weconsists of256NorthAmericancompaniesand91com-specify the following random effects negative binomialpaniesfromotherregions(45fromEuropeand46fromregression model:Asia).Our samplecanbeclassifiedas abalanced panelPir = exp(Xit-1y + Ait-1β1 + Ait-2β2datasetmeaningthatnofirmsexitedtheindustryorwereacquired by others during theperiod of our analysis.In+Ait-3P3 + Ait-4β4)addition, there were no firms that entered the sample at awhere Pir is a non-negative integer-valued count vari-later period in time. All the firms included in the sampleable for post-M&A innovativeperformance,measuredhavethesame startingpoint.by the number of patents achieved by firm i in year t,Our sample is also diverse in terms of the distributionXit-1 the vector of control variables affecting Pit (e.g.ofthe sizeof companies.About18%ofthecompaniesfirm size,industry,nationality,cultural distance,tmeandin our sample are relatively small withless than 1000employees.Almost the same percentage of companiesunobserved heterogeneity), Ait-year j the lagged vectorcan be characterized as very large with more than50,000of theindependentvariablesfor year j=1-4,ythevec-tor of regression coefficients for the control variablesemployees.Morethanhalf ofthesample(64%)canbeand theβs are the vectors of regression coefficients forfound in intermediate size-classesIntotal,weidentified2429M&Aeventsforourthe jth period lagged independent variables. By includ-ing lagged effects we can subsequently test the effect ofsample firms in the period 1985-1994. These M&Aacquisitionsfor upto4yearsaftertheyeartheM&Awasevents refer to the merging of two more or less equaloriginallymade.Thetotal impactof anM&Aacrosstimecompanies,as well as to acquisitions where one com-can be analyzed by summing theregression coefficientspany obtains majority ownership over another com-pany. To distinguish between technological and non-on the distributed lags.Bycalculating t-statistics we cantest the hypothesis that the total impact of acquisitionstechnological M&As,weanalyzed ifthetargetfirmhadsummed across all years,is zero and check whether it isanypatentingactivityin the5yearsprecedingtheM&Astatistically significant (Greene, 1993).Thevariance for(seeAhujaandKatila,2001).Of thetotal amountof
M. Cloodt et al. / Research Policy 35 (2006) 642–654 645 degree of differentiation in technological capabilities between the firms may enrich the acquiring firm’s knowledge base and create opportunities for learning (Ghoshal, 1987; Hitt et al., 1996). This enrichment of the acquiring firm’s knowledge base and a proper use of the external knowledge are relevant contributions to a firm’s innovative performance (Cohen and Levinthal, 1989; Griliches, 1990; Pakes and Griliches, 1984). In other words, we expect that one has to strive for moderate relatedness between knowledge bases. On the one hand, the acquired knowledge has to show enough overlap to facilitate the absorption process. On the other hand, the combination of knowledge bases requires enough diversity to make a substantial contribution to the post-M&A innovative performance. Hence, Hypothesis 4. The technological relatedness of the acquired knowledge base will be curvilinearly (inverse U-shaped) related to the post-M&A innovative performance of the acquiring firm. 3. Methods 3.1. Model This study uses a panel dataset model that combines time series and cross-sections to analyze our data and test the hypotheses. Following Ahuja and Katila (2001), we specify the following random effects negative binomial regression model: Pit = exp(Xit−1γ + Ait−1β1 + Ait−2β2 + Ait−3β3 + Ait−4β4) where Pit is a non-negative integer-valued count variable for post-M&A innovative performance, measured by the number of patents achieved by firm i in year t, Xit−1 the vector of control variables affecting Pit (e.g. firm size, industry, nationality, cultural distance, time and unobserved heterogeneity), Ait−year j the lagged vector of the independent variables for year j = 1–4, γ the vector of regression coefficients for the control variables, and the βs are the vectors of regression coefficients for the jth period lagged independent variables. By including lagged effects we can subsequently test the effect of acquisitions for up to 4 years after the year the M&A was originally made. The total impact of an M&A across time can be analyzed by summing the regression coefficients on the distributed lags. By calculating t-statistics we can test the hypothesis that the total impact of acquisitions, summed across all years, is zero and check whether it is statistically significant (Greene, 1993). The variance for the summed coefficients can also be computed with this model specification (Gujarati, 1988). To control for unobserved heterogeneity, we gathered pre-sample information of the unobserved differences in knowledge stocks between the sample firms. We include unobserved heterogeneity as an additional covariate in the model (Xit−1). However, possible unobserved firm effects can lead to serial correlation among the residuals of observations from the same firm. To address this issue of unobserved heterogeneity we used the generalized estimating equations (GEE) estimation procedure to estimate all models. This procedure provides a direct approach to modeling longitudinal count data with serial correlation (Liang and Zeger, 1986). 3.2. Sample and data The hypotheses are tested on a relatively large international sample of companies covering four high-tech industries: aerospace and defense (SIC-codes 372 and 376), computers and office machinery (SIC-code 357), pharmaceuticals (SIC-code 283) and electronics and communications (SIC-code 36). Our sample consists of 347 companies of which 21 (6.05%) operate in the aerospace and defense industry, 76 (21.9%) are found in computers and office machinery, 77 (22.19%) are active in pharmaceuticals, and 173 (49.86%) operate in the electronics and communications sector. The sample consists of 256 North American companies and 91 companies from other regions (45 from Europe and 46 from Asia). Our sample can be classified as a balanced panel dataset meaning that no firms exited the industry or were acquired by others during the period of our analysis. In addition, there were no firms that entered the sample at a later period in time. All the firms included in the sample have the same starting point. Our sample is also diverse in terms of the distribution of the size of companies. About 18% of the companies in our sample are relatively small with less than 1000 employees. Almost the same percentage of companies can be characterized as very large with more than 50,000 employees. More than half of the sample (64%) can be found in intermediate size-classes. In total, we identified 2429 M&A events for our sample firms in the period 1985–1994. These M&A events refer to the merging of two more or less equal companies, as well as to acquisitions where one company obtains majority ownership over another company. To distinguish between technological and nontechnological M&As, we analyzed if the target firm had any patenting activity in the 5 years preceding the M&A (see Ahuja and Katila, 2001). Of the total amount of
646M.Cloodt et al. / Research Policy 35 (2006)642-654M&As.1148mettheabove-mentionedcriterion andtheynumber of the patents that its acquisitions had obtainedare classified as technological M&As. The remainingduringthepreceding5yearsbeforetheparticularM&A1281M&Asareclassified as non-technological M&As.event.Thesepatents werethencombined withthepatentsForthefirms inthe sample,weobtained annual patentthat were cited by these companies.Duplicates werecount data for the period1980-1994 and acquisition andabstracted from the list to ensure that a patent codefirm-specific data for the years 1980-1993.The finalappears only once.The acquired knowledge base waspanel for the regression analysis amounts to 7 years fromthen calculated as the number of patents (i.e.knowledge1989to 1995.elements)onthis list.Itiswell known thatthere isno ‘official'databaseRelative size of acquired knowledge base.This vari-with a world-wide, industry level list of all companiesable was measured by dividing the absolute size offrom which one can draw a random sample. Our samplethe acquired knowledge base by the absolute size ofis taken from the Securities Data databank, which con-the acquiring firm's knowledge base.The absolute sizetains information on theyearanM&A was established,of theacquiring firm'sknowledgebasewas calculatedusing the same procedure as the absolute size of thethe acquirer.thetarget.theparent acquirerand thepar-ent target firm. Industry information is provided in SICacquired firm's knowledge base.In very few cases, thecodes of the acquiree and acquirer. Acquiring firms areacquired knowledge base was larger than the acquiringselected based on the industry information provided infirm'sknowledge base.In these cases,we usedthelargerSIC-codes which should cover one of the four high-technumber as the denominator. As we are interested in theindustries as mentioned above.Additional informationrelativeproportionofthemerged firm'sresourcesthatareonsizeandR&Dexpendituresofcompanieswasidenti-likely to be occupied with integrative rather than inven-fied through otherdatasets such as Amadeus,Compustat,tive activity,a number greaterthan one is not meaningful.and Worldscope.Data on patents and patentcitations areTechnologically related and technologically unre-takenfromtheUSPatentandTrademarkOfficedatabaselatedM&As.Tomeasuretherelatedness of theacquired(USDepartmentof Commerce).knowledge base,wecomposed a list of patent codes thatappeared in boththe acquiredfirm'sknowledge baseandin the acquiring firm's knowledge base.Thesepatents3.3.Variablesweredividedbythe absolute sizeof acquired knowledgebaseThedependent variable,post-M&A innovativeper-All independent variables described in the above con-formance of the acquiring firm, is measured by thesistof four lagged versions.number of patents granted to each acquiring firm.4 Wemeasure patentsi as the numberof successful patent3.4.Controlvariablesapplications orpatentsgranted,forthe acquiring firmi in year t.The dependent variable is based on the num-We control fora number of possible additional effectsber of patents of the acquiring firm obtained during 1-4on thepost-M&Ainnovativeperformanceof theacquir-years after theM&A.ing firm.Previous research on the effect of cultural dis-Number of non-technologicalM&As.M&Asaretance on post-M&Aperformance suggests bothpositivereported as technological acquisitions if the acquiredandnegative effects,but thenegative effects seemtobefirmhadanypatenting activityduringthe5yearspreced-dominant (Datta,1991;Haspeslagh and Jemison,1991).ing the acquisition.M&As that did not meet the above-WeusetheKogut and Singh (1988)modified indexofmentioned criterion are considered as non-technologicalHofstedetocontrolfor international cultural differencesM&As. To distinguish non-technological acquisitionsbetween companies involved in M&As.When calculat-fromtechnologicalacquisitions,weanalyzedthepatentingthisvariablewehavetomakeacorrectionasazeroingactivityof theacquiredfirm in the5yearsprecedingfor an observation in a certain year can representboththe M&A event.a domesticM&A (no cultural distance)and no M&AAbsolute sizeof acquiredknowledgebase.Foreach(technological or non-technological)in that particularacquiring firm and foreach year, a list was madewith theyear.We use a dummy variable to correct for this by set-ting the values of a dummy variable cultural distance toone,each year no M&A took place.4 Contrary to Ahuja and Katila (2001), our dependent variable isStudies by Griliches (1998) and Pakes and Grilichesmeasured by patentsgranted and we do not assign a granted patent(1984)indicatea statistical relationshipbetweenR&Dto the year in which it was originally applied for due to right handcensoring and data limitations.and the number of patents,although patent output
646 M. Cloodt et al. / Research Policy 35 (2006) 642–654 M&As, 1148 met the above-mentioned criterion and they are classified as technological M&As. The remaining 1281 M&As are classified as non-technological M&As. For the firms in the sample, we obtained annual patent count data for the period 1980–1994 and acquisition and firm-specific data for the years 1980–1993. The final panel for the regression analysis amounts to 7 years from 1989 to 1995. It is well known that there is no ‘official’ database with a world-wide, industry level list of all companies from which one can draw a random sample. Our sample is taken from the Securities Data databank, which contains information on the year an M&A was established, the acquirer, the target, the parent acquirer and the parent target firm. Industry information is provided in SIC codes of the acquiree and acquirer. Acquiring firms are selected based on the industry information provided in SIC-codes which should cover one of the four high-tech industries as mentioned above. Additional information on size and R&D expenditures of companies was identi- fied through other datasets such as Amadeus, Compustat, and Worldscope. Data on patents and patent citations are taken from the US Patent and Trademark Office database (US Department of Commerce). 3.3. Variables The dependent variable, post-M&A innovative performance of the acquiring firm, is measured by the number of patents granted to each acquiring firm.4 We measure patentsit as the number of successful patent applications or patents granted, for the acquiring firm i in year t. The dependent variable is based on the number of patents of the acquiring firm obtained during 1–4 years after the M&A. Number of non-technological M&As. M&As are reported as technological acquisitions if the acquired firm had any patenting activity during the 5 years preceding the acquisition. M&As that did not meet the abovementioned criterion are considered as non-technological M&As. To distinguish non-technological acquisitions from technological acquisitions, we analyzed the patenting activity of the acquired firm in the 5 years preceding the M&A event. Absolute size of acquired knowledge base. For each acquiring firm and for each year, a list was made with the 4 Contrary to Ahuja and Katila (2001), our dependent variable is measured by patents granted and we do not assign a granted patent to the year in which it was originally applied for due to right hand censoring and data limitations. number of the patents that its acquisitions had obtained during the preceding 5 years before the particular M&A event. These patents were then combined with the patents that were cited by these companies. Duplicates were abstracted from the list to ensure that a patent code appears only once. The acquired knowledge base was then calculated as the number of patents (i.e. knowledge elements) on this list. Relative size of acquired knowledge base. This variable was measured by dividing the absolute size of the acquired knowledge base by the absolute size of the acquiring firm’s knowledge base. The absolute size of the acquiring firm’s knowledge base was calculated using the same procedure as the absolute size of the acquired firm’s knowledge base. In very few cases, the acquired knowledge base was larger than the acquiring firm’s knowledge base. In these cases, we used the larger number as the denominator. As we are interested in the relative proportion of the merged firm’s resources that are likely to be occupied with integrative rather than inventive activity, a number greater than one is not meaningful. Technologically related and technologically unrelated M&As. To measure the relatedness of the acquired knowledge base, we composed a list of patent codes that appeared in both the acquired firm’s knowledge base and in the acquiring firm’s knowledge base. These patents were divided by the absolute size of acquired knowledge base. All independent variables described in the above consist of four lagged versions. 3.4. Control variables We control for a number of possible additional effects on the post-M&A innovative performance of the acquiring firm. Previous research on the effect of cultural distance on post-M&A performance suggests both positive and negative effects, but the negative effects seem to be dominant (Datta, 1991; Haspeslagh and Jemison, 1991). We use the Kogut and Singh (1988) modified index of Hofstede to control for international cultural differences between companies involved in M&As. When calculating this variable we have to make a correction as a zero for an observation in a certain year can represent both a domestic M&A (no cultural distance) and no M&A (technological or non-technological) in that particular year. We use a dummy variable to correct for this by setting the values of a dummy variable cultural distance to one, each year no M&A took place. Studies by Griliches (1998) and Pakes and Griliches (1984) indicate a statistical relationship between R&D and the number of patents, although patent output