The Moderating Role of Technological Dynamism in
the Financial Performance of Exploratory and
Exploitative Acquisitions: An Empirical Analysis of
European Firms
Master Thesis
by D.N.C. Wefers Bettink
S1780441
d.wefersbettink@gmail.com
Business Administration – Strategic Innovation Management
Rijksuniversiteit Groningen
First supervisor: Dr. I. Estrada Vaquero,
i.estrada.vaquero@rug.nl
Second supervisor: Dr. P. de Faria,
p.m.m.de.faria@rug.nl
Table of contents
1. Introduction... 3
2. Literature review The importance of a balance between exploration and exploitation... 7
Acquisitions and financial performance... 8
Acquisitions as external source for knowledge... 9
Technological dynamism and the effects of acquisition performance... 10
Hypothesis development... 11
3. Methodology Data collection and sample... 14
Measures... 15
Model specification and methods of analysis... 21
4. Results and Discussion Correlations... 23
Regression Analyses... 24
Discussion... 27
5. Conclusion and Implications Implications for research... 30
Implications for practice... 31
Limitations...…... 31
Future research... 32
References... 33
1. Introduction
Firms have increasingly recognized that they may increase their knowledge base not only by developing knowledge internally, but also through acquisitions. Several studies have found a positive effect of knowledge-related acquisitions on the acquiring firm’s innovation performance (Ahuja and Katila, 2001; Schildt, Maula and Keil, 2005; Makri, Hitt and Lane, 2010). However, little research has been done into the effects of knowledge related acquisitions on the financial performance of an acquiring firm.
A firm’s knowledge base can generally be divided into two types of knowledge, exploratory and exploitative knowledge. March (1991) explains:
“Exploration includes things captured by terms such as search, variation, risk taking, experimentation, play, flexibility, discovery, innovation. Exploitation includes such things as refinement, choice, production, efficiency, selection, implementation, execution. Adaptive systems that engage in exploration to the exclusion of exploitation are likely to find that they suffer the costs of experimentation without gaining many of its benefits. They exhibit too many undeveloped new ideas and too little distinctive competence. Conversely, systems that engage in exploitation to the exclusion of exploration are likely to find themselves trapped in suboptimal stable equilibria. As a result, maintaining an appropriate balance between exploration and exploitation is a primary factor in system survival and prosperity.” (p.71)
Secondly, the pace at which technological trajectories become obsolete is particularly fast in high tech industries and that leaves little time for a firm to develop all of the necessary knowledge internally (Eisenhardt and Schoonhoven, 1996).
The positive effect on innovation performance is more pronounced when the target firm possesses exploitative knowledge rather than exploratory knowledge. The reasons are that such knowledge and therefore such a target firm can be valued more accurately, that it is easier to merge two exploitative knowledge bases of a target and acquiring firm and that it is more likely that synergies can be achieved between them (Ahuja and Katila, 2001). The cost of merging two businesses will be higher when their knowledge bases are more heterogeneous. Future returns for exploratory knowledge are systematically less certain, and more remote in time than for exploitative acquisitions (March, 1991).
But there is a catch. The choice of target firms with a similar knowledge base can result in the acquiring firm’s R & D being only focused on close by solutions, derived from exploitative knowledge, and failure to recognize emerging trends and develop radical solutions, derived from exploratory knowledge. This is called the familiarity trap, favoring the development of a familiar technology, rather than investing in the development of new technologies (Ahuja and Morris Lampert, 2001).
Despite the inherent difficulty of merging two exploratory knowledge bases, acquisitions are often used to obtain such knowledge (Schildt, Maula and Keil, 2005; Lavie and Rosenkopf, 2006). Eisenhardt and Martin (2000) stress that not using external sources for knowledge will lead to obsolescence of the firm.
have a decisive influence on whether or not a balance is achieved. However, the level of technological dynamism is a more generic factor.
Sorensen and Stuart (2000) argue that high technological dynamism, a feature of high-tech industries, requires a firm in those industries to invest in exploratory-based knowledge in order to maintain and improve its performance. Conversely, in these industries firms that are mainly investing in exploitative knowledge will see a decreased performance over time. From this point on, low and high tech, and low and high technological dynamism are used synonymously.
As set out above, the characteristics of low tech industries generally allow firms enough time to develop knowledge internally. On the contrary, in high tech industries the fast and diversified changes in technology make it almost a necessity to acquire a company to obtain the necessary knowledge.
A study by Capron, Dussauge and Mitchell (1998) focused on high tech industries and the effect of acquisitions. They studied how firms in high tech industries use acquisitions in order to reconfigure their existing businesses, thereby proving that acquisitions in high tech industries can facilitate exploration. The most important aspect of these exploratory acquisitions was that the target firm can bring new routines, people, and skills to the acquiring firm.
Therefore the following research gap was found:
There is a lack of scientific research regarding the effects of exploratory and exploitative acquisitions on financial performance of firms in high tech versus low tech industries.
The sample will be based on firms obtaining knowledge externally through acquisitions, consisting of European firms regardless of their size, and measuring their financial performance through Earnings Before Interest Taxes, Depreciation and Amortization.
This study aims to determine what the financial effects of either an exploratory or exploitative acquisition are, and what the moderating effects of operating in a high or low tech industry are. Therefore the follow research question was developed:
• To what extent do exploratory and exploitative acquisitions influence the acquiring firm’s financial performance in low tech versus high tech industries?
Data was gathered from three databases: the Thomson SDC database, Thomson Datastream, and Orbis. These databases helped to compile a sample of European firms and information regarding the target firm they have acquired, the size of both the target firm and the acquirer, the financial performance of the acquirer before and after the acquisition, and the acquisition experience of the acquirer. All of the data was compiled in Excel and the analyses were conducted in SPSS.
Two Independent samples t-tests were conducted in order to determine if there was any significant difference between the means of an acquiring firm’s financial performance operating in high and low tech industries and between the means of acquiring firm’s financial performance which acquired a firm in a similar and different industry. Next, regression analyses were conducted in order to determine the predictive power of the comprehensive model.
b) the level of technological dynamism has a significant effect on the financial performance of either of these types of acquisitions. The comprehensive model does not have a high explanatory power and when looking at the reduced model, one can see the small explanatory power of the independent variables alone. However this study contributes to the existing academic research by proposing alternative measures, warning for potential pitfalls and suggesting research areas where more academic evidence is necessary.
In the discussion section a number of reasons are suggested for the lack of significant results and in the conclusion-section several alternative options are presented for future research which could lead to significant results.
This paper is structured as follows: in the next section, the literature review is developed. The third section focuses on the data, sample and methodology used to provide a structure for the results, which are discussed in the fourth section. In the fifth section, the conclusion is given, along with the implications for managers, the limitations, and suggestions for future research.
2. Literature Review
In this section, the case for the hypotheses is built, derived from relevant literature. This section is divided into several important concepts, relevant to the the general area of study in order to provide a complete and clear overview.
The importance of a balance between exploration and exploitation
Volberda; 2006). This mutual interdependence is clearly portrayed in the model developed by Lavie, Stettner and Tushman (2010), shown in figure 1.
Figure 1: model by Lavie, Stettner, and Tushman, 2010
This figures shows that investments in exploitation will generate income which should be used to invest in exploration. These investments in exploration should in turn lead to new technology which should provide new opportunities for exploitation. This is relevant for this study, because it shows the need for any firm to decide at any given time whether to invest (by acquisitions or otherwise) its finite amount of resources in exploration or in exploitation. Short-term returns can be achieved by investing in exploitative knowledge. However, in the long-run the incremental innovations derived from this knowledge will not help in cooping with new technological trajectories. Only exploratory knowledge will provide radical innovations meeting these requirements (March and Levinthal, 1981).
Acquisitions and financial performance
Whether acquisitions improve the performance of firms has been researched extensively and a broad distinction can be made between two streams of literature: Studies that focus on innovation performance of the acquiring firm (Ahuja and Katila, 2001; Makri, Hitt and Lane, 2010; Wijk, Jansen, Bosch and Volberda, 2012), and studies that focus on financial performance (Deusen and Mueller, 1999; Karim, Ameen and Ayaz, 2011).
2000-2008 is valued at 1.774 billion), and the majority of them fail to meet their intended objectives (Hitt et al. 2001).
However, Sundaramurthy (2000) points out that the general discussion should not necessarily focus on whether acquisitions increase a firm’s financial performance, but rather under which conditions acquisitions facilitate an increase in financial performance. King, Dalton and Daily (2004) performed a meta-analysis covering 93 studies which measured the relationship between acquisitions and financial performance. After reducing their sample size to only those focusing on the acquiring firm and their post-acquisition financial performance, seven studies remained. The results of these studies showed that the acquisitions either had none or even a slightly negative impact on a firm’s financial performance. The authors keep in mind that a cancelling effect of both positive and negative results from the studies they researched, could be the reason for their neutral findings.
Acquisitions as external source for knowledge
As set out above, an alternative way for assessing the results from an acquisition is by measuring the change in innovative performance (Ahuja and Katila, 2001; Schildt, Maula and Keil, 2005; Makri, Hitt and Lane, 2010). These studies divide the acquisition sample based upon the similarity of knowledge between the target and acquiring firm and do find significant effects. Therefore this study will use a similar independent variable (exploratory vs exploitative) to test the financial effects of acquisitions.
Some of the research shows that because of the relatedness of the knowledge of the acquired and the acquiring firm, it is easier to choose for acquisitions which increase the acquiring firm’s exploitative knowledge (exploitative acquisitions; (Ahuja and Katila, 2001 ). This relatedness of knowledge makes it possible to value the target firm more accurately, and makes it easier to merge with the acquiring firm. However, the choice of target firms with similar knowledge as the acquirer can lead to R&D which is focused on close by solutions (derived from exploitative knowledge) and fails to recognize emerging trends and develop radical solutions (derived from exploratory knowledge).
However, exploratory knowledge is always used for developing technologies meeting future market trends and technological trajectories and is therefore different from the knowledge used in current technologies. This creates some important obstacles when attempting to acquire this knowledge. Besides the tendency of managers to limit their choice of target firm to those which have similar knowledge (thus mainly exploitative), the risk associated with choosing a target firm which owns highly unrelated (i.e. exploratory) knowledge also plays a part. The cost of merging two businesses will be higher when their knowledge bases are divergent, and the potential of future returns are systematically less certain, and more remote in time (March, 1991).
Phene, Tallman and Almeida (2010) focus on the question when acquisitions facilitate exploration or exploitation. Their article mentions three factors from a learning theory perspective that would foster exploratory or exploitative innovation: the opportunity available to the acquiring firm (determined by the uniqueness of the knowledge embedded in the target firm); the absorptive capacity of the acquiring firm; and lastly, the level of control the acquiring firm has over the target firm, as demonstrated by the mode of acquisition.
Investing in absorptive capacity is regarded by Rothaermel and Alexandre (2009) as a way to balance the pressures for exploratory and exploitative knowledge. By developing their absorptive capacity firms can improve their ability to recognize, access and assimilate knowledge from external sources, (Cohen and Levinthal 1990). While absorptive capacity therefore allows a firm to improve its choice of target firms, it has a more direct positive influence on exploitative acquisitions than on exploratory acquisitions (Song & Shin, 2008).
Technological dynamism and the effects of acquisition performance
factors had no significant influence in explaining the variance in post-acquisition performance.
There is also a limited number of studies which consider environmental factors. Jansen et al. (2006) aim to provide empirical evidence for the moderating effect of environmental factors on the relationship of exploration/exploitation and financial performance. They found that exploration has a positive effect in the presence of dynamic environments and exploitation has a negative effect. The study by Uotila et al. (2009) proposes technological dynamism as most important moderator for the financial effects of exploration and exploitation. However, these studies look at exploration and exploitation as a given for a firm, not considering the way in which these types of knowledge are obtained.
Capron, Dussauge and Mitchell (1998) show how firms in high-technology industries use acquisitions in order to reconfigure their existing businesses, thereby proving that acquisition can also facilitate exploration. The most important aspect of these exploratory acquisitions is that the target firm can bring new routines and people with skills and expertise to the acquiring firm. However, these results only account for firms operating under high technological dynamism (high tech industry). Consequently, Uotila et al. (2009) found evidence that the need to balance exploration and exploitation might not be necessary for successful performance when a firm’s industry has low technological dynamism (low tech industry). Indicating that the need to obtain target firms with exploratory knowledge in order to be successful is not paramount in such industries.
Hypothesis development
March (1991) argues that a firm’s financial performance will be positively influenced when it pursues both exploratory and exploitative knowledge and achieves a balance between them. Such knowledge can be developed internally or externally, e.g. through acquisitions. Since an exploitative acquisition is easier to achieve than an exploratory acquisition, the nature of the acquisition plays an important role in determining the effects of the acquisition on the financial performance of the acquiring firm.
Exploratory acquisitions are characterized by being long-term oriented and the returns from investing in such knowledge is structurally less certain and more remote in time (March, 1991). Therefore, it is difficult to ascribe positive financial effects (especially on short-term) to such investments. On the other hand, exploitative acquisitions have a short-term focus aimed to improve any feature of existing products which will directly increase returns. Investments in firms with this type of knowledge will overall result in clear positive effects due to the relative ease of assimilating such knowledge and their focus on improving existing technologies.
The effects on financial performance are more clearly visible in firms operating in high tech industries than in low tech industries (high versus low technological dynamism).
long, investing in exploitative knowledge is a good strategy because it increases a firm’s income, while the threat of failing to recognize ermerging trends and technological trajectories is low. Therefore the following hypotheses were constructed:
Hypothesis 1a:
When technological dynamism is low, exploratory acquisitions have a negative effect on a firm’s financial performance.
Hypothesis 1b:
When technological dynamism is low, exploitative acquisitions have a positive effect on a firm’s financial performance.
Uotila et al. (2009) also found that exploration has a positive effect on a firm’s financial performance when the industry in which it operates is subject to a high technological dynamism. An exploitative acquisition in an industry with high technological dynamism, would be very risky due to the risk of the familiarity trap, explained earlier in the introduction section, and the failure to recognize emerging market trends and technological trajectories (Ahuja and Morris Lampert, 2001). Additionally, Zahra (2008) found that primarily large companies aspire to perform exploratory acquisitions, especially in industries with high technological dynamism. This is due to the fact that they are able to exploit exploratory knowledge better compared to small firms, and that large firms recognize the threat of emerging trends and the finiteness of the current technological trajectory better than small firms. Therefore the following hypotheses are developed:
Hypothesis 2a:
When technological dynamism is high, exploratory acquisitions have a positive effect on a firm’s financial performance.
Hypothesis 2b:
3. Methodology
This section starts with a brief description of how the data was gathered, followed by an explanation and justification for the reductions used to obtain the final sample. Secondly, the independent, dependent, and control variable are discussed, and finally the model used for this study is provided with the methods of analysis that were used.
Data collection and Sample
The data used for this study has been obtained from two databases, Thomson’s Financial Securities Data Corporation (SDC) database, and Thomson’s Datastream Financial database. The SDC database is used for identifying acquiring firms and providing detailed information regarding the target firm, transaction value, and the Standard Industrial Classification (SIC) codes of both acquiring and target firms. The SDC database is also used to determine the acquisition experience of each acquirer, which is used as a control variable.
The Datastream database is on firm-specific information of the acquiring firms over time, which is used for determining the effects of the two types (exploratory and exploitative) of acquisitions on a firm’s financial performance, by providing the Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA).
The base sample was constructed by first looking at acquisitions within the SDC database and compiling all of the European firms with acquisitions in 2010. An acquisition is characterised by one firm having less than 50% ownership of another firm prior to the transaction, and owning more than 50% of the target firm after the transaction (Desyllas and Hughes, 2008). Since this research is on knowledge adoption by acquirers from target firms, the sample is only constructed of acquisitions in which firms were acquired entirely, otherwise the percentage of ownership could influence the level of knowledge adoption by the acquirer (Moeller, Schlingeman, and Stulz, 2005).
relevant for future research, while still allowing for the measurement of financial effects after the acquisitions (by looking at the EBITDA of acquirers for 2011 and 2012).
From the initial sample of 1050 acquisitions, all of the acquirers which conducted multiple acquisitions in 2010 were excluded because for these cases the dependent variable, financial performance, cannot be assigned to one acquisition in particular. Furthermore, all of the firms who since their acquisition were either acquired themselves, have merged or went bankrupt were also excluded, leaving a sample of 349 acquirers. Next, acquirers were removed from the sample when the effective date was later than 2010, due to the inability to measure accurately the financial consequences for the two following years. This left a sample of 289 acquisitions. Finally, the sample was adjusted for the relative size of the target firm compared to acquirer. This last threshold is necessary because in order for an acquisition to influence the financial performance of a firm, it should be of a certain minimum size. A common threshold for the size of a target firm, is a minimum amount of monetary value for the transaction (Amihud, Lev and Travlos, 1990). The problem with this threshold is that it skews the outcomes by removing small firms which acquire other small firms, while including acquisitions by large multinationals in the sample which are relatively insignificant compared to their market value. Moeller, Schlingemann and Stulz, (2004) use the threshold of market value, arguing that a target firm should be at least 1% of market value compared to the acquirer. This study takes a similar measure, comparing the EBITDA of acquirers and the market value of target firms in the year of the acquisition (2010). The sample includes only those acquisitions for which an acquirer had to pay at least 10% of it’s EBITDA in 2010. Due to missing value paid for target firms, the sample is reduced from 289 to 133 acquisitions.
Measures
Industry Similarity
In order to determine whether an acquisition is exploratory or exploitative, a similar variable as that of Jiang, Tao and Santoro (2007) was used. Their study focuses on alliances rather than acquisitions; but their independent variable ‘industry diversity’ determines whether the alliance partners are from the similar or different industries. This same measure will be used to determine whether an acquisition is exploratory (target and acquiring firm are from a different industry) or exploitative (the target and acquiring firm are from the same industry). They have developed five categories based on SIC codes, in order to determine how similar the industries of both alliance partners are: a ‘4’ is given to alliance partners of which all four digits of each other’s SIC codes were the same; a ‘3’ was given to a three-digit similarity; a ‘2’ to a two digit-similarity; a ‘1’ to a one-digit similarity; and a ‘0’ was given when the SIC codes were completely different. The descriptive statistics of this measurement are shown in table 1.
Table 1: Division of sample based on Industry Similarity (categorical variable)
Industry Similarity (categorical) EBITDA 2012
Mean -91.77 N 47 0 Standard Deviation 303.98 Mean 6.38 N 15 1 Standard Deviation 122.06 Mean 125.16 N 13 2 Standard Deviation 157.62 Mean -646.35 N 17 3 Standard Deviation 2392.20 Mean 34.60 N 41 4 Standard Deviation 421.64 Mean -91.43 N 133 total Standard Deviation 913.16
as 0>1. This leads to the division of 71 exploitative acquisitions and 62 exploratory acquisitions.
Table 2: Division of sample based on Industry Similarity (dummy variable)
Industry Similarity (dummy) !"#$%&'()*('
Mean -111.86 N 71 0 Standard deviation 1227.292 Mean -68.02 N 62 1 Standard deviation 273,68 Mean -91.43 N 133 Total Standard deviation 913.16 Technological dynamism
The term ‘technological dynamism’ does not have one clear and broadly accepted definition. Albu and Bell (1999) have organized the results of several studies which (partly) include this phenomenon, shown in appendix A. They conclude from these studies that technological dynamism encompasses:
- product specifications and designs; materials and component specifications and properties; machinery and its range of operating characteristics; together with the various kinds of know-how, operating procedure and organizational arrangement needed to integrate these elements in an enormously variable range of different production systems (rather than just machinery-related innovations as was the general perception in the 1960s).
- Both an innovation by itself and the diffusion of an innovation, as any innovation requires adaption, the diffusion also contributes to technological change.
- External sources of innovations, such as suppliers and customers, are also part of technological dynamism. Feedback from customers can be an instigator of technological change.
types of acquisitions (exploratory and exploitative) on financial performance. In order to determine whether or not an acquirer operates in a high tech industry, Standard Industry Classification (SIC) codes are used again. This classification system uses a four-digit code to distinguish different industries (an overview of the industries and their codes can be found in appendix X). In the past two decades, more modern industry classification coding systems (like NAICS and NACE) have been developed. These systems have adopted far more sub-industries, especially in digital-related sub-industries, and could therefore be regarded as being more useful in distinguishing a firm’s industry. However, the SIC codes system has been widely used in previous studies to determine high tech industries, and Kile and Phillips (2009) have provided a clear overview of all of these studies (appendix B). Their main results show that industries starting with the following first three digits have been widely accepted as being a high tech industry: 357 for computer equipment industries; 737 for software industries; 283, 382, 873 for medical technology industries; 366, and 481 for communication industries; 361-365, and 367 for electrical industries; and 371-379, and 282-289 for other industries. The sample of 133 acquirers can now be divided into those with a corresponding SIC code with their target firm, and those which do not have corresponding codes. This leads to 56 firms operating within high tech industries, and 77 firms which are operating in low tech industries, as can be seen in table 3.
Table 3: Division of sample based on whether the acquiring firm operates in a high or low tech industry (dummy variable)
High Tech Industry (dummy variable) EBITDA 2012
Mean 12.94 N 77 0 Standard deviation 375.74 Mean -234.94 N 56 1 Standard deviation 1330.40 Mean -91.43 N 133 Total Standard deviation 913.16 Financial Performance
Most studies which focus on the effects of new knowledge obtained by firms, measure innovation rather than financial performance (Ahuja, and Katila, 2001; Schildt, Maula, Keil, and 2005; Desyllas, and Hughes, 2008; Makri, Hitt, and Lane, 2010; Yang, and Li, 2011). In particular, the number of patent citations in the years after the new knowledge is obtained are chosen as measurement, which is a direct and significant way to measure the effect of new knowledge being obtained by a firm (Sorensen, and Stuart, 2000). However, patent citations are more important in high tech industries than in low tech industries, making this measure not useful for this study.
Studies focused on the effects of acquisitions in general and on financial effects, prefer to focus on abnormal stock returns of an acquiring firm in the days after an acquisition (Singh and Montgomery, 1987; Bruner, 2002). The problem with this measure is that it does not look at the financial effects of new knowledge obtained, it only measures the expectations the market has for the acquiring firm after acquisition.
Finnerty and Emery (2004) describe several financial measurements in their study but argue that EBITDA is the best measure for comparing firms on their financial performance. This is due to the fact that firms adopt heterogeneous interest, depreciation, and amortization policies, while national governments set different tax rates. When looking at annual income based on EBITDA, none of these reductions have taken place and these differences are therefore eliminated.
The EBITDA of 2011, a year after the acquisition, is taken to see whether the newly obtained knowledge has a significant impact on the financial performance of the acquiring firm.
In order to avoid multicollinearity issues between the two alternative independent measures for Financial Performance (EBITDA 2011 and 2012) and the control measure EBITDA 2009, the two dependent measures were transformed from absolute figures to percentages, taking EBITDA 2010 as the base year (100%).
Control variables
performance is another control variable, measured by looking at the EBITDA of the acquirer
prior to the year of acquisition (2010). By including this variable, biased results coming from already highly (un)successful firms are mitigated. Lastly, acquisition experience is used, due to its influence on a firm to become better at obtaining knowledge (either exploratory or exploitative) from a target firm (Phene et al. 2010). A time-span of five years prior to 2010 is taken into account for this control variable.
In table 4 below, a short overview of the descriptive statistics of all of the variables used in this study is given. EBITDA 2010 is included here to provide a complete picture; however it is not included in the regression analysis, because of the relative short time-span between the acquisition (in some cases only days apart) and this measurement of financial performance.
Table 4: overview of Control Variables
Past Performance Acquisition Experience
Acquisition Size
Mean 155261.35 2.20 96.27
N 133 133 133
Standard Deviation 843219.93 2.85 282.36
Finally an overview is given of all of the variables used for this study and the measures adopted for these variables, displayed in table 5 below.
Table 5: overview of variables and the measures
Variable Measure Description
Industry Similarity
Industry Similarity (categorical)
Measure divides sample in 5 categories based upon a SIC code division developed by Jiang, Tao, and Santoro (2007). Industry Similarity
(Dummy)
Measure divides the categorical measure above giving a value of 0 to categories ‘0’and ‘1’, and a value of 1 to categories ‘3’, ‘4’, and ‘5’.
Technological Dynamism
High Tech Industry (Dummy)
Measure divides industries of acquiring firms into high and low tech, based upon a study by Kile and Phillips (2009). Financial
Performance
EBITDA 2011 (%) Earnings Before Interest, Taxes Depreciation, and Amortization of the acquiring firm for 2011.
EBITDA 2012 (%) Earnings Before Interest, Taxes Depreciation, and Amortization of the acquiring firm for 2012.
Past
Performance
EBITDA 2009 Earnings Before Interest, Taxes Depreciation, and Amortization of the acquiring firm for 2009.
Acquisition Experience
Number of
Acquisitions
Measure looks at the acquisitions executed by the acquiring firm, from 2006 until 2010.
Acquisition Size Monetary value of Acquisition
Model specification and methods of analysis
First the model will be discussed and all of its components explained, than the methods used to analyse are shown. Two alternative methods of analyses were conducted in order to increase the robustness of the findings. First the full sample will be used, and secondly the sample is divided based upon the independent dummy variables Industry Similarity, and Technological Dynamism.
Compounding all of the information previously mentioned, this leads to two alternative models:
1. Financial Performance= !0 + !1*[Industry Similarity Categorical] + !2*[Technological Dynamism Dummy] + !3*[Industry Similarity*Technological Dynamism Dummy] + !4*[Past performance] + !5*[Acquisition Experience] + !6*[Acquisition Size] +
!
And
2. Financial Performance= !0 + !1*[Industry Similarity Dummy] + !2*[Technological Dynamism Dummy] + !3*[Industry Similarity*Technological Dynamism Dummy] + !4*[Past performance] + !5*[Acquisition Experience] + !6*[Acquisition Size] +
!
The variable Past Performance is the first control variable and looks at the EBITDA of 2009. Acquisition Experience is the second control variable, taking into account the previous acquisitions of the last five years executed by the acquiring firm. Lastly, Acquisition Size focuses on the monetary value of the acquired firm in millions of Euros.
In general, this study adopts two alternative methods of analysis, based on how the sample is used. Firstly, the full sample is used to determine both the significance of the dependent variable Financial Performance by looking at both EBITDA 2011 and EBITDA 2012 as measure for this variable. For each of these measures, again two different analyses were conducted, one including the categorical measure for Industry Similarity, and one with the dummy measure for this variable.
For the second method, two different analyses are conducted based upon division of the independent variables. First, the sample is divided based on the similarity between the industries of the target and the acquiring firm (exploratory vs. exploitative). Secondly, the sample is divided based on the nature of the industry of the acquiring firm (high tech vs. low tech). The sample is relatively even distributed among the two subgroups (for Industry Similarity 71 vs. 62; and for Technological Dynamism 77 vs. 56).
These divisions of the sample are used for the hypotheses in the following manner:
• For hypotheses 1A and 1B, the sample was divided to those acquiring firms which operate in low tech industries (low technological dynamism). For Hypothesis 1A within this reduced sample, the focus is on those firms which acquired a target firm from a different industry (exploratory acquisition). For hypothesis 2B, within the reduced sample, the focus is on those firms which acquired a target firm of a similar industry (exploitative acquisition).
4. Results and discussion
Correlations
In table 4 the correlations of all of the variables involved in this study are shown.
Table 6: Correlations table Industry Similarity Industry Similarity Dummy High Tech Industry Dummy Indsim * Hightech Dummy EBITDA 2009 EBITDA 2011 EBITDA 2012 Acquisition Experience Acquisition Size Industry Similarity 1 Industry Similarity Dummy .000** 1 High Tech Industry Dummy .821 .700 1 Indsim * Hightech Dummy .000** .000** .000** 1 EBITDA 2009 .771 .506 .641 .693 1 EBITDA 2011 .276 .185 .967 .263 .710 1 EBITDA 2012 .976 .784 .123 .659 .001** .000** 1 Acquisition Experience .914 .734 .554 .966 .891 .545 .877 1 Acquisition Size .283 .789 .609 .989 .621 .577 .562 .001** 1 N = 133
**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
Regression analysis
As set out in table 8, eight models were constructed with models 7 and 8 as comprehensive models. Model 1 shows the significance of the control variables, the next three models are simple models, testing the independent variables individually. The fifth and sixth model are reduced models, looking at the significance of all of the independent variables combined. Model 5 is developed by using the categorical variable for industry similarity, while model 6 uses the alternative dummy measure for this variable. Finally, model 7 is the comprehensive model, again using the former measure for industry similarity, and model 8 uses the latter measure for this variable. Below, a short explanation is given how the models were developed:
• Model 1: Financial performance= !0 + !1*[EBITDA 2009] + !2*[Acquisition Experience] + !3*[Acquisition Size] + !
EBITDA 2009 is used for measuring the past performance, while Acquisitions experience was compiled from an accumulation of all of the acquisitions executed by the acquirer in a 5-year period prior to the acquisition in 2010. Acquisition Size moderates the effect of the relative size of the focal acquisition.
• Model 2: Financial performance = !0 + !1*[Industry Similarity Categorical] + !2*[EBITDA 2009] + !3*[Acquisition Experience] + !4*[Acquisition Size] + !
Industry Similarity is developed by dividing acquiring firms into five categories based upon the number of SIC code digits in common with the target firm (Jiang, Tao and Santoro 2007). With category 0 describing firms with no digit in common with their target firm, and category four including firms that have the same four digit SIC code as their target firm.
• Model 3: Financial performance = !0 + !1*[Industry Similarity Dummy] + !2*[EBITDA 2009] + !3*[Acquisition Experience] + !4*[Acquisition Size] + !
• Model 4: Financial performance = !0 + !1*[High Tech Industry Dummy] + !2*[EBITDA 2009] + !3*[Acquisition Experience] + !4*[Acquisition Size] + !
The variable High Tech Industry Dummy is compiled based on whether or not an acquiring firm had a SIC code which was classified as high tech industry according to the study of Kile and Phillips (2009).
• Model 5: Financial performance = !0 + !1*[Industry Similarity Categorical] + !2*[High Tech Industry Dummy] + !
This is a reduced model of the regression analysis, in which the measures Industry similarity and High Tech Industry Dummy are tested without any of the control variables. This is to test is useful in determining the explanatory power of these two independent variables combined.
• Model 6: Financial performance = !0 + !1*[Industry Similarity Dummy] + !2*[ High Tech Industry Dummy] + !
Similar to model five, this model shows the explanatory power of the alternative measure for industry similarity (the dummy variable) and High Tech Industry Dummy.
• Model 7: Financial performance = !0 + !1*[Industry Similarity Categorical] + !2*[High Tech Industry Dummy] + !3*[EBITDA 2009] + !4*[Acquisition Experience] + !5*[Acquisition Size] + !
This is the first of two comprehensive models, which includes the categorical measurement for Industry Similarity, the High Tech Industry Dummy, and the control variables. This model determines the predictive power of this research and is constructed with the purpose of testing all of the hypotheses.
• Model 8: Financial performance = !0 + !1*[Industry Similarity Dummy] + !2*[High Tech Industry Dummy] + !3*[EBITDA 2009] + !4*[Acquisition Experience] + !5*[Acquisition Size] + !
Table 6: Regression analysis models with EBITDA 2012 as dependent variable
Model 1 Model 2 Model3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9
Industry Similarity .971 ..581 .479 Industry Similarity Dummy .798 .823 .827 High Tech Industry Dummy .135 .157 .126 .137 .138 Industry Similarity*High Tech Dummy .664 .552 461 x* EBITDA 2009 .893 .893 .879 .818 .906 .723 .808 Acquisition Experience .744 .744 .754 .828 .742 .838 .836 Acquisition Size .564 .565 .576 .646 .561 .594 .656 Rsquared .060 .060 .064 .145 .071 .144 .136 .159 .146 Note: N = 133, *p < .05, **P < .01.
*Due to multicollinearity issues with the other two other independent variables, Industry Similarity*High Tech Inustry was removed from model 9.
As can be seen in the regression analysis performed in table 6, no significance was found for the independent variables, despite several alternative analyses performed and transformation of some variables. Another issue with this regression analysis is the very small Rsquared, indicating that the independent variables included have only limited explanatory power for the dependent variable. These results indicate that none of the hypotheses is supported, severely constraining the ability to draw conclusions from this research.
In order to verify the findings and determine their robustness, a number of alternative analyses were conducted.
industry. Unfortunately, these analyses did not provide any significance either. When using the full sample, taking EBITDA 2011 as depended variable gave even less significant outcomes than those for EBITDA 2012.
The two alternative measures for the independent variable Industry similarity were adopted to increase the robustness of this study. However, this caused the variable Industry Similarity*High Tech Industry Dummy to be removed. The reason for this removal is due to the possibility of transforming it only into a dummy or categorical measure which would cause multicollinearity with either one of the independent variables
Discussion
The goal for this study was to examine the effect of exploratory and exploitative acquisitions on a firm’s financial performance under low and high technological dynamism. The results of the study show no significant effect of either type of acquisition, neither in the low nor in the high tech industries. This indicates that there is no difference in financial performance of exploratory or exploitative acquisitions, regardless of whether they took place in a high tech or low tech industry. This is contrary to what previous studies suggested and therefore a few alternative explanations are provided below.
One explanation may be that the lack of significant results comes from the lack of variables which may help to distinguish more clearly between firms within each group. Cohen and Levinthal (1990) state that absorptive capacity is the ability of a firm to exploit external knowledge. They describe it as recognizing the value of external knowledge, the ability to assimilate such knowledge within the firm, and to derive financial gain from it. Firms with a relatively low absorptive capacity will have more difficulty in recognizing the appropriate knowledge, take more time to assimilate it, and will on average have less financial gain from it (Cohen and Levinthal, 1990).
negatively related. The more an acquiring firm exerts control over a target firm, the less the target firm will, after being acquired, have the ability to develop exploratory innovations. This last function could also play a large role in the findings for this study, given that a threshold for sample reduction included only fully acquired companies, instead of including acquisitions where a majority was taken in the firm. When an acquiring firm takes over 100% of a firm, the level of control over the target firm is naturally higher than if only a majority share was acquired. According to Phene et al. (2010) this would impede the ability to develop exploratory innovations derived from the acquired knowledge. This would mean that by reducing the sample to full acquisitions, the effects of exploratory and exploitative acquisitions on the financial performance may have been reduced.
performance measurements, looking both at a firm’s market value and its patent citations. They use Tobin’s q (the ratio between the market value and replacement value of the same physical asset) to determine the financial performance. If this value is greater than one, it means the market values the firm more than the aggregate value of its recorded assets, which might be caused by the newly obtained knowledge. Combining the two types of measurement allows for a more complete overview of the effects of knowledge (obtained through acquisitions) on a firm’s performance than if only the effect on financial performance is measured.
A third cause for the insignificant findings from this research might be the choice of technological dynamism as environmental moderator, and how this variable was constructed for this research. Jansen et al. (2006) used environmental dynamism rather than technical dynamism. They did not only include the pace of technological change in the relevant industry sector, but also incorporated the variations in customer preferences, and the fluctuations in product demand and supply of materials. The study used a five point scale to categorize the firms based on the level of environmental dynamism instead of a dichotomous variable (high or low tech firm) used in this research. The results of this study show evidence for exploration to be more effective in dynamic environments.
When comparing the choice of moderators with the study by King et al. (2004), it is not the choice but rather the number of moderators. They argue that four rather than two moderators should be included when measuring financial performance. This study used the relatedness of the target and acquiring firm as independent variable (industry similarity) and acquisitions experience as a control variable, but did not include whether the acquiring firm is part of a conglomerate, nor the method of payment (cash or equity).
one industry in one country (Schildt, Maula and Keil, 2005). The study by Desyllas and Hughes (2008) did not limit their sample to one country or one industry or even one year (as is the case for this study), but their sample consisted of 6106 acquiring firms, which reduces the threat of too much diversity and too small sub samples.
When comparing industries with low and high technological dynamism, it is not possible to focus on one industry. Limiting the number of industries, in each group (low or high tech) might also reduce the diversity issue.
Therefore, alternative selection criteria for the sample could have improved the results, either taking a predetermined selection of industries or one country, which would allow for more pronounced results. A second option in this regard, would be to increase the focal years, which would lead to a larger sample.
6.Conclusion
This study has aimed to clarify the effects of exploratory and exploitative acquisitions on the financial performance of firms in low and high-tech industries. However, the research has not resulted in sufficient evidence to reject the null-hypotheses. Based upon the findings, there seems to be no significant difference in the financial effects of exploratory and exploitative acquisitions, or whether the acquiring firm operates in a high or low tech industry.
Implications for research
The implications for research are limited as none of the hypotheses were confirmed. The findings indicate that studies on heterogenous financial effects between exploratory and exploitative acquisitions, performed in high or low tech industries would not yield significant differences. This is contrary to what previous studies have hinted towards and therefore some suggestions for increased awareness are provided in the future research section.
Implications for practice
taken into account when making the decision to acquire. However, the findings indicate that firms are relatively insensitive financially when it comes to either performing exploratory or exploitative acquisitions, or when operating in high or low tech industries. This suggests that other variables, not addressed in this study, play a much larger role when acquiring knowledge.
Limitations
This section describes in short, the main issues which have led to the limited generalizability of the results.
This study has several limitations.
Firstly, given the fact that the sample consists of different firms from numerous industries and (European) nationalities, the amount of firms (133) might be too small too make any generalizable statements.
Secondly, measuring the effects of knowledge adoption on financial performance has the inherent threat of being skewed by many different unidentified variables, such as the level of embeddedness in a network (van der Ven 1986). In order to tackle this problem, including these variables as control variables might have helped in reducing the background noise of these variables.
Thirdly, developing two independent dichotomous variables, rather than applying a more nuanced distribution for these variables, might have limited the generalizability of the results. The distribution of high tech and low tech firms is based on the SIC system, which has not been adjusted for the boom in the digital industry of the last 20 years. As its last update was in 1987, it does not distinguish between the many subindustries that sprung up as a result of technological advances like the internet and mobile phones.
Fourthly, the fact that no variable represented the absorptive capacity, the ability to exploit external knowledge, can also be considered a limitation of this study. Accurately recognizing the value of a target firm, the speed at which the knowledge from an acquisitions is assimilated, and the ability to financially gain from it can all play a part in explaining the effects of an acquisition on a firm’s financial performance.
risky step of acquiring another firm. Furthermore, this study only focuses on target firms which were acquired entirely, while Phene et al. (2010) argues that the larger share an acquiring firm obtains from a target firm, the more control it exerts over the target firm and the less likely it is that the target firm is able to generate exploratory innovations.
Future research
Due to the insignificant results, many suggestions for future research could be given. However, two general streams can be distinguished: Altering the foundations (like the variables, or the sample) of the research in order to achieve significant results, or building further upon the findings of this research, assuming these results are representative, despite previous studies pointing to the contrary). These are elaborated below.
First, one remarkable finding that also might inspire future research is discussed.
From the data it appears that large firms in industries with high technological dynamism, prefer performing exploitative rather than exploratory acquisitions. Below, this result is further discussed and some possible explanations are given for the other results.
Zahra (2008) argues that primarily large companies aspire to perform exploratory acquisitions, especially in industries with high technological dynamism. However graphs 1 and 2, derived from data from this study, show that the firms engaging in such acquisitions have a smaller-than-average EBITDA, suggesting that these are only small and medium-sized firm
The sample was divided into four groups based on the two dichotomous independent variables, Industry Similarity and Technological Dynamism. The sample is rather equally divided: 40 firms performed a exploitative acquisitions in a low tech industry (value 0), 37 firms performed an exploratory acquisition in a low tech industry (value 1), 31 firms performed an exploitative acquisition in a high tech industry (value 2), and 25 firms performed an exploratory acquisition in a high tech industry (value 3). The first graph, showing the means of the EBITDA of firms for 2009, indicates a contradicting finding to the study by Zahra (2008). It shows that large firms in high tech industries on average choose for exploitative rather than exploratory acquisitions, while exploratory acquisitions are mostly executed by smaller firms. Kelly and Amburgey (1991) provide an explanation for the high mean of firms in high tech industries which perform an exploitative acquisition: When the size of a firm increases (assuming that a firm with a large EBITDA will also be of relatively large size), the firm is more likely to focus on exploitation rather than exploration. Large firms in high-tech industries apparently seem to be good at performing exploitative acquisitions, and this could be explained by their absorptive capacity (Cohen and Levinthal, 1990). As set out in the literature review, absorptive capacity is concerned with the ability of a firm to recognize, assimilate and exploit external knowledge. This ability seems to be greatest for firms of group two, contradicting the findings of Uotila et al. (2009), who found that exploration, as opposed to exploitation, has a more positive effect on a firm’s financial performance when a firms operates in a high tech industry.
The two general streams for future research, identified above, can be characterized as follows.
The first general area for future research is to alter something in the structure of the research, like its variables or the sample, in order to get significant results. For instance, replacing technological dynamism by other environmental variables to measure their influence on the relationship between exploratory and exploitative acquisitions and financial performance. Phene et al. (2010) have identified several of these variables such as the level of control over an acquired firm, and the absorptive capacity of the acquiring firm.
add a measure for the dependent variable innovation performance which would make it hybrid, like Wang and Li (2008) have done.
Lastly the nature and scope of the sample could be changed by focusing on more than one year which reduces the threat of biased results derived from one particular year, and limiting the sample to one country or selection of industries might provide valuable insights regarding a certain group of firms.
The second general stream for future research would be to build upon the fact that no significance was found, ignoring previous studies which suggest the opposite. When taking this approach, future research should take into account that it was only under the specific conditions of this research that insignificance was found. Any of the alterations suggested above could change the results towards significance.
Acknowledgements
Firstly, I would like to thank my first supervisor Isabel Estrada Vaquero for guiding me through the whole process from beginning to end, without her help I would not have come this far. Secondly, I would like to thank dr. Killian McCarthy for providing me access to the SDC database. Thirdly, I would thank dr. Pedro Faria, my second supervisor, for taking the time to give is opinion on this study. Finally, my gratitude goes out to dr. Thijs Broekhuizen, coordinator of my study, along with all of my professors of the Strategic Innovation Management MBA for preparing me through their courses for this study.
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