The relationship between board characteristics and diversification: do the

differences between one- and two-tier boards have an effect?

M.P.H.J. (Michel) Witte

Final Version: 19-01-2012

The relationship between board characteristics and diversification: do the

differences between one- and two-tier boards have an effect?

Master Thesis of the department of Organization & Strategy

Tilburg School of Economics and Management

Tilburg University

ANR: 115960

Name: Michel Witte

Supervisor: Dr. E. Golovko

Second Reader: Drs. J.M. Dumas

Date of defense: 09-02-2012

I

Management Summary

What influences the decisions to diversify the product portfolio or stick to the core product has been the

subject of research by strategic management scholars for some time. Since this decision is made by the

board, it can be seen as not solely based on incentives on firm level, as most managers personally tend to

profit as well from diversifying. It can reduce risk and increases job security (Baysinger & Hoskisson, 1990).

Furthermore, some literature states that strategic choices are partially predicted by managerial background

characteristics (e.g. Hambrick & Mason, 1984). Therefore board characteristics as, for instance, age,

education and gender can have an effect on diversification.

This study tried to determine the exact role of the management and supervisory boards as well as the

characteristics of the people on these boards, in the strategic choice for diversification. Special attention

was given to the difference between one-tier and two-tier boards. The decision to diversify has been

looked at from several different perspectives. This paper merely used a strategic management perspective

and has combined arguments from the Resource-Based View, Agency theory and Stewardship theory.

These theories were used to formulate five hypotheses that were tested cross-sectional using a sample of

165 corporations from Germany, the Netherlands, the UK, Ireland, Belgium and Luxemburg.

This paper analyzed the differences between boards with respect to diversification by giving the answer to

the two research questions: first, which board characteristics affect the decision to engage in product

diversification and secondly, does the difference in board structure have an effect on this decision?

The results of the empirical study of the sample, which included the statistical testing of the hypotheses, as

well as t-tests, gave a clear answer to the first question. The data gave no support for the influence on

diversification by any of the board characteristics, although these were proposed by other scholars and as

such could be reliably retested in this paper.

The answer to the second question, the main topic of this research, the relationship between Board type

and diversification, is less simple. The correlation matrix and t-test showed an, albeit weak, positive

relationship. The regression analysis, however, turned out insignificant. Therefore it can be concluded that

there are indeed differences between one-tier and two-tier boards. But they cannot be explained by the

regressions used in this research, which is why in this study it is suggested how this relationship can

possibly be re-tested.

II

Foreword

This master thesis is the final product of over six years of studying at Tilburg University. It was a memorable

period, to say the least. The experiences I had over the years, the labor I put in and the friends I have made,

all lead to the final goal: graduation.

First, I have a more serious note. The inspiration for making this Master thesis mainly came from one man:

Dr. A. van Oijen. His inspirational classes on Corporate-Level Strategy made me think about the possibilities

to write my thesis on a topic in this field. After I consulted him whether my initial idea was good enough, he

told me straight up that I would be nearly impossible to make a decent thesis from my first research

proposal. Nevertheless he stimulated me to look further in the field of diversification. The end-result lies in

front of you. I hope it speaks for itself.

It is thanks to many people that have come this far. First of all, much gratitude goes to my parents. Not only

did they provide the (much-needed) financial support, but their wisdom and occasional words of

motivation and encouragement were the main reason I could eventually complete my studies. Special

thanks go to my brothers Philippe, for stealing my idea and using it in his own Master thesis and Didier for

the continued interest in what I was doing. For keeping me busy, while not working on this thesis I thank

my friends of R.A.F. and CCT. Without them my time in Tilburg would have been a lot more boring.

Next I also owe many thanks to my university supervisor Dr. E. Golovko for her advice and support. Without

it I would probably be still (re-)writing. Finally I would like to thank Drs. J.M. Dumas for taking seat in the

examining committee.

Michel Witte

Tilburg, January 2012

III

Table of Contents

MANAGEMENT SUMMARY .......................................................................................................................................... I

FOREWORD ................................................................................................................................................................ II

TABLE OF CONTENTS ................................................................................................................................................. III

1. INTRODUCTION ................................................................................................................................................. 1

2. THEORY AND BACKGROUND ............................................................................................................................. 3

2.1 THE MOTIVES TO DIVERSIFY ......................................................................................................................................... 3 2.1.1 Resource-Based View .................................................................................................................................. 3 2.1.2 Agency Theory ............................................................................................................................................. 5 2.1.3 Stewardship Theory .................................................................................................................................... 5

2.2 EMPIRICAL EVIDENCE ON THE FACTORS THAT INFLUENCE DIVERSIFICATION ............................................................................ 6 2.2.1 What affects diversification? ..................................................................................................................... 6 2.2.2 The influence on the level of diversification by different characteristics of the board ............................... 7

3. DATA AND METHODS .......................................................................................................................................15

3.1 SAMPLE SELECTION AND DATA COLLECTION .................................................................................................................. 15 3.2 VARIABLE SPECIFICATION AND MEASUREMENT............................................................................................................... 16 3.3 EMPIRICAL MODEL ................................................................................................................................................... 20

4. RESULTS ...........................................................................................................................................................22

4.1 DESCRIPTIVE STATISTICS ............................................................................................................................................ 22 4.2 PRELIMINARY T-TESTS ............................................................................................................................................... 24 4.3 REGRESSION RESULTS ............................................................................................................................................... 25

5. CONCLUSIONS AND RECOMMENDATIONS .......................................................................................................30

5.1 SUMMARY AND CONCLUSION ..................................................................................................................................... 30 5.2 LIMITATIONS .......................................................................................................................................................... 30 5.3 SUGGESTIONS FOR FUTURE RESEARCH ......................................................................................................................... 31

REFERENCES ..............................................................................................................................................................32

APPENDICES ............................................................................................................................................................ VIII

APPENDIX 1: INDUSTRY DUMMY LABEL CATEGORIZATION ...................................................................................................... VIII APPENDIX 2: THE COMPANIES IN THE SAMPLE ....................................................................................................................... IX

1

1. Introduction

The strategic decision whether to diversify the product portfolio or focusing on the core business has been

since long a source of heated debates. The decision to diversify ultimately lies in the hands of the board.

However, this decision can be seen as not solely based on incentives on firm level, as most managers

personally tend to profit as well from diversifying, as it reduces risk for them regarding bonuses and

increases job security (Baysinger & Hoskisson, 1990). Furthermore, strategic management literature states

that strategic choices are partially predicted by managerial background characteristics (Hambrick & Mason,

1984). Therefore board characteristics, such as age, education and gender can have an effect on

diversification.

Westphal and Frederickson (2001) found that supervisory board members exert relatively little influence

over major decisions such as corporate diversification. Still, their influence should not be neglected as

Anderson, Bates, Bizjak and Lemmon (2000) found. They did research on whether an inadequately

performing supervisory board led to an increased incentive to diversify, but found no evidence that this

was the case. Furthermore, Goranova, Alessandri, Brandes and Dharwadkar (2007) even suggested that a

good supervisory board, one that is strict and has a ‘hands-on’ mentality, even results in increased

incentive alignment and therefore is negatively related to corporate diversification. Therefore, if, for

instance, people on the management board were best friends in university, this could be potentially

harmful for the company. These findings are of particular interest for the so-called two tier board system as

is in place in countries as Germany and the Netherlands. Ruigrok, Peck and Keller (2006) found that

supervisory boards greatly mitigate the harmful behavior that can go hand in hand with unnecessary

diversification.

This study will try to determine the exact relationship between the strategic choice for diversification and

the role of boards, as well as the influence of the characteristics of the people on these boards in this

decision. This topic was recently investigated to some extent by Chen, Dyball and Wright (2009). Special

attention will be given to the difference between one-tier and two-tier boards. In one-tier boards there is a

division between executive directors and independent directors, the latter forming the ‘control’ mechanism

in the board. Most of the time, these independent directors have a lot of experience with other large

companies. In the two-tier board system the controlling element is a supervisory board, which, in most

cases, has law-mandated representation from the employees. Social and legal developments have

influenced that the one-tier board system in prevalent in the so called Anglo-Saxon countries, such as the

UK and Ireland. The two-tier board system is mainly used in Rhineland countries, such as Germany and the

Netherlands. These differences could potentially heavily influence the role of management regarding the

diversification decision.

2

To summarize, there are two central problem statements that will be used to come to conclusions:

Which board characteristics affect the decision to engage in product diversification?

Does the difference in board structure have an effect on this decision?

To extract the answer to this problem statement, some research questions will be posed to systematically

come to sub-conclusions, which in turn can give a wide understanding of the problem area.

First, which characteristics have a significant impact on eventual diversification decision?

In order to understand research on diversification, Ramanujam and Varadarajan (1989) suggested that four

influences induce a firm to diversify: the general environment, the industry environment, firm

characteristics and overall performance. The specific characteristics of the firms are an interesting

influence. It covers everything from how the company is governed to demographics, such as the average

age. Especially in this last category there are variables that this study will try to investigate and will focus on

the demographics of the people on the board in detail. Furthermore, top management teams’ demographic

characteristics can help to predict changes in diversification (Wiersema & Bantel, 1992). From this starting

point the influence of board characteristics will be explored further.

Secondly, what is the influence of the type of board system on diversification decisions?

Hoskisson and Hitt (1990) established that managerial motives for diversification may exist independent of

resources and incentives. Therefore these may serve as a motive for increased diversification. They also

found that these motives could be limited by governance. Mechanisms such as a strict board of directors

and corporate control limit tendencies to over diversify. As already mentioned, the difference between

one-tier and two-tier boards is interesting in this case. And from a broader perspective this also means

difference between countries, namely Anglo-Saxon countries and Rhineland countries. Ground-breaking

work in the clarification of the distinctions between these two models was done by Albert (1993). He

described the Anglo-Saxon model as market-oriented, more focused on short-term profits. In contrast the

Rhineland model is more society-oriented and opts for a more sustainable, future-oriented approach. In

essence, the only goal of an Anglo-Saxon company is to maximize profits. This is why board remuneration in

these countries often is linked to bonuses which, as already mentioned, could lead to misaligned

managerial motives. This as opposed to the Rhineland modeled companies, which opt for a more

sustainable way of doing business. Striking differences between the Rhineland and Anglo-Saxon are for

instance the obligation for large companies to have employee representation on the board (Koen, 2005)

and the increased involvement of banks (Aguilera & Jackson, 2003). As such, the board system will receive

extra attention in this study.

3

2. Theory and Background

This chapter will give a first introduction on why firms can decide to diversify. Three perspectives from

management literature will be looked at. Next, empirical studies will be analyzed to establish which

variables have proven to influence the diversification decision. Then also the specific focus of this study will

be discussed in-depth. The differences in one-tier and two-tier management boards, differences in

governance and also the parallels between this difference and the division between Anglo-Saxon and

Rhineland countries will be described.

2.1 The motives to diversify

The decision to diversify has been looked at from several different perspectives. Scholars in industrial

organization, strategic management and finance have all looked at the upsides and downsides of

diversification. This paper merely uses a strategic management perspective, stemming greatly from ground-

breaking work by Rumelt (1974).

There are two main motives suggested in the literature on why firms diversify. They will do so for either

synergistic or financial reasons (Amit & Livnat, 1988). However, the rationale behind diversification focuses

on three mayor stances that have dominated the strategic management literature on this subject: the

Resource-Based View, Agency theory and, more recently, Stewardship theory.

2.1.1 Resource-Based View

The resource-based view (RBV) has been an important theory in the strategic management literature for

some time now. It argues that firms diversify in response to excess capacity in productive factors

(Montgomery, 1994). Though this description does not completely cover how and why this excess capacity

is utilized. There are several lines of reasoning that scholars have used to argument for this statement. For

instance, some argue that resources are to some degree transferable across products and industries.

Because these are valuable, it may not be optimal for a firm to go slowly out of business as sales of its

products decline. Rather it may be better to move into areas where potential demand for new products is

greater than existing output, by diversifying (Chenhall, 1984; Matsusaka, 2001). This notion is supported by

Hoskisson and Hitt (1990), who found that excess capacity of tangible assets, such as plants and

warehouses, can be utilized for very closely related products. However, sometimes this reasoning can prove

to be not valid anymore. Namely, in a situation where the firm is financially in such dire straits that

divesting parts of the company to raise cash is the only option for the company. In which case decreases in

diversification can also be associated with financial distress (Denis, Denis & Sarin, 1997).

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An extension of this theory claims that firms not only use resources to diversify out of self-preservation but

also to exploit operating synergies (Amit & Livnat, 1988; Ramanujam & Varadarajan, 1989). A tactic that is

often used by multi-business organizations, which try to achieve economies of scope by sharing rare and

costly to imitate strategic assets among their businesses (Wang & Barney, 2006). Examples of this are

sharing customer data and marketing information, or joint production by two entities at the same location

(Klein & Saidenberg, 2000).

Others argue that firms have different incentives if excess capacity occurs. They propose that firms diversify

in order to utilize excess resources which could not be otherwise sold or leased because of the high

transaction costs that this would entail (Teece, 1982; Chatterjee & Wernerfelt, 1991; Fox & Hamilton,

1994). Especially companies in niche markets or with highly specific assets are likely to diversify for this

reason. Companies can choose to sometimes only break-even on the non-core product, as it enables them

to amortize their assets over more units.

The final argument RBV proposes is that diversification is used to reduce risk (McDougall & Round, 1984,

Hoskisson, Hitt & Hill, 1991). It can be a defensive move by management to mitigate undesirable

characteristics in a firm's dominant industrial environment (Wiersema & Bantel, 1992). For instance, firms

in an industry that is highly dependent on the state of the economy, such as luxury goods. These might be

tempted to also invest in something more secure, such as foods. However, firms can also have periods that

induce and reduce risk taking, depending on the level diversification and associated control system

attributes (Hoskisson, Hitt & Hill, 1991). A positive forecast for the economy might tempt companies to

differentiate less in comparison what would have happened in a normal situation.

Reducing risk can also be associated with the value of core firm resources (Wang and Barney, 2006). An

example of this reasoning is the situation for firms that are greatly affected by seasonality (Penrose, 1995

[1959]). For example an ice-cream shop can sell ice-cream during the summer, but sell skis from the same

building during the winter. This way the core firm resource, the building, is optimally used. Besides reducing

risk, another positive consequence of this strategy is that firms have more stable cash flow as seasonality

effects are smoothed. As a result firms can attempt to gain financial benefits from their ability to increase

leverage (Amit & Livnat, 1988). Furthermore, firms can use so-called cross-subsidization, meaning that the

firm uses its profits from one market to support predatory pricing activities in another (Penrose, 1995

[1959]). The losses that normally would have occurred can be covered by the extra income. Firms can use

this tactic so to keep their market share or try to push competitors out of the market, after which they can

bring back the price to a normal level.

5

Concluding, the resource-based view proposes four main reasons on why and how firms diversify. They do

so to reduce risk, to overcome transaction costs, to exploit operating synergies and sometimes out of self-

preservation of the firm to keep market-share in existing markets or get a toehold in new markets.

2.1.2 Agency Theory

Agency theory mainly focuses on the relationship managers have with the firm and the effect this has on

the company (Jensen & Meckling, 1976). A popular explanation of agency theory emphasizes the ‘dark side’

of diversification: firms are plagued with agency problems that allow managers to enter new businesses,

getting benefits in the process that they may reap at the expense of its shareholders (Montgomery, 1994;

Ramanujam & Varadarajan, 1989; Matsusaka, 2001). These private benefits may come from a variety of

sources. They may arise from prestige or better career prospects associated with running a more diversified

firm. Private benefits may arise because running a more diversified firm increases managers’ pay (Aggarwal

& Samwick, 2003). As already mentioned in section 2.1.1, diversification may allow cross-subsidization of

unprofitable divisions. This not only can stimulate the core product of the firm, but also can be a way to

cover up losses, as they might not appear on consolidated balance sheets (Klein & Saidenberg, 2000). This

practice can be harmful since the use of financial controls becomes more common as firms diversify

(Hoskisson, Hitt & Hill, 1993). This means that board members can influence the height of a bonus or

increase the likelihood of one being paid by means of creative accounting.

Beside direct benefits, managers can also benefit themselves indirectly. Managers may diversify to protect

their specific human capital from firm risk (Amihud & Lev 1981; Anderson, Bates, Bizjak & Lemmon, 2000).

If, for example, the current manager is about to be replaced by someone who would run the firm better

than him, he has an incentive to diversify into areas where he has a comparative management advantage

(Shleifer & Vishny, 1989). These scholars go even further by claiming that in some cases managers can

diversify to industries where their skills are essential. In this way they entrench themselves in the company

and increase job security.

Concluding, agency theory claims that managers have several reasons to diversify. They do so out of self-

interest: gaining prestige, to increase pay-checks, protecting their human capital or increasing job security

by entrenching themselves.

2.1.3 Stewardship Theory

The newest of the three stances is stewardship theory, that proposes that managers are not motivated by

individual goals, but rather are stewards whose motives are aligned with the objectives of their principals

(Davis, Schoorman & Donaldson, 1997). In other words, managers and staff work in the best interest of the

company and act accordingly. This creates some interesting reasons for diversification. For instance, Hyland

6

and Diltz (2002) suggested that diversifying firms have not engaged in as much research and development

as their non-diversifying counterparts. In order to grow or perhaps even maintain their current status, they

must buy growth in areas outside of where they are currently operating. Following somewhat the same

reasoning Montgomery (1994) found that young and growing businesses have plenty of profitable

opportunities in which to re-invest earnings. However, as businesses mature, these opportunities become

scarce, and managers begin to use cash flows from earlier innovative efforts to invest in other areas.

Another argument stemming from stewardship theory is that diversification can be used by managers to

increase profitability (McDougall & Round, 1984; Fox & Hamilton, 1994). However, this is a highly contested

argument since the increase in profitability caused by diversification has both been confirmed (Palepu,

1985; Amit & Livnnat, 1988) as well as rejected (McDougall & Round, 1984; Montgomery, 1994; Matsuaka,

2001). Furthermore it has been shown that the effect diversification has on performance can differ

between countries (Mayer & Whittington, 2003). Concluding, stewardship theory suggests that companies

diversify for several reasons: to ensure growth, to invest free cash or to increase profitability.

2.2 Empirical evidence on the factors that influence diversification

While the theory in the previous sections already gives a good overview on what exactly drives

management to diversify their company, there have also been some studies that have been testing these

theories statistically. Therefore, in this paragraph an overview will be given on the factors that have been

proven by management literature to have an influence on the diversification decision. In addition, this

study focuses particularly on the different characteristics of the board and proposes to add a variable,

namely the way the board is structured.

2.2.1 What affects diversification?

Strategic management literature, and to a lesser extent the financial literature, identified numerous factors

that affect diversification decision. Among these factors are also a lot of variables that have a link with how

and by whom the company is governed. There are two streams in the literature on governance: one that

focuses especially on the CEO and a stance that looks at governance in a much broader perspective.

Some scholars see the role of the CEO, as key decision maker, as vital in the process of change. Jin (2002)

and Field and Keys (2003) did research on CEO characteristics and decisions that affect firm risk. They found

that a higher level of non-diversifiable wealth the CEO has invested in the firm, the more likely that an

acquisition will be diversifying in nature. Thus, they provided evidence for the argument from resource-

based view theory that diversification is part of protecting their personal risk. Ruigrok, Peck and Keller

(2006) found even more evidence for this problem as they showed that the level of diversification, for

personal reasons, increases when a company combines the roles of the CEO and chairman.

7

Other researchers however, mainly focus on the role of governance. A subject that is heavily discussed

from this stance is the relationship between remuneration of board members and diversification. In

conjunction with the agency theory discussed earlier, increases in payment and incentive schemes have a

great correlation with diversification. Higher levels of pay, for instance, lead to increased diversification

(Anderson, Bates, Bizjak & Lemmon, 2000). Also the proportion received as a bonus will increase the

diversification in firms (Napier & Smith, 1987). But remuneration policies do not only cover direct monetary

incentives. Larger companies often offer packages with shares and options as a part of the total

remuneration. Board members in diversified firms tend to have lower stock ownership (Anderson, Bates,

Bizjak & Lemmon, 2000) which suggests support for the employment risk-reduction perspective (Goranova,

Alessandri, Brandes & Dharwadkar, 2007). This is a reason widely regarded as one of the problems

associated with diversification from an agency theory view. The level of diversification is not only negatively

related to managerial equity ownership, but also the equity ownership of outside block holders (Denis,

Denis & Sarin, 1997). Still, even if management does not have a stake in the company, ownership can

influence diversification. For instance, it was proven that dominant family ownership also leads to less

diversification (Chenhall, 1984). Most of the companies that are family-owned want to continue the

tradition and the line of trade of the family for as long as possible and do not want to spread their chances

but protect the heritage instead.

This study is seeking to cover both streams to some extent. It will look at how the level of diversification is

influenced by a number of variables. These will vary from the personal characteristics of the all the

executives on the board to the governance system that is used by the company.

2.2.2 The influence on the level of diversification by different characteristics of the board

In this section an overview will be given on the characteristics of the board that already have been proven

by other scholars to have an influence on the diversification decision. Examples of such characteristics are

age, tenure in the organization, education and socioeconomic roots (Hambrick & Mason, 1984). This paper

re-assesses these first three variables and additionally looks at the variables gender and, in particular,

board structure. Most scholars see the influence of the age of board members on diversification as positive.

It influences diversification positively as a higher age provides more incentives for entrenchment. Other

effects include risk-aversion of older people and increase of pay. Education also provides some direction for

the level of diversification. Educated people will be more knowledgeable on diversification and thus will be

better equipped to make risk assessments. The average number of years of tenure in some ways combines

the good and bad points of age and education. A long tenure means directors have a better overview on

the good points of a firm, but also the weaknesses. These can be potentially exploited for personal gains.

Lastly, Gender is also brought forward, as women tend to be more risk-averse and in accordance to social

changes become more involved in the boardroom. The effect this has on diversification has already been

8

researched to some extent. Early work by Sexton and Bowman-Ufton (1990) showed that this influence

might be exaggerated in strategic management studies. Later studies (Hillman, Shropshire & Cannella,

2007; Miller & Del Carmen Triana, 2009) predict that women are believed to have a positive effect on

diversification. Therefore this variable will be re-examined. In addition to these variables this study

proposes to add a variable, namely the way the board is structured. The variables will be explained further

in the next section.

Age

As already mentioned, risk management is an important argument in the discussion on the influence of age

on diversification. Young managers can be willing to take more risk in comparison to their older peers

(Hambrick & Mason, 1984), but at the same time they will be more willing to establish themselves in the

company. They have the drive to work hard and be innovative, which can lead to the company looking at

things from a different perspective. During the process the might find, for instance, new growth markets for

the current product or to streamline the current operation. This can mean that diversification on grounds

suggested by RBV and stewardship theory, such as ensuring growth or finding synergies to operate more

efficiently, may not be necessary. On the other hand, the experience and know-how of the older board

members makes them both indispensable and a risk to the business. Since they know every detail of the

company they can provide excellent insight in where opportunities for potential synergies with other

companies are. However manager age also gauges stamina for a demanding job. Many believe that

management is so demanding that the negative impact of age on stamina leads to poorer performance

(Golec, 1996). This can lead to a situation where the company is looking to bring fresh into the board in

order to ensure stable growth. When this is the case the entrenchment (Schleifer & Vishny, 1989) argument

presented earlier can present itself, because age also measures time until retirement and, hence, the

importance of future job income to the manager (Golec, 1996). The old manager in question will want to

continue his job until the end of his current term to be sure of a steady income, as well as aware of a

situation where he is forced to look for a new job in a climate where it is very difficult for older people to

find a new job. Therefore, taking the entrenchment argument into account, this study proposes the

following hypothesis:

H1: A higher average age of the management team leads to a higher degree of diversification.

Education

The next variable that will be considered is education. Wiersma and Bantel (1992) proposed that

management teams with higher education level were more open to change. In fact, managers in larger

multinationals tend to at least have completed a university education while a considerable number also has

a MBA or PhD. It is this extra knowledge that this study proposes makes a difference for the level of

9

diversification. Professional education in management is associated with moderation. MBA candidates by

their nature probably are less risk prone, as the analytic techniques learned in an MBA program are geared

primarily to avoiding big losses or mistakes (Hambrick & Mason, 1984). Their peers that lack this education

will more willing to take risks of invest in opportunities that in hindsight can prove to be wasteful.

Therefore, a board member with such a degree should know some basic tenets of investing as well as how

to recognize firms with good management (Golec, 1996). This knowledge makes them able to make better

educated choices. In addition, as all highly educated managers are trained to take more risk-averse

decisions they are more able to look at things with the bigger picture in mind. This stance is associated with

diversification. Therefore, regarding education, this study proposes:

H2: A higher level of average education of the management team leads to a higher level of diversification.

Tenure

The level of education is not the only variable that will lead to better informed choices, the level of tenure

as well influences the decision. Managers are better equipped to assess the dynamics of the organization

and the strengths and weaknesses when they have been employed at the company for a long time. As such,

tenure is a better measure of experience than age, since it measures the manager’s survivorship at the job.

Long tenure implies that the management company finds the manager’s ability and performance

satisfactory (Golec, 1996). This argument rules out the stamina argument presented earlier and can be seen

as a legitimate argument against entrenchment. Additionally it strengthens the argument that young

managers can have more of a “gung-ho” mentality, which leads them to try and find growth fast, without

proper consideration of the facts. Indeed, several scholars found that management teams with shorter

organizational tenure have more diversification (Michel & Hambrick, 1992; Wiersema & Bantel, 1992).

However, there is also a negative argument for a long tenure, since it may also indicate that the manager

has few better opportunities because of specialized skills or an unspectacular performance record (Golec,

1996), which brings back the entrenchment argument (Schleifer and Vishny, 1989) yet again. This can also

be difficult if the influence of seniority, especially in hierarchical organizations, is considered. People who

are longer in the organization can push decisions their way, based on the fact that they are more

experienced and as such could be respected by younger managers. However, it can be said that since most

companies are increasingly held accountable by their actions by the public and investors that this scenario

gets more unlikely by the day. Therefore, following the aforementioned scholars, this study proposes:

H3: Longer average organizational tenure leads to a higher level of diversification.

10

Gender

A variable that has become increasingly important as companies try to fulfill equal opportunities policies is

the role of gender in boards. Whether the presence of women in the board has an influence on

diversification has been researched to some extent. However, this research gave some mixed results.

Therefore the role of gender in relation to the level of diversification can be open to interpretational

disputes. This suggests that this variable can have both positive and negative effects. In the section below

the reasoning behind this suggestion is explained further.

As mentioned before, RBV proposed that one of the arguments for diversification is that it is used to reduce

risk (McDougall & Round, 1984). Risk aversion is an important argument in the differences in diversification

posture between men and women. With respect to the risk management, women tend to take lower risks

and try to avoid losses. Also females are less willing than males to become involved in situations with

uncertain outcomes (Sexton & Bowman-Ufton, 1990). The need for insurance is therefore low and

diversification appears a much more appropriate strategy for women (Dwyer, Gilkeson & List, 2002;

Schubert, 2006). This would implicate that more women on the board would lead to more diversification.

But there are also other arguments, stemming from agency and stewardship theory. For instance the

growth, free cash and profitability arguments proposed by stewardship theory are considered. Sexton and

Bowman-Ufton (1990) for example, found that females have less stamina that is needed to maintain a

growth-oriented business. Males are therefore more likely to pursue the growth opportunities presented

by diversifying. The quest for higher profitability also applies this method and would suggest that the lack

of stamina by women would implicate that men are more eager to look for, diversified, profit

opportunities. When following the reasoning of low insurance (Dwyer, Gilkeson & List, 2002; Schubert,

2006), males are more likely to spend their free cash on diversifying acquisitions. When these arguments

are considered, it would mean that less women on the board lead to more diversification.

It seems that there is evidence that a difference in diversification could be attributed by gender, but it

should be noted that studies directly researching the relationship between gender and diversification level

(Hillman, Shropshire & Cannella, 2007; Miller & Del Carmen Triana, 2009) found only a weak, lowly

significant, correlation. The problem, according to Donnell and Hall (1980), is that women do not differ

from men in the ways in which they administer the management process. Therefore, the arguments

stemming from agency theory are applicable to both men and women. In addition, strategic management

literature overuses gender related managerial differences (Sexton & Bowman-Ufton, 1990). Taking all

aforementioned arguments into account, studying gender seems worthwhile, so this paper proposes that:

H4a: A higher percentage of women on the board leads to a lower level of diversification

11

H4b: A higher percentage of women on the board leads to a higher level of diversification

Board Structure

The aforementioned variables are all tested before and will be used to see whether they are also supported

by this research. However, an increasingly important theme in comparative cross-country research is the

differences in organizational form in diverse national settings (Kogut, Walker & Anand, 2002; Wan &

Hoskisson, 2003). This has also an influence on diversification (Wan & Hoskisson, 2003). Therefore, this

study will thoroughly discuss the influence of the differences between several countries in the way the

board is structured. With board structure this paper refers to how boards are organized. For instance, how

higher management cooperates, which control mechanisms are in place and how decision making

procedures work (Aguilera, 2005). This is important because they influence the quality of directors’

deliberation and decisions, the ability of directors to protect shareholder interests and the ability to provide

strategic direction (Pearce & Zahra, 1992). Furthermore, as diversification increases the span of control of

corporate executives, they are no longer able to fully understand the operations of the multiple divisions

(Hoskisson, Hitt & Hill, 1993).

The board structure cannot be determined by preferences of the company alone. Demands from

governments, legal obligations and social advances have created a way in how decisions are being dealt

with. These distinctions can lead to totally different business environments per country and therefore each

company can have a unique board structure. Though, academic literature acknowledges that, generally

speaking, European companies can have one of two systems in place: a one-tier board system or a two-tier

board system.

One-tier boards

Anglo-Saxon countries can be described as ‘liberal market capitalism societies’ (Albert, 1993), which is the

foundation for some essential characteristics of the system. First of all, since the market is supposed to

regulate and support business life, financing is mostly done with equity. Furthermore, the management in

these countries acts out of one sole purpose and that is to make profit. As a consequence shareholders and

institutional investors are relatively passive and ownership can be quite dispersed. Therefore boards are

not always independent of management. Lastly, there are active markets for corporate control and flexible

labor markets (Gedajlovic & Shapiro, 1998; Aguilera & Jackson, 2003) to stimulate entrepreneurial

behavior.

The one-tier boards especially prevail in these countries. In this model, executive directors and non-

executive directors operate together in one organizational layer. Some one-tier boards are dominated by a

majority of executive directors while others are composed of a majority of non-executive directors

12

(Maasen, 1999). Leadership is divided between the CEO and the chairman. This can be the same person.

One-tier boards often use board committees like audit, remuneration and nomination committees

(Maassen, 1999) to cope with problems that the board alone cannot cope with.

Two-tier boards

Rhineland countries adhere to ‘social capitalism’ (Albert, 1993). The increased involvement of banks,

institutions and the government (Kogut, Walker & Anand, 2002) means that shareholders and stakeholders

participate actively and dynamically in the economy. That is why investments are mostly done using long-

term debt. More control elements are present in the system, such as boards that are more independent of

management ownership by large block holders and weak markets for corporate control. The increased

protection of stakeholders and the workers in particular results in rigid labor markets (Gedajlovic & Shapiro,

1998; Aguilera & Jackson, 2003).

A striking element is the influence of banks in the Rhineland system. Especially German banks typically hold

both large debt and equity positions in companies. This can be explained by the fact that shareholders

typically deposit their shares with these financial institutions. This contributes to a relatively high degree of

ownership concentration (Gedajlovic & Shapiro, 1998). Another example is the strong role of employees

(Olie & van Iterson, 2004) in companies. In Germany (Aufsichtsrat) and the Netherlands

(Ondernemingsraad) the supervisory board includes employee representatives (50% in companies with

more than 2000 employees) (Aguilera, 2005).

Due to these social elements, the two-tier board is prevalent in Rhineland countries. It is composed of a

Board of Management (or decision-making unit) and a Supervisory Board (or monitoring unit) (Aguilera,

2005). The management board is usually composed of executive managing directors. Law forbids that

directors combine the CEO and chairman roles in two-tier boards. Because the CEO has no seat in the

supervisory board, its board leadership structure is formally independent from the executive function of

the board (Maassen, 1999). The supervisory board constitutes entirely of non-executive supervisory

directors who protect interests in a company for unions, government or investors.

The functions of the supervisory board are three-fold: counseling, ratifying decisions made by the

managing board and monitoring the managing board (Douma, 1997). The role of monitoring is a central

element of agency theory and fully consistent with the view that the separation of ownership from control

creates a situation conducive to managerial opportunism (Daily, Dalton & Canella Jr., 2003). One of the key

goals of this board structure is to ensure the independence of the two boards by making sure that

executives are not too powerful (Goold, 1996).

13

In both systems there are several incentives present that either stimulate or discourage to diversify, as well

as measures to deal with unwanted diversification. First there will be looked at one-tier boards. The

greatest risks companies with these boards face are related to agency theory. Highly diversified firms often

display agency problems where governance has been ineffective and the agents (top executives) diversified

the firm in their own self interests (Johnson, Hoskisson & Hitt, 1993). The lack of a governing body makes it

easier for managers to entrench themselves or diversify for other personal reasons (Schleifer and Vishny,

1989), such as financial gains or protecting their human capital. However, agency theory suggests that the

market will resolve this problem as investors have some form of control as they have voting rights when

they feel the board is performing inadequately. Though, boards generally prefer to promote firm efficiency,

and hence help shareholder wealth preservation, before letting the market impose discipline (Singh,

Mathur & Gleason, 2004). As described before, in countries where the one-tier board system is prevalent

the main goal of doing business is making profit. This means that unwanted diversification can continue as

long as investors are not disappointed at the business end: when decent growth, profits and dividends are

ensured they will have little reason for complaints. However the heavily discussed profitability argument,

put forward by stewardship theory, also provides a countermeasure for unwanted diversification. As said

before some scholars argue that diversification has a negative influence on performance. When this is

indeed the case the investors in a one-tier board situation will be quicker to act against poor performance

by management (Westphal & Frederickson, 2001).

Two-tier boards, as explained in earlier sections, have an element of control build into the system, which

mitigates effects suggested by agency theory. Therefore companies with this structure are more likely to

diversify for reasons put forward by RBV and stewardship theory. Especially the role of the employees in

two-tier boards will increase the odds that the decision to diversify is made to reduce risk. But also the free

cash flow argument presented earlier can, when employees are involved, have a negative influence for this

system. Rather than spending this cash for new growth opportunities, they can feel that these funds can

better be invested in other areas. Furthermore, the counseling and monitoring functions of the supervisory

board can lead to slower decision making. Nevertheless there appears to be overwhelming support, in

particular among financial researchers, for supervisory boards providing beneficial monitoring and advisory

functions to firm shareholders (Fields & Keys 2003). Their argument is that people on the supervisory

boards are most of the times older businesspeople, whose experience makes them valuable to companies

from and sometimes vital to improve performance. For this study this implies, following stewardship

theory, that a higher age, longer tenure and higher level of education can also positively influence the

diversification level for two-tier boards.

Other reasons, such as overcoming transaction costs, exploiting operating synergies, preservation of the

firm or ensuring growth apply to both systems. Agency problems are associated with bad behavior by

14

individuals, while risk-reduction is a strategy that is made for the good of the firm and as such will lead to

quicker consensus on the righteousness of the diversification decision. Taking this and all the arguments

presented earlier into account, this paper proposes that companies with two-tier boards will be more

diversified.

H5: Companies with a two-tier board system are more diversified than those that have one-tier boards

All the aforementioned variables and their according hypotheses can be found in Table 1.

TABLE 1

Hypotheses and direction

Variable Hypothesis Effect

Age H1: A higher average age of the management team leads to a higher degree of diversification +

Education H2: A higher level of average education of the management team leads to a higher level of

diversification.

+

Tenure H3: Longer average organizational tenure leads to a higher level of diversification. +

Gender H4a: A higher percentage of women on the board leads to a lower level of diversification -

H4b: A higher percentage of women on the board leads to a higher level of diversification +

Board Structure H5: Companies with a two-tier board system are more diversified than those that have one-tier

boards

+

15

3. Data and Methods

3.1 Sample Selection and data collection

The hypotheses are tested cross-sectional using a sample of corporations for the financial year 2010. A

cross-sectional approach was chosen to control for the many factors external to the corporation, but

related to its diversification levels that vary over time (Chen, Dyball & Wright, 2009). A cross-sectional

approach is justified for this research as the composition of the board changes somewhat every year, but

these changes are marginal as most directors are appointed for certain terms. Only in rare situations these

terms are not completed. Therefore can be argued that the level of diversification in 2010 in the result of

the decisions by boards that had similar characteristics some years back. A sample was obtained using the

Orbis company database.

A random sample was selected by searching companies that matched several preconditions. First of all,

companies should be located in one of the 6 research areas: Germany, the Netherlands, the UK, Ireland,

Belgium or Luxemburg. These countries were chosen as they minimized a host of exogenous influences,

such as regional economic shocks and geographical remoteness (Wan & Hoskisson, 2003), which made it

easier to interpret the results within the variation of board structure. Also these countries are prime

examples of the different board structures in the same economic zone. The study tries to be as recent as

possible, so the companies should have been active in the last financial year (2010). Financial companies

were not included in the sample and as such only companies classified by Orbis as industrial companies

were selected. This was done especially since financial companies are obliged to adhere to strict laws that

forbid them to heavily diversify. The law was also used positively, since other laws mandate public

companies to make more information available, mainly for investors. However, all this information can also

be used for scientific purposes. Another positive ruling used to the advantage of this study was the

existence of international accounting standards. Especially German and British companies can provide

different accounting numbers for the same financial results. For instance the way in which assets can be

amortized is totally different and can heavily affect the results. Therefore companies using the same

accounting standard were used. Furthermore, the companies had at least €2.500.000 in assets in 2010 and

therefore could be considered a large company. Very large companies were chosen because their

management has more discretion in the choice of whether to operate as a single or diversified business,

compared to smaller corporations (Chen, Dyball & Wright, 2009).

After this selection, this initial database consisted of 174 companies. Then data, concerning financial year

2010, for this sample were gathered using Orbis, company websites and the Bloomberg Investor Website.

These data concerned financial performance, company size, board system and characteristics of people on

16

the board, such as gender, age, education and tenure. Hyland and Diltz (2002) also highly recommended

the inclusion of the height of the research and development budget, as it proved to be a significant

predictor of the level of diversification. However, this would have severely restricted the sample size

(Hoskisson, Hitt, Johnson & Moesel, 1993). Some companies lacked data needed for this study, as they

could not be found using aforementioned and other sources. Therefore 9 companies were taken out of the

sample, after which 165 companies were in the sample. Of these 165 companies 95 had a one-tier board

and 70 had a two-tier board.

3.2 Variable specification and measurement

Dependent variable

Diversification

The dependent variable for this study is diversification. There are several measurements for product

diversification that can be taken into consideration. The measures can be classified into three different

types. Firstly, the measure proposed by Rumelt (1974), which is a categorical measure. This measure

focuses on how the revenues of the different businesses are distributed. Secondly, Chatterjee and

Wernerfelt (1991) used a measure based on the Resource-Based View. The focus of these measures is on

the spread of strategic assets and competencies between different units (Markides & Williamson, 1996).

The third measure uses product-count measures and can be divided into two measures: simple product

count measure and the weighted product count measure.

The measure of Rumelt (1974) is based on the distribution of the revenues of the firm. A company is

classified as a single business if 95% of the sales are caused by one business of the firm. A business is said to

be a dominant one if between 70% and 94% of the company’s sales is generated by one business. When

the turnover of a company is below 70% Rumelt is also distinguishing a company as being related or

unrelated diversified.

Markides and Williamson (1996) first measured relatedness in the traditional (Rumelt) way, using a

dichotomous dummy variable: firms classified as related took the value one, and firms classified as

unrelated or dominant took the value zero. Single-business firms were excluded from the analysis. They

then replaced the related variable with structural indicators of relatedness to estimate the equation. As

such they calculated the level of diversification.

The method of Varadarajan and Ramanujam (1987) takes and easier approach and is an example of the

simple product count measure. Entropy measures, such as the one by Palepu (1985), are an example of the

weighted product count measure. Varadarajan and Ramanujam (1987) studied US companies in order to

17

find values for their model. They proposed two categories to measure the degree of related or unrelated

diversification; the ‘Mean Narrow Spectrum Diversification’ (MNSD) to measure the degree of related

diversification and the ‘Broad Spectrum Diversification’ (BSD) to measure the degree of unrelated

diversification. Companies were put in the respective categories based on the number of industries they

were active in. Palepu (1985) takes somewhat the same approach, but controls for the distribution of sales

between business units. Due to the absence of these data for most sample companies, the entropy

measure was not taken into consideration.

However, this study preferably needs a variable that is measured on a quantitative scale, to make it easier

to compute the regression equation. Therefore, diversification in this study will be operationalized by using

an un-weighted product-count measure. These are reliable, simple and easy to compute (Lubatkin,

Merchant & Srinivasin, 1993). Also, when circumstances prevent the use of entropy measures, as is the

case, the use of a product-count measure is appropriate (Hoskisson, Hitt, Johnson & Moesel, 1993). The

product count is based on SIC typology. The Standard Industrial Classification (SIC) system is a numerical

system developed by the US government for classifying all types of economic activity and is based on

establishment classifications, which are classified according to its primary activity (Montgomery, 1982). In

this way companies can be assigned codes according to the industries they operate in. There is high degree

of correspondence between the SIC-based diversification measures and Rumelt’s (1974) categorical

measures (Montgomery, 1982; Lubatkin, Merchant & Srinivasan, 1993). Furthermore, the most significant

studies relating structure to diversity have used business count measures to prove this (Pitts & Hopkins,

1982). Therefore the dependent variable will be a continuous variable, namely, the number of 4-digit SIC

categories the company in the sample was active in, in the year 2010.

Independent variables

In the previous chapter variables that seem to have an influence on diversification were already presented.

Amongst others, Wiersema and Bantel (1992) reported that top management teams’ demographic

characteristics help to predict changes. In this paragraph the demographics used in this study will be

introduced as well as will be explained how they will be operationalized.

Board Age

Age also measures time until retirement (Golec, 1996), but a lower age also means an increased willingness

to risk (Hambrick & Mason, 1984). This paper follows Wiersma and Bantel (1992) and has operationalized

the variable Board Age as the average age of the board members. In the average age all board of directors

that have executive power (Marlin, Lamont & Geiger, 2004) will be included. The average age of board

members will be calculated as the sum of the age of these people, divided by the number of people. In this

case the age will be the number that follows from 2010 minus the year of birth.

18

In sections 4.2 and 4.3 variables will be operationalized differently. In these sections it is necessary to

transform the variables to dummies in order to do t-tests and re-testing the regression to make the data

conclusions more rigid. Therefore Board Age will be recalculated as follows: all the data below the mean

age will be assigned dummy value zero and all the data points above mean will be assigned value one.

Board Tenure

Management teams with shorter organizational tenure have more diversification (Michel & Hambrick,

1992; Wiersema & Bantel, 1992) but could also point to entrenchment (Schleifer and Vishny ,1989).

Therefore, based on research by Michel and Hambrick (1992), Board Tenure will be measured as the mean

number of years the members of board have spent with a firm in their current position. As such the

average will point towards the normal level of diversification. Variation will point towards behavior

proposed by the theories presented earlier. Again, only the people on the board of directors that have

executive power (Marlin, Lamont & Geiger, 2004) are included. The calculation will be made with the

following formula: 2010 minus the year the board member started working in their current position.

As already mentioned, in sections 4.2 and 4.3 Board Tenure will also be operationalized differently. This

means that all the data below the mean tenure will be assigned dummy value zero and all the data points

above mean will be assigned value one.

Board Education

Also Board Education will be part of this study. For his study Golec (1996) used both the total number of

year of education as well as a dummy variable to account for whether a MBA was held. However, this paper

reasoned that while the total years of education measures only measures accumulated general knowledge,

a MBA measures business-specific knowledge. Therefore, in order stress the importance of this extra

education, this variable will be operationalized by the percentage of people on the board, that have

executive power, with a MBA, PhD, or equivalent, titles such as granted from a Doctorate or Professorship.

Again, only the people on the board of directors that have executive power (Marlin, Lamont & Geiger,

2004) are included.

In sections 4.2 and 4.3 also Board Education will be operationalized differently as well. This means that all

boards that have no-one at executive level with an advanced education will be assigned dummy value zero

and all the boards that do have executives with an advanced education will be assigned value one.

Board Gender

The Gender variable has been present in a large number of studies already presented (Sexton & Bowman-

Ufton 1990; Dwyer, Gilkeson & List, 2002; Schubert, 2006). But every time the variable was adapted the

19

specific needs of the study. Thus this study will do the same and follow previous studies (Hillman,

Shropshire & Cannella, 2007; Miller & Del Carmen Triana, 2009). The Board Gender variable will be

operationalized by the percentage of people on the board that is female. Also for this variable only the

people on the board of directors that have executive power (Marlin, Lamont & Geiger, 2004) are included.

Board Gender will be operationalized differently in sections 4.2 and 4.3, in a similar way as Board

Education. All boards that have no women in executive positions will be assigned value zero and all the

companies that have boards with women will be assigned value one.

Board Type

The Board type variable is the one that this study wants to introduce and consequently add to the

regression equation that models the relationship with the level of diversification. The rationale behind it

has been discussed in detail in the previous chapters. This variable will be operationalized by introducing a

dummy variable that will denote whether the company has an one-tier, in which case the dummy takes

value zero, or a two-tier board, in which case it takes value one.

Control variables

To make the statistical analysis more solid, this study also proposes some control variable to account for

effects other than those created by board characteristics.

Firm Size

The first control variable will be firm size, as it heavily affects differentiation and as such is very common in

the diversification literature (Chatterjee & Wernerfelt, 1991). A lot of large multinationals are in fact heavily

diversified. Therefore this relationship will be controlled by the natural logarithm of the assets, a widely

used measurement in the strategic management literature (Pearce & Zahra, 1992; Gedajlovic and Shapiro,

1998; Anderson, Bates, Bizjak & Lemmon, 2000; Mayer & Whittington, 2003).

Industry Effect

The type of industry firms operate in may provide incentives for firms to change their diversification level

(Hoskisson & Hitt, 1990). In addition, the economic concentration of an industry may influence the

likelihood of strategic change (Wiersema & Bantel, 1992). Indeed, some industries display higher levels of

diversification than other industries. As shown in Table 2 the case can be made that this study is no

exception. The industry dummy groups, their mean diversification and accompanying frequencies in the

sample can be found in this table. Controlling for the industry effect is not uncommon in strategic

management literature (Palepu, 1985) and so also for this study industry dummies were taken into account.

These were created by transforming the 2-digit SIC codes of the core business of the companies into

20

dummies of the sectors these codes belonged to. To which dummy groups, according to their 2-digit codes,

the companies in the sample were assigned can be found in Appendix 2. Since companies in the

manufacturing sector dominate this sample they provide the dummy base level.

TABLE 2

Diversification and Industry Dummies

Industry Dummy Mean Diversification Level % of total sample

Mining 2,31 7,9%

Construction 3,09 6,7%

Transport 1,67 20,0%

Wholesale 2,63 4,8%

Retail 1,71 8,5%

Services 1,60 9,1%

Manufacturing 2,34 43,0%

Total 2,15 100,0%

Firm Age

The last control variable will be firm age. Singh, Mathur and Gleason (2004) suggested that observed board

differences between heavily and less diversified focused firms are due to their being at different stages of

corporate evolution. Furthermore firm age must be controlled since young organizations have a lower

boundary on team tenure than old organizations (Michel & Hambrick, 1992). It will be operationalized as

the number that follows from the equation: 2010 minus the year of incorporation (found in the Orbis

database).

3.3 Empirical model

Because of the nature of the hypotheses and the characteristics of the data, several analytical procedures

were used to test this study's hypotheses. First the descriptive statistic s will provide an insight in the

correlations between the variables and possible outliers. Then t-tests are used as a preliminary test to see

whether there are the differences between the subsamples, which are based on the differently

operationalized variables of interest. These tests can also point out whether the individual variation in the

variable can be significant, but is possibly affected by the other data in the regression. Lastly, this

regression analysis will prove how the several variables interact with each other. To be able to draw

conclusions from this analysis the Hypotheses 1 through 6 were tested using multiple least-squares

regression. The overall model that applies to this paper is the following:

(1) Diversification level = β1+ β2 Age + β3 Tenure + β4 Education + β5 Gender + β6 Board

Type + β7 Firm Size + β8 Firm Age + β9 Firm Industry

21

However, to re-test the individual relationship between diversification and each variable the reduced

models (2) up to and including (6) will be used. This can confirm whether the direction of the un-

standardized regression coefficient is indeed correct and is not influenced by multi-collinearity within the

complete model. These models will have the variable of interest as only independent variable while there

will be controlled for firm size, firm age and industry. So models will be:

(2) Diversification level = β1+ β2 Age + β7 Firm Size + β8 Firm Age + β9 Firm Industry

(3) Diversification level = β1+ β3 Tenure + β7 Firm Size + β8 Firm Age + β9 Firm Industry

(4) Diversification level = β1+ β4 Education + β7 Firm Size + β8 Firm Age + β9 Firm Industry

(5) Diversification level = β1+ β5 Gender + β7 Firm Size + β8 Firm Age + β9 Firm Industry

(6) Diversification level = β1+ β6 Board Type + β7 Firm Size + β8 Firm Age + β9 Firm Industry

22

4. Results

4.1 Descriptive statistics

In this section will be looked at the characteristics of the companies in the sample. The descriptive statistics

of these companies are presented in Table 3. As already mentioned the sample consists of 165 companies,

of which 58% have a one-tier board and 42% has a two-tier board. Larger companies tend to have higher

levels of diversification. Therefore the sample studied consisted mostly of larger companies to be able to

pinpoint significant variances. This was also the reasoning behind the sample selection threshold of

€2.500.000 in assets. Furthermore, the companies are at different stages of corporate evolution. On

average they exist for nearly 60 years and vary from recent start-ups and spin-offs that are in business for

just a year to companies with a rich history spanning 197 years. On average they have 55.905 people

employed.

Looking at the variables of specific interest to this study, several remarkable results are worth mentioning.

Only around 1 in 20 board members studied in the sample are female. Even though this variable is not

really part of this study and as such was not studied in depth, it turned out while the data were gathered,

that most female director positions were in either the supervisory board or in non-executive function in

one-tier boards.

TABLE 3

Descriptive Statistics (N=165)

Diversification

Level

Board

Type

Board

Gender

Board

Tenure

Board

Education

Board

Age

Firm

Size

Firm Age

Mean 2,15 ,42 ,0493 3,8445 ,2253 53,030 16,0923 58,95152

Std. Error of Mean ,112 ,039 ,00749 ,20226 ,02027 ,3112 ,09109 3,907

Median 2,00 ,00 ,0000 3,5 ,17 52,500 15,7804 38

Std. Deviation 1,432 ,496 ,09615 2,59810 ,26039 3,9969 1,17001 50,183

Minimum 1 0 0 0 0 42,3 14,73 1

Maximum 10 1 ,50 14,00 1,00 65,5 19,30 197

Variables:

Board Type = dummy variable to difference between one-tier (D=0) and two-tier (D=1) boards

Board Gender = percentage of women in executive positions

Board Tenure = average tenure of people in executive positions for their current position

Board Education = the percentage of people in executive positions with an advanced (MBA/PhD) degree

Board Age = average age of people in executive positions

Firm Size = natural logarithm of the total assets of the company

Firm Age = the number of years since incorporation

The men and women that are working in executive positions have a maximum tenure in their current

position of 14 years, while on average they are working in that position for almost 4 years. The companies

employ on average 22,5% of their executives with people who followed an advance education such as a

23

MBA of PhD, but in some companies all the executives have an advanced education. The average board

age for the sample is 53 years.

Table 4 summarizes the correlations between the independent variables. The correlations between the

level of diversification and the independent variables already give a first insight into how strong the

relationship between them is. Furthermore it shows the strength of the relationship. Therefore it provides

the expected direction of the values that will be obtained with the regression analysis. As can be seen in the

table the relationship between diversification and the independent variables Gender, Tenure and Age show

negligible correlation and are statistically insignificant as well. The control variables Firm Size and Firm Age

are both highly significant, but at the same time are only lowly correlated with the diversification level.

However, the very high significance justifies the use of these variables as control variables in this research.

The last two variables, Board Type and Board Education, have a pretty high significance, but are weakly

correlated to diversification.

The mutual correlations between the independent variables also gave some interesting results. As shown in

the second column, fourth row of the table, the relationship between education and board system is the

only relationship that is both highly significant and has a moderate correlation. At first sight this seems

quite odd. However, an explanation can be found in the characteristics of the sample. Namely, in German

companies it is very common to have doctors or professors leading the company. These titles have been

assigned to matter in this study. Especially for high-tech companies this is the case. As Germany is an

important research nation for this study, this could have affected this relationship. This argument can be

proven with a t-test. A comparison between board type, as grouping variable, and education, as dependent

TABLE 4

Correlations Matrix (N=165)

Diversification Board Type Board Gender Board Tenure Board Education Board Age Firm Size

Board Type ,179**

Board Gender -,020 -,084

Board Tenure -,076 -,115* -,145**

Board Education ,136** ,477**** -,096 ,090

Board Age -,012 -,230*** -,008 ,279**** -,004

Firm Size ,250**** ,082 ,208*** ,033 ,120* ,219***

Firm Age ,243**** ,381**** -,082 -,060 ,147** -,057 ,002

Significance:

* = p < .10

** = p < .05

*** = p < .01

**** p < .001

Variables:

Diversification = level of diversification based on SIC 4-digit product-count

Board Type = dummy variable to differentiate between one-tier (D=0) and two-tier (D=1) boards

Board Gender = percentage of women in executive positions

Board Tenure = average tenure of people in executive positions for their current position

Board Education = the percentage of people in executive positions with an advanced (MBA/PhD) degree

Board Age = average age of people in executive positions

Firm Size = natural logarithm of the total assets of the company

Firm Age = the number of years since incorporation

24

variable, showed at 99% confidence level that in one-tier boards only 12% of people has an advanced

education versus 37% of people on two-tier boards.

Also some other highly significant relationships, albeit with a lower correlation are worth mentioning.

Board type does namely not only significantly correlate with education but also with board age and firm

age. Regarding the former, there is not really a logical explanation why this correlation exists. An

independent t-test confirmed that one-tier boards have on average older boards, but this test was not very

significant. Therefore this correlation is probably caused by sample selection bias. The correlation between

Firm Age and Board Type can be explained. However, this explanation has an argument based on sample

selection. Larger German companies are namely often a continuation of old German family-firms.

Therefore it is well possible that these firms have adjusted the results somewhat. The main topic of this

research, however, is the relationship between Board Type and the level of diversification. The correlation

between these variables was low and also not extremely significant. This gives doubts for usefulness of the

variable board type to explain diversification. Although the regression analysis will offer the full extent of

this relationship, a preliminary t-test confirmed that there are indeed differences in diversification between

one-tier and two-tier boards. At a 98% confidence-level the means of the one- and two-tier boards were

1,93 and 2,44 respectively.

The variable Firm Size has as well two highly correlating variables: Board Gender and Board Age. For the

former can be argued that larger companies feel more responsible to react to pressure put on them by

governments and public opinion to employ a balanced and diverse workforce. For the latter can be argued

that larger firms tend to appointed older, more experienced executives. Lastly, the correlation between

Board Age and Board Tenure can be explained by the reasoning that people get older while they are

accumulating years of their tenure at a company.

Collinearity checks gave no reasons for concern and it is therefore assumed that the models are not

affected by potential multi-collinearity in the regressions.

4.2 Preliminary t-tests

In order to get a first impression of the probable results of the regression and to check the direction of the

coefficients, all independent variables will be t-tested. This means comparison will be done using the

following procedure: all the independent variables that used a continuous or proportional scale were

operationalized differently. All data points were assigned dummy values. For the variable Age this meant

that the dummy took variable zero if the data point was below the mean and value one if it was above the

mean. The same went for the variable Tenure. For the variable board gender the dummy took value zero if

there were no women on the board and value one if there was female representation on the board.

25

Following somewhat the same principle the Education variable was treated. It took value zero if no people

on the board had followed more education after graduating from university and value one is this was the

case. This measurement for education was already proposed in the first place by Golec (1996).The dummy

variable for board type remained. All these dummy variables were treated with an independent variables t-

test, were diversification was the dependent variable and the assigned dummies provided the compared

groups. The results can be found in Table 5.

TABLE 5

Independent variables t-test

N Mean Std. Deviation t sig

Board System 0 95 1,93 1,331 -2,320 0,011

1 70 2,44 1,519

D Board Gender 0 123 2,08 1,239 -,984 0,163

1 42 2,33 1,896

D Board Education 0 73 1,90 1,169 -1,944 0,027

1 92 2,34 1,592

D Board Age 0 89 2,24 1,365 ,878 0,191

1 76 2,04 1,509

D Board Tenure 0 94 2,26 1,579 1,135 0,129

1 71 2,00 1,207

As can be seen, the t-tests show that the means of the variables Age, Gender and Tenure do not

significantly differ between groups. This is in line with what was concluded from the correlations matrix

(Table 4) where already was shown that these variables had no relation with diversification. The expectancy

is that this will be confirmed by the regression analysis. For both Age and Tenure the t-test, as well as the

correlation matrix, predict a negative direction of the relationship with diversification. For Gender the

correlation matrix predicted a negative direction of the relationship, while the t-test is shows a positive

sign. This could be caused by the transformation of the variable and will be checked with the regression.

Again, these tests are not significant, but could further strengthen the conclusions that follow from the

regression. The t-tests for Board System and Education give more hopeful results as they are both

significant at least at a 97% confidence level. The direction of the tests confirms what the correlation matrix

also brought forward, namely that the relation should be positive and as such the regression should give

the same result.

4.3 Regression results

The results of the six regression models that are used to test the hypotheses can be found in Table 6. Each

of these models is used to explain total diversification. First of all, the full model (1) will be considered.

Around 20,6% of the variation in the level of diversification of the companies in the sample can be

explained by this model. The F-value of 3,021 confirms the model is significant. The un-standardized

regression coefficients (B) of the independent variables give some interesting results.

26

Board age has a negative coefficient, both in the full as in the reduced model. This is in line with what was

expected based on the correlation matrix and t-test. However, this is not in line with the effect proposed in

Hypothesis 2. However, these results are not significant, so there can be concluded that no effect is

present.

Models (1) and (3) predict a negative coefficient for the relation between Board Tenure and the level of

diversification. This confirms the expectation based on the correlation matrix and t-test, but again it goes

against what was proposed in Hypothesis 2, which proposed a positive relationship between tenure and

age. All the statistical results prove otherwise. The coefficient is not significant and provides no support for

Hypothesis 2.

The coefficient between Board Education and the level of diversification is positive, which is in line with

what was proposed by both model (1) and (4) and congruent with the statistical procedures in earlier

sections. However, yet again the coefficient does not have a high significance level, which means there is

no support for Hypothesis 3 either.

The relation between Board Gender and the level of diversification is remarkable. Because the correlation

matrix and the regression seems to point out that relationship between them is negative. The t-test on the

other hand, perhaps due to how the dummy was constructed, turned out positive. All these results are

somewhat in line with what was expected for Hypotheses 4a and 4b. All results were not significant though,

so no effect is found.

Lastly, the main topic of this research: the relationship between Board type and diversification. The

correlation matrix proved an, albeit weak, positive relationship. This was backed up by the t-test that

showed a positive, and more significant, relationship. It is also confirmed to be positive by model (1) and (6)

but yet again these results are not statistically significant. As such, Hypothesis 5 cannot be confirmed and

no effect is found.

To account for problems arising from how the independent variables were operationalized, the models

were re-tested. In this test some variables were subjected to regression analysis again, while they were

operationalized differently. The variables Gender and Education, which were measured on a proportional

scale, were operationalized by the dummy variables created to do the t-tests in section 4.1. Also the

variables Age and tenure were calculated differently. After that the regressions were re-run, for which the

results can be found in Table 7. The complete model (1) improved ever so slightly. The R-square of 0,208

and an F-value of 3,059 are slightly higher than in the original model. Also the models (2) and (3) improved

slightly in comparison to the original model. One explanation is that this difference can probably be

27

attributed to the fact that the dummies have less variance. Therefore the role of the, already highly

significant, Firm Size variable becomes even more powerful as it is the only variable with a lot of variance.

Individual significance of the coefficients in all the re-tested models was not different than the original

model. The variable models (4) and (5) did not improve either. There is however an interesting finding in

this retest. The direction of the coefficient of the variable Gender changed from negative, in the original

regression, to positive. This is quite strange, but it was already hypothesised the coefficient in theory could

have gone both ways. Though this finding is not significant, the fact that the retest gave other results could

point towards evidence for this proposition.

28

Variables:

Board Type = dummy variable to differentiate between one-tier (D=0) and two-tier (D=1) boards

Board Gender = percentage of women in executive positions

Board Tenure = average tenure of people in executive positions for their current position

Board Education = the percentage of people in executive positions with an advanced (MBA/PhD) degree

Board Age = average age of people in executive positions

Firm Size = natural logarithm of the total assets of the company times number of employees

Firm Age = the number of years since incorporation

Industry Dummies = dummy variable to differentiate between several industries, assigned as the dominant

2-digit SIC code sector. Base value = ID Manufacturing

TABLE 6

Regression results (N=165)

Diversification

B Model (1) B Model (2) B Model (3) B Model (4) B Model (5) B Model (6)

Constant -2,932 -2,269 -3,625** -3,867** -2,669 -3,808**

Board Age -,019 -,031

Board Tenure -,051 -,059

Board Education ,403 ,461

Board Gender -,371 -,031

Board Type 0,085 ,261

Firm Size ,373**** ,385**** ,359**** ,350**** ,385**** ,349****

Firm Age ,004 ,005** ,004* ,005** ,005** ,004

Industry Dummies

ID Mining ,169 ,152 ,054 ,130 ,152 ,157

ID Construction 1,084** 1,102** 1,052** 1,130** 1,102** 1,099**

ID Transport -,503 -,449 -,484 -,410 -,449 -,435

ID Wholesale ,587 ,567 ,563 ,643 ,567 ,605

ID Retail -,411 -,433 -,523 -,281 -,433 -,331

ID Services -,083 -,055 -,197 -,095 -,055 -,133

R2 ,206 ,192 ,195 ,192 ,192 ,192

Adjusted R2 ,138 ,145 ,149 ,145 ,145 ,145

F 3,021 4,092 4,183 4,092 4,092 4,100

Significance:

* = p <.10

** = p < .05

*** = p < .01

**** p < .001

Models:

(1) Diversification Level = β1+ β2 Age + β3 Tenure + β4 Education + β5 Gender + β6 Board Type + β7 Firm Size + β8 Firm Age

+ β9 Firm Industry

(2) Diversification Level = β1+ β2 Age + β7 Firm Size + β8 Firm Age + β9 Firm Industry

(3) Diversification Level = β1+ β3 Tenure + β7 Firm Size + β8 Firm Age + β9 Firm Industry

(4) Diversification Level = β1+ β4 Education + β7 Firm Size + β8 Firm Age + β9 Firm Industry

(5) Diversification Level = β1+ β5 Gender + β7 Firm Size + β8 Firm Age + β9 Firm Industry

(6) Diversification Level = β1+ β6 Board Type + β7 Firm Size + β8 Firm Age + β9 Firm Industry

29

Variables:

DBoard Type = dummy variable to differentiate between one-tier (D=0) and two-tier (D=1) boards

DBoard Gender = dummy variable to differentiate boards with (D=1) and without (D=1) women

DBoard Tenure = dummy variable to differentiate between boards with mean below (D=0) and above (D=1)

mean tenure

DBoard Education = dummy variable to differentiate between boards without (D=0) and with (D=1) people with

advanced education in executive positions

DBoard Age = dummy variable to differentiate between boards with mean below (D=0) and above (D=1)

mean age

Firm Size = natural logarithm of the total assets of the company times number of employees

Firm Age = the number of years since incorporation

Industry Dummies = dummy variable to differentiate between several industries, assigned as the dominant 2-digit

SIC code sector. Base value = ID Manufacturing

TABLE 7

Re-Test: Regression with variable dummies, results (N=165)

Diversification

B Model (1) B Model (2) B Model (3) B Model (4) B Model (5)

Constant -3,767** -4,293*** -3,653*** -3,772** -3,676**

DBoard Age -,297 -,367*

DBoard Tenure -,181 -,287

DBoard Education ,000 ,087

DBoard Gender ,127 ,100

Board Type ,167

Firm Size ,362**** ,393**** ,356**** ,348**** ,343****

Firm Age ,004 ,005* ,004* ,005** ,005

Industry Dummies

ID Mining ,114 ,142 ,005 ,097 ,077

ID Construction 1,123** 1,124* 1,064** 1,113** 1,108**

ID Transport -,489 -,424 -,507 -,406 -,408

ID Wholesale ,559 ,556 ,551 ,591 ,604

ID Retail -,493 -,435 ,492 ,347 -,408

ID Services -,051 -,010 ,175 ,131 -,152

R2 ,208 ,200 ,195 ,186 ,186

Adjusted R2 ,140 ,154 ,148 ,139 ,139

F 3,059 4,317 4,165 3,947 3,947

Significance:

* = p < .10

** = p < .05

*** = p < .01

**** p < .001

30

5. Conclusions and Recommendations

5.1 Summary and conclusion

This paper analyzes the differences between characteristics of boards with respect to diversification, with a

special focus on the differences between one-tier and two-tier boards. Particularly by giving the answer to

the two research questions: first, which board characteristics affect the decision to engage in product

diversification and secondly, does the difference in board structure have an effect on this decision?

The results of the empirical study of a sample with 165 companies provide a clear answer to the first

question. They gave no support for any of the board characteristics, although these were proposed by

other scholars and could be safely retested in this paper.

The hypothesis for Board age proposed a positive relationship between age and the level of diversification,

but a negative relationship arose from the regression, correlation matrix and t-test. However, the variable

Board age was insignificant. The same problem was seen for the relationship between Board Tenure and

the level of diversification.

Board Education and the level of diversification were confirmed to have a positive relationship by the t-test,

which was even highly significant. Still, no result arose from the regression analysis, which could mean that

for this variable there is no variance in the sample. Board Education was insignificant. The relation between

Board Gender and the level of diversification gave mixed results in all the statistical procedures. Therefore

the presumption that it could provide both a positive as a negative relationship between the two may be

correct. However, also this variable proved insignificant, so a definite conclusion cannot be given.

Lastly, the main topic of this research: the relationship between Board type and diversification. The

correlation matrix and t-test showed an, albeit weak, positive relationship. The regression analysis,

however, turned out insignificant. Nevertheless, a conclusion is that there are indeed differences between

one-tier and two-tier boards. But they cannot be explained by the regression used in this research. In

section 5.3 there will be suggested how this relationship can possibly be re-tested.

5.2 Limitations

There are some limitations to this study that have to be taken into account. First, the insignificance of the

models is potentially caused by the sample. Only a small sub-set of countries is researched and also only

the companies that fulfilled certain requirements were introduced into the sample. The high presence of

manufacturing companies in the sample might also have influenced the results. These companies

31

accounted for almost half of the sample. Since in the manufacturing dummy both companies that are

sensitive as well as less sensitive to changes in the economy this could have made the sample too

homogenous. The sample might have also been too small to properly investigate differences in variation.

Secondly, construct validity could also pose a problem. For product diversification an un-weighted product

count was used. This measure is less refined than weighted measures, like the entropy measure (Palepu,

1985), and far less refined then the measures based on RBV (Markides & Williamson, 1996). A business

count measure is deemed particularly suitable for research comparing diversified and non-diversified firms,

but less for investigating differences among diversified firms (Pitts & Hopkins, 1982). Another objection to

the use of the product count measures has to do with the SIC system which underlies this product count

measure. As already mentioned, manufacturing firms accounted for a large proportion of the sample. This

is caused by two flaws in the SIC system, which inevitably could influence this study. First, if an industry is

classified according to market-based criteria, products produced through radically different processes could

be classified in one category (Montgomery, 1982). Secondly, the manufacturing category groups an

enormous amount of different products, from snacks to paints and furniture. The markets for these

products can have a totally different stance towards diversification, though the SIC system puts them in the

same category. Lastly, the proportional measurement of Board Education and Board Gender may not be

enough to fully explain the variations in these variables between companies. Although the measurement

with a dummy variable was also tested, this could not improve the model. Therefore the insignificance of

these variables can probably be attributed to the absence of enough variation in the sample.

Thirdly, the relationship between board characteristics and diversification may be sensitive to factors other

than the variables that were used. These influences are indeed very complex and maybe some explanatory

variables were missing.

Fourthly, the cross-sectional approach might not have been appropriate. Some other studies used panel

data to properly account for the characteristics some firms have developed as they are in further state of

their corporate evolution (Singh, Mathur & Gleason, 2004). The cross-sectional approach is a snapshot of

the situation in a certain year. Therefore companies can have made rigorous decisions based on factors

that lie out the range of normal influences. The cross-sectional approach disregards these influences, but

the use of panel data approach does account for these influences as multiple years are under investigation

and as such a more stable behavioral pattern can be perceived.

5.3 Suggestions for Future Research

As shown in the limitations as well as some sub-conclusions in the section 4.1 this research suffered from a

selection bias. The inclusions of selection threshold of €2.500.000 in assets to which companies should

32

comply to enter the sample made it a not-random sample. Therefore other companies which could

potentially increase the variation and therefore could lead to better results were potentially excluded.

Future research should, therefore, re-examine the variables used in this study in a larger sample with

possibly a larger geographical scope. That could mitigate the socio-cultural effects that hampered this

study, as for instance the tendency described in section 4.1 that for German companies it is very common

to have doctors or professors leading the company. These titles have been assigned to matter in this study.

Especially for high-tech companies this is the case. As Germany is an important research nation for this

study, this could have affected this relationship. More countries under investigation could have an effect

on this. The research could also be extended to see whether differences in diversification for the board

systems are also present for related and non-related product diversification. A longitudinal study or panel

data study looking how an evolving board composition changes the stance towards diversification would

also heavily complement this study. Especially the influence of equal opportunities schemes should provide

an interesting topic. Another suggestion is that this research is redone, without the use of un-weighed

product-count measure, but with weighted measures product measures or the entropy measure instead.

Also the inclusion of some other variables could have improved the research. For instance, American

literature that investigated the relationship between board and diversification only form a one-tier

perspective included more differences. For example, internal differences between the one-tier boards,

concerning whether board members were possibly independent or outside directors. These are not

accounted for by this study.

The use of sampling, while collecting the data and the inability to generalize the results from the use of only

one year give reason for more testing. The results of a longitudinal study could therefore complement this

study.

The most important implication of this study for the research into product diversification is that the role of

firm size is validated again. In all the models this variable proved most of the variation in the data. Also the

inclusion of industry dummies can be mentioned as a valuable addition to this study. Both are therefore

recommended as control variables in future research.

Besides that fact that this paper aimed to fill a gap in the strategic management literature concerning the

influence of boards on diversification, also a practical answer could be obtained. As explained in section 2.2

the largest danger for diversifying companies lies in the fact that board members do so for personal

reasons. This assumption is based around agency theory and as such, this paper tried to find out whether

the appointment of people with certain characteristics could overcome these problems. This study has,

however, not found a solid answer for this question, and perhaps future research could validate this claim.

IV

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VIII

Appendices

APPENDIX 1

Industry dummy label categorization

CONSTRUCTION TRANSPORTATION, COMMUNICATIONS, ELECTRIC, GAS, AND SANITARY

15 - - GENERAL BUILDLING CONTRACTORS 40 - - RAILROAD TRANSPORTATION

16 - - HEAVY CONSTRUCTION, EXCEPT BUILDING 41 - - LOCAL AND INTERURBAN PASSENGER TRANSIT

17 - - SPECIAL TRADE CONTRACTORS 42 - - TRUCKING AND WAREHOUSING

43 - - U.S. POSTAL SERVICE

MINING 44 - - WATER TRANSPORTATION

10 - - METAL MINING 45 - - TRANSPORTATION BY AIR

12 - - COAL MINING 46 - - PIPELINES, EXCEPT NATURAL GAS

13 - - OIL AND GAS EXTRACTION 47 - - TRANSPORTATION SERVICES

14 - - NONMETALLIC MINERALS, EXCEPT FUELS 48 - - COMMUNICATION

49 - - ELECTRIC, GAS, AND SANITARY SERVICES

MANUFACTURING

20 - - FOOD AND KINDRED PRODUCTS WHOLESALE TRADE

21 - - TOBACCO PRODUCTS 50 - - WHOLESALE TRADE - DURABLE GOODS

22 - - TEXTILE MILL PRODUCTS 51 - - WHOLESALE TRADE - NONDURABLE GOODS

23 - - APPAREL AND OTHER TEXTILE PRODUCTS

24 - - LUMBER AND WOOD PRODUCTS RETAIL TRADE

25 - - FURNITURE AND FIXTURES 52 - - EATING AND DRINKING PLACES

26 - - PAPER AND ALLIED PRODUCTS 53 - - GENERAL MERCHANDISE STORES

27 - - PRINTING AND PUBLISHING 54 - - FOOD STORES

28 - - CHEMICALS AND ALLIED PRODUCTS 55 - - AUTOMOTIVE DEALERS & SERVICE STATIONS

29 - - PETROLEUM AND COAL PRODUCTS 56 - - APPAREL AND ACCESSORY STORES

30 - - RUBBER AND MISC. PLASTICS PRODUCTS 57 - - FURNITURE AND HOMEFURNISHINGS STORES

31 - - LEATHER AND LEATHER PRODUCTS 58 - - EATING AND DRINKING PLACES

32 - - STONE, CLAY, AND GLASS PRODUCTS 59 - - MISCELLANEOUS RETAIL

33 - - PRIMARY METAL INDUSTRIES

34 - - FABRICATED METAL PRODUCTS SERVICES

35 - - INDUSTRIAL MACHINERY AND EQUIPMENT 70 - - HOTELS AND OTHER LODGING PLACES

36 - - ELECTRONIC & OTHER ELECTRIC EQUIPMENT 72 - - PERSONAL SERVICES

37 - - TRANSPORTATION EQUIPMENT 73 - - BUSINESS SERVICES

38 - - INSTRUMENTS AND RELATED PRODUCTS 75 - - AUTO REPAIR, SERVICES, AND PARKING

39 - - MISC. MANUFACTURING INDUSTRIES 76 - - MISCELLANEOUS REPAIR SERVICES

78 - - MOTION PICTURES

79 - - AMUSEMENT & RECREATION SERVICES

80 - - HEALTH SERVICES

81 - - LEGAL SERVICES

82 - - EDUCATIONAL SERVICES

83 - - SOCIAL SERVICES

84 - - MUSEUMS, BOTANICAL, ZOOLOGICAL GARDENS

86 - - MEMBERSHIP ORGANIZATIONS

87 - - ENGINEERING & MANAGEMENT SERVICES

88 - - PRIVATE HOUSEHOLDS

89 - - SERVICES, (NOT ELSEWHERE CLASSIFIED)

Retrieved 14-10-2011 from: http://www.gti.net/njchamber/index-sic.htm

NOTE: This appendix refers to how the

industry dummies are categorized. They

are based on the US government

guidelines, where the first two digits of

the SIC-code of the core operation of the

company is used to assign the sub-

category. To which category the

company belongs can be found in

Appendix 2.

IX

APPENDIX 2

The companies in the sample

Company Name Core Business Core 4-digit SIC Code Industry Dummy Label Diversification Product Count

ROYAL DUTCH SHELL PLC Crude petroleum and natural gas 1311 Mining 10

BP P.L.C. Petroleum refining 2911 Manufacturing 2

VOLKSWAGEN AG Motor vehicles 3711 Manufacturing 5

E.ON AG Electric services 4911 Transport 4

DAIMLER AG Motor vehicles 3711 Manufacturing 5

SIEMENS AG Electrical apparatus 3825 Manufacturing 8

TESCO PLC Grocery stores 5411 Retail 1

METRO AG Department stores 5311 Retail 4

BASF SE Miscellaneous chemical products 2899 Manufacturing 2

DEUTSCHE TELEKOM AG Communications services 4899 Transport 2

BAYERISCHE MOTOREN WERKE AG - BMW Motor vehicles 3711 Manufacturing 1

VODAFONE GROUP PUBLIC LIMITED COMPANY Communications services 4899 Transport 1

ARCELORMITTAL S.A. Iron and steel foundries 3325 Manufacturing 1

RWE AG Electric services 4911 Transport 3

RIO TINTO PLC Miscellaneous metal ores 1099 Mining 1

EUROPEAN AERONAUTIC DEFENCE AND SPACE COMPANY EADS N.V. Aircraft and parts 3728 Manufacturing 1

THYSSENKRUPP AG Steel works 3312 Manufacturing 4

BHP BILLITON PLC Coal mining services 1241 Mining 3

AUDI AG Motor vehicles 3711 Manufacturing 2

BAYER AG Drugs 2834 Manufacturing 6

SCOTTISH AND SOUTHERN ENERGY PLC Electric services 4911 Transport 3

GLAXOSMITHKLINE PLC Drugs 2834 Manufacturing 1

IMPERIAL TOBACCO GROUP PLC Cigarettes 2111 Manufacturing 3

KONINKLIJKE AHOLD NV Grocery stores 5411 Retail 1

DEUTSCHE LUFTHANSA AG Air transportation 4512 Transport 1

ANHEUSER-BUSCH INBEV Beverages 2082 Manufacturing 1

CONTINENTAL AG Tires and inner tubes 3011 Manufacturing 2

J SAINSBURY PLC Grocery stores 5411 Retail 1

ASTRAZENECA PLC Drugs 2834 Manufacturing 4

X

Company Name Core Business Core 4-digit SIC Code Industry Dummy Label Diversification Product Count

KONINKLIJKE PHILIPS ELECTRONICS NV Household appliances 3639 Manufacturing 2

BAE SYSTEMS PLC Aircraft and parts 3721 Manufacturing 2

BT GROUP PLC Communications services 4899 Transport 1

CELESIO AG Drugs 5122 Wholesale 2

XSTRATA PLC Miscellaneous metal ores 1099 Mining 1

HOCHTIEF AG Highway and street construction 1611 Construction 3

WM MORRISON SUPERMARKETS PLC Grocery stores 5411 Retail 1

ENBW ENERGIE BADEN-WÜRTTEMBERG AG Electric services 4911 Transport 2

BRITISH AMERICAN TOBACCO P.L.C. Cigarettes 2111 Manufacturing 3

NATIONAL GRID PLC Gas production and distribution 4923 Transport 1

COMPASS GROUP PLC Eating and drinking places 5812 Retail 1

CRH PUBLIC LIMITED COMPANY Concrete 3272 Manufacturing 2

TUI AG Travel 4725 Transport 4

HEINEKEN NV Beverages 2082 Manufacturing 1

FRESENIUS SE & CO. KGAA Medical instruments 3841 Manufacturing 1

WOLSELEY PLC Hardware wholesale 5074 Wholesale 4

HENKEL AG & CO. KGAA Soap and toilet preparations 2841 Manufacturing 4

AKZO NOBEL NV Drugs 2834 Manufacturing 3

DIAGEO PLC Beverages 2085 Manufacturing 1

KONINKLIJKE KPN NV Communications services 4899 Transport 1

ROLLS-ROYCE HOLDINGS PLC Aircraft and parts 3724 Manufacturing 1

LINDE AG Industrial inorganic chemicals 2813 Manufacturing 4

BG GROUP PLC Crude petroleum and natural gas 1311 Mining 3

SAP AG Computer related services 7372 Services 1

KINGFISHER PLC Variety stores 5331 Retail 1

ADIDAS AG Rubber and plastics footwear 3021 Manufacturing 5

JOHNSON MATTHEY PLC Miscellaneous chemical products 2899 Manufacturing 1

HEIDELBERGER ZEMENT AG Cement, hydraulic 3241 Manufacturing 4

MARKS AND SPENCER GROUP P.L.C. Department stores 5311 Retail 3

ASSOCIATED BRITISH FOODS PLC Food 2099 Manufacturing 3

BALFOUR BEATTY PLC Heavy construction 1629 Construction 3

THOMAS COOK GROUP PLC Travel 4724 Transport 1

XI

Company Name Core Business Core 4-digit SIC Code Industry Dummy Label Diversification Product Count

DIXONS RETAIL PLC Consumer electronics 5731 Retail 4

AURUBIS AG Copper ores 1021 Mining 1

RECKITT BENCKISER GROUP PLC Soap and toilet preparations 2841 Manufacturing 1

N.V. UMICORE S.A. Copper ores 1021 Mining 2

MERCK KGAA Drugs 2834 Manufacturing 2

DCC PUBLIC LIMITED COMPANY Petroleum 5172 Wholesale 3

KONINKLIJKE DSM N.V. Miscellaneous chemical products 2899 Manufacturing 1

G4S PLC Miscellaneous business services 7382 Services 1

VEDANTA RESOURCES PLC Nonferrous foundries (castings) 3365 Manufacturing 3

X5 RETAIL GROUP N.V. Grocery stores 5411 Retail 1

SALZGITTER AG Steel works 3312 Manufacturing 1

BILFINGER BERGER SE Heavy construction 1622 Construction 3

BAYWA AG Machinery 5083 Wholesale 4

STMICROELECTRONICS N.V. Electronic components 3674 Manufacturing 2

KONINKLIJKE BAM GROEP NV Contractor 1521 Construction 4

LANXESS AG Plastics materials 2821 Manufacturing 3

HOME RETAIL GROUP PLC Department stores 5311 Retail 1

SOLVAY SA Drugs 2834 Manufacturing 3

BELGACOM SA Communications services 4899 Transport 1

BRITISH SKY BROADCASTING GROUP PLC Cable services 4841 Transport 1

INCHCAPE PLC Motor vehicles 5012 Retail 5

PEARSON PLC Books 2731 Manufacturing 1

SMURFIT KAPPA GROUP PLC Paper 2671 Manufacturing 1

SUDZUCKER AG Sugar and confectionery products 2063 Manufacturing 5

BEIERSDORF AG Soap and toilet preparations 2844 Manufacturing 3

GKN PLC Motor vehicles 3714 Manufacturing 2

BUNZL PUBLIC LIMITED COMPANY Paper 2671 Manufacturing 1

RTL GROUP SA Broadcasting 4833 Transport 3

REXAM PLC Metal cans and containers 3411 Manufacturing 2

KLÖCKNER & CO SE Metals and minerals 5051 Wholesale 1

K+S AKTIENGESELLSCHAFT Agricultural chemicals 2874 Manufacturing 3

XII

Company Name Core Business Core 4-digit SIC Code Industry Dummy Label Diversification Product Count

SERCO GROUP PLC Management services 8741 Services 1

CARILLION PLC Contractor 1522 Construction 2

KERRY GROUP PUBLIC LIMITED COMPANY Food 2099 Manufacturing 3

CARNIVAL PLC Cruises 4481 Transport 1

WACKER CHEMIE AG Miscellaneous chemical products 2899 Manufacturing 1

GEA GROUP AG Technology 8711 Services 4

IMTECH N.V. Electrical work 1731 Construction 2

LOGICA PLC Computer related services 7373 Services 2

INTERNATIONAL POWER PLC Electric services 4911 Transport 2

RHEINMETALL AG Vehicle parts 3714 Manufacturing 3

SCA HYGIENE PRODUCTS SE Paper personal car 2676 Manufacturing 1

RYANAIR HOLDINGS PUBLIC LIMITED COMPANY Airline 4512 Transport 2

TRAVIS PERKINS PLC Construction materials 5031 Wholesale 1

ANTOFAGASTA PLC Copper ores 1021 Mining 1

EASYJET PLC Airline 4512 Transport 1

AMEC P L C Contractor 1522 Construction 5

FREENET AG Telephone communications 4813 Transport 1

INFINEON TECHNOLOGIES AG Electronic components 3679 Manufacturing 1

BABCOCK INTERNATIONAL GROUP PLC Miscellaneous business services 7389 Services 1

AGFA GEVAERT NV Photographic equipment 3861 Manufacturing 2

BEKAERT SA/NV Steel works 3315 Manufacturing 2

TATE & LYLE PUBLIC LIMITED COMPANY Sugar and confectionery products 2062 Manufacturing 3

PETROFAC LIMITED Oil and gas field services 1389 Mining 1

SMITHS GROUP PLC Technology 3812 Manufacturing 1

CAPITA GROUP PLC (THE) Management services 8744 Services 2

EXPERIAN PLC Information services 7323 Services 1

TAYLOR WIMPEY PLC Contractor 1522 Construction 4

AXEL SPRINGER AG Publishing and printing 2711 Manufacturing 3

PROSIEBENSAT1 MEDIA AG Broadcasting 4833 Transport 1

CSM NV Sugar and confectionery products 2064 Manufacturing 3

COOKSON GROUP PLC Electrics 3679 Manufacturing 2

XIII

Company Name Core Business Core 4-digit SIC Code Industry Dummy Label Diversification Product Count

SMITH & NEPHEW PLC Medical instruments 3845 Manufacturing 2

MILLICOM INTERNATIONAL CELLULAR SA Communication services 4899 Transport 1

HEIDELBERGER DRUCKMASCHINEN AG Machinery 3555 Manufacturing 1

PREMIER FOODS PLC Food 2099 Manufacturing 2

RHOEN-KLINIKUM AG Clinics 8011 Services 2

CABLE & WIRELESS WORLDWIDE PLC Communication services 4899 Transport 1

MTU AERO ENGINES HOLDINGS AG Aircraft and parts 3721 Manufacturing 1

KONINKLIJKE BOSKALIS WESTMINSTER NV Heavy construction 1629 Construction 3

TOGNUM AG Machinery 5084 Wholesale 1

NATIONAL EXPRESS GROUP PLC Trains and buses 4131 Transport 2

DEBENHAMS PLC Department stores 5311 Retail 1

KAZAKHMYS PLC Copper ores 1021 Mining 1

ITV PLC Broadcasting 4833 Transport 1

FUGRO NV Surface data services 8713 Services 1

BARRATT DEVELOPMENTS P L C Operative builders 1531 Construction 3

SBM OFFSHORE N.V. Oil and gas field services 1389 Mining 3

MITCHELLS & BUTLERS PLC Eating and drinking places 5813 Retail 2

YELL GROUP PLC Advertising 7311 Services 1

COBHAM PLC Aircraft and parts 3728 Manufacturing 2

FRAPORT AG Airport 4581 Transport 1

SEVERN TRENT PLC Water supply 4941 Transport 2

WHITBREAD PLC Hotels and motels 7011 Services 3

PERSIMMON PUBLIC LIMITED COMPANY Contractors 1521 Construction 2

UNITED UTILITIES GROUP PLC Water supply 4941 Transport 2

SES S.A. Communication services 4899 Transport 1

MAINOVA AG Electric services 4911 Transport 4

AEGIS GROUP PLC Advertising 7319 Services 2

THE SAGE GROUP PLC. Computer related services 7372 Services 1

STADA ARZNEIMITTEL AG Drugs 2834 Manufacturing 2

TOMTOM NV Communications equipment 3669 Manufacturing 1

PUNCH TAVERNS PLC Eating and drinking places 5813 Retail 2

XIV

Company Name Core Business Core 4-digit SIC Code Industry Dummy Label Diversification Product Count

DYCKERHOFF AG Cement, hydraulic 3241 Manufacturing 1

INFORMA PLC Publishing and printing 2721 Manufacturing 1

PENNON GROUP PLC Sanitary services 4952 Transport 1

SOLARWORLD AG Electric services 4911 Transport 1

GREENE KING PLC Beverages 2082 Manufacturing 4

CAIRN ENERGY PLC Crude petroleum and natural gas 1311 Mining 2

LONMIN PUBLIC LIMITED COMPANY Gold and silver ores 1041 Mining 1

KONINKLIJKE VOPAK N.V. Public warehousing and storage 4226 Transport 1

MILLENNIUM & COPTHORNE HOTELS PLC Hotels and motels 7011 Services 1

QIAGEN NV Instruments 3826 Manufacturing 1

MARSTON'S PLC Beverages 2082 Manufacturing 4