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.”

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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

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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.

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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

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(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.

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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

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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.

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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

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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.

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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

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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

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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

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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

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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 + 𝛽1𝑆𝑖𝑧𝑒𝐴𝑐𝑞 + 𝛽2ValueAcq + 𝛽3𝐷𝑒𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝐴𝑐𝑞 + 𝛽41dYear02 +

𝛽42dYear03 + 𝛽43dYear04 + 𝛽44dYear05 + 𝛽45dYear06 + 𝛽46dYear07 +

𝛽47dYear08 + 𝛽48dYear09 + 𝛽49dYear10 +𝛽5𝐶𝑟𝑜𝑠𝑠𝐵𝑜𝑟𝑑𝑒𝑟𝐴𝑐𝑞 + 𝛽6Relatedness + 𝜀,

𝜀 ~ 𝑛(0, 𝜎)

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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

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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

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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

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).

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.

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.

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).

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 Unstandardized

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

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 Unstandardized

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.

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 Unstandardized

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.

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 Unstandardized

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.

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.

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

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,

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

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.

30

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34

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