The influence of familiness on R&D, internationalization and capital structure

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The influence of

Familiness on R&D,

Internationalization

and Capital structure

Author

Martijn Claessen 11419482

Supervisor

Dr. R. van der Voort

MSc Entrepreneurship thesis

Vrije Universiteit Faculty of Economics and Business Administration

Universiteit van Amsterdam Economics and Business

Academic year: 2016/2017 Submission date: 30-06-2017

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Abstract

In the family business literature, consensus is still missing on what defines a family firm and its appropriate setup to examine the involvement of the family within businesses. The solution to this heterogeneity is still under debate resulting in a so-called family business theory jungle. Delightfully, it has become a predominant topic in family business literature since the introduction of the F-PEC scale by Klein, Astrachan and Smyrnios in 2002. This standardized and valid instrument revolutionizes the way in that family firms are measured in degrees of ‘familiness’, instead of dichotomously categorizing firms in family or nonfamily. In essence, the three-construct index measures the degree of family influence using the constructs Power, Experience and Culture. Each construct is measured by a set of questions.

The present study deploys the F-PEC scale for the first time in the Netherlands using a sample of Dutch businesses. The main purpose of the present research is to measure how the level of familiness influences a selection of business strategies, namely R&D, capital structure and internationalization. Consequently, the research question posed is:

What is the

effect of the level of familiness (measured by the F-PEC scale) on R&D,

internationalization and capital structure?

Despite the lack of significance for the majority of results, still a lot of valuable insights are to be seen. Firstly, it is shown that the level of familiness does not have a significant effect on the capital structure although a negative dynamic can be seen. Next to this, positive relations between familiness and the business strategies R&D and internationalization are detected. Furthermore, each business strategy has also been linked to six financial performance indicators. The results indicate that international diversification, measured by exports, is positively related to financial performance. The research and development and capital structure strategies showed mixed results.

Keywords: Familiness, F-PEC, Capital Structure, Internationalization, R&D and Financial Performance.

The copyright rests with the authors. The authors are solely responsible for the content of the thesis, includin g mistakes. The university cannot be held liable for the content of the author's thesis.

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3 Table of Contents

Abstract ... 2

1.0 Introduction ... 4

1.1 Gaps and main research question ... 8

2.0 Business Strategies and Familiness ... 11

2.1 Capital Structure ... 12

2.2 Research & Development ... 13

2.3 International Diversification and Orientation ... 14

3.0 Business Strategies on Financial Performance ... 17

4.0 The Research Models ... 18

4.1 F-PEC Scale: Reliability, Validity and Generalizability ... 18

4.2 Familiness on Capital structure, R&D and Internationalization ... 19

4.3 Capital structure, R&D and Internationalization on financial performance ... 20

5.0 Methodology... 21

5.1 Sample ... 21

5.2 Data Collection ... 21

5.3 Questionnaire and variables ... 22

5.4 Financial Indicators ... 27

5.5 Data analysis... 29

6.0 Results ... 30

6.1 Overview ... 30

6.2 Sector analysis ... 33

6.3 Familiness to business strategies ... 37

6.3.1 Capital structure ... 37

6.3.2 Internationalization: Exports... 38

6.3.3 Internationalization: International orientation ... 39

6.3.4 Research and development expenditures ... 40

6.4 Business Strategies to financial performance ... 41

6.4.1 Capital structure ... 41

6.4.2 Internationalization: exports ... 43

6.4.3 Research and development ... 45

7.0 Discussion and conclusion ... 47

7.1 Limitations ... 49

7.2 Contributions ... 51

7.3 Avenues for Future Research ... 52

References ... 54

Appendix ... 59

Questionnaire ... 59

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4

1.0 Introduction

For the ordinary mortal, the two most important things in life are family and employment. The combination of these two has been an evolving field of inquiry over the last decades. However, due to the multidisciplinary character of a ‘family business’ there is still much debate and heterogeneity to solve (Carney

et al.

2015; O’Boyle

et al.

, 2012; Anderson & Reeb, 2003). The heterogeneity and complexity of the subject could be a result of unknown moderators or mediators that have not been included in available papers yet (Mazzi, 2011).

Figure 1: Topics of family involvement articles - Source: Harms (2014, p.291)

Harms (2014) analysed 267 journal articles to draw a picture of the academic landscape with regards to family involvement topics. As can be seen in the figure above, performance studies, which mostly address financial (e.g. Return on Assets) performance in dicators have been the predominant topic for family business research (i.e. 151 studies). The results of many of these studies are mixed (e.g. Anderson & Reeb, 2003; Martinez, Stohr & Quiroga, 2007; Sraer & Thesmar, 2007; Flören

et al.

, 2010).

The definitional issue and search for clarification on what defines a ‘family business’ has been largely to the concern of the past, to a great deal of researchers in the respective research arena. Rutherford

et al.

(2008) refer to this as the ‘family business theory jungle’ to point out the lack of consensus because of many competing theories that examine familiness related to

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5 certain performance measures1_{. Harms (2014) attempted to clarify the definitional issue by} systematically clustering the relevant articles to obtain an overview of their definitional disparity.

Figure 2: Articles per cluster - Source: Harms (2014, p. 296)

As can be seen in the figure above, the majority of the articles in the author’s sample are without an explicit definition. Thereafter, a quarter of the articles consist of self-developed definitions. Accordingly, one can get a good intuition in how this missing unified definition concerning family businesses can cause problems in the research field. As Dyer (2006, p.254) concludes:

‘Thus, some studies likely included ﬁrms in their ‘family ﬁrm’ sample that would

not have been included in other studies’ samples and this mixing of ‘apples and oranges’

might account for the ambiguous ﬁndings’

. Consequently, it does not come as a surprise that in the twenty-three studies Rutherford

et al.

(2008) reviewed between family involvement and firm performance, nine demonstrated neutrality, nine studies showed a positive relationship, four studies provided partial support for a positive relationship and only one study found a negative relationship.

In general, family firms have advantages over ‘regular’ firms in the long term since it is understood that positive factors such as orientation, loyalty, teamwork, shared values and (reciprocal) altruism are more established within these firms; also known as the ‘bright side’

1_{See for example Kraiczy, 2013; p. 22-30, for an overview of different definitions of authors, with the}

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6 of family involvement. Contrarily, the ‘dark side’ poses possible downsides of family involvement such that businesses could be subject to conservatism, conflict, free riding, deviance, and lack of professionalism (O’Boyle, Pollack, & Rutherford, 2012). The bundles of resources, capabilities and characteristics, which are distinct for family firms, are referred to as ‘familiness’ (Bromiley & Rau, 2015). Many studies try to answer whether t hese capabilities are leading to a higher performance compared to companies that are considered to not possess such capabilities.

In order to measure the aforementioned familiness and break free from the definitional issues, a scale called ‘The F-PEC scale’ is introduced. This scale is created by Astrachan, Klein & Smyrnios (2002), and measures family influence (familiness) as a degr ee, instead of a dichotomous (i.e. you are, or you are not a family firm) manner. To overcome preceding definitional obstacles and measurement incongruities, the author of this paper adopted the respective measurement scale. Additionally, the F-PEC has been validated and operationalized in the form of a questionnaire by Astrachan, Klein & Smyrnios (2005) and is further tested by Holt

et al.

(2010).

As mentioned before, there is often much debate about the definition of a family business. Furthermore, with such definition approaches, a business is either a family business or not. In order to cope with these issues, the F-PEC scale is created. The F-PEC scale is an index to help understand the dynamics of family businesses on a continuous, rather than a dichotomous (i.e. simplistic categorization) scheme (Harms, 2014; Holt

et al.,

2010). This means that businesses are not merely defined as family or nonfamily, but that there is a certain degree of influence from the family, which is most often referred to as ‘familiness’. In this paper, the F-PEC scale has been chosen as it allows to measure the amount of ‘family’ within businesses, which allows to examine the impact that family influence (familiness) has on several strategies.

Three constructs of involvement of the family within the business are measured according to the F-PEC scale. The abbreviation stands for: Family influence on (1) Power: the influence the family has on governance and management of the firm. (2) Experience: the information knowledge, judgement, and intuition that comes through successive generations. The last construct is (3) Culture: the alignment of the family’s goals and with the firm’s (Cliff &

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7 Jennings, 2005 p.342). The figure below arrives at all constructs with its corresponding factors of the F-PEC scale.

Figure 3: The F-PEC scale - Source: Astrachan et al., 2002 (p.52)

The first construct of the F-PEC scale is power. Power looks at the amount of ‘power’ a family has over a company. In order to measure power, the theory takes into account the degree of ownership within a firm and the degree of family members within the go vernance and management boards (Astrachan

et al.

, 2002).

The second construct of the F-PEC scale is experience. Experience mainly focuses on the different generations of the family present within the business. This is done by looking at the generation(s) of the family member(s) that own the company, generation(s) of family member(s) that are active in the management team and generation(s) of family member(s) that are active in the governance board (Astrachan

et al.

, 2002).

The third construct of the F-PEC scale is culture. Culture mainly focuses on identifying to what extent the family goals are aligned with the goals of the firm. It further focuses on identifying the commitment of the family towards the business (Astrachan

et al.

, 2002).

Astrachan

et al.

(2002) believe that these three constructs capture a solid figure of how much familiness a business possesses. After having measured each construct of the F -PEC scale, a final familiness score can be calculated. Thus, familiness tries to express the am ount of family

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8 influence, involvement and commitment into one number. The final familiness score (existing out of Power, Experience and Culture) is the main variable, which will be used in this study. In the methodology and appendix, a further explanation and overview of the questions and measurements related to familiness and its constructs will be provided.

1.1 Gaps and main research question

A large piece of the family business literature focuses on the impact of family involvement on financial performance. This often translates into literature that compares the differences in financial performance between family firms and non-family firms. However, since the introduction of the F-PEC scale, such measurements can also be conducted between a sample of business that are considered to have

any

degree of family in their organization. For example, research between a sample of only family businesses is now easier to apply, because the F-PEC scale allows to give any business a degree of familiness. Since these studies are fairly new, applying the F-PEC scale on sample of family businesses can often be considered to be unique. Since the F-PEC scale has never been applied on a sample of Dutch businesses, this is considered to be the first research gap and at the same time a valuable contribution to existing literature.

The second research gap can be found when looking at the linkages of familiness. Fam ily business literature and F-PEC studies are most often linked to measure any differences in financial performance. However, in the currently available literature there are more aspects to be detected outside financial performance, that could be affected by family influence. Examples of this can be seen in the article of Carney

et al.

(2015). The paper focussed on how the capital structure, internationalization and research and development strategies are affected by family influence. Next to this, there are more papers that address these topics. A selection of these papers will be incorporated in chapter two. For this reason, this research focuses on three business strategies and its relations with familiness.

The strategies that have been chosen are research and development, capital structure and internationalization. The strategies have been chosen since each topic has been a dominant subject in the currently available family business research, however lacks d irect studies related to the F-PEC scale. Further research about how these strategies are influenced by familiness can be considered as valuable contributions to the existing literature. Namely, it

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9 allows further understanding of family businesses and may provide an indication of why family firms are often considered to be different from non -family firms. Moreover, due to the multidimensional F-PEC scale, it also allows for further understanding about differences between family firms, as some family firms face higher family influence than others. By multidimensional, it is referred to the three construct index (Power, Experience and Culture), which takes into account a wide variety of elements in order to show the representation and influence of the family within a company. All together, the main research question of this paper is:

What is the effect of the familiness index level on R&D, internationalization and capital

structure on a sample of Dutch businesses?

In summary, this research contributes to the literature as it is uses the F -PEC scale on a sample of Dutch businesses for the first time, and looks beyond the frequently carried out performance studies as indicated in the article of Harms (2014), by linking the level of familiness to R&D, internationalization and capital structure.

Next to this, this research makes maximal use of its database by adding an extra research section to the study. The extra section is focussed on testing how each of the aforementioned business strategies (

R&D, internationalization and capital structure)

are linked to financial performance within this study’s sample. Although this extra research is not directly linked to the main research question, it does provide interesting information ab out the influence of each of these strategies on financial performance. This extra section exist s out of three separate hypotheses (H4, H5 and H6) and can be considered as an addition to this database research. It is decided to add this section in this study because the data had to be gathered regardless and it can be considered as a valuable information to the existing literature. Namely, Carney et al. (2015) already performed a similar study, which makes it interesting to see whether the results are in line with this particular research.

Besides the discussed gaps in literature, this research is also conducted as this was requested by EY. The company initially requested a study about the differences between listed firms and private family firms. This research is part of the Capita Selecta course and can be considered as part two of this project. However, in dialog with EY, it was decided to dive

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10 deeper into the influences that families have on family businesses. Following the discussions with EY, four individual studies (parts 1) and one collective study (part 2) have been made. Each study made use of the same database.

This study starts off by introducing three different business strategies that could be affected by the level of familiness. Furthermore, in chapter three, three hypotheses are introduced which try to test how different levels of each business strategy have its effect on financial performance. After having introduced the literature and hypotheses, two models and a methodology will be provided in order to deliberately explain the research. This is followed by a results section, which will be further discussed in the discussion and conclusion.

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2.0 Business Strategies and Familiness

The previous chapter explained the three different constructs of F-PEC scale and why it has been created. As discussed in the introduction, a large piece of the currently available family business literature relates family involvement to financial performance . However, this study introduces other variables that could be affected by familiness as well. The goal is to indicate other areas that are affected by family involvement, influence and commitment (familiness) within a firm, rather than only focussing on financial performance.

In order to indicate other areas that may be affected by familiness, the article of Carney

et al.

(2015) who performed a META analysis, is used as the foundation to provide further hypotheses about specific strategies that may be affected by the degree of familiness. This study has been chosen because it addresses the impact of the family on both financial performance as well as a selection of different businesses strategies. As the research of Carney

et al.

(2015) has much common ground with what this current study is aiming to carry out, it has been decided to make the META analysis an important source for the creation of the hypotheses. Furthermore, the META analysis combined the results of 48 studies, and therefore can be considered to give a robust representation of the findings of the already existing academic landscape regarding these topics. The characteristics provided above are the main reasons why Carney

et al.

(2015) has been chosen.

The three business strategies that will be taken into account are capital structure, R&D and internationalization. The authors of Carney

et al.

(2015) did not relate the constructs of the F-PEC scale directly to these factors. However, the authors measured the differences between family firms and non-family firms in relation to the aforementioned strategies in order to measure the effect of family involvement on these strategies. The findings, in combination with other family business studies, will provide ground to construct hypotheses about the potential impact of familiness on each business strategy. In chapter 6, each hypothesis will be tested.

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2.1 Capital Structure

First strategy that may be affected by the level of familiness is to be found in the capital structure of businesses, mainly focusing on the usage of leverage within firms. Carney

et al.

(2015) argue that family firms are less likely to make use of debt financing than non -family firms. This is mainly due to the family firm's preference to retain control over the business. For example, in some cases, debt can include covenants that harm th e degree of family control in the business, which makes debt capital unattractive because risk -averse family firms do not want to lose control over their business (Carney

et al.,

2015). To elaborate on this subject, Gonzalez

et al.

(2012) argue that when family members are present in the board of directors, the debt levels are expected to be lower. This could be explained by the suggestion that family directors are more risk averse and for this reason try to reduce the use of debt in order to lower the risk of losing control.

The so-called

‘under leveraging’

of family businesses, which is being referred to as making less use of debt than the non-family counterparts, has its consequences. On the one hand, it is being argued that the low usage of leveraged capital harms a family business, as it limits the growth opportunities due to capital and investment constraints. However, on the other hand there is also a view that under leveraging helps the business maintaining strong business practices and a long-term orientation, resulting in a positive effect on perform ance (Carney

et

al.,

2015). The authors studied whether there indeed is a difference in the use of debt capital between family firms and on-family firms, which was done by comparing the debt-to-equity ratios. Furthermore, the paper tried to seek to what extent the capital structure present in a family firm affects firm performance. Based on the results of the META analysis, the authors did not find evidence for a significant difference in the composition of the capital structure between family firms and non-family firms. However, it did find a relationship showing that the more private family firms rely on debt, the lower the performance of the firm.

Having introduced the capital structure phenomenon in family and non -family businesses, it allows for further combining this subject with the theory of the F-PEC scale. Molly

et al.

(2010) conducted a research about to what extent succession (i.e. part of the Experience construct of the F-PEC) affects the use of debt capital in family firms. The research analys ed a number of financial indicators from a sample of enterprises. The constructed model and regression analysis found evidence for a significant negative effect of succession on the leverage of a family firm after the business is transferred from the first to the second

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13 generation. This negative effect on the leverage of the business could be explained by Kaye & Hamilton (2004) who argue that family firms often become more risk averse after succession, as family members have been taught to avoid risk and preserve the inherited assets. This can lead to a larger focus on wealth preservation instead of wealth creation, which can result in a lower debt rate in a family firm (Molly

et al.

, 2010). However, when looking at succession in next-generation family businesses, a reversed result is identified. Namely, the regression identifies a significant increase in the adjusted debt rate of family firms (Molly

et al.

, 2010). Given the mixed results about the use of debt capital after succession in family firms and the finding that the use of debt capital is not significantly different between family firms and nonfamily firms, the following hypothesis is formulated:

H1:

The level of familiness does not have a relationship with the composition

of the capital structure in family firms.

2.2 Research & Development

The second business strategy, which may be affected by the level of familiness, is to be found in the field of research and development. To start off, literature argues that a firm’s ownership structure is able to affect corporate strategies such as a firm’s technological innovation activities, which can be explained because different types of owners, differ when looking at investment horizons, diversification plans, risk and return aspirations (De Massis

et al.,

2013). Since ownership is an important part of the F-PEC scale, thus familiness, this chapter’s subject may be influenced by the degree of familiness as well. Next to this, research and development activities are often considered to be risky and expensive. T aking into account the frequently mentioned characteristic that family businesses are more risk averse than non -family firms, it is interesting to study the effect of the -family on R&D in -family firms.

Managements in family firms tend to have the preference to maintain close control over the activities of the businesses. Since research and development often relies on highly specialized and outside activities and expertise, resulting in less control, this can partly explain why family businesses underinvest in R&D (Carney

et al.,

2015). Next to this, family firms are considered to behave ‘loss averse’ because management in family firms aims to preserve socio-emotional wealth and for this reason underinvest in R&D activities (Chrisman & Patel, 2012). Carney

et al

. (2015) used a META analysis to see if the statement holds true that family firms are underinvesting in research and development activities. The findings of the

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14 study indeed show that private family firms commit fewer resources on research and development than non-family firms. This can be explained by the risk and loss averse character of the family firm as touched upon before. Furthermore, the findin gs of the META analysis suggest that the underinvestment in research and development activities does l imit the performance of family firms, because the results show that firm performance and R&D have a positive relation between each other.

Besides the previously highlighted study, there is more research in the currently available literature that supports the findings of R&D activities being negatively related to family involvement. For instance, Chrisman & Patel (2012) found evidence that family firms spend less on R&D due to socio-emotional wealth preservation, Block (2010) found evidence that family ownership is negatively related to R&D and innovation, and Chen & Hsu (2009) found that family ownership is negatively related to R&D investments in Taiwanese companies. Since many academic papers support the negative relation between family involvement and R&D activities and expenditure, the following hypothesis is constructed:

H2:

The level of familiness is negatively related to R&D expenditure.

2.3 International Diversification and Orientation

The third additional strategy that may be affected by the level of familiness can be found in the field of internationalization. Internationalization within family firms is an important subfield in research regarding family businesses. In the literature, it is argued that despite international diversification having positive effects on business performance, the general tendency is that family firms are disinclined to pursue such international strategies (Fernández & Nieto, 2005). The arguments that family firms are avoiding internationalization activities are caused by the high costs, disparate family goals, values and difficulties in leadership and control when pursuing these activities (Fernández & Nieto, 2005). However, not all studies agree with the image that family involvement is negatively related to interna tionalization. For instance, Sciascia

et al.

(2012) found evidence for an inverted u-shaped curve between family ownership and internationalization. This means that international entrepreneurship in businesses is maximized at a moderate level of family own ership. Next to this article, Zahra (2003) found evidence that family firms are actively pursuing internationalization, finding a positive association of family ownership and involvement with internationalization.

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15 Carney

et al.

(2015) used a META-analysis to measure the differences in international diversification and its impact on performance. The research looked at the ratio of exports to total sales in order to measure the internationalization. The results of the research provided evidence that private family firms are less internationally diversified than non -family firms. This is referred to as the commitment of international diversification. This under-commitment of international diversification can be considered as disadvantageous for family firms because international diversification does have a positive relation to firm performance according to the findings of Carney

et al.

(2015).

Calabro

et al.

(2016) also focussed on the impact of family involvement on internationalization. The study investigates to what extent the composition of the governance structure, focussing on the involvement of non-family members in the management, supervisory and advisory boards, affects the internationalization of a family firm. This can be related back to the F-PEC scale when looking at the Power construct. The authors show that a higher presence of

non-family

members in the governance structure has a positive impact on a family firm's international orientation. This can be explained because family members often have less experience outside their own firm compared to non -family members. Furthermore, non-family members often provide the family business with new connections, experience, know-how and reputation (Calabro

et al.,

2016). When looking at the resource based theory, increasing the amount of non-family members in the board, allows for better internationalization processes as they have less resource dependencies and are able to use more outside experience and connections (Calabro

et al.,

2016). Next to this, it is argued that the presence of non-family members in governance and management boards has a positive influence on the variety of knowledge and capabilities present within these boards, resulting in better attitudes towards international markets leading to a higher propensity to engage in international markets (Calabro

et al.,

2016).

All the arguments provided above, argue that higher involvement of

non-family

board members positively relates to the degree of international entrepreneurial orientation. International entrepreneurial orientation is defined as

‘a set of behaviours associated with the

potential creation of value, which manifest themselves as proactive and innovative methods,

risk-taking activity, autonomous actions, and an emphasis on outperforming rivals, all

variously aimed at discovering, enacting, evaluating, and exploiting opportunities across

national borders’

(Calabro

et al.

, 2016, p.240).

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16 The arguments of Fernández and Nieto (2005) in combination with the result that non -family firms extract a higher part of their sales from abroad compared to family firms, and the result that a higher involvement of

non-family

members present in the boards stimulates international orientation led to the composition of the following hypothesis:

H3:

The level of familiness is negatively related with international orientation and

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3.0 Business Strategies on Financial Performance

According to the previously explained literature, family firms can have different strategies when looking at dimensions such as capital structure, R&D and internationalization, especially when being compared to non-family businesses. For this reason, it is argued that these factors are likely to be influenced by the level of familiness within a business.

However, it is also interesting to see to what extent these strategies affect the financial performance of the businesses present in this study’s sample. The META analysis of Carney

et al.

(2015) tested besides the differences in business strategies between family and non -family firms, also the effect these strategies have on financial performance. Therefore, in this study, it is also tested in separation, if there is any relation between capital structure, R&D and internationalization when being linked to financial performance.

According to Carney

et al.

(2015) family firms are less likely to make use of debt financing than non-family firms. This has to do with the risk averseness of the family business owners who fear the risk of losing control and financial distress (Santos

et al.

2013). Carney

et al.

(2015) showed that the more private family firms rely on debt, the lower the performance of the firm. The authors used the debt-to-equity ratio as the indicator to measure the effect on performance. For this reason, the following hypothesis is constructed:

H4:

The level of debt is negatively related to Financial Performance

Furthermore, the authors studied whether research and development expenditures hinder financial performance (e.g. high costs could lead to lower performance) or increase financial performance (e.g. the creation of competitive advantages). The META analysis of Carney

et

al.

(2015) resulted in the findings that firm performance and R&D have a positive relation. For this reason, the following hypothesis is constructed:

H5:

The level of research and development expenditure is positively related to Financial

Performance

The last strategy, which could be related to financial performance, is internationalization. Like the other strategies, the META analysis of Carney

et al.

(2015) investigated the relation between internationalization and financial performance and found out that international diversification (measured by exports) does have a positive relation to firm performance. For this reason, the following hypothesis is constructed:

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4.0 The Research Models

The aim of this chapter is to introduce two research models which are created after having assessed the current literature and research gaps, as explained in the introduction and literature review. This chapter starts with discussing the reliability and validity of the F -PEC scale, which is an important part of the first research model.

4.1 F-PEC Scale: Reliability, Validity and Generalizability

Astrachan, Klein & Smyrnios (2002), could be regarded as ‘the founding fathers’ with respect to the inception of the F-PEC scale. In order to give meaningfulness to the latter scale, the authors and a team of skilled people, such as academic researchers, practitioners and family business owners developed the questions necessary to develop the F-PEC scale in aggregate. Indeed, the authors proceeded with pilot testing and focus group discussions with a number of family businesses in order to validate the scale with analysis of modelling techniques.

Evidently, items that were shown to demonstrate ambiguity, lack of discriminatory power or redundancy were eliminated (Astrachan

et al.,

2002). Furthermore, external validity was tested on a large sample of groups (i.e.

n

> 500) by means of cross-cultural comparison. As Astrachan

et al

, (2002, p. 52) conclude: ‘

The F-PEC index of family influence on the business

provides researchers, for the first time, with a tested standardized instrument that allows

integration of different theoretical positions as well as comparisons of different types of data’

. Later on, Klein, Astrachan & Smyrnios (2005), further tested the F -PEC scale in a sample of 10.000 randomly selected company CEOs through exploratory and factor analytical techniques, to prove reliability (Klein

et al.

, 2005).

In addition, Holt, Rutherford & Kuratko (2010) further tested the measurement properties of the F-PEC scale. Hence, further testing the items and making the operationalization more robust. Thereby, giving power to the generalizability, internal consi stency and reliability of the construct. Exploratory and confirmatory factor analysis were conducted and provided support to the model. As Holt

et al.

, (2010, p. 84) discuss: ‘

Although we are not by any means

suggesting that the F-PEC represents an end to the search for definitional clarity, our

findings do suggest that the F-PEC offers family business researchers a valid and reliable

scale with which to more finely classify family firms. Moreover, it measures several intangible

factors that may be used as the dimensions of involvement and essence (a yet to be measured

construct) are refined and converge

’.

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4.2 Familiness on Capital structure, R&D and Internationalization

Several hypotheses have been constructed at the end of ea ch topic introduced in chapter two. Each hypothesis links the level of familiness to a specific business strategy. An overview covering each hypothesis is provided below:

H1 The level of Familiness does not have a relationship with the composition of the capital structure in family firms.

H2 The level of Familiness is negatively related to research and development expenditure.

H3 The level of Familiness is negatively related with internationalization.

As can be seen when looking at the hypotheses above, this study focuses on measuring the impact that the level of familiness has on capital structure, R&D and internationalization. After having indicated the amount of familiness present within the business es, measured by the F-PEC scale, it will be assessed whether there are any trends and relations visible between familiness (independent variable) and capital structure, R&D and internationalization (dependent variables). The three hypotheses above resulted in the following model:

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4.3 Capital structure, R&D and Internationalization on financial performance

As explained in chapter three, this research also takes into account whether the business strategies (capital structure, R&D and internationalization) have any relation to financial performance. The aforementioned strategies could be influenced by the level of familiness, and simultaneously the strategies can influence financial performance as well. Therefore, it is also tested, if different levels of the business strategies have an effect on financial performance. In this case, the business strategies are considered as the independent variables whereas the financial performance indicators are the dependent variables. The hypotheses explained in chapter 3 about capital structure, research and development and exports resulted in the construction of the following hypotheses and model:

H4 The level of debt is negatively related to financial performance.

H5 The level of R&D expenditure is positively related to financial performance.

H6 The level of exports is positively related to financial performance.

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21

5.0 Methodology

This chapter explains how the study is executed. It begins with a description of the sample. After having touched upon this topic, the different kinds of data and its collection are described. Lastly, it is explained how the tests are carried out in SPSS.

5.1 Sample

The final sample used for testing the F-PEC scale exists out of 38 Dutch family businesses. The sample has been selected by looking at the ‘Elsevier Top 100 familiebedrijven’ and the ‘FBned’ database. The Elsevier top 100 list of family businesses is a yearly recurring list and provides an overview of the top 100 largest Dutch family businesses when loo king at the company's turnover. This study looks at the 2016 edition of the Elsevier Top 100 family business (Elsevier, 2016). The sample mainly includes large firms. This has been chosen because as addressed in Rutherford

et al.

(2008), there is more robustness in the relationship between familiness and performance for studies with samples of larger firms. After having collected all the results, the lowest revenue out of all the respondents was 14 million euro. The final sample was constructed by selecting 93 companies out of the two databases. Eventually 41 companies filled in the questionnaire, resulting in a response rate of 44%. However, three companies have been removed from the sample because there was not enough financial data available. The exclusion of these companies led to a total of 38 companies, which entirely filled in the questionnaire. All the businesses embedded in the sample are part of four different sectors. These sectors are food & drinks, construction, industry and consumer goods. The sectors have been chosen in consultation with EY.

5.2 Data Collection

In order to measure the effects of different levels of familiness on several aspects of a business, information gained from questionnaires and year reports have been collected. An overview of this process can be found in this sub-chapter.

To every business in the sample, a questionnaire has been sent in order to collect the information that was needed to fill in the modelled variables and to test the hypotheses. The questionnaire has been made in Qualtrics and sent by E-mail. The contact-details of each company have been obtained through the databases called Company.Info, LinkedIn and Orbis. An Excel spreadsheet was made to organize the progress of the respondents. This

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22 spreadsheet existed out of the following items: name of the company, industry, client of EY, names and functions of (executive) board members, phone numbers, email addresses, previous activity and the next activity, and lastly some notes. An example of this framework can be found below.

Client EY Company Name Function

Company

phone Previous activity Next

activity Notes/specialties

1 Yes Company X Mister X CEO 020-xxxxx

Introduction call ( 30-5)

Check-call (2-6)

Family company of the year 2017, positive first reaction.

Source - own composition

Before the questionnaires were sent, each company had been contacted by phone in order to try to increase the response rate and to ask for email addresses to send the introduction mail together with the link to the questionnaire. The questionnaire was sent to participants of the founding family or members of the highest management or governance boards. Due to the extensive network of EY and the personal way of reaching out to the companies the e ventual number of responses were positive. In chapter 5.4, it will be addressed how the financial data has been collected.

5.3 Questionnaire and variables

Most of the variables that are to be tested in this research have been obtained through the questionnaire. The majority of questions included in the questionnaire have been used and validated by previous academic literature. The main sources of these quest ions are Klein

et al.

(2005), Holt

et al.

(2010) and Astrachan

et al.

(2002). An overview of the complete questionnaire is provided in the appendix.

The goal of the questionnaire was to acquire information about the levels of familiness and the different business strategies. In the end, each business could score a familiness rate from 0 to 1. As explained before, familiness is measured by three constructs wherewith each of the constructs (Power, Experience, Culture) is weighted equally. The remainder of this chapter explains how the total familiness and each construct is calculated.

The first construct is power. The three factors representing the Power construct are ownership, management and governance. Each of the factors within the Power construct accounts for ⅓ of the total Power score. The scores of these factors are expressed in percentages. For instance, if the management board consists of three family members and two

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23 non-family members, the score attributed hereunto is 0.6, (i.e. 60%). An example of how each factor of the power construct is calculated can be found below:

Item: Answer: Score:

1. Family ownership 100% 100% = 1

2a. Management team 6 people 2b. Family members in

management team

3 people 3 / 6 = 50% = 0.5

3a. Governance board 4 people 3b. Family members in

governance board

2 people 2 / 4 = 50% = 0.5

Power = ( 1 + 0.5 + 0.5 ) / 3 = 0.6667

Calculation 1: Power construct

The second construct is experience. The experience construct is divided in three factors. The factors are generation of ownership, generation of the management board and the generation active in the governance board. Each factor is weighted as ⅓ of the total experience construct. The experience questions could be answered from generation 1 to generation 8+. As discussed in literature, each generation has a different impact on experience. For this reason, the generations are weighted as follows: G1 = 0; G2 = 0.5; G3 = 0.75; G4 = 0.875; G5 = 0.9375; G6 = 0.96875; G7 = 0.996775 and G8+ = 1 (Holt

et al.,

2010). An example of how experience is calculated can be found below:

Generation ownership 2nd_generation _0.5

Generation of family in management board 3rd_generation _0,75 Generation of family in governance board 3rd_generation _0,75 Experience = ( 0,5 + 0.75 + 0.75 ) / 3 = 0.5833

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24 Lastly, the culture construct is explained. The culture construct is divided in 14 different questions. Each question has the same weight, resulting in 7.14% per question. Furthermore, each question was asked in the form of a 5-point Likert scale, meaning that the weights of the scores are as follows: fully agree = 1.0, agree = 0.75, neutral = 0.5, disagree = 0.25, fully disagree = 0. After having analysed the responses, the results showed that the Cronbach’s alpha of the culture scale is 0.801. An example of how culture is calculated can be found below:

Culture answer 1 Disagree 0.25

Culture answer 2 Agree 0,75

Culture answer 4 Neither disagree nor agree 0,5

Culture answer 5 Fully agree 1

Culture answer 7 Fully disagree 0

Culture answer 9 Disagree 0,25

Culture answer 10 Disagree 0,25

Culture answer 14 Neither disagree nor agree 0,5

Culture = (0.25 +0.75 + 0.75 + 0.5 + 1 + 1 + 0 + 0.75 + 0.25

+ 0.25 + 0.75 + 1 + 1) / 14 = 0,5893

Calculation 3: Culture construct

In order to measure the total familiness level, the results of the power, experience and culture constructs, all account for 1/3rd_{of the total score. In the end, familiness is expressed in a score} between 0 and 1. An example of how the total familiness score is calculated, based on the examples above, can be found below:

Familiness ( Power + Experience + Culture ) / 3

( 0,6667 + 0,5833 + 0,5893 ) / 3 = 0,6131

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25 After completing the F-PEC part of the questionnaire, the following section of the questionnaire was aimed on acquiring information about the degree of international diversification and international orientation within the companies of the sample. The qualitative questions in this section have been derived from Calabro

et al.

(2016). The quantitative question is derived from Carney

et al.

(2015). Firstly, five questions were asked on the 5-point Likert scale: Top management tends to see the world, instead of just the Netherlands, as our firm's marketplace; The prevailing organizational culture at our firm (management's collective value system) is conducive to active exploration; Management develops human and other resources for achieving our goals in international markets; Our top management is experienced in international business; In international markets, our top managers have a proclivity for high-risk projects (with chances for high returns). The questions targeting international orientation were expressed in 5 -point Likert scales as well. For this reason, the score of each Likert scale item is similar to the ones expressed above. Simultaneously, the questions within each topic are weighted equally. After having analysed the responses, the results showed that the Cronbach’s alpha of the internationalization questions was 0.944.

An example of how the international orientation questions are calculated can be found below:

International or. answer 1 Disagree 0.25

International or. answer 2 Agree 0,75

International or. answer 3 Agree 0,75

International or. answer 4 Neither disagree nor agree 0,5 International or. answer 5 Fully agree 1

International orientation:

(0,25 + 0,75 + 0,75 + 0,5 + 1) / 5 = 0,65

Calculation 5: International orientation

After asking these questions, a direct question was asked to measure the exports of the companies. Namely, what was the approximate revenue obtained from activities/sales outside the Netherlands in 2015? (Exports). This could be answered on a scale from 0% to 100%. Furthermore, the respondent was asked whether there were significant changes in this rate since 2010. If the answer was

‘no’

, one proceeds to the next block. If the answer was

‘yes’

, the following question was posed to specify the changes: What w ere the significant changes

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26 in revenue obtained from sales/activities outside the Netherlands since 2010? However, the majority of respondents did not face major changes.

The last section of the questionnaire focussed on measuring R&D within the companies . The question has been derived from Carney

et al.

(2015); what was your company’s annual R&D budget in euros? Furthermore, it was also asked if there were significant changes in this rate since 2010. If ‘no’ was answered, the questionnaire proceeds to the next block, with ‘yes’ as an answer, this question was asked to specify the changes: What have been the significant changes in annual R&D budget since 2010? After analysing the answers, the majority of respondents did not face major changes. The annual R& D expenditures had been asked in euros. In order to get comparable numbers, the research and development expenditures had been divided by the revenues, which resulted in percentages indicating how much of the firms revenues had been invested in researched and development activities.

Lastly, no questions were included regarding the capital structure of the firm. In order to measure this, the debt-to-equity ratios are used to represent the composition of the capital structure. These figures are derived from the annual year reports of th e companies. The average debt-to-equity ratios over the years 2010 until 2015 have been taken into account.

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27

5.4 Financial Indicators

For the data collection of the financial statistics, the 38 companies provided 1.248 results, which resulted in the calculation of 1.094 financial ratios. All the financial indicators have been collected from 2010 until 2015. The seven financial indicators used to measure financial performance were chosen in consultation with EY and are as follows:

1. Revenue growth rate, which is calculated by dividing revenue 2010 by revenue 2011 in a percentage. This calculation is done for each year in the sample. This is a general indicator of a company's growth over the particular year.

2. Solvency, which is equity divided by total assets, ultimately expressed in a percentage. This will show the ratio between equity and liabilities on the balance sheet. This gives an indication if the company is able to pay its debts (Heaton, 2007).

3. Net profit margin, is calculated by dividing the net profit with the revenue multiplied by 100%. This will show an indication of the profitability of the business . (Blaine, 1994).

4. Return on assets (ROA), which is the net profit divided by the assets in a percentage. It is a key figure indicating the profitability of the average total assets before interest deduction.

5. Return on equity (ROE), is the net profit divided by the equity in a percentage. It is a key figure indicating the profitability of equity before interest deduction. (Cochran & Wood, 1984).

6. Return on capital (ROC) is the net profit, divided by the equity and the long -term debts. The ROC attempts to measure the return earned on capital invested in an investment (Damodaran, 2007).

7. Debt-to-equity. This ratio is calculated by dividing the total debt (short-term and long-term) through the equity. This will show an indication of how well and how fast a company is able to pay its debts (Oxford, 2017).

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28 It was decided to look at the year reports of 2010 until 2015 to be able to get an average image of the performances of the businesses, thus reducing the chance of bias and outliers. For the data analysis, the average financial indicators have been chosen to work with. The year reports of 2016 have not been included in the sample because many rep orts of this particular year have not been available yet. The financial indicators had been manually derived from the in total 366 reports. The main source that was used to obtain these year reports was the database Company.Info, which was granted access t o by Ernst & Young (EY).

The database Company.info allows downloading the available year reports of a variety of Dutch businesses. Besides this, the database also provides automatically generated annual summary of the years 2010 till 2015 including the financial indicators. To be sure, this document was checked with the actual annual reports, and lots discrepancies were found. For this reason, these automatically generated documents were not used. According to Company.info these automatically combined statements were retrieved from the annual reports by using Optical Character Recognition (OCR) software. OCR is able to automatically read and recognize a character (revenue for example) in a report. This software is known as an important innovation in a lot of technological applications, but is also known as a function that is not completely developed without errors. This is probably the reason why there were many mistakes in the combined reports. For this reason, all the data of this study was manually copied from the year reports in order to avoid these discrepancies.

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29

5.5 Data analysis

This study makes use of the Mann-Whitney U test in order to analyse its data. This test is a non parametric test and allows for testing differences between two samples. In this research several Mann-Whitney U tests will be conducted. Every test has been performed in SPSS.

For the first three hypotheses, three different grouping variables are incorporated in the Mann-Whitney U test. The first type of tests captures two groups, namely it compares one group with a familiness score above average with the group of companies that have a familiness score below average. For example, the average level of familiness in this study is 0,71. This means that for the first tests, one group existed out of companies with a familiness level higher than 0,71, and that the other group existed out of companies with a familiness level lower than 0,71. Consequently, these groups will be linked to the capital structure, internationalization and research and development measures. In these tests, the test variables are capital structure, internationalization and research and development, whereas the group ing variable is familiness (e.g. group 1 = above average, group 2 = below average). The second type of tests also captures two groups, namely it compares the ten companies with the highest familiness scores with the ten companies with the lowest familiness scores. These groups will be linked to the aforementioned business strategies as well. Furthermore, a similar test will be conducted between the five highest and five lowest companies with regards to their familiness scores. The test variables in this case are the same as explained above. The group ing variable still remains familiness, however group 1 = top 10/5 firms and group 2 = bottom 10/5 firms.

For the second part of this study, which is focussed on testing hypotheses four, five and six, it has been looked at multiple groups as well. It compares the group of companies which have levels of business strategies above average with the group of companies that have business strategy levels below average. These groups are linked to ROA, ROE, ROC, Solvency, Turnover growth and net profit margin (which are called test variables). The grouping variables are capital structure, internationalization and research and development (group 1 = strategy above average, group 2 = strategy below average). Although the main focus and hypothesis tests are put on the groups which compared companies above and below average, this test also included comparisons between groups focussing on the top 10/5 companies and bottom 10/5 companies. For all tests performed in this study, p-values of 0.05 and lower are considered to be significant.

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30

6.0 Results

This chapter starts off by showing the survey results of each company that has participated in the research. Furthermore, a sector analysis is provided. This is followed by linking the levels of familiness to the three different business strategies. Lastly, this chapter shows how different levels of certain business strategies affect financial performance.

6.1 Overview

The table below provides an overview of each company’s sector, familiness level, power level, experience level and culture level. It is sorted from highest to lowest familiness level.

N Sector Familiness Power Experience Culture

1 Consumer G 0,99 1 1 0,98 2 Construction 0,98 1 1 0,95 3 Food Drinks 0,86 0,87 0,99 0,73 4 Construction 0,86 1 0,91 0,66 5 Industry 0,81 1 0,75 0,68 6 Construction 0,81 0,78 0,67 1 7 Consumer G 0,8 0,67 0,94 0,79 8 Industry 0,78 0,75 0,75 0,86 9 Consumer G 0,78 1 0,5 0,84 10 Consumer G 0,77 0,67 0,81 0,82 11 Industry 0,77 0,56 0,87 0,89 12 Industry 0,77 0,56 0,87 0,88 13 Industry 0,76 0,42 0,94 0,93 14 Industry 0,74 0,88 0,5 0,86 15 Construction 0,74 0,38 0,87 0,96 16 Construction 0,73 0,75 0,75 0,68 17 Industry 0,73 0,49 0,83 0,86 18 Industry 0,72 0,57 0,83 0,77 19 Industry 0,72 0,62 0,83 0,71 20 Food Drinks 0,72 0,71 0,58 0,88 21 Food Drinks 0,71 0,55 0,75 0,84 22 Industry 0,69 0,6 0,58 0,89 23 Construction 0,69 0,42 0,94 0,71 24 Industry 0,68 0,48 0,58 0,98 25 Industry 0,67 0,5 0,75 0,77 26 Food Drinks 0,67 0,51 0,75 0,75 27 Industry 0,66 0,67 0,5 0,82 28 Industry 0,66 0,47 0,75 0,77 29 Industry 0,66 0,58 0,58 0,8 30 Food Drinks 0,64 0,5 0,67 0,75 31 Consumer G 0,64 0,55 0,67 0,71 32 Consumer G 0,61 0,56 0,33 0,95 33 Construction 0,59 0,56 0,5 0,71 34 Construction 0,57 1 0 0,7 35 Industry 0,57 0,44 0,5 0,77 36 Industry 0,53 0,75 0 0,84 37 Construction 0,5 0,26 0,5 0,73 38 Food Drinks 0,4 0,52 0 0,68 Average 0,71 0,65 0,67 0,81

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31 The table below provides an overview of each company’s sector, familiness level, power level, experience level and culture level. The table is sorted per sector, after which the familiness levels are ranked from high to low.

N Sector Familiness Power Experience Culture

1 Industry 0,81 1 0,75 0,68 2 Industry 0,78 0,75 0,75 0,86 3 Industry 0,77 0,56 0,87 0,89 4 Industry 0,77 0,56 0,87 0,88 5 Industry 0,76 0,42 0,94 0,93 6 Industry 0,74 0,88 0,5 0,86 7 Industry 0,73 0,49 0,83 0,86 8 Industry 0,72 0,57 0,83 0,77 9 Industry 0,72 0,62 0,83 0,71 10 Industry 0,69 0,6 0,58 0,89 11 Industry 0,68 0,48 0,58 0,98 12 Industry 0,67 0,5 0,75 0,77 13 Industry 0,66 0,67 0,5 0,82 14 Industry 0,66 0,47 0,75 0,77 15 Industry 0,66 0,58 0,58 0,8 16 Industry 0,57 0,44 0,5 0,77 17 Industry 0,53 0,75 0 0,84 18 Food Drinks 0,86 0,87 0,99 0,73 19 Food Drinks 0,72 0,71 0,58 0,88 20 Food Drinks 0,71 0,55 0,75 0,84 21 Food Drinks 0,67 0,51 0,75 0,75 22 Food Drinks 0,64 0,5 0,67 0,75 23 Food Drinks 0,4 0,52 0 0,68 24 Consumer G 0,99 1 1 0,98 25 Consumer G 0,8 0,67 0,94 0,79 26 Consumer G 0,78 1 0,5 0,84 27 Consumer G 0,77 0,67 0,81 0,82 28 Consumer G 0,64 0,55 0,67 0,71 29 Consumer G 0,61 0,56 0,33 0,95 30 Construction 0,98 1 1 0,95 31 Construction 0,86 1 0,91 0,66 32 Construction 0,81 0,78 0,67 1 33 Construction 0,74 0,38 0,87 0,96 34 Construction 0,73 0,75 0,75 0,68 35 Construction 0,69 0,42 0,94 0,71 36 Construction 0,59 0,56 0,5 0,71 37 Construction 0,57 1 0 0,7 38 Construction 0,5 0,26 0,5 0,73 Average 0,71 0,65 0,67 0,81

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32 The table below provides an overview of each company’s familiness level, debt-to-equity ratio, export level, international orientation and research and development expenditure level. It is sorted from highest to lowest familiness level.

N Sectors Familiness Debt-Equity Export International

orientation R&D 1 Consumer G 0,99 7,97 0,7 0,8 - 2 Construction 0,98 3,24 1 0 - 3 Food Drinks 0,86 0,74 0,76 0,8 0,0565 4 Construction 0,86 0,75 0 0 0,0055 5 Industry 0,81 0,93 0,64 0,75 0,0001 6 Construction 0,81 1,69 0,2 1 0,2057 7 Consumer G 0,8 1,59 0,15 0,8 0 8 Industry 0,78 1,47 0,7 0,8 0 9 Consumer G 0,78 0,66 0 0,2 - 10 Consumer G 0,77 1,22 0,8 0,75 0,0082 11 Industry 0,77 2,6 0 0 0,0054 12 Industry 0,77 1,98 0,3 0,85 0,0082 13 Industry 0,76 0,95 0,9 0,8 - 14 Industry 0,74 0,84 0,5 0,9 0,0024 15 Construction 0,74 0,77 0,93 0,7 0,0012 16 Construction 0,73 0,77 0,21 0,35 0,0072 17 Industry 0,73 0,94 0,25 0,7 - 18 Industry 0,72 1 0,7 0,85 0,0116 19 Industry 0,72 0,45 0,6 0,8 - 20 Food Drinks 0,72 1,17 0,25 0,7 - 21 Food Drinks 0,71 1,99 0,98 0,8 - 22 Industry 0,69 0,81 0,99 0,85 - 23 Construction 0,69 2,69 0 0,25 0,0021 24 Industry 0,68 1,17 0,11 0,55 0,0082 25 Industry 0,67 1,12 0,54 0,75 0,008 26 Food Drinks 0,67 0,61 0,5 0,7 0,0058 27 Industry 0,66 0,84 0,3 0,6 - 28 Industry 0,66 1,12 0,75 0,85 0,008 29 Industry 0,66 0,63 0,8 0,7 0,03 30 Food Drinks 0,64 1,65 0 0,35 - 31 Consumer G 0,64 1,47 0,2 0,65 0 32 Consumer G 0,61 3,41 0,95 0,95 0,0007 33 Construction 0,59 1,93 0,3 0,5 0,0136 34 Construction 0,57 2,24 0,04 0,45 - 35 Industry 0,57 1,76 0,6 0,75 0,02 36 Industry 0,53 1,19 0,02 0 0 37 Construction 0,5 4,5 0,05 0,35 0,0029 38 Food Drinks 0,4 3,38 0,95 0,7 - Average 0,71 1,69 0,465 0,613 0,016

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33

6.2 Sector analysis

This part of the study provides an overview of how the scores of every variable measured are different between the four sectors. To start off, in table four and five, the average scores per sector can be seen.

Sector Familiness Debt/Equity Export International

orientation

R&D

Food and drinks 0,6667 1,59 0,573 0,675 0,03115

Construction 0,7189 2,06 0,303 0,400 0,03402

Industry 0,7011 1,16 0,512 0,676 0,00849

Consumer goods 0,7650 2,72 0,467 0,691 0,00222

Table 4. Sectors and business strategy

Sector Familiness Solvency Turnover

Growth

Net profit

ROA ROE ROC

Food and drinks 0,6667 0,41 0,03 0,03 0,05 0,13 0,11

Construction 0,7189 0,43 0,06 0,02 0,03 0,07 0,07

Industry 0,7011 0,46 0,06 0,03 0,04 0,09 0,08

Consumer goods 0,7650 0,38 0,09 0,05 0,12 0,35 0,35

Table 5. Sectors and financial performance

The remainder of chapter 7.2 provides indications of the sectors and its average scores from high to low. In table six, it is shown that the sector ‘consumer goods’ has the highest familiness score.

Sector Familiness

1. Consumer goods 0,765

2. Construction 0,7189

3. Industry 0,7011

4. Food and drinks 0,6667

Table 6. Sectors and familiness

In table seven, it is shown that the sector ‘consumer goods’ has the highest debt to equity ratio.

Sector Debt/Equity Ratio

4. Industry 1,16

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34 In table eight, it is shown that the sector ‘food and drinks’ has the highest export level.

Sector Export

2. Industry 0,512

Table 8. Sectors and export level

In table nine, it is shown that the sector ‘consumer goods’ has the highest international orientation score.

Sector International

orientation

2. Industry 0,676

Table 9. Sectors and international orientation

In table ten, it is shown that the sector ‘construction’ has the highest R&D ratio.

Sector R&D

3. Industry 0,00849

Table 10. Sectors and R&D

In table eleven, it is shown that the sector ‘industry’ has the highest solvency score.

Sector Solvency

1. Industry 0,46