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Technovation 29 (2009) 704–713

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Technovation

0166-49

doi:10.1

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journal homepage: www.elsevier.com/locate/technovation

Post-merger performance in the software industry: The impact ofcharacteristics of the software product portfolio$

Pierre-Majorique L�eger �, Louis Quach 1

HEC Montr�eal, Department of IT, 3000 chemin de la Cote-Ste-Catherine, Montr�eal, Qu�ebec, Canada H3T 2A7

a r t i c l e i n f o

Keywords:

Software portfolio

Merger

Network effect

72/$ - see front matter Crown Copyright & 20

016/j.technovation.2009.05.016

would like to thank Carl St-Pierre, Mohamme

earch assistance. We would also like to reco

the FQRSC and NSERC.

esponding author.

ail address: pierre-majorique.leger@hec.ca (P.

ow works at SAP Canada.

a b s t r a c t

This article studies the impact of the characteristics of software product portfolios on the performance

of firms involved in a merger of software companies. The short-term financial results reveal that

markets generally seem to neglect the characteristics of software product portfolios when the merger is

announced. Nevertheless, such portfolios appear to have a positive impact on the price/book value ratio

of merged software firms. The empirical evidence presented in this paper suggests that, in the long

term, the performance of business combinations in the software industry is related to certain factors

that are attributable to virtual network effects.

Crown Copyright & 2009 Published by Elsevier Ltd. All rights reserved.

1. Introduction

Many information technology (IT) specialists agree that thesoftware industry has entered a phase of maturity in the last fewyears. Probably as a result, the merger-and-acquisition activity inthe IT industry has intensified to unprecedented levels in recentyears. Examples of high-profile deals concluded recently includethe acquisition of Cognos by IBM ($5.0 billion), Business Objectby SAP ($6.5 billion), Hyperion ($3.3 billion) and BEA Systems($6.5 billion) by Oracle, MySQL by Sun Microsystems ($1.0 billion),and Mercury Interactive ($4.5 billion) and Opsware ($1.6 billion)by HP. And this does not even take into account the $7 billionacquisitions made by EMC and Computer Associates’ more than150 acquisitions over the last decade.

The merger-and-acquisition literature reveals that few busi-ness combinations achieve the performance level that wasanticipated at the time the decision to merge was made (Dattaet al., 1992). It is therefore legitimate to ask the followingquestion: What factors determine the performance of a mergeror acquisition in information technology? Few studies havespecifically covered the software sector. More specifically, nostudy has yet considered the characteristics of the productportfolios held by the firms involved, which may well prove tobe a determining factor in explaining the performance of mergersand acquisitions of software firms.

09 Published by Elsevier Ltd. All

d Jabir and Karine Marion for

gnize the financial contribu-

-M. L�eger).

Based on network theory, this article hypothesizes that,beyond the traditional antecedents of the performance of businesscombinations, the performance of combinations of softwarecompanies should be positively impacted by the virtual networkeffects that result from the compatibility and complementarity ofthe new entity’s software products. More specifically, we examinethe impact of these two factors on the short-term marketperformance of both entities’ stock at the time of the announce-ment, on the value of the transaction and on the long-termfinancial performance of both entities, when measured alongsidethe impact of more traditional variables.

2. Review of the literature: the performance of businesscombinations

In the financial literature, mergers and acquisitions havealways been a topic of great interest, and this continues to betrue. A considerable proportion of these studies are interested inthe creation of value for shareholders. For many authors, valuecreation is a good indicator of the performance of a businesscombination (Agrawal et al., 1992; Datta et al., 1992; Lubatkin,1987; Singh and Montgomery, 1987). From a strictly financialpoint of view, the impact of the business combination is oftenassessed based on share value. However, other specialistsalso measure performance in accounting and economic terms(e.g., return on investment) (Pautler, 2003) or by the level ofsynergy achieved (Larsson and Finkelstein, 1999). In all thesecases, performance is associated with value creation. Conse-quently, it is important to investigate factors that are likely toresult in value (Seth, 1990b). In this context, Brouthers et al.(1998) suggest that the motivations for mergers should be

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perceived as key success factors. More than motivations, these keysuccess factors can be termed performance drivers. In light of theexisting literature, we propose to define the performance of abusiness combination by the achievement of certain motivationsand goals; the degree of achievement should be reflected in thecreation of value for shareholders.

A number of authors have studied the factors influencing theperformance of business combinations (Brouthers et al., 1998;Lehto and Lehtoranta, 2004; Seth, 1990b). Taken together, thesestudies suggest that the main performance antecedents relate tofour factors justifying the combination’s economic potential: thepotential for market growth, the potential for economies ofscale, the potential for economies of scope, and the potential toacquire competencies. The following paragraphs define each ofthese antecedents:

�

Economies of scale constitute a classic motivation for mergersand acquisitions (Brouthers et al., 1998). Combining into a newentity makes it possible to reduce average costs by consolidat-ing production, as well as administrative, commercial, logis-tical and research and development services. � In the context of a merger, economies of scope emerge when it

costs less for the new entity to produce different varieties of asingle product, primarily because of the consolidation ofpurchases, advertising and distribution, which are now doneon a larger scale (Priest, 1994). Thus, economies of scope areone of the main reasons for marketing strategies such asthe combined sale of products, the sale of related products orthe sale of products under a single brand name (Cabral, 2001).

� Market growth suggests a gain in market share and an

improvement in competitive positioning. This concept alsointegrates the notion of increased market power, that is, afirm’s ability to better control the prices, quantities or nature ofthe products it sells (Seth, 1990b).

� A business combination that is motivated by the acquisition of

competencies has the goal of acquiring skills that are difficultor would take too long to develop internally, or that cannot bepurchased in a transaction (Capron, 2005). The recentliterature suggests that this factor may be crucial to the futuresuccess of the new entity (Gammelgaard, 2004).

The literature indicates that the performance of a merger oracquisition is closely connected to each of the above-mentionedmotivations. Thus, the studies by Seth (1990a) and Pautler (2003)show that value creation in acquisitions is associated witheconomic performance that emerges from economies of scale,economies of scope and market power. Moreover, Capron (1999)suggests that value creation in acquisitions results not only from areduction in costs or the ability to dictate prices, but also from theopportunity to use a specialized resource that results from themerger and its potential to create synergies.

However, the potential for synergies rarely guarantees thesuccess of mergers or technological alliances. King et al. (2004)find that, on average, acquisition activity does not contributepositively to the performance of acquiring firms. According toBayona et al. (2006), share prices generally do not react to theannouncement of technological alliances. Furthermore, Tuch andO’Sullivan (2007) observe that, in the short run, acquisitions haveat best an insignificant impact on shareholder wealth, and thatlong-term performance analysis reveals overwhelmingly negativereturns. Accordingly, the literature also offers a number ofexplanations for the surprising proportion of acquisitions thathave failed in recent years, especially in technologically intensiveindustries, and much of the research points to integration as thedecisive factor.

Prabhu et al. (2005) argue that, for acquisitions to promoteinnovation, firms must first engage in internal knowledgedevelopment. Bannert and Tschirky (2004) also concentrate onthe importance of internalizing external knowledge, and identifythe lack of integrative decision-making, of systemic processes andof a holistic change of both companies during the integration asthe main causes of failed acquisitions. Along similar lines, Yooet al. (2007) develop a framework that reveals that mergersrepresent a discontinuity in knowledge sharing. Puranam andSrikanth (2007) offer a balanced point of view on integration byaccounting for the qualitatively distinct ways in which acquirersleverage technology acquisitions. They contend that acquirers usethe acquired firm’s existing knowledge as an input to their owninnovation processes but do not rely on it as an independentsource of ongoing innovation. To address this issue, Ku et al.(2007) propose a virtual collaborative framework to fosterknowledge sharing within the new entity.

From a managerial point of view, Graebner (2004) placesparticular emphasis on the role of managers from the acquiredcompany in solving implementation dilemmas. Paruchuri et al.(2006) take these results one step further by hypothesizing thatthe productivity of corporate scientists at acquired companies isgenerally impaired by integration, due to the loss of social statusand centrality in the process. This can be assumed to beparticularly true of technological firms. Tsai and Hsieh (2006)claim that the application of ‘‘two-stage grey decision-making’’can assist corporations in selecting technological assets to createwealth through mergers, whereas Haro-Dominguez et al. (2007)find that the degree of ‘‘absorptive capacity’’ has a positiveinfluence on both internal and external acquisitions of technology.

Shaver (2006) describes two mechanisms he calls thecontagion effect and the capacity effect, which establish the‘‘paradox of synergy,’’ whereby the excessive attention given toachieving synergy actually distracts the firm from its environ-ment. Sorescu et al. (2007) point to a firm-driven, marketing-driven variable—product capital, which they define as productdevelopment and support assets—to explain why some firmsmake smarter acquisition decisions. Wang and Zajac (2007) positthat firms should examine factors such as resource similarity andcomplementarity, relational capabilities and partner-specificknowledge before considering an acquisition.

The performance of a financial combination is also influencedby financial factors. It is not uncommon for the often astronomicalcosts incurred in a merger to result in intense pressures on thenew firm’s short-term market profitability, and also on its long-term accounting profitability; often the amounts invested gen-erate little return. Thus, any study of the performance of businesscombinations must take the financial context in which the mergeroccurs into account.

The amount of the merger or acquisition transaction has asignificant impact on the new entity’s future level of profits.However, in itself, the amount of a transaction alone does not takesufficient account of the reasons justifying this transaction. If anacquirer firm takes over another firm for a certain consideration, italso takes possession of the target firm’s assets. In addition, theratio of the amount of the merger-and-acquisition transactionover the total value of the target firm’s assets would constitute amore appropriate performance antecedent than the amount of thetransaction considered in isolation. In the financial literature, thisratio is referred to as the ‘‘price/book value ratio’’ (P/B). If this ratiois greater than 1, it shows that the acquirer firm is ready to paymore than the value of the firm it is acquiring. An overly high P/Bratio tends to increase the new entity’s debt burden, therebyhampering its future development; this situation is likely to beseverely punished by the financial markets (Leroy, 2003). Moreimportantly, assessing the performance of a business combination

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also requires one to take into consideration the target’s profit-ability before the merger. Indeed, the ex ante profitability of thetarget firm should be reflected not only in the P/B ratio but also inthe new entity’s performance (Hollender, 1967). It should alsobe noted that the merger’s economic potential appears to bediscounted in the P/B ratio (Varaiya, 1987).

To sum up, we can summarize our current knowledge of theperformance of business combinations in the model presented inFig. 1. Thus, the empirical literature claims that there is (i) anegative correlation between the P/B ratio and the new entity’sfinancial performance; (ii) a positive correlation between thetarget firm’s ex ante performance and the new entity’s financialperformance; and (iii) a positive correlation between the factorsinfluencing the merger’s economic potential and the new entity’sperformance; finally, (iv) the P/B ratio is positively influenced bythe target firm’s ex ante profitability and by the merger’seconomic potential.

3. Research question and conceptual framework

Although an extensive body of literature has examined theperformance of business combinations, few studies have at-tempted to understand the antecedents of the performance ofmergers of software firms. The software industry is characterizedby a number of individual criteria that cumulatively justify ourinterest in it.

The most noteworthy criterion is inherent in the intangiblenature of software products. Essentially based on knowledge, thecombination of software firms is associated with certain economicphenomena that are specific to the IT industry and that emergefrom the characteristics of the product portfolio. More specifically,this article will attempt to prove that, beyond the above-mentioned antecedents to the performance of mergers andacquisitions, the potential for a network effect is a phenomenonthat must be taken into consideration in evaluating the perfor-mance of a combination of businesses specializing in software.In other words, we are asking the following research question:

Is the financial performance of the firms involved in a softwarebusiness combination influenced by the potential networkeffect resulting from the characteristics of the new entity’sportfolio of software products?

We will first present the concept of virtual network effects.Then we will operationalize this concept within two differentnotions: software compatibility and software complementarity.

Amount of transaction

Value of target firm’s assets

Ex ante profitability of target firms

Factors influencing the business combination’s economic potential

+

+

Fig. 1. Factors cited in the literature as affecting

Finally, we will propose a research model incorporating thesevariables.

3.1. Virtual network effects and the performance of combinations of

specialized software firms

Network effects have been defined as a gain in the value that aparticipant derives from a good in an industry when the numberof participants consuming the same type of good increases(Economides, 1996; Katz and Shapiro, 1985). The higher thecritical mass of users of a network of goods, the greater the valueof the good is for each user. The fax and the telephone are classicexamples of this phenomenon. Thus, a telephone has no value inthe absence of a telecommunications network. In such bidirec-tional networks, the value of the network is equal to n*(n�1),where n is the number of items. When a new item is inserted intothe network, its value will increase by 2n.

Economides (2001) introduced the concept of virtual networks,in which network effects also exist but are expressed in a slightlydifferent way. He defines a virtual network as a collection ofcomplementary and compatible technologies based on the sametechnological platform. A network effect is manifested in a virtualnetwork by the mutual creation of value among the complemen-tary and compatible technological components.

In view of its nature, the software industry has properties thatmake it a particularly propitious business environment for themanifestation of virtual network effects. Thus, two compatibleand complementary programs such as the Microsoft operatingsystem and the Microsoft Office suite of applications can easilycreate value mutually and thereby promote the distribution ofboth. In this context, Ende and Wijnberg (2003) demonstratethrough case studies that network effects offer an explanation forthe increasing returns in the software sector. In other words,network effects contribute to the marginal profits of a product,which increase with the total quantity consumed or produced.

Pehrsson (2006) identifies four business relatedness classes,and finds that technology relatedness has the strongest positiveperformance effect. In order to measure the potential impact ofvirtual network effects on the performance of a combination ofsoftware firms, we propose to examine two main notions under-lying the emergence of this phenomenon: software compatibilityand software complementarity. As illustrated above, these twonotions appear to be a sine qua non for the emergence of a virtualnetwork effect. Compatibility and complementarity should there-fore constitute indicators of the potential of network externalitieswithin a product portfolio.

Financialperformance of

business combination

+

+

the performance of business combinations.

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3.2. The economic potential of software compatibility within the

new entity’s product portfolio

Software compatibility is defined as ‘‘the extent to whichprograms can work together and share data.’’2 Compatibilityis assured by the issuance of standards by companies(e.g., compatible with Microsoft’s .NET standard) or consortiumsof industry stakeholders (e.g., compatible with the XML standard).In this context, the results of the study by Kauffman and Li (2005)suggest that if a company wants to adopt a technology, it is moreprofitable to wait until this technology becomes a standard orattains a critical mass threshold before investing.

Moreover, it is important to think of compatibility as relative; aprogram will be more compatible in the absence of limitations onfunctionalities, in the presence of portability, and by assuring thatit functions with older versions (downward compatibility) orfuture versions (upward compatibility) of other programs.

Several other authors have studied compatibility and itsadvantages. According to Farrell and Saloner (1986), the threemain benefits of compatibility are the interchangeability ofcomplementary products, ease of communication, and costsavings. In the view of Economides (1991), incompatibility is nota technical problem so much as a cost problem: it is possible tomake any two products compatible provided that one accepts thecosts of transitioning from incompatibility to compatibility.

In the context of a business combination, if the productsowned by the firms involved in the merger are compatible, thisshould reduce the investments the new entity needs to make tomarket a unified product portfolio. In addition, software compat-ibility can be perceived as a benefit for customers in the sense thatit allows the joint use of software and thus gives them access tonew functionalities without making any additional investments.

In other words, in addition to conferring technical advantages,compatibility is directly related to financial investments: the morecompatible the software products are, the lower the financialinvestments required to make them work together. The compat-ibility of the products held by the new entity should thereforehave a positive impact on the performance of the merged firm, butit should also increase the P/B ratio of the transaction, given theanticipated economic potential of this factor. However, it isimportant to keep in mind that, even though compatibility isexpected to have a positive influence on both these measures, theP/B ratio can be expected to have a negative impact on long-termfinancial performance if the acquisition calls for a significantincrease in debt levels. We therefore formulated the followinghypotheses:

H1a. The greater the compatibility between the product portfo-lios in a business combination of software companies, the betterthe target’s short-term market performance will be.

H1b. The greater the compatibility between the product portfo-lios in a business combination of software companies, the higherthe value of the transaction will be.

H1c. The greater the compatibility between the product portfo-lios in a business combination of software companies, the betterthe new entity’s financial performance will be.

2 Microsoft Computer Dictionary, Fourth Edition. Redmond, WA: Microsoft Press,

1999, p. 100.

3.3. The economic potential of software complementarity within the

new entity’s product portfolio

Two products are described as being complementary whentheir joint use adds more value for the customer than the sum ofthe separate use of the same products (Carlaw and Lipsey, 2002).Product complementarity within a single portfolio is operationa-lized by the presence of functionalities within the differentprograms that create added value for the user when used jointly.This union leads to a gain in value for the user in the form of anaddition to or a functional enrichment of the product portfolio.

The merger of firms that have complementary product lines isalso a frequent motivation for a combination. By offering anintegrated product line, the new entity hopes to acquire anadditional economic rent corresponding to the benefit to users.Empirical evidence shows that resource complementarity in-creases the potential to create greater synergies in acquisitions,which leads to better performance in the long run. Thus, theresults of studies by Harrison et al. (2001) clearly show thatresource complementarity is a common characteristic of allacquisitions that perform well. Seth (1990b) also suggests thatmergers and acquisitions in related industries may benefit certainparties more but generally create as much value as mergers andacquisitions involving firms whose activities are not related.

According to Harrison et al. (2001), it is important not toconfuse complementarity and similarity: two activities or pro-ducts may be complementary without necessarily being similar.This distinction is all the more important given that, before 1991,research suggested that acquisitions in related sectors should leadto better performance by the acquirer firm (Kusewitt, 1985; Singhand Montgomery, 1987). Harrison et al. (1991), on the other hand,show that synergistic gains from combinations of resources aremuch greater when the resources are complementary, rather thansimilar.

Finally, by establishing a classification of three layers ofcomplementary software activity, namely (i) systems software,(ii) middleware software, and (iii) applications software, Silva andIyer (2006) emphasize the importance of software complemen-tarity in a context of mergers and acquisitions. According to theseauthors, in a merger or acquisition between software firms, whenthe acquiring firm’s product is complementary to that of thetarget firm, in accordance with their classification of softwareproducts, better performance is observed in terms of abnormalreturns than in mergers and acquisitions involving at least onenon-software firm. In other words, complementarity is a source ofvalue creation in software mergers and acquisitions.

To sum up, a positive relationship should exist betweenproduct complementarity in a single software portfolio and theperformance of the merged firm. Moreover, the economicpotential associated with complementarity should also increasethe transaction’s P/B ratio. Once again, the inverse relationshipbetween the two dependent variables must be kept in mind.Based on these arguments, we made the following hypotheses:

H2a. The greater the complementarity between the productportfolios in a business combination of software companies, thebetter the target’s short-term market performance will be.

H2b. The greater the complementarity between the productportfolios in a business combination of software companies, thehigher the value of the transaction will be.

H2c. The greater the complementarity between the productportfolios in a business combination of software companies, thebetter the new entity’s financial performance will be.

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3.4. A revisited model specific to the software industry

Beyond the traditional antecedents of performance in mergersand acquisitions presented in Section 2, we propose that thespecific elements in the software industry related to the potentialfor externalities in virtual networks should also be considered asfactors explaining the performance of combinations of softwarecompanies. The following conceptual model presents an overviewof the performance antecedents of software business combina-tions (Fig. 2).

4. Methodology

To answer the research question, we tested the research modelon the 60 largest (in terms of the acquisition value) combinationsof public firms specializing in software (SIC 7373 and 7372) thattook place in the United States between 1990 and 2003.The following sections present the operationalization of theperformance variables and the independent financial and quali-tative variables.

4.1. Operationalization of performance variables

The performance of each business combination was assessedover the short and long term. We used event studies to measurethe short-term market performance of the acquirer firm and thetarget firm. An event study is a method of measuring marketperformance that is based on the theory of efficient and rationalfinancial markets (Fama, 1970). According to this theory, the effectof a significant event is reflected immediately in the value of afinancial security (MacKinlay, 1997). Event study constitutes amethodological approach that makes it possible to studyhow financial markets assess the impact of an event on a firm’svalue. This method is widely used to study the performance ofbusiness combinations as anticipated by the markets (Agrawalet al., 1992; Das et al., 1998; Lubatkin, 1987). In informationtechnology, the approach has been used by Dos Santos et al.(1993), among others.

Factors associated with thepotential for network effects

HIB

and

H2B

: +

Amount of transaction

Value of target firm’s assets

Ex ante profitability of target firms

Factors influencing the business combination’s economic potential

+

+

Fig. 2. Proposed re

Event study makes it possible to calculate the abnormalreturn on a security at the time an event is announced. Abnormalreturn corresponds to the difference between the firm’s actualobserved return and the normal return that should have beenobserved if the event had not occurred. Each firm’s normal returnwas estimated using the standard approach proposed by MacK-inlay (1997), and the calculation was done using the Eventuss

module in SASs software. The market information needed tomeasure abnormal return was obtained from the CRSP (Center forResearch in Security Prices) database. The abnormal return wascalculated for the following analysis windows: �1 day from theannouncement date (�1,0), �1 day to +1 day after the announce-ment (�1,1), �3 days to +3 days (�3,3), �5 days to +5 days (�5,5)and �10 days to +10 days (�10,10). The model for estimating thenormal return was calculated over the days prior to theannouncement.

Long-term performance was measured by the change invarious accounting indicators that are widely used to assess theprofitability of the new entity (Banker et al., 2004; Miller, 2000).The information that we used to measure the new entity’s long-term performance included: (i) return on assets (ROA), (ii) returnon equity (ROE), (iii) profit margin, and (iv) annual sales. The dataneeded to calculate these indicators were obtained from theCOMPUSTATs (Standard & Poor) and OSIRISs (Bureau Van Dick)financial databases. We measured the growth in these variablesover periods of one year and two years after the announcement ofthe merger.

4.2. Operationalization of independent financial variables

The P/B ratio and the target’s ex ante profitability wereobtained from the SDC Platinum database. More specifically,the P/B ratio corresponds to the transaction value divided bythe value of the target firm’s assets. As for ex ante performance, weused the target firm’s earnings per share (EPS) at the time themerger was announced, as suggested by Burger and Webster(1978).

Financial performance of combination of

software companies

H1A and H2A : +

+

+

-

search model.

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4.3. Operationalization of independent qualitative variables

There is no structured data source documenting the composi-tion of software firms’ product portfolios. In order to assess thecharacteristics of the new entities’ software portfolios, we didhistorical research in order to determine their composition at thetime of the decision to merge. This historical research was basedon a survey of articles published in the press at the time eachmerger was announced that described the makeup of the softwareportfolios of the parties involved. Using Proquest, we chose tocollect announcements of mergers and acquisitions in specializedpublications such as The Wall Street Journal, Dow Jones &Company, the Financial Times, and any other relevant newspaperarticles that captured information concerning software portfoliosat the time of the announcement. An average of three articles wasfound for each announcement.

Using the coding approach suggested by Larsson (1993) andBullock and Tubbs (1987), a coding scheme for the newspaperarticles was developed in order to measure the six variables(acquisition of competencies, economies of scale, economies ofscope, market growth, software compatibility and softwarecomplementarity) representing antecedents to the performanceof business combinations. Appendix 1 presents the operationaldefinition of each variable and an example given to the coder tomake it easier to understand.

With regard to the operationalization of the measurement ofthese factors, we decided to assess each variable at the time ofcoding on a scale of three levels of intensity: from no intensity (0)to high intensity (2). Therefore, based on the evidence gatheredfrom the articles, the characteristics of the software portfolioresulting from each merger were evaluated with regard to the sixresearch variables using this three-level intensity scale. Theclassification scheme was pretested on ten business combina-tions, which enabled us to refine the measurement tool. A codingguide was also created based on the recommendations of Bullockand Tubbs (1987). This document included the coding scheme andpresented the necessary information to code the case studies andthe process that the coder had to follow in identifying the researchvariables. The coding was done by two coders; one third of thecoding was evaluated by both coders, with an interrater reliabilityrate of 80%. Any variances were discussed and recoded byconsensus, as recommended by Larsson (1993).

4.4. Descriptive statistics and correlations among research variables

No correlation among the different independent variables wasgreater than 0.5, which indicates a satisfactory level of discrimi-nating validity among the variables (Tabachnick and Fidell, 2001).The P/B ratio, EPS, software compatibility and economies of scalewere transformed to ensure that their distribution was normal.

5. Analysis and discussion

A path analysis using the observed variables was done to testthe research model based on the data gathered on the 60 softwarecompany combinations. Path analysis is a type of multipleregression analysis belonging to the class of structural equationmodeling (SEM) techniques (Joreskog and Sorbom, 1995). Pathanalysis is commonly used in many disciplines (e.g., psychologyand marketing), and is considered especially appropriate fortheory testing (Bagozzi, 1980). SEM goes beyond ordinaryregression models to incorporate multiple independent anddependent variables. Path analysis is appropriate for this paperbecause (i) it directly uses observed variables (all our variables are

financial values, i.e., single indicators); (ii) it allows theseindicators to be treated as dependent and independent variablesin the model (i.e., P/B ratio is considered in our model as both adependent and an independent variable); and (iii) it considers theintercorrelations within each set of criterion variables. In otherwords, path analysis allows one to calculate parameter estimatesand fit estimates for simultaneous equations. LISREL software(Submodel 2) was used to do this analysis (Joreskog and Sorbom,1995).

5.1. Short-term model

When we analyzed performance in terms of cumulativeabnormal return (CAR), in the case of acquirer firms, only softwarecompatibility seems to have a significant positive influence in the(�3,3) window. These results support hypothesis H1a. Targetfirms’ CAR is negatively affected by the P/B ratio in all analysiswindows. As previously mentioned, this is largely attributable tothe perception of high P/B ratios as involving unsustainableamounts of debt. With regard to complementarity, our resultsrevealed an unexpected impact. In the short term, complemen-tarity has a negative impact on market performance in certainanalysis windows. Thus, the results of our research do not allowus to accept hypothesis H2a. Finally, in the (�3,3) and (�5,5)windows, CAR is also positively impacted by economies of scope(Table 1).

The results obtained are in agreement with those of previousstudies; within short windows of analysis, the CAR of target firmsis clearly superior to that of acquiring firms. From a financial pointof view, this is also aligned with the convergence of the stock priceof the acquirer and that of the target in anticipation of the mergerat a previously specified price, which normally lies between thatof the acquirer and that of the target. In addition, the difference inperformance in favor of target firms seems to be much morepronounced in the software industry than in other industries.

Our results show that software compatibility is taken intoconsideration by acquirers in determining the acquisition value ofthe target firm. Indeed, Table 2 reveals that in addition to thetarget firm’s EPS, the acquisition of competencies, and marketgrowth, software compatibility has a positive impact on the P/Bratio. In other words, acquiring firms are willing to pay asubstantial premium for a target whose product is readilycompatible with their own. This ultimately confirms hypothesisH1b. However, the results are inconclusive in terms ofcomplementarity, so we cannot accept hypothesis H2b.

These findings indicate that the characteristics of productportfolios have an impact not on short-term market performancebut on the price/book value ratio. More specifically, they suggestthat financial markets tend to neglect the characteristics ofproduct portfolios in evaluating the impact of a combination onthe value of the acquirer firm’s shares. Thus, firms themselvesseem better able to evaluate product compatibility and comple-mentarity than financial agents, who appear to be less sophisti-cated in this regard.

5.2. Long-term model

In the long term (Table 3), the results show that softwarecompatibility, economies of scale and market growth have apositive impact on sales growth. As expected, performance interms of return on assets (ROA) and profit margin is negativelyaffected by P/B ratio, again due to increased debt ratios, andpositively affected by the target firm’s EPS, the acquisition ofcompetencies and economies of scope. Performance in terms ofreturn on equity (ROE) is influenced by the P/B ratio, the target

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Table 1Influence of performance factors on short-term market performance.

CUMULATIVE ABNORMAL RETURN

CAR �1,0 CAR �1,1 CAR �3,3 CAR �5,5 CAR �10,10

Beta Sig. Beta Sig. Beta Sig. Beta Sig. Beta Sig.

Acquirer firmsPrice/book value ratio �0.23 * �0.18 NS �0.11 NS �0.02 NS �0.04 NS

Target firm EPS �0.07 NS �0.05 NS 0.0005 NS �0.06 NS �0.16 NS

Acquisition of competencies �0.03 NS 0.19 NS 0.22 * �0.1 NS �0.25 *

Compatibility 0.17 NS 0.15 NS 0.04 NS �0.05 NS 0.03 NS

Complementarity �0.19 NS �0.26 * �0.15 NS 0.04 NS �0.13 NS

Economies of scale �0.11 NS �0.07 NS �0.02 NS 0.004 NS �0.11 NS

Economies of scope �0.1 NS �0.07 NS 0.08 NS 0.13 NS �0.02 NS

Market growth 0.15 NS 0.08 NS 0.02 NS �0.004 NS 0.005 NS

R2 (%) 11.69 7.87 5.28 3.51 12.12

Target firmsPrice/book value ratio 0.32 ** 0.37 *** 0.33 *** 0.24 ** 0.18 NS

Target firm EPS 0.16 NS 0.1 NS 0.11 NS 0.11 NS 0.02 NS

Acquisition of competencies 0.08 NS �0.002 NS 0.0002 NS �0.09 NS �0.07 NS

Compatibility �0.04 NS �0.03 NS �0.19 NS �0.22 * 0.04 NS

Complementarity �0.17 NS �0.23 * �0.22 * �0.12 NS �0.2 NS

Economies of scale �0.13 NS �0.26 ** �0.25 ** �0.25 * �0.22 *

Economies of scope 0.09 NS 0.17 NS 0.26 ** 0.27 ** 0.11 NS

Market growth �0.07 NS �0.08 NS �0.19 * �0.26 ** �0.09 NS

R2 (%) 13.73 20.36 26.16 22.35 8.92

****po.001; ***po.01; **po.05; *po.10; NS: Not significant.

Table 2Influence of performance factors on the value of the transaction.

Price/book value ratio

Gamma Sig.

Price/book value ratioTarget firm EPS 0.22 **

Acquisition of competencies 0.31 **

Compatibility 0.3 **

Complementarity 0.06 NS

Economies of scale �0.01 NS

Economies of scope �0.24 **

Market growth 0.26 **

R2 (%) 30.59

Table 3Influence of performance factors on the new entity’s performance.

Sales growth Return on assets

1 year 2 years 1 year 2 years

Beta Sig. Beta Sig. Beta Sig. Beta Sig.

Price/book value ratio 0.17 NS �0.07 NS 0.62 **** 0.52 ****

Target firm EPS �0.02 NS �0.04 NS 0.28 *** 0.21 **

Acquisition of competencies 0.18 NS �0.14 NS �0.32 *** �0.16 NS

Compatibility 0.41 ** 0.3 * �0.01 NS 0.11 NS

Complementarity �0.12 NS 0.06 NS 0.25 * 0.13 NS

Economies of scale 0.34 ** 0.26 * �0.01 NS 0.01 NS

Economies of scope �0.12 NS �0.05 NS �0.26 ** �0.32 **

Market growth 0.4 *** 0.17 NS �0.08 NS 0.06 NS

R2 (%) 34.34 20.11 57.82 48.45

Return on equity Profit margin

1 year 2 years 1 year 2 years

Beta Sig. Beta Sig. Beta Sig. Beta Sig.

Price/book value ratio 0.63 **** �0.002 NS 0.56 **** �0.34 **

Target firm EPS 0.24 ** �0.13 NS 0.22 ** �0.19 *

Acquisition of competencies �0.17 NS �0.13 NS �0.39 *** 0.04 NS

Compatibility 0.01 NS �0.22 NS �0.12 NS �0.31 **

Complementarity �0.23 * 0.27 NS 0.43 *** �0.07 NS

Economies of scale 0.01 NS 0.09 NS 0.1 NS �0.19 NS

Economies of scope �0.25 ** 0.13 NS �0.4 *** 0.3 **

Market growth 0.01 NS �0.24 * �0.13 NS �0.29 **

R2 (%) 57.56 12.83 60.12 39.18

****po.001; ***po.01; **po.05; *po.10; NS: Not significant.

P.-M. L�eger, L. Quach / Technovation 29 (2009) 704–713710

firm’s EPS and economies of scope. In the long term,complementarity has a negative effect on the profit margin oneyear after the announcement of the merger, so hypothesis H2c isnot supported.

Our results indicate that, in the long run, financial factors(especially the price/book value ratio) tend to influence thecombination’s performance in terms of ROA, ROE and profitmargin. Nevertheless, contrary to the results of the short-termmodel, our analysis suggests that characteristics of the softwareproduct portfolio play a significant role in the performance ofmerged software firms. More specifically, software compatibilityappears to be an antecedent to the new entity’s long-termperformance as measured by increased sales one and two yearsafter the announcement of the merger. This supports hypothesisH1c. As for the main antecedents of the financial performance ofthe business combination mentioned in the literature, the resultsof our study confirm that they have a positive impact, dependingon the type of performance considered (ROA, ROE, increased salesor profit margin).

6. Conclusion

The goal of this study was to better understand theperformance of mergers and acquisitions in the software sectorin the short and long term. The short-term results reveal that

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financial markets generally seem to neglect the characteristics ofsoftware product portfolios. Nevertheless, such portfolios appearto have a positive impact on the price/book value ratio ofcombined software firms. In the long term, the empirical evidencepresented in this paper suggests that the performance of businesscombinations in the software industry is connected to certainelements that are attributable to virtual network effects.

The importance of software compatibility, which is associatedwith virtual network effects, in explaining the performance ofcombinations of software firms, constitutes the most importantfinding of this paper. Compatibility constitutes a factor with apositive influence on the performance of the new entity in termsof increased sales in the short run after the announcement of themerger. This confirms that acquirers could potentially gain bybetter integrating technology issues into their M&A decision-making (James et al., 1998). Acquirers are fully aware of thebenefits associated with merging a software portfolio that iscompatible, and our results show that this business potential isaccounted for in the acquisition value of the target firm. However,it appears that financial markets are more shortsighted and tendto neglect the long-term benefits of software compatibility; theyfail to take the potential synergy of the combined softwareportfolio into account when valuing the acquirer firm’s shares.Financial professionals focusing on long-term growth wouldprobably benefit from being better able evaluate factors that areattributable to virtual network effects.

There are several limitations on this study. Only US firms weretaken into consideration, because of the wealth of informationavailable on these mergers in the documentary databases. It is notnecessarily possible to generalize these results to the European andAsian markets, which may have different attitudes. The firms’short-term market performance represents the financial markets’perception of the combination’s potential and not its actual short-term performance. Finally, it should be mentioned that theinstruments used to measure performance factors in this studyhave certain inherent limitations. First of all, the measurement isbased on public information or, in other words, ‘‘secondary data.’’ Inaddition, the qualitative variables used in this study were measuredon a scale with only three levels of intensity. Although the use of amore precise scale would have allowed for greater discrimination,it would also have increased the subjectivity applied in assessingfairly general information. Furthermore, this study did not take intoaccount organizational factors affecting post-acquisition perfor-mance. According to Chakrabarti (1990), the post-acquisitionsuccess of firms depends on the strategic fit between the mergingcompanies as well as on their organizational integration.

Our results are specific to the software industry and we haveno evidence that they are generalizable outside the context of thisstudy. However, compatibility and complementarity are likely tobe important drivers in other technological industries wherenetwork externalities are important, such as industries wheresoftware is embedded in equipment, as in the telecommunica-tions sector. Investigating recent mergers and alliances in soft-ware-intensive sectors would provide a better perspective on thegeneralizability of the current results.

This research makes contributions that are relevant to both thefinancial and IT industries. First of all, the study emphasizes that itis important for financial specialists to take the contents of aproduct portfolio into consideration. In the short term, financialmarkets often appear to ignore certain key characteristics ofproduct portfolios, and especially compatibility, which does in facthave an impact on the success of software business combinationsin the long term. It is important for managers to better positionthese characteristics in the rationale they use to justify thesynergy between the partners involved in a combination ofsoftware firms.

Appendix 1. Operationalization of variables (extract of codingscheme)

Variable

Operationaldefinition

Example

Acquisition ofcompetencies

By acquisition ofcompetencies, wemean the acquisitionof technical know-howor specifictechnologies, whichare difficult to imitateor copy and whichrequire acorrespondingfinancial investment.

‘‘SilverStream’s product

portfolio, particularly

its J2EE-based

application server, will

be a linchpin of Novell’s

transformation into an

e-business solutions

and platform company.’’(‘‘Novell gets J2EEboost fromSilverStream deal,’’Elizabeth Montalbano.CRN. Jericho: June 17,2002, No. 1000; p. 8)

Softwarecompatibility

Compatible programsare based on the samestandards and requirefew or no investmentsto make them worktogether.

‘‘Like most Web services

companies,

SilverStream’s

development tools are

based on Sun’s Java

computing system,

which makes it easier to

write code that runs on

many different

operating systems.’’(‘‘Transforming NovellSilverStream dealmoves firm deeperinto web software,’’Hiawatha Bray. Boston

Globe, June 11, 2002;p. C1)

Softwarecomplementarity

Two products are saidto be complementarywhen their joint useadds more value forthe customer than thesum of the values ofthe separate use ofthese products.

‘‘The goal is to integrate

BMC’s Patrol reporting

tools with BGS’s Best/I

analysis tools. The

combined tools would

let information systems

managers detect and

predict problems with

business-critical servers

and applications.’’(‘‘BMC buys BGSsystems to help ISmaintain service,’’Patrick Dryden.Computerworld.Framingham: Feb. 9,1998. Vol. 32, No. 6;p. 24)

Economies ofscale

Economies of scalerefer to the reductionin average per-unitcosts because ofincreased volume orrationalization ofoperations.

‘‘Rather than trying to

integrate the

companies’ systems and

technologies, which

makes such mergers

complex, Oracle will

simply milk ongoing

revenues from

PeopleSoft’s customers,

transfer the best

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P.-M. L�eger, L. Quach / Technovation 29 (2009) 704–713712

salesmen, developers

and technology to

Oracle, and close much

of the rest down’’ (‘‘Thesoft sell,’’ Financial

Times. London: June 7,2003. p. 16)

Economies ofscope

Economies of scopeare defined as anincrease in efficiencyresulting from theexpansion of the rangeof goods or servicesproduced by thecompany.

‘‘Network Associates,

formerly called McAfee

Associates, said it will

use TIS software in a

suite of programs,

similar to the way

Microsoft Corp. has

bundled collections of

personal computer

software’’ (‘‘Networkassociates to pay $300million to buy trustedinformation systems,’’Don Clark. Wall StreetJournal. New York:Feb. 24, 1998. p. 1)

Market growth

Growth refers to theconcept of increasedmarket share andpower.

‘‘The opportunity to

gain small and midsize

users was also a factor

for Peregrine whose

46,000 customers are

primarily large

companies’’ (‘‘Peregrineswoops in to buyRemedy,’’ AnnSullivan. Network

World. Framingham:June 18, 2001. Vol. 18,No. 25; p. 16)

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