Do high-tech acquisitions pay off? -

A study on the effect of acquisitions and motives on

innovation performance in the high-tech industry

Jorèn Aveskamp*

MSc Thesis Strategic Innovation Management

University of Groningen

Supervisor: Killian McCarthy

June 21, 2021

Abstract

This thesis examines the effect of acquisitions on the innovation performance of a company in

the high-tech industry. As an extra measure, we also examine the effect of the motive behind

an acquisition on innovation performance. We measured this effect with the Ansoff-matrix,

where we distinguish different motives between technological and non-technological motives

behind an acquisition. Furthermore, we control for other factors that may impact the

innovation performance of a company. We find evidence for the impact of acquisitions on

innovation performance that this has a significant negative impact. However, for the motives

of acquisitions, we find evidence that this is positively affecting a company's innovation

performance.

Word count: 8,705

Keywords:

Acquisitions, high-tech, innovation performance, technological, motives

* Student number: 4031431, E-mail: j.j.l.aveskamp@student.rug.nl

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Table of content

1. INTRODUCTION ..............................................................................................3

2. LITERATURE REVIEW ...................................................................................5

2.1. THE RESOURCE-BASED VIEW (RBV) ............................................................6

2.2. M&AS – ACQUISITION OF RESOURCES .........................................................7

2.3. ACQUISITIONS AND INNOVATION..................................................................8

2.4. M&A AND MOTIVES LITERATURE .............................................................. 10

2.5. ANSOFF-MATRIX ......................................................................................... 11

2.5.1. Technological .......................................................................................... 12

2.5.2. Non-technological ................................................................................... 13

3. METHODOLOGY ........................................................................................... 13

3.1. DATA COLLECTION ..................................................................................... 13

3.2. VARIABLES ................................................................................................. 15

3.3. RESEARCH DESIGN...................................................................................... 16

3.4. MODERATOR .............................................................................................. 16

4. DATA ................................................................................................................ 16

5. RESULTS .......................................................................................................... 21

6. DISCUSSION AND IMPLICATIONS ............................................................ 25

7. CONCLUSION ................................................................................................. 27

APPENDIX A – SEARCH STRATEGY ZEPHYR ............................................... 28

APPENDIX B – US SIC CODES HIGH-TECH SECTOR .................................... 29

BIBLIOGRAPHY .................................................................................................... 30

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1. Introduction In a constantly evolving environment, companies in high-tech industries are expected

to constantly update their technological knowledge base to remain competitive in a constantly

changing industry. M&As have always been seen as a means to gain market share, achieve

economies of scale, or for geographical expansion (Scherer and Ross, 1990). Recently, the

relevance of technological motives for M&As has increased and particularly in the high-tech

industries. Acquiring external knowledge remains a crucial strategy to maximize

technological performance and thus innovation activities (Kang, Jo, and Kang, 2015). M&As

as a form of acquiring new knowledge and technological capabilities, have increased in

popularity since the early 2000s. This phenomenon is especially prevalent in the

biopharmaceutical industries, where R&D expenditures are some of the highest in the world

(DiMasi, Hansen and Grobowski, 2003). Through M&As, firms are attempting to gain highly

developed technological expertise and R&D skills (Bower, 2001; Inkpen, Sundaran, and

Rockwood, 2000).

As a result of solid development in recent years of mergers and acquisitions (M&As),

they were considered special operations and they have become usual business development

options nowadays. Mergers & acquisitions is a term used to refer to the combination of

different companies or assets through various types of financial transactions. This includes

mergers, acquisitions, consolidations, and the purchase of assets (Hayes, 2020). The M&As

continue to be a prevalent form of corporate development (Cartwright and Schoenberg, 2006)

and one of the most essential strategies for external growth. When it comes to unique markets

and environmental conditions, external growth is preferable to a domestic one.

It is crucial to account that the technological performance of M&As reflects the long-

term effects on M&As. The technology-related incentives for M&As affect long-term

strategic variables, which often is underestimated in substantial of current empirical research,

which usually focuses on short-term, economic effects on M&As (Chakrabarti et al., 1994).

While in long-term effects on M&As, the expected synergetic elements can contribute to

technological performance by inventions of new process-related technologies or new product-

related technologies. These technologies can result in improved profitability for M&As if

these technologies are transformed into innovations. For example, new products and processes

are introduced into the market successfully. When the acquirer plans to get access to R&D

and the technological capabilities of the target firm, only to produce an already existing,

combined technological output. In this case, acquiring companies is a relatively fast strategy

to connect resources compared to external collaboration or internal development. Also,

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research has dispensed outcomes that acquisitions do impact innovation performance (Hitt et

al. 2001; Bena and Li, 2014; Cassiman et al., 2005). Now, in the fact of the recognized effect

of acquisitions on innovation input and output, both in the short- and long term, the outcomes

of previous studies are far from conclusive. Some studies suggest results that are positive-

significant, some show negative-significant, but also non-significant effects of acquisitions on

innovation performance (Aalbers and McCarthy, 2016)

Technological motives regarding M&As seem to be only moderately crucial across

various industries. However, further studies do argue that M&As are an important element in

the technology acquisition strategy of firms, especially in R&D concentrated industries. More

specifically, these industries tend to be high-tech sectors. We will continue along this line and

study the effect of M&As on innovation performance in the high-tech sector. Of course,

M&As are essential in other sectors as well, but the relation between M&As and innovation

performance is most obvious in high-tech sectors. Most studies explore the direct results of

acquisitions; however, firms are different in their competencies and resource usage to process

external resources into innovation outcomes (Prabhu, Chandy and Ellis, 2005). Therefore, it is

important not only to analyze an output measure of knowledge following M&A, but it is also

essential to compare the nature of the knowledge, as well as overlapping and non-overlapping

knowledge between the acquirer and target firm, is an interesting topic. One example of an

acquisition that amplifies the above is Gilead Sciences' acquisition of Triangle

Pharmaceuticals in 2003. In this case, both companies had high-quality knowledge with

overlapping strengths in fighting diseases. After the acquisition of the HIV treatment Emtriva,

Gilead Sciences combined the acquired treatment with its own treatment. The result was the

new drug Truveda, which became the standard drug in treating the infectious disease HIV

(Han, Jo, and Kang, 2016). This is an example of how combined knowledge can lead to

meaningful and new developments in the high-tech industry.

There is a growing empirical literature that is studying the relationship between M&As

and innovation. However, the empirical evidence about the effect of M&As on innovation is

not yet conclusive. Some studies find a positive effect on R&D and innovation activities by

the merging firms (Ahuja and Katila, 2001; Cassiman et al., 2003; Cloodt et al., 2006; Cefis

and Marsili, 2015), while others have found a negative impact of mergers and acquisitions on

innovation in the post-M&A stage (Blonigen and Taylor, 2003; Harrison et al., 1991; Szucs,

2014). The critical indication here is the motive of why a company acquires another company.

These motives give arguments on what the acquiring company is planning to do. There are

many studies with different conclusions regarding the motives behind an acquisition. Mostly,

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the outcomes are synergy, agency, or hubris-related. Whereas in the high-tech sector, one

would expect the main reason for an acquisition to be the improvement of a product portfolio

or to enhance products as this sector is innovation-driven.

This study aims to clarify our current understandings of acquisitions by addressing

this literature gap. For example, are the effects of acquisitions positive or negative regarding

innovation outcome? Therefore, we first examine the relationship between acquisitions and

innovation. This study is structured as follows; first, the resource-based view will examine

why companies tend to acquire resources. After that, we will discuss how M&As can jump

into that by acquiring firms, followed by evidence on acquisitions and innovations. After that,

we will present literature review on M&As and motives literature and the theoretical section

will be finalized by the findings of Ansoff.

We will define our conceptual model as follows:

Figure 1: Conceptual model

This study aims to determine the effect of acquisitions in the high-tech sector on the

innovation performance of companies in the high-tech sector. Besides that, we will check

whether technological motives or non-technological motives will have a positive/negative

impact on the innovation performance of companies in the high-tech sector. For this, we will

use literature on M&As but we are mainly focused on acquisitions.

2. LITERATURE REVIEW

A firm's motivation for M&As is for various reasons, with the realization that business

combinations provide opportunities to create new value to the economic wealth for their

shareholders (Krishna and Paul, 2007). This value can be created by taking advantage of

Acquisitions in the high-

tech sector

Innovation performance of

companies in the high-tech

sector

The motive behind an

acquisition

R&D

expenses

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economies of scale that can be achieved through a combination. This is because a new firm

might perform more efficiently and effectively than two separate firms. This value can also be

increased by combining two firms that complement each other with resources to increase

efficiency in the business operations. Some M&As involve significant changes in the

technological capabilities of these firms involved in M&As. However, the existing literature

shows some inconclusive results about the nature of those effects. On the one hand, there is a

negative relationship between R&D expenses and the number of M&As in which a particular

firm has been involved (Cassiman et al., 2005).

2.1. The resource-based view (RBV)

According to the resource-based and knowledge-based view (RBV and KBV) of the

firm, differences in innovation performance result from different knowledge sources (Bierly

and Chakrabarti, 1996). Therefore, a firm must have the ability to acquire, transfer and

integrate the knowledge of an acquired firms’ knowledge base into their own knowledge base

as this can create a competitive advantage (Barney, 1986). The RBV of a firm explores the

origins of competitive advantage and superior performance (Michalisin et al., 1997; Barney et

al., 2011). RBV explains the differences in performance among firms about the effects of

innovation as a firm-specific resource on firm performance (Wernerfelt, 1984). The RBV

aspires to explain the internal sources of a firm’s sustainable competitive advantage. In this

case, Barney (1991, 1994, 2002) argues that a firm’s resources must be valuable, rare,

inimitable, and non-substitutable (VRIN) to absorb and apply these resources. Following

Barney (1991), we define resources as "… all assets, capabilities organizational processes,

firm attributes, information and knowledge that is controlled by a firm that enables the firm to

conceive of and implement strategies that improve its efficiency and effectiveness." (Barney,

1991: 101.). Acquiring firms fail in their M&A due to one or two errors. First, the acquirer

overvalues the target – or it ineffectively integrates the target into its operation. With the

emphasis on these VRIN resources, the RBV has a potential to facilitate the underlying

assumptions of the valuation and integration that come to light in an M&A. The study of

Capron (1999), who examined horizontal acquisitions from an RBV perspective, explored on

how post-acquisition resource deployment, influences acquisition performance. In a survey,

she concluded that "… there is a significant risk of damaging acquisition performance in the

process of divesting and redeploying the target's assets and resources." (Capron, 1999: 988.).

King et al. (2008) examined the role of resource interactions in describing firm performance

in an environment of acquisitions. Although King et al. (2008) argued that acquisitions do not

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lead to a higher firm performance on average, they did confirm that complementary resources

in the acquiring and target firms are associated with deviating results. More precisely, they

found that the marketing resources of the acquiring firm and the technology resources of the

target firm positively complement each other. In contrast, the technology resources of the

acquiring firm and the marketing resources of the target firm negatively substitute each other.

The KBV, on the other hand, would argue that maximizing the value deriving from

these existing resources builds from superior knowledge (Costello et al., 2011). The KBV

argues that knowledge is strategically the most important to a firm's resources. The ability to

produce low-cost or unique products and services is the result of superior knowledge. With

knowledge, firms become more effective and efficient in the use of (scarce) resources. This

'new' view is gaining increased attention because of the rapid movements towards a more

knowledge-based economy. Therefore, the KBV of a firm is more like an outgrowth of the

existing RBV of a firm (Costello et al., 2011). According to Leonard (1998), the KBV is

linkable to innovation, and she claims that the most successful innovators are organizations

that build and manage knowledge effectively and efficiently. These organizations are the most

enthusiastic about pursuing new knowledge and most likely to use the power of innovations.

2.2. M&As – acquisition of resources

Acquisitions can be a suitable tool to gain VRIN resources. The RBV perceives a firm

as a unique bundle of capabilities and resources. Therefore, the primary task is to maximize

value by deploying these existing capabilities and resources optimal (Kraaijenbrink et al.,

2009). With acquisitions, an acquiring firm takes over all available resources from the target

firm. This means that the acquiring firm automatically gains new external knowledge that

becomes internal knowledge from the moment the acquisition is made. For an acquiring

company, this could be for the benefit of the innovation performance. Therefore, M&As can

contribute to a company's innovation performance, especially in a high-tech sector where the

degree of innovation can be decisive for the future of a company (Van Dijk, 2008). Barney

(1988) argues that acquirers can capture value by creating new combinations of resources and

capabilities from their own and those of the target company. A more recent study provides

evidence that following such a strategy of new combinations motivates many acquisitions

(Larsson & Finkelstein, 1999). The important factor here is the degree of overlap in the

technological knowledge base of both companies. Prior studies have measured technological

overlap as the knowledge base of the target that the acquirer already possesses (Ahuja &

Katila, 2001) or the sum of this overlap from the target and the acquiring firm (Mowery,

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Oxley & Silverman, 1996, 1998). Makri et al. (2009) argue that when the knowledge overlap

regarding the target firm is low, many opportunities for new combinations are possible.

However, then the acquirer lacks the absorptive capacity to recognize and execute the

combinations. The resources of the target company are then mostly weakened. Conversely,

when the knowledge overlap is high, the acquirer possesses the absorptive capacity but the

consequence that, due to knowledge excess, only a few new combinations are possible.

2.3. Acquisitions and innovation

Increasing control over the ongoing or the new environment of companies cannot be

taken as a goal itself. This search for new, rewarding opportunities needs to be part of the

process of absorption of a company’s environment. Thus, for a company to book successes,

the objective of increasing control and the integration of M&As ultimately must guide the

company to improved performance. Regarding the high-tech sector, improved performance

insinuates that integration through M&As needs to support the ongoing search for new,

radical technological capabilities (Barkema and Vermeulen, 1993). M&As are contingent

upon 'strategic fit', but also 'organizational fit'. The combination of these two enables M&A

partners to collaborate in current and future activities. This means that to achieve synergetic

effects using M&As, the 'strategic fit' through the market, product and technological

relatedness of companies must be complemented by an ‘organizational fit’ in which the

merged organizational structure appears to match.

According to Crossan and Apaydin (2010), innovation is both outcome and process.

Because of that, corporate innovation can be divided into two parts: innovation process

performance and innovation output performance. The first part reflects on the management

level of corporate technological innovation activities, where the second part reflects R&D

performance. Therefore, in this research, the acquirers' innovation performance refers to

'innovation output performance'. Moreover, R&D is the primary driver of innovation as

through R&D expenditures, companies can innovate their products. Because of that, R&D

intensity is one of the most widely used measures of innovation performance (Savrul and

Incekara, 2015). Furthermore, companies that are in the high-tech sector are focused on

innovation and thus their R&D expenditures. Therefore, R&D can have a significant impact

on a company's innovation performance.

Some scholars have found mixed results regarding the effect of R&D on innovation

and firm performance (Baumann and Kritikost, 2016). The extent to which a company

introduces new products or new product innovations is argued to mediate the effect of R&D

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on firm performance (DeCarolis and Deeds, 2000). Also, product innovation is an important

measure of the R&D effectiveness of a firm. This indicates how effectively the firm is using

new technological knowledge (Coombs and Bierly, 2006). Also, knowledge is the main

source of competitive advantage (Imran et al., 2016). The overall presumption is that a

company can achieve efficiency in the market if it has better operational and learning

capabilities than its competitors. Therefore, the most important resources to acquire from the

target companies are knowledge-based resources. This will lead to a critical process of

integrating the knowledge of the target when it comes to mergers and acquisitions. Integrating

these targets' knowledge can lead to better innovation outcomes. Hitt et al. (1991, 1996, 1997)

analyzed innovation performance and they found that acquisitions may not always guide to

positive firm performance. Hitt et al. (1991) presumed that acquisitions had a negative effect

on R&D intensity and patent intensity. By contrast, Ernst and Vitt (2000) analyzed the

behaviour of key inventors who are responsible for many high-quality patents for their

companies. An analysis of 43 acquisitions showed that many of these key inventors left the

company they worked or significantly reduced their patenting performance after the

acquisition was made. The main reasons that affect the behaviour of key inventors after the

acquisition are the size of the acquired firm, technological complementarity and the cultural

differences in R&D between both acquired and acquiring firms.

The relatedness in terms of specific fields of technology that the acquiring and

acquired firms share is an essential factor regarding M&A (Cassiman et al., 2003; Hagedoorn

and Duysters, 2002). It is crucial to have the ability to evaluate and utilize related externally

acquired knowledge over unrelated externally acquired knowledge (Cohen and Levinthal,

1990). This is because a firm's absorptive capacity mainly depends on the level of knowledge

in its specific field (Cohen and Levinthal, 1990; Duysters and Hagedoorn, 2000). If the

acquirer's knowledge base is not adequately adapted to the externally acquired knowledge, the

absorption process will become challenging (Duysters and Hagedoorn, 2000). Hence, Kogut

and Zander (1992) argue that unrelated externally acquired knowledge often requires a radical

change in the method of organizing research, which can easily be ineffective (Ahuja and

Katila, 2001; Dosi, 1988).

However, when the technological knowledge and capabilities of the acquiring firm are

too similar to their already existing knowledge base, it will have little contribution to the

innovation performance post-M&A deal. On the other hand, Ghoshal (1987) and Hitt et al.

(1996) argue that differences in technological capabilities may enhance the acquiring firm’s

knowledge base and generate opportunities to gain new knowledge. This enrichment of both

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the acquiring firm’s knowledge base and the use of its knowledge can be considered as

appropriate contributions to improve the acquiring firm’s innovation performance (Cohen and

Levinthal, 1989; Griliches, 1990; Pakes and Griliches, 1980). According to these researchers,

the main difference is in the degree of the knowledge bases. Moreover, M&As can further

enhance innovation processes by facilitating the achievement of financial synergies when

acquired firms can receive cheaper access to capital for growth. Granstrand and Sjolander

(1990) argue that acquired high-tech based firms grow faster than comparable non-acquired

firms as the non-acquired firms lack the financing ability to manage the development of a new

product generation. This is more developed in a high-tech sector company as these types of

companies always need to enhance technologies to stay upright.

Additionally, Hagedoorn and Duysters (2000) argue that the effect of technologically

related M&As is marginally significant on firms' innovation performance in the high-tech

industry. Their study is showing that M&As can contribute to improving the technological

capabilities of the new entity. Moreover, as some studies reveal the impact of R&D intensities

on innovation performance, we came up with the following hypotheses:

Hypothesis 1: Acquisitions in the high-tech sector have a positive effect on a firm’s

innovation performance.

Hypothesis 2: R&D expenses have a positive moderating effect on the relationship between

technological motives of acquisitions and innovation performance of a firm.

2.4. M&A and motives literature

In this section, we will discuss the motives for M&As and reasons as well as the

success of M&As. The most cited motives for an M&A are 1) the potential growth or

expansion of the acquirer and 2) the hope to create synergies. Other motives can be

diversification, financial or economic motives such as economies of scale or vertical

integration, higher market share and improved R&D or innovation (Chesbrough and

Crowther, 2006; Antoniou, Petmezas and Zhao, 2007). However, we do understand that not

all M&As are attached to technological reasons with the sole intention to absorb new

knowledge (Hamel, 1991). There are other motives for a firm to engage in M&As, such as

market-entry, market-structure, or the desire to expand internationally. These motives are

non-technological rationales that are less likely to deliver technological knowledge to the

acquiring firm (Cloodt et al., 2006). In these circumstances, M&As are expected to have little

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or even no effect on the innovation performance of the acquiring firm. It is important to note

that sometimes personal reasons and feelings may be the key triggers to an M&A rather than

rational motives alone.

Moreover, when an M&A can be considered a success, some negative effects will still

be there. One negative effect that will affect the new entity is, for example, the decrease of the

company’s innovation performance (Schenk, 2002). Possible reasons for this failure may be

the unpredictability of the market and managerial errors. Another study also indicates that if

M&As create a disturbance of the existing innovation routines and, on top of that, consuming

managerial time and efforts, they can negatively affect the post-M&A innovation performance

(Ahuja and Katila, 2001; Hitt et al., 1997). Several studies bring up the advantages of creating

a broader knowledge base by acquiring new knowledge. These advantages could be 1)

sustainable competitive advantage, 2) increased strategic flexibility, and 3) increased firm

performance (Henderson and Cockburn, 1994; Bierly and Chakrabarti, 1996; Reed and

DeFillippi, 1990).

The study of MacDonald (1985) indicates that R&D intensive firms, like firms in the

high-tech industry, aim at M&As with firms from other R&D intensive industries that are

similar in their R&D orientation to reach synergies. Hall (1990) mentions the importance of

synergies for explaining innovation performance in R&D-intensive industries. Another

primary motive for M&As that contain companies with higher R&D intensities is that these

companies are expected to possess research capabilities and skills that may play an essential

role in the future of technology. This is probably applicable in distinct industries but

predominantly in a high-tech industry where R&D output is crucial for the growth and

development of companies (Henderson & Cockburn, 1994).

2.5. Ansoff-matrix

These different understandings of M&As can confuse whether to use this tool to

improve innovation performance. To deal with this ambiguity, it is important to understand

the product-market framework in which each innovation type finds an appropriate space.

Thus, the Ansoff matrix can help a company to improve and clarify its strategy regarding

innovation.

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2.5.1. Technological

There are other motives that could be categorized with the help of the Ansoff Matrix.

The Ansoff matrix is one of the most important tools for strategic planning to diversify risks.

These categories are market development, market penetration, product development and

diversification. The Ansoff matrix is a good measure when describing whether an M&A deal

is focused on enhancing innovation or expanding the market. Within the Ansoff matrix, we

distinguish four different types of growth strategies. Diversification (D) involves creating a

new customer base that expands the original product's market potential. This is when a firm is

offering new products to a new market. It includes brand extensions or new brands. It usually

is the final option to pursue as this is the option where the risk is the highest (Ansoff, 1957).

On the other hand, there is product development (PD) which means that a firm

delivers new products to the same market. Contrary to that, there is market development,

which means that a firm delivers the same product to new markets. Product development or

new product development (NPD) pays attention to developing organized ways of guiding all

the processes regarding a new item or consumption to market. New products to be marketed

to existing customers may increase sales for a firm with a decline in existing products. A firm

can develop new products or offerings to renew existing ones to boost their market share

compared to rival firms (Ansoff, 1957). Gima et al. (2001) argued that product innovation

strategy and the performance of new technologies are closely correlated. According to Porac

et al. (2004), there is a significant relationship between NPD and a firm's growth. Product

development and diversification are highly focused on creating new products and thus

innovation but still are both very different approaches. This led to the following hypotheses:

Hypothesis 3a: Acquisitions motivated by product development or diversification positively

affect innovation performance within the high-tech sector.

Product development and diversification still do differ from each other. For example,

with product development as a motive, the focus is mainly on developing the product. On the

other hand, with diversification, the focus is on product development and the search for new

markets (Ansoff, 1957). This led to the following hypothesis:

Hypothesis 4a: Acquisitions with a motive categorized as 'product development' have a

higher impact on innovation performance than acquisitions with ‘diversification’ as a

motive.

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2.5.2. Non-technological

Market penetration (MP) focuses on the same market with the same product. It is seen

as the most straightforward and first option to achieve growth in most firms. It is an attempt to

create more company sales without leaving the original product-market strategy. As the

product is not changing and there is no need to look for new customers, it is only a new way

to promote or reposition the product (Eagle and Brennan, 2007). Market development (MD) is

a marketing strategy to enhance a firm's income by increasing sales in new markets.

Marketing existing product range in new markets is a technique that is used for growth by

owners (Ansoff, 1957). The product remains identical, but the marketing is more focused on

new targeted potential customers. This includes exporting the product or marketing it in new

regions. Porac, Pollock and Mishina (2004) argue that product extension and market

development significantly affect a firm's growth, but it is not affecting a firm's technological

capabilities. This led to the following hypothesis:

Hypothesis 3b: Acquisitions motivated by market penetration or market development

negatively affect innovation performance within the high-tech sector?

With market penetration as a motive for an acquisition, the primary purpose would be

to expand the company, where innovation is not a driver (Eagle and Brennan, 2007). With

market development, the purpose is to develop ‘the market’ (Ansoff, 1957). This development

could lead to new insights towards products and services as well. This led to the following

hypothesis:

Hypothesis 4b: Acquisitions with a motive categorized as 'market penetration' have a lower

impact on innovation performance than acquisitions with 'market development' as a

motive.

3. METHODOLOGY

3.1. Data collection

In this study, the level of analysis refers to the companies that are engaged in

acquisition deals and not to individual acquisitions. The reason for this approach is mainly

that technological performance is usually measured at the company level and not at the level

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of an individual acquisition. It is difficult to trace the technological performance of each

individual acquisition where the acquisition is small. By contrast, the combined effect of

several acquisitions of a company is easy to trace. Furthermore, the registration of

technological performance or innovation performance, e.g., through patents, usually occurs at

the company level at large and not at the level of the acquired or merged business unit. As

mentioned above, previous research reveals that M&As are expected to affect the

technological performance of companies in R&D-intensive industries. Therefore, we chose to

focus on companies in the international high-tech sector. Furthermore, we focus on the entire

high-tech sector instead of a single sub-sector because we retrieve a sample as large as

possible through this way. An important side note is that we are focused on acquisitions only,

so we exclude the mergers from our study.

First, we have obtained a dataset from Thomson Reuters SDC, which included 5325

acquisitions in the high-tech industry. Subsequently, we filled in the motive behind the

acquisition using Ansoff categories. These categories were coded as; MP (market

penetration), MD (market development), PD (product development) and D (diversification).

We then retrieved M&A data about this sector from Zephyr (see Appendix A) by using the US

SIC codes (see Appendix B) that indicate the sector an industry is in. The codes have been

obtained from the SDC dataset as well. Besides the US SIC codes, we also used “completed”

acquisitions only, set the date of the acquisition from 2000 to 2020 and inserted the Ticker

symbols* as well to get the data as representative as possible. Through this way, we matched

acquisitions based on the strategy stated above. We did not need any specific information

about the acquisition except information about the company that made the acquisition.

Therefore, we retrieved the BvD ID numbers from Zephyr from the acquiring companies. We

used these BvD ID numbers to retrieve company data from ORBIS to use for control

variables. For the patent data, we made use of the European patent and trademark office

(EPO).

As we look at the descriptive statistics, we have a minimum of 1 with a maximum of

2499 patents on 3,940 observations. The mean was approximately 181 patents filed per

company per year. We see that the acquisitions gave us 5,052 observations with a mean of

17.7 acquisitions per company. The minimum was one acquisition with a maximum of 84 per

company. The difference in patent_count and TotalAcquisitions is because not all acquiring

companies have filed patents for each observation.

*Ticker symbols: representing specific assets or securities listed on a stock exchange or traded publicly.

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3.2. Variables

We took the patent intensity of internationally filed patents of the companies in the

sample from 2000 to 2020 as the dependent variable. This patent data has been obtained from

the European Patent Office (EPO). This patent indicator for technological performance is

subject to a debate regarding its shortcomings and bias (Archibuci, 1992). However, despite

these shortcomings, it is accepted as the most suitable indicator that authorizes us to compare

the technological performance of firms (Acs and Audretsch, 1989). Even researchers that are

critical about the overall use of patents as an indicator of technological- and innovation

performance acknowledge that patents are appropriate in the context of the high-tech sector

(Arundel and Kabla, 1998). Moreover, patents correlate well with other measures of

innovation output such as new products, innovation and invention counts, and sales growth

(Achilladelis, Schwarzkopf and Cines, 1987). However, to use patents as a measure of

innovation performance comes with limitations. Sometimes, innovations are not patentable

where others are not patented, and patented innovations can differ in economic value (Cohen

and Levin, 1989). As we only focus on the high-tech sector, which is one single industrial

sector, such problems will be minimized due to the factors that affect the patent tendency,

which is likely to be stable within such context (Basberg, 1987; Cohen and Levin, 1989;

Griliches, 1990).

As an independent variable, we took the total amount of acquisitions a company made

per year. These deals were retrieved from Zephyr in combination with the SDC dataset as we

merged both datasets on US SIC codes, Ticker symbols, the year of the deal and the

'completed' deals only. Also, we took six dummy variables for Ansoff: TECH for

technological motivated acquisitions and NONtech for non-technological motivated

acquisitions. Besides, we will use all four motives of Ansoff to show the relation of each of

these motives with innovation performance. For this, we used the variables PDtech, Dtech,

MPnontech and MDnontech.

As we need to control for other factors that may influence the innovation performance,

we took the logarithms of the control variables. We did this because after running the swilk

test in Stata, we found out that all control variables seemed to have a prob>z of 0.00000,

which means that we can reject the hypothesis that the control variables are normally

distributed. For that sake, we needed to create the log variables to transform the highly

skewed variables into a more normalized dataset. For the controls we used for every firm the;

firm’s size as the natural logarithm of the number of employees (log_employees), total net

income (log_netincome), total R&D expenses (log_rdexpenses), deal value (log_dealvalue)

16

and total return on assets (log_roa). These control variables enhance the study's validity as

they limit the impact of confounding and other irrelevant variables. This will help us to

establish a causal relationship between our variables of interest.

3.3. Research design

We will run a negative binomial regression because our dependent variable is a count

variable (patent_count). The Poisson regression was an option, but we saw that the mean and

variance of our dependent variable are very different. Therefore, we ignored this option. Also,

the linear regression was an option. However, it does not fit as we have created a scatterplot

regarding the dependent and independent variables, and we did not find coherence between

them. In addition, we found that we have repeated measures in our sample, which indicates

that a linear regression is not suitable for results in this study. As we have a count variable as

our dependent variable for every measurement, we chose to run the negative binomial

regression to measure all our hypotheses.

3.4. Moderator

We also include a regression table with a moderator to measure the effect between TECH

motives and R&D expenses on the dependent variable patent_count. With this interaction

effect, we want to measure whether the moderator R&D expenses will influence the

relationship of TECH motives and innovation performance, as we mentioned in subsection

2.3.

4. Data

All the data is retrieved from various databases. We used the SDC database, Zephyr,

ORBIS and the European Patent Office (EPO). After thoroughly selecting the companies in

our sample, we wanted to consider in our analysis; we were left with a sample of 5,052

observations on acquisitions in the high-tech sector. This sample resulted from matching the

companies from the SDC database with the acquisition data from Zephyr. In this section, we

will discuss the various variables that were considered while doing the research. Also, we will

show some tables on the different descriptive statistics and a correlation matrix that will

represent the correlation between the variables that were used for the results. Table 1 reports

17

the definitions of the variables that were used in the analysis. In Table 2, the descriptive

statistics are shown.

Table 1:

Description of the variables

Variable Description

Patent_count

TotalAcquisitions

The total amount of patents that were

granted to a company. The patent count is

per company per year. The data on this

variable has been retrieved from a patent

database provided by Killian McCarthy.

The total acquisitions that a company made

per company per year. This variable has

been retrieved from the SDC database in

combination with Zephyr.

Log_employees*

Log_netincome*

Log_revenues*

The logarithm of the total number of

employees. The logarithm is calculated as

the ln(Totalnumberofemployees) and is

measured per company per year.

The logarithm of the total net income. The

logarithm is calculated as the ln(NetIncome)

and is measured per company per year.

The logarithm of the total revenues. The

logarithm is calculated as the

ln(TotalRevenues) and is measured per

company per year.

Log_roa*

Log_dealvalue

The logarithm of the total return on assets.

The logarithm is calculated as the ln(ROA)

and is measured per company per year.

The logarithm of the deal value of the

acquisition that the acquiring company

made. The logarithm is calculated as

ln(DealValue) and is measured per company

per year. The data on this variable has been

retrieved from the SDC database.

Log_rdexpenses*

TECH

The logarithm of the total R&D expenses.

The logarithm is calculated as

ln(RDExpenses) and is measured per

company per year.

The dummy variable is measured through '0'

or '1' where 0 = non-technological motive

18

NONtech

PDtech

Dtech

MPnontech

MDnontech

Multiple

i.YearOfAcquisition

and 1 = technological motive while the other

variables remain the same.

The dummy variable is measured through '0'

or '1' where 0 = technological motive and 1

= non-technological motive, while the other

variables remain the same.

The dummy variable that is measured

through ‘0’ or ’1’ where 0 = other motive

and 1 = product development motive

The dummy variable is measure through '0'

or '1' where 0 = other motive and 1 =

diversificiation motive

The dummy variable that is measured

through ‘0’ or ’1’ where 0 = other motive

and 1 = market penetration motive

The dummy variable that is measured

through ‘0’ or ’1’ where 0 = other motive

and 1 = market development motive

The dummy variable that is measure

through ‘0’ or ‘1’ where = other motive and

1 = multiple motives

Extra control variable on the year a

company made the acquisition.

*All data on these variables are retrieved from ORBIS, where all company data is available.

19

Table 2:

Descriptive statistics

Every variable reports the number of observations (Obs), mean, standard deviation, the

minimum and maximum value of the observations in the sample. The definitions of the

variables are mentioned in Table 1.

VARIABLES OBS MEAN STD.

DEV.

MIN MAX

patent count 3940 180.7 323.2 1 2499

TotalAcquisitions 5052 17.7 22.7 1 84

Numberofemployees 1968 53,574 59,230 452 305,000

NetIncome 2028 4,908,432 5,506,055 -542,422 20,997,452

TotalRevenues 2010 28,908,537 33,104,399 158,205 1.876e+08

ROA 2046 6.458 4.975 -23.475 21.622

DealValue ($mil) 5052 1434 4726 2.23 68445

RDExpenses 1746 3,577,754 3,403,296 0 13,086,060

NONtech 5052 .635 .481 0 1

TECH 5052 .365 .481 0 1

Table 2 reports the descriptive statistics of the variables of the sample over the period

2000-2020. We see that the observations differ, which can be explained by the fact that we

could not obtain all values from the Orbis database for certain control variables. This

difference in observations has been corrected by the robustness check. We also see that the

number of patents granted to a company has a mean of approximately 181 patents for 3940

observations. The standard deviation is high because the maximum number of patents granted

to a company is 2499 while the mean is around 181. This is the reasoning on why we have

this high standard deviation. This also means that the data on patent_count is more spread out.

By contrast, we see that the mean and standard deviation of TotalAcquisitions are close to

each other, which means that the data are clustered around the mean. For all the other

variables, this is the case from which we can conclude that our data is relatively reliable. An

important side note is that we see that there are 36,5% of the total acquisitions are

technological motivated and we see 63,5% of the acquisitions that are not technological

motivated. This gives us a total of 5,052 (100%) observations. To see whether these different

variables are correlated with each other, we composed a correlation matrix in which we have

20

left out the NONtech variable. We did this because we are mainly focused on the TECH

variable as this is the focus of the research. In Table 3, we illustrate the correlations between

the variables in the correlation matrix.

Table 3: Correlation matrix

VARIABLES (1) (2) (3) (4) (5) (6) (7)

(1) patent_count 1.000

(2) TotalAcquisitios -0.009 1.000

(3) ROA 0.019 0.303 1.000

(4) RDExpenses 0.518 0.590 0.481 1.000

(5) Numberofemployees 0.707 0.246 -0.032 0.688 1.000

(6) DealValue -0.003 -0.042 0.024 0.109 0.098 1.000

(7) TECH -0.036 0.037 0.105 0.066 -0.071 0.007 1.000

In this correlation matrix, we show the correlations between all the variables that we have

used. We note that most of the variables are positively correlated with each other. Before we

created the correlation matrix, we regressed these variables and after that, we did a VIF test.

The VIF value indicates whether multicollinearity is present between two or more variables.

Multicollinearity is a statistical appearance in which two or more explanatory variables in a

regression model are strongly correlated. At least one of them can be predicted by the model.

As we saw in our VIF test that the value of NetIncome and TotalRevenues were above 5, we

decided to drop these variables in the negative binomial regression and in the correlation

matrix to keep the research as representative and feasible as possible.

We see that the correlation between patent_count and Numberofemployees is slightly

above 0.7, but we decided to leave this variable in as this 0.007 surplus is negligible. When

we look at the dependent variable patent_count, we see that it is positive towards the variables

ROA, RDExpenses and Numberofemployees, whereas it is negative for TotalAcquisitions,

DealValue and TECH. This means that, for every variable except the three latter variables

mentioned, if one score increases, so does the other. We left the control variable

i.YearOfAcquisition out of the correlation because this is a factor-variable operator which is

not allowed to run in a correlation. For the variable TotalAcquisitions, we see that all different

variables are significant, which means that if the total number of acquisitions of a company

increases, we will expect that all variables, except DealValue, will increase as well, which

makes total sense.

21

5. Results

For the results section, we chose to run a negative binomial regression. Usually, the

Poisson regression will fit the best when the dependent variable is a count variable. This is the

case in our research, as patent_count is a count variable. However, we did not go for the

Poisson regression because the mean and variance of our dependent variable, patent_count,

were very far apart from each other. This gives the indication of not running a Poisson

regression but a negative binomial regression. This regression is also applicable for a

dependent variable that is a count variable. We ran a linear regression for the same variables

as we added in our negative binomial regression to see for the variance inflation test (VIF)

results. Although, we will not use that regression for our results as the linear regression is not

fitting our research.

In Table 4, we will show the results of the negative binomial regression. To determine the

impact of acquisitions on the innovation performance of a firm, we have included

TotalAcquisitions as the independent variable in our regression. This can be seen in Table 4,

where both values are shown for the TECH motives model but also for the NONtech motives

model. As we already discussed, we dropped the variables log_revenues and log_netincome

in our regression. We dropped these variables due to the high VIF value that it has, which

means that there is multicollinearity between log_netincome, log_revenues and the other

variables. We run four different models in our regression, one that holds for technological

motives (TECH; Model1) and one that holds for non-technological motives (NONtech;

Model2). Also, we run two models that are representing product development and

diversification (PDtech and Dtech; Model3) against market penetration and market

development (MPnontech and MDnontech; Model4). The last model represents the category

where multiple motives were included (Multiple; Model5). We did this to see the differences

in significance towards the dependent variable patent_count.

22

Table 4: Negative binomial regression

Model1 Model2 Model3 Model4 Model5

VARIABLES b/se b/se b/se b/se b/se

(sum)

patent_count

TotalAcquisitions -0.006*** -0.006*** -0.006*** -0.006*** -0.006***

[0.001] [0.001] [0.001] [0.001] [0.001]

log_employees 0.733*** 0.733*** 0.737*** 0.731*** 0.716***

[0.037] [0.037] [0.036] [0.037] [0.037]

log_roa 0.177*** 0.177*** 0.180*** 0.176*** 0.176***

[0.058] [0.058] [0.058] [0.058] [0.057]

log_dealvalue -0.041*** -0.041*** -0.043*** -0.047*** -0.046***

[0.015] [0.015] [0.015] [0.015] [0.016]

log_rdexpenses 0.057 0.057 0.054 0.063 0.078**

[0.041] [0.041] [0.040] [0.041] [0.040]

TECH 0.237***

[0.054]

NONtech

-0.237***

[0.054]

PDtech

0.253***

[0.056]

Dtech

0.052

[0.107]

MPnontech

-0.207***

[0.054]

MDnontech

-0.258***

[0.096]

Multiple

-0.287

[0.182]

Constant -4.304*** -4.067*** -4.282*** -4.091*** -4.270***

[0.500] [0.509] [0.499] [0.510] [0.515]

Adjusted R-

squared

Observations 1168 1168 1168 1168 1168

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 4 shows us the regression results and if we look at the values representing the

TECH variable (Model1), we see this value is positive. This contrasts with the value TECH in

the correlation matrix in Table 3 where this value is negative. This is due to the difference in

variables in the correlation matrix versus the regression table as we left the extra control

variable on year i.YearOfAcquisitions out of the correlation because, as mentioned earlier, this

23

is a factor-variable operator that cannot run in a correlation. When we look at Table 4, we see

that all outcomes on Models 1 and 2 are the same except for the values of TECH and

NONtech. When we focus on the variable TotalAcquisitions, which indicates the total

acquisitions a company has made, we find a significant negative effect for all models at the

1% level. This gives us enough evidence to note that we found support for Hypothesis 1.

Also, we see that the variables log_employees, log_roa and log_rdexpenses are positively

significant at the 1% level. Conversely, the variable log_dealvalue is negative significant at

the 1% level. Finally, when we focus on the motive variables, we see that the TECH variable

is positively significant to patent_count, whereas NONtech is negatively significant to

patent_count. Through this, we find a statistically positive significant relation at the 1% level

for TECH motives, which is one of the main findings of this study. Besides that, we find a

statistically negative significant relation at the 1% level for NONtech motives. Therefore, we

have enough evidence to support Hypothesis 3b.

When we look at Models 3 and 4 representing all Ansoff categories individually, we note

that the value for variable PDtech is positively significant at a 1% level. Moreover, when we

look at Dtech, the value shows that it is not significant at all. The variables MPnontech and

MDnontech are both negatively significant. MPnontech is significant at a 1% level, whereas

MDnontech is significant at a 5% level. Through these results, we find that we have enough

evidence to support Hypothesis 4a and 4b. When we look at the variable ‘multiple’, we see

that there is no significant value.

24

Table 5: Moderation effect

-1 -2

VARIABLES patent_count /

TotalAcquisitions -0.006***

(0.001)

log_employees 0.731***

(0.037)

log_roa 0.180***

(0.058)

log_dealvalue -0.042***

(0.015)

log_rdexpenses 0.051

(0.041)

0b.TECH#co.log_rdexpenses 0.000

(0.000)

1.TECH#c.log_rdexpenses 0.015***

(0.004)

lnalpha -0.276***

(0.045)

Constant -4.176***

(0.506)

Observations 1,168 1,168

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

In Table 5, we show the moderation effect regression. We see that the moderation effect

of TECH motive and R&D expenses is positively significant to patent_count on the 1% level.

This gives us enough evidence to support Hypothesis 2. We also have run regression models

to see the effects a third variable may have on the relationship between patent_count and

NONtech motives. However, we found out that there are no positive interaction effects for

any of the variables in the sample.

25

6. Discussion and implications

As we mentioned earlier, mergers and acquisitions are a great tool to increase innovation

performance for a company. In our study, we mainly focused on acquisitions. This has to do

with the amount of knowledge and resources that are brought in with an acquisition.

However, the motive on why the acquisition was made is an important factor as well. This

thesis investigates the effect of 1) the total acquisitions made by a company and 2) the motive

on why an acquisition was made, on innovation performance measured by the number of

granted patents. Specifically, we want to determine to what extent acquisitions and the

motives behind an acquisition are important for innovation performance.

First, we analyze the relations between the dependent, independent and control variables

in our sample of 5,025 observations in the high-tech industry. Secondly, we estimate a model

over the entire sample, including the Ansoff dummy variables, to precisely estimate the effect

of the acquisitions and the motives behind these deals on innovation performance. In this

model, we also include all Ansoff motives individually to see what effect all the motives have

individually on innovation performance. Our paper contributes to the current literature on the

impact on innovation performance that was mentioned in section 2. We provide deeper

insights by including the motives and motives literature in this study. Moreover, we control

for other factors and the implications of the results we can use for practical and theoretical

applications. This study provides important implications on what is or is not improving a

company's innovation performance in the high-tech sector.

For the impact of acquisitions on innovation performance, we find some interesting

results. The number of patents granted is negatively significant with the total acquisitions

made by a company. This tells us that innovation performance is not increasing by acquiring

companies. These findings contrast with the outcomes of the study of Hagedoorn and

Duysters (2000). They concluded that the effect of technologically related acquisitions is

marginally significant. Their study shows that acquisitions can contribute to improving the

technological performance of firms in a high-tech industry. However, prior studies reveal that

there are mixed findings on this topic as it depends on various factors. Our findings are in line

with the study of Schenk (2002), who argued that M&As have a negative impact on a firm's

innovation performance. This statement can be supported by possible causes of an M&A

failure which are mentioned in subsection 2.4.; M&As and motives.

When we look at the impact of the technological and non-technological motives on

innovation performance, we can conclude that technological motivated acquisitions improve

26

innovation performance. By contrast, non-technological motivated acquisitions are not

improving innovation performance. Hence, these results align with Hypothesis 3a and

Hypothesis 3b, which are defined in section 2.5. As we look deeper into the TECH and

NONtech motives and look at the results for every motive individually, we see significant

results as well. For example, the value for product development motive shows that

acquisitions with product development as a motive do increase a company's innovation

performance. By contrast, the diversification motive does not show a significant effect. This

gives us enough evidence to support Hypothesis 4a.

On the other hand, for NONtech motives, we see that both motives are not increasing

innovation performance and both are negatively significant on the 1% level. However, market

penetration is more negative than market development, giving us enough evidence to support

Hypothesis 4b. These findings align with the projections that Ansoff (1957) made in his

article about the different growth strategies. For the moderator effect, we can say that R&D

expenses as a moderator have a positive and significant impact on the relationship between a

technological motive and innovation performance. As a result, we have enough evidence to

support Hypothesis 5. This means that R&D expenses have a positive influence on innovation

performance when the motive is technological. Hence, DeCarolis and Deeds (1999) findings

are in line with our findings regarding the moderation effect of R&D expenses.

Since we could not generate significant positive results for the impact of acquisitions on

innovation performance, we suggest this topic for further research. We suggest this because,

in the literature, we found evidence that the innovation performance could increase by

focusing on acquisitions and we did not find this back in our results. This could be due to the

focus on the high-tech sector. Besides, in this thesis, we make a distinction between the

growth categories of Ansoff in the high-tech sector. A distinction between these categories in

another industry could potentially lead to interesting results.

27

7. CONCLUSION

This research aimed to identify the impact of acquisitions on innovation performance

expressed in the number of granted patents. Besides, it has also shown the effect of the

different motives behind an acquisition towards innovation performance. We selected

companies in the high-tech sector as this sector is the most related to innovation. Our results

showed that the extent to which a company is active in acquisitions does not positively

influence its innovation performance. In addition, we also tested whether technological vs

non-technological motives have an impact on innovation performance. This revealed that

acquisitions with a technological motive have a better effect on innovation performance. Non-

technological motives, on the other hand, score negatively. We also tested all motives

individually to check for which motives innovation performance would score best. We saw

that this was the case for the motive product development, which is, of course, a rather logical

outcome. For the other motives, we found no positively significant values. Finally, we looked

at an interaction effect between technological motives and R&D expenses versus innovation

performance. We found that the moderator, R&D expenses, positively affected the

relationship between technological motives and the dependent variable patent count.

28

Appendix A – search strategy Zephyr

29

Appendix B – US SIC codes high-tech sector

3721. Aircraft

3724. Aircraft engines and engine parts

3728. Aircraft parts and auxiliary equipment, not elsewhere specified

2836. Biological products, except diagnostic substances

3578. Calculating and accounting machines, except electronic computers

3624. Carbon and graphite products

3646. Commercial, industrial and institutional electric lighting fixtures

3669. Communications equipment, not elsewhere specified

7376. Computer facilities management services

3577. Computer peripheral equipment, not elsewhere specified

3572. Computer storage devices

3575. Computer terminals

3643. Current-carrying wiring devices

3357. Drawing and insulating of nonferrous wire

3634. Electric housewares and fans

3641. Electric lamp bulbs and tubes

3694. Electrical equipment for internal combustion engines

3629. Electrical industrial apparatus, not elsewhere specified

3699. Electrical machinery, equipment and supplies, not elsewhere specified

3671. Electron tubes

3675. Electronic capacitors

3677. Electronic coils, transformers and other inductors

3679. Electronic components, not elsewhere specified

3571. Electronic computers

3678. Electronic connectors

3676. Electronic resistors

3769. Guided missile and space vehicle parts and auxiliary equipment, not elsewhere

specified

3764. Guided missile and space vehicle propulsion units and propulsion unit parts

3761. Guided missiles and space vehicles

3639. Household appliances, not elsewhere specified

3651. Household audio and video equipment

3631. Household cooking equipment

3633. Household laundry equipment

3632. Household refrigerators and home and farm freezers

2835. In vitro and in vivo diagnostic substances

3648. Lighting equipment, not elsewhere specified

3695. Magnetic and optical recording media

2833. Medicinal chemicals and botanical products

3621. Motors and generators

3644. Noncurrent-carrying wiring devices

3579. Office machines, not elsewhere specified

2834. Pharmaceutical preparations

3652. Phonograph records and pre-recorded audio tapes and disks

3612. Power, distribution and specialty transformers

3692. Primary batteries, dry and wet

3672. Printed circuit boards

3663. Radio and television broadcasting and communications equipment

3625. Relays and industrial controls

3645. Residential electric lighting fixtures

3674. Semiconductors and related devices

3691. Storage batteries

3613. Switchgear and switchboard apparatus 3661. Telephone and telegraph apparatus

30

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