The Corporate Innovation Strategy of Serial Acquirers and Stock
Market Reactions: Balancing Exploration and Exploitation
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ABSTRACT
A firm’s corporate innovation strategy is pivotal for firm performance and has become more frequently executed through mergers and acquisitions (M&As) in high-tech industries. Subsequently, literature started to investigate serial acquirers, firms that frequently perform interrelated M&As in programs instead of isolated events. In this research, I investigate the effect of changing the corporate innovation strategy compared to previous acquisitions for serial acquirers on firm performance measured by reactions on the stock market. Subsequently, I make use of March’s (1991) innovation framework and differentiate between exploitative and explorative serial acquirers making a switch towards the other type of innovation in the focal acquisition. I test the hypotheses by investigating 204 serial acquirers, performing 1415 acquisitions of which the innovation strategy was identified. I find evidence consistent with the innovation literature that exploration and exploitation should be balanced in order to sustain firm performance. The results provide partial support for a positive relationship between a switch in strategy in the focal acquisition and firm performance; more specifically, results show that investors are reacting more positively on an exploratory acquisition after an exploitative strategy, compared to a switch towards exploitative innovation through the focal acquisition. My research thereby contributes to the understanding of the implications of a serial acquirer’s corporate innovation strategy on firm performance.
Keywords: Mergers and Acquisitions; Serial Acquirers; High-tech industries; Exploration and
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1. INTRODUCTION
One of the major decisions management of high-tech organizations has to make relates to its corporate innovation strategy. March (1991) describes two types of corporate innovation strategy; exploitative and exploratory innovation. Whereas exploration is about creating new knowledge and new markets, exploitation includes activities regarding efficiency improvement and increased performance in existing markets (Angwin, 2007; March, 1991). Through exploitative innovation, firms can secure a steady revenue stream, whereas exploratory innovation allows firms to search for knowledge that is necessary to secure long-term profitability (Levinthal and March, 1981).
Firms striving to increase their innovative performance rarely possess the requisite knowledge within their organizational boundaries. The search for improved innovative performance, therefore, has firms going beyond internal development (e.g. Cloodt, Hagedoorn, and Van Kranenburg, 2006; Lavie, Kang, and Rosenkopf, 2011). Mergers and acquisitions (M&As) have been a frequently used method to explore and exploit, using knowledge beyond a firm’s organizational boundaries (Cefis, Marsili, and Rigamonti, 2019; Cloodt et al., 2006; Hayward, 2002). M&As are argued to be beneficial for a firm’s innovation performance as it provides the acquirer with the potential for integrating the acquirer and target firm’s resources and subsequently recombine these to boost innovation performance (Hitt et al., 1996).
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these firms perform, are interrelated and create significant value over time (Frick and Torres, 2002; Rovit and Lemke, 2003; Schipper and Thompson, 1983). Subsequently, literature started to evaluate sequences of acquisitions as interrelated events performed by ‘serial acquirers’, instead of examining the focal acquisition in isolation (e.g. Brueller et al., 2015; Laamanen and Keil, 2008; Schipper and Thompson, 1983).
Literature has assumed repeatedly that acquisitions in high-tech industries are equal in the sense that they only focus on knowledge acquisition and neglect the rationale behind the focal acquisition (Cloodt et al., 2006). This is surprising, especially as Heimeriks, Schijven, and Gates (2012) pointed out that there are substantial differences in risk to which an acquirer is exposed to in different types of acquisitions. A firm following an exploitative innovation strategy might secure today’s performance, but risks to lose its capability to adapt to the external environment and experience obsolescence, which deteriorates its long-term performance (Uotila et al., 2009). On the other hand, following an exploratory innovation strategy endangers a firm its short-term performance, as performance improvements will likely appear in the long run (Levinthal and March, 1993). Furthermore, exploratory innovation is riskier by nature and therefore more uncertain in its performance (March, 1991; Uotila et al., 2009).
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M&As, which I measure as the relative weight of exploratory motives over accumulated exploratory and exploitative motives in its previous M&As. More specifically, I investigate the reaction of investors when serial acquirers switch their innovative strategy in the focal acquisition, compared to its previous acquisitions. Thus, the goal of this research is to uncover the effect of a switch that occurs when a serial acquirer pursues exploratory innovation in its focal acquisition, while it focused on exploitative innovation in previous M&As, and vice versa. A switch occurs when the corporate innovation strategy followed in the focal acquisition deviates from the corporate innovation strategy identified in previous three M&As. Therefore, the research question formulated in this research is: “What is the effect of switching its innovation strategy in the focal acquisition for serial acquirers with an extreme innovative strategy on firm performance?”
Serial acquirers are exposed to different risks when following an exploitative or exploratory innovation strategy through M&As, and need to balance exploitative and explorative innovation for a sustainable and superior firm performance (March, 1991). As serial acquirers perform interrelated acquisitions in order to achieve their goals (Schipper and Thompson, 1983), I argue that a strategy switch performed by the serial acquirer will result in a positive reaction of the market. Furthermore, I expect that a switch towards an exploratory M&A results in a more positive reaction than a switch towards an exploitative M&A as it is essential to avoid obsolescence in dynamic markets and the potential higher performance exploratory innovation can deliver.
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most dominant methods in M&A performance research (Mackinlay, 1997; McWilliams and Siegel, 1997). I find partial support for a positive relationship between an innovation strategy switch by a specialized serial acquirer and firm performance, and more particularly, partial support for a positive effect of a switch from exploitative acquisitions towards the focal exploratory acquisition. This indicates that investors take the corporate innovation strategy of serial acquirers followed in previous M&As into account when assessing the risk of the strategy of the focal acquisition, and are aware of the fact that serial acquirers should balance their innovation strategy. Moreover, it suggests that the market recognizes that the risk of a switch to exploratory innovation is ‘necessary evil’ which offers high potential performance in these industries.
I contribute to literature in several ways; Firstly, I contribute to literature by combining innovation and M&A streams to provide insight into the need for a balance between exploitative and explorative innovation in M&As performed by serial acquirers. Furthermore, I add to the limited body of literature investigating M&A motives (e.g. Aalbers, McCarthy, and Heimeriks, 2020; Haleblian et al., 2009) and its subsequent performance implications. Lastly, this research contributes to serial acquirer literature by incorporating motives from individual M&As in formulating a serial acquirer’s innovation strategy.
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2. LITERATUREREVIEW
2.1 M&A Motives in High-Tech Industries
M&As in high-tech industries have been integral in various literature streams, such as finance (Rossi, Yedidia Tarba, and Raviv, 2011), strategic management (He and Wong; McCarthy and Aalbers, 2016), organizational behavior (McNamara, Haleblian, and Dykes, 2008), and innovation literature streams (Cloodt et al., 2006). The most predominant view on technological M&As is that it provides good opportunities to create synergies between the knowledge and capabilities between the two firms (Larsson and Finkelstein, 1999). However, even though there have been requests in literature to delve deeper into motives behind M&As, little advancements have been made in describing those in more detail (e.g. Haleblian et al., 2009).
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An increasing amount of firms perform acquisitions in order to boost their innovative or financial performance. Instead of only performing a single acquisition during a long period, firms start to perform multiple acquisitions within a short period to achieve their desired strategic outcomes (Laamanen and Keil, 2008). Consequently, instead of evaluating and analyzing acquisitions in isolation, literature has started to consider sequences of acquisitions as interrelated events, defining these firms as ‘serial acquirers’ or firms performing acquisitions in ‘acquisition programs’ (e.g. Brueller et al., 2015; Schipper and Thompson, 1983).
2.2 Risk in Corporate Innovation Strategy through M&As
Not all acquisitions are the same in terms of the risk that they imply. Making use of March’s (1991) framework, it can easily be argued that explorative acquisitions, which concern “search, variation, risk-taking, experimentation, play, flexibility, discovery, innovation” are more risky than exploitative acquisitions, which concern “refinement, choice, production, efficiency, selection, implementation, execution” (March, 1991; p.171).
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scan external information and utilize it for commercial ends (Cohen and Levinthal, 1990; Lavie et al., 2011). As a result, it increases its adaptability to environmental changes and subsequently its ability to increase exploratory innovations (Atuahene-Gima, 2005; Brown and Eisenhard, 1997).
As exploratory and exploitative innovation can result in the development of different capabilities and distinct benefits in terms of profitability, both innovation types are fundamental for firm performance. Firms that heavily rely on M&As to execute their corporate innovation strategy should be aware of the consequences of focusing solely on one type of innovation, and the necessity of balancing their corporate innovation strategy through M&As as well (March, 1991). Exploitative innovation offers potential for incremental improvements and a continuous stream of revenues, whereas exploratory innovation can result in breakthrough innovations that open up new product markets (Angwin, 2007; Levinthal and March, 1981). Furthermore, an overinvestment of resources in exploitation can result in a depletion of opportunities, whereas too much emphasis on exploration leaves an organization with a surplus of underdeveloped ideas and untapped opportunities (March, 1991). Exploration provides the generation of new knowledge and opportunities, whereas exploitation leverages an organization’s current knowledge to generate short-term revenues (Tushman and O’Reilly, 1996; Uotila et al., 2009). For example, when a serial acquirer pursues exploitative innovation in previous acquisitions, it is beneficial to balance that sequence with an exploratory motivated acquisition to minimize risks and seek the complementary benefits of exploratory innovation (March, 1991). Therefore, it is essential for serial acquirers to balance the corporate innovation strategy pursued in their M&A programs.
2.3 Reacting to Risk Signals in M&A
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stock market (Zollo and Meier, 2008). Through official M&A announcements, the acquirer attempts to outline how this acquisition will increase firm performance according to those who initiated and completed the acquisition process, which subsequently will be valued by the market (Epstein, 2005; Hassan, Ghauri, and Mayrhofer, 2018).
Signaling theory suggests that the market will react to any announcement that describes the risk associated with an acquisition. This theory is used at times when information about an event is spread asymmetrically between several parties (Spence, 1974). In recent years, signaling theory has been applied to a multitude of contexts and types of signals (Connely et al., 2011). In signaling theory, a signaler has specific information about a variety of subjects e.g. products, firms (Kirmani and Rao, 2000; Ross, 1977) which is not accessible for outsiders (Connely et al., 2011).
A limited amount of research has probed into the reaction of capital markets resulting from a wide range of acquisition strategies. Previous research has argued that analysts active in the capital market are less confident in exploratory innovation strategies as a larger knowledge gap exists between managers and the capital market (Jia, 2017). Furthermore, investors react negatively to exploratory M&As, whereas they respond positively when an exploitative M&A is announced (Aalbers et al., 2020). In line with hat reasoning, Jia (2018) argues that firms pursuing exploratory innovation endeavors have a higher chance of stock price crashes due to higher risk in innovative activities, and subsequently more ‘bad news’ and less stable firm performance.
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conjunction with the innovation strategy the serial acquirer previously followed, will cause a change in firm value as a consequence of the reaction of investors.
3. HYPOTHESES
3.1 Market Reaction to Switching M&A Innovation Strategy
In order to assess the reaction of the market on the focal acquisition, it is highly relevant to include the strategy followed in previous acquisitions made by the serial acquirer; as Schipper and Thompson (1983) argue, acquisitions made by serial acquirers consist of a sequence of interrelated acquisitions, which cannot be seen as isolated, single acquisitions.
As exploratory innovation is focused more on the creation of new knowledge and risk-taking, it is less certain to show positive results than exploitative innovation, which aims for incremental improvements of current knowledge (Devos, Kadapakkam, and Krishnamurthy, 2009; March, 1991). However, firms should not refrain from exploratory innovation, but balance their innovative strategy between exploitative and exploratory activities (March, 1991; Uotila et al., 2009). A focus on one and disregarding the other, will result in either an excessive focus on short- (exploitation) or long-term (exploration) performance, neglecting the need for the other one (Levinthal and March, 1993).
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innovation strategy followed in those acquisitions, as the M&A announcements should reflect the aim of the acquisition as accurate as possible (Litov, Moreton, and Zenger, 2012).
When serial acquirers perform an acquisition which follows a different innovation strategy than previous acquisitions, it will face less exposure to hazards of reduced market performance by investing in both short- and long-term performance (March, 1991). As serial acquirers perform various acquisitions in a short time window, investors will be aware of previous events due to the M&A announcements that have been released. The focal M&A announcement shows investors that a switch is made by the serial acquirer, disseminating the different aim of the focal acquisition compared to previous acquisitions. Subsequently, investors will be aware that a sufficient balance in the innovation strategy, and consequently a sustainable firm performance is secured. Therefore, I argue:
Hypothesis 1: A switch of innovation acquisition in the focal M&A of a specialized serial acquirer has a positive relationship with firm performance
3.2 Market Reaction to Switches Towards Exploitation or Exploration
Besides the reasoning that a strategy switch made by a serial acquirer will result in a positive firm performance, I will specify the distinct consequences for serial acquirers specialized in either exploitative or exploratory motivated acquisitions. As exploitative and exploratory innovation is performed for different reasons and differ in terms of risk and benefits, I argue that investors will perceive a strategy switch made by a serial acquirer specialized in exploitative acquisitions differently than a strategy made by a specialized exploratory acquirer. Subsequently, these will cause different reactions to the stock market.
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1985; Benner and Tushman, 2002; Levinthal and March, 1993). However, firms relying too heavily on solely exploitative innovative activity can cause structural inertia and a decreased ability to adapt to a changing environment (Hannan and Freeman, 1984).
Furthermore, focusing solely on exploitative innovation can reduce the variability in a firm’s capabilities; for example, BancOne was a successful bank acquiring the same types of targets consistently, without considering other types of targets (Uyterhoeven, 1994). As time passed, BancOne became increasingly specialized and equipped itself with routines only focused on acquisitions with a narrow scope, only beneficial for these types of acquisitions (Szulanski, 1996). Consequently, it became incapable of acquiring other types of firms and failed to compete with other banks as they were not able to catch up with new initiatives, absent in their own acquisition target group (Hayward, 2002). In short, focusing exclusively on exploitation can hinder the organization in its ability to create the knowledge necessary for long-term performance. Exploration is pivotal in high-tech industries to remain competitive and create new knowledge searching for ground-breaking innovation (Jansen et al., 2005).
Moreover, exploitative innovation is a well-suited strategy in order to build up slack resources (Smith and Tushman, 2005). Consequently, a sequence of exploitative acquisitions can provide firms sufficient slack resources to be better prepared financially to take the ‘leap into the unknown’ and pursue exploratory innovation by acquiring a target firm accompanied with higher potential benefits and risks. As noted by Sorescu, Chandy, and Prabhu (2003), innovations resulting from exploratory activities provide better results than innovations created by exploitative innovation.
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serial acquirer previously performed acquisitions with higher potential performance. Furthermore, the creation of knowledge through exploratory innovation is a requisite in high-tech industries, whereas the benefits of exploitative innovation aimed at improving efficiencies and broadening the knowledge base will be short-lived (He and Wong, 2004; March, 1991).
To summarize, investors can interpret the previous exploitative acquisitions as a good ‘preparation’ to decrease burdensome financial consequences, and a suitable moment to invest in the creation of knowledge deemed necessary for long-term performance in high-tech industries. Furthermore, an exclusive focus on exploitative acquisitions could cause inertia and a narrow set of capabilities. Therefore, investors who are aware of a serial acquirers’ previous acquisitions, will react positively towards an exploratory acquisition, because it is the ‘necessary evil’ for an organization to perform in high-tech industries. On the other hand, serial acquirers previously performing exploratory acquisitions that switch towards an exploitative acquisition will cause a weaker reaction from investors. The exploitative acquisition allows for revenue generation, but will not allow for the magnitude of potential benefits of exploratory innovation, and these profits will be short-lived in the dynamic environment of high-tech industries (Jansen et al., 2005; Sorensen and Stuart, 2000). Therefore, I propose:
Hypothesis 2: A strategy switch performed by a specialized exploitative serial acquirer will result in a more positive reaction on the stock market compared to a strategy switch performed by a specialized exploratory serial acquirer
4. METHODOLOGY
4.1 Empirical Setting
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becomes trivial within these industries, M&As have been an important instrument to increase and renew knowledge bases in order to increase innovation and hold a competitive advantage (Bierly and Chakrabarti, 1996). Consequently, M&A strategies within these industries are a suitable setting in which the effects of my hypotheses can be investigated.
4.2 Sample
The data used in order to investigate the hypotheses is collected from Thomson Reuters SDC. I filtered the data to comprise: 1) all acquisitions, performed by 2) stock-listed companies, which were 3) announced within the period 01/01/2001 to 01/01/2016. These acquisitions had 4) a deal value of at least $10 million, 5) 100% of the target firm was acquired by the acquiring firm and 6) both firms are active in high-tech industries.
I follow the definition of high-tech industries applied by various authors in the M&A and innovation literature (e.g. Cloodt et al., 2006; McCarthy & Aalbers, 2016). The hypotheses are tested on a sample of global companies covering four high-tech industries: aerospace and defense (SIC-codes 372 and 376), computers and office machinery (SIC-code 357), pharmaceuticals (SIC-code 283) and electronics and communications (SIC-code 36). Furthermore, observations were eliminated when stock was repurchased, when the acquiring and target firm are both owned by the same parent firm, and lastly, removed the observations when they indicated a within-firm restructuring (Ikenberry, Lakonishok, and Vermaelen, 1995), even as observations in which the SIC code of the acquiring firm was administered incorrectly. In conclusion, the total sample includes 3,186 acquisitions.
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definition of serial acquirers, the sample consists of 1415 acquisitions, performed by 204 serial acquirers. For robustness checking purposes, however, I also used a more conservative definition of serial acquirers of at least 3 acquisitions performed within 5 years (e.g. Fuller, Netter, and Stegemoller, 2005), to create a sample 251 serial acquirers, performing a total of 1,266 acquisitions.
4.3 Dependent Variable
In this research, I use an event study to evaluate the reaction of investors to the M&A announcement and its corresponding innovation strategy for several reasons. Event study methodology has been the most commonly used method to evaluate the effect of M&As on firm performance measured by stock prices (Mackinlay, 1997). To illustrate, Zollo and Meier (2008) show that 41% of all M&A performance research uses event study methodology, followed by methods based on long-term accounting metrics (28%).
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4.4 Independent variable
I use official M&A announcements to identify the motives of each acquisition. I retrieved these from the Thompson database and identified the number of exploratory or exploitative motives announced per acquisition. In order to assess whether the serial acquirer performed a switch in its innovation strategy in the focal acquisition, I compare the strategy followed in the previous three acquisitions to the strategy of the focal acquisition.
Firstly, I identified the number of exploitative and exploratory motives per acquisition, and created a variable including the total sum of all motives per event and calculated the ratio of exploratory innovation strategy motives compared to exploitative motives. I calculate the strategy followed by the serial acquirer over the previous three acquisitions as follows:
SAstrategy𝑥= Exploration𝑥
ExplorationM𝑥+ ExploitationM𝑥∗ 100% eq(1)
SAstrategy is the aggregate innovation strategy of the serial acquirer performed in the previous three M&As. ExplorationM𝑥 expresses the total weight of exploratory motives
identified throughout the previous three acquisitions, whereas ExploitationM𝑥 is the total count
of exploitative motives identified. Thereafter, I identify a strategy switch when the focal acquisition differs 50%, 40% or 30% compared to the innovation strategy followed in previous acquisitions. An overview of the frequencies of switches per definition can be found in Appendix A.
4.5 Control Variables
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target firm characteristics and explicit deal aspects and contain: 1) motive count, defined as the total weight of all motives identified per acquisition, 2) the acquirer’s financial slack (Haleblian et al., 2009), measured as the normalized function of the acquiring firm’s operating cash flow over its total assets regarding the last year before the official announcement was released; 3), the percent of cash used in the deal (Hayward and Hambrick, 1997), 4) the value of the acquisition expressed in US$ millions (Beckman and Haunschild, 2002), 5) and the serial acquirer it’s prior performance, defined as its return on assets (ROA) in the year preceding the focal acquisition (Zollo and Singh, 2004), 6) the size of the acquirer, measured in 1000’s of US$ millions (Zollo and Singh, 2004), 7) the geographic distance between acquiring and target firm in kilometers, 8) the relatedness between both firms using SIC code, and lastly 9) the level R&D intensity.
Furthermore, I used a dummy variable to control for international deals (1=international, 0=domestic) and lastly, I controlled for year differences and industry differences using the 3-digit SIC code and the year in which the focal M&A announcement was released. If a value was missing, this was substituted by the average of the variable. Lastly, I performed the Shapiro-Wilk test to see if the variables were normally distributed; for the variables Acquirer Size, Deal Value, and Acquirer Financial Slack, I use the logs as they were not normally distributed.
4.6 Estimation Model
In order to calculate differences in the stock market reactions as a result of the M&A announcements of the serial acquirer, I use the following model:
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In this model, 𝐶𝐴𝑅𝑖𝑡 is the cumulative abnormal result of serial acquirer i in the measured time gap t around the announcement of the acquisition. 𝛽1𝑆𝑡𝑟𝑎𝑡𝑒𝑔𝑦𝑆𝑤𝑖𝑡𝑐ℎ𝑖𝑡 is a dummy
variable which can consist of various dummy variables; firstly, a dummy variable can be used that indicates whether the serial acquirer i made a switch in strategy; furthermore, a dummy variable is used to either measure a switch from an exploitative strategy towards an exploratory in the focal acquisition and vice versa. This dummy variable can only adopt a score of 1 if there were 3 previous acquisitions of which the motives were determined. Furthermore, 𝛽𝑗𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖𝑡 includes all control variables used in our baseline model which are described in section 4.5 ‘control variables’. Lastly, 𝜀𝑖𝑡 is a normally distributed error term.
Eventually, I include 1415 acquisitions into the regression model, making use of the sample consisting of serial acquirers that perform at least 4 acquisitions within 10 years. 26 observations were dropped while performing the regression due to collinearity issues, resulting in 1389 observations used in the model to test whether a switch -in general, and specifically for switches to exploitative and exploratory acquisitions- will result in a difference in CAR. I use a linear regression estimating the ordinary least squares (OLS). Besides, I firstly create a baseline model excluding an independent variable, only consisting of the dependent and control variables to check for multicollinearity. The variance-inflator test (VIF) shows that the highest score on this test consists of 1.96 (International Deal), which is well within the accepted threshold of 5, showing no signs of multicollinearity (Hair et al., 1987).
5. RESULTS
5.1 Descriptive Statistics
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identified as having no clear motive. Lastly, 551 acquisition announcements included both at least one exploratory and one exploitative motive.
[INSERT TABLE 1 HERE]
Furthermore, Table 2 presents the descriptive statistics of the industries in which the acquirers are active and whether were performed domestically or internationally. It shows the number of acquisitions performed by firms present in these industries, and includes a distinction made between either domestic and international acquisitions. The statistics tell us that of the final sample of 1415 acquisitions, 846 (60%) were acquisitions performed domestically, whereas 569 observations (40%) were international acquisitions. Besides, it shows that the largest share of the M&As was performed by firms active in the Electronics and Communications sector (41%), and the Aerospace and Defense Machinery sector contributes the smallest share to the sample (7%).
[INSERT TABLE 2 HERE]
Next, Table 3 shows the correlation matrix for correlations within the model variables. The Variance Inflation Factor (VIF) values show no serious issues of multicollinearity.
[INSERT TABLE 3 HERE]
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the strategy switch in comparison with the previous innovation strategy, being a switch of 50%, 40%, and 30%. The frequencies identified in each situation can be found in Appendix A.
5.2 Reaction of the Market on Strategy Switches
In order to test my hypotheses, several linear regressions were performed to estimate the ordinary least square models (OLS). Table 4 and Table 5 report the results of these tests. Furthermore, industry and year dummies were used to account for differences within years and industries in every model.
[INSERT TABLE 4 HERE]
Table 4 presents the results on the tests for Hypotheses 1. Each column provides a separate model for every strategy variable, being separate switches to exploitative and exploratory acquisitions for three groups; an extreme strategy defined as being outside the 35%-65% exploratory strategy threshold with a minimum switch of 30% after the focal acquisition ( and two groups of with extreme strategies outside 40%-60% exploratory strategy, with either a switch of 30% or 40%. Furthermore, for extreme strategies 30%-70% (with a switch of 50%,40%, and 30%), extreme strategies of 35%-65% (with a switch of either 50% and 40%), and lastly, an extreme strategy defined as 40%-60% (with a switch of 50%), separate models were created (in accordance with Table 4). These strategy switches have not been divided into exploratory and exploitative switches.
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is tested. Model 2 and 5 show significant results when a switch of at least 50% occurred, and show a positive coefficient. In Model 2, 1,3% of the total change in CAR is explained by the switch. Model 5 shows a 1,1% change in CAR is caused by the strategy switch. Lastly, Model 7 shows a positive coefficient explaining 0,8% of the change in CAR. All of these results are significant (p < 0.10). These results suggest that when the focal acquisition is announced and is a switch under these definitions, this will result in a positive reaction on the stock market. On the other hand, Models 3 through 6, and 8 through 10 show no significant results.
[INSERT TABLE 5 HERE]
Secondly, to test Hypothesis 2, I specifically zoom in on the differences for switches to an exploratory and exploitative strategy during the focal acquisition for the models in which the sample size allows us to evaluate these. Table 6, Model 1 displays an R-squared=0,059 on 1389 observations. Model 2 and Model 6 show a significant (both p < 0.05) positive coefficient, explaining 1,4% (Model 2) and 1,3% (Model 6) change in CAR. This indicates that when an exploratory acquisition is made by a serial acquirer specialized in exploitative acquisitions, the stock market reacts positively to this focal acquisition under these definitions. Inconsistent with Models 2 and 6, Model 4 shows no significant result for a switch to an exploratory acquisition. On the other hand, Models 3, 5, and 7 show no significant results; these tested for a switch to exploitative acquisitions by specialized exploratory acquirers. The results suggest that there is indeed a stronger effect for switches to an exploratory innovation strategy than to an exploitative innovation strategy through the focal acquisition. However, as there is no significant effect measured for switches to an exploitative acquisition, Hypothesis 2 is partially confirmed.
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performed in the 260-day time window, and one in which this was the case and results could be affected. The sample used to make the two subpopulations defines a strategic switch as one when the innovation strategy changed at least 30%, and an extreme strategy was followed when the strategy in previous M&As was outside the 40%-60% exploration threshold, as these included most observations of strategy switches and provide a strong basis to validate previous results. Subsequently, I performed additional linear regressions on both subpopulations, which show a positive significant result (p <0.10) on the subpopulation without M&As in the 260-day time gap and a non-significant result on the ‘contaminated’ subpopulation. This distinction could provide a possible explanation for other non-significant results in other tests.
[INSERT TABLE 6 HERE]
Altogether, the results from these tests indicate that, to a certain extent, a strategic switch affects the performance of the serial acquirer on the stock market. More specifically, the stock exchange market reacts differently on serial acquirers specialized in exploitative acquisitions performing an exploratory acquisition compared to serial acquirers following a different strategy. In fact, the market reacts strongly to a switch towards an exploratory acquisition, when it previously focused on exploitative innovation through M&As.
6. DISCUSSION
6.1 Key Findings
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I have researched the performance implications of switching the corporate innovation strategy for serial acquirers through the reaction of the stock market on those switches. Currently, literature still ignores the impact of previous acquisitions on how investors react to the focal acquisition, even though it is acknowledged that serial acquirers perform acquisitions which are interrelated to each other (Schipper and Thompson, 1983).
By applying March’s (1991) framework and integrating the perceived performance consequences in M&As of balancing a firm’s corporate innovation strategy with signaling theory (Spence, 1974), I pose that switching corporate innovation strategy, after a sequence of M&As focused on exploitation or exploration, will lead to a positive stock market reaction. The results of this paper indicate that investors indeed are reacting positively to a switch of corporate innovation strategy by serial acquirers if it was previously focused on one type of innovation. This is in line with existing literature (e.g. He and Wong, 2004; Jansen et al., 2006; Lavie et al., 2011; Uotila et al., 2009), arguing that an organization never should be overly dependent on either exploitation or exploration, as both provide the organization different, but crucial types of benefits. However, not in all tests the results provided significant results, in which different definitions of a specialized serial acquirer and strategy switch were applied.
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effect is measured, could imply that investors do not perceive it to be of significance, after completing a sequence of potentially high-risk and highly rewarding exploratory acquisitions.
6.2 Theoretical Contributions
This paper makes several contributions to literature. Firstly, I combine innovation and M&A literature streams by implementing the framework of March (1991) to formulate a corporate innovation strategy through M&As for serial acquirers. In current literature, the effect of the corporate innovation strategy in previous M&As on the reaction of the market has largely been ignored. This study contributes to innovation and M&A literature by showing that balancing the corporate innovation strategy after a sequence of one particular innovation strategy through M&As is relevant for investors as well. While my findings are in line with March’s (1991) statement that exploitative and exploratory innovation should be balanced, it is not in line with the findings of Aalbers et al. (2020), that, to my knowledge, is the only research that has focused on the effect of a corporate innovation strategy in the focal acquisition on firm performance.
Moreover, this research contributes to the widely examined literature investigating the implications of M&As on firm performance. It increases our understanding of balancing exploitation and exploration within high-tech industries, and confirms findings of Uotila et al. (2009) showing that in high R&D intensity industries, this balance is a prerequisite for increased firm performance. Furthermore, whereas Jia (2017) mainly focused on the role of corporate innovation strategy in M&As in influencing the perceived credibility of the forecast of analysts, this paper has aimed to clarify the relationship between the innovation strategy and the subsequent reaction on the stock market.
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and Keil, 2008). Prior work on serial acquirers has disregarded motives of individual M&As and did not include measures of individual M&As when using combined measures of sequences (with few exceptions, e.g. Stettner and Lavie, 2014). By incorporating the innovative motive of previous acquisitions’ strategy as an antecedent for firm performance, I contribute to serial acquirer literature.
6.3 Managerial implications
The managerial implications of this study are twofold; firstly, managers should keep in mind that it is very important to remain a balanced, but varied M&A program, when the firm is active through M&As. As mentioned before in the example of BancOne, managers should be aware of the implications of focusing solely on one type of innovation through M&As, as it can narrow the firm’s capabilities and hamper the performance of M&As outside that narrow focus (Hayward, 2002). This would reduce the potential for managers to balance their innovation strategy through varied M&As in terms of innovation. Additionally, this research reminds managers active in high-tech industries to the relevance of creating new knowledge through exploratory innovation. Whereas exploitative M&As might come at lower risk (Devos et al., 2009), it is very important for managers to be aware of benefits involved with exploratory M&As and the necessity to invest in such activities.
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types of risk present after a sequence of either exploratory or exploitative innovation and subsequently make use of this knowledge in order to assess the value of the focal acquisition.
6.4 Limitations and Future Research
This research pioneers unexplored grounds and provides a first step in understanding investor reactions on the corporate innovation strategy of serial acquirers. Yet, this research is subject to various limitations which can form a basis for future research.
Firstly, this research focuses on firm performance in high-tech industries by measuring the short-term reaction of investors by assuming that the capital markets are efficient and will reflect the value added by the event. Additionally, it will be interesting to investigate the consequences of focal acquisitions on innovation performance as well in order to verify the results of this study.
Secondly, the corporate innovation strategy followed by serial acquirers is based on its previous three M&As. However, the frequency of M&As between acquirers varies heavily in this sample, ranging from 56 acquisitions performed by Cisco Systems Inc within 10 years, to 4 acquisitions by a large number of firms in this sample. Various scholars argue that the time interval between acquisitions can significantly influence firm performance (e.g. Brown and Eisenhardt, 1997; Hayward, 2002). Furthermore, it could affect investors' awareness of the serial acquirer’s innovation strategy as well. Therefore, I invite future research to address the effect of time variation between M&As on firm performance for serial acquirers.
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1991; Uotila et al., 2009) and for the reaction of investors on ambidextrous M&As (Aalbers et al., 2020). Future research can clarify the consequences for serial acquirers, taking previous acquisitions and its innovative strategy into account.
28
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33
APPENDIXA
The tables below display the frequency of strategy switches and specific shifts towards an exploitative or exploratory M&A, making use of two different definitions of serial acquirers.
The horizontal bar displays the threshold outside which SAStragey should be in order to have an extreme strategy. The vertical bar displays the strength of the switch of the corporate innovation in the focal acquisition which is applied, compared to SAStrategy.
Furthermore, the numbers between brackets identify specific switches to an exploratory and exploitative innovation strategy through the focal M&A (shift to exploratory innovation* - shift to exploitative innovation).
Serial Acquirer Definition: at least 4 Acquisitions within 10 Years
Serial Acquirer Definition: at least 3 Acquisitions within 5 Years
High (0,3-0,7) Medium (0,35-0,65) Low (0,40-0,60)
50% Shift 102 136 174 (52* – 50) (68* – 68) (83* – 91) 40% Shift 120 157 196 (64* – 56) (81* – 76) (97* – 99) 30% Shift 150 228 312 (79* – 71) (112* – 116) (145* – 167)
High (0,3-0,7) Medium (0,35-0,65) Low (0,40-0,60)
34
Table 1 - Motives per Acquisition
35
Table 2 - Acquisitions by Industry and International Perspective
Industry Domestic (%) International (%) Total (%)
Aerospace and defense 54 46 100 (7%)
Computers and office machinery 235 82 317 (22%)
Pharmaceuticals 217 195 412 (29%)
Electronics and communications 340 246 586 (41%)
36
Table 3 - Correlation Matrix
1 2 3 4 5 6 7 8 9 10 11
1 CAR 1.000
2 Med strategy – switch to explore 30% 0.055 1.000
[0.039] 3 Prior Performance 0.081 0.041 1.000 [0.002] [0.128] 4 Geographic Distance 0.023 -0.022 0.076 1.000 [0.388] [0.400] [0.005] 5 Relatedness 0.049 -0.024 0.006 -0.033 1.000 [0.067] [0.375] [0.837] [0.221] 6 R&D intensity -0.037 -0.035 -0.183 -0.040 0.051 1.000 [0.161] [0.192] [0.000] [0.130] [0.054] 7 International deal 0.050 -0.038 0.105 0.668 0.022 0.009 1.000 [0.060] [0.158] [0.000] [0.000] [0.406] [0.731] 8 Motive Count 0.027 0.014 0.024 0.038 0.075 0.000 0.028 1.000 [0.305] [0.594] [0.375] [0.150] [0.005] [0.995] [0.300] 9 Acquirer Size -0.039 0.063 0.097 0.059 -0.223 -0.133 0.115 -0.002 1.000 [0.146] [0.020] [0.000] [0.029] [0.000] [0.000] [0.000] [0.941] 10 Deal Value -0.059 -0.033 0.044 -0.019 0.114 -0.016 -0.026 0.146 0.092 1.000 [0.026] [0.219] [0.100] [0.475] [0.000] [0.551] [0.329] [0.000] [0.001]
11 Acquirer Financial Slack 0.085 0.065 0.038 0.065 0.005 -0.283 0.149 0.010 0.148 0.045 1.000
[0.001] [0.015] [0.156] [0.015] [0.857] [0.000] [0.000] [0.720] [0.000] [0.090]
Mean 0 0.08 5.57 3536.7 2.07 14.36 0.4 1.29 37134.25 907.78 -0.03
Standard Deviation 0.08 0.27 15.54 3471.91 1.7 34.07 0.49 0.63 69221.63 4043.53 0.29
Min -0.31 0 -301.85 0 0 0.06 0 0 0 10 -0.96
37
Table 4 - Regression Results: Switches of Corporate Innovation Strategy through M&A
1 2 3 4 5 6 7 8 9 10
CAR3 CAR3 CAR3 CAR3 CAR3 CAR3 CAR3 CAR3 CAR3 CAR3
High strategy – switch 50% 0.013*
(1.714)
High strategy – switch 40% 0.007
(0.978)
High strategy – switch 30% 0.006
(0.955)
Med strategy – switch 50% 0.011*
(1.819)
Med strategy – switch 40% 0.006
(1.072)
Med strategy – switch 30% 0.008*
(1.701)
Low strategy – switch 50% 0.007
(1.311)
Low strategy – switch 40% 0.002
(0.474)
Low strategy – switch 30% 0.007
(1.552) Prior Performance 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** (2.300) (2.303) (2.300) (2.292) (2.310) (2.304) (2.300) (2.313) (2.304) (2.343) Geographic Distance -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 (-0.416) (-0.434) (-0.421) (-0.406) (-0.411) (-0.408) (-0.389) (-0.410) (-0.412) (-0.358) Relatedness 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 (1.163) (1.139) (1.148) (1.162) (1.124) (1.140) (1.171) (1.166) (1.164) (1.203) R&D Intensity -0.000** -0.000** -0.000** -0.000** -0.000** -0.000** -0.000** -0.000** -0.000** -0.000** (-2.185) (-2.243) (-2.207) (-2.204) (-2.231) (-2.203) (-2.203) (-2.206) (-2.188) (-2.142) International Deal 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 (1.076) (1.160) (1.112) (1.098) (1.150) (1.110) (1.137) (1.127) (1.090) (1.111) Motive Count 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 (0.545) (0.636) (0.566) (0.528) (0.669) (0.580) (0.563) (0.663) (0.567) (0.617)
(Log) Acquirer size -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002
(-1.091) (-1.170) (-1.140) (-1.142) (-1.170) (-1.144) (-1.210) (-1.138) (-1.109) (-1.196)
(-1.113) (-1.147) (-1.122) (-1.108) (-1.136) (-1.119) (-1.095) (-1.126) (-1.115) (-1.114)
(Log) Potential Slack 0.006* 0.006* 0.006* 0.006* 0.006* 0.006* 0.005* 0.006* 0.006* 0.006*
(1.923) (1.926) (1.918) (1.910) (1.879) (1.892) (1.841) (1.898) (1.910) (1.864)
Constant 0.066*** 0.066*** 0.066*** 0.066*** 0.065*** 0.066*** 0.066*** 0.065*** 0.065*** 0.065***
38
Table 4 - Regression Results: Switches of Corporate Innovation Strategy through M&A (Continued)
(Log) Deal Value -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002
(-1.113) (-1.147) (-1.122) (-1.108) (-1.136) (-1.119) (-1.095) (-1.126) (-1.115) (-1.114)
(Log) Acquirer Financial Slack 0.006* 0.006* 0.006* 0.006* 0.006* 0.006* 0.005* 0.006* 0.006* 0.006*
(1.923) (1.926) (1.918) (1.910) (1.879) (1.892) (1.841) (1.898) (1.910) (1.864)
Year dummy YES YES YES YES YES YES YES YES YES YES
Industry dummy YES YES YES YES YES YES YES YES YES YES
Constant 0.066*** 0.066*** 0.066*** 0.066*** 0.065*** 0.066*** 0.066*** 0.065*** 0.065*** 0.065***
(3.658) (3.678) (3.674) (3.672) (3.624) (3.653) (3.660) (3.586) (3.639) (3.582)
Observations 1,389 1,389 1,389 1,389 1,389 1,389 1,389 1,389 1,389 1,389
R-squared 0.059 0.060 0.059 0.059 0.060 0.059 0.060 0.059 0.059 0.060
r2 0.0585 0.0601 0.0590 0.0590 0.0600 0.0590 0.0597 0.0592 0.0586 0.0597
39
Table 5 - Regression Results: Switches to Exploration and Exploitation Through M&A
1 2 3 4 5 6 7
CAR3 CAR3 CAR3 CAR3 CAR3 CAR3 CAR3
Med strategy – switch to explore 30% 0.014**
(2.548)
Med strategy – switch to exploit 30% 0.000
(0.065)
Low strategy – switch to explore 40% 0.009
(1.458)
Low strategy – switch to exploit 40% -0.004
(-0.531)
Low strategy – switch to explore 30% 0.013**
(2.287)
Low strategy – switch to exploit 30% 0.000
(0.046) Prior Performance 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** (2.300) (2.253) (2.293) (2.287) (2.279) (2.311) (2.297) Geographic Distance -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 (-0.416) (-0.428) (-0.414) (-0.405) (-0.418) (-0.401) (-0.414) Relatedness 0.002 0.002 0.002 0.002 0.002 0.002 0.002 (1.163) (1.153) (1.162) (1.152) (1.153) (1.188) (1.161) R&D Intensity -0.000** -0.000** -0.000** -0.000** -0.000** -0.000** -0.000** (-2.185) (-2.194) (-2.186) (-2.191) (-2.180) (-2.119) (-2.183) International Deal 0.007 0.008 0.007 0.007 0.007 0.007 0.007 (1.076) (1.171) (1.077) (1.115) (1.071) (1.153) (1.073) Motive Count 0.002 0.002 0.002 0.002 0.002 0.002 0.002 (0.545) (0.514) (0.542) (0.569) (0.494) (0.567) (0.540)
(Log) Acquirer size -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002
(-1.091) (-1.223) (-1.094) (-1.147) (-1.082) (-1.208) (-1.093)
(Log) Deal Value -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002
(-1.113) (-1.060) (-1.112) (-1.088) (-1.093) (-1.059) (-1.110)
(Log) Acquirer Financial Slack 0.006* 0.005* 0.006* 0.006* 0.006* 0.005* 0.006*
(1.923) (1.806) (1.922) (1.877) (1.918) (1.826) (1.923)
Year dummies YES YES YES YES YES YES YES
Acquirer Industry dummies YES YES YES YES YES YES YES
Constant 0.066*** 0.065*** 0.066*** 0.065*** 0.066*** 0.064*** 0.066***
(3.658) (3.637) (3.660) (3.603) (3.660) (3.552) (3.656)
Observations 1,389 1,389 1,389 1,389 1,389 1,389 1,389
R-squared 0.059 0.061 0.059 0.059 0.059 0.061 0.059
r2 0.0585 0.0606 0.0585 0.0592 0.0587 0.0606 0.0585
40
Table 6 - Regression results: Additional Analysis with and without M&As in Time Window (Low strategy – switch 30%)
1 2 3
CAR3 CAR3 CAR3
Switch with M&As in time window -0.005
(-0.906)
Switch without M&As in time window 0.017*
(1.790) Prior Performance 0.000** -0.000 0.001*** (2.300) (-0.255) (2.756) Geographic Distance -0.000 0.000 -0.000 (-0.416) (0.143) (-0.249) Relatedness 0.002 0.005** -0.001 (1.163) (2.513) (-0.283) R&D Intensity -0.000** -0.000 -0.000 (-2.185) (-1.533) (-0.762) International Deal 0.007 -0.008 0.016* (1.076) (-0.995) (1.750) Motive Count 0.002 0.003 0.000 (0.545) (0.655) (0.048)
(Log) Acquirer size -0.002 -0.002 -0.003
(-1.091) (-0.603) (-1.096)
(Log) Deal Value -0.002 -0.004 -0.001
(-1.113) (-1.641) (-0.375)
(Log) Potential Slack 0.006* 0.005 0.006*
(1.923) (1.175) (1.684)
Year Dummy YES YES YES
Industry Dummy YES YES YES
Constant 0.066*** 0.016 0.039
(3.658) (0.348) (1.467)
Observations 1,389 647 741
R-squared 0.059 0.090 0.073
