Master Thesis Organizational & Management Control

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Master Thesis

Organizational & Management Control

‘The influence of national culture and legal systems

on the relationship between liquidity and performance’

Janieke Golbach

WA Scholtenstraat 11a

9711 XA Groningen

s1637916

06-18296615

[email protected]

November 26th 2012

Supervisor: J.S. Gusc and C.P.A. Heijes

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Abstract

In times of internationalization and worldwide competition, multinational companies

deliver a substantial contribution to the economy. In the field of research about

multinationals culture plays an important role. But also working capital management

techniques, which are crucial in maximizing organizational performance, are a well

research subject. In this study these subjects are combined and the influence of culture on

the relation between liquidity and performance is researched. Due to empirical

quantitative analyses, there is found a negative relation between the cash conversion

cycle (as measure for liquidity) and return on equity (as measure for performance). This

relation is stronger in countries that have higher uncertainty and it is stronger in countries

with a common law system.

Key words: culture, cash conversion cycle, performance, legal system

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Table of contents

Abstract………………………………………………………………………….. pp. 2

1. Introduction…………………………………………………………………… pp. 4

2. Literature review……………………………………………………………… pp. 7

2.1 Performance…………………………………………………………………... pp. 7

2.2 Liquidity……………………………………………………………………… pp. 8

2.3 Liquidity and performance…………………………………………………… pp. 10

2.4 Culture………………………………………………………………………... pp. 12

2.5 Culture and liquidity-performance relation…………………………………... pp. 17

2.6 Legal system………………………………………………………………….. pp. 20

2.7 Legal system and liquidity-performance relation…………………………….. pp. 22

2.8 Overview …………………………………………………………………….. pp. 23

3. Method………………………………………………………………………… pp. 24

3.1 Concept operationalization…………………………………………………… pp. 24

3.2 Sample………………………………………………………………………… pp. 28

3.3 Statistical analysis…………………………………………………………….. pp. 28

4. Results…………………………………………………………………………. pp. 32

4.1 Sample………………………………………………………………………… pp. 32

4.2 Correlations…………………………………………………………………… pp. 33

4.3 Regression analysis…………………………………………………………… pp. 34

5. Conclusion and discussion……………………………………………………. pp. 40

5.1 Conclusion…………………………………………………………………….. pp. 40

5.2 Discussion……………………………………………………………………... pp. 41

6. References……………………………………………………………………… pp. 46

7. Appendix……………………………………………………………………….. pp. 52

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1. Introduction

The business world is globalizing and more firms are seeking for opportunities

outside their domestic borders. Nowadays there are more than 63.000 multinationals with

821.000 subsidiaries spread all over the world. Together they produce 25 percent of the

world’s gross product (Gabel and Bruner, 2003). Multinational organizations offer a

substantial contribution to the world economy. With respect to multinationals, the

concept of culture has proven to be an interesting research subject of which the results

can be very useful to managers in organizing their business to maximize performance.

One of the possibilities to maximize performance is to have an efficient working capital

management, because working capital virtually affects the firms overall profitability,

solvency and liquidity (Attari and Raza, 2012).

This research focuses on the influence of culture on the relationship between liquidity

and performance. Previous research has shown that there exists a relationship between

capital structure and performance (Gleason et al., 2000; Chou and Lee, 2010). Liquidity

is an important part on the assets side of the capital structure of a firm and can be

measured by the cash conversion cycle (CCC). Attari and Raza (2012) state that the

length of the CCC is considered among the fundamental ingredients of working capital

management. And due to the large influence of working capital management practices on

performance is a crucial ingredient. Research shows that there is a negative relation

between the CCC and performance of a firm. This means the shorter the CCC, the higher

the performance (Jose et al., 1996; Wang, 2002; Eljelly, 2004; Hutchison et al., 2007;

Attari and Raza, 2012).

Furthermore, Gleason et al. (2000) found strong evidence that the capital structure is

influenced by culture, and therefore performance is also influenced by national culture.

Ramirez and Tadesse (2009) state that there is a significant variation in liquid asset

holdings across firms as well as across countries. They point out national culture as an

economically and statistically significant predictor of firms’ liquid asset holding.

Therefore this research will include national culture as an influencing factor on the

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relationship between liquidity and performance. This has been done by the following

research question:

How does culture influence the relation between liquidity and performance of an

organization?

The goal of this research is to contribute to the understanding of the relation between

culture, liquidity and performance and to give an insight in how particular working

capital management practices can be addressed to different cultural dimensions, in order

to achieve the higher organizational goal of maximizing performance. This research can

be especially relevant to managers of multinational and culturally diverse organizations.

There have been some previous studies on this topic, but most research have lacked a

cross country comparison as data from one country was used (Ebben and Johnson, 2011)

and thereby only one dimension of culture (Ramirez and Tadesse, 2009) or no separation

of the different cultural dimensions was presented (Gleason et al., 2000). Moreover, often

the CCC as a whole has been tested, while the different parts are neglected (Jose et al.,

1996). Acknowledging these limitations of the current literature, this study focuses on

three cultural dimensions (the ones that are most relevant according to the literature),

three countries to execute a cross country comparison and data from a time period of 5

years. Also, the separate parts of the CCC in relation with performance will be tested.

Finally, the influence of the legal system of a country, as a controlling variable against

national culture, is included.

The research has been approached by a statistical analysis of the influence of liquidity

on performance with the inclusion of different culture variables. The data was collected

from more than 2200 companies in the United States, Japan and France over the period of

2006-2010.

After this introduction, the literature will be reviewed in which the most important

concepts and theories are highlighted and the hypotheses will be formed (chapter 2, pp.

7). Subsequently, the methodology will be explained and the concepts operationalized

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(chapter 3, pp. 24), followed by the analysis and the results of the regressions (chapter 4,

pp.32). Finally, the confirmation of the hypothesis will be considered and summarized in

the conclusion (chapter 5, pp. 40). The paper will end with the discussion and some

recommendations for future research (chapter 5, pp. 40).

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2. Literature review

In order to be able to answer the research question (‘How does culture influence the

relation between liquidity and performance of an organization?’), the most important

concepts (culture, liquidity, performance, legal system) will be defined and explained in

the following literature review. Based on these concepts and their underlying

relationships and theories, several hypotheses and expected relations will be stated.

2.1 Performance

Performance systems are used within organizations for the monitoring and evaluation

of the performance of different areas of business, usually by comparing actual

performance with the targets (Westerman et al., 2010). However, measuring performance

is also needed to compare the company with competitors.

McGee et al. (2005) indicate three perspectives of performance; financial

performance, operations performance and organizational effectiveness. Financial

performance assumes the dominances of financial goals and is associated with accounting

based or financial market based measures. Operational performances focus on internal

performance factors which might lead to success for the company and its set of

businesses. These included market position, growth, market share, efficiency, value

added in manufacturing or product quality. Organizational effectiveness goes beyond

economic and operational performances and focuses on the strategic goals of the entire

firm and creates value for all stakeholders. A properly fitted strategy is therefore highly

important.

Moreover, a division can be made between financial and non-financial performance

can be made. The balanced score card is a performance evaluation instrument that

combines financial and non-financial factors. The balance score card often includes

profitability measures, customer satisfaction measures internal measures of efficiency,

quality and time and innovation measures (Bhimani et al., 2008).

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In this research the focus is on financial performance. Managers and analysts use

accounting earnings and accounting profits as a benchmark because financial accounts

are readily available and the measures themselves are easy to calculate (McGee et al.,

2005). These measures are also used in external reporting. Due accounting measures,

there is an objective way of evaluating, which makes comparison with other companies

easier (Westerman et al., 2010).

Evaluation of financial performance can be done in different areas. One possibility is

due to operating performance, for example profitability or return on assets, as has been

done in research of Wang (2002) and Deloof (2003). Or it can be done based on

corporate value, for example, return on equity, as has been done in Wang (2002).

In this research, performance will be evaluated based on corporate value, especially

the return on equity, and operating performance, especially by the return on assets, which

has also been done by Jose et al. (1996). The major difference between these two types of

methods is the influence of the capital structure, which is only visible in the return on

equity (Jose et al., 1996). Because the expected influence of culture on the capital

structure of a firm, both measurements, return on equity and return on assets, will be used

in this study.

2.2 Liquidity

Liquidity refers to the ease and rapidity with which assets can be converted into cash,

without significant loss in value. The more liquid a firm’s assets, the less likely the firm

is to experience problems meeting short-term obligations. Thus, the probability that a

firm will avoid financial distress can be linked to the firm’s liquidity (Hillier et al., 2010).

Due to its direct influence on the organizational state of being, liquidity can be seen as an

important variable in evaluating the total organizational performance.

Liquidity can be evaluated in different ways. Traditionally, the current ratio (current

assets divided by current liabilities) is often used as a key indicator of a firm’s liquidity.

However, this method must be viewed with caution. It is often not recognized that the

basic liquidity protection against unanticipated discrepancies in the amount and timing of

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operating cash inflows and outflows is provided by a firm’s cash reserve investments in

combination with its unused borrowing capacity rather than by total current asset

coverage of outstanding current liabilities. This could potentially lead to the

misinterpretation of a firm’s liquidity position. To overcome this problem, the cash

conversion cycle (CCC) is often used today. The CCC is an ongoing liquidity

management measure and includes the operational cycle and excludes the time element of

its cash outflows. Its application will assure the proper amount and timing of funds

available to meet a firm’s liquidity needs (Richard & Laughlin, 1980; Jose et al., 1996). It

combines both balance sheet and income statement data in order to create a measure with

a time dimension (Jose et al., 1996).

The cash conversion cycle is the net time interval between actual cash expenditures

on a firm’s purchase of productive resources and the ultimate recovery of cash receipts

from product sales, established the period of time required to convert a dollar of cash

disbursement back into a dollar of cash inflow from a firm’s regular course of operations

(Richard & Laughlin, 1980). In other words, it measures the number of days that funds

are committed to inventories and receivables, minus the number of days that payment to

suppliers is deferred (Gentry et al., 1990).

The CCC can be broken down into three parts (as shown in figure 2.1). It consists of

the collection period, the credit period and the inventory conversion period. The

collection period measures the average number of days from the sales of goods to the

collection of the resulting receivables (account receivable/sales x 365). The credit period

measures the average length of time between the purchase of goods and the payment of

them (account receivables/costs of goods sold x 365). The inventory conversion period

measures the length of the average time between the acquisition and the sale of products

(inventory/costs of goods sold x 365). The CCC is calculated as the collection period plus

the inventory conversion period minus the credit period (Banomyong, 2005; Hillier et al.,

2010).

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Figure 2.1: Cash Conversion Cycle (taken from Jose et al., 1996)

If a shorter CCC is wanted, the collection period and the inventory conversion period

need to be decreased while the credit period should be increased. However, if a longer

CCC is desired, the collection period and the inventory conversion period need to be

increased while the credit period is decreased. In general, a longer CCC will produce a

larger required commitment to cash and non-cash, current asset investments and a less

extensive relative ability to finance these investments with current liabilities (Richards

and Laughlin, 1980).

2.3 Liquidity and performance

Attari and Raza (2012) state that having an efficient working capital management,

which includes liquidity management, is one of the possibilities to maximize

performance. Working capital management affects the firm’s overall profitability,

solvency and liquidity. Knowledge on factors that influence performance and their causal

relationships can be of great importance in reaching the overall organizational goal of

maximizing performance.

The available literature identifies a negative relation between the CCC and

performance. In other words, a shorter CCC leads to a higher performance. Jose et al.

(1996) state that more aggressive liquidity management, which results in a shorter CCC,

is associated with higher profitability for several industries. They claim that there is a

significant negative relationship between CCC and profitability and that this relationship

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is not driven by size (Jose et al., 1996). Wang (2002) confirms this relation and states that

an aggressive liquidity management leads to higher corporate values. Deloof (2003)

found that by reducing the collection period and the inventory conversion period the

corporate profitability can be increased. Organizations with high liquidity are waiting

longer to pay their bills, which indicate a positive relation between the performance and

the credit period. Also the studies of Eljelly (2004) and Hutchison et al. (2007) found a

negative relation between liquidity levels/CCC and profitability.

Ebben and Johnson (2011) state that effective working capital management increases

returns by reducing the costs of capital and by allowing firms to achieve higher levels of

asset turnover. Reducing the days of inventory and the days of receivables (and thereby

creating a shorter CCC) have a positive impact on the return on assets. Firms that have a

longer CCC will have larger working capital investments and will therefore be more cash

constrained.

Attari and Raza (2012) state that there is a positive relationship between the length of

CCC and the profitability of firms in terms of return on assets. This is a strong indication

to the firm managers that the longer the CCC, the less capital will be deployed in current

assets and eventually there will be more capital investment leading towards a higher

profitability. They indicate that there is a negative relation between CCC and return on

equity. A shorter CCC period eventually results in a high profitability of the firm because

due to the efficient working capital management practices the costs of using the funds are

decreased.

Managing the elements of the CCC involves balancing between profitability and

liquidity. If the days of the inventory conversion period are reduced too far, the firms risk

lost sales due to stock outs. If the days of the collection period are reduced too far, the

firms lose sales from customers requiring credit. If the firm increases the days of the

credit period too much, discounts for early payments and flexibility for future debt are

lost (Jose et al., 1996).

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The results of this literature review lead to the following hypotheses concerning the

relationship between liquidity (CCC) and performance (ROE/ROA).

Hypothesis 1: There is a negative relation between CCC and firm performance

Hypothesis 1A: There is a negative relation between the days in the collection

period and firm performance

Hypothesis 1B: There is a positive relation between the days in the credit period

and firm performance

Hypothesis 1C: There is a negative relation between the days in the inventory

conversion period and firm performance.

2.4 Culture

National culture has been an important influencing factor in many studies in the field

of finance, accounting, psychological and social literature. Different definitions,

dimensions and measurements are used in defining the concept of national culture. For

example, the research of Hofstede (1980), Trompenaars and Hampden-Turner (1998),

Hall and Hall (2011) and the GLOBE study have made important statements about the

definition of culture (Scheffknecht, 2011).

Hofstede (1980) states that culture is based on values, which are broad tendencies to

prefer certain states of affairs over others. These values form the core elements in culture

(Hofstede, 1980; McSweeney, 2002). Hofstede identities five dimensions to

operationalize culture; individualism, uncertainty avoidance, masculinity, power distance

and long-term orientation. Differences in national culture can be made clear based on the

scores on these dimensions (Hofstede, 1980; Hofstede, 1993).

Trompenaars (1998) defines culture as a series of rules and methods that a society has

evolved to deal with the recurring problem it faces. He believes that the resolving of the

dilemmas between people and the natural environment lies at the heart of the modern

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organizational challenge. Managing people from different cultures is crucial. He

identifies seven continues that characterize the dilemmas that need reconciliation;

universalism-particularism, individualism-communitarianism, neutral-affective, specific-

diffuse, achievement-ascription, sequential-synchronic and internal-external control

(Bickerstaffe, 2002).

Hall and Hall (2011) developed a cultural model that emphasized the importance of

nonverbal signals and modes of awareness over explicit messages. These insights proved

highly valuable in studying how members of different cultures interact and how they

often fail to understand one another. Hall and Hall (2011) define high and low context

cultures and use a time and space dimension for cultural differences (Scheffknecht,

2011).

The GLOBE study state that there exist nine cultural dimensions. Each of them is

presented in two variants; society as it is and society as it should be, according to the

respondents. These dimensions are; in-group collectivism, uncertainty avoidance, future

orientation, institutional collectivism, gender egalitarianism, power distance, performance

orientation, humane orientation and assertiveness. The GLOBE study is partly based on

the methodology of Hofstede and therefore many similarities exist (Minkov and Blagoev,

2012).

For this study the dimensions of Hofstede (1980, 1993) will be used to define national

culture. Despite the critiques (McSweeney, 2002; Harrison and McKinnon, 1999),

Hofstede is still one of the most widely used methodologies in the existing culture

literature. His dimensions are linked to a wide range of managerial decisions varying

from research and development to earnings manipulation (Ramirez and Kwok, 2009).

The five used dimensions are clearly defined and operationalized. The scores on the

different dimensions are known for 93 countries all over the world and can be easily

accessed. This makes comparison between countries and their differences between

national cultures easy. Also, due to the broad and frequent use of the Hofstede

dimensions in other research, it is possible to compare the present research to previous

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studies. Based on the previous arguments, the Hofstede dimensions are used to

operationalize national culture in this research.

As stated above, Hofstede identifies five dimensions; individualism, uncertainty

avoidance, masculinity, power distance and long-term orientation (Hofstede 1980, 1993).

Individualism refers to the degree people in a country prefer to act as individuals

rather than as member of a group. In societies with high individualism the ties between

individuals are loose and individuals are expected to take care of themselves and their

direct families only. In collectivistic societies there is a tight framework in which

individuals can expect their relatives or members of the same group look after them in

exchange for unquestioning loyalty.

Uncertainty avoidance is the degree to which people in a country feel comfortable or

threatened with uncertainty and ambiguity. Societies with high uncertainty avoidance

have rigid codes of belief and behavior and are intolerant of unorthodox behavior and

ideas. In low uncertainty avoidance societies there is a more relaxed attitude in which

practices counts more than principles.

Masculinity is the degree to which tough values like assertiveness, performance,

heroism and material reward for success are represented. A masculine society is more

competitive. While a feminine society is more consensus oriented and values like

cooperation, modesty, care for the weak and quality of life are more important.

Power distance is the degree of inequality among people which the population of a

country considers normal. In other words, the extent to which the less powerful members

of organizations within a country expect and accept that power is distributed unequally.

In societies with high power distance hierarchical order is accepted. In societies with low

power distance people want to equalize the distribution of power.

Long-term orientation refers to values orientated to the future, for example saving,

persistence adapting to changed conditions, or values oriented to the past and present, for

example respect for tradition, fulfilling social obligations and quick results. (Yoo et al,

2011; Hofstede, 1980; Hofstede, 1993; www.geert-hofstede.com).

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For this research three countries will be used; the United States (US), Japan and

France. They have different scores on the five culture dimensions which makes

comparison interesting. The exact scores (see figure 2.2 and table 2.1) and further

explanation can be found below.

The US scores low on power distance. They focus on equal rights. Hierarchy is

established for convenience, superiors are always accessible and managers rely on

individual employees and teams for their expertise. Information is shared frequently and

communication is informal, direct and participative. The US has a very individualistic

culture. Employees are expected to be self-reliant and display initiative. Promotion

decisions are based on evidence or merit. The US is uncertainty accepting. There is

acceptance for new ideas and innovative products and they do not require a lot of rules.

The US is a masculine society. Competition is high and people talk freely about

successes and achievements. Conflicts are solved individual and the goal is to win.

Finally the US is short-term oriented country. Results are short-term based and there is a

focus on tradition and fulfilling social obligations (http://geert-

hofstede.com/countries.html).

Japan scores medium on power distance. Japanese are conscious of their hierarchical

position but it is not as hierarchical as most other Asian cultures. Decision making is slow

but there is a strong belief that everyone is born equal and anyone can get ahead and

become anything if he works hard enough. Japan have many characteristics of a

collectivistic society, such as harmony of the group above individual opinions, but Japan

is more individualistic (private and reserved) than other Asian countries. Japan scores

very high on uncertainty avoidance. Life is high ritualized, planned and predicted. Japan

is a very masculine society. There is much competition and workaholism. It is still hard

for women to climb up the corporate ladders. Japan is very long-term oriented. There is a

high rate of investment in R&D and priority of steady growth rather than quarterly profits

(http://geert-hofstede.com/countries.html).

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France scores high on power distance. The power is highly centralized and the

attitude towards managers is more formal. The information flow is hierarchical and

information is controlled with power (unequally distributed). France also scores high on

individualism. They favor individual and private opinions. The relationship with work is

contract based, the focus is on the task and autonomy is favored. The communication is

direct and everyone is allowed to speak up. The management is focused on the

individuals and individual work recognition is expected. They also score high on

uncertainty avoidance. Certainty is reached through academic work. In management rules

and security are present and if lacking, it creates stress, even as changing policies. France

is relative feminine country. They care for the quality of life, competition amongst

colleagues is not favored and the management is supportive and conflicts are solved with

dialogue. Finally France is a short-term oriented society. It focuses on quick results.

Consumption is driven by immediate gratification and is sensitive to social trends and

rituals. Management is based on self-reliance, personal achievement, hard work and

managers are judged on short-term results (http://geert-hofstede.com/countries.html).

Figure 2.2: The scores of the three countries on the Hofstede dimensions

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US Japan France

Individualism 91 46 71

Uncertainty avoidance 46 92 86

Long-term orientation 29 80 39

Table 2.1: The scores of the three countries on the Hofstede dimensions

While all cultural dimensions are always present, they do not all influence the

liquidity of a firm. It can be said that culture is shared but also situational. In daily life,

not all five dimensions are used in every situation. Masculinity and power distance are

helpful in explaining other business practices such as differences in organizational

structures, but do not have a clear theoretical implication for liquid asset holding, debt

ratios and capital structure (Ramirez and Tadesse, 2009; Ramirez and Kwok, 2009). Also

the scores on power distance (see figure 2.2 above) differ not that much, which leads to

the expectation that the results will not be substantial different. Therefore the focus of

this research is on the dimensions individualism, uncertainty avoidance and long-term

orientation.

2.5 Culture and liquidity-performance relation

Culture has proven to be a broad influencing factor on many organizational facets, for

example on management control systems (Harrison and McKinnon, 1999; Efferin and

Hopper, 2007; Jansen et al., 2009) and organizational design and structure (Harrison et

al., 1994; Ramirez and Kwok, 2009), as well as on the capital structure and liquidity

(Gleason et al., 2000; Ramirez and Tadesse, 2009; Ramirez and Kwok, 2009). This may

lead to different management practice preferences in different countries under the

influence of different cultures.

In countries that score high on individualism, managers are more emphasized on self-

sufficiency and self-interest (Ramirez and Kwok, 2009). Gleason et al. (2000) stated that

there is a strong relation between the cultural dimension individualism and the amount of

debt in an organization. Managers in a high individualistic culture may choose lower debt

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levels to maximize success and enhance their personal reputations. Due to the

individualistic orientation of managers, their focus on personal results and achievements

and their independence of others, the expectation is that all kinds of debt (liquidity can be

seen as negative debt) remain low. This leads to the expectation that there is a negative

relation between individualism and the CCC. This means that firms in more

individualistic countries have a shorter CCC and thus higher liquidity. Due to the

individualism, managers in organizations inclined to focus on their own success, which

means maximizing their own performance. The focus is on collecting the money and less

on the payment to suppliers. In other words, collecting their money fast (a relatively short

collection and inventory conversion period), while waiting with the payment of others (a

relative long credit period). This indicates a stronger relationship between the collection

period and inventory conversion period and performance in individualistic countries and

a weaker relationship between the credit period and performance in individualistic

countries. This will be tested according to the following hypotheses.

Hypothesis 2: The relationship between CCC and performance is stronger in

countries that are more individualistic

Hypothesis 2A: The relationship between the collection period and performance is

stronger in countries that are more individualistic

Hypothesis 2B: The relationship between the credit period and performance is

weaker in countries that are more individualistic

Hypothesis 2C: The relationship between the inventory conversion period and

performance is stronger in countries that are more individualistic

Uncertainty avoidance is related to the level of stress in a society while confronted

with an unknown future. A high score on uncertainty avoidance indicates that the country

has a low tolerance for uncertainty. This leads to a rule-oriented society to reduce the

unpredictability of future events. Managers will be less willing to take risk and use risk-

management tools to cope with this risk against undesired future states of nature. Liquid

assets can be seen as negative debt which can be quickly deployed when needed. Thus

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risk averse managers should hold higher levels of liquid assets (Ramirez and Tadesse,

2009; Chang, 2009). Gleason et al. (2000) also stated that there is a positive relation

between uncertainty avoidance and CCC. Firms from highly uncertainty avoidance

cultures are less willing to take the risk of stock outs due to a short inventory conversion

period and potentially loose sales due to short credit period. This means that companies

in a country with higher uncertainty avoidance have a stronger relationship between the

collection period, credit period and the inventory conversion period and performance,

because they want to keep their liquidity high, they do not want to lose potential sales and

they do not want to risk stock outs. This leads to the following hypotheses.

Hypothesis 3: The relationship between the CCC and performance is stronger in

countries that have higher uncertainty avoidance


stronger in countries that have higher uncertainty avoidance


stronger in countries that have higher uncertainty avoidance


performance is stronger in countries that have higher uncertainty

avoidance

Long-term orientation is associated with values oriented to the future, for example

saving, investments in R&D and goals for steady yearly growth instead of quarterly

profits. The CCC is a short-term liquidity measurement that is focused on efficiency and

not on long-term orientation activities such as saving. It is expected that there is a

positive relation between long-term orientation and the CCC. A long-term oriented

company has a longer CCC. In other words, an organization with a short time orientation

has a shorter CCC. Short-term oriented organizations focus more on short-term measures

such as the CCC. It can be expected that the more short-term oriented a company is, the

more value is emphasized on the CCC. Thus you can say the relation between CCC and

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performance is stronger in countries that are more short-term oriented. This will be tested

according to the following hypothesis.


countries that are more short-term orientated.


stronger in countries that are more short-term orientated.


weaker in countries that are more short-term orientated.


performance is stronger in countries that are more short-term

orientated.

2.6 Legal system

Besides the influence of culture on the liquidity-performance relation, several other

intra- and extra-organizational factors could be identified that have a possible effect on

this liquidity-performance relationship, for example process management techniques,

behavior of competitors and the national legal system. It is beyond the scope of this

research to investigate all factors, so this research is limited to the influence of legal

system and culture. The national legal system is an interesting influencing factor, because

it shares certain similarities with national culture. The fundamental principles of a legal

system are also based on values, its long development and its relatively static and

stability in the time are equal to national culture (Harrison and McKinnon, 1999).

However, legal systems have prescribed rules that have to be observed and followed,

while cultures have more indirect influences on the behavior and decision-making of the

organizations management. It is especially interesting to see to what extent the influence

of culture and legal system on the liquidity-performance is comparable.

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In general, legal systems can be divided into two categories; civil law and common

law. Civil law is based on the law system in the Roman Empire. Laws and rules are

clearly, completely and coherently stated and there is no need for judges to deliberate

publicly about which laws, customs and past experiences apply to new, evolving

situations. There is a high degree of procedural formalism to reduce the discretion of

judges. A critique is that the excessive judicial formalism may not allow judges sufficient

discretion to apply laws fairly to changing conditions and therefore not support evolving

commercial needs (Menard and Shirley, 2005).

Common law is based on the English law, developed since the seventeenth century. It

typically imposes less rigid and formalistic requirements. Judges have a broad

interpretation power and laws can be created in court as circumstances change rather than

adhering to the logical principles of codified law. It is based on experience, whereas civil

law is based on rules and logic (Menard and Shirley, 2005).

The most important difference between civil and common law, according to the law

and finance view, is the difference in protecting the rights of private investors relative to

the rights of the state. Private property rights protection forms the foundation for financial

development. Also the ability to adjust to changing circumstances is an important

determining factor for the financial needs and development of an economy. Civil law

countries will have weaker property rights protection and lower levels of financial

development than countries with other legal systems. Also the adaptability in civil law

(especially of French legal origin) countries is lower which means that they have a lower

probability of developing efficiently flexible financial systems than common law

countries and civil law countries (from German legal origin) (Menard and Shirley, 2005).

French is a civil law country. The French law is evolved as a combination of different

regional customary law, law based on Justinian texts and case law. The US is a common

law country, mainly based on the British law. Japan is a civil law country, based on the

German law. Especially commercial and company law are civil law based. However,

public law in Japan has some common law characteristics (Menard and Shirley, 2005).

22

2.7 Legal system and liquidity-performance relation

The influence of the country’s legal system on shareholder rights and thereby indirect

on the capital structure of firms can be an influencing factor on the relationship between

liquidity and performance.

Chang (2009) states that managers in countries with the absence of sufficient

shareholder protection rights prefer to hold cash rather than dispersing it among their

shareholders. Managers in common law countries (with stronger shareholder protection

rights) tend to keep less cash and other liquid assets. In countries with strong private

property rights protection (common law) firms tend to reinvest their profits, but where

property rights are relatively weakly enforced, entrepreneurs are less inclined to invest

the retained earnings (Menard and Shirley, 2005). This will be confirmed by McLean et

al. (2012) who state that investor protection laws encourage efficient investment. Chang

(2009) state that in countries with poor shareholder protection rights organizations hold

higher cash levels which can be easily invested in value-reducing investments with little

or no scrutiny from their shareholders.

Due to efficient reinvestment of cash in the firm the overall performance and value of

the firm will be higher. Reinvesting the profits leads to a higher (starting) cash balance.

Efficient use of the CCC maximizes cash generation for the business, which means better

performance and more growth developments (Reider, 2010).

In common law countries, where the shareholder rights are stronger and reinvestment

of profits is more common and efficient, there is more focus on the CCC to generate cash

and thereby generate a higher performance. In other words, the relationship between CCC

and performance is expected to be stronger in common law countries than in civil law

countries. This will be tested according to the following hypothesis.


common law countries than in civil law countries.

23

2.8 Overview

In figure 2.3 an overview of the concepts, their underlying relations and the proposed

hypotheses can be found.

Figure 2.3: Conceptual model

24

3. Method

Based on the research question ‘How does culture influence the relation between

liquidity and performance of an organization?’ this study can be marked as an

explanatory study. An explanatory study goes beyond description and attempts to explain

the reasons for the phenomenon that the descriptive study only observed. The hypotheses

are tested to explain the proposed relationships. An explanatory study often answers a

‘how’ question. (Cooper and Schindler, 2008). Here the influence of culture, an

explaining factor, on the relation between liquidity and performance, the phenomenon, is

tested.

The main concepts, which have been explained in the literature review in the previous

chapter, can be clearly and objectively measured. Due to the large amount of available

data on these concepts in organizations and the possibility of quantitative measurability, a

quantitative study seems to be the best fit to answer the research question.

Empirical data about liquidity and performance is obtained from Orbis, a global company

data base with financial data from balance sheets and income statements of companies all

over the world. The data on culture is conducted from the Hofstede studies (Hofstede,

1980; Hofstede, 1993; http://www.geert-hofstede.com)

These data are the input of the statistical analysis, which will be executed to indicate

the strength of the causal relationships.

To perform this research first the different concepts (culture, performance, cash

conversion cycle (CCC)) will be operationalized, then the data collection is executed and

finally the statistical analyses with their assumptions are introduced.

3.1 Concept operationalization

3.1.1 Countries

The research will be executed with data from firms from three different countries; the

US, Japan and France. These countries have a gross domestic product (GDP) per capita

that are relatively close in number (see table 3.1), which indicates that their economic

25

development is relatively similar. This is relevant because large differences in economic

development could lead to deviating results, in which other factors (for example large

growth rates) outside of culture can be explanatory. Besides the scores of these countries

on the three cultural dimensions differ (see table 3.2). The US and Japan are often at one

end whereas France is found in the middle. This allows us to test if there is a linear

relationship between culture and CCC and performance, thus if the relation between CCC

and performance is stronger when the culture is, for example, more individualistic.

2006 2007 2008 2009 2010

US 44.623 46.349 46.760 45.192 46.702

Japan 34.102 37.972 39.473 43.063 45.903

France 35.467 40.342 43.992 40.477 39.170

Table 3.1: GDP per capita in US dollar

(Source: World Bank national accounts data and OECD National Accounts data files)

US Japan France

Individualism 91 46 71

Uncertainty avoidance 46 92 86

Long-term orientation 29 80 39

Table 3.2: Scores on the Hofstede dimensions

(Source: www.geert-hofstede.com)

3.1.2 Performance

The performance will be evaluated according to the return on equity and the return on

assets. Return on equity (ROE) is the total net income (profit) divided by the total amount

of equity. Return on assets (ROA) is the total net income divided by the total assets.

Return on assets indicates how effectively or efficiently a firm uses its assets (Johnson

and Soenen, 2003). The ROE and ROA are measured in percentages, indicating which

percentage of the total amount of equity (ROE) of total assets (ROE) is determined by the

net income. This can vary from 0% till 100%. ROE and ROA can be extracted from the

database Orbis.

26

3.1.3 Liquidity

The liquidity is measured by the CCC. This is calculated as the collection period plus

the inventory conversion period minus the credit period. The collection period measures

the average number of days from the sales of goods to the collection of the resulting

receivables (account receivable/sales x 365). The credit period measures the average

length of time between the purchase of goods and the payment of them (account

receivables/costs of goods sold x 365). The inventory turnover measures the length of the

average time between the acquisition and the sale of products (365/inventory turnover).

All the periods are measured in number of days. The scale can range from eternity

negative to eternity positive. These three measures can be extracted separately for the

database Orbis. Together they form the CCC.

3.1.4 Culture

Culture is measured by the Hofstede scores as indicated above in table 1 and 3. Only

the dimensions individualism, uncertainty avoidance and long-term orientation are

investigated here. The dimensions are used as dummy variables, where a low score on a

dimension is 0 and a high score on a dimension is 1. The dummy variables can be found

below in table 4.

US Japan France

Individualism 91 (high=1) 46 (low=0) 71 (high=1)

Uncertainty avoidance 46 (low=0) 92 (high=1) 86 (high=1)

Long-term orientation 29 (low=0) 80 (high=1) 39 (low=0)

Table 3.3: Hofstede scores and dummy variables

3.1.5 Legal system

The legal system can be identified as common law and civil law. The US is a

common low country, while France and Japan are civil law countries. These variables

will be used as a dummy variable, where common law is 0 and civil law is 1.

27

3.1.6 Control variables

Size will be used as a control variable. Size can influence the liquidity of an

organization. Larger firms tend to be more profitable, have a higher ROA and ROE and

tend to have a shorter CCC. Size will be measured by the log of total net sales, which is

measured in thousands of Euros. Log sales (LS) will be used as a means of obtaining a

normal distribution (Ebben and Johnson, 2011). Log sales will be measured in Euros and

vary from zero Euros till eternity.

An overview of the different variables and their notation can be found below in table 3.4.

The abbreviations are used in the rest of the tables. The first part variable is the name of

the measure, followed by the year in which it is measured. For example, ROE06 means

the return on equity in 2006.

Variable Measure

Independent Performance Return on equity (ROE)

Return on assets (ROA)

Dependent Liquidity Cash Conversion Cycle (CCC)

Collection period (COP)

Credit period (CRP)

Inventory conversion period (ICP)

Culture

Legal system

Individualism (IND)

Uncertainty avoidance (UA)

Long-term orientation (LTO)

Common law

Civil law

Control Size Sales (LS)

Table 3.4: Overview of the variables and measures

28

3.2 Sample

3.2.1 Firms

To study the influence of the legal system equally, only listed firms are selected. The

influence of the legal system is mostly due to the shareholder rights that differ between

the two types of legal system. To measure the influence of the legal system, firms at least

have to have shareholders. To make sure this is the case, only listed firms are selected.

3.2.2 Industry

Only organizations that operate in the manufacturing industry are used. The

relationship between CCC and ROA/ROE is very sensitive to factors such as capital

intensity, product durability, production process, channels of marketing and competitive

forces (Jose et al., 1996). To exclude these effects, only data for companies in the

manufacturing industry are used.

3.2.3 Period

The research is conducted with accounting data of a time period of five years. The

model is tested for each year separately. Results are compared to see if the conclusion

holds for several years and therefore make them more generalizable for the longer time

period. Balances and income statements from 2006-2010 have been used.

3.3 Statistical analysis

3.3.1 Assumptions

To test the hypotheses, linear regression analyses will be used. Before carrying out

regression analysis, several assumptions have to be met; linear correlation, normal

distribution, no multicollinearity and no heteroscedasticity of the variances.

Before testing the assumptions, outliers must be detected and removed. This can be

done by inspecting scatter plots of box plots. Tabachnick and Fidel (2007) define outliers

that have a standard residual of more than 3.3 or less than -3.3 (Ebben and Johnson,

29

2011; Field, 2005). All values that differ more than 3.3 standard deviations of the mean,

the organization will be removed from the data set.

A linear correlation can be tested by executing a scatter plot and by performing

correlation analysis. This correlation analysis can be done by generating correlation

tables in Eviews or test bivariate correlation in SPSS. This way the correlation between

the dependent and independent variables can be shown.

Normal distribution can be examined by the skewness and kurtosis or a normal

probability plot (P-P plot) or histogram. The skewness is a measure of asymmetry of the

distribution of the series around its mean. The skewness of a symmetric distribution is

zero. Kurtosis measures the peakedness or flatness of the distribution of the series. The

kurtosis of a normal distribution is 3 (Brooks, 2008; Huizingh, 2006). Based on previous

experience and the opinion of fellow researchers, for this research the margins for a

normal distribution are skewness between -1 and 1 and a kurtosis between 2 and 4.

Multicollinearity implies that the explanatory or independent variables are correlated

with each other. This results in a high correlation between the explanatory variables,

which result in unreliable outcomes. Multicollinearity will be tested by testing

correlations in Eviews or bivariate correlations in SPSS. For this research the reference is

that if there is a correlation higher than ρ=0.9 multicollinearity will be a problem and the

variables will be investigated within two different regression models (Pallant, 2010).

Heteroscedasticity states that the errors do not have a constant variance. If the variances

of the errors are constant there is homoscedasticity (or homogeneity). This can be tested

with the White test in Eviews or Levene’s test in SPSS. If p<0.05, the variances in a

group differ significantly and homogeneity of variances has been violated and thus there

is heteroscedasticity (Brooks, 2008; Field, 2005). Heteroscedasticity may lead to

overestimation of the Pearson correlation coefficient.

30

3.3.2 Analysis

All the hypotheses will be tested with linear regression analysis to see if there is a

relationship between the CCC and performance. An overview of the executed regression

models can be found in table 6 below. The results of the regressions for the different

countries are compared in order to see in which country or for which cultural dimensions

the relationships are the strongest. Also the coefficients are tested in a new, overall

regression that indicates if there are significant differences between the coefficients. All

tests are executed using both Eviews and SPSS. A significance level of 0.05 or higher

will be used.

3.3.3 Expected results

According to the hypotheses the expectation is that there is negative relation between

CCC and firm performance, a negative relation between collection period and firm

performance, a positive relation between credit period and firm performance and a

negative relation between inventory conversion period and firm performance. This

relation will be stronger in individualistic countries, which means that the results of the

regression analysis of the US and France are stronger than Japan. The relation will also

be stronger in countries with a higher uncertainty avoidance, which means that the results

of the regression analysis of Japan and France are stronger than the US. The relation will

also be stronger in countries that are more long-term oriented, which means that the

results of the regression analysis of Japan is stronger than the US and France. According

to the hypothesis the expectation is that the relationship between CCC and performance is

stronger in common law countries than in civil law countries, meaning that the results of

the regression analysis of the US are stronger than Japan and France.

31

Model Hypotheses Regression

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

H1, H2, H3, H4, H5

H1, H2, H3, H4, H5

H1, H2, H3, H4, H5

H1, H2, H3, H4, H5

H1a, H2a, H3a, H4a

H1a, H2a, H3a, H4a

H1a, H2a, H3a, H4a

H1a, H2a, H3a, H4a

H1b, H2b, H3b, H4b

H1b, H2b, H3b, H4b

H1b, H2b, H3b, H4b

H1b, H2b, H3b, H4b

H1c, H2c, H3c, H4c

H1c, H2c, H3c, H4c

H1c, H2c, H3c, H4c

H1c, H2c, H3c, H4c

Relation between CCC and ROE/ROA

YCCC= B0+B1ROE+B2ROA+B3Size

Relation between CCC and ROE/ROA in the US


Relation between CCC and ROE/ROA in Japan


Relation between CCC and ROE/ROA in France


Relation between the collection period and ROE/ROA

Ycollection period= B0+B1ROE+B2ROA+B3Size

Relation between the collection period and ROE/ROA in the US


Relation between the collection period and ROE/ROA in Japan


Relation between the collection period and ROE/ROA in France


Relation between the credit period and ROE/ROA

Ycredit period= B0+B1ROE+B2ROA+B3Size

Relation between the credit period and ROE/ROA in the US


Relation between the credit period and ROE/ROA in Japan


Relation between the credit period and ROE/ROA in France


Relation between the inventory conversion period and ROE/ROA

Yinventory conversion period= B0+B1ROE+B2ROA+B3Size

Relation between the inventory conversion period and ROE/ROA in the US


Relation between the inventory conversion period and ROE/ROA in Japan


Relation between the inventory conversion period and ROE/ROA in France


Table 3.5: Regression models

32

4. Results

4.1 Sample

Based on the availability of data in Orbis, 2266 companies from the US, Japan and

France are selected for this study. First, the outliers are removed, based on the scatterplot

(as an example the scatterplot of CCC06 has been added in the appendix, figure 7.1, pp.

55) and when the data differs more than 3.3 standard deviation of the mean. This has

been done for every individual variable. If an outlier is detected the total firm is removed

from the dataset. It appears that if a firm has an outstanding value in one variable, it often

shows outstanding values in other variables as well. In that case, the complete firm was

removed from this research. After deleting these outliers, the final data set includes 2054

companies. There are 751 US firms, 1133 Japanese firms and 170 France firms.

The descriptive statistics can be found per country in table 7.1 (US, pp. 52), 7.2

(Japan, pp. 53) and 7.3 (France, pp. 54) in the appendix. These tables show that there is

some difference between the lengths of the cash conversion cycle (CCC) in the three

countries. In the US and Japan the mean length of the CCC of the five selected years is

respectively 61,610 days and 62,685 days, while in Japan this is 71,078 days. However,

in the US the collection period (COP) (44,715 days) and the credit period (CRP) (23,739

days) are lower than in Japan (respectively 69,083 and 40,549 days) and in France

(respectively 71,900 and 50,954 days). Japan has the lowest inventory conversion period

(ICP) of 34,150 versus 40,634 days in the US and 50,132 days in France.

It is striking that the sales (LS) of the three countries are very close together, but the

return on equity (ROE) and return on assets (ROA) show great differences. The ROE and

ROA in Japan (7,362 percent and 3,737 percent) are lower than the ROE and ROA in the

US (15,612 and 7,851 percent) and in France (12,920 and 5,280 percent). This can

indicate a different way of doing business.

33

4.2 Correlations

The correlation matrix (table 7.4, pp. 58 (US), table 7.5, pp. 59 (Japan) and table 7.6,

pp. 60 (France) in the appendix) measures the strength of the correlation between the

different variables. In these tables the correlation between the different variables is

measured. Correlations are always conducted in a specific year. For example the CCC06

is correlated with the ROE06 and ROA06 and CCC07 is correlated with ROE07 and

ROA07.

The values of most correlations are relatively constant over the years (no large

differences in and between the countries). Also, the direction of the correlation is often

constant. There is a negative correlation between CCC and ROE and a positive

correlation between CCC and ROA. This holds for all the countries in every year. This

means that when the CCC becomes shorter, the ROE becomes higher and the ROA

becomes lower. However not all these correlations are significant. In France the

correlations are the least significant (only CCC en ROA correlate significant in 2008). In

Japan the significance is the highest (only no significance between CCC and ROA in

2008 and 2009). In the US not all the correlations are significance.

The separate parts of the CCC do not have a constant relationship with the ROE and

ROA. The direction of this relationship is not consistent and also the strength of the

correlation differs between the countries.

For the US there is a positive relationship between COP and ROE (except in 2009

and 2010) and also a positive relationship between COP and ROA (except in 2010). The

correlation between CRP and ROE is positive in 2006 and 2008 and negative in 2007,

2009 and 2010, but the correlation between CRP and ROA is negative. For ICP the

correlation with ROE is significantly negative, while the correlation with ROA is positive

(except in 2009 and 2010).

For Japan there is a significant negative correlation between COP and ROE and COP

and ROA is overall negative (except in 2007 and 2010). The correlation between CRP

34

and ROE and CRP and ROA is significantly negative. The correlation between ICP and

ROE and ICP and ROA is significantly negative (except for ROA in 2010).

For France the correlation between COP and ROE is positive (except for 2007) and

the correlation between COP and ROA is positive for 2006, 2008, 2009 and negative for

2007 and 2010. The correlation between CRP and ROE is positive in 2006 and 2007 and

negative in 2008, 2009, 2010 and the correlation between CRP and ROA is significant

negative. The correlation between ICP and ROE is negative and the correlation between

ICP and ROA is negative for 2006, 2009 and 2010 and positive for 2007 and 2008). In

France the correlations are thus the least constant in comparison to the other countries.

There is a relatively constant correlation with the same direction between the

dependent variables (CCC, COP, CRP, ICP) and the control variable sales. Also the

correlation between sales and the independent variables (ROE, ROA) is relatively

constant and with the same direction (with exception of in Japan in 2006 and 2008).

For all the countries, in all the years, the correlation between ROE and ROA never

exceeds 0.9. This correlation indicates that these two variables can be used in the same

regression model.

4.3 Regression analysis

4.3.1 Assumptions

The general assumptions for using ordinary least square (OLS) regression analysis are

not all met. There is a linear correlation. This is tested due the correlation tables (table

7.4, 7.5 and 7.6 in the appendix, pp. 58-60) and scatter plot (figure 7.1 in the appendix,

pp. 55). There is no multicollinearity of the independent variables, because no

correlations have a value above 0,900. However there is some evidence

heteroscedasticity of the variances. The Levene test is often not significant for the

different variables. For example the Levene statistic for CCC06 is 14.884 (p < 0,01). Due

the significance of this test, there is no homogeneity of the variances. Allison (1999)

35

states that the reason for OLS not being optimal when heteroscedasticity is present, is that

it gives equal weight to all observations when actually observations with a larger

disturbance variance contain less information than observations with smaller disturbance

variance. The standard errors are biased when heteroscedasticity is present. This can lead

to bias in test statistics and confidence intervals.

Brooks (2008) state that if heteroscedasticity is known, then an alternative estimation

method can be used which takes this into account. One possibility is to use generalized

least squares (GLS). This regression is based on the same basic assumptions as the

assumptions for ordinary least square regressions. Eviews always conducts these

generalized least squares, so this will not be a problem. However in SPSS the method of

GLS is not available. An alternative can be found in Weighted Least Squares. However,

this method is very difficult and the wrong choice of weights can produce biased

estimates of the standard errors or if the weights are correlated with the disturbance term,

the WLS slope estimates will be inconsistent (Allison, 1999).

Allision (1999) states that unless heteroscedasticity is ‘marked’, significance tests are

virtually unaffected and thus OLS estimation can be used without concern of serious

distortion. Severe heteroscedasticity can sometimes be a problem. Due to the focus on the

coefficient of the regression, and not on the standard errors, OLS regression has been

used to analyze the data.

Moreover not all the individual variables have a normal distribution. This has been

tested by an analysis of the skewness and kurtosis and a normal probability plot (P-P

plot) or histogram (see paragraph 3.3.1, pp. 29). There are particularly problems with the

skewness and kurtosis of ROE and ROA. This can be partly explained by the fact that

that only companies with a positive ROE and ROA have been selected from the data

base. Companies with a negative ROE and ROA are not included in the analysis. This

can lead to a higher skewness. Because of the large sample size, non-normality of the

individual variables does not have to form a problem for the regression analysis. Brooks

(2008) state that for sample sizes that are sufficiently large, violation of the normality

assumption is virtually inconsequential. Non-normality in financial data could also arise

from certain types of heteroscedasticity (Brooks, 2008). Therefore, the regression

36

analysis itself is also tested on normality due the skewness and kurtosis and a P-P Plot

and a histogram for every variable (for example see figure 7.2 and 7.3 in the appendix,

pp. 53-54). Only if the normality of the regression is between previous determined

margins of normality, the results of this specific regression model will be used.

The results of the regression analysis can be found in table 7.7 till 7.10 in the

appendix (pp. 61-64). In each table four regression models are tested for one year. For

example, in table 7.7 (pp. 61) the regression model between CCC and ROE/ROA in 2006

for the US, Japan, France and for the total of countries can be found. The regressions are

always conducted with variables within one country, thus the regression of CCC06 of the

US is always conducted with ROE06 and ROA06 of the US. This is an important fact to

keep in mind when reading the tables.

In table 7.11 and 7.12 in the appendix (pp. 65-66), the regression analysis between the

coefficients of the previous regression can be found. Here the coefficients of Japan will

be compared to the coefficients of the US.

4.3.2 Results

Only if the regression has a normal distribution (skewness between -1 and +1 and

kurtosis between 2 and 4), the histogram, the P-P plot and the scatter plot look normal

and the F-value is statistically significant at a 0,05 level, the results of the regression can

be used to draw conclusions. This is tested for all the regression models. An example of

the histogram, P-P plot and scatter plot of model 1 in 2006 can be found in figure 7.1 till

7.3 in the appendix (pp. 52-54).

Based on non-normality and non-significant F-values the following regression models

(for the specific years) are excluded; model 4 (2006, 2008, 2009), model 6 (2006), model

8 (2006, 2008, 2009, 2010), model 9 (2006, 2008), model 10 (2006, 2007, 2008, 2009,

2010), model 12 (2006, 2008, 2009, 2010), model 13 (2006, 2007, 2008, 2009, 2010),

model 15 (2010), model 16 (2006, 2007, 2008, 2009, 2010). Explanation of the model

can be found in table 3.5 (pp. 31) and the results can be found in table 7.7 till table 7.10

(pp. 61-64). Because France has problems with normality in every regression model and

the correlations between the different variables are often not significant, the reliability of

37

the results is questionable. Therefore, it has been decided not to include France in the

conclusions and only compare the US and Japan. Japan and US have opposite scores on

the culture dimensions and legal system, so comparison of only these two countries is

still valid.

The R squared of the regressions is often low. In definition, R squares is the

suitability of statistic fit, thus that is how well the regression model actually fits the data

(Huizingh, 2006). However as a rule of thumb a low R squared is not uncommon in

financial data analysis. R squared is very important when the sample size is low, for

example N is 10. However in this study the sample sizes are large (N>800), which

reduces the focus on low R squared values. The validity of the regression analysis will

not be evaluated based on the R squared values.

4.3.3 Hypotheses

Hypothesis 1 states that there is a negative relation between CCC and firm

performance. The results show that there is a significant negative relationship between

CCC and ROE in the US in every year (model 2, table 7.7, pp. 61). Also for Japan a

significant negative relationship between CCC and ROE is found (except in 2008) (model

3, table 7.7, pp. 61). The results show that there is a significant positive relationship

between CCC and ROA for the US (model 2, table 7.7, pp. 61) and Japan (model 3, table

7.7, pp. 61).

Hypothesis 1 can thus be confirmed when performance is measured with ROE. When

performance is measured with ROA the relationship is positive, which leads to the

rejection of hypothesis 1.

For every year, the relation between CCC and ROE in Japan is stronger and more

significant compared with the strength of the relation in the US (table 7.7, pp. 61). In

2006, 2008 and 2009 the differences between the coefficients are significant (table 7.11,

pp. 65). Also, the strength of the relation between CCC and ROA is stronger in Japan

(table 7.7, pp. 61). However, these differences are not significant.

38

Hypothesis 2 states that the relation between CCC and performance is stronger in

countries that are more individualistic. This expects a stronger relation between CCC and

ROE/ROA in the US compared to Japan. Based on the results states above, this

hypothesis is not confirmed.


countries that have higher uncertainty avoidance. This expects a stronger relation in

Japan compared to the US. Based on the results stated above, hypothesis 3 can be

confirmed if performance is measured with ROE. However if performance is measured

with ROA, hypothesis 3 is rejected.


countries that are more short-term oriented. This expects a stronger relation in the US

compared to Japan. Based on the results above, hypothesis is not confirmed.


common law countries than in civil law countries. This expects a stronger relation in

Japan compared to the US. This hypothesis is confirmed, but only if performance is

measured with ROE. With ROA as measurement hypothesis 5 is rejected.

Hypothesis 1a states that there is a negative relation between the collection period

(COP) and firm performance. The results show that the relation between COP and ROE

is significantly negative in Japan (model 7, table 7.8, pp. 62). The relation between COP

and ROA is significantly positive (model 7, table 7.8, pp. 62). The results for the US are

not significant (model 6, table 7.8, pp. 62) so these results cannot be included in the

conclusion. This means that hypothesis 1a is only confirmed for Japan if performance is

measured with ROE.

Due to the insignificance of the results of the US, no comparison can be made

between the US and Japan. This means that hypothesis 2a (the relation between COP and

performance is stronger in countries that are more individualistic), hypothesis 3a (the

relation between COP and performance is stronger in countries that have higher

uncertainty avoidance) and hypothesis 4a (the relation between COP and performance is

stronger in countries that are more short-term oriented) are all rejected.

39

Hypothesis 1b states that there is positive relation between the credit period (CRP)

and firm performance. The relation between CRP and ROE is significantly positive in the

US and Japan (model 10 and 11, table 7.9, pp. 63). The relation between CRP is

significantly negative for the US and Japan (model 10 and 11, table 7.9, pp. 63).

However, due to normality problems, model 10 is not reliable (see paragraph 4.3.2) and

thus not included in further analysis and conclusion making. This means that hypothesis

1b is confirmed for Japan and if performance is measured with ROE, but this hypothesis

is rejected if performance is measured with ROA.

Due to the exclusion of model 10 of the US, no reliable comparison can be made

between the US and Japan. This means that hypothesis 2b (the relation between CRP and

performance is weaker in countries that are more individualistic), hypothesis 3b (the

relation between CRP and performance is stronger in countries that have higher

uncertainty avoidance) and hypothesis 4b (the relation between CRP and performance is

weaker in countries that are more short-term oriented) are all rejected.

Hypothesis 1c states that there is a negative relation between the inventory conversion

period (ICP) and firm performance. The results show that there is a negative relation

between ICP and ROE in every year in both the US and Japan (model 14 and 15, table

7.10). However, this relation is significant in the US in 2007 and 2010 and significant in

Japan in 2006, 2009 and 2010. Model 15 for the year 2010 is excluded from further

analysis due to normality problems (see paragraph 4.3.2, pp. 36). The relation between

ICP and ROA is not constant for the US and Japan. In some years it is positive and in

other years negative (model 14 and 15, table 7.8, pp. 62). Also the results are not

significant. This means that hypothesis 1c is rejected.

Due to the insignificance of the results, no comparison can be made between the US

and Japan. Therefore hypothesis 2c (the relation between ICP and performance is

stronger in countries that are more individualistic), hypothesis 3c (the relation between

ICP and performance is stronger in countries that have higher uncertainty avoidance) and

hypothesis 4c (the relation between ICP and performance is stronger in countries that are

more short-term oriented) are all rejected.

40

5. Conclusion and discussion

5.1 Conclusion

In order to answer the main research question ‘how does culture influence the relation

between liquidity and performance of an organization?’ five hypotheses have been

developed and tested by empirical quantitative research. This has led to the following

results.

A significant negative relation between cash conversion cycle (CCC) and return on

equity (ROE) in the US and Japan is found. This relation appeared to be stronger in

countries with more uncertainty avoidance and in countries that have a common law

system. This means that three hypotheses are confirmed and uncertainty avoidance and

legal system have an influence on the relation between CCC and ROE. In countries with

higher uncertainty avoidance or a common law system the relation between CCC and

ROE is stronger. However, against expectations, there is found a significant positive

relation between CCC and ROA in the US and Japan. This makes that these hypotheses

cannot be confirmed, but this inverse relationship is still an interesting result.

The hypotheses on the relation between the separate parts of the CCC and ROE have

been confirmed. Also here the relation with ROA is, against expectations, often reversed.

A negative relationship between the collection period and ROE and a positive

relationship between the collection period and ROA in Japan is found. There is also

found a positive relation between the credit period and ROE and a negative relation

between the credit period and ROA. There is found a negative relation between the

inventory conversion period and ROE. Unfortunately, the results of the US turned out to

be not significant in most years, which make comparison not possible. Due to this

insignificance, no statements can be made on the influence of culture on the relationship

between the different parts of the cash conversion cycle and organizational performance.

In conclusion, uncertainty avoidance and legal system have a statistically proven

influence on the liquidity-performance relation. For the other cultural dimensions, no

influence can be indicated. Based on these results, there must be concluded that the

41

influence of culture, which is questioned in the main research question, is very limited in

this research.

5.2 Discussion

As for every study, this research has several methodological and general limitations

and recommendations for future research. Also, possible explanations for the limited

influence of culture can be indicated.

France is not included in the conclusions. The results of the regression analyses did

not meet the normality assumptions or the F-test is not significant. The sample size of

French firms was a lot smaller than the sample size of the US and Japan. This can be a

reason for this insignificance.

The coefficients of the regression results of Japan are always stronger than the US

(see table 7.7 till 7.10 in the appendix, pp. 61-64). Is this because the relation between

CCC and performance in Japan is actually stronger? Or does it depend on the sample of

this particular set of organizations? However, there is not always a significant difference

between the regression coefficients, which indicates that the liquidity-performance

relation in Japan is not always significantly stronger than in the US. This is an interesting

but contradictory result. Future research will have to show if this relation is indeed much

stronger in Japan in comparison to other countries.

It is striking that the hypotheses and expected relations between liquidity and

performance are only true when performance is measured with ROE. In fact, the

relationship between liquidity and ROA is often exactly the opposite of what is expected.

For example, the expected negative relation between CCC and ROA appears to be

positive. ROE and ROA are both measures of organizational effectiveness. However, the

distinctive factor is the amount of debt in an organization. The amount of debt is only

reflected in the measure ROA. When the ROE and ROA are equal, this means that there

is no debt in the organization (Hillier et al., 2010). If the amount of debt rises, while the

42

net income will stay equal, ROE stays equal but ROA will decrease. If the days in the

CCC decrease, the liquidity increases, the total assets increases, which leads to a lower

ROA (the denominator will be higher, so ROA will be lower). This explains the positive

relation; when CCC decreases, ROA decreases. Liquidity has no direct influence on ROE

calculation. ROE only changes if a lower CCC and then a higher liquidity an increase of

the net income will be accomplished. This explains the contrary relation between CCC

and ROE and ROA.

For this research, the specific influence of culture and legal system on the liquidity-

performance relation is investigated. These two factors are only a small part of all factors

that can influence this relationship. That culture and legal system only explain a small

part of this relation could also be observed by means of the low values of R squared.

However, low values of R squared are not uncommon for statistical testing with financial

data. On the other hand though, these R squared values should not be ignored completely.

The most important conclusion from these values is that this research should be seen in a

larger picture, in which a lot of other variables have an influence as well. Therefore, the

validity of this research can be considered not that high. This validity can be increased by

including more variables that influence CCC or performance or their relationship. This is

a good recommendation for future research. Variables that can also influence the

liquidity-performance relation are for example the influence of the economical crisis,

organizational culture, process management or competitors.

The research is conducted with data from 2006 till 2010. In this period also the

worldwide economical crisis started. The financial crisis and the associated recession led

to difficult times for organizations in general (Campello et al., 2010). Campello et al.

(2010) state that financially constrained firms planned to cut more investment,

technology, marketing and employment relative to financially unconstrained firms. They

were also forced to burn a sizeable portion of their cash savings and display a much

higher propensity to sell off assets as a way to generate funds during the crisis.

Unconstrained firms do not display this behavior and their investment levels and cash

savings stay relatively constant. The listed firms in the data set can be considered as

43

unconstrained firms, because they are able to acquire external funds, for example by

share issuing. Hereby the expectation is that there is no influence of the financial crisis on

the liquidity of the investigated firms and thus the liquidity-performance relation is not

affected. However, it is possible that the financial crisis can influence the net income in

of an organization, for example due to less sales or higher costs of goods. Future research

is necessary to investigate how large the influence of this crisis has been on

organizational performance in general.

Furthermore, organizational culture could also have an influence on the liquidity-

performance relation, which has not been included in this study. Organizational culture

refers to the underlying, shared values that provide employees with behavioral norms in

the firms (Webster and White, 2010). Especially in multinational organizations, where

different ethnicities and national cultures come together, organizational culture can

organize individual behavior and provides organizational members with structure (De

Witte and van Muijen, 1999). Jung et al. (2008) and Webster and White (2010) state that

essence of organizational culture is significantly influenced by the national culture in

which the company is located. However, organizational culture is transferred into foreign

subsidiaries and these subsidiaries are managed in accordance with the culture of the

parent, which suggests the importance of organizational culture over or besides national

culture (Chang and Tayler, 1999). The role and importance of organizational and national

culture can differ between organizations. Especially due to the variety of national cultures

in a multinational organization organizational culture can be more important and

determining for management practices than national culture. Listed firms are often large

and internationally oriented.

However, organizational culture is very subjective and specific for every single

organization. This makes it difficult to measure, especially in a quantitative manner

within this research design. A qualitative way of doing research with a smaller data

sample fits better when organizational culture is included. Based on the availability of

resources and data and personal preferences this quantitative research design is chosen.

44

Further research is needed to see how large this influence of organizational culture on

the relationship between liquidity and performance is and if it is less, more or equal

important compared to the influence of national culture.

Finally, process management techniques, for example just in time, or competitors can

influence the cash conversion cycle and thereby the liquidity-performance relation. For

example, due to just-in-time management the inventory conversion period will be as short

as possible and this decreases the length of the total cash conversion cycle. Management

decisions about the used process management techniques will be made under the

influence of national or organizational culture. But also the strategy and structure of an

organization is determinative in making these decisions. At the same time the behavior of

competitors and their procedures and rules about collection and payment terms can

influence the cash conversion cycle of the company as well. The influence of process

management techniques and competitors behavior are additional influencing factors.

However, in large, established organizations the influence of process management

techniques is very determining for the performance of an organization. National culture

can be an influencing factor on this process management decisions but finally the

influence of process design can overrule the influence of national culture.

In conclusion, the relation between liquidity and performance is partially influenced

by the national culture (only statistical prove for the culture dimension uncertainty

avoidance) and the legal system that exists in a country. However, the absence of

significant statistical results of the US has unfortunately made a comparison impossible,

which pressures conclusion making. Based on the results of the literature review and the

statistical analysis, questions have arisen on how large the actual influence of national

culture on this relationship is. Since national culture demonstrates to have an influence on

many business subjects, assumptions were made that this relationship is also under

influence of national culture. It can be concluded that this influence of culture is not that

large as the expectation was. There are many much influencing variables which make this

relationship complicated. Possibly the role of national culture is overrated and factors

such as managerial preference for different process management techniques are more

determinative for the importance of this relation in different countries. Therefore, no

45

direct conclusions can be made without taking into account all the other variables that

influence the way of doing business of an organization. This is, however, beyond the

scope of this research, but definitely a good recommendation for future research to

aggregate all the influencing factors in one study so a complete picture can be created.

46

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52

7. Appendix

Figure 7.1 Scatterplot CCC06 (example)

53

Figure 7.2: Normality plot of CCC06

54

Figure 7.3: Histogram CCC06

55

Variable Mean Median Std. Dev. Minimum Maximum

CCC06

CCC07

CCC08

CCC09

CCC10

COP06

COP07

COP08

COP09

COP10

CRP06

CRP07

CRP08

CRP09

CRP10

ICP06

ICP07

ICP08

ICP09

ICP10

ROE06

ROE07

ROE08

ROE09

ROE10

ROA06

62,263

61,186

60,323

62,443

61,836

46,267

45,694

42,291

44,443

44,878

24,774

24,872

22,145

22,982

23,920

40,770

40,365

40,177

40,982

40,878

16,860

17,027

16,352

13,254

14,568

8,485

59,207

57,889

57,114

58,200

57,951

45,800

46,190

42,130

44,190

44,570

22,760

22,860

19,900

20,560

22,000

37,785

36,210

36,573

35,784

36,537

14,690

15,000

13,850

11,170

12,510

7,520

41,004

40,829

39,708

41,781

41,189

26,990

25,565

24,169

25,465

25,294

13,533

13,829

12,663

13,491

13,730

29,384

29,467

29,424

30,198

29,561

10,179

10,349

11,399

9,951

9,523

5,596

-34,738

-43,681

-27,212

-39,342

-39,383

0,000

0,000

0,000

0,000

0,000

0,000

0,000

0,000

0,000

0,000

0,518

0,519

0,515

0,653

0,388

0,160

0,250

0,550

0,010

0,220

0,050

184,313

196,201

204,888

210,838

211,506

168,300

147,830

137,240

154,600

142,410

81,070

81,400

82,250

83,170

91,340

152,083

145,418

160,088

152,720

150,826

69,190

81,530

91,590

99,450

89,630

45,590

ROA07

ROA08

ROA09

ROA10

LS06

LS07

LS08

LS09

LS10

8,547

7,902

6,757

7,566

2,588

13,486

13,636

13,557

13,743

7.740

7.080

5.710

6.250

2.612

13.605

13.769

13.694

13.874

5.219

5.215

4.895

5.007

0.163

2.063

2.057

2.032

2.024

0,140

0,110

0,010

0,100

1,869

6,580

6,490

6,657

6,708

33,450

28,880

34,030

29,290

2,967

19,396

19,615

19,493

19,545

Table 7.1: Descriptive statistics, US, 751 companies

56


CCC06

CCC07

CCC08

CCC09

CCC10

COP06

COP07

COP08

COP09

COP10

CRP06

CRP07

CRP08

CRP09

CRP10

ICP06

ICP07

ICP08

ICP09

ICP10

ROE06

ROE07

ROE08

ROE09

ROE10

ROA06

ROA07

ROA08

ROA09

ROA10

LS06

LS07

LS08

LS09

LS10

62,034

61,101

61,640

65,269

63,383

72,606

68,850

63,647

70,959

69,352

44,796

42,011

37,012

39,719

39,207

34,224

34,262

35,005

34,020

33,238

8,856

8,764

6,358

6,114

6,718

4,313

4,380

3,279

3,165

3,548

12,727

12,767

12,938

12,900

13,027

57,513

57,257

55,776

58,730

58,618

74,390

70,150

62,880

73,270

71,350

40,340

38,030

32,820

35,590

35,240

29,153

29,177

28,427

27,916

27,948

7,770

7,900

5,540

5,270

5,990

3,730

3,830

2,710

2,650

3,050

12,615

12,659

12,852

12,817

12,910

46,930

45,497

45,976

48,472

46,556

42,055

39,269

37,172

40,559

40,024

27,905

25,512

24,101

24,425

24,499

25,962

26,053

27,813

27,462

26,468

5,273

5,236

4,392

4,119

4,248

2,808

2,844

2,495

2,255

2,404

1,600

1,605

1,616

1,599

1,602

-69,123

-64,781

-71,798

-70,492

-49,915

0,000

0,000

0,000

0,000

0,000

0,000

0,000

0,000

0,000

0,000

0,370

0,439

0,388

0,433

0,365

0,050

0,040

0,020

0,010

0,010

0,010

0,030

0,000

0,010

0,000

4,727

4,999

5,396

5,603

5,266

227,140

216,614

216,092

240,086

209,124

205,580

181,490

174,500

190,890

187,770

145,070

126,170

138,110

122,440

128,360

143,137

142,578

152,720

158,696

139,313

34,650

47,280

29,140

21,950

26,160

17,280

16,550

13,240

11,390

12,520

18,075

18,144

18,195

18,210

18,284

Table 7.2: Descriptive statistics, Japan, 1133 companies

57


CCC06

CCC07

CCC08

CCC09

CCC10

COP06

COP07

COP08

COP09

COP10

CRP06

CRP07

CRP08

CRP09

CRP10

ICP06

ICP07

ICP08

ICP09

ICP10

ROE06

ROE07

ROE08

ROE09

ROE10

ROA06

ROA07

ROA08

ROA09

ROA10

LS06

LS07

LS08

LS09

LS10

70,126

72,707

71,281

71,647

69,627

75,961

75,700

71,489

68,939

67,412

53,972

53,004

51,930

47,501

48,365

48,137

50,012

51,722

50,209

50,580

14,853

14,966

12,675

10,421

11,683

5,852

5,993

5,061

4,419

5,076

13,019

13,128

13,216

2,565

13,278

67,543

67,432

69,329

67,304

65,942

72,300

71,930

67,680

66,560

64,160

50,075

48,685

46,275

44,230

45,210

43,272

44,594

45,890

42,369

46,946

14,190

14,505

12,520

9,535

11,095

4,880

4,790

4,315

3,630

4,050

12,637

12,672

12,857

2,554

12,878

52,176

53,376

54,922

55,157

49,497

36,811

37,607

37,119

34,093

33,244

23,686

21,099

22,751

19,769

18,848

41,584

42,039

46,465

47,360

41,791

7,458

7,775

6,747

6,590

7,714

3,686

3,577

3,334

3,321

3,465

2,259

2,228

2,223

0,166

2,197

-51,870

-40,855

-79,910

-49,899

-58,008

3,080

2,780

0,000

0,000

0,000

4,940

4,860

4,740

6,900

6,320

1,041

0,881

0,833

0,595

0,478

0,510

1,050

0,480

0,050

0,380

0,190

0,590

0,260

0,030

0,190

8,372

8,568

8,653

2,136

8,561

272,872

236,382

287,268

341,773

230,872

226,850

214,450

191,390

215,420

223,290

172,810

128,900

130,770

126,400

109,640

309,322

268,382

331,818

357,843

241,722

43,950

46,350

38,880

30,750

71,600

20,370

17,260

17,510

16,550

16,250

18,704

18,734

18,893

2,920

18,761

Table 7.3: Descriptive statistics, France, 170 companies

58

CCC COP CRP ICP ROE ROA

COP

2006

2007

2008

2009

2010

0,672**

0,654**

0,656**

0,650**

0,673**

CRP 2006

2007

2008

2009

2010

-0,085*

-0,147**

-0,110**

-0,137**

-0,094*

0,204**

0,159**

0,140**

0,186**

0,206**

ICP

2006

2007

2008

2009

2010

0,740**

0,749**

0,763**

0,775**

0,774**

0,113**

0,113**

0,124**

0,138**

0,177**

0,155**

0,128**

0,166**

-0,101**

0,157**

ROE 2006

2007

2008

2009

2010

-0,045

-0,061

-0,023

-0,106**

-0,126**

0,006

0,021

0,072*

-0,050

-0,081*

0,005

-0,004

0,007

-0,064

-0,028

-0,066

-0,104**

-0,087*

-0,133**

-0,199**

ROA 2006

2007

2008

2009

2010

0,103**

0,125**

0,154**

0,040

0,052

0,034

0,086*

0,122**

0,009

-0,028

-0,208**

-0,205**

-0,196**

-0,236**

-0,216**

0,016

0,025

0,023

-0,058

-0,004

0,749**

0,679**

0,662**

0,718**

0,701**

LS 2006

2007

2008

2009

2010

-0,310**

-0,302**

-0,319**

-0,328**

-0,337**

-0,203**

-0,174**

-0,162**

-0,187**

-0,178**

0,160**

-0,179**

0,214**

0,227**

0,245**

-0,173**

-0,179**

-0,206**

-0,195**

-0,203**

0,159**

0,209**

0,273**

0,200**

0,230**

-0,121**

-0,148**

-0,072*

-0,076*

-0,091*

Correlation is statistically significant at ** p<0,01, * p<0,05

Table 7.4: Correlation between variables, US

59


COP

2006

2007

2008

2009

2010

0,738**

0,734**

0,727**

0,748**

0,740**

CRP 2006

2007

2008

2009

2010

0,059*

0,048

0,050

0,078**

0,064*

0,561**

0,537**

0,520**

0,546**

0,539**

ICP

2006

2007

2008

2009

2010

0,675**

0,687**

0,725**

0,729**

0,699**

0,317**

0,299**

0,315**

0,328**

0,228**

0,273**

0,255**

0,255**

0,221**

0,223**

ROE 2006

2007

2008

2009

2010

-0,199**

-0,082**

-0,112**

-0,160**

-0,067*

-0,123**

-0,057

-0,086**

-0,148**

-0,090**

-0,096**

-0,036

-0,050

-0,088**

-0,052

-0,119**

-0,093**

-0,113**

-0,142**

-0,029

ROA 2006

2007

2008

2009

2010

0,079**

0,101**

0,046

0,015

0,118**

-0,029

0,028

-0,002

-0,054

0,016

-0,262**

-0,221**

-0,203**

-0,225**

-0,177**

-0,091**

-0,082**

-0,097**

-0,094**

0,019

0,711**

0,731**

0,786**

0,789**

0,802**

LS 2006

2007

2008

2009

2010

-0,184**

-0,194**

-0,181**

-0,196**

-0,195**

-0,106**

-0,123**

-0,149**

-0,134**

-0,154**

0,151**

0,150**

0,102**

0,141**

0,118**

0,002

-0,006

-0,012

-0,021

-0,001

-0,021

0,027

-0,046

0,076*

0,156**

-0,147**

-0,127**

-0,174**

-0,059*

-0,017


Table 7.5: Correlation between variables, Japan

60


COP

2006

2007

2008

2009

2010

0,527**

0,562**

0,517**

0,508**

0,522**

CRP 2006

2007

2008

2009

2010

-0,184*

-0,177*

-0,130

-0,147

-0,161*

0,248**

0,180*

0,190*

0,091

0,116

ICP

2006

2007

2008

2009

2010

0,684**

0,678**

0,706**

0,738**

0,696**

-0,083

-0,091

-0,095

-0,091

-0,125

0,120

0,116

0,184*

0,180*

0,168*

ROE 2006

2007

2008

2009

2010

-0,083

-0,124

-0,013

-0,003

-0,057

0,072

-0,062

0,040

0,079

0,017

0,067

0,101

-0,038

-0,130

-0,131

-0,130

-0,051

-0,066

-0,115

-0,140

ROA 2006

2007

2008

2009

2010

0,078

0,043

0,156*

0,129

0,099

0,016

-0,105

0,049

0,082

-0,022

-0,159*

-0,209**

-0,271**

-0,282**

-0,332**

-0,008

0,043

0,013

-0,026

-0,014

0,623**

0,625**

0,675**

0,767**

0,746**

LS 2006

2007

2008

2009

2010

-0,214**

-0,252**

-0,215**

-0,230**

-0,241**

-0,234**

-0,206**

-0,175*

-0,197*

-0,150

0,048

0,182*

0,144

0,217**

0,273**

-0,034

-0,045

-0,044

-0,036

-0,043

0,064

0,173*

0,086

0,027

-0,020

-0,204**

-0,146

-0,240**

-0,214**

-0,194*


Table 7.6: Correlation between variables, France

61

CCC Total (Model 1) CCC US (Model 2) CCC Japan (Model 3) CCC France (Model 4)

Constant

2006

2007

2008

2009

2010

120,485(7,061)**

118,623 (7,159)**

121,955 (7,261)**

135,118 (7,519)**

128,007 (7,320)**

132,219 (10,201)**

124,838 (10,634)**

130,053 (10,156)**

145,763 (10,548)**

144,261 (10,717)**

121,489 (11,329)**

115,281 (10,982)**

120,261 (11,262)**

129,333 (11,584)**

110,953 (11,266)**

127,522 (25,503)**

144,691 (26,222)**

116,823 (28,165)**

128,974 (27,739)**

129,800 (24,680)**

Size 2006

2007

2008

2009

2010

-10,171 (1,238)**

-10,270 (1,254)**

-10,873 (1,260)**

-12,087 (1,309)**

-11,316 (1,270)**

-12,271 (1,704)**

-11,366 (1,764)**

-12,963 (1,693)**

-14,027 (1,758)**

-13,586 (1,772)**

-9,908 (1,961)**

-9,511 (1,912)**

-9,479 (1,930)**

-10,197 (2,029)**

-8,531 (1,982) **

-9,279 (4,219)**

-11,529 (4,424)*

-8,457 (4,677)

-10,286 (4,632)*

-10,330 (3,999)*

ROE

2006

2007

2008

2009

2010

-1,174 (0,189)**

-0,942 (0,176)**

-0,688 (0,172)**

-1,180 (0,217)**

-1,244 (0,208)**

-0,496 (0,227)*

-0,499 (0,206)*

-0,193 (0,177)

-0,508 (0,223)*

-0,655 (0,228)**

-2,965 (0,361)**

-2,639 (0,367)**

-3,712 (0,488)**

-4,767 (0,556)**

-4,259 (0,541)**

-1,124 (0,695)

-1,076 90,698)

-1,223 (0,880)

-1,382 (1,025)

-1,540 (0,729)*

ROA 2006

2007

2008

2009

2010

2,400 (0,347)**

2,236 (0,341)**

1,906 (0,354)**

2,064 (0,422)**

2,564 (0,387)**

1,191 (0,411)**

1,344 (0,403)**

1,291 (0,372)**

0,887 (0,446)*

1,085 (0,425)*

5,068 (0,686)**

5,024 (0,684)**

5,615 (0,873)**

7,004 (1,012)**

8,262 (0,944)**

2,011 (1,433)

1,644 (1,511)

3,654 (1,829)*

3,615 (2,082)

3,426 (1,653)*

R squared 2006

2007

2008

2009

2010

0,074

0,074

0,067

0,074

0,082

0,106

0,104

0,120

0,114

0,124

0,092

0,087

0,081

0,097

0,100

0,062

0,077

0,069

0,070

0,085

R Adjusted

squared

2006

2007

2008

2009

2010

0,073

0,073

0,066

0,073

0,081

0,103

0,101

0,117

0,111

0,120

0,090

0,085

0,078

0,095

0,098

0,045

0,060

0,052

0,053

0,069

F value 2006

2007

2008

2009

2010

54,478**

54,536**

48,832**

54,659**

61,025**

29,646**

28,950**

34,098**

32,091**

35,108**

38,003**

35,566**

32,800**

40,389**

41,865**

3,640*

4,605**

4,073**

4,148**

5,169**

Coefficient with standard errors in parentheses

Coefficient is statistically significant at ** p<0,01, * p<0,05

Table 7.7: Regression model 1-4 (CCC and ROE/ROA)

62

COP Total (Model 5) COP US (Model 6) COP Japan (Model 7) COP France (Model 8)

Constant

2006

2007

2008

2009

2010

121,494 (6,123)**

112,550 (5,963)**

106,175 (5,712)**

115,038 (6,043)**

114,062 (6,029)**

83,605 (6,946)**

72,018 (6,921)**

66,620 (6,452)**

75,841 (6,709)**

77,148 (6,910)**

111,679 910,491)**

102,582 (9,792)**

108,051 (9,332)**

113,826 (9,991)**

109,721 (10,004)**

130,396 (17,856)**

137,857 (18,560)**

112,347 (19,368)**

108,612 (17,347)**

103,874 (17,054)**

Size 2006

2007

2008

2009

2010

-8,796 (1,073)**

-7,983 (1,044)**

-7,825 (0,991)**

-8,071 (1,052)**

-7,961 (1,046)**

-6,627 (1,161)**

-4,988 (1,148)**

-4,935 (1,075)**

-5,273 (1,118)**

-5,089 (1,142)**

-5,750 (1,816)**

-5,753 (1,705)**

-7,161 (1,599)**

-6,384 (1,750)**

-6,627 (1,760)**

-10,430 (2,954)*

-10,001 (3,131)*

-7,696 (3,216)*

-7,681 (2,897)**

-6,150 (2,763)*

ROE

2006

2007

2008

2009

2010

-0,323 (0,164)*

-0,221 (0,147)

-0,144 (0,135)

-0,517 (0,174)**

-0,491 (0,172)**

0,219 (0,155)

0,076 (0,134)

0,189 (0,112)

-0,050 (0,142)

-0,050 (0,147)

-1,557 (0,334)**

-1,092 (0,328)**

-1,620 (0,404)**

-2,344 (0,480)**

-2,156 (0,481)**

0,927 (0,486)

0,566 (0,494)

0,566 (0,605)

0,728 (0,641)

0,539 (0,504)

ROA 2006

2007

2008

2009

2010

-0,742 (0,301)*

-0,576 (0,284)*

-0,617 (0,278)*

-0,675 (0,339)*

-0,559 (0,319)

-0,262 (0,280)

0,188 (0,262)

0,228 (0,237)

0,046 (0,284)

-0,154 (0,274)

1,575 (0,635)*

1,831 (0,610)**

1,937 (0,723)**

2,294 (0,873)**

3,283 (0,838)**

-1,577 (1,003)

-2,270 (1,069)*

-0,763 (1,257)

-0,741 (1,302)

-1,436 (1,143)

R squared 2006

2007

2008

2009

2010

0,059

0,047

0,048

0,066

0,065

0,044

0,034

0,042

0,035

0,034

0,031

0,025

0,036

0,042

0,041

0,076

0,068

0,036

0,048

0,032

R Adjusted

squared

2006

2007

2008

2009

2010

0,057

0,046

0,047

0,064

0,063

0,040

0,030

0,038

0,031

0,030

0,029

0,023

0,034

0,040

0,038

0,059

0,052

0,018

0,031

0,014

F value 2006

2007

2008

2009

2010

42,542**

33,578**

34,389**

47,895**

47,201**

11,399**

8,827**

10,893**

9,105**

8,742**

12,075**

9,686**

14,138**

16,527**

15,993**

4,543**

4,059**

2,051

2,773*

1,823



Table 7.8: Regression model 5-8 (COP and ROE/ROA)

63

CRP Total (Model 9) CRP US (Model 10) CRP Japan (Model 11) CRP France (Model 12)

Constant

2006

2007

2008

2009

2010

46,882 (3,900)**

41,201 (3,709)**

36,089 (3,594)**

30,853 (3,545)**

31,078 (3,608)**

22,172 (3,371)**

18,870 (3,647)**

12,273 (3,294)**

10,508 (3,422)**

9,659 (3,609)**

29,969 (6,749)**

27,320 (6,163)**

31,987 (5,942)**

25,238 (5,883)**

27,961 (6,058)**

59,100 (11,512)**

46,196 (10,033)**

54,053 (11,389)**

36,675 (9,706)**

33,260 (8,931)**

Size 2006

2007

2008

2009

2010

-0,004 (0,684)

0,627 (0,650)

0,630 (0,623)

1,944 (0,617)**

1,864 (0,626)**

0,759 (0,563)

1,511 (0,605)*

2,122 (0,549)**

2,725 (0,570)**

2,934 (0,597)**

4,103 (1,168)**

3,491 (1,073)**

1,514 (1,018)

3,582 (1,031)**

2,740 (1,066)*

-0,987 (1,904)

1,284 (1,693)

0,362 (1,891)

2,767 (1,621)

3,744 (1,447)*

ROE

2006

2007

2008

2009

2010

0,548 (0,104)**

0,490 (0,091)**

0,353 (0,085)**

0,273 (0,102)**

0,298 (0,103)**

0,449 (0,075)**

0,260 (0,070)**

0,177 (0,057)*

0,167 (0,072)*

0,195 (0,077)*

0,861 (0,215)**

1,187 (0,206)**

1,506 (0,257)**

1,276 (0,282)**

1,279 (0,291)**

0,905 (0,314)**

0,964 (0,267)**

0,875 (0,356)*

0,443 (0,359)

0,512 (0,264)

ROA 2006

2007

2008

2009

2010

-2,557 (0,192)**

-2,332 (0,176)**

-2,062 (0,175)**

-2,221 (0,199)**

-2,027 (0,191)**

-1,100 (0,136)**

-0,852 (0,138)**

-0,707 (0,121)**

-0,856 (0,145)**

-0,801 (0,143)**

-3,556 (0,409)**

-3,416 (0,384)**

-3,944 (0,460)**

-4,208 (0,514)**

-3,604 (0,507)**

-2,220 (0,647)**

-2,493 (0,578)**

-3,021 (0,739)**

-2,182 (0,729)**

-2,457 (0,598)**

R squared 2006

2007

2008

2009

2010

0,113

0,108

0,095

0,109

0,096

0,104

0,083

0,090

0,106

0,105

0,092

0,088

0,073

0,084

0,061

0,072

0,135

0,112

0,113

0,174

R Adjusted

squared

2006

2007

2008

2009

2010

0,111

0,108

0,094

0,107

0,095

0,101

0,079

0,087

0,102

0,102

0,090

0,086

0,071

0,081

0,058

0,055

0,119

0,096

0,097

0,159

F value 2006

2007

2008

2009

2010

86,292**

82,171**

71,763**

83,224**

72,400**

29,028**

22,546**

24,756**

29,416**

29,301**

37,890**

36,154**

29,441**

34,228**

24,290**

4,309**

8,641**

7,013**

7,068**

11,664**



Table 7.9: Regression model 9-12 (CRP and ROE/ROA)

64

ICP Total (Model 13) ICP US (Model 14) ICP Japan (Model 15) ICP France (Model 16)

Constant

2006

2007

2008

2009

2010

45,873 (4,701)**

47,273 (4,870)**

51,869 (5,144)**

50,932 (5,146)**

45,023 (5,002)**

70,786 (7,604)**

71,691 (7,955)**

75,705 (7,841)**

80,430 (7,906)**

76,772 (8,012)**

39,779 (6,533)**

40,019 (6,553)**

44,196 (7,052)**

40,745 (6,832)**

29,193 (6,726)**

56,226 (20,713)**

53,031 (21,367)*

58,529 (24,561)*

57,037 (24,414)*

59,186 (21,370)**

Size 2006

2007

2008

2009

2010

-1,378 (0,824)

-1,660 (0,853)

-2,418 (0,892)**

-2,072 (0,896)*

-1,491 (0,868)

-4,885 (1,270)**

-4,867 (1,320)**

-5,906 (1,307)**

-6,029 (1,318)**

-5,562 (1,325)**

-0,055 (1,131)

-0,267 (1,141)

-0,805 (1,209)

-0,231 (1,197)

0,837 (1,183)

0,164 (3,426)

-0,244 (3,605)

-0,399 (4,078)

0,162 (4,077)

-0,436 (3,463)

ROE

2006

2007

2008

2009

2010

-0,304 (0,126)*

-0,231 (0,120)

-0,191 (0,122)

-0,390 (0,148)**

-0,455 (0,142)**

-0,266 (0,169)

-0,316 (0,154)*

-0,205 (0,136)

-0,291 (0,167)

-0,412 (0,171)*

-0,547 (0,208)**

-0,360 (0,219)

-0,586 (0,306)

-1,147 (0,328)**

-0,824 (0,323)*

-1,146 (0,564)*

-0,678 (0,569)

-0,914 (0,768)

-1,666 (0,902)

-1,567 (0,631)*

ROA 2006

2007

2008

2009

2010

0,586 (0,231)*

0,480 (0,232)*

0,460 (0,251)

0,518 (0,289)

1,096 (0,265)**

0,353 (0,307)

0,305 (0,302)

0,355 (0,288)

-0,015 (0,334)

0,438 (0,317)

-0,062 (0,396)

-0,223 (0,408)

-0,266 (0,546)

0,502 (0,597)

1,374 (0,563)*

1,367 (1,164)

1,421 (1,231)

1,396 (1,595)

2,174 (1,833)

2,406 (1,432)

R squared 2006

2007

2008

2009

2010

0,007

0,006

0,008

0,009

0,013

0,033

0,038

0,045

0,047

0,049

0,014

0,009

0,013

0,020

0,006

0,026

0,012

0,010

0,023

0,038

R Adjusted

squared

2006

2007

2008

2009

2010

0,005

0,005

0,006

0,007

0,011

0,029

0,034

0,041

0,043

0,045

0,011

0,006

0,010

0,018

0,003

0,008

-0,006

-0,008

0,005

0,021

F value 2006

2007

2008

2009

2010

4,455**

4,237**

5,259**

6,063**

8,744**

8,571**

9,891**

11,803**

21,345**

12,841**

5,132**

3,201*

4,832**

7,757**

2,296

1,453

0,664

0,579

1,276

2,191



Table 7.10: Regression model 13-16 (ICP and ROE/ROA)

65

CCC COP CRP ICP

Constant

2006

2007

2008

2009

2010

71,383 (2,584)**

67,337 (2,526)**

69,009 (2,270)**

76,696 (2,419)**

68,272 (2,466)**

81,319 (2,128)**

72,572 (1,997)**

68,282 (1,702)**

79,850 (1,865)**

75,026 (1,936)**

49,314 (1,348)**

43,520 (1,254)**

38,759 (1,063)**

42,896 (1,102)**

41,196 (1,161)**

39,379 (1,583)**

38,285 (1,584)**

39,486 (1,480)**

39,742 (1,507)**

34,442 (1,538)**

Dummy 2006

2007

2008

2009

2010

-6,123 (4,048)

-2,067 (3,971)

-7,388 *3,582)*

-8,376 (3,672)*

1,489 (3,851)

-35,339 (3,334)**

-27,750 (3,139)**

-28,488 (2,685)**

-33,721 (2,831)**

-27,013 (3,023)**

-24,659 (2,112)**

-18,559 (1,971)**

-16,746 (1,677)**

-18,763 (1,674)**

-16,687 (1,814)**

4,557 (2,481)

7,124 (2,490)**

4,354 (2,334)

6,581 (2,288)**

11,814 (2,402)**

ROE

2006

2007

2008

2009

2010

-1,059 (0,251)**

-0,714 (0,247)**

-1,167 (0,294)**

-1,879 (0,328)**

-0,732 (0,310)

-0,985 (0,206)**

-0,426 (0,196)*

-0,726 (0,220)**

-1,457 (0,253)**

-0,847 (0,244)**

-0,511 (0,131)**

-0,174 (0,123)

-0,272 (0,138)*

-0,521 (0,150)**

-0,297 (0,146)*

-0,584 (0,154)**

-0,462 (0,155)**

-0,712 (0,191)**

-0,943 (0,204)**

-0,182 (0,194)

USROE 2006

2007

2008

2009

2010

0,879 (0,297)**

0,474 (0,291)

1,087 (0,325)**

1,435 (0,368)**

0,188 (0,354)

1,002 (0,245)**

0,477 (0,230)*

0,879 (0,224)**

1,330 (0,284)**

0,632 (0,278)*

0,518 (0,155)**

0,169 (0,145)

0,280 (0,512)

0,434 (0,168)

0,257 (0,167)

0,395 (0,182)*

0,166 (0,183)

0,488 (0,212)*

0,540 (0,229)

-0,187 (0,221)

R squared 2006

2007

2008

2009

2010

0,010

0,006

0,009

0,022

0,009

0,120

0,100

0,100

0,135

0,112

0,158

0,132

0,116

0,141

0,116

0,023

0,021

0,018

0,033

0,025

R Adjusted

squared

2006

2007

2008

2009

2010

0,009

0,004

0,007

0,020

0,007

0,119

0,099

0,098

0,133

0,111

0,156

0,130

0,114

0,139

0,114

0,022

0,020

0,017

0,031

0,023

F value 2006

2007

2008

2009

2010

6,376**

3,591

5,495**

13,849**

5,446**

85,385**

69,518**

69,255**

97,415**

79,184**

117,107**

94,898**

82,024**

102,581**

82,019**

14,826**

13,676**

11,783**

21,312**

15,874**



Table 7.11: Regression model differences between countries (CCC/COP/CRP/ICP and

ROE)

66

CCC COP CRP ICP

Constant

2006

2007

2008

2009

2010

56,288 (2,426)**

23,990 (2,372)**

58,821 (2,130)**

64,206 (2,353)**

55,272 (2,347)**

74,466 (2,007)**

67,115 (1,881)**

63,782 (1,601)**

74,029 (1,810)**

68,388 (1,851)**

56,020 (1,228)**

50,662 (1,153)**

43,451 (0,978)**

47,434 (1,038)**

45,580 (1,089)**

37,842 (1,489)**

37,537 (1,497)**

38,490 (1,392)**

37,661 (1,460)**

32,464 (1,470)**

Dummy 2006

2007

2008

2009

2010

-0,430 (3,821)

-1,139 (3,858)

-7,758 (3,578)*

-4,053 (3,701)

3,320 (3,754)

-29,609 (3,161)**

-25,008 (3,061)**

-25,942 (2,689)**

-29,895 (2,847)**

-22,456 (2,961)**

-26,982 (1,933)**

-21,151 (1,876)**

-17,536 (1,643)**

-20,063 (1,632)**

-17,189 (1,742)**

2,197 (2,345)

2,718 (2,434)

0,6487 (2,339)

5,778 (2,297)*

8,587 (2,352)**

ROA

2006

2007

2008

2009

2010

1,325 (0,471)**

1,617 (0,454)**

0,844 (0,157)

0,316 (0,605)

2,279 (0,548)**

-0,433 (0,390)

0,393 (0,360)

-0,036 (0,388)

-0,976 (0,466)*

0,267 (0,432)

-2,602 (0,238)**

-1,978 (0,221)**

-1,958 (0,237)**

-2,439 (0,267)**

-1,799 (0,254)**

-0,844 (0,289)**

-0,754 (0,286)**

-1,078 (0,338)**

-1,148 (0,376)**

0,213 (0,343)**

USROA 2006

2007

2008

2009

2010

-0,570 (0,554)

-0,642 (0,546)

0,328 (0,599)

0,023 (0,695)

-1,850 (0,636)**

0,600 (0,458)

0,027 (0,433)

0,599 (0,450)

1,021 (0,535)

-0,407 (0,501)

2,100 (0,280)**

1,435 (0,266)**

1,480 (0,275)**

1,790 (0,307)**

1,208 (0,295)**

0,930 (0,340)**

0,767 (0,345)*

1,209 (0,392)**

0,791 (0,432)

-0,236 (0,398)

R squared 2006

2007

2008

2009

2010

0,008

0,012

0,009

0,002

0,010

0,110

0,100

0,096

0,121

0,106

0,206

0,172

0,150

0,179

0,144

0,018

0,015

0,014

0,021

0,018

R Adjusted

squared

2006

2007

2008

2009

2010

0,006

0,011

0,008

0,000

0,009

0,109

0,098

0,095

0,120

0,104

0,205

0,171

0,149

0,178

0,142

0,017

0,014

0,012

0,019

0,017

F value 2006

2007

2008

2009

2010

4,892**

7,662**

5,980**

0,962

6,539**

77,500**

69,258**

66,711**

86,162**

73,962**

162,447**

130,074**

110,538**

136,688**

105,073**

11,613**

9,836**

8,642**

13,137**

11,595**



Table 7.12: Regression model differences between countries (CCC/COP/CRP/ICP and

ROA)

Documents

Master Thesis Organizational & Management Control