Strategic complementarities in M&As: evidence from ...

Inf Technol Manag DOI 10.1007/s10799-014-0194-0

Strategic complementarities in M&As: evidence from the US information retrieval services industry Olga Bruyaka • Tabitha James • Deborah F. Cook Reza Barkhi

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Springer Science+Business Media New York 2014

Abstract The information age has increased our dependency on data, and consequently the economic value of information retrieval services (IRS) companies. While mergers and acquisitions (M&As) are a popular means to sustain growth for these companies, they often fail to fulfill the promise of shareholder value creation. This makes the inquiry into market valuation of M&As in the IRS industry timely and important. Using the concept of strategic complementarity that is relatively new in the M&A literature, we study industry and geographic complementarities between acquirers and targets as well as acquirer- and market-specific contingency factors to better understand market valuation of M&As. In an empirical study of 821 M&As by 150 firms in the US IRS industry between 1993 and 2006, we show that the two types of complementarities have contrasting effects on market valuation of M&As. While the effect is positive for geographic complementarity at both state and division levels, the effect of industry complementarity is found to be negative except for acquirers in the Internet software and services midindustry. Additionally, our findings provide insights on the role of three contingent factors—acquirers’ age, size and O. Bruyaka Department of Management, Pamplin College of Business, Virginia Tech, Blacksburg, VA 24061-0101, USA T. James D. F. Cook Department of Business Information Technology, Pamplin College of Business, Virginia Tech, Blacksburg, VA 24061-0101, USA R. Barkhi (&) Department of Accounting and Information Systems, Pamplin College of Business, Virginia Tech, Blacksburg, VA 24061-0101, USA e-mail: [email protected]

stock market growth—that can help better understand diverging effects of industry and geographic complementarities. Keywords Information retrieval services Firm complementarities Mergers and acquisitions Market value

1 Introduction The current era, deemed the ‘‘information age’’, corresponds to the rise of computing and its ability to allow for the storage, processing, and use of large amounts of data in manners not previously possible. This has led to information driven economies and in many ways has changed the way society functions. The information retrieval services (IRS) companies have supported the growth of electronic information services by providing access and data analytic capabilities to the Internet and digital data. By the late 1990s, information services, which include IRS companies, were a major part of the US economy and a major factor in the US economy’s rapid growth through that decade [14]. The value and importance of the IRS industry explains the continued investment in effective companies in this industry by shareholders who benefit from the growth of such companies. The competitive dynamics of the companies operating in this industry necessitates strategies that preempt competition. In the late 1990s and early 2000s, information providers turned to mergers and acquisitions (M&As) as one way to adapt to the new marketplace and increase their profits and market value. According to existing studies on M&As the achievement of benefits such as economies of scale and scope by acquiring firms is related to strategic complementarities between an acquirer

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and a target [38, 39, 43]. Strategic complementarity occurs ‘‘when merging firms have different resources, capabilities, and/or strategies that can potentially be combined or reconfigured to create value that did not exist in either firm before the acquisition’’ [38]. While scholars have recently begun to examine acquisition complementarity, there is still little evidence concerning how complementarity influences acquisition performance [7, 38]. Furthermore, in the relatively scarce literature on acquisition complementarity, empirical evidence is sometimes mixed. In particular, while both industry and geographic complementarity in M&As have been subjects of research, the findings of their effects on acquirers’ performance are contradictory. In particular, Zaheer et al. [59] found that when the degree of integration is low, geographic complementarity leads to higher performance for the acquiring firm. In contrast, Kim and Finkelstein [38] found that market complementarity in the banking industry is negatively related to acquisition performance. In the present study we examine the effect of industry and geographic complementarities in a different context (IRS industry), which directly addresses the call for more research on acquisition complementarity. It also allows us to expand the boundaries of strategic complementarity effect by providing additional empirical evidence on the nature of the effect of strategic complementarity on acquirers’ performance because the logic of value creation in M&As differs noticeably for each type of strategic complementarity [59]. It is notable that the IRS industry has received very little research attention in prior literature despite a surge of M&A activities in this industry. In addition, our study explores the role of three contingent factors that can help better understand diverging effects of industry and geographic complementarities. Contrasting findings regarding the effects of industry and geographic complementarities in acquisitions suggest ‘‘that important contingencies are at play and, thus, researchers need to dig deeper’’ [4: 595]. Previous studies showed that strategic focus, out-of-market acquisition experience [38] and integration level [60] moderate the direct effects of acquisition complementarities. However, as Zaheer et al. [59] noted ‘‘our findings with regard to product complementarity are less straightforward … this indicates that a further contingency beyond the level of integration is necessary to understand how value is created from product complementarity’’. In our study we complement previous research by exploring moderating effects of different factors—acquirers’ size, IPO age and stock market growth— on acquirers’ performance. More broadly speaking, research on different types of acquisition complementarity complements existing research predominantly focused on strategic similarity. Despite the dominant logic that strategic similarity fosters

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value creation, there are fundamental arguments that complementary differences are more crucial for M&A success [7]. Thus, complementarity, as a relatively new concept in the M&A literature, has a promising denotation [26, 43, 59]. Our study of strategic complementarities in ISR industry M&As contributes to augment the understanding of the M&A phenomenon. The remainder of this paper is organized as follows. We first define our core constructs—types of strategic complementarity—in the context of the IRS industry. Then we follow with hypotheses development section and empirical analysis. Finally, we discuss the results and limitations, and suggest avenues for future research.

2 The US information retrieval industry Companies in the US IRS sector (SIC 7375) are ‘‘primarily engaged in providing on-line information retrieval services on a contract or fee basis. The information generally involves a range of subjects and is taken from other primary sources’’ (http://www.osha.gov/pls/imis/sic_manual. display?id=152&tab=description). Through the 1990s and into the 2000s, the 7375 subsector grew at a fast rate: the revenues increased from $5.3 billion to $9.1 billion in just 3 years (1995–1997) [14]. The rapid rise of the IRS industry sector was fueled by the growth of the Internet which enabled the competitive dynamics for the emergence of companies seeking to establish valuable capabilities to provide on-line information services. Although the IRS industry has become integral to the US economy, the results of investments in IRS companies was not always positive. The mid-1990s to 2000 defined the period of growth of what was known as the ‘‘dot com bubble’’ where stock prices were quickly driven up by what could be argued as unreasonable speculation in technology companies, including many of the IRS companies. The subsequent collapse of the bubble (2000-2002) had negative consequences on the US economy as the stock prices plunged. Interest in recent IPO possibilities such as Facebook, Pandora, LinkedIn, and Twitter provides some evidence of a potential second bubble within the same types of companies [48]. The widespread interest in and influence of electronic information service companies lends credence to our choice of IRS as our empirical context. M&As are one strategy for IRS companies to provide attractive new services to consumers whose demand for data analytics increases with the growth of the IRS industry. For example, Lycos, Yahoo!, and Google began with a focus on search but continued to expand their offerings account for 103 of the acquisitions in our sample from 1993 to 2006. These companies like many others

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involved in the M&As in the IRS industry hoped to achieve economies of scale and scope through industry and geographic complementarities with their acquisition targets. To illustrate industry complementarities, one of the most prominent M&As is Google’s (7375) acquisition of Android Inc. (SIC 7372—prepackaged software). The acquisition of a software company specializing in mobile wireless software provided Google the opportunity to better integrate its service offerings with an increasingly popular platform of mobile wireless devices [9]. Over time the services that the search companies provided expanded into communication services such as: email, instant messaging (IM), voice and video chat over the Internet. To help integrate and expand their product offerings, search companies conducted acquisitions of companies in complementary industry sectors. Examples include Yahoo!’s acquisition of the VoIP Dialpad Inc., a telephone communication firm (SIC 4813) [56] and Google’s acquisition of GrandCentral Communications Inc., a software company whose primary focus was to integrate multiple phone numbers (SIC 7372) [3]. An example of geographic complementarity in the IRS industry is Iron Mountain Inc.’s (7375), a company specializing in records management and data protection, acquisition of a New Zealand-based records management company, SIRVA New Zealand Ltd, [36] to expand into the South Pacific. Yet another example is PSINet Inc.’s, a Virginia, US-based Internet service provider (ISP)’s (7375), acquisition of Tokyo Internet Corporation (CBR 1998) to further move into the Japanese market. Overall, the IRS industry is a relevant empirical context to study strategic complementarities given the increasing number of M&As this industry has seen throughout the 1990s and into the 2000s. The importance of complementary capabilities is significant in this industry due to the potential expansion into new markets with both industry and geographic complementarities. Additionally, geographic complementarities are of special interest in the IRS industry as the Internet breaks some of the traditional geographic boundaries.

3 Theory and hypotheses development 3.1 Industry complementarity and acquirer market value When a company diversifies by acquiring a business in a new industry, such an acquisition is characterized by industry complementarity. There are several different mechanisms by which industry complementarity in M&As can be value creating. First, an important benefit of industry complementarity arises from the potential of

leveraging the acquirer’s core competencies and sharing activities leading to economies of scope [19]. Economies of scope arise when the cost of joint production of two goods by a multi-product firm is less than the combined costs of production of these goods by two single-product firms [51]. An example of such an acquisition in the IRS industry includes Google expanding its offerings related to non-PC platforms (mobile) (acquisition of Android Inc.). Second, complementary acquisitions bring together firms with different but compatible resources and product strategies. According to the Resource-Based-View (RBV), postacquisition resource-deployment and the resulting product mix are important sources of value-creation in acquisitions [38]. Bundling products and services create new opportunities to expand the customer base and improve the acquirer’s competitive position on the market by offering better customer service options. A good illustration in the IRS industry is Google’s expansion into Internet-based video (acquisition of YouTube) and Yahoo’s acquisition of Dialpad Communications. Third, acquisitions of a target operating in a different industry can potentially increase the acquirer’s market power: market strength in one particular industry may be used to sustain low price strategies in other industries with tougher competition or the success in one industry can be used to compensate for temporary downturns in different industries [55]. For example, a firm could leverage its success to temporarily sustain low profits in another industry to create barriers to entry and enhance monopoly power. Consider the Knot Inc.’s acquisition of a traditional publisher, WeddingPages. It is possible that this choice was made to secure content in a long-term strategic move to gain a competitive edge. In sum, there are a variety of mechanisms through which industry complementarity can increase stock market return of an acquiring firm leading to the first hypothesis: H1 Industry complementarity between an acquiring IRS firm and its target is value generating to the acquiring firm’s stock price following an M&A announcement. 3.2 Geographic complementarity and acquirer market value In addition to industry complementarity and the resulting value gained from economies of scope, firms can expand into new geographic markets to enlarge their customer base and gain additional economies of scale. M&As that expand firms’ geographic boundaries open the door for a wider network of customers and suppliers, more accessible markets, and more favorable growth opportunities [57]. According to the market-based view [10], quick entrance into new markets and the resulting improved economies of

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scale drive M&As. Such geographic expansion has also been called internationalization and globalization in prior literature [34], when partners are in different countries. Whether domestic or international, however, M&As between geographically distant firms have been shown to generate firm value through the acquisition of additional resources [6], location advantages [21], and greater economies of scale and scope [40]. Geographically-distant M&As allow the exploitation of cheaper resources available at the target location, such as labor, and are thus capable of generating tremendous cost savings by requiring only minimal integration. For IRS companies, opening new geographic opportunities are value-creating as they make it easier to capture a wider customer base and gain competitive advantage over their rivals [46], especially if the new geographic markets are sufficiently different. IRS firms can benefit from expanded geography even more than traditional firms given that information goods are not subject to physical transportation costs. Given the forward looking nature of stock market valuation, we expect the acquisition of geographical complementarities that boost revenues and lower costs to entail a positive market reaction vis-a`-vis the stock price of the acquiring firm, suggesting the following hypothesis: H2 Geographical complementarity between an acquiring IRS firm and its target is value generating to the acquiring firm’s stock price following an M&A announcement. 3.3 Moderating effects of acquirers’ size, IPO age and stock market growth Various authors [10, 50] have noted that there is a complex pattern of motives behind the decision to merge or acquire another company and that no single approach is likely to fully explain the motivation. This complex pattern of motives can differ depending on a number of factors including specific characteristics of the companies and industries involved as well as the economic and political conditions in the marketplace. Larsson et al. [42] pointed out that identifying the direct factors that cause high versus low M&A performance is crucial to understanding M&A dynamics. Studying the variables that moderate the relationship between M&A performance and these direct factors is necessary to predict post-M&A performance in the long-run. Authors have identified acquirers’ size [1, 15, 18, 24, 39], IPO age [2], and stock market growth [12, 37] as important moderators of this relationship. 3.3.1 Moderating effect of acquiring firms’ size A main factor determining M&A success is the ability to integrate the target’s resources—knowledge bases,

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employees, or customer bases and markets with the existing resources of the acquirer. For smaller acquirers, postM&A integration was shown to be particularly difficult as it takes away resources necessary to undergo innovative endeavors [1, 18] and was thus found to be more risky and time consuming [15, 16]. In particular, when targets, such as startups, are in financial distress, only large acquirers possess the financial and managerial support to overcome post-M&A hurdles [25]. According to the RBV [5, 47], for IRS companies, knowledge and technological integrations are particularly crucial for creating and sustaining competitive advantage. For larger acquirers, fewer political and financial struggles are expected to stand in the way of successful integration [58], allowing more visible economies of scope. Further, with large acquirers, it is easier to spot redundancies in resources [38] and consequently eliminate these redundancies and achieve cost savings [24]. Consistent with the efficient market hypothesis [23] we propose that the stock market will positively react to an M&A announcement when an acquiring firm is relatively large. This leads us to propose the following hypothesis: H3a The moderating effect of an acquiring IRS firm’s size is positive for the industry complementarity–market performance relationship, as an M&A is announced. We expect firm size to play a positive role in absorbing the shocks of introducing new products and services resulting from industry complementarity during M&As. However, when large firms try to expand into new geographic regions, they are likely to face some challenges resulting from their reduced flexibility, agility, and adaptability. For example, a larger acquirer has more complex organizational structures, which increases bureaucracies and makes it harder to construct a unified management hierarchy [50]. Within the same industry, geographically distant M&As could lead to the exodus of key innovators at the target firm, given that innovative employees tend to prefer less rigid organizational structure and less bureaucracy [61]. This exodus could result in significant costs due to losing talented workers who may prefer to work for smaller firms with less rigid structures. In addition to structural shocks, cultural changes due to geographic disparities can hamper the ability of large firms to become flexible and respond to local constraints that are imposed on them by the norms of different regions. For example, Google’s move into the Chinese search engine market faced such challenging constraints that the company withdrew its plans for geographic expansion. The biggest obstacle to this expansion was the fundamental difference between Google’s philosophy and that of the new geographic region. For smaller firms, less structure and more flexibility make it easier to maneuver in the new geographic markets to gain competitive advantage. As a result,

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we expect fewer synergies achieved by geographic complementarities for larger acquirers. H3b The moderating effect of an aquiring IRS firm’s size is negative for the geographic complementarity–market performance relationship, as an M&A is announced. 3.3.2 Moderating effect of acquiring firms’ IPO age In general, acquirers’ longer tenure in business (i.e., industry experience) positively contributes to the success of their announced M&A initiatives, as they are more likely to have had M&A experience before [4, 32]. M&A experience can manifest itself into various proficiencies and capabilities for public and private firms. While private firms can merely focus on internal operations and innovations, public firms are accountable to all their shareholders ranging from the major equity owners of the firm to the small investors. There are endless operational, financial, and regulatory requirements that public firms need to abide by when undergoing an M&A. IPO age is indicative of the extent that the acquirers have faced similar challenges and experiences in their past as public firms. We therefore expect investors to be more optimistic about the success of an M&A for acquirers who have been public for a longer period of time. Existing research corroborates this argument. For example, Brau et al. [13] found that IPOs acquiring within a year of going public (young IPO age) significantly underperform for 1- through 5-year holding periods following the first year. As managers become more experienced in running a publicly traded company (a firm’s IPO age increases), they may become more realistic about the benefits and costs of their investment decisions. We propose that an acquirer’s IPO age can moderate the effect of complementarity. Previous research has shown that integrating a company from an unrelated industry— industry complementarity—is more challenging than integrating a company that serves a different geographic market–geographic complementarity [59]. Therefore, in the case of industry complementarity we expect that older firms will apply their industry experience and benefit more from every acquisition, compared to younger firms that have less M&A experience. H4a The moderating effect of an acquiring IRS firm’s IPO age is positive for the industry complementarity– market performance relationship, as an M&A is announced. Firms with high IPO age tend to become less flexible and may not be able to adapt as easily when they enter into a new geographic market, and consequently may be less likely to benefit from the same competitive dynamics that they enjoy in their familiar geographic region. Firms with low IPO age may be more flexible to respond to new

market requirements more easily. On the other hand, high IPO age may make it harder for firms to modify their strategy when facing new markets given their reduced adaptability and flexibility. This suggests that the moderating effect of IPO age on geographic complementarity of M&A performance is negative. H4b The moderating effect of an aquiring IRS firm’s IPO age is negative for the geographic complementarity–market performance relationship, as an M&A is announced. 3.3.3 Moderating effect of stock market growth Existing research has shown that firms’ market valuations are significantly correlated with industry-wide M&A activities. For instance, Bouwman et al. [12] found that acquirers buying during high-valuation markets have significantly higher announcement returns than those buying during low-valuation markets. Jovanovic and Rousseau [37] showed that merger waves tend to occur during stockmarket booms when price-earnings ratios are high. It has also been observed that during bull markets, managers and investors are usually very optimistic and tend to take more risks including M&As involving fewer synergies [27]. When market indicators suggest an upward trending market, companies tend to utilize their increasing revenues to benefit from the up-trend and find opportunities to invest in M&A activity. During down markets, M&As are often paid for with equity rather than cash, although it has been shown that equity-based M&As are damaging to the value of the acquirer [54]. This finding is based on the equity signaling and overvaluation hypotheses, stating that when firms pay for their M&As with equity rather than cash, they signal to the market that their stock is overvalued and their stock price is not backed by strong fundamentals [45]. Hence, we expect that upward or downward market tendencies moderate the effect of industry and geographic complementarities on stock market returns of an acquiring IRS firm following a M&A announcement. When markets are on a downward trend, we expect this to push the stock price of each company and the resulting M&A down, indicating less realistic indications of the success of the acquisition. When the stock market is trending upwards, firms are optimistic and the expectation of positive trend will encourage them to take more risks and build their business by trying to benefit from business opportunities such as expanding into new product markets. This suggests that upward trending stock markets will positively moderate industry complementarity performance. H5a Stock market growth at a time of an M&A announcement positively moderates the industry complementarity- market performance relationship, as an M&A is announced.

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Similar arguments set forth in hypothesis H5a apply for geographic complementarity and the effect of the stock market’s upward or downward trends as moderators of M&A performance. When the market is trending upwards, companies would want to expand to make more money given the good economic conditions and their positive cash flows. Hence, they consider expanding into new geographic markets to benefit from economies of scale. This suggests that upward-trending stock markets will positively moderate geographic complementarity performance. H5b Stock market growth at a time of an M&A announcement positively moderates geographical complementarity–market performance relationship, when an M&A is announced.

4 Methods 4.1 Data and sample The sample for the study was the US IRS (SIC code 7375) industry from 1993 to 2006. We collected data on acquisitions made by all publicly traded companies in this industry from the SDC Platinum Database. Since we also required data on the acquiring firms from the Standard and Poor’s Compustat and Center for Research in Security Prices (CRSP) databases, we had to retain only those firms whose data was available in all three data sources. This requirement reduced our observations to 821 acquisitions by 150 unique acquiring firms. Although the 7375 classification is primarily described as companies whose focus includes processing and making data available for consumption over the Internet, the classification actually includes a mix of companies that provide data access platforms for a myriad of user types, as well as, services that allow individuals and companies to access and more easily use the Internet. To provide an overview of the companies in the sample, we loosely grouped them into categories based on their major product offering, as described in the short business description provided by the SDC database. These categories, provided in Table 1, help illustrate the acquiring companies in the sample. We also provide a view of the number of companies by geography (US Census Division) and by size (amount of staff) in Table 2.

identifying the event of interest, an acquisition announcement in our case. After the event is defined, the period of time over which the stock price of the firm experiencing the event is determined. Then, the stock price changes beyond the ‘‘normal,’’ or expected changes (i.e., abnormal returns), in response to the event announcement, are examined to determine the extent to which the event changes the market participants’ evaluation of the firm (an IRS firm as an acquirer) [41]. The event study methodology allows us to compute abnormal returns, as the difference between an acquirer’s expected stock price, had the M&A not been announced, and its actual stock price around the day that the M&A was announced. According to the EMH [23], and assuming investors are rational, the potential impact of an M&A on acquirers’ bottom line ought to be factored in their stock prices around the M&A announcement date. Aggregating the abnormal returns over an extended window around the M&A announcement day accounts for possible information leakage prior to the M&A, and potential spill-over and momentum effects, following the M&A. As in [31] and [38], we use the market model, shown in Eq. (1), to estimate firm expected returns. In Eq. (1), each firm’s stock market return Rît at time t is estimated as a function of the market return Rmt. Rît ¼ aî þ bî Rmt þ eît

where

Our dependent variable is an acquiring firm’s market value, measured as the cumulative abnormal returns of the acquiring firms’ stock prices that are obtained by running event study analyses [44]. An event study first requires

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ð1Þ

As in [30] and [38], we proxy the market return using the value-weighted CRSP market index,1 which is computed using the New York Stock Exchange (NYSE), American Stock Exchange (Amex), the S&P 500 index, and the NASDAQ Composite Index. We estimate the parameters of Eq. (1) using a 100-day estimation period that ends 15 days prior to the announcement of each M&A. The evaluation period spans a 21-day event window, covering 10 days prior to the M&A, the day that the M&A was announced, and 10 days after the M&A was announced. This is a reasonable event window in a situation in which information could be leaked prior to the event, as is the case in an M&A, which involves an extensive pre-planning process [44, 51]. The estimated abnormal return, ARi,t, for acquirer i’s stock market return on day t is given in Eq. (2), as the difference between the observed (actual) market return of acquirer i’s stock market return on day t, Rit, and the estimated return of firm i’s stock on day t, aî bî Rm;t ; obtained from Eq. (1). ARi;t ¼ Rit a^;i bî Rm;t

4.2 Dependent variable

eît White noise

ð2Þ

The mean cumulative abnormal return of firm i’s stock over the 21-day evaluation windows, CARi,(t-10,t?10), expressed in Eq. (3), is the sum of the abnormal returns of 1

http://www.crsp.com/documentation/product/stkind/index_meth odologies/stock_file_indices.html.

Overview Internet Service Providers: primarily dial-up and broadband connectivity service providers

Network & network application service providers: Provide support systems for the Internet and network application services

Software Providers: Develop software for Internet use

Pure Internet Search Providers: Provide search engines to locate information on the Internet

Provide data retrieval and processing services: primarily for entertainment and lifestyle content

Category (# in category)

Connectivity Services (38)

Internet Services (24)

Software Providers (9)

Search Providers (13)

Information Processing Providers – Lifestyle and Entertainment (26)

Table 1 Examples of categories used to classify companies in the sample

Google Inc

Knot Inc Pricelinecom Inc SportsLine USA Inc MP3.COM Inc

Provide Internet wedding info svcs Provide online travel support svcs Provide sports info retrieval services Provide online music distn services

ZDNet (Ziff-Davis Inc)

Hollywood Media Corp iVillage Inc Provided ent info services Provide online networking services

Provide online news, ent services

Gaiam Inc Provide lifestyle-media services

Lycos Inc

Infoseek Corp

Yahoo! Inc

Edgar Online Inc

Answers Corp

Provide Internet Services

Syntellect Inc

Provide Internet search engine svcs

SumTotal Systems Inc

Develop Internet software

Network Solutions Inc

Provide domain registration svcs

SonicWALL Inc

American Software Inc

Provide ecommerce services

Develop e-learning software

Vicinity Corp

Provide online enterprise loc svs

National Info Consortium Inc

Rackspace Hosting Inc Keynote Systems Inc

Develops Internet sec software

Terremark Worldwide Inc

Provide web hosting services Provide Internet test services

Develop Internet portals for gov

Sunhawk.com Corp

United Online Inc

Provide Internet subscription svcs

Own, op Internet exchanges

Time Warner Cable Inc

Provide cable TV Services

Provide digital rights mgmt svcs

Clearwire Corp

Provide wireless Internet Services

NeuStar Inc

CenturyTel Inc

LivePerson Inc

Fusion Telecom Intl Inc

Provide commun Services

Provide clearinghouse Services

Eagle Broadband Inc

Provide VoIP Services

Application Services Provider {ASP}

EarthLink Inc

Provide broadband services

Examples of companies

Internet Service Provider {ISP}

Examples of short business descriptions

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24/7 Real Media Inc

Global Payments Inc

Thomson Reuters Corp Provide integrated info services

Provide online advg services

Provide electn processing services

Attorneys.com Inc

Kforce Inc

WebMD Health Corp Provide online health info services

Provide attorney referral services

Provide RE info services Provide finl info services

Provide online auction services

acquirer i’s stock price, ARi,t, on each of the 21 days around the M&A announcement date.

Provide online staffing services

Fidelity Natl Info Solutions FactSet Research Systems Inc

Escala Group Inc

DoubleClick Inc

CheckFree Corp Provide electronic billing services

Provide data retrieval and processing services: primarily for business content (e.g. direct marketing, billing, finance, healthcare support, payment processing)

Provide Internet advg services

Examples of short business descriptions Overview

Examples of companies

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CARi;ðt10;tþ10Þ ¼

t10

Information Processing Providers – Business Applications (40)

Category (# in category)

Table 1 continued

ARi;t ¼

tX þ10

ðRit a^;i bî Rm;t Þ

ð3Þ

t10

4.3 Independent variables and moderators 4.3.1 Industry complementarity For industry complementarity, we employed two variables—General industry complementarity (SIC code differences) and Mid-industry complementarity (productmarket differences). The first examines the Standard Industry Classification (SIC) codes. All acquirers are in the 7375 (Information Retrieval Services) industry, while the target’s SICs vary. This construct provides a high-level view of the focal industry. We used the SIC codes obtained from SDC Platinum Database provided by Thomson Reuters. A field was created that denotes whether the target and acquirer SIC codes match (denoted by 0) or are different (denoted by 1). In addition, a percentage was calculated for the exact difference in digits. For example, Google’s (the acquirer) SIC is 7375 and YouTube’s (the target) SIC is 7372, which means the percentage difference is 0.25 (1 digit is different out of 4). The second measure relies on the SDC mid-industry classification. The SDC mid-industry classification is determined by Thomson Reuters and is ‘‘based on SIC Codes, NAIC Codes and overall company business description’’.2 ‘‘There are more than 85 mid-level industry classifications grouped by 14 macro-level categories.3’’ The mid-industry classification lends a further level of detail to the analysis. The acquirers in our sample belong to one of 7 different mid-industry classifications (cable, computers and peripherals, E-commerce/B2B, Internet software and services, IT consulting and services, publishing, and software). The majority of the acquirers in our sample belong to either the ‘‘Internet software and services’’ mid-industry or the ‘‘software’’ mid-industry. The targets in our sample belong to one of 45 different midindustry classifications.4 The field was set to one if the 2

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tX þ10

http://mergers.thomsonib.com/td/DealSearch/help/nidef.htm. http://mergers.thomsonib.com/td/DealSearch/help/nidef.htm. 4 Target firms’ mid-industry classification includes advertising and marketing, asset management and components, automotive retailing, banks, broadcasting, brokerage, building/construction and engineering, cable, chemicals, computers and electronics retailing, computers and peripherals, credit institutions, discount and department store retailing, E-commerce/B2B, electronics, employment services, healthcare equipment and supplies, healthcare providers and services (HMOs), hospitals, insurance, Internet and catalog retailing, Internet software and services, IT consulting and services, motion pictures/ audio visual, national agency, other consumer products, other 3

Inf Technol Manag Table 2 Sample by geography and size US census divisions

Staff amount Less than 100

Division 1 (New England)

Less than 500

Less than 1,000

10

2

4

19

4

2

Division 2 (Mid-Atlantic)

3

Division 3 (East North Central) Division 4 (West North Central)

2

Division 5 (South Atlantic)

4

9

1

2

2

6

1

2

Division 6 (East South Central) Division 7 (W est South Central)

Less than 2,000

Less than 5,000

Less than 10,000

Greater than 10,000

1

17 1

3

4

29

1

Division 8 (Mountain)

1 1

14

23

8

3

5

Total By Staff Amount

24

71

27

15

6

4.3.2 Geographic complementarity We measure the geographic location complementarity via three variables using the geographic location of the firms— State Difference, Region Difference and Division Difference variables. Data was available from the SDC database on the US state the company listed. State Difference variable was assigned the value of one if the US state of the acquirer and target were different and zero if they were the same. In order to provide higher levels of granularity to the measure of geographic complementarity two variables were created: a Region Difference variable, which was one if the regions of the acquirer and target were different, and zero if they were the same, and a Division Difference variable, which was 1 if the acquirer and target were located in different divisions and 0 if they were in the same division. We used the US census region and division classifications. The US census region and division was determined for each state (using Wikipedia to get the US census regions and division5) for both the acquirer and target. The regional divisions used were the Northeast: which contains the New England division and the MidAtlantic division the Midwest: which contains the East North Central division and the West North Central division Footnote 4 continued financials, other high technology, other industrials, other media and entertainment, other retailing, other telecom, professional services, publishing, real estate management and development, recreation and leisure, software, space and satellites, telecommunications equipment, telecommunications services, transportation and infrastructures, travel services, water and waste management, and wireless. 5 http://www.census.gov/geo/maps-data/maps/pdfs/reference/us_regdiv. pdf.

6

1

Division 9 (Pacific)

acquirer and targets’ mid-industry classifications were different and zero if they were not.

29 2 4

2 9

Total by US census division

1

9

2

150

53 5

the South: which contains the South Atlantic division, the East South Central division, and the West South Central division, and the West: which contains the Mountain division and the Pacific division. 4.3.3 Moderators We test moderating effects of three variables—acquirers’ IPO age, acquirers’ size and stock market growth. Variable Acquirer’s IPO age captures the number of years since the acquirer has been trading its stock. Variable Acquirer’s size was measured as a number of employees of an acquiring firm. In order to reduce the effect of outliers, the log of the number of employees was calculated and utilized in the models. Finally, we measured Stock market growth as a daily change in Nasdaq index: (Day2 – Day1)/Day1. 4.4 Control variables M&A performance can be affected by variables other than industry and geographic complementarity and our moderating variables, so, based on prior empirical studies on corporate acquisitions, we employed a set of control variables designed to eliminate potentially confounding and extraneous factors that could influence acquisition performance. We controlled for various characteristics of the acquiring firms, the targets, the acquisition deals and market conditions. Acquirer’s slack or excess resources can allow better management of the difficulties of an acquisition and support the activities required for the integration of the acquired company. Following [52], we calculated Acquirer’s slack available as a current ratio and Acquirer’s slack recoverable as selling, general, and administrative expenses divided by sales. Acquirer’s experience (the number of acquisitions a firm made prior to the acquisition announcement at time t)

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Inf Technol Manag

and Years since last M&A (ln) could be expected to influence acquisition performance as experienced acquirers could have developed facilitation procedures [33]. We also control for number of (#) Alliances before an M&A—number of alliances a firm recorded before the announced acquisition at time t—as an indicator of experience in joint value creation. A dummy variable Prior alliance ties indicator controls for whether a strategic alliance between an acquirer and a target existed prior to the acquisition. The stock market may place higher value on acquisitions of former alliance partners given lesser uncertainty surrounding the transaction [49]. We also control for Acquirer-specific uncertainty, which can stem from internal changes like entering a new market or experiencing a turnover in top management, and influences perception and managers’ decision-making [8] that can in turn influence stock market valuation of the acquisition. Following [8], Acquirer-specific uncertainty is operationalized as the standardized monthly volatility of the focal firm’s stock in the year prior to the M&A announcement. The monthly volatility is calculated as the coefficient of variation for firm j’s annual monthly stock closing price; or: Standard Deviation ðFirm0 s Monthly Closing Price; Year i; Firm jÞ Average ðFirm’s Monthly Closing Price,Year i; Firm jÞ

where i = 1993,…, 2006. The index j represents each of the firms in the sample. If a firm’s stock price experiences high variance relative to its average price, the focal firm is experiencing high firm-specific uncertainty. Monthly stock price data were obtained from Compustat. We also control for target firms’ characteristics such as their ownership (Private target indicator), overall financial ranking dummy variable (Target Fortune 500 indicator) as well as the characteristic of the deal (Cash for M&A indicator). Finally, we accounted for the Industry volatility (measured as industry earnings volatility following [11] and included year dummies (1993–2006) in all our statistical models.

5 Analysis and results 5.1 Estimation method Our data include 821 focal acquisition deals made by 150 firms from 1993 to 2006. Pooling repeated observations on the same firm violates the assumption of independence required for ordinary least squares regression, resulting in serial correlation of the model’s residuals [38]. Thus, a generalized least squares (GLS) model is preferred [28]. When making the choice between random- or fixed-effects GLS model we took into account the nature of our data and based our decision on the results of Hausman test. Thirtyseven percent of our focal firms (56 out of 150) had a single acquisition during 1993–2006 observation period. This

123

makes a within-group fixed-effect model, which is frequently used to control for firm fixed effects, not appropriate for our data. The use of a within-group fixed-effects model with panel data that are more cross-sectional in nature than longitudinal may not only result in less efficient estimators due to the large number of parameters being estimated, but it may also lead to estimation bias [35]. The Hausman specification test was performed to compare the estimators of the within-group fixed-effects model and the random-effects model, and the results indicated that there was no systematic difference in the estimated coefficients between the two models (v2 = 23.01), suggesting that the random-effects model is appropriate for the data [28, 29]. Based on the above considerations we estimated randomeffects GLS models, which correct for serial correlation of disturbances6. The GLS model is specified as follows [28]: y ¼ x0ij b þ ða þ ui Þ þ eij ;

j ¼ 1; . . .; ni ;

i ¼ 1; . . .; I;

where y is the response for the jth observation in firm i; x is a p 9 1 vector of covariates associated with that response; b is the vector of regression coefficients that are of scientific interest; (a ? uj) is the heterogeneity, or individual effect, where a is a constant term and uj is the random heterogeneity specific to the jth observation and is constant through time, and e is an independent error term. Furthermore, given that our data have unbalanced panels and uneven temporal spacing, which could result in poor estimation of autocorrelation coefficients, we used the Swamy–Arora method for unbalanced panels, which provides a precise small-sample adjustment [38]. 5.2 Results Descriptive statistics and a correlation matrix for the variables used in the study are shown in Table 3. As expected state, region and division differences between partners reflecting geographic complementarity are highly correlated with each other. Therefore, we run separate statistical models with each of these variables. General industry and Mid-industry complementarity variables are also highly correlated (0.52), mid-industry being a refined measure of the general industry complementarity. We run separate models for these two variables. Most importantly, there is no strong correlation between two types of strategic complementarities—industry and geographic—which is consistent with our hypothesis that each type of complementarity represents an independent factor potentially affecting firm’s performance.

6

We also ran fixed-effects models. The results generally confirmed the effects found in random-effects GLS models. The results are available from the authors.

Inf Technol Manag Table 3 Descriptive statistics Mean

SD

1

2

1.

Acquirer’s market value

0.00

0.06

2.

General industry complementarity

0.35

0.39

-0.03

3

4

5

6

3.

Mid-industry complementarity

0.66

0.47

-0.06

4.

State difference

0.77

0.42

0.07

-0.04

-0.04

5

Region difference

0.60

0.49

0.04

-0.04

-0.05

0.66*

6.

Division difference

0.69

0.46

0.04

-0.03

-0.03

0.80*

0.83*

7.

Acquirer’s size

5.29

1.69

-0.03

-0.14*

-0.02

0.09*

0.08

8.

Acquirer’s IPO age

1.38

0.80

0.04

0.02

0.12*

0.12*

9.

Stock market growth

0.45

0.51

-0.08

-0.04

-0.05

0.03

10

Acquirer’s experience

8.24

12.54

-0.03

-0.12*

-0.14*

0.11*

11. 12.

Acquirer-specific uncertainty Acquirer’s slack available

0.31 4.59

0.23 4.84

-0.01 -0.01

-0.03 -0.02

-0.03 -0.06

0.10*

-0.08 -0.09*

13.

Acquirer’s slack recoverable

1.42

3.30

-0.11*

-0.02

-0.06

0.02

Prior alliance ties indicator

0.02

0.14

0.02

0.03

0.04

-0.02

15.

# Alliances before the acquisition

7.39

16.96

-0.05

0.00

0.04

0.02

16.

Years since last M&A (ln)

0.22

0.40

0.06

0.01

17.

Private target indicator

0.68

0.47

-0.04

-0.07

18.

Target Fortune500 indicator

0.04

0.19

-0.01

0.04

19.

Cash for M&A indicator

0.19

0.36

0.03

20.

Industry volatility

-0.31

2.78

-0.08

1.

Acquirer’s market value

2.


3.


4.

State difference

5

Region difference

6.

Division difference

7.

Acquirer’s size

8.


9. 10

Stock market growth Acquirer’s experience

11.

Acquirer-specific uncertainty

-0.14*

12.

Acqurer’s slack available

-0.08 -0.02

11

8

9

0.52*

14.

10

7

0.13* -0.06 -0.08 -0.12 * -0.01 0.10*

0.12*

-0.07

0.02

-0.05

0.14*

-0.13*

-0.18* 0.30*

0.22*

-0.06 -0.11*

0.02 -0.04

-0.17* -0.29*

0.05 0.19*

-0.31*

0.22*

0.01

-0.03

-0.01

0.05

0.06

-0.07

0.03

0.03

0.28*

0.12* -0.10*

0.04

0.04

0.03

-0.01

0.31*

-0.02

-0.01

-0.02

-0.04

-0.10*

0.02

0.04

0.01

0.04

0.05

0.03

-0.02

0.01

0.05

0.02

0.04

0.04

0.00

0.22*

-0.17*

0.05

0.06

0.01

-0.04

-0.02

-0.07

-0.01

0.12*

15

16

17

18

19

12

0.06

-0.02

0.11*

-0.18*

13

14

0.12*

-0.05

13.


14.


0.05

-0.02

0.28*

-0.04

0.07 -0.04

15.

# Alliances before the acquisition

0.49*

-0.10*

-0.13*

-0.09*

16.


-0.12*

-0.04

-0.08

-0.10*

17.


0.07

-0.11*

0.15*

18.

Target Fortune500 indicator

-0.02

-0.07

0.00

19.


-0.10*

-0.16*

20.

Industry volatility

0.02

0.07

-0.04 0.03

0.15* -0.06

-0.10*

-0.13*

-0.02

-0.08

-0.02

0.00

-0.05

-0.15*

-0.09*

0.01

-0.06

-0.11*

0.03

0.00

0.01

0.10*

0.00

-0.15* 0.05 -0.04

-0.11* 0.00

0.00

* p \ 0.10

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Inf Technol Manag Table 4 Summary of GLS results: direct effects Variables

Industry complementarity Model 1


Model 2

Geographic complementarity Model 3

Model 4

Model 5

Model 6

Model 7

-0.007


-0.011***

State difference

-0.010** 0.010**

Region difference

0.006

Division difference

0.009**

0.009**

Acquirer’s size

-0.003*

-0.003*

-0.003**

-0.003**

-0.003*

-0.003**

-0.003**

Acquirer’s IPO age Stock market growth

0.005 0.033*

0.005 0.032***

0.007 0.031***

0.005 0.032***

0.005 0.032***

0.005 0.033***

0.005 -0.003**


0.000

0.000

0.000

0.000

0.000

0.000

0.000

Acquirer-specific uncertainty

0.005

0.005

0.002

0.006

0.005

0.005

0.001

Acquirer’s slack available

0.000

0.000

0.000

0.000

0.000

0.000

0.000


0.001

0.001

0.001

0.001

0.001**

0.001

0.001**


0.015

0.016

0.017

0.016

0.016

0.016

0.017

# Alliances before M&A

0.000

0.000

0.000

0.000

0.000

0.000

0.000


0.004

0.004

0.004

0.004

0.004

0.004

0.004


-0.003

-0.004

-0.004

-0.003

-0.003

-0.003

-0.004

Target Forune500 indicator

-0.002

-0.002

-0.002

-0.001

-0.001

-0.001

0.000


0.005

0.005

0.006

0.006

0.005

0.006

0.007

Industry volatility

-0.008

-0.008

-0.006

-0.006***

-0.007

-0.007

-0.035**

Year 1994 (dummy)

0.076

0.078

0.059

0.061

0.070

0.065

0.389**

Year 1995 (dummy)

-0.087

-0.089

-0.065

-0.067

-0.078

-0.068

-0.538**

Year 1996 (dummy) Year 1997 (dummy)

0.037 0.030

0.038 0.031

0.036 0.029

0.034 0.031

0.034 0.030

0.034 0.030

0.051** 0.068***

Year 1998 (dummy)

0.041**

0.041

0.040**

0.039

0.041***

0.040***

0.081***

Year 1999 (dummy)

0.003

0.004***

0.004

0.003***

0.003

0.003

0.035***

Year 2000 (dummy)

-0.035***

-0.034***

-0.032***

-0.035

-0.034***

-0.035***

0.039***

Year 2001 (dummy)

0.029**

0.028**

0.030**

0.028***

0.028**

0.029**

0.057***

Year 2002 (dummy)

0.024*

0.025*

0.022

0.024**

0.024*

0.025*

0.053***

Year 2003 (dummy)

0.026**

0.026**

0.023*

0.027***

0.026**

0.027**

0.058***

Year 2004 (dummy)

0.002

0.002

0.000

0.001***

0.001

0.002

0.029**

Year 2005 (dummy)

0.012

0.013

0.008

0.012***

0.012

0.012

0.051***

Year 2006 (dummy)

-0.011

-0.011

-0.010

-0.008***

-0.009

-0.008

-0.042

Year 2007 (dummy)

0.042**

0.040**

0.042**

0.041**

0.040**

0.041***

0.036**

_cons

-0.012**

-0.009

-0.010*

-0.017***

-0.046***

-0.048***

-0.036**

Wald chi2

51.41***

60.43***

61.91***

61.99***

60.4***

61.85***

65.96***

R-sq

0.15

0.16

0.16

0.19

0.16

0.16

0.17

N

821

821

821

821

821

821

821

n

150

150

150

150

150

150

150

*** p \ 0.01; ** p \ 0.05; * p \ 0.10

Table 4 reports random-effects GLS estimates of industry and geographic complementarities direct effects on acquiring firms’ abnormal returns. Model 1 includes only control variables; Model 2 and Model 3 show the direct effect of industry complementarity variables and Model 4 through Model 6 include geographic complementarity measures at different levels of refinement. Model 7 is a full model with both types of complementarity—

123

industry (as measured at mid-industry level) and geographic (measured as Division Difference).7 Contrary to the 7

Given that various measures of industry complementarity are correlated (the same is true for the measures of geographic complementarity), we tested several full models with different combinations of measures for industry and geographic complementarities. We report here the model with significant coefficients for both types of complementarities when included together in the model.

Inf Technol Manag

predictions of hypothesis 1 we found that Mid-industry complementarity had significant negative effect (b = -0.011, p \ 0.01, Table 4, Model 3) while geographic complementarity (consistent to the predictions of hypothesis 2) had significant positive effect on acquirers’ cumulative abnormal returns: State Difference (b = 0.010, p \ 0.05, Table 4, Model 4) and Division Difference (b = 0.009, p \ 0.01, Table 4, Model 6). Specifically, acquiring companies from different states and geographical divisions has positive effect on performance, which can be explained by the effects of economies of scale (market extension through acquisition). In contrast, acquiring firms from different sub-industries (Mid-industry complementarity), which increases product-market scope, decreases acquisition performance. Finally, neither General industry complementarity nor Region Difference as a measure of geographic complementarity had significant effects on acquirers’ performance, which suggests that more finegrained decomposition of strategic complementarity measures is needed to capture their direct effect on acquiring firms’ abnormal returns. Table 5 reports random-effects GLS estimates of firms’ acquisition performance including interaction effects of different types of strategic complementarity with firm- and industry characteristics, specifically with acquirer’s IPO age and size and stock market growth. Model 8 through Model 10 include interaction effects with industry complementarity measured at the aggregated level (General industry complementarity) and Model 11 through Model 16 were run with geographic complementarity measures—State and Division Difference. Interestingly, while we found a significant direct effect of the Mid-industry complementarity variable, its interaction effects with acquirers’ age, size and stock market growth were not significant. In contrast, General industry complementarity becomes significant when interaction terms are included in the model (we report interaction effects models with General industry complementarity only). One explanation of such findings may be that industry complementarity measured at an aggregate level does not signal much information to the market unless market analysts take into account acquiring firms’ characteristics and the timing of the acquisition (stock market growth). Specifically, we found significant positive interaction effects between General industry complementarity and Acquirer’s size (b = 0.006, p \ 0.05, Table 5, Model 8) and Acquirer’s IPO age (b = 0.012, p \ 0.10, Table 5, Model 9), which provides support for hypotheses H3A and H4A. This means that for larger and older firms the effect of industry complementarity on acquisition performance is positive and significant. Contrary to hypothesis 5A, we found a significant negative interaction effect between General industry complementarity and Stock market growth (b = -0.001, p \ 0.01, Table 5, Model 10), meaning that when the market

is growing, industry complementarity (at aggregated level) has adverse effects on acquirers’ performance as perceived by the market. In the case of geographic complementarity, consistent with the predictions of hypothesis H3B, we found that Acquirers’ Size significantly and negatively moderates the relationship between geographic complementarity and acquirers’ market value: State Difference b = -0.007, p \ 0.01 (Table 5, Model 11); Division Difference b = -0.005, p \ 0.05 (Table 5, Model 14). Thus, the benefits of economies of scale provided by increased market exposure through acquisition of geographically complementary firms wanes when the size of a company increases. However, we did not find significant interaction effects for Acquirer’s IPO age (Hypothesis H4B was not supported) nor for Stock market growth (H5B was not supported). Figure 1 illustrates the interaction effects. 5.3 Additional analysis In order to further explore the negative effect of industry complementarity that is counter to the hypothesized effect, we examined whether the types of acquirers mattered in acquisitions involving ISR firms. Given that we measured industry complementarity based on the companies’ midindustry description provided in SDC Platinum database, we identified seven mid-industry categories (cable, computers and peripherals, e-commerce/B2B, Internet software and services, IT consulting & services, publishing, and software). The majority of the observations included acquiring firms in either the Internet software and services mid-industry (503 out of 821 acquisitions) and the software mid-industry (250 out of 821 acquisitions). Examples of firms in the Internet software and services industry include: Yahoo!, Ticketmaster, DoubleClick, American Online, Earthlink, and Google. Examples of firms in the software mid-industry include: Global Payments Inc., Internet.com Corp, PSINet Inc, Fidelity Natl Info Solutions, ZDNet (Ziff-Davis Inc), and SportsLine USA Inc. We ran the regression on the full sample of acquisitions with a dummy variable for each type of acquirer (except for one, which served as a baseline category; we tried all the dummies as a baseline category) based on their mid-industry, but found no significant results. We then tested the interaction effect between mid-industry complementarity dummy and the types of acquirers. With this analysis, we found that compared to other types of acquirers realizing both complementary and non-complementary acquisitions, acquirers in the software mid-industry conducting complementary acquisitions had lower abnormal returns (b = -0.02, p \ 0.01). For comparison, there was a negative effect of mid-industry complementarity without differentiating between the acquirers types of b = -0.01, p \ 0.01.

123

123 0.047***

0.003

-0.007***

-0.003**

-0.006

0.017*

9Aquirer’s IPO age

0.000 0.005 -0.039** Included -0.027* 64.28*** 0.17 821 150

Private Target indicator

Target Forune500 indicator


Industry volatility

Year dummies

_cons

Wald chi2

R-sq

N

n

*** p \ 0.01; ** p \ 0.05; * p \ 0.10

0.005 -0.004


0.018 0.000

0.001


# Alliances before M&A

0.003 0.000

Acquirer-specific uncertainty Acquirer’s slack available


0.032*** 0.000


0.005


Stock market growth

-0.005***

Acquirer’s size

9Stock market growth

150

821

0.16

63.70***

-0.033**

Included

-0.042**

0.006

0.000

-0.004

0.005

0.000

0.015

0.001

0.003 0.000

0.000

0.032***

0.000

150

821

0.16

64.27***

-0.044***

Included

-0.041**

0.007

0.000

-0.004

0.004

0.000

0.016

0.001

0.005 0.000

0.000

0.032***

0.005

150

821

0.16

68.97***

-0.079***

Included

-0.042**

0.008

0.000

-0.004

0.004

0.000

0.016

0.001

0.004 0.000

0.000

0.030***

0.005

150

821

0.12

62.90***

-0.055***

Included

-0.040**

0.006

0.000

-0.004

0.005

0.000

0.016

0.000

0.004 0.000

0.000

0.030***

0.010

150

821

0.13

61.95***

-0.050***

Included

-0.040**

0.007

0.000

-0.004

0.004

0.000

0.016

0.001

0.004 0.000

0.000

0.030***

0.005

-0.003**

0.000

0.010*

150

821

0.16

66.39***

-0.068***

Included

-0.042***

0.007

0.000

-0.004

0.005

0.000

0.016

0.001

0.003 0.000

0.000

0.032***

0.005

0.001

0.036***

-0.003*

-0.001***

0.002

Model 14

-0.005**

-0.003*

0.012*

-0.024**

Model 13

9Acquirer’s size

-0.037** 0.006**

Model 12

Model 11

Model 10

Model 8

Model 9

Interactions with geographic complementarity

Interactions with industry complementarity

Division difference


9Acquirer’s IPO age

9Acquirer’s size

State difference


9Acquirer’s IPO age

General industry complementarity 9Acquirer’s size

Variables

Table 5 Summary of GLS results: interaction effects

150

821

0.12

62.04***

-0.047**

Included

-0.040**

0.006

0.000

-0.004

0.005

0.000

0.016

0.001

0.004 0.000

0.000

0.032***

0.004

-0.003**

0.001

0.006

Model 15

150

821

0.13

63.19***

-0.050**

Included

-0.041**

0.006

0.000

-0.004

0.005

0.000

0.016

0.001

0.003 0.000

0.000

0.032***

0.005

-0.003**

0.000

0.010*

Model 16

Inf Technol Manag

Inf Technol Manag

Fig. 1 Illustration of interaction effects. a General industry complementarity and interaction effects. b Geographic complementarity (state and division differences) and interaction with acquirer’s size

We then restricted our sample to the observations with mid-industry complementarity (n = 628, N = 121) and tested the effects of acquirer types on their abnormal returns. We confirmed the negative effect of software acquirers conducting mid-industry complementary acquisitions (b = -0.02, p \ 0.06). In addition, we found that in contrast to other types of acquirers realizing mid-industry complementary acquisitions, acquirers in the Internet software and services industry had positive abnormal returns (b = 0.02, p \ 0.05). Thus, when finer-grained

measures are used, industry background of the acquirers significantly affects their abnormal returns from complementary acquisitions with acquirers in Internet software and services industry gaining highest returns. This finding is interesting because many of the generally recognizable, consumer focused companies fall in the Internet software and services mid-industry (Yahoo!, Ticketmaster, DoubleClick, American Online, Earthlink, and Google). Google’s acquisitions of both DoubleClick and YouTube were both mid-industry similar (both Internet software and

123

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services), whereas Google’s acquisition of Android was not (Android was in the Software mid-industry). This finding suggests that within the IRS industry, subsets of industry complementarity are in fact responded to positively as we expected, but that this trend is not true overall.

6 Discussion 6.1 Key findings This paper extends our understanding of the role strategic complementarities play in market valuation of IRS industry M&As. Specifically, we studied whether and how two types of strategic complementarities—industry and geographic—affect market value of acquiring firms in the IRS industry, and how firm-specific and environmental contingencies affect the value of strategic complementarities. Our findings show that industry and geographic complementarities in M&As do matter and significantly impact acquiring IRS firms’ stock market value following the announcement of an M&A. Beyond the simple ‘yes’ or ‘no’ answer this paper provides empirical evidence about the differing effects of strategic complementarity—positive in the case of geographic complementarity and negative in the case of industry complementarity—on firm market value. Our findings suggest that not all strategic complementarities in the IRS industry send positive signals to the stock market. Therefore, it is important to identify the most valuable types of complementarities with the potential to increase firms’ market valuation at a time of an M&As and beyond. Our finding of a positive effect of geographic complementarity on acquiring firms’ market value corroborates previous studies’ findings that geographically distant firms generate firm value through the acquisition of additional resources [6], location advantages [21], and greater economies of scale [40], which makes the effect of geographic complementarity generalizable to the IRS industry. While in line with our prediction and existing research evidence, this result is important because the effect of geographic complementarity was measured at three levels of analysis—state, region and division—in contrast to Zaheer et al. [59] measure of geographic complementarity based on self-reported data from the acquiring firms’ managers and Kim and Finkelstein’s [38] measure of geographic distance. Our empirical results indicate that in the IRS industry acquiring companies from different states and geographical divisions had positive effect on performance. We believe that this finer grained measure of geographic complementarity provides a more robust evidence of the positive effect of geographic complementarity in acquisitions.

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We predicted that industry complementarity between an acquiring IRS firm and its target would be value-generating to the acquiring firm’s stock price following an M&A announcement. This prediction was based on three arguments advanced in the financial and strategic management perspectives on M&A [7]: economies of scope [19]; synergy based on complementary resources and product strategies; potential increase in the acquirer’s market power [55]. Contrary to our prediction, we found that industry complementarity has a negative effect on the acquiring IRS firms’ stock market value confirming the findings of Ellis et al. and Zaheer et al. [22, 60]. Information technology services are complementary to many other industries. However, when IRS companies realize acquisitions of companies in different industries, we found that their market valuation drops. This finding supports the argument that industry relatedness rather than differences between the target and the acquirer is expected to offer more synergy potential [17, 53]. In our empirical context of the IRS industry, we believe that the potential loss of focus or a perception of movement away from core competency manifests from the diversity in product offerings. There is significant diversity in the primary product offerings of the acquiring companies (as was illustrated in Table 1), resulting in a large variation in the targets acquired, as can be seen through the variety of the mid-industry classifications for the targets. Combined with the industry volatility in 7375 classification resulting from a temperamental economy (dot com bubble) and rapid technological innovation, it could be suggested that the market is still trying to learn how to assess M&A activity in this industry segment. The fervor surrounding the financial evaluations of recent social media companies also supports the suggestion that market players are having some difficulty assigning value to transactions in this industry segment. It is important to note that the largely negative effect of industry complementarity does not hold in all cases. Our findings revealed significant differences in industry complementarity effects depending on the type of the acquirer. In particular, while acquirers in the software industry had lowest abnormal returns on their complementary M&As compared to all acquiring companies in the ISR industry sample, Internet software and services acquirers had significant positive valuation of their M&As. These findings suggest that the value of industry complementarity should be considered together with the type of the acquiring firm. Other key findings of this study are the moderating effects of the acquiring firms’ size, IPO age and the stock market growth effect. As predicted, stock market valuation of larger acquiring firms decreased at the announcement of an M&A featuring geographic complementarity, while the market valued geographic complementarity in M&As performed by smaller firms. We explain these results by the

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fact that the market attributes more value to the potential of smaller firms to grow bigger through geographic expansion, while exhibiting caution toward larger firms becoming too big and geographically spread, potentially subjecting them to cultural barriers. Also the magnitude effect for small firms may be larger resulting in more observable significance as these firms expand to larger geographic markets. Many small firms are local or regional, and hence expanding into new geographic markets may mean expanding to a new state within the US. On the other hand, larger firms are often more geographically diverse and expanding into new geographic markets may mean going to other countries where the risk of doing business may be high, e.g. Google’s failed experiment expanding geographically into China. In contrast, we found that larger firms acquiring targets in related but complementary industries (industry complementarity) exhibited higher market valuation. For example, when Google bought YouTube or DoubleClick, the M&A was considered successful given the relatedness of these companies. A similar moderating effect was found for the acquiring firms’ IPO age when the buyers realized M&As with industry complementarity. Consistent with the RBV [5, 47], smaller and younger firms may lack diverse, heterogeneous capabilities, which may be valuable in integrating and managing targets from different industries. In other words, larger firms have more resources to absorb companies in different industries and develop a strategy that results in successful integration. However, we did not find a significant impact of the IPO age moderator on geographic complementarity with respect to the acquiring IRS firm’s market value. This could be due to the youth of the companies in the sample we are examining. Most of the companies in our sample went public between 1990 and 2005, so there is not much variability in the length of time since the IPOs of the acquirers. While our results confirmed that expanding geographically when the stock market is growing increases the acquiring firms’ market value, we found the opposite moderating effect of stock market growth when firms’ realize M&As with industry complementarity. This implies that when market is up, companies are better off focusing on their niche and expanding into new geographic locations rather than expanding into new product markets. Stock market growth is a measure of the economic vitality and the resulting outcome is that the economic pie is expanding. As the economic pie expands, there are more opportunities to serve more customers and expand into new geographies to increase ‘‘reach’’ benefiting from low entry barriers for IRS firms when there are no significant cultural barriers such as when Google attempted to penetrate the China. Focusing on core competency will be a strategy that seems to pay most handsomely because there is less risk

associated with this strategy than with a strategy that expands into new product markets and new industries. Overall, significant effects of the moderating variables are consistent with the contingency theory [20] and mean that the value an acquiring firm creates through a M&A depends on firm’s characteristics (size and age) as well as external environmental conditions (stock market growth). This study, while providing useful insights, is not without limitations. First, the scope of the study was limited to the IRS industry and one has to be careful about generalizability of the results. Second, this study is based on a snapshot that focused on a period of time and while it is a traditional research approach, one has to be careful about extending the results beyond the time period from which the data was gathered. Future research may inquire whether the effects of strategic complementarities are sensitive to the observation period. Additionally, the time frame of this study included the adoption of the North American Industry Classification System (NAICS) to ultimately replace SICs and subsequent changes to NAICS occurred in 2002 which dramatically impacted the information sector (http://www.census.gov/econ/census02/data/ bridge/). Since our targets were being acquired throughout this shift, we were unable to use NAICS codes for our analysis and instead utilized SDC’s mid-industry classification that considers NAICS. Future work should utilize NAICS since it should ultimately improve the granularity of the SICs. Third, the measures we used for geographic and industry complementarity are reasonable but not without their limitations. For example, IRS firms are often involved with ‘‘information goods’’ that are easily transportable on the Internet regardless of geographic proximity. 6.2 Implications for practice The importance of information services to the global economy, as well as the unique characteristics of on-line information services, makes the exploration of strategic complementarities in M&As conducted by the IRS firms beneficial. The results help inform both scholarly research and management practice in the industries particularly crucial to future economic performance. First, our findings imply that managers of acquiring firms should take into account and carefully examine the types of complementarity between their firm and the target company because the nature of complementarity and the ‘‘fit’’ determines the potential for value creation. In the IRS industry context we established that industry complementarity in M&As does not clearly explain value creation for an acquiring firm, whereas achieving economies of scale through geographic complementarities is clearly a value-creating strategy. Second, our results stress the importance of contingency factors in extracting value from M&As. This finding, we

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believe, is very useful for managers when they evaluate their targets and try to predict the performance of the M&A as it shows that the focus on complementarity and the timing of the merger can influence the success of the M&A relative to market reaction. In particular, we show that the effects of industry and geographic complementarities of M&As vary depending on the size and IPO age of the acquiring firms, reflecting these firms’ resources and experience respectively. In addition, we found differing moderating effects of stock market growth in the case of industry and geographic complementarity indicating that timing of a M&A significantly effects how the market attributes value to the acquiring firm’s stock depending on the type of the complementarity the M&A involves.

7 Conclusion Value creation in the IRS industry is closely tied with value creation in many other industries in the national economy including prepackaged software and communications, among others. This paper advances our understanding of strategic factors determining value creation through M&As of the IRS firms. The results of this study stress the importance of considering strategic complementarities (i.e., industry and geographic) between acquirers and their targets, which are among critical characteristics of M&As that determine value creation for the acquiring firms. Acknowledgeing that the value of strategic complementarities in M&As might depend on firm-specific factors and the general condition of the stock market, we adopted a contingency approach and tested moderating effects of acquiring firms’ size, age and stock market growth. Strategic complementarities are critical in achieving economies of scale and scope in an acquisition. However, certain types of complementarity such as industry complementarity may be difficult to handle specifically for smaller and younger firms. Our results showed that in a larger time window these types of firms were systematically underperforming larger and older firms acquiring targets in different industries. Smaller and younger firms may lack diverse, heterogeneous capabilities, hampering them in their attempts to integrate and manage targets from different industries. In other words, larger firms have more resources to absorb companies in different industries and develop a strategy that results in successful integration. At the same time, our findings revealed that leveraging economies of scale through geographic complementarities proved to be a value creating strategy particularly for smaller and younger acquirers in the US IRS industry during 1993–2006. The positive effect of geographic complementarity on value creation from M&As transactions did not depend on the ups and downs of the stock market in 1993–2000. This contrasts with our finding of

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increasingly negative effects of industry complementaritiy when the market was high. In sum, smaller and younger firms were better off leveraging economies of scale through geographic complementarity, while larger and older firms were expected to be better at realizing the benefits of industry complementarity as reflected in stock market reaction to M&A announcements in the IRS industry. However, given lower valuation associated with acquisitions of targets from different industries than IRS, we conclude that focusing on the core competencies within the IRS industry and leveraging economies of scale were the key factors explaining value creation in the IRS M&As in 1993–2006. We believe that the results of this study complement existing studies of M&As in information systems research considering how acquisition and integration of information companies can create value in various complementary industries. Specifically, we considered whether IRS companies can be good acquirers and create value for their shareholders. The insights provided in the current study can, we hope, encourage future research on different types of strategic complementarities and various conditions of value creation by IRS companies.

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