1. Erasmus University Rotterdam
M&A relatedness effects on economic performance
Research Training & Bachelor Thesis
TEAM 9 - 03/06/2015
Niceasia Mc Perry 420120
Heleen Tsang 417451
Ennis Rastoder 420624
Instructor: Riccardo Valboni
“This document is written by Niceasia Mc Perry, Ennis Rastoder and Heleen Tsang, who
declare that each individual takes responsibility for the full contents of the whole document.
We declare that the text and the work presented in this document is original and that no
sources other than mentioned in the text and its references have been used in creating it. RSM
is only responsible for supervision of completion of the work but not for the contents.”

2. 1
Table of contents
Abstract..................................................................................................................................... 2
Introduction.............................................................................................................................. 2
Literature Study & Critical Review ...................................................................................... 5
Hypothesis & Research Question........................................................................................ 11
Methods .................................................................................................................................. 11
Research strategy.................................................................................................................. 11
Sample .................................................................................................................................. 12
Variables............................................................................................................................... 13
Results..................................................................................................................................... 16
Supplementary results analysis............................................................................................ 18
Discussion............................................................................................................................... 25
Lessons learned...................................................................................................................... 28
References............................................................................................................................... 30
Appendix I: SIC-code list ..................................................................................................... 34

3. 2
M&A Relatedness Effects on Economic Performance
NICEASIA MC PERRY, HELEEN TSANG & ENNIS RASTODER
ABSTRACT Mergers and Acquisitions (M&A) in the high-tech industry have
experienced a tremendous growth in the past two decades, namely due to the exponential
growth of technology capabilities, and new business models that have introduced many
new products and services. These have made the high-tech industry one of the most
advanced in a globalized world. However, there is little empirical study on the effects
industry relatedness has on M&A’s in the high-tech industry. Drawing from data of
132 M&As for North-America and Western Europe, this study analyses the relationship
between industry relatedness and acquisition performance (ROA) between acquirer and
target in the high-tech industry. The results find no evidence to support the hypothesis
that related M&A’s outperform unrelated M&A’s. The results indicate that a
convergence is taking place across multiple sectors in the high-tech industry where
companies diversify in order to increase the ROA. Managers responsible for M&As need
to therefore assess the implications of industry relatedness carefully, and diversify
through acquisitions their resources, knowledge and patents across multiple sectors in
the high-tech industry in order to improve ROA and to gain a strong market position
in promising high-tech markets.
Introduction
In recent years, companies in the high-tech industry have emerged in prominence and
offer high growth potential. Due to the convergence of high-tech products and
services, where everything is becoming ever more connected, M&As in the high-tech
industry have become crucial to understanding the exponential growth the industry
has experienced in especially the past two decades. In the last 15 years, the high-tech
industry has had a significant impact on acquisitions and experienced high volume of
M&A activity. It has been reported that mergers and acquisitions in the technology
industry exceeded mergers and acquisitions in any other industry (PwC’s Technology
Institute, 2013). Amid the popularity of mergers and acquisitions in the high-tech
industry, value creation and performance outcomes remain of great importance. One
way to examine the performance outcomes of M&As in the high-tech industry is to
examine the relationship between industry relatedness and economic returns.

4. 3
The study of industry relatedness is of great importance to the high-tech industry in
order to examine the potential growth it can provide to strategy and innovation for
companies. For instance, industry relatedness realizes synergy effects, which arise
from related sources in order to benefit from economies of scope and scale.
Moreover, industry relatedness is of importance to the high-tech industry as it allows
the expansion of market share or improvements of market positioning which can exist
if one benefits from the first-mover advantage. However, contemporary literature has
not yet enlightened the implications of relatedness on the economic performance in
high-tech industries.
Specifically, considering the fast development of the high-tech industry due to its
innovative and ever-changing environment, M&As are used to gain quick access to
assets, patents and technology; these are resources that deliver great advantage. Thus,
to understand how the M&A process can deliver opportunities, it is important to
determine if the acquirer desires to diversify into a related or unrelated industry.
Therefore, the relatedness of the target company becomes a crucial factor.
On the one hand, companies can benefit from related acquisitions by acquiring
additional assets that can broaden their economies of scale and increase their market
share and market power. But also to be ahead of competition in acquiring a target, and
by decreasing the overall competition in the market, the company can increase its
leverage over consumers, and thus, improve its ROA. On the other hand, companies
can benefit from unrelated acquisitions by acquiring assets in a new/dissimilar market
to their own. This gives them access to new markets, diversifies their portfolio and
retains new assets and patents that can improve ROA.
Previous research demonstrated that industry relatedness and performance outcomes
are positively related (Singh & Montgomery, 1987; Homberg et al., 2009). Though,
research literature from Singh & Montgomery (1987) and Homberg et al., (2009) on
industry relatedness and performance does not emphasise the high-tech industry
specifically, examples of industry relatedness and performance can be found in recent
real-life situations. To illustrate, HP, a hardware manufacturer who acquired the
British software company Autonomy experienced a sharp stock price decline

5. 4
(Economist, 2012). Another example demonstrating industry relatedness and
performance outcomes is the acquisition of Motorola Mobility, a telecom equipment
manufacturer, by the software giant Google. Google made a whopping $9.5 billion loss
when it sold Motorola Mobility to the Chinese hardware manufacturer Lenovo, for
$2.9 billion (Economist, 2014).
The benefits for Lenovo of this acquisition are not yet very clear, however, what
Lenovo did manage to make a success was its related acquisition of IBM’s PC business
in 2005. Lenovo managed to become the only PC vendor to increase sales in a declining
PC market (Economist, 2013). Both HP and Google were companies who acquired
target companies in an unrelated acquisition which both proved to be unsuccessful
and only strengthening proponents’ (Singh & Montgomery, 1987; Homberg et al.,
2009) arguments that related acquisitions outperform unrelated acquisitions.
However, other sources of literature have not been able to support the above-
mentioned statement and provide contradictory statements and believe that related
acquisitions do not necessarily outperform unrelated acquisitions. (Kennedy & Payne
2002). Case in point, Singh & Montgomery (1987) even argued that economic returns
from especially related acquisitions could be mitigated by the excessive valuations of
targets in the bidding process.
In order to examine the effects relatedness has on the performance (ROA) of
companies; we first present a critical review of empirical literature and our theory that
form the fundamentals of this study. Then we present our research question and
hypothesis, along with our own assumptions on the causal relation between
relatedness and economic performance. We outline the methodology used to
empirically test our hypothesis. We then provide empirical results drawn from a
sample of 132 M&As from North America and Western Europe with a deal value of
above $250 million. Lastly, in the discussion, we emphasize on our interpretation and
assumptions of the results.

6. 5
Literature Study & Critical Review
This literature review contains a discussion of eight empirical studies that provide a
framework for the hypothesis. The empirical literature is summarized in Table 1 and
Table 2.
Assessing the prior studies it comes to notice that there are 3 issues that have limited
the availability of the empirical literature for usefully testing the hypothesis; (1) the
high-tech industry is a fairly young industry with major developments in the industry
being established only just recently. (2) Moore’s law speeding up technological
development rapidly, quickly outdating the theories established in past empirical
literature, implying that M&A decisions now can have a totally unexpected outcome
in the near future due to rapid transformations in the high-tech industry. (3) No clear
and consistent definitions of what a related or unrelated acquisition is, specifically to
the high-tech industry.
Conventional wisdom and previous research have demonstrated that related
acquisitions are associated with greater M&A success and higher returns (e.g. Singh &
Montgomery, 1987; Homberg et al., 2009). However, the causal mechanism behind this
reasoning is that synergies exist in mergers and acquisitions and are the highest in
related acquisitions (Homberg et al. 2009). To illustrate, Hagendoorn and Duysters
(2002) mentions that related M&As are expected to generate higher returns from
economies of scale and scope that could produce more synergetic effects than in cases
of unrelated M&As. This view has also been supported by Homberg et al. (2009),
which will be discussed in a subsequent paragraph.
Interestingly, Hagendoorn and Duysters (2002) have attempted to debate on the
subject matter by studying the industry relatedness of acquirer and target companies
and the effect it has on the technological performance of the combined companies. The
study demonstrates that related acquisitions have a significant and positive
relationship with the technological performance of the companies. Higher
technological performance of the combined companies create better synergies,
technological activities and innovative potential of M&As, which is crucial to M&A
success. The findings of the study only add additional support to the argument that

7. 6
related acquisitions are associated with greater performance outcomes than unrelated
acquisitions and thus support our hypotheses.
Moreover, findings from Homberg et al. (2009) have also suggested that acquisitions
in the high-tech industry have a positive effect on the performance outcome of related
acquisitions as it enhances transfer and combination of skills and resources. Similar to
Hagendoorn and Duysters (2002) point of view, Homberg et al. (2009) concluded that
in the short-run M&As in the high-tech sector whom work in similar industries and
build on complementary technologies experience a rise in synergy effects.
Nevertheless, to the contrary to both studies of Homberg et al. (2009) and Hagendoorn
and Duysters (2002) Seth (1990) proposed an alternative point of view.
Seth (1990) discussed that when the literature regarding potential sources of gains
from acquisitions is re-examined, it is evident that there also exist some sources of
value creation, which are more likely to be available to unrelated acquisitions than to
related acquisitions, i.e. coinsurance and financial diversification. Therefore, the
strongest conclusion is that different types of acquisitions are associated with different
sources of value creation. Seth identified theoretical arguments and discussed why
related acquisitions might not be expected to create more value than unrelated
acquisitions, on average. By creating new measurement of value creation (synergy),
Seth (1990) hopes that difficulties will be solved in measurements, which were used
by earlier researchers.
Kennedy (2002) mentioned that in the high-tech industry, where innovation is of key
importance, acquirers tend to acquire majority stakes in related targets, implying that
top managers realize that economic returns from related acquisitions are higher than
those of unrelated acquisitions, so managers feel more confident and comfortable
participating in a high transaction. Likewise, Kennedy (2002) stated that both
unrelated and related acquisitions are also subject to innovation as a causal
mechanism. Cloodt (2006) supports this argument, by claiming that a higher
innovative performance creates an indirect effect for a higher firm performance, which
eventually leads to a higher ROA.

8. 7
Cloodt (2006) bases the results on a sample that includes high-tech companies from
Asia, USA and Europe, which makes the sample a representative of the whole
population. Cloodt (2006) also states that non-technological M&As contribute little to
the innovative output of the acquiring firm or that there might even be a negative
impact on the post-M&A innovative performance, because non-technological M&As
do not create additional technological learning or make any other contribution to the
post-M&A innovative performance.
In this sense, the conclusion can be derived that two related high-tech organisations
that merge or where one acquires the other, are better able to innovate as they (1)
expand their economies of scale and production capabilities, (2) expand the economies
of scope and align their resources (such as R&D), and (3) market power which allows
the acquirer to exercise more influence on the market (Singh & Montgomery 1987). So
an acquisition of related knowledge will have the most positive impact on a firm’s
post-M&A innovative performance. However, the acquisition of knowledge that is too
similar to the already existing knowledge base is disadvantageous, as the acquiring
firm will have to bear the costs of obtaining and transferring external knowledge
without any relevant enrichments of its existing knowledge base (Cloodt, 2006). This
implies that there is not only a cost attached to unrelated acquisitions, but also to
acquisitions that are too related.
Alternatively, another causal mechanism behind the hypothesis is resource
complementarity, which is critical to successful related acquisitions as it provides more
opportunities for complementary resources to join forces (Hitt et. al 2001). Singh &
Montgomery (1987) identifies resource complementarity for related acquisitions in
three broad categories as when acquirer and target can (1) align distribution channels,
(2) product technologies and (3) scientific research (R&D). Considering that Singh &
Montgomery’s (1987) study focuses on M&As overall and not specifically on high-tech,
the way resource complementary is defined is considered to be too broad and quite
simplistic for the high-tech industry. Moreover, since Singh & Montgomery (1987)
focuses on essentially out-dated data, many technological developments have

9. 8
occurred after that study, which have resulted in new business models, new
technologies and a complex interconnectivity between industries.
In addition, Harrison et al. (2001), demonstrates that firms that have complementary
resources are most likely to produce competitive advantage by combining resources
and providing unique and difficult-to-imitate value (Harrison et al., 1991). The paper
explains that complementary resources are most likely to create unique valuable
synergies deriving from economies of scope, which could in turn lead to higher
returns.
To conclude, given the prominence of M&As in the high-tech industry, value creation
and performance outcomes remain of great importance. Therefore, performance
outcomes are examined to research the relationship between industry relatedness and
economic returns. Correspondingly, our hypothesis focuses on the economic returns
obtained from ‘industry relatedness’. By testing our hypothesis and answering our
research question, our study will contribute to the existing limited literature about
industry relatedness and performance outcomes in the high-tech industry.
The literature study showed that there are three broad causal mechanisms that give
rise to economic returns in related acquisitions: Synergies, Complementary resources
and Innovation. In our interpretation it is still difficult to claim whether related
acquisitions will outperform unrelated acquisitions in the high-tech industry and vice-
versa, because of the limited quantity of the empirical studies in the high-tech industry
that provides strong evidence of economic returns. We assume that the business
strategy and core business of an acquiring firm has a significant coherence with the
type of acquisition and whether this will have a positive impact on the performance of
post-acquisition or not. We believe a successful acquisition in the high-tech industry is
dependent on the fit between business strategy of the acquirer and target. In extent,
the acquiring firm can still have positive economic returns post-acquisition whether it
is related or unrelated to their core business. The period of growth and change of
demand in technology are also important elements to consider when making claims.

10. 9
Table 1: Summary of empirical literature
Title Researcher Year Study field Type of study Sample size Effect size Hypothesis
Supported?
Do synergies exist in related
acquisitions?
A meta-analysis of acquisition studies
Homberg et al., 2009 M&A Relatedness and
Synergies
Quantitative n = 12,268 M&A’s Related;
0.213****
Yes
The Effect of Mergers
and Acquisitions on the
Technological Performance
of Companies in a High-tech
environment
Hagedoorn &
Duysters
2010 Technological
performance of M&A
Quantitative n = 201 M&A’s Related;
0.591***
Yes
Resource complementarity in business
combinations:Extending the logic to
organizational alliances
Harrison et al, 2001 Resource complementary Qualitative N/A N/A Yes
Corporate acquisition strategies and
economic performance
Singh &
Montgomery
1987 Economic performance &
relatedness
Quantitative n = 105 M&A’s Related;
0.359****
Unrelated;
0.219*****
Yes
Matching industries between target and
acquirer in high-tech mergers and
acquisitions
Kennedy et al., 2002 Matching industry
between acquirer and
target in an M&A
Quantitative n = 456 M&A’s 0.461*
Yes
* p < 0.10 ** p < 0.05 *** p < 0.01 ****p <0.001 *****p <0.005

11. 10
Table 2: Summary of empirical literature (Continued)
Title Researcher Year Study field Type of study Sample size Effect size Hypothesis
Supported?
Market value effects of acquisitions
involving internet firms: A resource-based
analysis
Mergers and acquisitions: Their effect on
the innovative performance of companies
in high-tech industries
Value creation in acquisitions: A re-
examination of performance issues
Uhlenbruck,
Hitt, Semadeni
Cloodt,
Hagedoorn,
Kranenburg
Seth
2006
2006
1990
Acquisitions in online and
offline firms
Post-M&A innovative
performance of high-tech
acquiring firms
Synergy creations in
M&A’s
Quantitative
research
Quantitative
research
Quantitative
research
n= 798 M&A’s
n= 2429 M&A’s
n= 208
Related:
0.05**
N/A
N/A
Yes
Yes
Yes
* p < 0.10 ** p < 0.05 *** p < 0.01 ****p <0.001 *****p <0.005

12. 11
Hypothesis & Research Question
Though the literature on the hypothesis and research question are limited, the
literature that is reviewed in this study gives an important overview of the prevailing
theories and identifies the general importance and significance of the empirical
literature that is intended to support the hypothesis. Using the literature review as a
base for the hypothesis, clearly defining relatedness, and placing this into context of
real examples, the hypothesis can help both investors and managers make better
decisions in an industry that is growing exponentially. (Muehlhauser, 2014)
The purpose of the literature review is to identify empirical literature relevant to the
hypothesis:
The economic return from M&A is larger in high-tech sectors when an acquisition is a related
acquisition than when it is an unrelated acquisition.
This study will contribute to the existing limited literature about industry relatedness
and performance outcomes in the high-the industry by answering the research
question:
Do related M&A’s in the high-tech industry provide stronger economic returns than unrelated
M&A’s in the high-tech industry?
Methods
The methods of this study refer to the design and methodology used; outlining the
appropriate research strategy, population, sampling methods and measurement
procedures. Concisely, this section explains how this study was carried out, by which
the validity and credibility of the study was assessed. Moreover, it provides an answer
to the ‘how’ the research question has been investigated.
Research strategy
This study uses a cross-sectional research strategy as the basis for assessing industry
relatedness in the high-tech sector. A cross-sectional research strategy is used when
studying one or more independent variables on a dependent variable within a given
population at one point in time (Mann 2003). A cross-sectional research strategy is
deemed appropriate as the research question merely investigates whether there is an

13. 12
association between the independent variable (industry relatedness) and dependent
variable (acquisition performance). As a result, the study investigates whether a
change in industry relatedness co-occurs with change in acquisition performance.
However, if the research question of this study entailed a causal claim or attempted to
discover cause-and-effect relationships between variables (e.g. higher measure
industry relatedness leads to better acquisition performance) then an experimental
study would be more appropriate.
Moreover, there is no experimental procedure possible when analyzing M&A
transactions because the researcher cannot manipulate variables. Contrary to an
experiment, a cross-sectional research strategy is observational in nature, as is the
nature of this study. No interaction with subjects or manipulations is needed to draw
a conclusion, which is required in an experimental study.
Sample
The sample used to test the hypothesis consists of 132 corporations that completed an
M&A transaction that occurred between January 2001 and January 2010. M&A
transactions are defined as acquisitions involving at least a significant percent of the
ownership and/or shares acquired of the target company, which involves only two
entities; the acquiring company and the target company. No private but only public
companies were included in the study to ensure that financial information about the
M&A transactions and companies were available for public view. Furthermore, the
M&A transactions were also defined as follows; “Western M&A transactions”
including North American and West-European target and acquiring companies. The
transactions represent both domestic but also cross-border mergers and acquisitions
(e.g. Canadian company acquiring Canadian company or North-American
corporation acquiring Swedish company).
Additionally, the M&A transactions are defined as having a minimum deal value of
$250 million. M&A deal values with a minimum of $250 million typically include
acquisitions that involve intellectual property assets (patents, trademarks, trade
secrets etc.), which are essential in high-tech mergers and acquisitions to accrue value.
Deal values lower than $250 million involve purchases of material assets such as
plants, buildings and property etc. which are not the focus of this study and have a

14. 13
lesser impact on market valuation (Haleblian & Finkelstein, 1999) and performance
outcomes. Moreover, the M&A transactions were limited to acquirer and target
companies in the high-tech industry. The sectors within the high-tech industry
include, inter alia; semiconductor, software, computer storage device among others.
Appendix I provides a full overview of the selected sectors within the high-tech
industry and the accompanied SIC codes. The data set includes companies such as
Google, Microsoft Corporation, Hewlett Packard Corporation and IBM. The M&A
transactions were taken from January 2001 until January 2010. The effective date
duration for the M&A transaction is 10 years due to the nature and growth potential
of the high-tech industry.
The high-tech industry is highly transformative and has a short product life cycle. A
time span of 10 years ensures that the performance indicators materialize.
Furthermore, the M&A transactions were also defined as ‘completed’ meaning that
during the effective time period of January 2001 until January 2010, the M&A
transaction has been finalized and successfully closed. The database Thomson One
was used to find the effective data for the study. The Thomson ONE database provides
information on (individual) M&A deals and timely corporate transaction data. Finally,
the performance outcome indicators were obtained from DataStream.
Variables
The study considers the regression model for the cross sectional data to calculate the
relationship between the independent and dependent variables. The model implies
that the performance indicator (return on assets) is a linear function of (degree of)
industry relatedness and accompanied control variables (e.g. deal value, deal to asset
ratio etc.). The regression model represents the following:
Return on Assets = 𝛼 + 𝛽1 𝑆𝑖𝑧𝑒𝐴𝑐𝑞 + 𝛽2ValueAcq + 𝛽3 𝐷𝑒𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝐴𝑐𝑞 + 𝛽41dYear02 +
𝛽42dYear03 + 𝛽43dYear04 + 𝛽44dYear05 + 𝛽45dYear06 + 𝛽46dYear07 +
𝛽47dYear08 + 𝛽48dYear09 + 𝛽49dYear10 +𝛽5 𝐶𝑟𝑜𝑠𝑠𝐵𝑜𝑟𝑑𝑒𝑟𝐴𝑐𝑞 + 𝛽6Relatedness + 𝜀,
𝜀 ~ 𝑛(0, 𝜎)

15. 14
Independent variable
Industry relatedness is one of the primary variables of interest and is defined as the
alignment between core industries of two corporations (Kennedy & Payne, 2002).
Industry relatedness is measured on the on basis of SIC-codes (Standard Industrial
Classification codes). These codes constitute a four-digit number and are one of the
most effective ways to classify industries as the SIC-system captures the distance
between industries. (Lien, 2008) It is used as a proxy to indicate whether two entities
are operating in the same industry with core resources. Industries between the target
and acquiring companies are deemed related if the two entities share the same four-
digit SIC code. This implies that the acquirer and target operate in the same industries
with the same operations. Industries are classified as ‘somewhat related’ if two
companies share three or two out of the four-digit SIC codes. Last but not least,
industries are classified as unrelated if two entities do not share the same four-digit
SIC code, implying that the target and acquiring companies do not work in similar
industries and presumably with different operations.
Additionally, there are two alternative ways in measuring Industry relatedness on the
basis of SIC-codes. First, Industry relatedness can be measured by assigning greater
weight to matching four-digit SIC codes and less weight to matching one, two or three-
digit SIC codes (Haleblian & Finkelstein, 1999). To illustrate, two entities that share the
same four-digit SIC codes were assigned the number 9, while two entities that had
matching three-digit SIC codes, the number 5, two entities that had matching two-digit
SIC codes, the number 3 and two entities that had matching one-digit SIC code, the
number 1.
Secondly, following the weighting scheme described in the previous paragraph.
Industry relatedness can also be measured by classifying industry relatedness as
horizontal, related and conglomerate (Haleblian & Finkelstein, 1999). Two entities that
had a matching four-digit SIC code were classified as horizontal, implying that the
acquisition takes place in the exact same industry. Two entities that matched on the
two-digit level were classified as related and two entities that had no matching SIC-
code were classified as conglomerate, meaning that the acquisition took place in two
distinct industries and conduct business separately. The SIC codes for this study were

16. 15
selected from the SICCODE database and output was obtained from the Thomson
ONE database.
Dependent variable
The second primary variable of interest is the dependent variable, a performance
indicator measured as the Return on Assets (ROA) that is generated three years after
the acquisition. The Return on Assets (ROA) is measured as Net Income/Total Assets.
Return on Assets measures a company’s ability to manage its resources (assets,
investments) and generate subsequent returns.
Control variables
Next to the independent and dependent variable, several control variables are used to
clean for the effects of confounding variables and spurious relationships. The control
variables are related to the dependent variable. However, there might be variables not
included in the regression but which are still of influence on the performance indicator
(Haleblian & Finkelstein, 1999).
The dependent variable is controlled for;
1. Size of the acquirer (SizeAcq). This control variable serves to indicate whether the
size of the acquirer has any influence on the ROA. It is assumed that the larger the
acquirer, the stronger its relation is with the performance indicator ROA. Large
acquirers have more resources and capabilities to generate a higher ROA.
2. Deal-to-assets ratio (DealAssetAcq). This control variable measures the influence a
deal value has compared to the size of the assets of the target company on the return
on the assets. In case the deal is valued higher than the actual value of the target, it can
mean that the acquirer believes the target can generate a higher ROA, since the
acquirer is willing to pay more than the actual value of the company. Thus, we assume
if the deal-to-asset ratio is higher, then the ROA is expected to be higher as well, as the
acquirer expects the future cash flow of the target to be higher.
3. The year of acquisition (YearAcq). The year of acquisition indicates the year the
acquisition took place. This is important as the study generates the ROA three years

17. 16
after the acquisition took place. Events that take place in a particular year may have an
impact on the ROA.
4. Total value of the deal (ValueAcq). The data set includes deal values with a
minimum of $250 million. Deal values with a minimum of $250 million, will provide
us with better results as it excludes insignificant deal values that might not relate to
acquiring intellectual property assets, but rather the purchase of material objects such
as plants, buildings, property and equipment. It is then assumed that deal value and
ROA are positively correlated, as the acquiring company will pay a premium for target
companies that generate a higher forecasted ROA. The total value of the deal could
indicate whether the acquisition is ‘overvalued’ or ‘undervalued’.
5. Cross Border Acquisition (CrossBordAcq). This control variable differentiates
between domestic and cross border acquisitions. Domestic acquisitions are ought to
outperform cross border acquisitions due to the risks that arise with cross border
acquisitions. Since we live in an era of globalization, it is important to check for any
differences that might arise from domestic and cross border acquisitions.
Results
This chapter gives an insight in the relationship between the industry relatedness of
acquisitions in the high-tech industry and its accompanied return on assets. Based on
the results from the study we can draw the conclusion as to whether the hypothesis
should be rejected or accepted.
Table 3 outlines descriptive statistics and correlation coefficients for the variables used
in the study. The correlation table measures the strength and nature of the relationship
between two variables and provides preliminary support for the hypothesis. The
correlation table indicates that the industry relatedness and return on assets are
slightly negatively correlated, which does not provide enough preliminary evidence
to support the hypothesis.
Considering that the data are cross-sectional, a regression analysis is conducted based
on the data collection methods that is described in the previous paragraph to test the
hypothesis. The results in Table 4 demonstrate that there is insufficient evidence at a
0.05 significance level to support the claim that the economic return from M&A is

18. 17
larger in high-tech sectors when an acquisition is a related acquisition than when it is
an unrelated acquisition. Statistical significance is considered when the p-value is
smaller than the significance level ɑ. The explanatory power of the regression model
is measured by R2. The R2 measures how close the data are fitted into the regression
line (van Dalen 2009) and the explanatory power of our regression model is 14.0%,
which indicates that the model explains 14.0% of the variability of the data around the
mean. The explanatory power of the regression model may be on the low side,
however, this just indicates that there are predictors not in the model (e.g. financial
distress of a company) that cannot be accounted for. Moreover, there are no signs of
multicollinearity, as Table 3 shows that there are no two or more variables correlated
with a correlation > 0.890 (van Dalen 2009).
The results demonstrate that collectively, the corresponding variables are not
significant predictors of the Y variable. Specifically, in relation to the hypothesis,
industry relatedness, albeit having a positive relationship with return on assets and
thus improve return on assets, is not a significant predictor of the return on assets. In
other words, as the industries between the acquiring company and the target company
get more related, the return on assets increases in tandem. Likewise, when analysing
the control variables; the Deal-to-Asset ratio of the acquisition has a positive
relationship with return on assets, whereas, the value of the acquisition (ValueAcq) has
a negative relationship with the return on assets, both with effects not being
statistically significant. On the contrary, the size of the acquirer generates a significant,
positive relationship between the size of the acquirer and return on assets. Thus, an
increase in the size of the acquirer implies a ‘parallel’ growth in return on assets.
Moreover, cross border mergers and acquisitions result in a lower return on assets than
domestic mergers and acquisitions. Also, the relationship between this variable and
the independent variable is not significant (CrossBorder). The control variables
demonstrate that for all the mergers and acquisitions taking place between 2001 and
2010 (except for the year 2008) have a negative relationship with the return on assets,
with its effects not being statistically significant (YearAcq).

19. 18
Supplementary results analysis
To assess the appropriateness of the regression model, the model is examined by
plotting residuals. The difference between the observed value of the dependent
variable (Return on Assets) and the predicted value are the residuals (van Dalen 2009).
Table 5 demonstrates that there are three residuals. Running the regression again
excluding the three residuals, the R2 of the regression model increases from 14.0% to
19.2%. Additionally, the regression model as a whole is then significant (Table 6).
However, the analysis shows that contrary to the previous model, industry relatedness
generates a negative (yet not statistically significant) relationship with return on assets.
The three cases will be highlighted and discussed in the discussion section.
Moreover, the methods section discussed two additional measures in measuring
industry relatedness: measuring industry relatedness by assigning greater weight to
matching four-digit SIC codes and less weight to matching one, two or three-digit SIC
codes and additionally by classifying industry relatedness as horizontal, related and
conglomerate. Each method has been regressed and the results of both methods are
reported in tables 7 and 8. The third regression model which assigned greater weight
to matching four-digit SIC code suggests there is insufficient evidence at a 0.05
significance level to support the claim that the economic return from M&A is larger in
high-tech sectors when an acquisition is a related acquisition than when it is an
unrelated acquisition. In fact, the third regression model shows a positive relationship
with return on assets as well, however, the nature and strength of the relationship to
the Y predictor is less strong (lower beta coefficient, higher p-value) as compared to
what is presented in the first regression model.
Secondly, the fourth regression model presented in Table 8 measured industry
relatedness by classifying industry relatedness as horizontal, related and
conglomerate. The fourth model suggests that there is insufficient evidence at a 0.05
significance level to support the claim that the economic return from M&A is larger in
high-tech sectors when an acquisition is a related acquisition than when it is an
unrelated acquisition. The R2 of the regression model did increase from 14.0% to 16.8%
but still remains not significant.

20. 19
The results indicate that conglomerate acquisitions and related acquisitions have a
negative relationship with return on assets in comparison to horizontal acquisitions.
Each model and its corresponding table (tables 4, 6, 7 and 8) provides a 95% confidence
interval for the independent variables Industry Relatedness. For most purposes, the 95%
confidence interval shows that the lower bound of Industry Relatedness has a negative
relationship with the dependent variable Return on Assets, while the upper bound is
positive, irrespective of the unstandardized or standardized coefficient of B. Table 8
(model 4) however, shows a 95% confidence interval for which the lower bound and
the upper bound of Industry Relatedness is negative [-16,936 ; -,512] meaning that in
this range the true value of Industry Relatedness will be negative. Therefore, it can be
concluded that the point estimate for Industry Relatedness varies, several regression
models have been presented and the results seem inconclusive. The most reasonable
conclusion that can be given is that the performance outcome of Industry Relatedness
depends. There seem to be no clear effect and thus Industry Relatedness does not
determine the value of a deal.
In this study, the high-tech industry consists of sectors such as semiconductor,
software, and computer storage device among others. Appendix I provides a full
overview of the selected sectors within the high-tech industry and the accompanied
SIC codes. The high-tech industry continues to see an increased convergence, taking
place on multiple levels. The convergence of sectors within the high-tech industry
creates uncertainty that characterizes technological and economic development within
the high-tech industry. This endogenous change has led to lateral entry of companies
in multiple sections of the high-tech industry causing companies to be related to a
variety of industries. (Hagedoorn 2002). Therefore, the suggested reference is that in
relation to the different methods of measuring industry relatedness as outlined in
tables 7 and 8 industry relatedness might not pay off and the perceived benefit is less.
In brief, whether the industry relatedness is formulated and modelled in various ways,
the findings indicate that as found in some previous research, industry relatedness
does not seem to influence the performance outcomes.

21. 20
Table 3: Univariate statistics and Pearson correlation coefficients, n = 131
Mean
Std.
Deviation N 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
ReturnOnAssets 4.50 15.85 131.00 1.00
SizeAcq 22434.55 34248.33 131.00 .223** 1.00
ValueAcq 2105.87 4585.48 131.00 0.02 0.06 1.00
DealAssetAcq 5.08 10.86 131.00 -0.01 -0.05 .613** 1.00
dYear01 0.15 0.36 131.00 0.08 -
.176*
0.09 .170* 1.00
dYear02 0.05 0.23 131.00 -0.02 -0.05 0.12 -0.02 -0.10 1.00
dYear03 0.07 0.25 131.00 -0.12 -0.03 -0.07 -0.06 -0.12 -0.06 1.00
dYear04 0.05 0.21 131.00 -0.09 -0.12 -0.04 -0.01 -0.09 -0.05 -0.06 1.00
dYear05 0.15 0.35 131.00 -0.14 -0.07 0.02 0.04 -
.175*
-0.10 -0.11 -0.09 1.00
dYear06 0.14 0.35 131.00 -0.06 0.12 -0.04 -0.01 -
.169*
-0.09 -0.11 -0.09 -
.164*
1.00
dYear07 0.13 0.34 131.00 0.08 -0.07 -0.06 -0.06 -
.164*
-0.09 -0.10 -0.08 -
.159*
-
.154*
1.00
dYear08 0.11 0.32 131.00 .153* 0.09 0.04 -0.07 -
.153*
-0.09 -0.10 -0.08 -
.148*
-0.14 -0.14 1.00
dYear09 0.04 0.19 131.00 0.05 0.00 -0.01 -0.05 -0.08 -0.05 -0.05 -0.04 -0.08 -0.08 -0.08 -0.07 1.00
dYear10 0.11 0.32 131.00 0.03 .264** -0.04 0.00 -
.153*
-0.09 -0.10 -0.08 -
.148*
-0.14 -0.14 -0.13 -0.07 1.00
CrossBorderAcq 0.12 0.33 131.00 -0.12 0.01 -0.07 -0.10 -0.09 0.12 -0.01 -0.08 -0.02 .257** -0.14 0.09 -0.07 -0.06 1.00
Relatedness 2.27 0.88 131.00 -0.05 -
.504**
0.08 0.11 0.11 0.00 -0.09 0.14 0.02 -0.07 0.03 0.02 -0.02 -0.14 0.02 1.00
**. Correlation is significant at the 0.01 level (1-tailed).
*. Correlation is significant at the 0.05 level (1-tailed).

22. Table 4: Regression estimates of the influence of M&As (2001-2010) on the
return on assets in the high-tech industry, n = 131 (Model 1)
Model 1 Unstandard
ized
Coefficients
Standardized
Coefficients
t Sig. 95.0% Confidence
Interval for B
B Beta Lower
bound
Upper
bound
(Constant) 3.740 0.627 0.532 -8.066 15.547
SizeAcq 0.000 0.267 2.500 0.014 0.000 0.000
ValueAcq 0.000 -0.034 -0.305 0.761 -0.001 0.001
DealAssetAcq 0.000 0.006 0.054 0.957 -0.003 0.003
dYear02 -3.501 -0.050 -0.498 0.619 -17.413 10.411
dYear03 -10.327 -0.165 -1.626 0.107 -22.903 2.250
dYear04 -10.025 -0.133 -1.373 0.173 -24.490 4.441
dYear05 -8.940 -0.199 -1.781 0.078 -18.884 1.003
dYear06 -6.355 -0.139 -1.188 0.237 -16.947 4.237
dYear07 -0.920 -0.020 -0.177 0.860 -11.232 9.392
dYear08 1.986 0.040 0.360 0.720 -8.956 12.928
dYear09 -0.329 -0.004 -0.042 0.967 -15.888 15.231
dYear10 -5.603 -0.113 -0.999 0.320 -16.712 5.506
CrossBorderAcquisition -5.563 -0.115 -1.250 0.214 -14.379 3.253
Industry Relatedness 1.298 0.072 0.705 0.482 -2.349 4.944
* p < 0.05
R2 = 0.140; Adj R2 = 0.036; Std Error 15,558; Sig. 0.192.
Regression (df ) 14; error (df ) 116 ; Total (df ) 130.
Table 5: Residual analysis and statistics
Case Return on Assets Predicted Value Residual
iSOFT Group PLC -76 -9.57 -66.429
Cadence Design Systems Inc -70 -2.25 -67.745
Peregrine Systems Inc 88 7.77 80.233

23. 22
Table 6: Regression estimates of the influence of M&As (2001-2010) on the
return on assets in the high-tech industry, without residuals n = 128 (Model
2)
Model 2 Unstandard
ized
Coefficients
Standardized
Coefficients
t Sig. 95.0% Confidence
Interval for B
B Beta Lower
bound
Upper
bound
(Constant) 2.527 0.664 0.508 -5.008 10.063
SizeAcq 0.000 0.312 2.984 0.003 0.000 0.000
ValueAcq 0.000 -0.046 -0.415 0.679 -0.001 0.000
DealAssetAcq 0.000 0.041 0.366 0.715 -0.002 0.002
dYear02 -0.532 -0.012 -0.119 0.906 -9.413 8.349
dYear03 2.467 0.058 0.584 0.560 -5.896 10.829
dYear04 -4.994 -0.102 -1.071 0.286 -14.230 4.242
dYear05 -1.106 -0.037 -0.339 0.736 -7.578 5.366
dYear06 -3.340 -0.113 -0.974 0.332 -10.135 3.456
dYear07 3.879 0.128 1.161 0.248 -2.743 10.501
dYear08 6.058 0.189 1.711 0.090 -0.957 13.073
dYear09 4.450 0.084 0.889 0.376 -5.468 14.369
dYear10 -0.885 -0.028 -0.247 0.806 -7.998 6.227
CrossBorderAcquisition 0.137 0.004 0.047 0.963 -5.668 5.942
Industry Relatedness -0.072 -0.006 -0.062 0.951 -2.393 2.248
* p < 0.05
R2 = 0.192; Adj R2 = 0.92; Std Error 9.859; Sig. 0.031.
Regression (df ) 14; error (df ) 113 ; Total (df ) 127.

24. 23
Table 7: Regression estimates of the influence of M&As (2001-2010) on the
return on assets in the high-tech industry, Proportional relatedness n = 131
(Model 3)
Model 3 Unstandard
ized
Coefficients
Standardized
Coefficients
t Sig. 95.0% Confidence
Interval for B
B Beta Lower
bound
Upper
bound
(Constant) 4.822 0.973 0.333 -4.992 14.636
SizeAcq 0.000 0.264 2.489 0.014 0.000 0.000
ValueAcq 0.000 -0.034 -0.303 0.762 -0.001 0.001
DealAssetAcq 0.000 0.006 0.056 0.956 -0.003 0.003
dYear02 -3.508 -0.050 -0.499 0.618 -17.423 10.406
dYear03 -10.342 -0.166 -1.628 0.106 -22.922 2.238
dYear04 -10.008 -0.133 -1.370 0.173 -24.476 4.461
dYear05 -8.942 -0.199 -1.781 0.078 -18.887 1.004
dYear06 -6.352 -0.139 -1.187 0.237 -16.946 4.243
dYear07 -0.917 -0.020 -0.176 0.860 -11.231 9.396
dYear08 2.001 0.040 0.362 0.718 -8.942 12.944
dYear09 -0.329 -0.004 -0.042 0.967 -15.892 15.233
dYear10 -5.634 -0.114 -1.004 0.317 -16.745 5.476
CrossBorderAcquisition -5.556 -0.115 -1.248 0.215 -14.373 3.262
Proportional Industry
Relatedness
0.310 0.068 0.673 0.502 -0.601 1.221
* p < 0.05
R2 = 0.139; Adj R2 = 0.36; Std Error 15.561; Sig. 0.194.
Regression (df ) 14; error (df ) 116 ; Total (df ) 130.

25. 24
Table 8: Regression estimates of the influence of M&As (2001-2010) on the
return on assets in the high-tech industry, Horizontal, Related and
Conglomerate, n = 131 (Model 4)
Model 4 Unstandard
ized
Coefficients
Standardized
Coefficients
t Sig. 95.0% Confidence
Interval for B
B Beta Lower
bound
Upper
bound
(Constant) 8.869 2.318 0.022 1.289 16.449
SizeAcq .000 .241 2.276 0.025 .000 .000
ValueAcq .000 -.058 -.515 0.608 -.001 .001
DealAssetAcq .000 .024 .215 0.830 -.003 .004
dYear02 -1.988 -.028 -.285 0.776 -15.808 11.832
dYear03 -11.449 -.183 -1.819 0.072 -23.918 1.021
dYear04 -10.011 -.133 -1.388 0.168 -24.295 4.274
dYear05 -9.429 -.210 -1.900 0.060 -19.261 .402
dYear06 -8.629 -.188 -1.597 0.113 -19.330 2.072
dYear07 -.039 -.001 -.008 0.994 -10.260 10.181
dYear08 .294 .006 .053 0.958 -10.642 11.229
dYear09 -1.861 -.023 -.239 0.812 -17.302 13.579
dYear10 -5.092 -.103 -.918 0.360 -16.073 5.890
CrossBorderAcquisition -3.381 -.070 -.747 0.457 -12.353 5.590
dRelated -8.724 -.203 -2.104 0.038 -16.936 -.512
dConglomerate -1.018 -.029 -.274 0.785 -8.389 6.353
* p < 0.05
R2 = 0.168, Adj R2 = 0.060; Std Error 15.362; Sig. 0.098.
Regression (df ) 15; error (df ) 115 ; Total (df ) 130.

26. 25
Discussion
Our results indicate that relatedness has no significant influence on the ROA.
There is no proof that related acquisitions outperform unrelated acquisitions.
Companies in the high-tech sector seem to benefit from both related and
unrelated acquisitions. It is important to note, that technology transfer can
result in increased ROA. Therefore, companies in the high-tech industry might
recognize that unrelated acquisitions, where the acquirer obtains new
technological resources, can be complemented with the primary activities of
the acquirer, and therefore still provide a strong ROA.
The low explanatory power of our results can mostly be prescribed to factors
that are of influence on the ROA but that are not included in our variables such
as financial distress, risk aversion of managers, company structure and
decision-making process. Factors as these can be of influence on the ROA since
some managers could take more risk than others, or the company structure
which can lead to different approaches to M&As. Also the decision-making
process in companies can affect the ROA. It is not solely certain that companies
make acquisition decisions purely to increase ROA. There might be other
reasons, such as being ahead of a competitor to acquire a target, even if this will
affect the ROA in the short to –mid term.
In the results chapter, we mentioned that the explanatory power of the
regression model can be improved by omitting three residuals. (See tables 5
and 6). The three unusual cases (residuals) have a large deviating return on
assets (-76%, -70% and 88% respectively). This is due to, inter alia, the following
two reasons; iSoft evaporated earnings in 2006 due to an accounting scandal.
In that year, iSoft shares lost around 90 percent of its value (Wash, 2006). Also,
Cadence Design System faced financial troubles that resulted in huge layoffs
and resignations of multiple board members (Moretti, 2008). However, these
three residuals do not violate any underlying assumptions of the regression
model and can be included in the regression model.

27. 26
In first instance, we expected the relation between relatedness and acquisition
performance to be positive. Surprisingly, there seems to be little evidence for
such a relation between relatedness and acquisition performance. This might
be because, high-tech companies can only make significant gains in growth by
acquiring unrelated targets that will broaden their reach in different high-tech
markets instead of just one. Since the dotcom crash, high-tech markets have
grown exponentially. Due to this fast growth, high-tech companies seem to
have invested more in unrelated acquisitions, aiming to increase their ROA in
new, high-growth markets. Therefore, this might be the reason that related
acquisitions have not outperformed unrelated acquisitions in the high-tech
industry.
Interestingly, when we look at the relation between relatedness and ROA for
cross-border acquisitions, the relation is negative. In this case, domestic
acquisitions outperform foreign acquisitions due to the associated risk, cultural
dissonance, and effort needed to implement target in acquirer. The risk factor
seems to be stronger for both related and unrelated acquisitions, when cross-
border M&A come into play.
Other than for the year 2008, there is no significance for the relation between
relatedness and the year of acquisition. The positive relation in 2008 might be
explained by the risk aversion caused to companies due to the financial crisis.
Companies might have invested more in related acquisitions as a safer option
while on the other side there was a significant decrease in unrelated
acquisitions.
Lastly, since the size of the acquirer seems to have an influence on the ROA, we
can argue that larger acquirers are better capable to generate a higher ROA on
their acquisitions than smaller acquirers. This can be caused by their large
resources and capabilities, which enable these companies to make better use of
their targets. Also, since they are larger companies, they have developed more

28. 27
acquisition experience, which helps them to better integrate targets and
generate more ROA.
We expected to sustain our hypothesis that related acquisitions generate a
higher ROA. However, this seems not to be the case in the high-tech industry.
There can be several reasons for this; the first reason is that high-technology is
a booming market, where sub-industries are growing rapidly that companies
continuously engage in unrelated acquisitions in order to get a foothold in an
unrelated high-tech market, in order to find new sources to generate ROA, or
even to secure a strong position in a new, risky but promising market. The
second reason is that the high-tech industry is a fairly young industry, which
is less conservative and willing to engage in more diverse markets. The third
reason is more complex. The fast technological developments have a
deflationary effect on ROA; meaning that high-tech goods and services today
become less valuable in the future due to Moore’s law. In this case, high-tech
companies take more risk and engage in unrelated acquisitions to sustain their
ROA.
Managerial Implications
Managers in the high-tech industry need to be aware that relatedness is not a
guarantee for generating well-performing ROA. As demonstrated in the
results, industry relatedness does not determine deal value. If a manager
decides to execute a financial merger and/or acquisition (a financial M&A
solely focuses on improving financial performance or reducing risk) then the
company might not get the most out of the deal, especially if a related target
inflates the acquisition price in the bidding process. Therefore, it is advised that
managers do not use industry relatedness as the sole basis or reference point in
determining the performance outcome of an M&A deal. Since the high-tech
industry is a very transformative industry with much potential across many
different sectors, it is highly advisable for high-tech companies to diversify, as
they will be better able to complement the different type of resources,

29. 28
knowledge and patents into better existing and new products and services that
can secure higher ROA in the future. Lastly, as the high-tech sectors are
converging, managers need to take notice that competitors that diversify
through unrelated acquisitions and gain new resources, knowledge and
patents can use these tools to gain an advantage over the respective
competitors, by being better able to create new products and services.
Lessons learned
In this chapter we evaluate our learning process while conducting this study
by discussing our experience with research-in-practice and discussing the
comparisons between our own research and previous research.
After we had been assigned to study the M&A topic, we agreed to research a
topic that is new to us yet also very contemporary in management
sciences/business studies. We believed that we needed to explore a topic that
differs from prior research and has not been researched heavily so that a
“significant” contribution could be made with our study to that research
domain. Subsequently, studying the industry relatedness in the high-tech
sector was a good choice as there were limited similar empirical studies in this
domain available, challenging us to find our own way in creating and testing a
hypothesis. While conducting the study we learned to work step-by-step in
order to deliver a well-structured thesis with elements that were new to us. We
learned to evaluate the relevance of other empirical research to our own study,
how to create our own research strategy with the necessary tools and how to
analyze and to interpret the results.
Finding empirical research to use as a basis of our research was quite a
challenge but we still managed to learn a lot about mergers and acquisitions in
general. After analyzing prior empirical studies we concluded that our
hypothesis was hardly tested by any previous researchers. It may be so that
certain parts of previous empirical research are related to this study, but it was

30. 29
definitely not exactly the same. This made our research interesting but quite
challenging. However, all in all, we are almost certain to say that industry
relatedness in the high-tech industry does not have a significant influence on
the performance of a firm, specifically the return on assets.

31. 30
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Appendix I: SIC-code list
3571 Electronic Computers (Primary)*
3572 Computer Storage Devices (Primary)
3575 Computer Terminals
3661 Telephone and Telegraph Apparatus (Primary)
3663 Radio and Television Broadcasting and Communications Equipment
(Primary)
3669 Communications Equipment, not elsewhere classified
3674 Semiconductors and Related Devices (Primary)
3691 Storage Batteries
7311 Advertising Agencies (Primary)
7313 Radio, Television, and Publishers' Advertising Representatives
7319 Advertising, not elsewhere classified
7371 Computer Programming Services (Primary)
7372 Prepackaged Software (Primary)
7373 Computer Integrated Systems Design
7374 Computer Processing and Data Preparation and Processing Services
(Priamry)
7375 Information Retrieval Services
7382 Security Systems Services